From 8e57a2e5a8b07937a6c84d40ec8868020ad8bb41 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Mon, 10 Mar 2025 11:17:38 -0700 Subject: [PATCH 01/28] First commit for forked branch. --- models/retinalOCT_RPD_segmentation/LICENSE | 25 + .../configs/inference.yaml | 23 + .../configs/inference_yb.yaml | 26 + .../configs/large_files.yaml | 5 + .../configs/metadata.json | 101 ++ .../docs/Figure1.jpg | Bin 0 -> 192692 bytes .../docs/README.md | 155 +++ .../docs/Table2.gif | Bin 0 -> 39501 bytes .../scripts/Base-RCNN-FPN.yaml | 41 + .../scripts/Ensembler.py | 117 +++ .../scripts/__init__.py | 1 + .../scripts/analysis_lib.py | 946 ++++++++++++++++++ .../scripts/datasets/__init__.py | 0 .../scripts/datasets/data.py | 112 +++ .../scripts/datasets/volReader.py | 335 +++++++ .../scripts/inference.py | 237 +++++ .../scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml | 56 ++ .../scripts/table_styles.py | 41 + .../scripts/working.yaml | 56 ++ .../scripts/ybpres.mplstyle | 6 + 20 files changed, 2283 insertions(+) create mode 100644 models/retinalOCT_RPD_segmentation/LICENSE create mode 100644 models/retinalOCT_RPD_segmentation/configs/inference.yaml create mode 100644 models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml create mode 100644 models/retinalOCT_RPD_segmentation/configs/large_files.yaml create mode 100644 models/retinalOCT_RPD_segmentation/configs/metadata.json create mode 100644 models/retinalOCT_RPD_segmentation/docs/Figure1.jpg create mode 100644 models/retinalOCT_RPD_segmentation/docs/README.md create mode 100644 models/retinalOCT_RPD_segmentation/docs/Table2.gif create mode 100644 models/retinalOCT_RPD_segmentation/scripts/Base-RCNN-FPN.yaml create mode 100644 models/retinalOCT_RPD_segmentation/scripts/Ensembler.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/__init__.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/datasets/__init__.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/datasets/data.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/inference.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml create mode 100644 models/retinalOCT_RPD_segmentation/scripts/table_styles.py create mode 100644 models/retinalOCT_RPD_segmentation/scripts/working.yaml create mode 100644 models/retinalOCT_RPD_segmentation/scripts/ybpres.mplstyle diff --git a/models/retinalOCT_RPD_segmentation/LICENSE b/models/retinalOCT_RPD_segmentation/LICENSE new file mode 100644 index 00000000..dc32dc5a --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/LICENSE @@ -0,0 +1,25 @@ +BSD 2-Clause License + +Copyright (c) 2022, uw-biomedical-ml +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/models/retinalOCT_RPD_segmentation/configs/inference.yaml b/models/retinalOCT_RPD_segmentation/configs/inference.yaml new file mode 100644 index 00000000..35336773 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/configs/inference.yaml @@ -0,0 +1,23 @@ +imports: +- $import scripts +- $import scripts.inference + +args: + run_extract : False + input_dir : "/path/to/data" + extracted_dir : "/path/to/extracted/data" + input_format : "dicom" + create_dataset : True + dataset_name : "my_dataset_name" + + output_dir : "/path/to/model/output" + run_inference : True + create_tables : True + +# create visuals + binary_mask : False + binary_mask_overlay : True + instance_mask_overlay : False + +inference: +- $scripts.inference.main(@args) \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml b/models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml new file mode 100644 index 00000000..488de340 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml @@ -0,0 +1,26 @@ +imports: +- $import scripts +- $import scripts.inference + +args: + run_extract : False + # input_dir : "/data/amd-data/cera-rpddata/Himeesh DICOM/Folder 1" + # extracted_dir : "/data/yelena/monaiRPD/extracted_dicom" + # input_format : "dicom" + input_dir : "/data/amd-data/cera-rpddata/RPD_Himeesh_v2" + extracted_dir : "/data/yelena/monaiRPD/extracted_vol" + input_format : "vol" + create_dataset : False + dataset_name : "monai_vol" + + output_dir : "/data/yelena/monaiRPD/output_vol" + run_inference : False + create_tables : False + +# create visuals + binary_mask : False + binary_mask_overlay : False + instance_mask_overlay : False + +inference: +- $scripts.inference.main(@args) \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/configs/large_files.yaml b/models/retinalOCT_RPD_segmentation/configs/large_files.yaml new file mode 100644 index 00000000..e4e5e95d --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/configs/large_files.yaml @@ -0,0 +1,5 @@ +large_files: + - path: "models/models.zip" + url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/models.zip" + hash_val: "" + hash_type: "" \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json new file mode 100644 index 00000000..cbea63c3 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -0,0 +1,101 @@ +{ + "version": "0.0.1", + "changelog": { + "0.0.1": "Initial version" + }, + "monai_version": "1.1.0", + "pytorch_version": "1.10.0", + "numpy_version": "1.21.2", + "required_packages_version": { + "opencv-python-headless": "4.11.0.86", + "pandas": "1.3.5", + "seaborn": "0.12.2", + "scikit-learn": "1.0.2", + "configargparse": "1.7", + "progressbar": "2.5", + "pydicom": "2.4.4", + "fire": "0.7.0", + "jinja2": "3.1.5", + "detectron2": "0.6+cu113" + }, + "task": "Reticular Pseudodrusen (RPD) instance segmentation.", + "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", + "authors": "Yelena Bagdasarova, Scott Song", + "copyright": "Copyright (c) 2022, uw-biomedical-ml", +"network_data_format": { + "inputs": { + "image": { + "type": "image", + "format": "PNG", + "modality": "OCT", + "num_channels": 1, + "spatial_shape":[496,1024], + "dtype": "int16", + "value_range": [0,256], + "is_patch_data": false, + "channel_def": {"0": "image"} + } + }, + "preprocessed_data_sources": { + "vol_file": { + "type": "image", + "format": "VOL", + "modality": "OCT", + "num_channels": 1, + "spatial_shape":[496,1024,"D"], + "dtype": "int16", + "value_range": [0,256], + "description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to ////_oct_.png on disk, where is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " + + }, + "dicom_series": { + "type": "image", + "format": "DICOM", + "modality": "OCT", + "SOP class UID" : "1.2.840.10008.5.1.4.1.1.77.1.5.4", + "num_channels": 1, + "spatial_shape":[496,1024,"D"], + "dtype": "int16", + "value_range": [0,256], + "description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to //_oct_.png on disk, where is the slice number. " + } + }, + "outputs": { + "instances": { + "type": "dictionary", + "format": "COCO", + "modality": "n/a", + "num_channels": 1, + "spatial_shape": [496,1024], + "channel_def": {"0": "RPD"}, + "description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances." + } + }, + "post_processed_outputs": { + "binary segmentation": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [496,1024], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices." + }, + "binary segmentation overlay": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [496,1024], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." + }, + "instance segmentation overlay": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [496,1024], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." + } + } + } +} \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/docs/Figure1.jpg b/models/retinalOCT_RPD_segmentation/docs/Figure1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c3964f0ef7647c1ccdec8ad9b59df9b4fd0d2fc1 GIT binary patch literal 192692 zcmb5VWn7z05HA`uxJ!WGuEpIc?(Q0#;_g;x3&9I5Qb>!tTX78(Enci>u|jc|llRE) z+{W`nPj{IkKcV?cKrI!r=ftr%45&#J?xRAI2fR`130stKq4IK>?9UToF2t>!g zBEZ7J#Ka=O!^a^YCjo)TNyx}3shMdhsTjdzWOQ6~j4Z6|9PAXd+`Qauyv%IuZ2x@( z2?zvYVPFwsVG*-Yl2Nk#{~j;B0748j016ZZi2;C2h=f9j^fCyb1^|%I{^J(#e+DWN zG75rG00t(4Y)Ak=MnXYBMny#iqNAXqqv9i|08~OWB6>bKbYccwAiu3QNi+s2qr6Yr zS2A*u`o5x#r)RGX9Tfc$pQS|Xhw}g26u|`w zDl!^6g2qpX1VBaxpdq6G(Gau#V+*k-A?klS66=!Kenn&8_fG3OCDn_r-(-~kdG@jl zz(GN*Aw(er$N&Z(4Q~^-C1?|*S!OR^03@yfSy?v?$~b&uaf;Lc>NtDat%5;R?gDZ* zAPU`w@(4jvT9Bk=eyPsK2*cQ5LV1?)DBrxtIjY^;;_bi4>%0rf-OLlbj|OM=S!>(4 z7B&u%?hExQo9b1b7f6eB_Xb2NsL5S&Qre=h^~jKX8Ny1L#4GQPWmmf}le3F%@}cEX znl0BM@Nm`FKCAwWaS0Jtw4fiCuoaP^6xt;CgLUOG?Zct#qv_e5=I`Y-2nGD<%5K-; z{IS`B2KJES5@3MY=_QUN*G5a)P1kTj!!v)D3OugR{-+QBrdhpBZ4DY_@W_mH3E=Ie zi+()F_XTBZtKn{k8K3QkS8n6lrp%am-Ii}3L*c{Xb=!>BPj09l{H3$;j2?;{Wrli6 z|NJM=k0Us4AMw)_wQ08KR|i$LZ#qAK8y|pQ)P_#nLs0p*nSipk=+b^KGqV?LuF9 zn7xg{jz5d2Jvd=G(r&#iJVQ2uUk?~t`|cSDPx5i9fNY|BrPyuXSLK?Qh?n0_yX_*_ zUP`7NrW}rZ^cU7n=pjJ;Y)yXn5I(+Cl@Ehyk)s8VKqj!g0kWmzT=4Z+x2MBLxe?&d zeH&}*mmH(p$}=S8=S4TFWM0X`euwQerqL_f13gZ`hK?FF{SkVl#;~so5-$6nmgMRsDJq2 zv}8v}U7d#mitp@-1BrraB?P{gDq9K2wR$E-NE?ox1=JUKiswF^+`a(7w;je)FM#AK z>#QGDs;ITPS7n9tJ|_0pv}U*~VWzjyQN}~d^`OzG9<`gB*6r!Sz{D$P!PAZF;9vL8 ztL+i7&4MMp3T&*UBvd{BAn+BW=fxumxVCtqbz+1JEA8@{%~?K6mBB%agHAw9VV49Z zQ63~xtBFeY%niOI(P>)=jr>=RKh#4Dc>%axMIUE>UjUOhf2LLUf0zwO8%9CqFNqEM zO(spp4!G?wTGawv98{kC3}o)47~0!dI-LH z@6tfUA)JvHs;|H^6Evj}&L*OM<_Ui=C?I#;mXh(*q$QW818)y~?^b?(t~(qgBC&^n z=h7;qMF55Euw{e=^QD&~uR4rJUI4%SYef+1sQLL+Gk+fPyj}+bm=Jj{r}0fjKKoHk z{d0rwetwmB6^56cTg`gYdWHgg0hs%Tr93^^7D{=kFs2Q=C|tB{5MLh4wLITM&_&*w zSH1x5?lT^iZeyFxA1o^`7O&@{r>-zo@O=SPFZXDqAR-uN&C()kUaDenc0d#ouxC9s z^yzPyEEC#UL{-q#)00p)_w6IgzvGD7DNpgb%Jw2OZNVBO2OT(atVaY-m@IDW)*3|S zS^U$(%5ChVqFypy_7yU0)H~k zR&^(0G(y!*>t;dx_gMXiuN-)<+6B3_qHRD&nQ7_Au&OX^-jun^hx`#nYPnMY0JW0%x3chDm#_SUgY z#>!+BjZ_!w(Q7uG3KUw|Q9-KLw~gC>P+tIVguJ8kg69?yc0Pj!br_2wU}i+AByaBF zJoz)FruiOW{emhEl2`C%^nVY_ zXU~YpdiWn_UN57BMT#?Hgb3xKWSBLeTT>RCXY%wseC zZTt4A?DL{4{8-9Pr&54|3bvi_`ch1cg1T#87{2u{WdMSR2;qS(! 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Hickey, Amy C. Cohn, Mali Okada, Robert P. Finger, Jan H. Terheyden, Ruth E. Hogg, Pierre-Henry Gabrielle, Louis Arnould, Maxime Jannaud, Xavier Hadoux, Peter van Wijngaarden, Carla J. Abbott, Lauren A.B. Hodgson, Roy Schwartz, Adnan Tufail, Emily Y. Chew, Cecilia S. Lee, Erica L. Fletcher, Melanie Bahlo, Brendan R.E. Ansell, Alice Pébay, Robyn H. Guymer, Aaron Y. Lee, Zhichao Wu + +### **Tags** +Reticular Pseudodrusen, AMD, OCT, Segmentation + +## **Model Description** +This model detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans. The instance segmentation model used a Mask-RCNN [1] head with the ResNeXt-101-32x8d-FPN [2] backbone (pretrained on ImageNet) implemented via the Detectron2 framework [3]. The model produces outputs that consist of bounding boxes and segmentation masks that delineate the coordinates and pixels of each instance detected, which are assigned a corresponding output probability. A tuneable probability threshold can then be applied to finalise the binary detection of an RPD instance. + +Five segmentation models using these RPD instance labels on the OCT B-scans were trained based on five-fold cross-validation which were used to form a final ensemble model using soft voting (see supplementary material of paper for more information on model training.) + +## **Data** +The model was trained using the prospectively-collected, baseline OCT scans (prior to any treatments) of individuals enrolled in the LEAD study [4] imaged using Heidelberg Spectralis HRA+OCT. OCT B-scans from 200 eyes from 100 individuals in the LEAD study were randomly selected to undergo manual annotations of RPD by a single grader (HK) at the pixel level, following training from two senior investigators (RHG and ZW). Only definite RPD lesions, defined as subretinal hyperreflective accumulations that altered the contour of, or broke through, the overlying photoreceptor ellipsoid zone on the OCT B-scans were annotated. + +The model was then internally tested in a different set of OCT scans from 125 eyes from 92 individuals from the LEAD study, and externally tested on five independent datasets: the MACUSTAR study [5], the Northern Ireland Cohort for Longitudinal Study of Ageing (NICOLA) study [6], the Montrachet study [7], AMD observational studies at the University of Bonn, Germany (UB), and a routine clinical care cohort seen at the University of Washington (UW). The presence of RPD was graded either as part of each study (MACUSTAR and UB datasets) or graded by one of the study investigators (HK; in the NICOLA, UW, and Montrachet datasets). All these studies defined RPD based on the presence of five or more definite lesions on more than one OCT B-scan that corresponded to hyporeflective lesions seen on near-infrared reflectance imaging. + +#### **Preprocessing** +Scans were kept at native resolution (1024 x 496 pixels). + +## **Performance** +In the external test datasets, the overall performance for detecting RPD in a volume scan was (AUC = 0·94; 95% CI = 0·92–0·97). In the internal test dataset, the Dice coefficient (DSC) between the model and manual annotations by retinal specialists for each B-scan was caculated and the average over the dataset is listed in the table below. Note that the DSC was assigned a value of 1·0 to all pairwise comparisons where no pixels on a B-scan were labelled as having RPD. + +![](Table2.gif) + +![](Figure1.jpg) + +For more details regarding evaluation results, please see Results section of paper. + +## INSTALLATION +This bundle can be installed using docker by navigating to the RPDBundle directory and running +``` +docker build -t : . +``` + +## USAGE +The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: + +``` +imports: +- $import scripts +- $import scripts.inference + +args: + run_extract : False + input_dir : "/path/to/data" + extracted_dir : "/path/to/extracted/data" + input_format : "dicom" + create_dataset : True + dataset_name : "my_dataset_name" + + output_dir : "/path/to/model/output" + run_inference : True + create_tables : True + +# create visuals + binary_mask : False + binary_mask_overlay : True + instance_mask_overlay : False + +inference: +- $scripts.inference.main(@args) +``` +Then in your bash shell run +``` +BUNDLE="/path/to/budle/RPDBundle" + +python -m monai.bundle run inference \ + --bundle_root "$BUNDLE" \ + --config_file "$BUNDLE/configs/inference.yaml" \ + --meta_file "$BUNDLE/configs/metadata.json" +``` +### VOL/DICOM EXTRACTION +If extracting DICOM or VOL files: +* set `run_extract` to `True` +* specify `input_dir`, the path to the directory that contains the VOL or DICOM files +* specify `extracted_dir`, the path to the directory where extracted images will be stored +* set `input_format` to "dicom" or "vol" + +The VOL or DICOM files can be in a nested hierarchy of folders, and all files in that directory with a VOL or DICOM extension will be extracted. + +For DICOM files, each OCT slice will be saved as a png file to `//_oct_.png` on disk, where `` is the slice number. + +For VOL files, each OCT slice will be saved as a png file to `////_oct_.png` on disk, where `` is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " + +### DATASET PACKAGING +Once you have the scans in PNG format, you can create a "dataset" in Detectron2 dictionary format for model consumption: +* set `create_dataset` to `True` +* set `dataset_name` to the chosen name of your dataset + +The dataset dictionary will be saved as pickle file in `////RPDBundle/datasets/.pk` + +### INFERENCE +To run inference on your dataset: +* set `dataset_name` to the name of your dataset which you create with the previous step and resides in `////RPDBundle/datasets/.pk` +* set `output_dir`, the path to the directory where model predictions and other data will be stored. +* set `run_inference` to `True` + +The final ensembled predictions will be saved in COCO Instance Segmentation format in `coco_instances_results.json` in the output directory. The output directory will also be populated with five folders with the preffix 'fold' which contain predictions from the individual models of the ensemble. + +### SUMMARY TABLES and VISUAL OUTPUT +Tables and images can be created from the predictions and written to the output directory. +These tables can be created by setting `create_tables` to `True`: +* HTML table called `dfimg_.html` indexed by OCT-B scan with columns listing the detected number of RPD instances (dt_instances), pixels (dt_pixels), and horizontal pixels (dt_xpxs) in that B-scan. +* HTML table called `dfvol_.html` indexed by OCT volume with columns listing the detected number of RPD instances (dt_instances), pixels (dt_pixels), and horizontal pixels (dt_xpxs) in that volume. + +The predicted segmentations can be output as multi-page TIFFs, where each TIFF file corresponds to an input volume of the dataset, and each page to an OCT slice from the volume in original order. The output images can be binary masks, binary masks overlaying the original B-scan, and instance masks overlaying the original B-scan. Set the `binary_mask`, `binary_mask_overlay` and `instance_mask_overlay` flags in the yaml file to `True` accordingly. + +## **System Configuration** +Inference on one Nvidia A100 gpu takes about 0.041 s/batch of 14 images, about 3G of gpu memory, and 6G of RAM. + +## **Limitations** +This model has not been tested for robustness of performance on OCTs imaged with other devices and with different scan parameters. + +## **Citation Info** + +``` +@article {Kumar2024.09.11.24312817, + author = {Kumar, Himeesh and Bagdasarova, Yelena and Song, Scott and Hickey, Doron G. and Cohn, Amy C. and Okada, Mali and Finger, Robert P. and Terheyden, Jan H. and Hogg, Ruth E. and Gabrielle, Pierre-Henry and Arnould, Louis and Jannaud, Maxime and Hadoux, Xavier and van Wijngaarden, Peter and Abbott, Carla J. and Hodgson, Lauren A.B. and Schwartz, Roy and Tufail, Adnan and Chew, Emily Y. and Lee, Cecilia S. and Fletcher, Erica L. and Bahlo, Melanie and Ansell, Brendan R.E. and P{\'e}bay, Alice and Guymer, Robyn H. and Lee, Aaron Y. and Wu, Zhichao}, + title = {Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration on Optical Coherence Tomography}, + elocation-id = {2024.09.11.24312817}, + year = {2024}, + doi = {10.1101/2024.09.11.24312817}, + publisher = {Cold Spring Harbor Laboratory Press}, + URL = {https://www.medrxiv.org/content/early/2024/09/12/2024.09.11.24312817}, + eprint = {https://www.medrxiv.org/content/early/2024/09/12/2024.09.11.24312817.full.pdf}, + journal = {medRxiv} +} +``` + +## **References** +[1]: He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask R-CNN." In Proceedings of the IEEE international conference on computer vision (ICCV), pp. 2961-2969. 2017. + +[2]: Xie, Saining, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. "Aggregated residual transformations for deep neural networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1492-1500. 2017. + +[3]: Wu, Yuxin, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. "Detectron2." arXiv preprint arXiv:1902.09615 (2019). + +[4]: Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health. 2019;1(6):e271–e97. + +[5]: Finger RP, Schmitz-Valckenberg S, Schmid M, et al. MACUSTAR: Development and Clinical Validation of Functional, Structural, and Patient-Reported Endpoints in Intermediate Age-Related Macular Degeneration. Ophthalmologica. 2019;241(2):61–72. + +[6]: Hogg RE, Wright DM, Quinn NB, et al. Prevalence and risk factors for age-related macular degeneration in a population-based cohort study of older adults in Northern Ireland using multimodal imaging: NICOLA Study. Br J Ophthalmol. 2022:bjophthalmol-2021-320469. + +[7]: Gabrielle P-H, Seydou A, Arnould L, et al. Subretinal Drusenoid Deposits in the Elderly in a Population-Based Study (the Montrachet Study). 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"build_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res2", "res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map + ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) + RPN: + IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] + PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level + PRE_NMS_TOPK_TEST: 1000 # Per FPN level + # Detectron1 uses 2000 proposals per-batch, + # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) + # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. + POST_NMS_TOPK_TRAIN: 1000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "StandardROIHeads" + IN_FEATURES: ["p2", "p3", "p4", "p5"] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 +SOLVER: + IMS_PER_BATCH: 14 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (496,) +VERSION: 2 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: False diff --git a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py new file mode 100644 index 00000000..d57a76e6 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py @@ -0,0 +1,117 @@ +from pycocotools.coco import COCO +import pandas as pd +from torchvision.ops.boxes import box_iou,box_convert +import torch +import numpy as np +import os +import json +from tqdm import tqdm + +class NpEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, np.integer): + return int(obj) + elif isinstance(obj, np.floating): + return float(obj) + elif isinstance(obj, np.ndarray): + return obj.tolist() + else: + return super(NpEncoder, self).default(obj) + +class Ensembler(): + def __init__(self,output_dir,dataset_name,grplist,iou_thresh,coco_gt_path=None,coco_instances_results_fname=None): + self.output_dir = output_dir + self.dataset_name=dataset_name + self.grplist = grplist + self.iou_thresh=iou_thresh + self.n_detectors = len(grplist) + + if coco_gt_path is None: + fname_gt = os.path.join(output_dir,dataset_name +"_coco_format.json") + else: + fname_gt = coco_gt_path + + if coco_instances_results_fname is None: + fname_dt = "coco_instances_results.json" + else: + fname_dt = coco_instances_results_fname + + #load in ground truth (form image lists) + coco_gt = COCO(fname_gt) + #populate detector truths + dtlist = [] + for grp in grplist: + fname = os.path.join(output_dir,grp,fname_dt) + dtlist.append(coco_gt.loadRes(fname)) + print('Successfully loaded {} into memory. {} instance detected.\n'.format(fname,len(dtlist[-1].anns))) + + self.coco_gt = coco_gt + self.cats = [cat['id'] for cat in self.coco_gt.dataset['categories']] + self.dtlist = dtlist + self.results=[] + + print('Working with {} models, {} categories, and {} images.'.format(self.n_detectors,len(self.cats),len(self.coco_gt.imgs.keys()))) + + def mean_score_nms(self): + def nik_merge(lsts): + """Niklas B. https://github.com/rikpg/IntersectionMerge/blob/master/core.py""" + sets = [set(lst) for lst in lsts if lst] + merged = 1 + while merged: + merged = 0 + results = [] + while sets: + common, rest = sets[0], sets[1:] + sets = [] + for x in rest: + if x.isdisjoint(common): + sets.append(x) + else: + merged = 1 + common |= x + results.append(common) + sets = results + return sets + winning_list = [] + print('Computing mean score non-max suppression ensembling for {} images.'.format(len(self.coco_gt.imgs.keys()))) + for img in tqdm(self.coco_gt.imgs.keys()): + #print(img) + df = pd.DataFrame() #a dataframe of detections + obj_set = set() #a set of objects (frozensets) + for i,coco_dt in enumerate(self.dtlist): #for each detector append predictions to df + df = df.append(pd.DataFrame(coco_dt.imgToAnns[img]).assign(det=i),ignore_index=True) + if not df.empty: + for cat in self.cats: #for each category + dfcat = df[df['category_id']==cat] + ts = box_convert(torch.tensor(dfcat['bbox']),in_fmt='xywh',out_fmt='xyxy') #list of tensor boxes for cateogory + iou_bool = np.array((box_iou(ts,ts)>self.iou_thresh)) #compute IoU matrix and threshold + for i in range(len(dfcat)): #for each detection in that category + fset = frozenset(dfcat.index[iou_bool[i]]) + obj_set.add(fset) #compute set of sets representing objects + #find overlapping sets + + # for fs in obj_set: #for existing sets + # if fs&fset: #check for + # fsnew = fs.union(fset) + # obj_set.remove(fs) + # obj_set.add(fsnew) + obj_set = nik_merge(obj_set) + for s in obj_set:#for each detected objects, find winning box and assign score as mean of scores + dfset = dfcat.loc[list(s)] + mean_score = dfset['score'].sum()/max(self.n_detectors,len(s)) #allows for more detections than detectors + winning_box = dfset.iloc[dfset['score'].argmax()].to_dict() + winning_box['score']=mean_score + winning_list.append(winning_box) + print('{} resulting instances from NMS'.format(len(winning_list))) + self.results = winning_list + return self + + def save_coco_instances(self,fname = "coco_instances_results.json"): + if self.results: + with open(os.path.join(self.output_dir,fname),'w') as f: + f.write(json.dumps(self.results,cls=NpEncoder)) + f.flush() + +if __name__ == "__main__": + ens = Ensembler('dev',["fold1", "fold2", "fold3", "fold4","fold5"],.2) + ens.mean_score_nms() \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/scripts/__init__.py b/models/retinalOCT_RPD_segmentation/scripts/__init__.py new file mode 100644 index 00000000..29348081 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/__init__.py @@ -0,0 +1 @@ +from.inference import main # Import main from inference.py \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py new file mode 100644 index 00000000..961d2b09 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py @@ -0,0 +1,946 @@ +#!/usr/bin/env python + +""" +Utiltites for analyizing and visualizing model segmentations on dataset. +Yelena Bagdasarova, Scott Song +""" + +import os +import pickle +import numpy as np +import pandas as pd +import warnings +from tqdm import tqdm +import json +import sys + +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from pycocotools.mask import decode + +import torch +from detectron2.data import ( + DatasetCatalog, + MetadataCatalog) +import detectron2 +import detectron2.utils.comm as comm +from detectron2.engine import DefaultPredictor +from detectron2.utils.visualizer import Visualizer +from detectron2.evaluation import COCOEvaluator + +import cv2 +from PIL import Image +import matplotlib.pyplot as plt +plt.style.use('ybpres.mplstyle') +#plt.style.use('/data/ssong/detectron2-rpd-yb/detectron2-rpd-pkg/src/detectron2-rpd/ybpres.mplstyle') +from matplotlib.backends.backend_pdf import PdfPages +import seaborn as sns + + +def grab_dataset(name): + def f(): + return pickle.load( open( "datasets/"+name+".pk", "rb" ) ) + return f + + +class OutputVis(): + + def __init__(self,dataset_name,cfg=None,prob_thresh=0.5,pred_mode='model',pred_file=None,has_annotations=True, draw_mode = 'default'): + self.dataset_name = dataset_name + self.cfg = cfg + self.prob_thresh = prob_thresh + self.data = DatasetCatalog.get(dataset_name) + if pred_mode =='model': + self.predictor = DefaultPredictor(cfg) + self._mode = 'model' + elif pred_mode =='file': + with open(pred_file,'r') as f: + self.pred_instances = json.load(f) + self.instance_img_list = [p['image_id'] for p in self.pred_instances] + self._mode = 'file' + else: + sys.exit('Invalid mode. Only "model" or "file" permitted.') + self.has_annotations = has_annotations + self.permitted_draw_modes = ['default','bw'] + self.set_draw_mode(draw_mode) + self.font_size = 16 #28 for ARVO + self.annotation_color = 'r' + self.scale = 3.0 + + def set_draw_mode(self,draw_mode): + if draw_mode not in self.permitted_draw_modes: + sys.exit('draw_mode must be one of the following: {}'.format(self.permitted_draw_modes)) + self.draw_mode = draw_mode + + def get_ori_image(self,ImgId): + """[summary] + + Args: + ImgId (str): Value of image_id in image data structure. + + Returns: + PIL.Image: Original image fed into the model scaled up by a factor of 3 for visualization. + """ + dat = self.get_gt_image_data(ImgId) #gt + im = cv2.imread(dat['file_name']) #input to model + v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) + result_image = v_gt.output.get_image() #get original image + img = Image.fromarray(result_image) + return img + + def get_gt_image_data(self,ImgId): + """Returns image ground truth image entry for corresponding to ImgId. + + Args: + ImgId (str): Value of image_id in image data structure. + + Returns: + dict: Dictionary for the image with corresponding ImgId. + """ + gt_data = next(item for item in self.data if (item['image_id'] == ImgId)) + return gt_data + + def produce_gt_image(self,dat,im): + """Returns image im overlayed with ground truth instances found in dat if there are any. Depending on visualizer mode, the instances are separate colors or monochrome. + + Args: + dat (dict): Dictionary for the image im containing ground truth annotations. + im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to + the height and width of the image respectively. C is the number of color channels. The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. + + Returns: + PIL.Image: The resulting original image overlayed with ground truth instances. + """ + v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) + if (self.has_annotations): #ground truth boxes and masks + segs = [ddict['segmentation'] for ddict in dat['annotations']] + if self.draw_mode is 'bw': + BBoxes = None + assigned_colors = [self.annotation_color]*len(segs) + else: #default behavior + bboxes = [ddict['bbox'] for ddict in dat['annotations']] + BBoxes = detectron2.structures.Boxes(bboxes) + BBoxes = detectron2.structures.BoxMode.convert(BBoxes.tensor,from_mode=1,to_mode=0) #0= XYXY, 1 = XYWH + assigned_colors = None + + result_image = v_gt.overlay_instances(boxes=BBoxes,masks=segs,assigned_colors=assigned_colors, alpha=1.0).get_image() + else: + result_image = v_gt.output.get_image() #get original image if no annotations + img = Image.fromarray(result_image) + return img + + def produce_model_image(self,ImgId,dat,im): + """Returns image im overlayed with instances predicted by the model. Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on the given ImgId. + + Args: + ImgId (str): Value of image_id in image data structure. + dat (dict): Dictionary for the image im. Used for height and width parameters. + im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to + the height and width of the image respectively. C is the number of color channels. The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. + + Returns: + PIL.Image: The resulting original image overlayed with model-predicted instances. + """ + v_dt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) + v_dt._default_font_size = self.font_size + + #get predictions from model or file + if self._mode=='model': + outputs = self.predictor(im)["instances"].to("cpu") + elif self._mode=='file': + outputs = self.get_outputs_from_file(ImgId,(dat['height'],dat['width'])) + outputs = outputs[outputs.scores>self.prob_thresh] #apply probability threshold to instances + if self.draw_mode is 'bw': + result_model = v_dt.overlay_instances(masks=outputs.pred_masks,assigned_colors=[self.annotation_color]*len(outputs), alpha=1.0).get_image() + else: #default behavior + result_model = v_dt.draw_instance_predictions(outputs).get_image() + img_model = Image.fromarray(result_model) + return img_model + + def get_image(self,ImgId): + """Returns the ground truth and model prediction overlays for the image corresponding to ImgId. + + Args: + ImgId (str): Value of image_id in image data structure. + + Returns: + (PIL.Image,PIL.Image): Tuple of PIL.images corresponding to ground truth and model prediction respectively. + """ + dat = self.get_gt_image_data(ImgId) #gt + im = cv2.imread(dat['file_name']) #input to model + img = self.produce_gt_image(dat,im) + img_model = self.produce_model_image(ImgId,dat,im) + return img, img_model + + def get_outputs_from_file(self,ImgId,imgsize): + """For image with image_id ImgId, reads in and converts instances from coco format in self.pred_file to a detectron2 Instances structure required for the visulizer utility. + + Args: + ImgId (str): Value of image_id in image data structure. + imgsize (tuple): Height and width of the image in pixels. + + Returns: + detectron2.structures.Instances: The correctly formated data structure to be used to the detectron2 visualizer utility. + """ + + pred_boxes = [] + scores = [] + pred_classes = [] + pred_masks = [] + for i,img in enumerate(self.instance_img_list): + if img==ImgId: + pred_boxes.append(self.pred_instances[i]['bbox']) + scores.append(self.pred_instances[i]['score']) + pred_classes.append(int(self.pred_instances[i]['category_id'])) + #pred_masks_rle.append(self.pred_instances[i]['segmentation']) + pred_masks.append(decode(self.pred_instances[i]['segmentation'])) + BBoxes = detectron2.structures.Boxes(pred_boxes) + pred_boxes = detectron2.structures.BoxMode.convert(BBoxes.tensor,from_mode=1,to_mode=0) #0= XYXY, 1 = XYWH + inst_dict = dict(pred_boxes = pred_boxes,scores=torch.tensor(np.array(scores)),pred_classes=torch.tensor(np.array(pred_classes)),pred_masks = torch.tensor(np.array(pred_masks)).to(torch.bool))#pred_masks_rle=pred_masks_rle) + outputs = detectron2.structures.Instances(imgsize,**inst_dict) + return outputs + + @staticmethod + def height_crop_range(im,height_target=256): + """Find the range of pixels in the height dimension spanning the height height_target which contain the brightest regions of the image. + + Args: + im (numpy array): a numpy array of shape (H, W, C) + height_target (int, optional): The desired span of the image height. Defaults to 256. + + Returns: + range: The starting and stopping pixels for cropping the image height. + """ + yhist = im.sum(axis=1) #integrate over width of image + mu = np.average(np.arange(yhist.shape[0]),weights = yhist) + h1 = int(np.floor(mu-height_target/2)) #inclusive + h2 = int(np.ceil(mu+height_target/2)) #exclusive + if h1<0: + h1 = 0 + h2 = height_target + if h2>yhist.shape[0]: + h2 = yhist.shape[0] + h1 = h2-height_target + return range(h1,h2) + + def output_to_pdf(self,ImgIds,outname,dfimg=None): + """Create pdf with name outname displaying ground truth and model prediction overlays for image ids listed in ImgIds. + + Args: + ImgIds (list(str)): List of image_id values to output. + outname (str): path name for pdf + dfimg (pandas.DataFrame, optional): A dataframe of stats to display for each image. Defaults to None. + """ + + gtstr = '' + dtstr = '' + + if dfimg is not None: + gtcols = dfimg.columns[['gt_' in col for col in dfimg.columns]] + dtcols = dfimg.columns[['dt_' in col for col in dfimg.columns]] + + with PdfPages(outname) as pdf: + for imgid in tqdm(ImgIds): + img, img_model = self.get_image(imgid) + #pdb.set_trace() + crop_range = self.height_crop_range(np.array(img.convert('L')),height_target=256*self.scale) + img = np.array(img)[crop_range] + img_model = np.array(img_model)[crop_range] + + fig, ax = plt.subplots(2,1,figsize=[22,10],dpi=200) + ax[0].imshow(img) + ax[0].set_title(imgid+' Ground Truth') + ax[0].set_axis_off() + ax[1].imshow(img_model) + ax[1].set_title(imgid+' Model Prediction') + ax[1].set_axis_off() + if dfimg is not None: #annotate with provided stats + gtstr = ['{:s}={:.2f}'.format(col,dfimg.loc[imgid,col]) for col in gtcols] + ax[0].text(0,.05*(ax[0].get_ylim()[0]),gtstr,color='white',fontsize=14) + dtstr = ['{:s}={:.2f}'.format(col,dfimg.loc[imgid,col]) for col in dtcols] + ax[1].text(0,.05*(ax[1].get_ylim()[0]),dtstr,color='white',fontsize=14) + pdf.savefig(fig) + plt.close(fig) + + def save_imgarr_to_tiff(self,imgs,outname): + """Save array of images in stacked tiff format (one image per page). + + Args: + imgs (array(PIL.Images)): An array of PIL.Images to save. + outname (str): Path name to save to. + """ + if len(imgs) > 1: + imgs[0].save(outname,dpi=(400,400), tags = "", compression = None, save_all=True, append_images=imgs[1:]) + else: + imgs[0].save(outname) + + def output_ori_to_tiff(self,ImgIds,outname): + """Save list of original images corresponding to ImgIds in stacked tiff format. + + Args: + ImgIds (list(str)): A list of image_ids for images to save. + outname (str): Path name to save to. + """ + imgs = [] + for imgid in tqdm(ImgIds): + img_ori = self.get_ori_image(imgid) #PIL Image + imgs.append(img_ori) + self.save_imgarr_to_tiff(imgs,outname) + + def output_pred_to_tiff(self,ImgIds,outname,pred_only=False): + """Save list of images overlayed with the model predictions in stacked tiff format. + + Args: + ImgIds (list(str)): A list of image_ids for images to save. + outname (str): Path name to save to. + """ + imgs = self.output_pred_to_list(ImgIds,pred_only) + self.save_imgarr_to_tiff(imgs,outname) + + def output_pred_to_list(self,ImgIds,pred_only=False): + """Return list of images overlayed with the model predictions. + + Args: + ImgIds (list(str)): A list of image_ids for images to save. + outname (str): Path name to save to. + """ + imgs = [] + for imgid in tqdm(ImgIds): + dat = self.get_gt_image_data(imgid) #gt + if pred_only: + im = np.zeros((dat['height'],dat['width'],3)) #blank image for overlay + assert self._mode == 'file', 'pred_mode must be "file" when pred_only flage is set to True.' #fix this later + else: + im = cv2.imread(dat['file_name']) #input to model + img_dt = self.produce_model_image(imgid,dat,im) + imgs.append(img_dt) + return imgs + + def output_all_to_tiff(self,ImgIds,outname): + """Save list of images (original, ground truth overlay, and model prediction overlay) to stacked tiff format. + + Args: + ImgIds (list(str)): A list of image_ids for images to save. + outname (str): Path name to save to. + """ + imgs = [] + for imgid in tqdm(ImgIds): + img_gt, img_dt = self.get_image(imgid) + img_ori = self.get_ori_image(imgid) + hcrange = list(self.height_crop_range(np.array(img_ori.convert('L')),height_target=256*self.scale)) + img_result = Image.fromarray(np.concatenate((np.array(img_ori.convert('RGB'))[hcrange,:],np.array(img_gt)[hcrange,:],np.array(img_dt)[hcrange]))) + imgs.append(img_result) + self.save_imgarr_to_tiff(imgs,outname) + + def get_enface_dt(self,grp,scan_height,scan_width,scan_spacing): + """Return enface perspective of model predictions for a single scan volume whose imgids are listed in the index of grp. + + Args: + grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. + scan_height (int): Image height in pixels. + scan_width (int): Image width in pixels. + scan_spacing (float): The spacing between scan centers in pixels. + + Returns: + np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. + """ + grp = grp.sort_index() + nscans = len(grp) + enface_height = int(np.ceil((nscans-1)*scan_spacing)) + enface = np.zeros((enface_height,scan_width,3),dtype=int) + for i,imgid in enumerate(grp.index): + pos = int(np.clip(np.floor(scan_spacing*i),0,scan_width-1)) #vertical enface position + + + outputs = self.get_outputs_from_file(imgid,(scan_height,scan_width)) + outputs = outputs[outputs.scores>self.prob_thresh] + instances = outputs.pred_boxes[:,(0,2)].round().clip(0,scan_width-1).to(np.int) + + for inst in instances: + try: + enface[max(pos-4,0):min(pos+4,scan_width-1),inst[0]:inst[1]]=np.array([255,255,255])#random_color(rgb = True) + except(IndexError): + print( pos, inst[0],inst[1]) + return enface + + def get_enface_gt(self,grp,scan_height,scan_width,scan_spacing): + """Return enface perspective of ground truth annotations for a single scan volume whose imgids are listed in the index of grp. + + Args: + grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. + scan_height (int): Image height in pixels. + scan_width (int): Image width in pixels. + scan_spacing (float): The spacing between scan centers in pixels. + + Returns: + np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. + """ + grp = grp.sort_index() + nscans = len(grp) + enface_height = int(np.ceil((nscans-1)*scan_spacing)) + enface = np.zeros((enface_height,scan_width,3),dtype=int) + if not self.has_annotations: + enface[:,:] = np.array([100,100,100]) + + else: + #minx = scan_width + for i,imgid in enumerate(grp.index): + pos = int(np.clip(np.floor(scan_spacing*i),0,scan_width-1)) + instances = self.get_gt_image_data(imgid)['annotations'] + for inst in instances: + x1= inst['bbox'][0] + #minx = min(minx,x1) + x2 = x1 + inst['bbox'][2] + try: + enface[max(pos-4,0):min(pos+4,scan_width-1),x1:x2]=np.array([255,255,255])#random_color(rgb = True) + except(IndexError): + print( pos, x1,x2) + return enface + + def compare_enface(self,grp,name,scan_height,scan_width,scan_spacing): + """Return figure comparing detector enface perspective of model prediction with ground truth. + + Args: + grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. + name (str): The name of the group identified by scan volume ID. + scan_height (int): Image height in pixels. + scan_width (int): Image width in pixels. + scan_spacing (float): The spacing between scan centers in pixels. + + Returns: + (matplotlib.figure.Figure, array(AxesSubplot)): Tuple of figure handle and array of subplot axes. + """ + fig, ax = plt.subplots(1,2,figsize=[18,9],dpi=120) + + enface = self.get_enface_dt(grp,scan_height,scan_width,scan_spacing) + ax[0].imshow(enface) + ax[0].set_title(str(name) + ' DT') + ax[0].set_aspect('equal') + + + enface = self.get_enface_gt(grp,scan_height,scan_width,scan_spacing) + ax[1].imshow(enface) + ax[1].set_title(str(name) + ' GT') + ax[1].set_aspect('equal') + return fig,ax + +def Wilson_CI(p,n,z): + if (p<0 or p>1 or n==0): + if (p<0 or p>1): + warnings.warn(f'The value of proportion {p} must be in the range [0,1]. Returning identity for CIs.') + else: + warnings.warn(f'The number of counts {n} must be above zero. Returning identity for CIs.') + return (p,p) + sym = z*(p*(1-p)/n + z*z/4/n/n)**.5 + asym = p + z*z/2/n + fact = 1/(1+z*z/n) + upper = fact*(asym+sym) + lower = fact*(asym-sym) + return (lower,upper) + +class EvaluateClass(COCOEvaluator): + def __init__(self,dataset_name, output_dir,prob_thresh=0.5,iou_thresh = 0.1,evalsuper=True): + super().__init__(dataset_name,tasks={'bbox','segm'},output_dir = output_dir) + self.dataset_name = dataset_name + self.mycoco=None #pycocotools.cocoEval instance + self.cocoDt=None + self.cocoGt=None + self.evalsuper = evalsuper # if True, run COCOEvaluator.evaluate() when self.evaluate is run + self.prob_thresh = prob_thresh #instance probabilty threshold for scalars (precision,recall,fpr for scans) + self.iou_thresh = iou_thresh #iou threshold for defining precision,recall + self.pr = None + self.rc = None + self.fpr = None + def reset(self): + super().reset() + self.mycoco=None + def process(self, inputs, outputs): + super().process(inputs,outputs) + def evaluate(self): + #with nostdout(): #suppress the coco summarize statment (the one with APs) + if self.evalsuper: + _ = super().evaluate() #this call populates coco_instances_results.json + comm.synchronize() + if not comm.is_main_process(): + return () + self.cocoGt = COCO(os.path.join(self._output_dir,self.dataset_name +'_coco_format.json')) #produced when super is initialized + self.cocoDt = self.cocoGt.loadRes(os.path.join(self._output_dir,'coco_instances_results.json')) #load detector results + self.mycoco = COCOeval(self.cocoGt,self.cocoDt,iouType ='segm') + self.num_images = len(self.mycoco.params.imgIds) + print('Calculated metrics for {} images'.format(self.num_images)) + self.mycoco.params.iouThrs = np.arange(.10,.6,.1) + #mycoco.params.recThrs = [0,.5,.75,1] + self.mycoco.params.maxDets = [100] + #mycoco.params.imgIds=[imgId] + self.mycoco.params.areaRng = [[0, 10000000000.0]] + + self.mycoco.evaluate() + self.mycoco.accumulate() + + self.pr = self.mycoco.eval['precision'][:, #iouthresh + :, #recall level + 0, #catagory + 0, #area range + 0] #max detections per image + self.rc = self.mycoco.params.recThrs + self.iou = self.mycoco.params.iouThrs + self.scores = self.mycoco.eval['scores'][:,:,0,0,0] #unreliable if GT has no instances + p,r = self.get_precision_recall() + return p,r + + def plot_PRcurve(self,ax=None): + if ax==None: + fig, ax = plt.subplots(1,1) + for i in range(len(self.iou)): + ax.plot(self.rc,self.pr[i],label = '{:.2}'.format(self.iou[i])) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_title('') + ax.legend(title='IoU') + + def plot_recall_vs_prob(self): + plt.figure() + for i in range(len(self.iou)): + plt.plot(self.rc,self.scores[i],label = '{:.2}'.format(self.iou[i])) + plt.ylabel('Model probability') + plt.xlabel('Recall') + plt.legend(title='IoU') + + def get_precision_recall(self): + iou_idx,rc_idx = self._find_iou_rc_inds() + precision = self.pr[iou_idx,rc_idx] + recall = self.rc[rc_idx] + return precision,recall + + def _calculate_fpr_matrix(self): + #FP rate, 1 RPD in image = FP + if (self.scores.min()==-1) and (self.scores.max()==-1): + print('WARNING: Scores for all iou thresholds and all recall levels are not defined. This can arise if ground truth annotations contain no instances. Leaving fpr matrix as None') + self.fpr = None + return + + fpr = np.zeros((len(self.iou),len(self.rc))) + for i in range(len(self.iou)): + for j,s in enumerate(self.scores[i]): #j -> recall level, s -> corresponding score + ng = 0 #number of negative images + fp = 0 #number of false positives images + for el in self.mycoco.evalImgs: + if el is None:#no predictions, no gts + ng=ng+1 + elif len(el['gtIds'])==0:# some predictions and no gts + ng=ng+1 + if (np.array(el['dtScores']) >s).sum() > 0: #if at least one score over threshold for recall level + fp=fp+1 #count as FP + else: + continue + fpr[i,j] = fp/ng + self.fpr = fpr + + def _calculate_fpr(self): + print('Using alternate calculation for fpr at instance score threshold of {}'.format(self.prob_thresh)) + ng = 0 #number of negative images + fp = 0 #number of false positives images + for el in self.mycoco.evalImgs: + if el is None:#no predictions, no gts + ng=ng+1 + elif len(el['gtIds'])==0:# some predictions and no gts + ng=ng+1 + if (np.array(el['dtScores']) >self.prob_thresh).sum() > 0: #if at least one score over threshold for recall level + fp=fp+1 #count as FP + else: #gt has instances + continue + return fp/(ng+1e-5) + + def _find_iou_rc_inds(self): + try: + iou_idx = np.argwhere(self.iou==self.iou_thresh)[0][0] #first instance of + except IndexError: + print('iou threshold {} not found in mycoco.params.iouThrs {}'.format(self.iou_thresh,self.mycoco.params.iouThrs)) + exit(1) + #test above for out of bounds + inds = np.argwhere(self.scores[iou_idx]>=self.prob_thresh) + if len(inds)>0: + rc_idx = inds[-1][0] #get recall index corresponding to prob_thresh + else: + rc_idx = 0 + return iou_idx,rc_idx + + def get_fpr(self): + + if self.fpr is None: + self._calculate_fpr_matrix() + + if self.fpr is not None: + iou_idx,rc_idx = self._find_iou_rc_inds() + fpr = self.fpr[iou_idx,rc_idx] + elif len(self.mycoco.cocoGt.anns)==0: + fpr = self._calculate_fpr() + else: + fpr=-1 + return fpr + + def summarize_scalars(self): #for pretty printing + p,r = self.get_precision_recall() + f1 = 2*(p*r)/(p + r) + fpr = self.get_fpr() + + #Confidence intervals + z=1.96 #95% Gaussian + #instance count + inst_cnt = self.count_instances() + n_r = inst_cnt['gt_instances'] + n_p = inst_cnt['dt_instances'] + n_fpr = inst_cnt['gt_neg_scans'] + + def stat_CI(p,n,z): + return z*np.sqrt(p*(1-p)/n) + + r_ci = Wilson_CI(r,n_r,z) + p_ci = Wilson_CI(p,n_p,z) + fpr_ci = Wilson_CI(fpr,n_fpr,z) + + #propogate errors for f1 + int_r = stat_CI(r,n_r,z) + int_p = stat_CI(p,n_p,z) + int_f1 =(f1)*np.sqrt(int_r**2 * (1/r - 1/(p+r))**2 + int_p**2 * (1/p - 1/(p+r))**2) + f1_ci = (f1-int_f1,f1+int_f1) + + dd = dict(dataset = self.dataset_name, precision = float(p),precision_ci = p_ci,recall=float(r), recall_ci = r_ci,f1 = float(f1),f1_ci = f1_ci, fpr=float(fpr),fpr_ci = fpr_ci, iou=self.iou_thresh,probability=self.prob_thresh) + return dd + + def count_instances(self): + gt_inst = 0 + dt_inst = 0 + gt_neg_scans = 0 + for key,val in self.cocoGt.imgs.items(): + imgid = val['id'] + #Gt instances + annIdsGt = self.cocoGt.getAnnIds([imgid]) + annsGt = self.cocoGt.loadAnns(annIdsGt) + gt_inst+=len(annsGt) + if len(annsGt)==0: + gt_neg_scans+=1 + + #Dt instances + annIdsDt = self.cocoDt.getAnnIds([imgid]) + annsDt = self.cocoDt.loadAnns(annIdsDt) + annsDt = [ann for ann in annsDt if ann['score']>self.prob_thresh] + dt_inst+=len(annsDt) + + return dict(gt_instances=gt_inst,dt_instances=dt_inst,gt_neg_scans = gt_neg_scans) + + +from sklearn.metrics import precision_recall_curve,average_precision_score +class CreatePlotsRPD(): + def __init__(self,dfimg): + self.dfimg = dfimg + self.dfvol = self.dfimg.groupby(['volID'])[['gt_instances','gt_pxs','gt_xpxs','dt_instances','dt_pxs','dt_xpxs']].sum() + + @classmethod + def initfromcoco(cls,mycoco,prob_thresh): + df = pd.DataFrame(index = mycoco.cocoGt.imgs.keys(), columns=['gt_instances','gt_pxs','gt_xpxs','dt_instances','dt_pxs','dt_xpxs'],dtype=np.uint64) + + for key,val in mycoco.cocoGt.imgs.items(): + imgid = val['id'] + #Gt instances + annIdsGt = mycoco.cocoGt.getAnnIds([imgid]) + annsGt = mycoco.cocoGt.loadAnns(annIdsGt) + instGt = [mycoco.cocoGt.annToMask(ann).sum() for ann in annsGt] + xprojGt = [(mycoco.cocoGt.annToMask(ann).sum(axis=0)>0).astype('uint8').sum() for ann in annsGt] + #Dt instances + annIdsDt = mycoco.cocoDt.getAnnIds([imgid]) + annsDt = mycoco.cocoDt.loadAnns(annIdsDt) + annsDt = [ann for ann in annsDt if ann['score']>prob_thresh] + instDt = [mycoco.cocoDt.annToMask(ann).sum() for ann in annsDt] + xprojDt = [(mycoco.cocoDt.annToMask(ann).sum(axis=0)>0).astype('uint8').sum() for ann in annsDt] + + dat = [len(instGt),np.array(instGt).sum(),np.array(xprojGt).sum(),len(instDt),np.array(instDt).sum(),np.array(xprojDt).sum()] + df.loc[key] = dat + + newdf = pd.DataFrame([idx.rsplit('.',1)[0].rsplit('_',1) for idx in df.index],columns=['volID','scan'],index = df.index) + df = df.merge(newdf,how='inner',left_index=True,right_index=True) + return cls(df) + + @classmethod + def initfromcsv(cls,fname): + df = pd.read_csv(fname) + return cls(df) + + def get_max_limits(self,df): + max_inst=np.max([df.gt_instances.max(),df.dt_instances.max()]) + max_xpxs = np.max([df.gt_xpxs.max(),df.dt_xpxs.max()]) + max_pxs = np.max([df.gt_pxs.max(),df.dt_pxs.max()]) +# print('Max instances:',max_inst) +# print('Max xpxs:',max_xpxs) +# print('Max pxs:',max_pxs) + return max_inst,max_xpxs,max_pxs + + def vol_level_prc(self,df,gt_thresh=5,ax=None): + + prc = precision_recall_curve(df.gt_instances>=gt_thresh,df.dt_instances) + if ax==None: + fig,ax = plt.subplots(1,1) + ax.plot(prc[1],prc[0]) + ax.set_xlabel('RPD Volume Recall') + ax.set_ylabel('RPD Volume Precision') + + + ap = average_precision_score(df.gt_instances>=gt_thresh,df.dt_instances) + return ap,prc + + def plot_img_level_instance_thresholding(self,df,inst): + + rc = np.zeros((len(inst),)) + pr = np.zeros((len(inst),)) + fpr = np.zeros((len(inst),)) + + fig, ax = plt.subplots(1,3,figsize = [15,5]) + for i,dt_thresh in enumerate(inst): + gt = df.gt_instances>dt_thresh + dt = df.dt_instances>dt_thresh + rc[i] = (gt&dt).sum()/gt.sum() + pr[i] = (gt&dt).sum()/dt.sum() + fpr[i] = ((~gt)&(dt)).sum()/((~gt).sum()) + + ax[1].plot(inst,pr) + ax[1].set_ylim(0.45,1.01) + ax[1].set_xlabel('instance threshold') + ax[1].set_ylabel('Precision') + + + ax[0].plot(inst,rc) + ax[0].set_ylim(0.45,1.01) + ax[0].set_ylabel('Recall') + ax[0].set_xlabel('instance threshold') + + + ax[2].plot(inst,fpr) + ax[2].set_ylim(0,0.80) + ax[2].set_xlabel('instance threshold') + ax[2].set_ylabel('FPR') + + plt.tight_layout() + return pr,rc,fpr + + def plot_img_level_instance_thresholding2(self,df,inst,gt_thresh,plot=True): + + #should be using global function + # def Wilson_CI(p,n,z): + + # sym = z*(p*(1-p)/n + z*z/4/n/n)**.5 + # asym = p + z*z/2/n + # fact = 1/(1+z*z/n) + # upper = fact*(asym+sym) + # lower = fact*(asym-sym) + # return (lower,upper) + + rc = np.zeros((len(inst),)) + pr = np.zeros((len(inst),)) + fpr = np.zeros((len(inst),)) + rc_ci = np.zeros((len(inst),2)) + pr_ci = np.zeros((len(inst),2)) + fpr_ci = np.zeros((len(inst),2)) + + + for i,dt_thresh in enumerate(inst): + gt = df.gt_instances>=gt_thresh + dt = df.dt_instances>=dt_thresh + rc[i] = (gt&dt).sum()/gt.sum() + pr[i] = (gt&dt).sum()/dt.sum() + fpr[i] = ((~gt)&(dt)).sum()/((~gt).sum()) + rc_ci[i,:] = Wilson_CI(rc[i],gt.sum(),1.96) + pr_ci[i,:]= Wilson_CI(pr[i],dt.sum(),1.96) + fpr_ci[i,:] = Wilson_CI(fpr[i],((~gt).sum()),1.96) + + if plot: + fig, ax = plt.subplots(1,3,figsize = [15,5]) + # ax[0].plot(rc,pr) + # ax[0].set_xlabel('Recall') + # ax[0].set_ylabel('Precision') + + ax[1].plot(inst,pr) + ax[1].fill_between(inst,pr_ci[:,0],pr_ci[:,1],alpha=.25) + #ax[1].set_ylim(0.45,1.01) + ax[1].set_xlabel('instance threshold') + ax[1].set_ylabel('Precision') + + + ax[0].plot(inst,rc) + ax[0].fill_between(inst,rc_ci[:,0],rc_ci[:,1],alpha=.25) + #ax[0].set_ylim(0.45,1.01) + ax[0].set_ylabel('Recall') + ax[0].set_xlabel('instance threshold') + + + ax[2].plot(inst,fpr) + ax[2].fill_between(inst,fpr_ci[:,0],fpr_ci[:,1],alpha=.25) + #ax[2].set_ylim(0,0.80) + ax[2].set_xlabel('instance threshold') + ax[2].set_ylabel('FPR') + + plt.tight_layout() + return dict(precision=pr,precision_ci = pr_ci,recall=rc,recall_ci = rc_ci, fpr=fpr,fpr_ci = fpr_ci) + + def gt_vs_dt_instances(self,ax=None): + df = self.dfimg + max_inst,max_xpxs,max_pxs = self.get_max_limits(df) + idx = (df.gt_instances>0)&(df.dt_instances>0) + + if ax==None: + fig = plt.figure(dpi=100) + ax = fig.add_subplot(111) + + y = df[idx].groupby('gt_instances')['dt_instances'].mean() + yerr = df[idx].groupby('gt_instances')['dt_instances'].std() + ax.errorbar(y.index,y.values,yerr.values,fmt='*') + plt.plot([0,max_inst],[0,max_inst],alpha=.5) + plt.xlim(0,max_inst+1) + plt.ylim(0,max_inst+1) + ax.set_aspect(1) + plt.xlabel('gt_instances') + plt.ylabel('dt_instances') + plt.tight_layout() + return ax + + def gt_vs_dt_instances_boxplot(self,ax=None): + df = self.dfimg + max_inst,max_xpxs,max_pxs = self.get_max_limits(df) + max_inst = int(max_inst) + if ax==None: + fig = plt.figure(dpi=100) + ax = fig.add_subplot(111) + + + + + ax.plot([0,max_inst+1],[0,max_inst+1],alpha=.5) + x = df['gt_instances'].values.astype(int) + y = df['dt_instances'].values.astype(int) + sns.boxplot(x,y, ax=ax,width=.5) + ax.set_xbound(0,max_inst+1) + ax.set_ybound(0,max_inst+1) + ax.set_aspect('equal') + + + ax.set_title('') + ax.set_xlabel('gt_instances') + ax.set_ylabel('dt_instances') + + import matplotlib.ticker as pltticker + loc = pltticker.MultipleLocator(base=2.0) + ax.xaxis.set_major_locator(loc) + ax.yaxis.set_major_locator(loc) + + + return ax + + + + def gt_vs_dt_xpxs(self): + df = self.dfimg + max_inst,max_xpxs,max_pxs = self.get_max_limits(df) + idx = (df.gt_instances>0)&(df.dt_instances>0) + dfsub = df[idx] + + fig1 = plt.figure(figsize = [10,10],dpi=100) + ax = fig1.add_subplot(111) + sc = ax.scatter(dfsub['gt_xpxs'],dfsub['dt_xpxs'],c =dfsub['gt_instances'] ,cmap='viridis') + ax.set_aspect(1) + #ax = dfsub.plot(kind = 'scatter',x=,y=,c='gt_instances') + plt.plot([0,max_xpxs],[0,max_xpxs],alpha=.5) + plt.xlim(0,max_xpxs) + plt.ylim(0,max_xpxs) + plt.xlabel('gt_xpxs') + plt.ylabel('dt_xpxs') + cbar = plt.colorbar(sc) + cbar.ax.set_ylabel('gt_instances') + plt.tight_layout() + + fig2 = plt.figure(figsize = [10,10],dpi=100) + ax = fig2.add_subplot(111) + sc = ax.scatter(dfsub['gt_xpxs'],dfsub['gt_xpxs']-dfsub['dt_xpxs'],c =dfsub['gt_instances'] ,cmap='viridis') + #ax = dfsub.plot(kind = 'scatter',x=,y=,c='gt_instances') + plt.plot([0,max_xpxs],[0,0],alpha=.5) + plt.xlabel('gt_xpxs') + plt.ylabel('gt_xpxs-dt_xpxs') + cbar = plt.colorbar(sc) + cbar.ax.set_ylabel('gt_instances') + plt.tight_layout() + + fig3 = plt.figure(dpi=100) + plt.hist(dfsub['gt_xpxs']-dfsub['dt_xpxs']) + plt.xlabel('gt_xpxs - dt_xpxs') + plt.ylabel('B-scans') + + return fig1,fig2,fig3 + + def gt_vs_dt_xpxs_mu(self): + df = self.dfimg + max_inst,max_xpxs,max_pxs = self.get_max_limits(df) + idx = (df.gt_instances>0)&(df.dt_instances>0) + dfsub = df[idx] + + from scipy import stats + mu_dt,bins,bnum = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['dt_xpxs'],statistic = 'mean',bins=10) + std_dt,_,_ = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['dt_xpxs'],statistic = 'std',bins = bins) + mu_gt,_,_ = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['gt_xpxs'],statistic='mean',bins=bins) + std_gt,_,_ = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['gt_xpxs'],statistic = 'std',bins = bins) + fig = plt.figure(dpi=100) + plt.errorbar(mu_gt,mu_dt,yerr = std_dt,xerr=std_gt,fmt='*') + plt.xlabel('gt_xpxs') + plt.ylabel('dt_xpxs') + plt.plot([0,max_xpxs],[0,max_xpxs],alpha=.5) + plt.xlim(0,max_xpxs) + plt.ylim(0,max_xpxs) + plt.gca().set_aspect(1) + plt.tight_layout() + return fig + + def gt_dt_FP_FN_count(self): + df = self.dfimg + fig,ax =plt.subplots(1,2,figsize=[10,5]) + + idx = (df.gt_instances==0)&(df.dt_instances>0) + ax[0].hist(df[idx]['dt_instances'],bins = range(1,10)) + ax[0].set_xlabel('dt instances') + ax[0].set_ylabel('B-scans') + ax[0].set_title('FP dt instance count per B-scan') + + idx = (df.gt_instances>0)&(df.dt_instances==0) + ax[1].hist(df[idx]['gt_instances'],bins = range(1,10)) + ax[1].set_xlabel('gt instances') + ax[1].set_ylabel('B-scans') + ax[1].set_title('FN gt instance count per B-scan') + + plt.tight_layout() + return fig + + def avg_inst_size(self): + df = self.dfimg + max_inst,max_xpxs,max_pxs = self.get_max_limits(df) + idx = (df.gt_instances>0)&(df.dt_instances>0) + dfsub = df[idx] + + fig = plt.figure(figsize=[10,5]) + plt.subplot(121) + bins = np.arange(0,120,10) + ax = (dfsub.gt_xpxs/dfsub.gt_instances).hist(bins = bins,alpha=.5,label='gt') + ax = (dfsub.dt_xpxs/dfsub.dt_instances).hist(bins=bins,alpha=.5,label='dt') + ax.set_xlabel('xpxs') + ax.set_ylabel('B-scans') + ax.set_title('Average size of instance') + ax.legend() + + plt.subplot(122) + bins = np.arange(0,600,40) + ax = (dfsub.gt_pxs/dfsub.gt_instances).hist(bins=bins,alpha=.5,label='gt') + ax = (dfsub.dt_pxs/dfsub.dt_instances).hist(bins=bins,alpha=.5,label='dt') + ax.set_xlabel('pxs') + ax.set_ylabel('B-scans') + ax.set_title('Average size of instance') + ax.legend() + + plt.tight_layout() + return fig + + diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/__init__.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py new file mode 100644 index 00000000..652baf84 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -0,0 +1,112 @@ +import os +import shutil +import cv2 +from PIL import Image +from .volReader import volFile +from tqdm import tqdm +import glob +import distutils.util +from pydicom import dcmread +from pydicom.fileset import FileSet +import pandas as pd + + +script_dir = os.path.dirname(__file__) +class Error(Exception): + """Base class for exceptions in this module.""" + pass + + + +# def extract_files(dirtoextract, extracted_path): +# proceed = True +# if ((os.path.isdir(extracted_path)) and (len(os.listdir(extracted_path))!=0)): +# val = input(f'{extracted_path} exists and is not empty. Files may be overwritten. Proceed with extraction? (Y/N)') +# proceed = bool(distutils.util.strtobool(val)) +# if proceed: +# print(f"Extracting files from {dirtoextract} into {extracted_path}...") +# files_to_extract = glob.glob(os.path.join(dirtoextract,'**/*.vol'),recursive=True) +# for i,line in enumerate(tqdm(files_to_extract)): +# fpath = line.strip('\n') +# vol = volFile(fpath) +# fpath = fpath.replace('\\','/') +# path, scan_str = fpath.strip('.vol').rsplit('/',1) +# extractpath = os.path.join(extracted_path,scan_str.replace('_','/')) +# os.makedirs(extractpath,exist_ok=True) +# preffix = os.path.join(extractpath, scan_str+'_oct') +# vol.renderOCTscans(preffix) +# else: +# pass + +def extract_files(dirtoextract, extracted_path, input_format): + assert input_format in ['vol','dicom'], 'Error: input_format must be "vol" or "dicom".' + proceed = True + if ((os.path.isdir(extracted_path)) and (len(os.listdir(extracted_path))!=0)): + val = input(f'{extracted_path} exists and is not empty. Files may be overwritten. Proceed with extraction? (Y/N)') + proceed = bool(distutils.util.strtobool(val)) + if proceed: + print(f"Extracting files from {dirtoextract} into {extracted_path}...") + if input_format == 'vol': + files_to_extract = glob.glob(os.path.join(dirtoextract,'**/*.vol'),recursive=True) + for i,line in enumerate(tqdm(files_to_extract)): + fpath = line.strip('\n') + vol = volFile(fpath) + fpath = fpath.replace('\\','/') + path, scan_str = fpath.strip('.vol').rsplit('/',1) + extractpath = os.path.join(extracted_path,scan_str.replace('_','/')) + os.makedirs(extractpath,exist_ok=True) + preffix = os.path.join(extractpath, scan_str+'_oct') + vol.renderOCTscans(preffix) + elif input_format =='dicom': + keywords = ['SOPInstanceUID', + 'PatientID', + 'ImageLaterality', + 'SeriesDate' + ] + list_of_dicts = [] + dirgen = glob.iglob(os.path.join(dirtoextract,'**/DICOMDIR'),recursive=True) + + for dsstr in dirgen: + fs = FileSet(dcmread(dsstr)) + fsgenOPT = genOPTfs(fs) + for fi in tqdm(fsgenOPT): + dd=dict() + #top level keywords + for key in keywords: + dd[key] = fi.get(key) + + volpath = os.path.join(extracted_path, f'{fi.SOPInstanceUID}') + #volpath = os.path.join(extracted_path, f'{fi.PatientID}_{fi.ImageLaterality}_{fi.SeriesDate}') #path for volume + shutil.rmtree(volpath,ignore_errors=True) + os.mkdir(volpath) + n = fi.NumberOfFrames + for i in range(n): + fname = os.path.join(volpath,f'{fi.SOPInstanceUID}_oct_{i:03d}.png') + Image.fromarray(fi.pixel_array[i]).save(fname) + list_of_dicts.append(dd.copy()) + dfoct = pd.DataFrame(list_of_dicts, columns = keywords) + dfoct.to_csv(os.path.join(extracted_path,'basic_meta.csv')) + else: + pass + +def rpd_data(extracted_path): + dataset = [] + instances = 0 + wrong_poly = 0 + extracted_files = glob.glob(os.path.join(extracted_path,'**/*.png'),recursive=True) + print("Generating dataset of images...") + for fn in tqdm(extracted_files): + fn_adjusted = fn.replace('\\','/') + imageid = fn_adjusted.split("/")[-1] + im = cv2.imread(fn) + dat = dict(file_name = fn_adjusted, height = im.shape[0], width = im.shape[1], image_id = imageid) + dataset.append(dat) + print(f"Found {len(dataset)} images") + print(f"Found {instances} instances") + print(f"Found {wrong_poly} too few vertices") + return dataset + +def genOPTfs(fs): + for instance in fs.find(Modality='OPT'): + ds = instance.load() + yield ds \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py new file mode 100644 index 00000000..92314063 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py @@ -0,0 +1,335 @@ +#!/usr/bin/env python +# +# Aaron Y. Lee MD MSCI (University of Washington) Copyright 2019 +# +# Code ported from Markus Mayer's excellent work (https://www5.cs.fau.de/research/software/octseg/) +# +# Also thanks to who contributed to the original openVol.m in Markus's project +# Radim Kolar, Brno University, Czech Republic +# Kris Sheets, Retinal Cell Biology Lab, Neuroscience Center of Excellence, LSU Health Sciences Center, New Orleans + + +import struct, array, datetime, codecs +import numpy as np +from collections import OrderedDict + +class volFile(): + def __init__(self, filename): + """ + Parses Heyex Spectralis *.vol files. + + Args: + filename (str): Path to vol file + + Returns: + volFile class + + """ + self.__parseVolFile(filename) + + @property + def oct(self): + """ + Retrieve OCT volume as a 3D numpy array. + + Returns: + 3D numpy array with OCT intensities as 'uint8' array + + """ + return self.wholefile["cScan"] + + @property + def irslo(self): + """ + Retrieve IR SLO image as 2D numpy array + + Returns: + 2D numpy array with IR reflectance SLO image as 'uint8' array. + + """ + return self.wholefile["sloImage"] + + @property + def grid(self): + """ + Retrieve the IR SLO pixel coordinates for the B scan OCT slices + + Returns: + 2D numpy array with the number of b scan images in the first dimension + and x_0, y_0, x_1, y_1 defining the line of the B scan on the pixel + coordinates of the IR SLO image. + + """ + wf = self.wholefile + grid = [] + for bi in range(len(wf["slice-headers"])): + bscanHead = wf["slice-headers"][bi] + x_0 = int(bscanHead["startX"] / wf["header"]["scaleXSlo"]) + x_1 = int(bscanHead["endX"] / wf["header"]["scaleXSlo"]) + y_0 = int(bscanHead["startY"] / wf["header"]["scaleYSlo"]) + y_1 = int(bscanHead["endY"] / wf["header"]["scaleYSlo"]) + grid.append([x_0, y_0, x_1, y_1]) + return grid + + def renderIRslo(self, filename, renderGrid=False): + """ + Renders IR SLO image as a PNG file and optionally overlays grid of B scans + + Args: + filename (str): filename to save IR SLO image + renderGrid (bool): True will render red lines for the location of the B scans. + + Returns: + None + + """ + from PIL import Image, ImageDraw + wf = self.wholefile + a = np.copy(wf["sloImage"]) + if renderGrid: + a = np.stack((a,)*3, axis=-1) + a = Image.fromarray(a) + draw = ImageDraw.Draw(a) + grid = self.grid + for (x_0, y_0, x_1, y_1) in grid: + draw.line((x_0,y_0, x_1, y_1), fill=(255,0,0), width=3) + a.save(filename) + else: + Image.fromarray(a).save(filepre) + + def renderOCTscans(self, filepre = "oct", renderSeg=False): + """ + Renders OCT images a PNG file and optionally overlays segmentation lines + Also creates a CSV file of vol file features. + + Args: + filepre (str): filename prefix. OCT Images will be named as "_001.png" + renderSeg (bool): True will render colored lines for the segmentation of the RPE, ILM, and NFL on the B scans. + + Returns: + None + + """ + from PIL import Image + wf = self.wholefile + for i in range(wf["cScan"].shape[0]): + a = np.copy(wf["cScan"][i]) + if renderSeg: + a = np.stack((a,)*3, axis=-1) + for li in range(wf["segmentations"].shape[0]): + for x in range(wf["segmentations"].shape[2]): + a[int(wf["segmentations"][li,i,x]),x, li] = 255 + + Image.fromarray(a).save("%s_%03d.png" % (filepre, i)) + + def __parseVolFile(self, fn, parseSeg=False): + print(fn) + wholefile = OrderedDict() + decode_hex = codecs.getdecoder("hex_codec") + with open(fn, "rb") as fin: + header = OrderedDict() + header["version"] = fin.read(12) + header["octSizeX"] = struct.unpack("I", fin.read(4))[0] # lateral resolution + header["numBscan"] = struct.unpack("I", fin.read(4))[0] + header["octSizeZ"] = struct.unpack("I", fin.read(4))[0] # OCT depth + header["scaleX"] = struct.unpack("d", fin.read(8))[0] + header["distance"] = struct.unpack("d", fin.read(8))[0] + header["scaleZ"] = struct.unpack("d", fin.read(8))[0] + header["sizeXSlo"] = struct.unpack("I", fin.read(4))[0] + header["sizeYSlo"] = struct.unpack("I", fin.read(4))[0] + header["scaleXSlo"] = struct.unpack("d", fin.read(8))[0] + header["scaleYSlo"] = struct.unpack("d", fin.read(8))[0] + header["fieldSizeSlo"] = struct.unpack("I", fin.read(4))[0] # FOV in degrees + header["scanFocus"] = struct.unpack("d", fin.read(8))[0] + header["scanPos"] = fin.read(4) + header["examTime"] = struct.unpack("=q", fin.read(8))[0] / 1e7 + header["examTime"] = datetime.datetime.utcfromtimestamp(header["examTime"] - (369*365.25+4) * 24*60*60) # needs to be checked + header["scanPattern"] = struct.unpack("I", fin.read(4))[0] + header["BscanHdrSize"] = struct.unpack("I", fin.read(4))[0] + header["ID"] = fin.read(16) + header["ReferenceID"] = fin.read(16) + header["PID"] = struct.unpack("I", fin.read(4))[0] + header["PatientID"] = fin.read(21) + header["unknown2"] = fin.read(3) + header["DOB"] = struct.unpack("d", fin.read(8))[0] - 25569 + header["DOB"] = datetime.datetime.utcfromtimestamp(0) + datetime.timedelta(seconds=header["DOB"] * 24 * 60 * 60 ) # needs to be checked + header["VID"] = struct.unpack("I", fin.read(4))[0] + header["VisitID"] = fin.read(24) + header["VisitDate"] = struct.unpack("d", fin.read(8))[0] - 25569 + header["VisitDate"] = datetime.datetime.utcfromtimestamp(0) + datetime.timedelta(seconds=header["VisitDate"] * 24 * 60 * 60) # needs to be checked + header["GridType"] = struct.unpack("I", fin.read(4))[0] + header["GridOffset"] = struct.unpack("I", fin.read(4))[0] + + wholefile["header"] = header + fin.seek(2048) + U = array.array("B") + U.frombytes(fin.read(header["sizeXSlo"] * header["sizeYSlo"])) + U = np.array(U).astype("uint8").reshape((header["sizeXSlo"],header["sizeYSlo"])) + wholefile["sloImage"] = U + + sloOffset = 2048 + header["sizeXSlo"] * header["sizeYSlo"] + octOffset = header["BscanHdrSize"] + header["octSizeX"] * header["octSizeZ"] * 4 + bscans = [] + bscanheaders = [] + bscanqualities = [] + if parseSeg: + segmentations = None + for i in range(header["numBscan"]): + fin.seek(16 + sloOffset + i * octOffset) + bscanHead = OrderedDict() + bscanHead["startX"] = struct.unpack("d", fin.read(8))[0] + bscanHead["startY"] = struct.unpack("d", fin.read(8))[0] + bscanHead["endX"] = struct.unpack("d", fin.read(8))[0] + bscanHead["endY"] = struct.unpack("d", fin.read(8))[0] + bscanHead["numSeg"] = struct.unpack("I", fin.read(4))[0] + bscanHead["offSeg"] = struct.unpack("I", fin.read(4))[0] + bscanHead["quality"] = struct.unpack("f", fin.read(4))[0] + bscanHead["shift"] = struct.unpack("I", fin.read(4))[0] + bscanheaders.append(bscanHead) + bscanqualities.append(bscanHead["quality"]) + + # extract OCT B scan data + fin.seek(header["BscanHdrSize"] + sloOffset + i * octOffset) + U = array.array("f") + U.frombytes(fin.read(4 * header["octSizeX"] * header["octSizeZ"])) + U = np.array(U).reshape((header["octSizeZ"],header["octSizeX"])) + # remove out of boundary + v = struct.unpack("f", decode_hex('FFFF7F7F')[0]) + U[U == v] = 0 + # log normalize + U = np.log(10000 * U + 1) + U = (255. * (np.clip(U, 0, np.max(U)) / np.max(U))).astype("uint8") + bscans.append(U) + if parseSeg: + # extract OCT segmentations data + fin.seek(256 + sloOffset + i * octOffset) + U = array.array("f") + U.frombytes(fin.read(4 * header["octSizeX"] * bscanHead["numSeg"])) + U = np.array(U) + print(U.shape) + U[U == v] = 0. + if segmentations == None: + segmentations = [] + for j in range(bscanHead["numSeg"]): + segmentations.append([]) + + for j in range(bscanHead["numSeg"]): + segmentations[j].append(U[j*header["octSizeX"]:(j+1) * header["octSizeX"]].tolist()) + wholefile["cScan"] = np.array(bscans) + if parseSeg: + wholefile["segmentations"] = np.array(segmentations) + wholefile["slice-headers"] = bscanheaders + wholefile["average-quality"] = np.mean(bscanqualities) + self.wholefile = wholefile + import csv + from pathlib import Path, PurePath + volFeatures = [PurePath(fn).name, wholefile["header"]["version"].decode("utf-8").rstrip('\x00'), wholefile["header"]["numBscan"], wholefile["header"]["octSizeX"], wholefile["header"]["octSizeZ"], wholefile["header"]["distance"], wholefile["header"]["scaleX"], \ + wholefile["header"]["scaleZ"], wholefile["header"]["sizeXSlo"], wholefile["header"]["sizeYSlo"], wholefile["header"]["scaleXSlo"], wholefile["header"]["scaleYSlo"], wholefile["header"]["fieldSizeSlo"], \ + wholefile["header"]["scanFocus"], wholefile["header"]["scanPos"].decode("utf-8").rstrip('\x00'), wholefile["header"]["examTime"], wholefile["header"]["scanPattern"], wholefile["header"]["BscanHdrSize"], \ + wholefile["header"]["ID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["ReferenceID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["PID"], wholefile["header"]["PatientID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["DOB"], wholefile["header"]["VID"], wholefile["header"]["VisitID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["VisitDate"], \ + wholefile["header"]["GridType"], wholefile["header"]["GridOffset"], wholefile["average-quality"]] + output_dir = PurePath(fn).parent + output_csv = output_dir.joinpath('vols.csv') + if not Path(output_csv).exists(): + print("Creating vols.csv as it does not exist.") + with open(output_csv, 'w', newline='') as file: + writer = csv.writer(file) + writer.writerow(["filename", "version", "numBscan", "octSizeX", "octSizeZ", "distance", "scaleX", \ + "scaleZ", "sizeXSlo", "sizeYSlo", "scaleXSlo", "scaleYSlo", "fieldSizeSlo", \ + "scanFocus", "scanPos", "examTime", "scanPattern", "BscanHdrSize", \ + "ID", "ReferenceID", "PID", "PatientID", "DOB", "VID", "VisitID", "VisitDate", \ + "GridType", "GridOffset", "Average Quality"]) + with open(output_csv, 'r', newline='') as file: + existing_vols = csv.reader(file) + for vol in existing_vols: + if vol[0] == PurePath(fn).name: + print("Skipping,", PurePath(fn).name, "already present in vols.csv.") + return + with open(output_csv, 'a', newline='') as file: + print("Adding", PurePath(fn).name, "to vols.csv.") + writer = csv.writer(file) + writer.writerow(volFeatures) + + + + @property + def fileHeader(self): + """ + Retrieve vol header fields + + Returns: + Dictionary with the following keys + - version: version number of vol file definition + - numBscan: number of B scan images in the volume + - octSizeX: number of pixels in the width of the OCT B scan + - octSizeZ: number of pixels in the height of the OCT B scan + - distance: unknown + - scaleX: resolution scaling factor of the width of the OCT B scan + - scaleZ: resolution scaling factor of the height of the OCT B scan + - sizeXSlo: number of pixels in the width of the IR SLO image + - sizeYSlo: number of pixels in the height of the IR SLO image + - scaleXSlo: resolution scaling factor of the width of the IR SLO image + - scaleYSlo: resolution scaling factor of the height of the IR SLO image + - fieldSizeSlo: field of view (FOV) of the retina in degrees + - scanFocus: unknown + - scanPos: Left or Right eye scanned + - examTime: Datetime of the scan (needs to be checked) + - scanPattern: unknown + - BscanHdrSize: size of B scan header in bytes + - ID: unknown + - ReferenceID + - PID: unknown + - PatientID: Patient ID string + - DOB: Date of birth + - VID: unknown + - VisitID: Visit ID string + - VisitDate: Datetime of visit (needs to be checked) + - GridType: unknown + - GridOffset: unknown + + """ + return self.wholefile["header"] + + def bScanHeader(self, slicei): + """ + Retrieve the B Scan header information per slice. + + Args: + slicei (int): index of B scan + + Returns: + Dictionary with the following keys + - startX: x-coordinate for B scan on IR. (see getGrid) + - startY: y-coordinate for B scan on IR. (see getGrid) + - endX: x-coordinate for B scan on IR. (see getGrid) + - endY: y-coordinate for B scan on IR. (see getGrid) + - numSeg: 2 or 3 segmentation lines for the B scan + - quality: OCT signal quality + - shift: unknown + + """ + return self.wholefile["slice-headers"][slicei] + + def saveGrid(self, outfn): + """ + Saves the grid coordinates mapping OCT Bscans to the IR SLO image to a text file. The text file + will be a tab-delimited file with 5 columns: The bscan number, x_0, y_0, x_1, y_1 in pixel space + scaled to the resolution of the IR SLO image. + + Args: + outfn (str): location of where to output the file + + Returns: + None + + """ + grid = self.grid + with open(outfn, "w") as fout: + fout.write("bscan\tx_0\ty_0\tx_1\ty_1\n") + ri = 0 + for r in grid: + r = [ri] + r + fout.write("%s\n" % "\t".join(map(str, r))) + ri += 1 + + diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py new file mode 100644 index 00000000..76d137a5 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -0,0 +1,237 @@ +import os +os.chdir(os.path.dirname(os.path.abspath(__file__))) +current_directory = os.getcwd() +print(current_directory) + + +from .datasets import data +from .analysis_lib import grab_dataset,EvaluateClass,CreatePlotsRPD,OutputVis +from .Ensembler import Ensembler +from .table_styles import styles + +import pickle +from detectron2.config import get_cfg +from detectron2.data import DatasetCatalog,MetadataCatalog +from detectron2.modeling import build_model +from detectron2.data import build_detection_test_loader +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.evaluation import inference_on_dataset, COCOEvaluator + +import pandas as pd +import logging +import configargparse +import progressbar +import urllib +logging.basicConfig(level=logging.INFO) + +import json +import urllib.request +import zipfile + +os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + +dpi= 120 +class MyProgressBar(): + # https://stackoverflow.com/a/53643011/3826929 + # George C + def __init__(self): + self.pbar = None + + def __call__(self, block_num, block_size, total_size): + if not self.pbar: + self.pbar=progressbar.ProgressBar(maxval=total_size) + self.pbar.start() + + downloaded = block_num * block_size + if downloaded < total_size: + self.pbar.update(downloaded) + else: + self.pbar.finish() + +def create_dataset(dataset_name, extracted_path): # Creates dataset and pk file from extracted images. + stored_data = data.rpd_data(extracted_path) + pickle.dump(stored_data, open(os.path.join(data.script_dir,f"{dataset_name}.pk"), "wb")) + +def configure_model(): + cfg = get_cfg() + moddir = os.path.dirname(os.path.realpath(__file__)) + name = 'working.yaml' + cfg_path = os.path.join(moddir, name) + cfg.merge_from_file(cfg_path) + return cfg + +def register_dataset(dataset_name): + for name in [dataset_name]: + try: + DatasetCatalog.register(name, grab_dataset(name)) + except: + print('Already registered.') + MetadataCatalog.get(name).thing_classes = ["rpd"] + +def run_prediction(cfg, dataset_name, output_path): + model = build_model(cfg) # returns a torch.nn.Module + myloader = build_detection_test_loader(cfg,dataset_name) + myeval = COCOEvaluator(dataset_name,tasks={'bbox','segm'},output_dir =output_path) #produces _coco_format.json when initialized + for mdl in ("fold1", "fold2", "fold3", "fold4","fold5"): + extract_directory = '../model' + if not os.path.isdir(extract_directory): + os.mkdir(extract_directory) + url = 'https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/models.zip' + path_to_zip_file, headers = urllib.request.urlretrieve(url, reporthook = MyProgressBar()) + with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref: + zip_ref.extractall(extract_directory) + file_name = mdl + "_model_final.pth" + model_weights_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), extract_directory, file_name) + print(model_weights_path) + DetectionCheckpointer(model).load(model_weights_path) # load a file, usually from cfg.MODEL.WEIGHTS + model.eval() #set model in evaluation mode + myeval.reset() + output_dir = os.path.join(output_path, mdl) + myeval._output_dir = output_dir + print("Running inference with model ", mdl) + results_i = inference_on_dataset(model, myloader, myeval) #produces coco_instance_results.json when myeval.evaluate is called + print("Done with predictions!") + +def run_ensemble(dataset_name, output_path, iou_thresh = 0.2): + ens = Ensembler(output_path,dataset_name,["fold1", "fold2", "fold3", "fold4","fold5"], iou_thresh=iou_thresh) + ens.mean_score_nms() + ens.save_coco_instances() + return ens + +def evaluate_dataset(dataset_name, output_path,iou_thresh = 0.2,prob_thresh = 0.5): + myeval = EvaluateClass(dataset_name, output_path, iou_thresh = iou_thresh, prob_thresh=prob_thresh,evalsuper=False) + myeval.evaluate() + with open(os.path.join(output_path,'scalar_dict.json'),"w") as outfile: + json.dump(obj=myeval.summarize_scalars(),fp=outfile) + return myeval + +def create_table(myeval): + dataset_table = CreatePlotsRPD.initfromcoco(myeval.mycoco,myeval.prob_thresh) + dataset_table.dfimg.sort_index(inplace=True) + return dataset_table + #dataset_table.dfimg['scan'] = dataset_table.dfimg['scan'].astype('int') #depends on what we want scan field to be + +def output_vol_predictions(dataset_table,vis,volID,output_path,output_mode='pred_overlay'): + dfimg = dataset_table.dfimg + imgids = dfimg[dfimg.volID ==volID].sort_index().index.values + outname = os.path.join(output_path,f'{volID}.tiff') + if output_mode=='pred_overlay': + vis.output_pred_to_tiff(imgids,outname,pred_only=False) + elif output_mode == 'pred_only': + vis.output_pred_to_tiff(imgids,outname,pred_only=True) + elif output_mode == 'originals': + vis.output_ori_to_tiff(imgids,outname) + elif output_mode == 'all': + vis.output_all_to_tiff(imgids,outname) + else: + print(f'Invalid mode {output_mode} for function output_vol_predictions.') + +def output_dataset_predictions(dataset_table,vis,output_path,output_mode = 'pred_overlay',draw_mode='default'): + vis.set_draw_mode(draw_mode) + os.makedirs(output_path,exist_ok=True) ## should this be set to exist_ok=False? + for volID in dataset_table.dfvol.index: + output_vol_predictions(dataset_table,vis,volID,output_path,output_mode) + +def create_dfvol(dataset_name, output_path, dataset_table): + dfvol = dataset_table.dfvol.sort_values(by=['dt_instances'],ascending=False) + with pd.option_context('styler.render.max_elements', int(dfvol.size)+1): + html_str = dfvol.style.format('{:.0f}').set_table_styles(styles).to_html() + html_file = open(os.path.join(output_path, 'dfvol_'+dataset_name+'.html'),'w') + html_file.write(html_str) + html_file.close() + +def create_dfimg(dataset_name, output_path, dataset_table): + dfimg = dataset_table.dfimg.sort_index() + with pd.option_context('styler.render.max_elements', int(dfimg.size)+1): + html_str = dfimg.style.set_table_styles(styles).to_html() + html_file = open(os.path.join(output_path, 'dfimg_'+dataset_name+'.html'),'w') + html_file.write(html_str) + html_file.close() + +def main(args): + dataset_name = None + input_dir = None + extracted = None + output = None + run_ext = True + run_inf = True + make_table = True + make_visuals = False + bm = False + bmo = False + imo = False + + print(args) + + dataset_name = args.get('dataset_name') # Access values from the dictionary + input_dir = args.get('input_dir') + extracted = args.get('extracted_dir') + input_format = args.get('input_format') + output = args.get('output_dir') + run_ext = args.get('run_extract', True) # Provide default values + make_dataset = args.get('create_dataset',True) + run_inf = args.get('run_inference', True) + make_table = args.get('create_tables', True) + bm = args.get('binary_mask', False) + bmo = args.get('binary_mask_overlay', False) + imo = args.get('instance_mask_overlay', False) + make_visuals = (bm | bmo | imo) + + iou_thresh = 0.2 + prob_thresh = 0.5 + if run_ext: + if not os.path.isdir(extracted): + print("Extracted dir does not exist! Making extracted dir...") + os.mkdir(extracted) + data.extract_files(input_dir, extracted, input_format) + print("Image extraction complete!") + if make_dataset: + print("Creating dataset from extracted images...") + create_dataset(dataset_name, extracted) + if run_inf: + print("Configuring model...") + cfg = configure_model() + print("Registering dataset...") + register_dataset(dataset_name) + if not os.path.isdir(output): + print("Output dir does not exist! Making output dir...") + os.makedirs(output) + print("Running inference...") + run_prediction(cfg, dataset_name, output) + print("Inference complete, running ensemble...") + run_ensemble(dataset_name, output) + print("Ensemble complete!") + if make_table or make_visuals: + print("Registering dataset...") + register_dataset(dataset_name) + print("Evaluating dataset...") + eval = evaluate_dataset(dataset_name, output, iou_thresh, prob_thresh) + print("Creating dataset table...") + table = create_table(eval) + if make_table: + create_dfvol(dataset_name, output, table) + create_dfimg(dataset_name, output, table) + print("Dataset htmls complete!") + if make_visuals: + vis = OutputVis(dataset_name, + prob_thresh = eval.prob_thresh, + pred_mode = 'file', + pred_file = os.path.join(output, 'coco_instances_results.json'), + has_annotations=False) + vis.scale=1.0 + if bm: + print("Creating binary masks tif (no overlay)...") + vis.annotation_color='w' + output_dataset_predictions(table,vis,os.path.join(output,'predicted_binary_masks'),'pred_only','bw') + if bmo: + print("Creating binary masks tif (with overlay)...") + output_dataset_predictions(table,vis,os.path.join(output,'predicted_binary_overlays'),'pred_overlay','bw') + if imo: + print("Creating instances masks tif (with overlay)...") + output_dataset_predictions(table,vis,os.path.join(output,'predicted_instance_overlays'),'pred_overlay','default') + print("Visualizations complete!") + +# if __name__ == "__main__": +# main(sys.argv[1:]) +# # main_alt() \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml new file mode 100644 index 00000000..47d3aa24 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml @@ -0,0 +1,56 @@ +_BASE_: "Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.001 + NMS_THRESH_TEST: .01 +INPUT: + MIN_SIZE_TRAIN: (496,) + MIN_SIZE_TEST: 496 +SOLVER: + BASE_LR: 0.02 + #GAMMA: 0.05 + #STEPS: (3000, 7000, 11000, 15000) + #MAX_ITER: 18000 + GAMMA: 0.1 + STEPS: (3000, 4500) + MAX_ITER: 6000 + CHECKPOINT_PERIOD: 300 + IMS_PER_BATCH: 14 +TEST: + DETECTIONS_PER_IMAGE: 30 # LVIS allows up to 300 + EVAL_PERIOD: 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 + NUM_WORKERS: 4 +# DATASETS: +# TRAIN: ("fold1","fold2","fold3","fold4",) +# TEST: ("fold5",) +# OUTPUT_DIR: "./output_valid_fold5" +# DATASETS: +# TRAIN: ("fold2","fold3","fold4","fold5",) +# TEST: ("fold1",) +# OUTPUT_DIR: "./output_valid_fold1" +# DATASETS: +# TRAIN: ("fold3","fold4","fold5","fold1",) +# TEST: ("fold2",) +# OUTPUT_DIR: "./output_valid_fold2" +# DATASETS: +# TRAIN: ("fold4","fold5","fold1","fold2",) +# TEST: ("fold3",) +# OUTPUT_DIR: "./output_valid_fold3" +# DATASETS: +# TRAIN: ("fold5","fold1","fold2","fold3",) +# TEST: ("fold4",) +# OUTPUT_DIR: "./output_valid_fold4" + +#modifiying to commit again \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/scripts/table_styles.py b/models/retinalOCT_RPD_segmentation/scripts/table_styles.py new file mode 100644 index 00000000..148e76b6 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/table_styles.py @@ -0,0 +1,41 @@ +def hover(hover_color="#add8e6"): + return dict(selector="tbody tr:hover", + props=[("background-color", "%s" % hover_color)]) + +styles = [ + #table properties + dict(selector=" ", + props=[("margin","0"), + ("font-family",'"Helvetica", "Arial", sans-serif'), + ("border-collapse", "collapse"), + ("border","none"), + ("border", "2px solid #ccf") + ]), + +# #header color - optional +# dict(selector="thead", +# props=[("background-color","#cc8484") +# ]), + + #background shading + dict(selector="tbody tr:nth-child(even)", + props=[("background-color", "#fff")]), + dict(selector="tbody tr:nth-child(odd)", + props=[("background-color", "#eee")]), + + #cell spacing + dict(selector="td", + props=[("padding", ".5em"),("text-align","center")]), + + #header cell properties + dict(selector="th", + props=[("font-size", "125%"), + ("text-align", "center")]), + + #caption placement + dict(selector="caption", + props=[("caption-side", "bottom")]), + + #render hover last to override background-color + hover() +] diff --git a/models/retinalOCT_RPD_segmentation/scripts/working.yaml b/models/retinalOCT_RPD_segmentation/scripts/working.yaml new file mode 100644 index 00000000..47d3aa24 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/working.yaml @@ -0,0 +1,56 @@ +_BASE_: "Base-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl" + PIXEL_STD: [57.375, 57.120, 58.395] + MASK_ON: True + RESNETS: + STRIDE_IN_1X1: False # this is a C2 model + NUM_GROUPS: 32 + WIDTH_PER_GROUP: 8 + DEPTH: 101 + ROI_HEADS: + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.001 + NMS_THRESH_TEST: .01 +INPUT: + MIN_SIZE_TRAIN: (496,) + MIN_SIZE_TEST: 496 +SOLVER: + BASE_LR: 0.02 + #GAMMA: 0.05 + #STEPS: (3000, 7000, 11000, 15000) + #MAX_ITER: 18000 + GAMMA: 0.1 + STEPS: (3000, 4500) + MAX_ITER: 6000 + CHECKPOINT_PERIOD: 300 + IMS_PER_BATCH: 14 +TEST: + DETECTIONS_PER_IMAGE: 30 # LVIS allows up to 300 + EVAL_PERIOD: 300 +DATALOADER: + SAMPLER_TRAIN: "RepeatFactorTrainingSampler" + REPEAT_THRESHOLD: 0.001 + NUM_WORKERS: 4 +# DATASETS: +# TRAIN: ("fold1","fold2","fold3","fold4",) +# TEST: ("fold5",) +# OUTPUT_DIR: "./output_valid_fold5" +# DATASETS: +# TRAIN: ("fold2","fold3","fold4","fold5",) +# TEST: ("fold1",) +# OUTPUT_DIR: "./output_valid_fold1" +# DATASETS: +# TRAIN: ("fold3","fold4","fold5","fold1",) +# TEST: ("fold2",) +# OUTPUT_DIR: "./output_valid_fold2" +# DATASETS: +# TRAIN: ("fold4","fold5","fold1","fold2",) +# TEST: ("fold3",) +# OUTPUT_DIR: "./output_valid_fold3" +# DATASETS: +# TRAIN: ("fold5","fold1","fold2","fold3",) +# TEST: ("fold4",) +# OUTPUT_DIR: "./output_valid_fold4" + +#modifiying to commit again \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/scripts/ybpres.mplstyle b/models/retinalOCT_RPD_segmentation/scripts/ybpres.mplstyle new file mode 100644 index 00000000..a0c9a964 --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/scripts/ybpres.mplstyle @@ -0,0 +1,6 @@ +axes.titlesize : 16 +axes.labelsize : 16 +lines.linewidth : 2 +lines.markersize : 6 +xtick.labelsize : 15 +ytick.labelsize : 15 From 1e02cc58850834f7b965921a342ed775830f504c Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Wed, 12 Mar 2025 14:03:44 -0700 Subject: [PATCH 02/28] Deleted the zip file download in inference.py. Updated urls in large_files.yaml. Fixed case-sensitivity for .png/.PNG in dataset/data.py. Made confidence threshold an external argument. --- .../configs/inference_yb.yaml | 26 ------------------- .../configs/large_files.yaml | 24 +++++++++++++++-- .../docs/README.md | 5 ++-- .../scripts/datasets/data.py | 2 +- .../scripts/inference.py | 11 +++----- 5 files changed, 29 insertions(+), 39 deletions(-) delete mode 100644 models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml diff --git a/models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml b/models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml deleted file mode 100644 index 488de340..00000000 --- a/models/retinalOCT_RPD_segmentation/configs/inference_yb.yaml +++ /dev/null @@ -1,26 +0,0 @@ -imports: -- $import scripts -- $import scripts.inference - -args: - run_extract : False - # input_dir : "/data/amd-data/cera-rpddata/Himeesh DICOM/Folder 1" - # extracted_dir : "/data/yelena/monaiRPD/extracted_dicom" - # input_format : "dicom" - input_dir : "/data/amd-data/cera-rpddata/RPD_Himeesh_v2" - extracted_dir : "/data/yelena/monaiRPD/extracted_vol" - input_format : "vol" - create_dataset : False - dataset_name : "monai_vol" - - output_dir : "/data/yelena/monaiRPD/output_vol" - run_inference : False - create_tables : False - -# create visuals - binary_mask : False - binary_mask_overlay : False - instance_mask_overlay : False - -inference: -- $scripts.inference.main(@args) \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/configs/large_files.yaml b/models/retinalOCT_RPD_segmentation/configs/large_files.yaml index e4e5e95d..72728060 100644 --- a/models/retinalOCT_RPD_segmentation/configs/large_files.yaml +++ b/models/retinalOCT_RPD_segmentation/configs/large_files.yaml @@ -1,5 +1,25 @@ large_files: - - path: "models/models.zip" - url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/models.zip" + - path: "models/fold1_model_final.pth" + url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold1_model_final.pth" + hash_val: "" + hash_type: "" + + - path: "models/fold2_model_final.pth" + url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold2_model_final.pth" + hash_val: "" + hash_type: "" + + - path: "models/fold3_model_final.pth" + url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold3_model_final.pth" + hash_val: "" + hash_type: "" + + - path: "models/fold4_model_final.pth" + url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold4_model_final.pth" + hash_val: "" + hash_type: "" + + - path: "models/fold5_model_final.pth" + url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold5_model_final.pth" hash_val: "" hash_type: "" \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index d717514c..e9695e33 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -100,8 +100,9 @@ To run inference on your dataset: The final ensembled predictions will be saved in COCO Instance Segmentation format in `coco_instances_results.json` in the output directory. The output directory will also be populated with five folders with the preffix 'fold' which contain predictions from the individual models of the ensemble. ### SUMMARY TABLES and VISUAL OUTPUT -Tables and images can be created from the predictions and written to the output directory. -These tables can be created by setting `create_tables` to `True`: +Tables and images can be created from the predictions and written to the output directory. A confidence threshold of 0.5 is applied to the scored predictions by default. To change the threshold, set the `prob_thresh` value between 0.0 and 1.0. + +The tables can be created by setting `create_tables` to `True`: * HTML table called `dfimg_.html` indexed by OCT-B scan with columns listing the detected number of RPD instances (dt_instances), pixels (dt_pixels), and horizontal pixels (dt_xpxs) in that B-scan. * HTML table called `dfvol_.html` indexed by OCT volume with columns listing the detected number of RPD instances (dt_instances), pixels (dt_pixels), and horizontal pixels (dt_xpxs) in that volume. diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py index 652baf84..31dbf260 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -93,7 +93,7 @@ def rpd_data(extracted_path): dataset = [] instances = 0 wrong_poly = 0 - extracted_files = glob.glob(os.path.join(extracted_path,'**/*.png'),recursive=True) + extracted_files = glob.glob(os.path.join(extracted_path,'**/*.[Pp][Nn][Gg]'),recursive=True) print("Generating dataset of images...") for fn in tqdm(extracted_files): fn_adjusted = fn.replace('\\','/') diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 76d137a5..055dddb0 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -75,12 +75,6 @@ def run_prediction(cfg, dataset_name, output_path): myeval = COCOEvaluator(dataset_name,tasks={'bbox','segm'},output_dir =output_path) #produces _coco_format.json when initialized for mdl in ("fold1", "fold2", "fold3", "fold4","fold5"): extract_directory = '../model' - if not os.path.isdir(extract_directory): - os.mkdir(extract_directory) - url = 'https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/models.zip' - path_to_zip_file, headers = urllib.request.urlretrieve(url, reporthook = MyProgressBar()) - with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref: - zip_ref.extractall(extract_directory) file_name = mdl + "_model_final.pth" model_weights_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), extract_directory, file_name) print(model_weights_path) @@ -156,6 +150,8 @@ def main(args): output = None run_ext = True run_inf = True + prob_thresh = 0.5 + iou_thresh = 0.2 make_table = True make_visuals = False bm = False @@ -172,14 +168,13 @@ def main(args): run_ext = args.get('run_extract', True) # Provide default values make_dataset = args.get('create_dataset',True) run_inf = args.get('run_inference', True) + prob_thresh = args.get('prob_thresh',0.5) make_table = args.get('create_tables', True) bm = args.get('binary_mask', False) bmo = args.get('binary_mask_overlay', False) imo = args.get('instance_mask_overlay', False) make_visuals = (bm | bmo | imo) - iou_thresh = 0.2 - prob_thresh = 0.5 if run_ext: if not os.path.isdir(extracted): print("Extracted dir does not exist! Making extracted dir...") From 3ac9f28aa369c5240d8b24c897f2a844b6ab45f1 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Tue, 25 Mar 2025 10:27:49 -0700 Subject: [PATCH 03/28] Implemented unit testing and added sample_data. --- ci/bundle_custom_data.py | 2 + .../test_retinalOCT_RPD_segmentation.py | 125 ++++++++++++++++++ .../configs/metadata.json | 54 ++++---- ...5a985fb301988fce780dcaad37e71a_oct_026.png | Bin 0 -> 207284 bytes ...5a985fb301988fce780dcaad37e71a_oct_051.png | Bin 0 -> 198775 bytes ...5a985fb301988fce780dcaad37e71a_oct_060.png | Bin 0 -> 198794 bytes ...abf1e4c558ab7b7637b88d962920af_oct_027.png | Bin 0 -> 217808 bytes ...abf1e4c558ab7b7637b88d962920af_oct_033.png | Bin 0 -> 201337 bytes .../scripts/Ensembler.py | 12 +- .../scripts/analysis_lib.py | 38 +++--- .../scripts/datasets/data.py | 11 +- .../scripts/datasets/volReader.py | 9 +- .../scripts/inference.py | 29 ++-- 13 files changed, 210 insertions(+), 70 deletions(-) create mode 100644 ci/unit_tests/test_retinalOCT_RPD_segmentation.py create mode 100644 models/retinalOCT_RPD_segmentation/sample_data/37721c93df11e35a8caa9b15616841ae985a985fb301988fce780dcaad37e71a_oct_026.png create mode 100644 models/retinalOCT_RPD_segmentation/sample_data/37721c93df11e35a8caa9b15616841ae985a985fb301988fce780dcaad37e71a_oct_051.png create mode 100644 models/retinalOCT_RPD_segmentation/sample_data/37721c93df11e35a8caa9b15616841ae985a985fb301988fce780dcaad37e71a_oct_060.png create mode 100644 models/retinalOCT_RPD_segmentation/sample_data/8c85a17e87eef485a975566dab6b54cafbabf1e4c558ab7b7637b88d962920af_oct_027.png create mode 100644 models/retinalOCT_RPD_segmentation/sample_data/8c85a17e87eef485a975566dab6b54cafbabf1e4c558ab7b7637b88d962920af_oct_033.png diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index 6f20d112..51cb50bf 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -23,6 +23,7 @@ "maisi_ct_generative", "cxr_image_synthesis_latent_diffusion_model", "brain_image_synthesis_latent_diffusion_model", + "retinalOCT_RPD_segmentation", ] # This list is used for our CI tests to determine whether a bundle contains the preferred files. @@ -47,6 +48,7 @@ "mednist_ddpm", "cxr_image_synthesis_latent_diffusion_model", "brain_image_synthesis_latent_diffusion_model", + "retinalOCT_RPD_segmentation", ] # This list is used for our CI tests to determine whether a bundle needs to be tested after downloading diff --git a/ci/unit_tests/test_retinalOCT_RPD_segmentation.py b/ci/unit_tests/test_retinalOCT_RPD_segmentation.py new file mode 100644 index 00000000..7f82570f --- /dev/null +++ b/ci/unit_tests/test_retinalOCT_RPD_segmentation.py @@ -0,0 +1,125 @@ +import glob +import json +import os +import shutil +import subprocess +import sys +import tempfile +import unittest + +import numpy as np +import pandas as pd +import yaml +from PIL import Image + + +class TestRPDInference(unittest.TestCase): + def setUp(self): + print(os.getcwd()) + # set the bundle root to the directory the test is being run from. + self.bundle_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..","..","models", "retinalOCT_RPD_segmentation")) + # Change the current working directory to bundle_root + os.chdir(self.bundle_root) + + # Create a temporary directory for test data + self.test_data_dir = tempfile.mkdtemp() + self.extracted_dir = os.path.join(self.bundle_root, "sample_data") + + + # create a dummy metadata.json file. + metadata_file = os.path.join(self.test_data_dir, "metadata.json") + metadata = { + "version": "0.0.1", + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", + } + with open(metadata_file, 'w') as f: + json.dump(metadata, f) + + # create output directory. + self.output_dir = os.path.join(self.test_data_dir, "output") + os.makedirs(self.output_dir) + + + + def tearDown(self): + # Clean up the temporary directory + shutil.rmtree(self.test_data_dir) + + + def test_inference_run(self): + # Override configuration parameters + override = { + "args": { + "extracted_dir": self.extracted_dir, + "output_dir": self.output_dir, + "run_extract": False, + "create_dataset": True, + "run_inference": True, + "binary_mask": True, + "binary_mask_overlay": True, + "instance_mask_overlay": True, + "dataset_name": "testDataset" + }, + } + + # Load the original inference.yaml + inference_yaml_path = "configs/inference.yaml" + with open(inference_yaml_path, 'r') as f: + inference_yaml = yaml.safe_load(f) + + # Modify inference.yaml with override parameters. + inference_yaml['args'].update(override['args']) + + # Create a new inference.yaml in the test_data_dir + test_inference_yaml_path = os.path.join(self.test_data_dir, "inference.yaml") + with open(test_inference_yaml_path, 'w') as f: + yaml.dump(inference_yaml, f) + + # Run the inference command using subprocess + cmd = [ + sys.executable, # Use the same Python interpreter + "-m", + "monai.bundle", + "run", + "inference", + "--bundle_root", + self.bundle_root, + "--config_file", + test_inference_yaml_path, # Use the new file + "--meta_file", + os.path.join(self.test_data_dir, "metadata.json"), + ] + + try: + subprocess.run(cmd, check=True) + except subprocess.CalledProcessError as e: + self.fail(f"Inference command failed: {e}") + + + # Add assertions to check the output + # Check if output files were created + output_files = os.listdir(self.output_dir) + self.assertTrue(len(output_files) > 0) + + # Check for the COCO JSON file + coco_file_found = glob.glob(os.path.join(self.output_dir,'**','coco_instances_results.json'),recursive=True) + print(coco_file_found) + self.assertTrue(len(coco_file_found)==6) + + # Check for the TIFF files. + tiff_files_found = glob.glob(os.path.join(self.output_dir,'**','*.tiff'),recursive=True) + self.assertTrue(len(tiff_files_found)==6) + + # Check for the html files. + html_files_found = glob.glob(os.path.join(self.output_dir,'*.html')) + self.assertTrue(len(html_files_found)==2) + + # At least 10 RPD present in sample data + dfvol = pd.read_html(os.path.join(self.output_dir,'dfvol_testDataset.html'))[0] + self.assertTrue(dfvol['dt_instances'].sum()[0]>10) + + + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index cbea63c3..45e3101a 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -1,28 +1,30 @@ { - "version": "0.0.1", - "changelog": { - "0.0.1": "Initial version" - }, - "monai_version": "1.1.0", - "pytorch_version": "1.10.0", - "numpy_version": "1.21.2", - "required_packages_version": { - "opencv-python-headless": "4.11.0.86", - "pandas": "1.3.5", - "seaborn": "0.12.2", - "scikit-learn": "1.0.2", - "configargparse": "1.7", - "progressbar": "2.5", - "pydicom": "2.4.4", - "fire": "0.7.0", - "jinja2": "3.1.5", - "detectron2": "0.6+cu113" - }, - "task": "Reticular Pseudodrusen (RPD) instance segmentation.", - "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", - "authors": "Yelena Bagdasarova, Scott Song", - "copyright": "Copyright (c) 2022, uw-biomedical-ml", -"network_data_format": { + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", + "version": "0.0.1", + "changelog": { + "0.0.1": "Initial version" + }, + "monai_version": "1.1.0", + "pytorch_version": "1.10.0", + "numpy_version": "1.21.2", + "optional_packages_version": { + "opencv-python-headless": "4.11.0.86", + "pandas": "1.3.5", + "seaborn": "0.12.2", + "scikit-learn": "1.0.2", + "configargparse": "1.7", + "progressbar": "2.5", + "pydicom": "2.4.4", + "fire": "0.7.0", + "jinja2": "3.1.5", + "detectron2": "0.6+cu113" + }, + "name": "retinalOCT_RPD_segmentation", + "task": "Reticular Pseudodrusen (RPD) instance segmentation.", + "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", + "authors": "Yelena Bagdasarova, Scott Song", + "copyright": "Copyright (c) 2022, uw-biomedical-ml", + "network_data_format": { "inputs": { "image": { "type": "image", @@ -61,10 +63,12 @@ } }, "outputs": { - "instances": { + "pred": { + "dtype": "dictionary", "type": "dictionary", "format": "COCO", "modality": "n/a", + "value_range": [0,1], "num_channels": 1, "spatial_shape": [496,1024], "channel_def": {"0": "RPD"}, diff --git a/models/retinalOCT_RPD_segmentation/sample_data/37721c93df11e35a8caa9b15616841ae985a985fb301988fce780dcaad37e71a_oct_026.png b/models/retinalOCT_RPD_segmentation/sample_data/37721c93df11e35a8caa9b15616841ae985a985fb301988fce780dcaad37e71a_oct_026.png new 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z{_(uS2$3VSgCBhA-@g9dG4{7F;vf9Vz`wepBcQHt{AnKfyz*a7_ygkLZ@&w2`T!Bo z*#*}-zY^Z{-ndGB)IB+13m=1`FXG3u|FCjXC+=U&JU(x6IY)lK`;BV+98iDvU^FLk z_&4){?OU_#`bBpB{MN+?d%@~+EgNsT1hf-04lL^{?mPc77vTQ_j~Y&kG@P+i00000 LNkvXXu0mjfC&)7i literal 0 HcmV?d00001 diff --git a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py index d57a76e6..61118df7 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py +++ b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py @@ -1,12 +1,14 @@ -from pycocotools.coco import COCO +import json +import os + +import numpy as np import pandas as pd -from torchvision.ops.boxes import box_iou,box_convert import torch -import numpy as np -import os -import json +from pycocotools.coco import COCO +from torchvision.ops.boxes import box_convert, box_iou from tqdm import tqdm + class NpEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): diff --git a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py index 961d2b09..7ba60fb5 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py +++ b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py @@ -5,36 +5,34 @@ Yelena Bagdasarova, Scott Song """ +import json import os import pickle -import numpy as np -import pandas as pd -import warnings -from tqdm import tqdm -import json import sys +import warnings -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval -from pycocotools.mask import decode - -import torch -from detectron2.data import ( - DatasetCatalog, - MetadataCatalog) +import cv2 import detectron2 import detectron2.utils.comm as comm +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch +from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.engine import DefaultPredictor -from detectron2.utils.visualizer import Visualizer from detectron2.evaluation import COCOEvaluator - -import cv2 +from detectron2.utils.visualizer import Visualizer from PIL import Image -import matplotlib.pyplot as plt +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from pycocotools.mask import decode +from tqdm import tqdm + plt.style.use('ybpres.mplstyle') +import seaborn as sns + #plt.style.use('/data/ssong/detectron2-rpd-yb/detectron2-rpd-pkg/src/detectron2-rpd/ybpres.mplstyle') from matplotlib.backends.backend_pdf import PdfPages -import seaborn as sns def grab_dataset(name): @@ -630,7 +628,9 @@ def count_instances(self): return dict(gt_instances=gt_inst,dt_instances=dt_inst,gt_neg_scans = gt_neg_scans) -from sklearn.metrics import precision_recall_curve,average_precision_score +from sklearn.metrics import average_precision_score, precision_recall_curve + + class CreatePlotsRPD(): def __init__(self,dfimg): self.dfimg = dfimg diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py index 31dbf260..6080184a 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -1,15 +1,16 @@ +import distutils.util +import glob import os import shutil + import cv2 +import pandas as pd from PIL import Image -from .volReader import volFile -from tqdm import tqdm -import glob -import distutils.util from pydicom import dcmread from pydicom.fileset import FileSet -import pandas as pd +from tqdm import tqdm +from .volReader import volFile script_dir = os.path.dirname(__file__) class Error(Exception): diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py index 92314063..c1cbbaab 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py @@ -9,10 +9,15 @@ # Kris Sheets, Retinal Cell Biology Lab, Neuroscience Center of Excellence, LSU Health Sciences Center, New Orleans -import struct, array, datetime, codecs -import numpy as np +import array +import codecs +import datetime +import struct from collections import OrderedDict +import numpy as np + + class volFile(): def __init__(self, filename): """ diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 055dddb0..1cb1fe5c 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -1,27 +1,28 @@ import os + os.chdir(os.path.dirname(os.path.abspath(__file__))) current_directory = os.getcwd() print(current_directory) -from .datasets import data -from .analysis_lib import grab_dataset,EvaluateClass,CreatePlotsRPD,OutputVis -from .Ensembler import Ensembler -from .table_styles import styles - +import logging import pickle -from detectron2.config import get_cfg -from detectron2.data import DatasetCatalog,MetadataCatalog -from detectron2.modeling import build_model -from detectron2.data import build_detection_test_loader -from detectron2.checkpoint import DetectionCheckpointer -from detectron2.evaluation import inference_on_dataset, COCOEvaluator +import urllib -import pandas as pd -import logging import configargparse +import pandas as pd import progressbar -import urllib +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_test_loader +from detectron2.evaluation import COCOEvaluator, inference_on_dataset +from detectron2.modeling import build_model + +from .analysis_lib import CreatePlotsRPD, EvaluateClass, OutputVis, grab_dataset +from .datasets import data +from .Ensembler import Ensembler +from .table_styles import styles + logging.basicConfig(level=logging.INFO) import json From 0676427d8decde316c57f813f78c6f2122cb8132 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Tue, 25 Mar 2025 11:44:29 -0700 Subject: [PATCH 04/28] Applied changes from isort, black, flake in runtests.sh. Ran pytype separately. --- .../test_retinalOCT_RPD_segmentation.py | 43 +- .../docs/README.md | 5 +- .../scripts/Ensembler.py | 97 +- .../scripts/__init__.py | 2 +- .../scripts/analysis_lib.py | 1077 +++++++++-------- .../scripts/datasets/data.py | 69 +- .../scripts/datasets/volReader.py | 149 ++- .../scripts/inference.py | 196 +-- .../scripts/table_styles.py | 65 +- 9 files changed, 922 insertions(+), 781 deletions(-) diff --git a/ci/unit_tests/test_retinalOCT_RPD_segmentation.py b/ci/unit_tests/test_retinalOCT_RPD_segmentation.py index 7f82570f..79c6c80d 100644 --- a/ci/unit_tests/test_retinalOCT_RPD_segmentation.py +++ b/ci/unit_tests/test_retinalOCT_RPD_segmentation.py @@ -7,24 +7,23 @@ import tempfile import unittest -import numpy as np import pandas as pd import yaml -from PIL import Image class TestRPDInference(unittest.TestCase): def setUp(self): print(os.getcwd()) # set the bundle root to the directory the test is being run from. - self.bundle_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..","..","models", "retinalOCT_RPD_segmentation")) + self.bundle_root = os.path.abspath( + os.path.join(os.path.dirname(__file__), "..", "..", "models", "retinalOCT_RPD_segmentation") + ) # Change the current working directory to bundle_root os.chdir(self.bundle_root) # Create a temporary directory for test data self.test_data_dir = tempfile.mkdtemp() self.extracted_dir = os.path.join(self.bundle_root, "sample_data") - # create a dummy metadata.json file. metadata_file = os.path.join(self.test_data_dir, "metadata.json") @@ -32,20 +31,17 @@ def setUp(self): "version": "0.0.1", "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", } - with open(metadata_file, 'w') as f: + with open(metadata_file, "w") as f: json.dump(metadata, f) # create output directory. self.output_dir = os.path.join(self.test_data_dir, "output") os.makedirs(self.output_dir) - - def tearDown(self): # Clean up the temporary directory shutil.rmtree(self.test_data_dir) - def test_inference_run(self): # Override configuration parameters override = { @@ -58,21 +54,21 @@ def test_inference_run(self): "binary_mask": True, "binary_mask_overlay": True, "instance_mask_overlay": True, - "dataset_name": "testDataset" - }, + "dataset_name": "testDataset", + } } # Load the original inference.yaml inference_yaml_path = "configs/inference.yaml" - with open(inference_yaml_path, 'r') as f: + with open(inference_yaml_path, "r") as f: inference_yaml = yaml.safe_load(f) # Modify inference.yaml with override parameters. - inference_yaml['args'].update(override['args']) + inference_yaml["args"].update(override["args"]) # Create a new inference.yaml in the test_data_dir test_inference_yaml_path = os.path.join(self.test_data_dir, "inference.yaml") - with open(test_inference_yaml_path, 'w') as f: + with open(test_inference_yaml_path, "w") as f: yaml.dump(inference_yaml, f) # Run the inference command using subprocess @@ -95,31 +91,28 @@ def test_inference_run(self): except subprocess.CalledProcessError as e: self.fail(f"Inference command failed: {e}") - # Add assertions to check the output # Check if output files were created output_files = os.listdir(self.output_dir) self.assertTrue(len(output_files) > 0) # Check for the COCO JSON file - coco_file_found = glob.glob(os.path.join(self.output_dir,'**','coco_instances_results.json'),recursive=True) + coco_file_found = glob.glob(os.path.join(self.output_dir, "**", "coco_instances_results.json"), recursive=True) print(coco_file_found) - self.assertTrue(len(coco_file_found)==6) + self.assertTrue(len(coco_file_found) == 6) # Check for the TIFF files. - tiff_files_found = glob.glob(os.path.join(self.output_dir,'**','*.tiff'),recursive=True) - self.assertTrue(len(tiff_files_found)==6) + tiff_files_found = glob.glob(os.path.join(self.output_dir, "**", "*.tiff"), recursive=True) + self.assertTrue(len(tiff_files_found) == 6) # Check for the html files. - html_files_found = glob.glob(os.path.join(self.output_dir,'*.html')) - self.assertTrue(len(html_files_found)==2) + html_files_found = glob.glob(os.path.join(self.output_dir, "*.html")) + self.assertTrue(len(html_files_found) == 2) # At least 10 RPD present in sample data - dfvol = pd.read_html(os.path.join(self.output_dir,'dfvol_testDataset.html'))[0] - self.assertTrue(dfvol['dt_instances'].sum()[0]>10) - - + dfvol = pd.read_html(os.path.join(self.output_dir, "dfvol_testDataset.html"))[0] + self.assertTrue(dfvol["dt_instances"].sum()[0] > 10) if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index e9695e33..11730193 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -89,7 +89,7 @@ Once you have the scans in PNG format, you can create a "dataset" in Detectron2 * set `create_dataset` to `True` * set `dataset_name` to the chosen name of your dataset -The dataset dictionary will be saved as pickle file in `////RPDBundle/datasets/.pk` +The summary tables and visual output is organized around OCT volumes, so please make sure that the basename of the PNG files looks like `_.` The dataset dictionary will be saved as pickle file in `////RPDBundle/datasets/.pk` ### INFERENCE To run inference on your dataset: @@ -108,6 +108,9 @@ The tables can be created by setting `create_tables` to `True`: The predicted segmentations can be output as multi-page TIFFs, where each TIFF file corresponds to an input volume of the dataset, and each page to an OCT slice from the volume in original order. The output images can be binary masks, binary masks overlaying the original B-scan, and instance masks overlaying the original B-scan. Set the `binary_mask`, `binary_mask_overlay` and `instance_mask_overlay` flags in the yaml file to `True` accordingly. +### SAMPLE DATA +As a reference, sample OCT-B scans are provided in PNG format under the `sample_data` directory. Set `extracted_dir` in `inference.yaml` to `sample_data` to run inference on these few set of images. + ## **System Configuration** Inference on one Nvidia A100 gpu takes about 0.041 s/batch of 14 images, about 3G of gpu memory, and 6G of RAM. diff --git a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py index 61118df7..b91d0771 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py +++ b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py @@ -20,16 +20,19 @@ def default(self, obj): else: return super(NpEncoder, self).default(obj) -class Ensembler(): - def __init__(self,output_dir,dataset_name,grplist,iou_thresh,coco_gt_path=None,coco_instances_results_fname=None): + +class Ensembler: + def __init__( + self, output_dir, dataset_name, grplist, iou_thresh, coco_gt_path=None, coco_instances_results_fname=None + ): self.output_dir = output_dir - self.dataset_name=dataset_name + self.dataset_name = dataset_name self.grplist = grplist - self.iou_thresh=iou_thresh + self.iou_thresh = iou_thresh self.n_detectors = len(grplist) - + if coco_gt_path is None: - fname_gt = os.path.join(output_dir,dataset_name +"_coco_format.json") + fname_gt = os.path.join(output_dir, dataset_name + "_coco_format.json") else: fname_gt = coco_gt_path @@ -38,21 +41,25 @@ def __init__(self,output_dir,dataset_name,grplist,iou_thresh,coco_gt_path=None,c else: fname_dt = coco_instances_results_fname - #load in ground truth (form image lists) + # load in ground truth (form image lists) coco_gt = COCO(fname_gt) - #populate detector truths + # populate detector truths dtlist = [] for grp in grplist: - fname = os.path.join(output_dir,grp,fname_dt) + fname = os.path.join(output_dir, grp, fname_dt) dtlist.append(coco_gt.loadRes(fname)) - print('Successfully loaded {} into memory. {} instance detected.\n'.format(fname,len(dtlist[-1].anns))) - + print("Successfully loaded {} into memory. {} instance detected.\n".format(fname, len(dtlist[-1].anns))) + self.coco_gt = coco_gt - self.cats = [cat['id'] for cat in self.coco_gt.dataset['categories']] + self.cats = [cat["id"] for cat in self.coco_gt.dataset["categories"]] self.dtlist = dtlist - self.results=[] + self.results = [] - print('Working with {} models, {} categories, and {} images.'.format(self.n_detectors,len(self.cats),len(self.coco_gt.imgs.keys()))) + print( + "Working with {} models, {} categories, and {} images.".format( + self.n_detectors, len(self.cats), len(self.coco_gt.imgs.keys()) + ) + ) def mean_score_nms(self): def nik_merge(lsts): @@ -74,46 +81,54 @@ def nik_merge(lsts): results.append(common) sets = results return sets + winning_list = [] - print('Computing mean score non-max suppression ensembling for {} images.'.format(len(self.coco_gt.imgs.keys()))) - for img in tqdm(self.coco_gt.imgs.keys()): - #print(img) - df = pd.DataFrame() #a dataframe of detections - obj_set = set() #a set of objects (frozensets) - for i,coco_dt in enumerate(self.dtlist): #for each detector append predictions to df - df = df.append(pd.DataFrame(coco_dt.imgToAnns[img]).assign(det=i),ignore_index=True) + print( + "Computing mean score non-max suppression ensembling for {} images.".format(len(self.coco_gt.imgs.keys())) + ) + for img in tqdm(self.coco_gt.imgs.keys()): + # print(img) + df = pd.DataFrame() # a dataframe of detections + obj_set = set() # a set of objects (frozensets) + for i, coco_dt in enumerate(self.dtlist): # for each detector append predictions to df + df = df.append(pd.DataFrame(coco_dt.imgToAnns[img]).assign(det=i), ignore_index=True) if not df.empty: - for cat in self.cats: #for each category - dfcat = df[df['category_id']==cat] - ts = box_convert(torch.tensor(dfcat['bbox']),in_fmt='xywh',out_fmt='xyxy') #list of tensor boxes for cateogory - iou_bool = np.array((box_iou(ts,ts)>self.iou_thresh)) #compute IoU matrix and threshold - for i in range(len(dfcat)): #for each detection in that category + for cat in self.cats: # for each category + dfcat = df[df["category_id"] == cat] + ts = box_convert( + torch.tensor(dfcat["bbox"]), in_fmt="xywh", out_fmt="xyxy" + ) # list of tensor boxes for cateogory + iou_bool = np.array((box_iou(ts, ts) > self.iou_thresh)) # compute IoU matrix and threshold + for i in range(len(dfcat)): # for each detection in that category fset = frozenset(dfcat.index[iou_bool[i]]) - obj_set.add(fset) #compute set of sets representing objects - #find overlapping sets + obj_set.add(fset) # compute set of sets representing objects + # find overlapping sets # for fs in obj_set: #for existing sets # if fs&fset: #check for # fsnew = fs.union(fset) # obj_set.remove(fs) - # obj_set.add(fsnew) - obj_set = nik_merge(obj_set) - for s in obj_set:#for each detected objects, find winning box and assign score as mean of scores + # obj_set.add(fsnew) + obj_set = nik_merge(obj_set) + for s in obj_set: # for each detected objects, find winning box and assign score as mean of scores dfset = dfcat.loc[list(s)] - mean_score = dfset['score'].sum()/max(self.n_detectors,len(s)) #allows for more detections than detectors - winning_box = dfset.iloc[dfset['score'].argmax()].to_dict() - winning_box['score']=mean_score + mean_score = dfset["score"].sum() / max( + self.n_detectors, len(s) + ) # allows for more detections than detectors + winning_box = dfset.iloc[dfset["score"].argmax()].to_dict() + winning_box["score"] = mean_score winning_list.append(winning_box) - print('{} resulting instances from NMS'.format(len(winning_list))) - self.results = winning_list + print("{} resulting instances from NMS".format(len(winning_list))) + self.results = winning_list return self - def save_coco_instances(self,fname = "coco_instances_results.json"): + def save_coco_instances(self, fname="coco_instances_results.json"): if self.results: - with open(os.path.join(self.output_dir,fname),'w') as f: - f.write(json.dumps(self.results,cls=NpEncoder)) + with open(os.path.join(self.output_dir, fname), "w") as f: + f.write(json.dumps(self.results, cls=NpEncoder)) f.flush() + if __name__ == "__main__": - ens = Ensembler('dev',["fold1", "fold2", "fold3", "fold4","fold5"],.2) - ens.mean_score_nms() \ No newline at end of file + ens = Ensembler("dev", ["fold1", "fold2", "fold3", "fold4", "fold5"], 0.2) + ens.mean_score_nms() diff --git a/models/retinalOCT_RPD_segmentation/scripts/__init__.py b/models/retinalOCT_RPD_segmentation/scripts/__init__.py index 29348081..79a1bc6b 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/__init__.py +++ b/models/retinalOCT_RPD_segmentation/scripts/__init__.py @@ -1 +1 @@ -from.inference import main # Import main from inference.py \ No newline at end of file +from .inference import main # Import main from inference.py diff --git a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py index 7ba60fb5..10241ba7 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py +++ b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py @@ -1,7 +1,7 @@ #!/usr/bin/env python """ -Utiltites for analyizing and visualizing model segmentations on dataset. +Utiltites for analyizing and visualizing model segmentations on dataset. Yelena Bagdasarova, Scott Song """ @@ -17,76 +17,86 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +import seaborn as sns import torch from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.engine import DefaultPredictor from detectron2.evaluation import COCOEvaluator from detectron2.utils.visualizer import Visualizer +from matplotlib.backends.backend_pdf import PdfPages from PIL import Image from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from pycocotools.mask import decode +from sklearn.metrics import average_precision_score, precision_recall_curve from tqdm import tqdm plt.style.use('ybpres.mplstyle') -import seaborn as sns -#plt.style.use('/data/ssong/detectron2-rpd-yb/detectron2-rpd-pkg/src/detectron2-rpd/ybpres.mplstyle') -from matplotlib.backends.backend_pdf import PdfPages +# plt.style.use('/data/ssong/detectron2-rpd-yb/detectron2-rpd-pkg/src/detectron2-rpd/ybpres.mplstyle') def grab_dataset(name): def f(): - return pickle.load( open( "datasets/"+name+".pk", "rb" ) ) - return f - - -class OutputVis(): - - def __init__(self,dataset_name,cfg=None,prob_thresh=0.5,pred_mode='model',pred_file=None,has_annotations=True, draw_mode = 'default'): + return pickle.load(open("datasets/" + name + ".pk", "rb")) + + return f + + +class OutputVis: + def __init__( + self, + dataset_name, + cfg=None, + prob_thresh=0.5, + pred_mode="model", + pred_file=None, + has_annotations=True, + draw_mode="default", + ): self.dataset_name = dataset_name self.cfg = cfg self.prob_thresh = prob_thresh self.data = DatasetCatalog.get(dataset_name) - if pred_mode =='model': + if pred_mode == "model": self.predictor = DefaultPredictor(cfg) - self._mode = 'model' - elif pred_mode =='file': - with open(pred_file,'r') as f: + self._mode = "model" + elif pred_mode == "file": + with open(pred_file, "r") as f: self.pred_instances = json.load(f) - self.instance_img_list = [p['image_id'] for p in self.pred_instances] - self._mode = 'file' + self.instance_img_list = [p["image_id"] for p in self.pred_instances] + self._mode = "file" else: sys.exit('Invalid mode. Only "model" or "file" permitted.') self.has_annotations = has_annotations - self.permitted_draw_modes = ['default','bw'] + self.permitted_draw_modes = ["default", "bw"] self.set_draw_mode(draw_mode) - self.font_size = 16 #28 for ARVO - self.annotation_color = 'r' + self.font_size = 16 # 28 for ARVO + self.annotation_color = "r" self.scale = 3.0 - def set_draw_mode(self,draw_mode): + def set_draw_mode(self, draw_mode): if draw_mode not in self.permitted_draw_modes: - sys.exit('draw_mode must be one of the following: {}'.format(self.permitted_draw_modes)) + sys.exit("draw_mode must be one of the following: {}".format(self.permitted_draw_modes)) self.draw_mode = draw_mode - def get_ori_image(self,ImgId): + def get_ori_image(self, ImgId): """[summary] Args: - ImgId (str): Value of image_id in image data structure. + ImgId (str): Value of image_id in image data structure. Returns: PIL.Image: Original image fed into the model scaled up by a factor of 3 for visualization. """ - dat = self.get_gt_image_data(ImgId) #gt - im = cv2.imread(dat['file_name']) #input to model + dat = self.get_gt_image_data(ImgId) # gt + im = cv2.imread(dat["file_name"]) # input to model v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) - result_image = v_gt.output.get_image() #get original image + result_image = v_gt.output.get_image() # get original image img = Image.fromarray(result_image) return img - def get_gt_image_data(self,ImgId): + def get_gt_image_data(self, ImgId): """Returns image ground truth image entry for corresponding to ImgId. Args: @@ -95,10 +105,10 @@ def get_gt_image_data(self,ImgId): Returns: dict: Dictionary for the image with corresponding ImgId. """ - gt_data = next(item for item in self.data if (item['image_id'] == ImgId)) - return gt_data + gt_data = next(item for item in self.data if (item["image_id"] == ImgId)) + return gt_data - def produce_gt_image(self,dat,im): + def produce_gt_image(self, dat, im): """Returns image im overlayed with ground truth instances found in dat if there are any. Depending on visualizer mode, the instances are separate colors or monochrome. Args: @@ -109,25 +119,29 @@ def produce_gt_image(self,dat,im): Returns: PIL.Image: The resulting original image overlayed with ground truth instances. """ - v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) - if (self.has_annotations): #ground truth boxes and masks - segs = [ddict['segmentation'] for ddict in dat['annotations']] - if self.draw_mode is 'bw': + v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) + if self.has_annotations: # ground truth boxes and masks + segs = [ddict["segmentation"] for ddict in dat["annotations"]] + if self.draw_mode == "bw": BBoxes = None - assigned_colors = [self.annotation_color]*len(segs) - else: #default behavior - bboxes = [ddict['bbox'] for ddict in dat['annotations']] + assigned_colors = [self.annotation_color] * len(segs) + else: # default behavior + bboxes = [ddict["bbox"] for ddict in dat["annotations"]] BBoxes = detectron2.structures.Boxes(bboxes) - BBoxes = detectron2.structures.BoxMode.convert(BBoxes.tensor,from_mode=1,to_mode=0) #0= XYXY, 1 = XYWH + BBoxes = detectron2.structures.BoxMode.convert( + BBoxes.tensor, from_mode=1, to_mode=0 + ) # 0= XYXY, 1 = XYWH assigned_colors = None - - result_image = v_gt.overlay_instances(boxes=BBoxes,masks=segs,assigned_colors=assigned_colors, alpha=1.0).get_image() + + result_image = v_gt.overlay_instances( + boxes=BBoxes, masks=segs, assigned_colors=assigned_colors, alpha=1.0 + ).get_image() else: - result_image = v_gt.output.get_image() #get original image if no annotations + result_image = v_gt.output.get_image() # get original image if no annotations img = Image.fromarray(result_image) return img - def produce_model_image(self,ImgId,dat,im): + def produce_model_image(self, ImgId, dat, im): """Returns image im overlayed with instances predicted by the model. Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on the given ImgId. Args: @@ -142,20 +156,22 @@ def produce_model_image(self,ImgId,dat,im): v_dt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) v_dt._default_font_size = self.font_size - #get predictions from model or file - if self._mode=='model': + # get predictions from model or file + if self._mode == "model": outputs = self.predictor(im)["instances"].to("cpu") - elif self._mode=='file': - outputs = self.get_outputs_from_file(ImgId,(dat['height'],dat['width'])) - outputs = outputs[outputs.scores>self.prob_thresh] #apply probability threshold to instances - if self.draw_mode is 'bw': - result_model = v_dt.overlay_instances(masks=outputs.pred_masks,assigned_colors=[self.annotation_color]*len(outputs), alpha=1.0).get_image() - else: #default behavior - result_model = v_dt.draw_instance_predictions(outputs).get_image() + elif self._mode == "file": + outputs = self.get_outputs_from_file(ImgId, (dat["height"], dat["width"])) + outputs = outputs[outputs.scores > self.prob_thresh] # apply probability threshold to instances + if self.draw_mode == "bw": + result_model = v_dt.overlay_instances( + masks=outputs.pred_masks, assigned_colors=[self.annotation_color] * len(outputs), alpha=1.0 + ).get_image() + else: # default behavior + result_model = v_dt.draw_instance_predictions(outputs).get_image() img_model = Image.fromarray(result_model) return img_model - def get_image(self,ImgId): + def get_image(self, ImgId): """Returns the ground truth and model prediction overlays for the image corresponding to ImgId. Args: @@ -164,13 +180,13 @@ def get_image(self,ImgId): Returns: (PIL.Image,PIL.Image): Tuple of PIL.images corresponding to ground truth and model prediction respectively. """ - dat = self.get_gt_image_data(ImgId) #gt - im = cv2.imread(dat['file_name']) #input to model - img = self.produce_gt_image(dat,im) - img_model = self.produce_model_image(ImgId,dat,im) + dat = self.get_gt_image_data(ImgId) # gt + im = cv2.imread(dat["file_name"]) # input to model + img = self.produce_gt_image(dat, im) + img_model = self.produce_model_image(ImgId, dat, im) return img, img_model - - def get_outputs_from_file(self,ImgId,imgsize): + + def get_outputs_from_file(self, ImgId, imgsize): """For image with image_id ImgId, reads in and converts instances from coco format in self.pred_file to a detectron2 Instances structure required for the visulizer utility. Args: @@ -185,22 +201,27 @@ def get_outputs_from_file(self,ImgId,imgsize): scores = [] pred_classes = [] pred_masks = [] - for i,img in enumerate(self.instance_img_list): - if img==ImgId: - pred_boxes.append(self.pred_instances[i]['bbox']) - scores.append(self.pred_instances[i]['score']) - pred_classes.append(int(self.pred_instances[i]['category_id'])) - #pred_masks_rle.append(self.pred_instances[i]['segmentation']) - pred_masks.append(decode(self.pred_instances[i]['segmentation'])) + for i, img in enumerate(self.instance_img_list): + if img == ImgId: + pred_boxes.append(self.pred_instances[i]["bbox"]) + scores.append(self.pred_instances[i]["score"]) + pred_classes.append(int(self.pred_instances[i]["category_id"])) + # pred_masks_rle.append(self.pred_instances[i]['segmentation']) + pred_masks.append(decode(self.pred_instances[i]["segmentation"])) BBoxes = detectron2.structures.Boxes(pred_boxes) - pred_boxes = detectron2.structures.BoxMode.convert(BBoxes.tensor,from_mode=1,to_mode=0) #0= XYXY, 1 = XYWH - inst_dict = dict(pred_boxes = pred_boxes,scores=torch.tensor(np.array(scores)),pred_classes=torch.tensor(np.array(pred_classes)),pred_masks = torch.tensor(np.array(pred_masks)).to(torch.bool))#pred_masks_rle=pred_masks_rle) - outputs = detectron2.structures.Instances(imgsize,**inst_dict) + pred_boxes = detectron2.structures.BoxMode.convert(BBoxes.tensor, from_mode=1, to_mode=0) # 0= XYXY, 1 = XYWH + inst_dict = dict( + pred_boxes=pred_boxes, + scores=torch.tensor(np.array(scores)), + pred_classes=torch.tensor(np.array(pred_classes)), + pred_masks=torch.tensor(np.array(pred_masks)).to(torch.bool), + ) # pred_masks_rle=pred_masks_rle) + outputs = detectron2.structures.Instances(imgsize, **inst_dict) return outputs @staticmethod - def height_crop_range(im,height_target=256): - """Find the range of pixels in the height dimension spanning the height height_target which contain the brightest regions of the image. + def height_crop_range(im, height_target=256): + """Find the range of pixels in the height dimension spanning the height height_target which contain the brightest regions of the image. Args: im (numpy array): a numpy array of shape (H, W, C) @@ -209,58 +230,58 @@ def height_crop_range(im,height_target=256): Returns: range: The starting and stopping pixels for cropping the image height. """ - yhist = im.sum(axis=1) #integrate over width of image - mu = np.average(np.arange(yhist.shape[0]),weights = yhist) - h1 = int(np.floor(mu-height_target/2)) #inclusive - h2 = int(np.ceil(mu+height_target/2)) #exclusive - if h1<0: + yhist = im.sum(axis=1) # integrate over width of image + mu = np.average(np.arange(yhist.shape[0]), weights=yhist) + h1 = int(np.floor(mu - height_target / 2)) # inclusive + h2 = int(np.ceil(mu + height_target / 2)) # exclusive + if h1 < 0: h1 = 0 h2 = height_target - if h2>yhist.shape[0]: + if h2 > yhist.shape[0]: h2 = yhist.shape[0] - h1 = h2-height_target - return range(h1,h2) + h1 = h2 - height_target + return range(h1, h2) - def output_to_pdf(self,ImgIds,outname,dfimg=None): + def output_to_pdf(self, ImgIds, outname, dfimg=None): """Create pdf with name outname displaying ground truth and model prediction overlays for image ids listed in ImgIds. Args: - ImgIds (list(str)): List of image_id values to output. - outname (str): path name for pdf + ImgIds (list(str)): List of image_id values to output. + outname (str): path name for pdf dfimg (pandas.DataFrame, optional): A dataframe of stats to display for each image. Defaults to None. """ - gtstr = '' - dtstr = '' - + gtstr = "" + dtstr = "" + if dfimg is not None: - gtcols = dfimg.columns[['gt_' in col for col in dfimg.columns]] - dtcols = dfimg.columns[['dt_' in col for col in dfimg.columns]] - + gtcols = dfimg.columns[["gt_" in col for col in dfimg.columns]] + dtcols = dfimg.columns[["dt_" in col for col in dfimg.columns]] + with PdfPages(outname) as pdf: for imgid in tqdm(ImgIds): img, img_model = self.get_image(imgid) - #pdb.set_trace() - crop_range = self.height_crop_range(np.array(img.convert('L')),height_target=256*self.scale) + # pdb.set_trace() + crop_range = self.height_crop_range(np.array(img.convert("L")), height_target=256 * self.scale) img = np.array(img)[crop_range] img_model = np.array(img_model)[crop_range] - - fig, ax = plt.subplots(2,1,figsize=[22,10],dpi=200) + + fig, ax = plt.subplots(2, 1, figsize=[22, 10], dpi=200) ax[0].imshow(img) - ax[0].set_title(imgid+' Ground Truth') + ax[0].set_title(imgid + " Ground Truth") ax[0].set_axis_off() ax[1].imshow(img_model) - ax[1].set_title(imgid+' Model Prediction') + ax[1].set_title(imgid + " Model Prediction") ax[1].set_axis_off() - if dfimg is not None: #annotate with provided stats - gtstr = ['{:s}={:.2f}'.format(col,dfimg.loc[imgid,col]) for col in gtcols] - ax[0].text(0,.05*(ax[0].get_ylim()[0]),gtstr,color='white',fontsize=14) - dtstr = ['{:s}={:.2f}'.format(col,dfimg.loc[imgid,col]) for col in dtcols] - ax[1].text(0,.05*(ax[1].get_ylim()[0]),dtstr,color='white',fontsize=14) + if dfimg is not None: # annotate with provided stats + gtstr = ["{:s}={:.2f}".format(col, dfimg.loc[imgid, col]) for col in gtcols] + ax[0].text(0, 0.05 * (ax[0].get_ylim()[0]), gtstr, color="white", fontsize=14) + dtstr = ["{:s}={:.2f}".format(col, dfimg.loc[imgid, col]) for col in dtcols] + ax[1].text(0, 0.05 * (ax[1].get_ylim()[0]), dtstr, color="white", fontsize=14) pdf.savefig(fig) plt.close(fig) - def save_imgarr_to_tiff(self,imgs,outname): + def save_imgarr_to_tiff(self, imgs, outname): """Save array of images in stacked tiff format (one image per page). Args: @@ -268,54 +289,56 @@ def save_imgarr_to_tiff(self,imgs,outname): outname (str): Path name to save to. """ if len(imgs) > 1: - imgs[0].save(outname,dpi=(400,400), tags = "", compression = None, save_all=True, append_images=imgs[1:]) + imgs[0].save(outname, dpi=(400, 400), tags="", compression=None, save_all=True, append_images=imgs[1:]) else: - imgs[0].save(outname) + imgs[0].save(outname) - def output_ori_to_tiff(self,ImgIds,outname): + def output_ori_to_tiff(self, ImgIds, outname): """Save list of original images corresponding to ImgIds in stacked tiff format. Args: ImgIds (list(str)): A list of image_ids for images to save. outname (str): Path name to save to. """ - imgs = [] + imgs = [] for imgid in tqdm(ImgIds): - img_ori = self.get_ori_image(imgid) #PIL Image + img_ori = self.get_ori_image(imgid) # PIL Image imgs.append(img_ori) - self.save_imgarr_to_tiff(imgs,outname) + self.save_imgarr_to_tiff(imgs, outname) - def output_pred_to_tiff(self,ImgIds,outname,pred_only=False): - """Save list of images overlayed with the model predictions in stacked tiff format. + def output_pred_to_tiff(self, ImgIds, outname, pred_only=False): + """Save list of images overlayed with the model predictions in stacked tiff format. Args: ImgIds (list(str)): A list of image_ids for images to save. outname (str): Path name to save to. """ - imgs = self.output_pred_to_list(ImgIds,pred_only) - self.save_imgarr_to_tiff(imgs,outname) + imgs = self.output_pred_to_list(ImgIds, pred_only) + self.save_imgarr_to_tiff(imgs, outname) - def output_pred_to_list(self,ImgIds,pred_only=False): - """Return list of images overlayed with the model predictions. + def output_pred_to_list(self, ImgIds, pred_only=False): + """Return list of images overlayed with the model predictions. Args: ImgIds (list(str)): A list of image_ids for images to save. outname (str): Path name to save to. """ - imgs = [] + imgs = [] for imgid in tqdm(ImgIds): - dat = self.get_gt_image_data(imgid) #gt + dat = self.get_gt_image_data(imgid) # gt if pred_only: - im = np.zeros((dat['height'],dat['width'],3)) #blank image for overlay - assert self._mode == 'file', 'pred_mode must be "file" when pred_only flage is set to True.' #fix this later - else: - im = cv2.imread(dat['file_name']) #input to model - img_dt = self.produce_model_image(imgid,dat,im) + im = np.zeros((dat["height"], dat["width"], 3)) # blank image for overlay + assert ( + self._mode == "file" + ), 'pred_mode must be "file" when pred_only flage is set to True.' # fix this later + else: + im = cv2.imread(dat["file_name"]) # input to model + img_dt = self.produce_model_image(imgid, dat, im) imgs.append(img_dt) return imgs - def output_all_to_tiff(self,ImgIds,outname): - """Save list of images (original, ground truth overlay, and model prediction overlay) to stacked tiff format. + def output_all_to_tiff(self, ImgIds, outname): + """Save list of images (original, ground truth overlay, and model prediction overlay) to stacked tiff format. Args: ImgIds (list(str)): A list of image_ids for images to save. @@ -325,12 +348,20 @@ def output_all_to_tiff(self,ImgIds,outname): for imgid in tqdm(ImgIds): img_gt, img_dt = self.get_image(imgid) img_ori = self.get_ori_image(imgid) - hcrange = list(self.height_crop_range(np.array(img_ori.convert('L')),height_target=256*self.scale)) - img_result = Image.fromarray(np.concatenate((np.array(img_ori.convert('RGB'))[hcrange,:],np.array(img_gt)[hcrange,:],np.array(img_dt)[hcrange]))) + hcrange = list(self.height_crop_range(np.array(img_ori.convert("L")), height_target=256 * self.scale)) + img_result = Image.fromarray( + np.concatenate( + ( + np.array(img_ori.convert("RGB"))[hcrange, :], + np.array(img_gt)[hcrange, :], + np.array(img_dt)[hcrange], + ) + ) + ) imgs.append(img_result) - self.save_imgarr_to_tiff(imgs,outname) + self.save_imgarr_to_tiff(imgs, outname) - def get_enface_dt(self,grp,scan_height,scan_width,scan_spacing): + def get_enface_dt(self, grp, scan_height, scan_width, scan_spacing): """Return enface perspective of model predictions for a single scan volume whose imgids are listed in the index of grp. Args: @@ -340,28 +371,29 @@ def get_enface_dt(self,grp,scan_height,scan_width,scan_spacing): scan_spacing (float): The spacing between scan centers in pixels. Returns: - np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. + np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. """ grp = grp.sort_index() nscans = len(grp) - enface_height = int(np.ceil((nscans-1)*scan_spacing)) - enface = np.zeros((enface_height,scan_width,3),dtype=int) - for i,imgid in enumerate(grp.index): - pos = int(np.clip(np.floor(scan_spacing*i),0,scan_width-1)) #vertical enface position + enface_height = int(np.ceil((nscans - 1) * scan_spacing)) + enface = np.zeros((enface_height, scan_width, 3), dtype=int) + for i, imgid in enumerate(grp.index): + pos = int(np.clip(np.floor(scan_spacing * i), 0, scan_width - 1)) # vertical enface position - - outputs = self.get_outputs_from_file(imgid,(scan_height,scan_width)) - outputs = outputs[outputs.scores>self.prob_thresh] - instances = outputs.pred_boxes[:,(0,2)].round().clip(0,scan_width-1).to(np.int) + outputs = self.get_outputs_from_file(imgid, (scan_height, scan_width)) + outputs = outputs[outputs.scores > self.prob_thresh] + instances = outputs.pred_boxes[:, (0, 2)].round().clip(0, scan_width - 1).to(np.int) for inst in instances: try: - enface[max(pos-4,0):min(pos+4,scan_width-1),inst[0]:inst[1]]=np.array([255,255,255])#random_color(rgb = True) - except(IndexError): - print( pos, inst[0],inst[1]) - return enface + enface[max(pos - 4, 0) : min(pos + 4, scan_width - 1), inst[0] : inst[1]] = np.array( + [255, 255, 255] + ) # random_color(rgb = True) + except IndexError: + print(pos, inst[0], inst[1]) + return enface - def get_enface_gt(self,grp,scan_height,scan_width,scan_spacing): + def get_enface_gt(self, grp, scan_height, scan_width, scan_spacing): """Return enface perspective of ground truth annotations for a single scan volume whose imgids are listed in the index of grp. Args: @@ -371,31 +403,33 @@ def get_enface_gt(self,grp,scan_height,scan_width,scan_spacing): scan_spacing (float): The spacing between scan centers in pixels. Returns: - np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. + np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. """ grp = grp.sort_index() nscans = len(grp) - enface_height = int(np.ceil((nscans-1)*scan_spacing)) - enface = np.zeros((enface_height,scan_width,3),dtype=int) + enface_height = int(np.ceil((nscans - 1) * scan_spacing)) + enface = np.zeros((enface_height, scan_width, 3), dtype=int) if not self.has_annotations: - enface[:,:] = np.array([100,100,100]) + enface[:, :] = np.array([100, 100, 100]) else: - #minx = scan_width - for i,imgid in enumerate(grp.index): - pos = int(np.clip(np.floor(scan_spacing*i),0,scan_width-1)) - instances = self.get_gt_image_data(imgid)['annotations'] + # minx = scan_width + for i, imgid in enumerate(grp.index): + pos = int(np.clip(np.floor(scan_spacing * i), 0, scan_width - 1)) + instances = self.get_gt_image_data(imgid)["annotations"] for inst in instances: - x1= inst['bbox'][0] - #minx = min(minx,x1) - x2 = x1 + inst['bbox'][2] + x1 = inst["bbox"][0] + # minx = min(minx,x1) + x2 = x1 + inst["bbox"][2] try: - enface[max(pos-4,0):min(pos+4,scan_width-1),x1:x2]=np.array([255,255,255])#random_color(rgb = True) - except(IndexError): - print( pos, x1,x2) + enface[max(pos - 4, 0) : min(pos + 4, scan_width - 1), x1:x2] = np.array( + [255, 255, 255] + ) # random_color(rgb = True) + except IndexError: + print(pos, x1, x2) return enface - def compare_enface(self,grp,name,scan_height,scan_width,scan_spacing): + def compare_enface(self, grp, name, scan_height, scan_width, scan_spacing): """Return figure comparing detector enface perspective of model prediction with ground truth. Args: @@ -408,323 +442,356 @@ def compare_enface(self,grp,name,scan_height,scan_width,scan_spacing): Returns: (matplotlib.figure.Figure, array(AxesSubplot)): Tuple of figure handle and array of subplot axes. """ - fig, ax = plt.subplots(1,2,figsize=[18,9],dpi=120) - - enface = self.get_enface_dt(grp,scan_height,scan_width,scan_spacing) - ax[0].imshow(enface) - ax[0].set_title(str(name) + ' DT') - ax[0].set_aspect('equal') + fig, ax = plt.subplots(1, 2, figsize=[18, 9], dpi=120) + enface = self.get_enface_dt(grp, scan_height, scan_width, scan_spacing) + ax[0].imshow(enface) + ax[0].set_title(str(name) + " DT") + ax[0].set_aspect("equal") - enface = self.get_enface_gt(grp,scan_height,scan_width,scan_spacing) + enface = self.get_enface_gt(grp, scan_height, scan_width, scan_spacing) ax[1].imshow(enface) - ax[1].set_title(str(name) + ' GT') - ax[1].set_aspect('equal') - return fig,ax - -def Wilson_CI(p,n,z): - if (p<0 or p>1 or n==0): - if (p<0 or p>1): - warnings.warn(f'The value of proportion {p} must be in the range [0,1]. Returning identity for CIs.') + ax[1].set_title(str(name) + " GT") + ax[1].set_aspect("equal") + return fig, ax + + +def Wilson_CI(p, n, z): + if p < 0 or p > 1 or n == 0: + if p < 0 or p > 1: + warnings.warn(f"The value of proportion {p} must be in the range [0,1]. Returning identity for CIs.") else: - warnings.warn(f'The number of counts {n} must be above zero. Returning identity for CIs.') - return (p,p) - sym = z*(p*(1-p)/n + z*z/4/n/n)**.5 - asym = p + z*z/2/n - fact = 1/(1+z*z/n) - upper = fact*(asym+sym) - lower = fact*(asym-sym) - return (lower,upper) - -class EvaluateClass(COCOEvaluator): - def __init__(self,dataset_name, output_dir,prob_thresh=0.5,iou_thresh = 0.1,evalsuper=True): - super().__init__(dataset_name,tasks={'bbox','segm'},output_dir = output_dir) + warnings.warn(f"The number of counts {n} must be above zero. Returning identity for CIs.") + return (p, p) + sym = z * (p * (1 - p) / n + z * z / 4 / n / n) ** 0.5 + asym = p + z * z / 2 / n + fact = 1 / (1 + z * z / n) + upper = fact * (asym + sym) + lower = fact * (asym - sym) + return (lower, upper) + + +class EvaluateClass(COCOEvaluator): + def __init__(self, dataset_name, output_dir, prob_thresh=0.5, iou_thresh=0.1, evalsuper=True): + super().__init__(dataset_name, tasks={"bbox", "segm"}, output_dir=output_dir) self.dataset_name = dataset_name - self.mycoco=None #pycocotools.cocoEval instance - self.cocoDt=None - self.cocoGt=None - self.evalsuper = evalsuper # if True, run COCOEvaluator.evaluate() when self.evaluate is run - self.prob_thresh = prob_thresh #instance probabilty threshold for scalars (precision,recall,fpr for scans) - self.iou_thresh = iou_thresh #iou threshold for defining precision,recall + self.mycoco = None # pycocotools.cocoEval instance + self.cocoDt = None + self.cocoGt = None + self.evalsuper = evalsuper # if True, run COCOEvaluator.evaluate() when self.evaluate is run + self.prob_thresh = prob_thresh # instance probabilty threshold for scalars (precision,recall,fpr for scans) + self.iou_thresh = iou_thresh # iou threshold for defining precision,recall self.pr = None self.rc = None self.fpr = None - def reset(self): + + def reset(self): super().reset() - self.mycoco=None + self.mycoco = None + def process(self, inputs, outputs): - super().process(inputs,outputs) + super().process(inputs, outputs) + def evaluate(self): - #with nostdout(): #suppress the coco summarize statment (the one with APs) + # with nostdout(): #suppress the coco summarize statment (the one with APs) if self.evalsuper: - _ = super().evaluate() #this call populates coco_instances_results.json + _ = super().evaluate() # this call populates coco_instances_results.json comm.synchronize() if not comm.is_main_process(): return () - self.cocoGt = COCO(os.path.join(self._output_dir,self.dataset_name +'_coco_format.json')) #produced when super is initialized - self.cocoDt = self.cocoGt.loadRes(os.path.join(self._output_dir,'coco_instances_results.json')) #load detector results - self.mycoco = COCOeval(self.cocoGt,self.cocoDt,iouType ='segm') + self.cocoGt = COCO( + os.path.join(self._output_dir, self.dataset_name + "_coco_format.json") + ) # produced when super is initialized + self.cocoDt = self.cocoGt.loadRes( + os.path.join(self._output_dir, "coco_instances_results.json") + ) # load detector results + self.mycoco = COCOeval(self.cocoGt, self.cocoDt, iouType="segm") self.num_images = len(self.mycoco.params.imgIds) - print('Calculated metrics for {} images'.format(self.num_images)) - self.mycoco.params.iouThrs = np.arange(.10,.6,.1) - #mycoco.params.recThrs = [0,.5,.75,1] + print("Calculated metrics for {} images".format(self.num_images)) + self.mycoco.params.iouThrs = np.arange(0.10, 0.6, 0.1) + # mycoco.params.recThrs = [0,.5,.75,1] self.mycoco.params.maxDets = [100] - #mycoco.params.imgIds=[imgId] + # mycoco.params.imgIds=[imgId] self.mycoco.params.areaRng = [[0, 10000000000.0]] self.mycoco.evaluate() self.mycoco.accumulate() - self.pr = self.mycoco.eval['precision'][:, #iouthresh - :, #recall level - 0, #catagory - 0, #area range - 0] #max detections per image + self.pr = self.mycoco.eval["precision"][ + :, :, 0, 0, 0 # iouthresh # recall level # catagory # area range + ] # max detections per image self.rc = self.mycoco.params.recThrs self.iou = self.mycoco.params.iouThrs - self.scores = self.mycoco.eval['scores'][:,:,0,0,0] #unreliable if GT has no instances - p,r = self.get_precision_recall() - return p,r + self.scores = self.mycoco.eval["scores"][:, :, 0, 0, 0] # unreliable if GT has no instances + p, r = self.get_precision_recall() + return p, r - def plot_PRcurve(self,ax=None): - if ax==None: - fig, ax = plt.subplots(1,1) + def plot_PRcurve(self, ax=None): + if ax is None: + fig, ax = plt.subplots(1, 1) for i in range(len(self.iou)): - ax.plot(self.rc,self.pr[i],label = '{:.2}'.format(self.iou[i])) - ax.set_xlabel('Recall') - ax.set_ylabel('Precision') - ax.set_title('') - ax.legend(title='IoU') - + ax.plot(self.rc, self.pr[i], label="{:.2}".format(self.iou[i])) + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") + ax.set_title("") + ax.legend(title="IoU") + def plot_recall_vs_prob(self): plt.figure() for i in range(len(self.iou)): - plt.plot(self.rc,self.scores[i],label = '{:.2}'.format(self.iou[i])) - plt.ylabel('Model probability') - plt.xlabel('Recall') - plt.legend(title='IoU') - + plt.plot(self.rc, self.scores[i], label="{:.2}".format(self.iou[i])) + plt.ylabel("Model probability") + plt.xlabel("Recall") + plt.legend(title="IoU") + def get_precision_recall(self): - iou_idx,rc_idx = self._find_iou_rc_inds() - precision = self.pr[iou_idx,rc_idx] + iou_idx, rc_idx = self._find_iou_rc_inds() + precision = self.pr[iou_idx, rc_idx] recall = self.rc[rc_idx] - return precision,recall + return precision, recall def _calculate_fpr_matrix(self): - #FP rate, 1 RPD in image = FP - if (self.scores.min()==-1) and (self.scores.max()==-1): - print('WARNING: Scores for all iou thresholds and all recall levels are not defined. This can arise if ground truth annotations contain no instances. Leaving fpr matrix as None') + # FP rate, 1 RPD in image = FP + if (self.scores.min() == -1) and (self.scores.max() == -1): + print( + "WARNING: Scores for all iou thresholds and all recall levels are not defined. This can arise if ground truth annotations contain no instances. Leaving fpr matrix as None" + ) self.fpr = None return - fpr = np.zeros((len(self.iou),len(self.rc))) + fpr = np.zeros((len(self.iou), len(self.rc))) for i in range(len(self.iou)): - for j,s in enumerate(self.scores[i]): #j -> recall level, s -> corresponding score - ng = 0 #number of negative images - fp = 0 #number of false positives images + for j, s in enumerate(self.scores[i]): # j -> recall level, s -> corresponding score + ng = 0 # number of negative images + fp = 0 # number of false positives images for el in self.mycoco.evalImgs: - if el is None:#no predictions, no gts - ng=ng+1 - elif len(el['gtIds'])==0:# some predictions and no gts - ng=ng+1 - if (np.array(el['dtScores']) >s).sum() > 0: #if at least one score over threshold for recall level - fp=fp+1 #count as FP + if el is None: # no predictions, no gts + ng = ng + 1 + elif len(el["gtIds"]) == 0: # some predictions and no gts + ng = ng + 1 + if ( + np.array(el["dtScores"]) > s + ).sum() > 0: # if at least one score over threshold for recall level + fp = fp + 1 # count as FP else: continue - fpr[i,j] = fp/ng - self.fpr = fpr + fpr[i, j] = fp / ng + self.fpr = fpr def _calculate_fpr(self): - print('Using alternate calculation for fpr at instance score threshold of {}'.format(self.prob_thresh)) - ng = 0 #number of negative images - fp = 0 #number of false positives images + print("Using alternate calculation for fpr at instance score threshold of {}".format(self.prob_thresh)) + ng = 0 # number of negative images + fp = 0 # number of false positives images for el in self.mycoco.evalImgs: - if el is None:#no predictions, no gts - ng=ng+1 - elif len(el['gtIds'])==0:# some predictions and no gts - ng=ng+1 - if (np.array(el['dtScores']) >self.prob_thresh).sum() > 0: #if at least one score over threshold for recall level - fp=fp+1 #count as FP - else: #gt has instances + if el is None: # no predictions, no gts + ng = ng + 1 + elif len(el["gtIds"]) == 0: # some predictions and no gts + ng = ng + 1 + if ( + np.array(el["dtScores"]) > self.prob_thresh + ).sum() > 0: # if at least one score over threshold for recall level + fp = fp + 1 # count as FP + else: # gt has instances continue - return fp/(ng+1e-5) + return fp / (ng + 1e-5) def _find_iou_rc_inds(self): try: - iou_idx = np.argwhere(self.iou==self.iou_thresh)[0][0] #first instance of + iou_idx = np.argwhere(self.iou == self.iou_thresh)[0][0] # first instance of except IndexError: - print('iou threshold {} not found in mycoco.params.iouThrs {}'.format(self.iou_thresh,self.mycoco.params.iouThrs)) + print( + "iou threshold {} not found in mycoco.params.iouThrs {}".format( + self.iou_thresh, self.mycoco.params.iouThrs + ) + ) exit(1) - #test above for out of bounds - inds = np.argwhere(self.scores[iou_idx]>=self.prob_thresh) - if len(inds)>0: - rc_idx = inds[-1][0] #get recall index corresponding to prob_thresh + # test above for out of bounds + inds = np.argwhere(self.scores[iou_idx] >= self.prob_thresh) + if len(inds) > 0: + rc_idx = inds[-1][0] # get recall index corresponding to prob_thresh else: rc_idx = 0 - return iou_idx,rc_idx + return iou_idx, rc_idx def get_fpr(self): - if self.fpr is None: self._calculate_fpr_matrix() - if self.fpr is not None: - iou_idx,rc_idx = self._find_iou_rc_inds() - fpr = self.fpr[iou_idx,rc_idx] - elif len(self.mycoco.cocoGt.anns)==0: + if self.fpr is not None: + iou_idx, rc_idx = self._find_iou_rc_inds() + fpr = self.fpr[iou_idx, rc_idx] + elif len(self.mycoco.cocoGt.anns) == 0: fpr = self._calculate_fpr() else: - fpr=-1 + fpr = -1 return fpr - - def summarize_scalars(self): #for pretty printing - p,r = self.get_precision_recall() - f1 = 2*(p*r)/(p + r) + + def summarize_scalars(self): # for pretty printing + p, r = self.get_precision_recall() + f1 = 2 * (p * r) / (p + r) fpr = self.get_fpr() - #Confidence intervals - z=1.96 #95% Gaussian - #instance count + # Confidence intervals + z = 1.96 # 95% Gaussian + # instance count inst_cnt = self.count_instances() - n_r = inst_cnt['gt_instances'] - n_p = inst_cnt['dt_instances'] - n_fpr = inst_cnt['gt_neg_scans'] - - def stat_CI(p,n,z): - return z*np.sqrt(p*(1-p)/n) - - r_ci = Wilson_CI(r,n_r,z) - p_ci = Wilson_CI(p,n_p,z) - fpr_ci = Wilson_CI(fpr,n_fpr,z) - - #propogate errors for f1 - int_r = stat_CI(r,n_r,z) - int_p = stat_CI(p,n_p,z) - int_f1 =(f1)*np.sqrt(int_r**2 * (1/r - 1/(p+r))**2 + int_p**2 * (1/p - 1/(p+r))**2) - f1_ci = (f1-int_f1,f1+int_f1) - - dd = dict(dataset = self.dataset_name, precision = float(p),precision_ci = p_ci,recall=float(r), recall_ci = r_ci,f1 = float(f1),f1_ci = f1_ci, fpr=float(fpr),fpr_ci = fpr_ci, iou=self.iou_thresh,probability=self.prob_thresh) + n_r = inst_cnt["gt_instances"] + n_p = inst_cnt["dt_instances"] + n_fpr = inst_cnt["gt_neg_scans"] + + def stat_CI(p, n, z): + return z * np.sqrt(p * (1 - p) / n) + + r_ci = Wilson_CI(r, n_r, z) + p_ci = Wilson_CI(p, n_p, z) + fpr_ci = Wilson_CI(fpr, n_fpr, z) + + # propogate errors for f1 + int_r = stat_CI(r, n_r, z) + int_p = stat_CI(p, n_p, z) + int_f1 = (f1) * np.sqrt(int_r**2 * (1 / r - 1 / (p + r)) ** 2 + int_p**2 * (1 / p - 1 / (p + r)) ** 2) + f1_ci = (f1 - int_f1, f1 + int_f1) + + dd = dict( + dataset=self.dataset_name, + precision=float(p), + precision_ci=p_ci, + recall=float(r), + recall_ci=r_ci, + f1=float(f1), + f1_ci=f1_ci, + fpr=float(fpr), + fpr_ci=fpr_ci, + iou=self.iou_thresh, + probability=self.prob_thresh, + ) return dd def count_instances(self): gt_inst = 0 dt_inst = 0 gt_neg_scans = 0 - for key,val in self.cocoGt.imgs.items(): - imgid = val['id'] - #Gt instances + for key, val in self.cocoGt.imgs.items(): + imgid = val["id"] + # Gt instances annIdsGt = self.cocoGt.getAnnIds([imgid]) annsGt = self.cocoGt.loadAnns(annIdsGt) - gt_inst+=len(annsGt) - if len(annsGt)==0: - gt_neg_scans+=1 + gt_inst += len(annsGt) + if len(annsGt) == 0: + gt_neg_scans += 1 - #Dt instances + # Dt instances annIdsDt = self.cocoDt.getAnnIds([imgid]) annsDt = self.cocoDt.loadAnns(annIdsDt) - annsDt = [ann for ann in annsDt if ann['score']>self.prob_thresh] - dt_inst+=len(annsDt) + annsDt = [ann for ann in annsDt if ann["score"] > self.prob_thresh] + dt_inst += len(annsDt) - return dict(gt_instances=gt_inst,dt_instances=dt_inst,gt_neg_scans = gt_neg_scans) + return dict(gt_instances=gt_inst, dt_instances=dt_inst, gt_neg_scans=gt_neg_scans) -from sklearn.metrics import average_precision_score, precision_recall_curve - - -class CreatePlotsRPD(): - def __init__(self,dfimg): +class CreatePlotsRPD: + def __init__(self, dfimg): self.dfimg = dfimg - self.dfvol = self.dfimg.groupby(['volID'])[['gt_instances','gt_pxs','gt_xpxs','dt_instances','dt_pxs','dt_xpxs']].sum() - - @classmethod - def initfromcoco(cls,mycoco,prob_thresh): - df = pd.DataFrame(index = mycoco.cocoGt.imgs.keys(), columns=['gt_instances','gt_pxs','gt_xpxs','dt_instances','dt_pxs','dt_xpxs'],dtype=np.uint64) + self.dfvol = self.dfimg.groupby(["volID"])[ + ["gt_instances", "gt_pxs", "gt_xpxs", "dt_instances", "dt_pxs", "dt_xpxs"] + ].sum() - for key,val in mycoco.cocoGt.imgs.items(): - imgid = val['id'] - #Gt instances + @classmethod + def initfromcoco(cls, mycoco, prob_thresh): + df = pd.DataFrame( + index=mycoco.cocoGt.imgs.keys(), + columns=["gt_instances", "gt_pxs", "gt_xpxs", "dt_instances", "dt_pxs", "dt_xpxs"], + dtype=np.uint64, + ) + + for key, val in mycoco.cocoGt.imgs.items(): + imgid = val["id"] + # Gt instances annIdsGt = mycoco.cocoGt.getAnnIds([imgid]) annsGt = mycoco.cocoGt.loadAnns(annIdsGt) instGt = [mycoco.cocoGt.annToMask(ann).sum() for ann in annsGt] - xprojGt = [(mycoco.cocoGt.annToMask(ann).sum(axis=0)>0).astype('uint8').sum() for ann in annsGt] - #Dt instances + xprojGt = [(mycoco.cocoGt.annToMask(ann).sum(axis=0) > 0).astype("uint8").sum() for ann in annsGt] + # Dt instances annIdsDt = mycoco.cocoDt.getAnnIds([imgid]) annsDt = mycoco.cocoDt.loadAnns(annIdsDt) - annsDt = [ann for ann in annsDt if ann['score']>prob_thresh] + annsDt = [ann for ann in annsDt if ann["score"] > prob_thresh] instDt = [mycoco.cocoDt.annToMask(ann).sum() for ann in annsDt] - xprojDt = [(mycoco.cocoDt.annToMask(ann).sum(axis=0)>0).astype('uint8').sum() for ann in annsDt] - - dat = [len(instGt),np.array(instGt).sum(),np.array(xprojGt).sum(),len(instDt),np.array(instDt).sum(),np.array(xprojDt).sum()] + xprojDt = [(mycoco.cocoDt.annToMask(ann).sum(axis=0) > 0).astype("uint8").sum() for ann in annsDt] + + dat = [ + len(instGt), + np.array(instGt).sum(), + np.array(xprojGt).sum(), + len(instDt), + np.array(instDt).sum(), + np.array(xprojDt).sum(), + ] df.loc[key] = dat - - newdf = pd.DataFrame([idx.rsplit('.',1)[0].rsplit('_',1) for idx in df.index],columns=['volID','scan'],index = df.index) - df = df.merge(newdf,how='inner',left_index=True,right_index=True) + + newdf = pd.DataFrame( + [idx.rsplit(".", 1)[0].rsplit("_", 1) for idx in df.index], columns=["volID", "scan"], index=df.index + ) + df = df.merge(newdf, how="inner", left_index=True, right_index=True) return cls(df) - + @classmethod - def initfromcsv(cls,fname): + def initfromcsv(cls, fname): df = pd.read_csv(fname) return cls(df) - - def get_max_limits(self,df): - max_inst=np.max([df.gt_instances.max(),df.dt_instances.max()]) - max_xpxs = np.max([df.gt_xpxs.max(),df.dt_xpxs.max()]) - max_pxs = np.max([df.gt_pxs.max(),df.dt_pxs.max()]) -# print('Max instances:',max_inst) -# print('Max xpxs:',max_xpxs) -# print('Max pxs:',max_pxs) - return max_inst,max_xpxs,max_pxs - - def vol_level_prc(self,df,gt_thresh=5,ax=None): - - prc = precision_recall_curve(df.gt_instances>=gt_thresh,df.dt_instances) - if ax==None: - fig,ax = plt.subplots(1,1) - ax.plot(prc[1],prc[0]) - ax.set_xlabel('RPD Volume Recall') - ax.set_ylabel('RPD Volume Precision') - - - ap = average_precision_score(df.gt_instances>=gt_thresh,df.dt_instances) - return ap,prc - - def plot_img_level_instance_thresholding(self,df,inst): + def get_max_limits(self, df): + max_inst = np.max([df.gt_instances.max(), df.dt_instances.max()]) + max_xpxs = np.max([df.gt_xpxs.max(), df.dt_xpxs.max()]) + max_pxs = np.max([df.gt_pxs.max(), df.dt_pxs.max()]) + # print('Max instances:',max_inst) + # print('Max xpxs:',max_xpxs) + # print('Max pxs:',max_pxs) + return max_inst, max_xpxs, max_pxs + + def vol_level_prc(self, df, gt_thresh=5, ax=None): + prc = precision_recall_curve(df.gt_instances >= gt_thresh, df.dt_instances) + if ax is None: + fig, ax = plt.subplots(1, 1) + ax.plot(prc[1], prc[0]) + ax.set_xlabel("RPD Volume Recall") + ax.set_ylabel("RPD Volume Precision") + + ap = average_precision_score(df.gt_instances >= gt_thresh, df.dt_instances) + return ap, prc + + def plot_img_level_instance_thresholding(self, df, inst): rc = np.zeros((len(inst),)) pr = np.zeros((len(inst),)) fpr = np.zeros((len(inst),)) - fig, ax = plt.subplots(1,3,figsize = [15,5]) - for i,dt_thresh in enumerate(inst): - gt = df.gt_instances>dt_thresh - dt = df.dt_instances>dt_thresh - rc[i] = (gt&dt).sum()/gt.sum() - pr[i] = (gt&dt).sum()/dt.sum() - fpr[i] = ((~gt)&(dt)).sum()/((~gt).sum()) - - ax[1].plot(inst,pr) - ax[1].set_ylim(0.45,1.01) - ax[1].set_xlabel('instance threshold') - ax[1].set_ylabel('Precision') - - - ax[0].plot(inst,rc) - ax[0].set_ylim(0.45,1.01) - ax[0].set_ylabel('Recall') - ax[0].set_xlabel('instance threshold') - - - ax[2].plot(inst,fpr) - ax[2].set_ylim(0,0.80) - ax[2].set_xlabel('instance threshold') - ax[2].set_ylabel('FPR') + fig, ax = plt.subplots(1, 3, figsize=[15, 5]) + for i, dt_thresh in enumerate(inst): + gt = df.gt_instances > dt_thresh + dt = df.dt_instances > dt_thresh + rc[i] = (gt & dt).sum() / gt.sum() + pr[i] = (gt & dt).sum() / dt.sum() + fpr[i] = ((~gt) & (dt)).sum() / ((~gt).sum()) + + ax[1].plot(inst, pr) + ax[1].set_ylim(0.45, 1.01) + ax[1].set_xlabel("instance threshold") + ax[1].set_ylabel("Precision") + + ax[0].plot(inst, rc) + ax[0].set_ylim(0.45, 1.01) + ax[0].set_ylabel("Recall") + ax[0].set_xlabel("instance threshold") + + ax[2].plot(inst, fpr) + ax[2].set_ylim(0, 0.80) + ax[2].set_xlabel("instance threshold") + ax[2].set_ylabel("FPR") plt.tight_layout() - return pr,rc,fpr - - def plot_img_level_instance_thresholding2(self,df,inst,gt_thresh,plot=True): + return pr, rc, fpr - #should be using global function + def plot_img_level_instance_thresholding2(self, df, inst, gt_thresh, plot=True): + # should be using global function # def Wilson_CI(p,n,z): # sym = z*(p*(1-p)/n + z*z/4/n/n)**.5 @@ -737,210 +804,200 @@ def plot_img_level_instance_thresholding2(self,df,inst,gt_thresh,plot=True): rc = np.zeros((len(inst),)) pr = np.zeros((len(inst),)) fpr = np.zeros((len(inst),)) - rc_ci = np.zeros((len(inst),2)) - pr_ci = np.zeros((len(inst),2)) - fpr_ci = np.zeros((len(inst),2)) - - - for i,dt_thresh in enumerate(inst): - gt = df.gt_instances>=gt_thresh - dt = df.dt_instances>=dt_thresh - rc[i] = (gt&dt).sum()/gt.sum() - pr[i] = (gt&dt).sum()/dt.sum() - fpr[i] = ((~gt)&(dt)).sum()/((~gt).sum()) - rc_ci[i,:] = Wilson_CI(rc[i],gt.sum(),1.96) - pr_ci[i,:]= Wilson_CI(pr[i],dt.sum(),1.96) - fpr_ci[i,:] = Wilson_CI(fpr[i],((~gt).sum()),1.96) + rc_ci = np.zeros((len(inst), 2)) + pr_ci = np.zeros((len(inst), 2)) + fpr_ci = np.zeros((len(inst), 2)) + + for i, dt_thresh in enumerate(inst): + gt = df.gt_instances >= gt_thresh + dt = df.dt_instances >= dt_thresh + rc[i] = (gt & dt).sum() / gt.sum() + pr[i] = (gt & dt).sum() / dt.sum() + fpr[i] = ((~gt) & (dt)).sum() / ((~gt).sum()) + rc_ci[i, :] = Wilson_CI(rc[i], gt.sum(), 1.96) + pr_ci[i, :] = Wilson_CI(pr[i], dt.sum(), 1.96) + fpr_ci[i, :] = Wilson_CI(fpr[i], ((~gt).sum()), 1.96) if plot: - fig, ax = plt.subplots(1,3,figsize = [15,5]) + fig, ax = plt.subplots(1, 3, figsize=[15, 5]) # ax[0].plot(rc,pr) # ax[0].set_xlabel('Recall') # ax[0].set_ylabel('Precision') - ax[1].plot(inst,pr) - ax[1].fill_between(inst,pr_ci[:,0],pr_ci[:,1],alpha=.25) - #ax[1].set_ylim(0.45,1.01) - ax[1].set_xlabel('instance threshold') - ax[1].set_ylabel('Precision') - - - ax[0].plot(inst,rc) - ax[0].fill_between(inst,rc_ci[:,0],rc_ci[:,1],alpha=.25) - #ax[0].set_ylim(0.45,1.01) - ax[0].set_ylabel('Recall') - ax[0].set_xlabel('instance threshold') + ax[1].plot(inst, pr) + ax[1].fill_between(inst, pr_ci[:, 0], pr_ci[:, 1], alpha=0.25) + # ax[1].set_ylim(0.45,1.01) + ax[1].set_xlabel("instance threshold") + ax[1].set_ylabel("Precision") + ax[0].plot(inst, rc) + ax[0].fill_between(inst, rc_ci[:, 0], rc_ci[:, 1], alpha=0.25) + # ax[0].set_ylim(0.45,1.01) + ax[0].set_ylabel("Recall") + ax[0].set_xlabel("instance threshold") - ax[2].plot(inst,fpr) - ax[2].fill_between(inst,fpr_ci[:,0],fpr_ci[:,1],alpha=.25) - #ax[2].set_ylim(0,0.80) - ax[2].set_xlabel('instance threshold') - ax[2].set_ylabel('FPR') + ax[2].plot(inst, fpr) + ax[2].fill_between(inst, fpr_ci[:, 0], fpr_ci[:, 1], alpha=0.25) + # ax[2].set_ylim(0,0.80) + ax[2].set_xlabel("instance threshold") + ax[2].set_ylabel("FPR") plt.tight_layout() - return dict(precision=pr,precision_ci = pr_ci,recall=rc,recall_ci = rc_ci, fpr=fpr,fpr_ci = fpr_ci) + return dict(precision=pr, precision_ci=pr_ci, recall=rc, recall_ci=rc_ci, fpr=fpr, fpr_ci=fpr_ci) - def gt_vs_dt_instances(self,ax=None): + def gt_vs_dt_instances(self, ax=None): df = self.dfimg - max_inst,max_xpxs,max_pxs = self.get_max_limits(df) - idx = (df.gt_instances>0)&(df.dt_instances>0) - - if ax==None: + max_inst, max_xpxs, max_pxs = self.get_max_limits(df) + idx = (df.gt_instances > 0) & (df.dt_instances > 0) + + if ax is None: fig = plt.figure(dpi=100) ax = fig.add_subplot(111) - y = df[idx].groupby('gt_instances')['dt_instances'].mean() - yerr = df[idx].groupby('gt_instances')['dt_instances'].std() - ax.errorbar(y.index,y.values,yerr.values,fmt='*') - plt.plot([0,max_inst],[0,max_inst],alpha=.5) - plt.xlim(0,max_inst+1) - plt.ylim(0,max_inst+1) + y = df[idx].groupby("gt_instances")["dt_instances"].mean() + yerr = df[idx].groupby("gt_instances")["dt_instances"].std() + ax.errorbar(y.index, y.values, yerr.values, fmt="*") + plt.plot([0, max_inst], [0, max_inst], alpha=0.5) + plt.xlim(0, max_inst + 1) + plt.ylim(0, max_inst + 1) ax.set_aspect(1) - plt.xlabel('gt_instances') - plt.ylabel('dt_instances') + plt.xlabel("gt_instances") + plt.ylabel("dt_instances") plt.tight_layout() return ax - def gt_vs_dt_instances_boxplot(self,ax=None): + def gt_vs_dt_instances_boxplot(self, ax=None): df = self.dfimg - max_inst,max_xpxs,max_pxs = self.get_max_limits(df) + max_inst, max_xpxs, max_pxs = self.get_max_limits(df) max_inst = int(max_inst) - if ax==None: + if ax is None: fig = plt.figure(dpi=100) ax = fig.add_subplot(111) - - - - ax.plot([0,max_inst+1],[0,max_inst+1],alpha=.5) - x = df['gt_instances'].values.astype(int) - y = df['dt_instances'].values.astype(int) - sns.boxplot(x,y, ax=ax,width=.5) - ax.set_xbound(0,max_inst+1) - ax.set_ybound(0,max_inst+1) - ax.set_aspect('equal') - + ax.plot([0, max_inst + 1], [0, max_inst + 1], alpha=0.5) + x = df["gt_instances"].values.astype(int) + y = df["dt_instances"].values.astype(int) + sns.boxplot(x, y, ax=ax, width=0.5) + ax.set_xbound(0, max_inst + 1) + ax.set_ybound(0, max_inst + 1) + ax.set_aspect("equal") - ax.set_title('') - ax.set_xlabel('gt_instances') - ax.set_ylabel('dt_instances') + ax.set_title("") + ax.set_xlabel("gt_instances") + ax.set_ylabel("dt_instances") import matplotlib.ticker as pltticker + loc = pltticker.MultipleLocator(base=2.0) ax.xaxis.set_major_locator(loc) ax.yaxis.set_major_locator(loc) - return ax - - def gt_vs_dt_xpxs(self): df = self.dfimg - max_inst,max_xpxs,max_pxs = self.get_max_limits(df) - idx = (df.gt_instances>0)&(df.dt_instances>0) + max_inst, max_xpxs, max_pxs = self.get_max_limits(df) + idx = (df.gt_instances > 0) & (df.dt_instances > 0) dfsub = df[idx] - - fig1 = plt.figure(figsize = [10,10],dpi=100) + + fig1 = plt.figure(figsize=[10, 10], dpi=100) ax = fig1.add_subplot(111) - sc = ax.scatter(dfsub['gt_xpxs'],dfsub['dt_xpxs'],c =dfsub['gt_instances'] ,cmap='viridis') + sc = ax.scatter(dfsub["gt_xpxs"], dfsub["dt_xpxs"], c=dfsub["gt_instances"], cmap="viridis") ax.set_aspect(1) - #ax = dfsub.plot(kind = 'scatter',x=,y=,c='gt_instances') - plt.plot([0,max_xpxs],[0,max_xpxs],alpha=.5) - plt.xlim(0,max_xpxs) - plt.ylim(0,max_xpxs) - plt.xlabel('gt_xpxs') - plt.ylabel('dt_xpxs') + # ax = dfsub.plot(kind = 'scatter',x=,y=,c='gt_instances') + plt.plot([0, max_xpxs], [0, max_xpxs], alpha=0.5) + plt.xlim(0, max_xpxs) + plt.ylim(0, max_xpxs) + plt.xlabel("gt_xpxs") + plt.ylabel("dt_xpxs") cbar = plt.colorbar(sc) - cbar.ax.set_ylabel('gt_instances') + cbar.ax.set_ylabel("gt_instances") plt.tight_layout() - - fig2 = plt.figure(figsize = [10,10],dpi=100) + + fig2 = plt.figure(figsize=[10, 10], dpi=100) ax = fig2.add_subplot(111) - sc = ax.scatter(dfsub['gt_xpxs'],dfsub['gt_xpxs']-dfsub['dt_xpxs'],c =dfsub['gt_instances'] ,cmap='viridis') - #ax = dfsub.plot(kind = 'scatter',x=,y=,c='gt_instances') - plt.plot([0,max_xpxs],[0,0],alpha=.5) - plt.xlabel('gt_xpxs') - plt.ylabel('gt_xpxs-dt_xpxs') + sc = ax.scatter(dfsub["gt_xpxs"], dfsub["gt_xpxs"] - dfsub["dt_xpxs"], c=dfsub["gt_instances"], cmap="viridis") + # ax = dfsub.plot(kind = 'scatter',x=,y=,c='gt_instances') + plt.plot([0, max_xpxs], [0, 0], alpha=0.5) + plt.xlabel("gt_xpxs") + plt.ylabel("gt_xpxs-dt_xpxs") cbar = plt.colorbar(sc) - cbar.ax.set_ylabel('gt_instances') + cbar.ax.set_ylabel("gt_instances") plt.tight_layout() - + fig3 = plt.figure(dpi=100) - plt.hist(dfsub['gt_xpxs']-dfsub['dt_xpxs']) - plt.xlabel('gt_xpxs - dt_xpxs') - plt.ylabel('B-scans') - - return fig1,fig2,fig3 - + plt.hist(dfsub["gt_xpxs"] - dfsub["dt_xpxs"]) + plt.xlabel("gt_xpxs - dt_xpxs") + plt.ylabel("B-scans") + + return fig1, fig2, fig3 + def gt_vs_dt_xpxs_mu(self): df = self.dfimg - max_inst,max_xpxs,max_pxs = self.get_max_limits(df) - idx = (df.gt_instances>0)&(df.dt_instances>0) + max_inst, max_xpxs, max_pxs = self.get_max_limits(df) + idx = (df.gt_instances > 0) & (df.dt_instances > 0) dfsub = df[idx] - + from scipy import stats - mu_dt,bins,bnum = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['dt_xpxs'],statistic = 'mean',bins=10) - std_dt,_,_ = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['dt_xpxs'],statistic = 'std',bins = bins) - mu_gt,_,_ = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['gt_xpxs'],statistic='mean',bins=bins) - std_gt,_,_ = stats.binned_statistic(dfsub['gt_xpxs'],dfsub['gt_xpxs'],statistic = 'std',bins = bins) + + mu_dt, bins, bnum = stats.binned_statistic(dfsub["gt_xpxs"], dfsub["dt_xpxs"], statistic="mean", bins=10) + std_dt, _, _ = stats.binned_statistic(dfsub["gt_xpxs"], dfsub["dt_xpxs"], statistic="std", bins=bins) + mu_gt, _, _ = stats.binned_statistic(dfsub["gt_xpxs"], dfsub["gt_xpxs"], statistic="mean", bins=bins) + std_gt, _, _ = stats.binned_statistic(dfsub["gt_xpxs"], dfsub["gt_xpxs"], statistic="std", bins=bins) fig = plt.figure(dpi=100) - plt.errorbar(mu_gt,mu_dt,yerr = std_dt,xerr=std_gt,fmt='*') - plt.xlabel('gt_xpxs') - plt.ylabel('dt_xpxs') - plt.plot([0,max_xpxs],[0,max_xpxs],alpha=.5) - plt.xlim(0,max_xpxs) - plt.ylim(0,max_xpxs) + plt.errorbar(mu_gt, mu_dt, yerr=std_dt, xerr=std_gt, fmt="*") + plt.xlabel("gt_xpxs") + plt.ylabel("dt_xpxs") + plt.plot([0, max_xpxs], [0, max_xpxs], alpha=0.5) + plt.xlim(0, max_xpxs) + plt.ylim(0, max_xpxs) plt.gca().set_aspect(1) plt.tight_layout() return fig def gt_dt_FP_FN_count(self): df = self.dfimg - fig,ax =plt.subplots(1,2,figsize=[10,5]) + fig, ax = plt.subplots(1, 2, figsize=[10, 5]) - idx = (df.gt_instances==0)&(df.dt_instances>0) - ax[0].hist(df[idx]['dt_instances'],bins = range(1,10)) - ax[0].set_xlabel('dt instances') - ax[0].set_ylabel('B-scans') - ax[0].set_title('FP dt instance count per B-scan') + idx = (df.gt_instances == 0) & (df.dt_instances > 0) + ax[0].hist(df[idx]["dt_instances"], bins=range(1, 10)) + ax[0].set_xlabel("dt instances") + ax[0].set_ylabel("B-scans") + ax[0].set_title("FP dt instance count per B-scan") - idx = (df.gt_instances>0)&(df.dt_instances==0) - ax[1].hist(df[idx]['gt_instances'],bins = range(1,10)) - ax[1].set_xlabel('gt instances') - ax[1].set_ylabel('B-scans') - ax[1].set_title('FN gt instance count per B-scan') + idx = (df.gt_instances > 0) & (df.dt_instances == 0) + ax[1].hist(df[idx]["gt_instances"], bins=range(1, 10)) + ax[1].set_xlabel("gt instances") + ax[1].set_ylabel("B-scans") + ax[1].set_title("FN gt instance count per B-scan") plt.tight_layout() return fig - + def avg_inst_size(self): df = self.dfimg - max_inst,max_xpxs,max_pxs = self.get_max_limits(df) - idx = (df.gt_instances>0)&(df.dt_instances>0) + max_inst, max_xpxs, max_pxs = self.get_max_limits(df) + idx = (df.gt_instances > 0) & (df.dt_instances > 0) dfsub = df[idx] - - fig = plt.figure(figsize=[10,5]) + + fig = plt.figure(figsize=[10, 5]) plt.subplot(121) - bins = np.arange(0,120,10) - ax = (dfsub.gt_xpxs/dfsub.gt_instances).hist(bins = bins,alpha=.5,label='gt') - ax = (dfsub.dt_xpxs/dfsub.dt_instances).hist(bins=bins,alpha=.5,label='dt') - ax.set_xlabel('xpxs') - ax.set_ylabel('B-scans') - ax.set_title('Average size of instance') + bins = np.arange(0, 120, 10) + ax = (dfsub.gt_xpxs / dfsub.gt_instances).hist(bins=bins, alpha=0.5, label="gt") + ax = (dfsub.dt_xpxs / dfsub.dt_instances).hist(bins=bins, alpha=0.5, label="dt") + ax.set_xlabel("xpxs") + ax.set_ylabel("B-scans") + ax.set_title("Average size of instance") ax.legend() plt.subplot(122) - bins = np.arange(0,600,40) - ax = (dfsub.gt_pxs/dfsub.gt_instances).hist(bins=bins,alpha=.5,label='gt') - ax = (dfsub.dt_pxs/dfsub.dt_instances).hist(bins=bins,alpha=.5,label='dt') - ax.set_xlabel('pxs') - ax.set_ylabel('B-scans') - ax.set_title('Average size of instance') + bins = np.arange(0, 600, 40) + ax = (dfsub.gt_pxs / dfsub.gt_instances).hist(bins=bins, alpha=0.5, label="gt") + ax = (dfsub.dt_pxs / dfsub.dt_instances).hist(bins=bins, alpha=0.5, label="dt") + ax.set_xlabel("pxs") + ax.set_ylabel("B-scans") + ax.set_title("Average size of instance") ax.legend() plt.tight_layout() return fig - - diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py index 6080184a..efb2075e 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -13,10 +13,12 @@ from .volReader import volFile script_dir = os.path.dirname(__file__) + + class Error(Exception): """Base class for exceptions in this module.""" - pass + pass # def extract_files(dirtoextract, extracted_path): @@ -39,75 +41,76 @@ class Error(Exception): # else: # pass + def extract_files(dirtoextract, extracted_path, input_format): - assert input_format in ['vol','dicom'], 'Error: input_format must be "vol" or "dicom".' + assert input_format in ["vol", "dicom"], 'Error: input_format must be "vol" or "dicom".' proceed = True - if ((os.path.isdir(extracted_path)) and (len(os.listdir(extracted_path))!=0)): - val = input(f'{extracted_path} exists and is not empty. Files may be overwritten. Proceed with extraction? (Y/N)') + if (os.path.isdir(extracted_path)) and (len(os.listdir(extracted_path)) != 0): + val = input( + f"{extracted_path} exists and is not empty. Files may be overwritten. Proceed with extraction? (Y/N)" + ) proceed = bool(distutils.util.strtobool(val)) if proceed: print(f"Extracting files from {dirtoextract} into {extracted_path}...") - if input_format == 'vol': - files_to_extract = glob.glob(os.path.join(dirtoextract,'**/*.vol'),recursive=True) - for i,line in enumerate(tqdm(files_to_extract)): - fpath = line.strip('\n') + if input_format == "vol": + files_to_extract = glob.glob(os.path.join(dirtoextract, "**/*.vol"), recursive=True) + for i, line in enumerate(tqdm(files_to_extract)): + fpath = line.strip("\n") vol = volFile(fpath) - fpath = fpath.replace('\\','/') - path, scan_str = fpath.strip('.vol').rsplit('/',1) - extractpath = os.path.join(extracted_path,scan_str.replace('_','/')) - os.makedirs(extractpath,exist_ok=True) - preffix = os.path.join(extractpath, scan_str+'_oct') + fpath = fpath.replace("\\", "/") + path, scan_str = fpath.strip(".vol").rsplit("/", 1) + extractpath = os.path.join(extracted_path, scan_str.replace("_", "/")) + os.makedirs(extractpath, exist_ok=True) + preffix = os.path.join(extractpath, scan_str + "_oct") vol.renderOCTscans(preffix) - elif input_format =='dicom': - keywords = ['SOPInstanceUID', - 'PatientID', - 'ImageLaterality', - 'SeriesDate' - ] + elif input_format == "dicom": + keywords = ["SOPInstanceUID", "PatientID", "ImageLaterality", "SeriesDate"] list_of_dicts = [] - dirgen = glob.iglob(os.path.join(dirtoextract,'**/DICOMDIR'),recursive=True) + dirgen = glob.iglob(os.path.join(dirtoextract, "**/DICOMDIR"), recursive=True) for dsstr in dirgen: fs = FileSet(dcmread(dsstr)) fsgenOPT = genOPTfs(fs) for fi in tqdm(fsgenOPT): - dd=dict() - #top level keywords + dd = dict() + # top level keywords for key in keywords: dd[key] = fi.get(key) - volpath = os.path.join(extracted_path, f'{fi.SOPInstanceUID}') - #volpath = os.path.join(extracted_path, f'{fi.PatientID}_{fi.ImageLaterality}_{fi.SeriesDate}') #path for volume - shutil.rmtree(volpath,ignore_errors=True) + volpath = os.path.join(extracted_path, f"{fi.SOPInstanceUID}") + # volpath = os.path.join(extracted_path, f'{fi.PatientID}_{fi.ImageLaterality}_{fi.SeriesDate}') #path for volume + shutil.rmtree(volpath, ignore_errors=True) os.mkdir(volpath) n = fi.NumberOfFrames for i in range(n): - fname = os.path.join(volpath,f'{fi.SOPInstanceUID}_oct_{i:03d}.png') + fname = os.path.join(volpath, f"{fi.SOPInstanceUID}_oct_{i:03d}.png") Image.fromarray(fi.pixel_array[i]).save(fname) list_of_dicts.append(dd.copy()) - dfoct = pd.DataFrame(list_of_dicts, columns = keywords) - dfoct.to_csv(os.path.join(extracted_path,'basic_meta.csv')) + dfoct = pd.DataFrame(list_of_dicts, columns=keywords) + dfoct.to_csv(os.path.join(extracted_path, "basic_meta.csv")) else: pass + def rpd_data(extracted_path): dataset = [] instances = 0 wrong_poly = 0 - extracted_files = glob.glob(os.path.join(extracted_path,'**/*.[Pp][Nn][Gg]'),recursive=True) + extracted_files = glob.glob(os.path.join(extracted_path, "**/*.[Pp][Nn][Gg]"), recursive=True) print("Generating dataset of images...") for fn in tqdm(extracted_files): - fn_adjusted = fn.replace('\\','/') + fn_adjusted = fn.replace("\\", "/") imageid = fn_adjusted.split("/")[-1] im = cv2.imread(fn) - dat = dict(file_name = fn_adjusted, height = im.shape[0], width = im.shape[1], image_id = imageid) + dat = dict(file_name=fn_adjusted, height=im.shape[0], width=im.shape[1], image_id=imageid) dataset.append(dat) print(f"Found {len(dataset)} images") print(f"Found {instances} instances") print(f"Found {wrong_poly} too few vertices") return dataset + def genOPTfs(fs): - for instance in fs.find(Modality='OPT'): + for instance in fs.find(Modality="OPT"): ds = instance.load() - yield ds \ No newline at end of file + yield ds diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py index c1cbbaab..0b366ff3 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py @@ -3,7 +3,7 @@ # Aaron Y. Lee MD MSCI (University of Washington) Copyright 2019 # # Code ported from Markus Mayer's excellent work (https://www5.cs.fau.de/research/software/octseg/) -# +# # Also thanks to who contributed to the original openVol.m in Markus's project # Radim Kolar, Brno University, Czech Republic # Kris Sheets, Retinal Cell Biology Lab, Neuroscience Center of Excellence, LSU Health Sciences Center, New Orleans @@ -18,7 +18,7 @@ import numpy as np -class volFile(): +class volFile: def __init__(self, filename): """ Parses Heyex Spectralis *.vol files. @@ -26,7 +26,7 @@ def __init__(self, filename): Args: filename (str): Path to vol file - Returns: + Returns: volFile class """ @@ -61,7 +61,7 @@ def grid(self): Returns: 2D numpy array with the number of b scan images in the first dimension - and x_0, y_0, x_1, y_1 defining the line of the B scan on the pixel + and x_0, y_0, x_1, y_1 defining the line of the B scan on the pixel coordinates of the IR SLO image. """ @@ -89,20 +89,21 @@ def renderIRslo(self, filename, renderGrid=False): """ from PIL import Image, ImageDraw + wf = self.wholefile a = np.copy(wf["sloImage"]) if renderGrid: - a = np.stack((a,)*3, axis=-1) + a = np.stack((a,) * 3, axis=-1) a = Image.fromarray(a) draw = ImageDraw.Draw(a) grid = self.grid - for (x_0, y_0, x_1, y_1) in grid: - draw.line((x_0,y_0, x_1, y_1), fill=(255,0,0), width=3) + for x_0, y_0, x_1, y_1 in grid: + draw.line((x_0, y_0, x_1, y_1), fill=(255, 0, 0), width=3) a.save(filename) else: - Image.fromarray(a).save(filepre) + Image.fromarray(a).save(filename) - def renderOCTscans(self, filepre = "oct", renderSeg=False): + def renderOCTscans(self, filepre="oct", renderSeg=False): """ Renders OCT images a PNG file and optionally overlays segmentation lines Also creates a CSV file of vol file features. @@ -116,14 +117,15 @@ def renderOCTscans(self, filepre = "oct", renderSeg=False): """ from PIL import Image + wf = self.wholefile for i in range(wf["cScan"].shape[0]): a = np.copy(wf["cScan"][i]) if renderSeg: - a = np.stack((a,)*3, axis=-1) + a = np.stack((a,) * 3, axis=-1) for li in range(wf["segmentations"].shape[0]): for x in range(wf["segmentations"].shape[2]): - a[int(wf["segmentations"][li,i,x]),x, li] = 255 + a[int(wf["segmentations"][li, i, x]), x, li] = 255 Image.fromarray(a).save("%s_%03d.png" % (filepre, i)) @@ -134,9 +136,9 @@ def __parseVolFile(self, fn, parseSeg=False): with open(fn, "rb") as fin: header = OrderedDict() header["version"] = fin.read(12) - header["octSizeX"] = struct.unpack("I", fin.read(4))[0] # lateral resolution + header["octSizeX"] = struct.unpack("I", fin.read(4))[0] # lateral resolution header["numBscan"] = struct.unpack("I", fin.read(4))[0] - header["octSizeZ"] = struct.unpack("I", fin.read(4))[0] # OCT depth + header["octSizeZ"] = struct.unpack("I", fin.read(4))[0] # OCT depth header["scaleX"] = struct.unpack("d", fin.read(8))[0] header["distance"] = struct.unpack("d", fin.read(8))[0] header["scaleZ"] = struct.unpack("d", fin.read(8))[0] @@ -144,24 +146,30 @@ def __parseVolFile(self, fn, parseSeg=False): header["sizeYSlo"] = struct.unpack("I", fin.read(4))[0] header["scaleXSlo"] = struct.unpack("d", fin.read(8))[0] header["scaleYSlo"] = struct.unpack("d", fin.read(8))[0] - header["fieldSizeSlo"] = struct.unpack("I", fin.read(4))[0] # FOV in degrees + header["fieldSizeSlo"] = struct.unpack("I", fin.read(4))[0] # FOV in degrees header["scanFocus"] = struct.unpack("d", fin.read(8))[0] header["scanPos"] = fin.read(4) header["examTime"] = struct.unpack("=q", fin.read(8))[0] / 1e7 - header["examTime"] = datetime.datetime.utcfromtimestamp(header["examTime"] - (369*365.25+4) * 24*60*60) # needs to be checked + header["examTime"] = datetime.datetime.utcfromtimestamp( + header["examTime"] - (369 * 365.25 + 4) * 24 * 60 * 60 + ) # needs to be checked header["scanPattern"] = struct.unpack("I", fin.read(4))[0] header["BscanHdrSize"] = struct.unpack("I", fin.read(4))[0] header["ID"] = fin.read(16) header["ReferenceID"] = fin.read(16) - header["PID"] = struct.unpack("I", fin.read(4))[0] + header["PID"] = struct.unpack("I", fin.read(4))[0] header["PatientID"] = fin.read(21) header["unknown2"] = fin.read(3) header["DOB"] = struct.unpack("d", fin.read(8))[0] - 25569 - header["DOB"] = datetime.datetime.utcfromtimestamp(0) + datetime.timedelta(seconds=header["DOB"] * 24 * 60 * 60 ) # needs to be checked + header["DOB"] = datetime.datetime.utcfromtimestamp(0) + datetime.timedelta( + seconds=header["DOB"] * 24 * 60 * 60 + ) # needs to be checked header["VID"] = struct.unpack("I", fin.read(4))[0] header["VisitID"] = fin.read(24) header["VisitDate"] = struct.unpack("d", fin.read(8))[0] - 25569 - header["VisitDate"] = datetime.datetime.utcfromtimestamp(0) + datetime.timedelta(seconds=header["VisitDate"] * 24 * 60 * 60) # needs to be checked + header["VisitDate"] = datetime.datetime.utcfromtimestamp(0) + datetime.timedelta( + seconds=header["VisitDate"] * 24 * 60 * 60 + ) # needs to be checked header["GridType"] = struct.unpack("I", fin.read(4))[0] header["GridOffset"] = struct.unpack("I", fin.read(4))[0] @@ -169,11 +177,11 @@ def __parseVolFile(self, fn, parseSeg=False): fin.seek(2048) U = array.array("B") U.frombytes(fin.read(header["sizeXSlo"] * header["sizeYSlo"])) - U = np.array(U).astype("uint8").reshape((header["sizeXSlo"],header["sizeYSlo"])) + U = np.array(U).astype("uint8").reshape((header["sizeXSlo"], header["sizeYSlo"])) wholefile["sloImage"] = U sloOffset = 2048 + header["sizeXSlo"] * header["sizeYSlo"] - octOffset = header["BscanHdrSize"] + header["octSizeX"] * header["octSizeZ"] * 4 + octOffset = header["BscanHdrSize"] + header["octSizeX"] * header["octSizeZ"] * 4 bscans = [] bscanheaders = [] bscanqualities = [] @@ -197,13 +205,13 @@ def __parseVolFile(self, fn, parseSeg=False): fin.seek(header["BscanHdrSize"] + sloOffset + i * octOffset) U = array.array("f") U.frombytes(fin.read(4 * header["octSizeX"] * header["octSizeZ"])) - U = np.array(U).reshape((header["octSizeZ"],header["octSizeX"])) - # remove out of boundary - v = struct.unpack("f", decode_hex('FFFF7F7F')[0]) + U = np.array(U).reshape((header["octSizeZ"], header["octSizeX"])) + # remove out of boundary + v = struct.unpack("f", decode_hex("FFFF7F7F")[0]) U[U == v] = 0 # log normalize U = np.log(10000 * U + 1) - U = (255. * (np.clip(U, 0, np.max(U)) / np.max(U))).astype("uint8") + U = (255.0 * (np.clip(U, 0, np.max(U)) / np.max(U))).astype("uint8") bscans.append(U) if parseSeg: # extract OCT segmentations data @@ -212,14 +220,14 @@ def __parseVolFile(self, fn, parseSeg=False): U.frombytes(fin.read(4 * header["octSizeX"] * bscanHead["numSeg"])) U = np.array(U) print(U.shape) - U[U == v] = 0. - if segmentations == None: + U[U == v] = 0.0 + if segmentations is None: segmentations = [] for j in range(bscanHead["numSeg"]): segmentations.append([]) for j in range(bscanHead["numSeg"]): - segmentations[j].append(U[j*header["octSizeX"]:(j+1) * header["octSizeX"]].tolist()) + segmentations[j].append(U[j * header["octSizeX"] : (j + 1) * header["octSizeX"]].tolist()) wholefile["cScan"] = np.array(bscans) if parseSeg: wholefile["segmentations"] = np.array(segmentations) @@ -228,35 +236,88 @@ def __parseVolFile(self, fn, parseSeg=False): self.wholefile = wholefile import csv from pathlib import Path, PurePath - volFeatures = [PurePath(fn).name, wholefile["header"]["version"].decode("utf-8").rstrip('\x00'), wholefile["header"]["numBscan"], wholefile["header"]["octSizeX"], wholefile["header"]["octSizeZ"], wholefile["header"]["distance"], wholefile["header"]["scaleX"], \ - wholefile["header"]["scaleZ"], wholefile["header"]["sizeXSlo"], wholefile["header"]["sizeYSlo"], wholefile["header"]["scaleXSlo"], wholefile["header"]["scaleYSlo"], wholefile["header"]["fieldSizeSlo"], \ - wholefile["header"]["scanFocus"], wholefile["header"]["scanPos"].decode("utf-8").rstrip('\x00'), wholefile["header"]["examTime"], wholefile["header"]["scanPattern"], wholefile["header"]["BscanHdrSize"], \ - wholefile["header"]["ID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["ReferenceID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["PID"], wholefile["header"]["PatientID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["DOB"], wholefile["header"]["VID"], wholefile["header"]["VisitID"].decode("utf-8").rstrip('\x00'), wholefile["header"]["VisitDate"], \ - wholefile["header"]["GridType"], wholefile["header"]["GridOffset"], wholefile["average-quality"]] + + volFeatures = [ + PurePath(fn).name, + wholefile["header"]["version"].decode("utf-8").rstrip("\x00"), + wholefile["header"]["numBscan"], + wholefile["header"]["octSizeX"], + wholefile["header"]["octSizeZ"], + wholefile["header"]["distance"], + wholefile["header"]["scaleX"], + wholefile["header"]["scaleZ"], + wholefile["header"]["sizeXSlo"], + wholefile["header"]["sizeYSlo"], + wholefile["header"]["scaleXSlo"], + wholefile["header"]["scaleYSlo"], + wholefile["header"]["fieldSizeSlo"], + wholefile["header"]["scanFocus"], + wholefile["header"]["scanPos"].decode("utf-8").rstrip("\x00"), + wholefile["header"]["examTime"], + wholefile["header"]["scanPattern"], + wholefile["header"]["BscanHdrSize"], + wholefile["header"]["ID"].decode("utf-8").rstrip("\x00"), + wholefile["header"]["ReferenceID"].decode("utf-8").rstrip("\x00"), + wholefile["header"]["PID"], + wholefile["header"]["PatientID"].decode("utf-8").rstrip("\x00"), + wholefile["header"]["DOB"], + wholefile["header"]["VID"], + wholefile["header"]["VisitID"].decode("utf-8").rstrip("\x00"), + wholefile["header"]["VisitDate"], + wholefile["header"]["GridType"], + wholefile["header"]["GridOffset"], + wholefile["average-quality"], + ] output_dir = PurePath(fn).parent - output_csv = output_dir.joinpath('vols.csv') + output_csv = output_dir.joinpath("vols.csv") if not Path(output_csv).exists(): print("Creating vols.csv as it does not exist.") - with open(output_csv, 'w', newline='') as file: + with open(output_csv, "w", newline="") as file: writer = csv.writer(file) - writer.writerow(["filename", "version", "numBscan", "octSizeX", "octSizeZ", "distance", "scaleX", \ - "scaleZ", "sizeXSlo", "sizeYSlo", "scaleXSlo", "scaleYSlo", "fieldSizeSlo", \ - "scanFocus", "scanPos", "examTime", "scanPattern", "BscanHdrSize", \ - "ID", "ReferenceID", "PID", "PatientID", "DOB", "VID", "VisitID", "VisitDate", \ - "GridType", "GridOffset", "Average Quality"]) - with open(output_csv, 'r', newline='') as file: + writer.writerow( + [ + "filename", + "version", + "numBscan", + "octSizeX", + "octSizeZ", + "distance", + "scaleX", + "scaleZ", + "sizeXSlo", + "sizeYSlo", + "scaleXSlo", + "scaleYSlo", + "fieldSizeSlo", + "scanFocus", + "scanPos", + "examTime", + "scanPattern", + "BscanHdrSize", + "ID", + "ReferenceID", + "PID", + "PatientID", + "DOB", + "VID", + "VisitID", + "VisitDate", + "GridType", + "GridOffset", + "Average Quality", + ] + ) + with open(output_csv, "r", newline="") as file: existing_vols = csv.reader(file) for vol in existing_vols: if vol[0] == PurePath(fn).name: print("Skipping,", PurePath(fn).name, "already present in vols.csv.") return - with open(output_csv, 'a', newline='') as file: + with open(output_csv, "a", newline="") as file: print("Adding", PurePath(fn).name, "to vols.csv.") writer = csv.writer(file) writer.writerow(volFeatures) - - @property def fileHeader(self): """ @@ -336,5 +397,3 @@ def saveGrid(self, outfn): r = [ri] + r fout.write("%s\n" % "\t".join(map(str, r))) ri += 1 - - diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 1cb1fe5c..272819a9 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -1,15 +1,8 @@ -import os - -os.chdir(os.path.dirname(os.path.abspath(__file__))) -current_directory = os.getcwd() -print(current_directory) - - +import json import logging +import os import pickle -import urllib -import configargparse import pandas as pd import progressbar from detectron2.checkpoint import DetectionCheckpointer @@ -18,22 +11,24 @@ from detectron2.evaluation import COCOEvaluator, inference_on_dataset from detectron2.modeling import build_model +os.chdir(os.path.dirname(os.path.abspath(__file__))) +current_directory = os.getcwd() +print(current_directory) from .analysis_lib import CreatePlotsRPD, EvaluateClass, OutputVis, grab_dataset from .datasets import data from .Ensembler import Ensembler from .table_styles import styles -logging.basicConfig(level=logging.INFO) -import json -import urllib.request -import zipfile +logging.basicConfig(level=logging.INFO) -os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "0" -dpi= 120 -class MyProgressBar(): +dpi = 120 + + +class MyProgressBar: # https://stackoverflow.com/a/53643011/3826929 # George C def __init__(self): @@ -41,7 +36,7 @@ def __init__(self): def __call__(self, block_num, block_size, total_size): if not self.pbar: - self.pbar=progressbar.ProgressBar(maxval=total_size) + self.pbar = progressbar.ProgressBar(maxval=total_size) self.pbar.start() downloaded = block_num * block_size @@ -50,100 +45,116 @@ def __call__(self, block_num, block_size, total_size): else: self.pbar.finish() -def create_dataset(dataset_name, extracted_path): # Creates dataset and pk file from extracted images. + +def create_dataset(dataset_name, extracted_path): # Creates dataset and pk file from extracted images. stored_data = data.rpd_data(extracted_path) - pickle.dump(stored_data, open(os.path.join(data.script_dir,f"{dataset_name}.pk"), "wb")) - + pickle.dump(stored_data, open(os.path.join(data.script_dir, f"{dataset_name}.pk"), "wb")) + + def configure_model(): cfg = get_cfg() moddir = os.path.dirname(os.path.realpath(__file__)) - name = 'working.yaml' + name = "working.yaml" cfg_path = os.path.join(moddir, name) cfg.merge_from_file(cfg_path) return cfg + def register_dataset(dataset_name): for name in [dataset_name]: try: DatasetCatalog.register(name, grab_dataset(name)) - except: - print('Already registered.') + except AssertionError as e: + print(f"Assertion failed: {e}. Already registered.") MetadataCatalog.get(name).thing_classes = ["rpd"] + def run_prediction(cfg, dataset_name, output_path): model = build_model(cfg) # returns a torch.nn.Module - myloader = build_detection_test_loader(cfg,dataset_name) - myeval = COCOEvaluator(dataset_name,tasks={'bbox','segm'},output_dir =output_path) #produces _coco_format.json when initialized - for mdl in ("fold1", "fold2", "fold3", "fold4","fold5"): - extract_directory = '../model' + myloader = build_detection_test_loader(cfg, dataset_name) + myeval = COCOEvaluator( + dataset_name, tasks={"bbox", "segm"}, output_dir=output_path + ) # produces _coco_format.json when initialized + for mdl in ("fold1", "fold2", "fold3", "fold4", "fold5"): + extract_directory = "../model" file_name = mdl + "_model_final.pth" model_weights_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), extract_directory, file_name) print(model_weights_path) - DetectionCheckpointer(model).load(model_weights_path) # load a file, usually from cfg.MODEL.WEIGHTS - model.eval() #set model in evaluation mode + DetectionCheckpointer(model).load(model_weights_path) # load a file, usually from cfg.MODEL.WEIGHTS + model.eval() # set model in evaluation mode myeval.reset() output_dir = os.path.join(output_path, mdl) myeval._output_dir = output_dir print("Running inference with model ", mdl) - results_i = inference_on_dataset(model, myloader, myeval) #produces coco_instance_results.json when myeval.evaluate is called + _ = inference_on_dataset( + model, myloader, myeval + ) # produces coco_instance_results.json when myeval.evaluate is called print("Done with predictions!") -def run_ensemble(dataset_name, output_path, iou_thresh = 0.2): - ens = Ensembler(output_path,dataset_name,["fold1", "fold2", "fold3", "fold4","fold5"], iou_thresh=iou_thresh) + +def run_ensemble(dataset_name, output_path, iou_thresh=0.2): + ens = Ensembler(output_path, dataset_name, ["fold1", "fold2", "fold3", "fold4", "fold5"], iou_thresh=iou_thresh) ens.mean_score_nms() ens.save_coco_instances() return ens -def evaluate_dataset(dataset_name, output_path,iou_thresh = 0.2,prob_thresh = 0.5): - myeval = EvaluateClass(dataset_name, output_path, iou_thresh = iou_thresh, prob_thresh=prob_thresh,evalsuper=False) + +def evaluate_dataset(dataset_name, output_path, iou_thresh=0.2, prob_thresh=0.5): + myeval = EvaluateClass(dataset_name, output_path, iou_thresh=iou_thresh, prob_thresh=prob_thresh, evalsuper=False) myeval.evaluate() - with open(os.path.join(output_path,'scalar_dict.json'),"w") as outfile: - json.dump(obj=myeval.summarize_scalars(),fp=outfile) + with open(os.path.join(output_path, "scalar_dict.json"), "w") as outfile: + json.dump(obj=myeval.summarize_scalars(), fp=outfile) return myeval + def create_table(myeval): - dataset_table = CreatePlotsRPD.initfromcoco(myeval.mycoco,myeval.prob_thresh) + dataset_table = CreatePlotsRPD.initfromcoco(myeval.mycoco, myeval.prob_thresh) dataset_table.dfimg.sort_index(inplace=True) return dataset_table - #dataset_table.dfimg['scan'] = dataset_table.dfimg['scan'].astype('int') #depends on what we want scan field to be + # dataset_table.dfimg['scan'] = dataset_table.dfimg['scan'].astype('int') #depends on what we want scan field to be + -def output_vol_predictions(dataset_table,vis,volID,output_path,output_mode='pred_overlay'): +def output_vol_predictions(dataset_table, vis, volID, output_path, output_mode="pred_overlay"): dfimg = dataset_table.dfimg - imgids = dfimg[dfimg.volID ==volID].sort_index().index.values - outname = os.path.join(output_path,f'{volID}.tiff') - if output_mode=='pred_overlay': - vis.output_pred_to_tiff(imgids,outname,pred_only=False) - elif output_mode == 'pred_only': - vis.output_pred_to_tiff(imgids,outname,pred_only=True) - elif output_mode == 'originals': - vis.output_ori_to_tiff(imgids,outname) - elif output_mode == 'all': - vis.output_all_to_tiff(imgids,outname) + imgids = dfimg[dfimg.volID == volID].sort_index().index.values + outname = os.path.join(output_path, f"{volID}.tiff") + if output_mode == "pred_overlay": + vis.output_pred_to_tiff(imgids, outname, pred_only=False) + elif output_mode == "pred_only": + vis.output_pred_to_tiff(imgids, outname, pred_only=True) + elif output_mode == "originals": + vis.output_ori_to_tiff(imgids, outname) + elif output_mode == "all": + vis.output_all_to_tiff(imgids, outname) else: - print(f'Invalid mode {output_mode} for function output_vol_predictions.') + print(f"Invalid mode {output_mode} for function output_vol_predictions.") -def output_dataset_predictions(dataset_table,vis,output_path,output_mode = 'pred_overlay',draw_mode='default'): + +def output_dataset_predictions(dataset_table, vis, output_path, output_mode="pred_overlay", draw_mode="default"): vis.set_draw_mode(draw_mode) - os.makedirs(output_path,exist_ok=True) ## should this be set to exist_ok=False? + os.makedirs(output_path, exist_ok=True) for volID in dataset_table.dfvol.index: - output_vol_predictions(dataset_table,vis,volID,output_path,output_mode) + output_vol_predictions(dataset_table, vis, volID, output_path, output_mode) + def create_dfvol(dataset_name, output_path, dataset_table): - dfvol = dataset_table.dfvol.sort_values(by=['dt_instances'],ascending=False) - with pd.option_context('styler.render.max_elements', int(dfvol.size)+1): - html_str = dfvol.style.format('{:.0f}').set_table_styles(styles).to_html() - html_file = open(os.path.join(output_path, 'dfvol_'+dataset_name+'.html'),'w') + dfvol = dataset_table.dfvol.sort_values(by=["dt_instances"], ascending=False) + with pd.option_context("styler.render.max_elements", int(dfvol.size) + 1): + html_str = dfvol.style.format("{:.0f}").set_table_styles(styles).to_html() + html_file = open(os.path.join(output_path, "dfvol_" + dataset_name + ".html"), "w") html_file.write(html_str) html_file.close() + def create_dfimg(dataset_name, output_path, dataset_table): dfimg = dataset_table.dfimg.sort_index() - with pd.option_context('styler.render.max_elements', int(dfimg.size)+1): + with pd.option_context("styler.render.max_elements", int(dfimg.size) + 1): html_str = dfimg.style.set_table_styles(styles).to_html() - html_file = open(os.path.join(output_path, 'dfimg_'+dataset_name+'.html'),'w') + html_file = open(os.path.join(output_path, "dfimg_" + dataset_name + ".html"), "w") html_file.write(html_str) html_file.close() + def main(args): dataset_name = None input_dir = None @@ -161,20 +172,20 @@ def main(args): print(args) - dataset_name = args.get('dataset_name') # Access values from the dictionary - input_dir = args.get('input_dir') - extracted = args.get('extracted_dir') - input_format = args.get('input_format') - output = args.get('output_dir') - run_ext = args.get('run_extract', True) # Provide default values - make_dataset = args.get('create_dataset',True) - run_inf = args.get('run_inference', True) - prob_thresh = args.get('prob_thresh',0.5) - make_table = args.get('create_tables', True) - bm = args.get('binary_mask', False) - bmo = args.get('binary_mask_overlay', False) - imo = args.get('instance_mask_overlay', False) - make_visuals = (bm | bmo | imo) + dataset_name = args.get("dataset_name") # Access values from the dictionary + input_dir = args.get("input_dir") + extracted = args.get("extracted_dir") + input_format = args.get("input_format") + output = args.get("output_dir") + run_ext = args.get("run_extract", True) # Provide default values + make_dataset = args.get("create_dataset", True) + run_inf = args.get("run_inference", True) + prob_thresh = args.get("prob_thresh", 0.5) + make_table = args.get("create_tables", True) + bm = args.get("binary_mask", False) + bmo = args.get("binary_mask_overlay", False) + imo = args.get("instance_mask_overlay", False) + make_visuals = bm | bmo | imo if run_ext: if not os.path.isdir(extracted): @@ -182,7 +193,7 @@ def main(args): os.mkdir(extracted) data.extract_files(input_dir, extracted, input_format) print("Image extraction complete!") - if make_dataset: + if make_dataset: print("Creating dataset from extracted images...") create_dataset(dataset_name, extracted) if run_inf: @@ -206,28 +217,37 @@ def main(args): print("Creating dataset table...") table = create_table(eval) if make_table: - create_dfvol(dataset_name, output, table) - create_dfimg(dataset_name, output, table) - print("Dataset htmls complete!") + create_dfvol(dataset_name, output, table) + create_dfimg(dataset_name, output, table) + print("Dataset htmls complete!") if make_visuals: - vis = OutputVis(dataset_name, - prob_thresh = eval.prob_thresh, - pred_mode = 'file', - pred_file = os.path.join(output, 'coco_instances_results.json'), - has_annotations=False) - vis.scale=1.0 + vis = OutputVis( + dataset_name, + prob_thresh=eval.prob_thresh, + pred_mode="file", + pred_file=os.path.join(output, "coco_instances_results.json"), + has_annotations=False, + ) + vis.scale = 1.0 if bm: print("Creating binary masks tif (no overlay)...") - vis.annotation_color='w' - output_dataset_predictions(table,vis,os.path.join(output,'predicted_binary_masks'),'pred_only','bw') + vis.annotation_color = "w" + output_dataset_predictions( + table, vis, os.path.join(output, "predicted_binary_masks"), "pred_only", "bw" + ) if bmo: print("Creating binary masks tif (with overlay)...") - output_dataset_predictions(table,vis,os.path.join(output,'predicted_binary_overlays'),'pred_overlay','bw') + output_dataset_predictions( + table, vis, os.path.join(output, "predicted_binary_overlays"), "pred_overlay", "bw" + ) if imo: print("Creating instances masks tif (with overlay)...") - output_dataset_predictions(table,vis,os.path.join(output,'predicted_instance_overlays'),'pred_overlay','default') + output_dataset_predictions( + table, vis, os.path.join(output, "predicted_instance_overlays"), "pred_overlay", "default" + ) print("Visualizations complete!") + # if __name__ == "__main__": # main(sys.argv[1:]) -# # main_alt() \ No newline at end of file +# # main_alt() diff --git a/models/retinalOCT_RPD_segmentation/scripts/table_styles.py b/models/retinalOCT_RPD_segmentation/scripts/table_styles.py index 148e76b6..eb32e97c 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/table_styles.py +++ b/models/retinalOCT_RPD_segmentation/scripts/table_styles.py @@ -1,41 +1,32 @@ def hover(hover_color="#add8e6"): - return dict(selector="tbody tr:hover", - props=[("background-color", "%s" % hover_color)]) + return dict(selector="tbody tr:hover", props=[("background-color", "%s" % hover_color)]) -styles = [ - #table properties - dict(selector=" ", - props=[("margin","0"), - ("font-family",'"Helvetica", "Arial", sans-serif'), - ("border-collapse", "collapse"), - ("border","none"), - ("border", "2px solid #ccf") - ]), - -# #header color - optional -# dict(selector="thead", -# props=[("background-color","#cc8484") -# ]), - - #background shading - dict(selector="tbody tr:nth-child(even)", - props=[("background-color", "#fff")]), - dict(selector="tbody tr:nth-child(odd)", - props=[("background-color", "#eee")]), - - #cell spacing - dict(selector="td", - props=[("padding", ".5em"),("text-align","center")]), - #header cell properties - dict(selector="th", - props=[("font-size", "125%"), - ("text-align", "center")]), - - #caption placement - dict(selector="caption", - props=[("caption-side", "bottom")]), - - #render hover last to override background-color - hover() +styles = [ + # table properties + dict( + selector=" ", + props=[ + ("margin", "0"), + ("font-family", '"Helvetica", "Arial", sans-serif'), + ("border-collapse", "collapse"), + ("border", "none"), + ("border", "2px solid #ccf"), + ], + ), + # #header color - optional + # dict(selector="thead", + # props=[("background-color","#cc8484") + # ]), + # background shading + dict(selector="tbody tr:nth-child(even)", props=[("background-color", "#fff")]), + dict(selector="tbody tr:nth-child(odd)", props=[("background-color", "#eee")]), + # cell spacing + dict(selector="td", props=[("padding", ".5em"), ("text-align", "center")]), + # header cell properties + dict(selector="th", props=[("font-size", "125%"), ("text-align", "center")]), + # caption placement + dict(selector="caption", props=[("caption-side", "bottom")]), + # render hover last to override background-color + hover(), ] From f7e9de6bbd862aed5a3b0d3ab68d8978bd96f33c Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Tue, 25 Mar 2025 13:17:36 -0700 Subject: [PATCH 05/28] Removed configargparser and added PNG naming instructions in readme. --- models/retinalOCT_RPD_segmentation/configs/metadata.json | 1 - models/retinalOCT_RPD_segmentation/docs/README.md | 6 +++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 45e3101a..189e932f 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -12,7 +12,6 @@ "pandas": "1.3.5", "seaborn": "0.12.2", "scikit-learn": "1.0.2", - "configargparse": "1.7", "progressbar": "2.5", "pydicom": "2.4.4", "fire": "0.7.0", diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index 11730193..9f0f698d 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -28,11 +28,11 @@ In the external test datasets, the overall performance for detecting RPD in a vo For more details regarding evaluation results, please see Results section of paper. -## INSTALLATION + ## USAGE The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: @@ -109,7 +109,7 @@ The tables can be created by setting `create_tables` to `True`: The predicted segmentations can be output as multi-page TIFFs, where each TIFF file corresponds to an input volume of the dataset, and each page to an OCT slice from the volume in original order. The output images can be binary masks, binary masks overlaying the original B-scan, and instance masks overlaying the original B-scan. Set the `binary_mask`, `binary_mask_overlay` and `instance_mask_overlay` flags in the yaml file to `True` accordingly. ### SAMPLE DATA -As a reference, sample OCT-B scans are provided in PNG format under the `sample_data` directory. Set `extracted_dir` in `inference.yaml` to `sample_data` to run inference on these few set of images. +As a reference, sample OCT-B scans are provided in PNG format under the sample_data directory. Set `extracted_dir` in `inference.yaml` to `sample_data` to run inference on these few set of images. ## **System Configuration** Inference on one Nvidia A100 gpu takes about 0.041 s/batch of 14 images, about 3G of gpu memory, and 6G of RAM. From 30dabf9a9b51704563dfc8cdfb1bf9fa735ab850 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Tue, 1 Apr 2025 14:14:41 -0700 Subject: [PATCH 06/28] Moved large_files.yaml into retinalOCT_RPD_Segmentation into bundle root. --- models/retinalOCT_RPD_segmentation/{configs => }/large_files.yaml | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename models/retinalOCT_RPD_segmentation/{configs => }/large_files.yaml (100%) diff --git a/models/retinalOCT_RPD_segmentation/configs/large_files.yaml b/models/retinalOCT_RPD_segmentation/large_files.yaml similarity index 100% rename from models/retinalOCT_RPD_segmentation/configs/large_files.yaml rename to models/retinalOCT_RPD_segmentation/large_files.yaml From efb5da88120321081feec58d1721a3644e7f8e32 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Tue, 29 Apr 2025 22:16:34 +0000 Subject: [PATCH 07/28] Updated dependencies. Updated depreciated pandas api. Updated install instructions in readme. --- ci/bundle_custom_data.py | 4 +++- ..._retinalOCT_RPD_segmentation_dependency.sh | 3 +++ .../test_retinalOCT_RPD_segmentation.py | 2 +- .../configs/metadata.json | 19 ++++++++++--------- .../docs/README.md | 11 +++++++++++ .../docs/requirements.txt | 7 +++++++ .../scripts/Ensembler.py | 5 +++-- .../scripts/inference.py | 2 +- 8 files changed, 39 insertions(+), 14 deletions(-) create mode 100644 ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh create mode 100644 models/retinalOCT_RPD_segmentation/docs/requirements.txt diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index 51cb50bf..2fcdd599 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -60,7 +60,9 @@ # This dict is used for our CI tests to install required dependencies that cannot be installed by `pip install` directly. # If a bundle has this kind of dependencies, please add the bundle name (key), and the path of the install script (value) # into the dict. -install_dependency_dict = {"maisi_ct_generative": "ci/install_scripts/install_maisi_ct_generative_dependency.sh"} +install_dependency_dict = { + "maisi_ct_generative": "ci/install_scripts/install_maisi_ct_generative_dependency.sh", + "retinalOCT_RPD_segmentation": "ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh"} # This list is used for our CI tests to determine whether a bundle supports TensorRT export. Related # test will be employed for bundles in the dict. diff --git a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh new file mode 100644 index 00000000..ca13eb69 --- /dev/null +++ b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh @@ -0,0 +1,3 @@ +# ensure that using evironment with python==3.9 +pip install -r ./docs/requirements.txt #includes monai +python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' diff --git a/ci/unit_tests/test_retinalOCT_RPD_segmentation.py b/ci/unit_tests/test_retinalOCT_RPD_segmentation.py index 79c6c80d..f245b0fe 100644 --- a/ci/unit_tests/test_retinalOCT_RPD_segmentation.py +++ b/ci/unit_tests/test_retinalOCT_RPD_segmentation.py @@ -111,7 +111,7 @@ def test_inference_run(self): # At least 10 RPD present in sample data dfvol = pd.read_html(os.path.join(self.output_dir, "dfvol_testDataset.html"))[0] - self.assertTrue(dfvol["dt_instances"].sum()[0] > 10) + self.assertTrue(dfvol["dt_instances"].sum().iloc[0] > 10) if __name__ == "__main__": diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 189e932f..4078d7c9 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -4,19 +4,20 @@ "changelog": { "0.0.1": "Initial version" }, - "monai_version": "1.1.0", - "pytorch_version": "1.10.0", - "numpy_version": "1.21.2", - "optional_packages_version": { + "monai_version": "1.4.0", + "pytorch_version": "2.7.0", + "numpy_version": "1.26.4", + "optional_packages_version": {}, + "required_packages_version": { "opencv-python-headless": "4.11.0.86", - "pandas": "1.3.5", - "seaborn": "0.12.2", - "scikit-learn": "1.0.2", + "pandas": "2.2.3", + "seaborn": "0.13.2", + "scikit-learn": "1.6.1", "progressbar": "2.5", "pydicom": "2.4.4", "fire": "0.7.0", - "jinja2": "3.1.5", - "detectron2": "0.6+cu113" + "detectron2": "0.6", + "lxml": "5.4.0" }, "name": "retinalOCT_RPD_segmentation", "task": "Reticular Pseudodrusen (RPD) instance segmentation.", diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index 9f0f698d..2ae275de 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -33,6 +33,17 @@ This bundle can be installed using docker by navigating to the RPDBundle directo ``` docker build -t : . ``` --> +## INSTALL +The following installation order works when installing into an environment with python==3.9. + +From the bundle directory, install the required packages using +``` +pip install -r ./docs/requirements.txt +``` +Install detectron2 using +``` +python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' +``` ## USAGE The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: diff --git a/models/retinalOCT_RPD_segmentation/docs/requirements.txt b/models/retinalOCT_RPD_segmentation/docs/requirements.txt new file mode 100644 index 00000000..c80d6e8a --- /dev/null +++ b/models/retinalOCT_RPD_segmentation/docs/requirements.txt @@ -0,0 +1,7 @@ +monai[fire,ninja,torchvision,pandas,pydicom] + +opencv-python-headless +seaborn +scikit-learn +progressbar +lxml diff --git a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py index b91d0771..28d47b51 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py +++ b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py @@ -88,10 +88,11 @@ def nik_merge(lsts): ) for img in tqdm(self.coco_gt.imgs.keys()): # print(img) - df = pd.DataFrame() # a dataframe of detections + dflist = [] # a dataframe of detections obj_set = set() # a set of objects (frozensets) for i, coco_dt in enumerate(self.dtlist): # for each detector append predictions to df - df = df.append(pd.DataFrame(coco_dt.imgToAnns[img]).assign(det=i), ignore_index=True) + dflist.append(pd.DataFrame(coco_dt.imgToAnns[img]).assign(det=i)) + df = pd.concat(dflist,ignore_index=True) if not df.empty: for cat in self.cats: # for each category dfcat = df[df["category_id"] == cat] diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 272819a9..45b8afe4 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -76,7 +76,7 @@ def run_prediction(cfg, dataset_name, output_path): dataset_name, tasks={"bbox", "segm"}, output_dir=output_path ) # produces _coco_format.json when initialized for mdl in ("fold1", "fold2", "fold3", "fold4", "fold5"): - extract_directory = "../model" + extract_directory = "../models" file_name = mdl + "_model_final.pth" model_weights_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), extract_directory, file_name) print(model_weights_path) From 7cd1394cc02316100550379a19f29dc07ce382ab Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Fri, 16 May 2025 04:22:58 +0000 Subject: [PATCH 08/28] fix isort and update cit for detectron2 Signed-off-by: Yiheng Wang --- ci/get_bundle_requirements.py | 3 + ..._retinalOCT_RPD_segmentation_dependency.sh | 1 - .../configs/inference.yaml | 2 +- .../configs/metadata.json | 240 ++++++++++-------- .../docs/README.md | 32 +-- .../docs/requirements.txt | 6 +- .../large_files.yaml | 10 +- .../scripts/inference.py | 1 - .../scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml | 6 +- .../scripts/working.yaml | 6 +- 10 files changed, 168 insertions(+), 139 deletions(-) diff --git a/ci/get_bundle_requirements.py b/ci/get_bundle_requirements.py index 9262b112..fbf0c56c 100644 --- a/ci/get_bundle_requirements.py +++ b/ci/get_bundle_requirements.py @@ -19,6 +19,7 @@ ALLOW_MONAI_RC = os.environ.get("ALLOW_MONAI_RC", "false").lower() in ("true", "1", "t", "y", "yes") +special_dependencies_list = ["detectron2"] def increment_version(version): """ @@ -79,6 +80,8 @@ def get_requirements(bundle, models_path, requirements_file): if package_key in metadata.keys(): optional_dict = metadata[package_key] for name, version in optional_dict.items(): + if name in special_dependencies_list: + continue libs.append(f"{name}=={version}") if len(libs) > 0: diff --git a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh index ca13eb69..ccf94ba5 100644 --- a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh +++ b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh @@ -1,3 +1,2 @@ # ensure that using evironment with python==3.9 -pip install -r ./docs/requirements.txt #includes monai python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' diff --git a/models/retinalOCT_RPD_segmentation/configs/inference.yaml b/models/retinalOCT_RPD_segmentation/configs/inference.yaml index 35336773..fd148017 100644 --- a/models/retinalOCT_RPD_segmentation/configs/inference.yaml +++ b/models/retinalOCT_RPD_segmentation/configs/inference.yaml @@ -20,4 +20,4 @@ args: instance_mask_overlay : False inference: -- $scripts.inference.main(@args) \ No newline at end of file +- $scripts.inference.main(@args) diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 4078d7c9..fe37a74b 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -1,105 +1,143 @@ { - "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", - "version": "0.0.1", - "changelog": { - "0.0.1": "Initial version" - }, - "monai_version": "1.4.0", - "pytorch_version": "2.7.0", - "numpy_version": "1.26.4", - "optional_packages_version": {}, - "required_packages_version": { - "opencv-python-headless": "4.11.0.86", - "pandas": "2.2.3", - "seaborn": "0.13.2", - "scikit-learn": "1.6.1", - "progressbar": "2.5", - "pydicom": "2.4.4", - "fire": "0.7.0", - "detectron2": "0.6", - "lxml": "5.4.0" - }, - "name": "retinalOCT_RPD_segmentation", - "task": "Reticular Pseudodrusen (RPD) instance segmentation.", - "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", - "authors": "Yelena Bagdasarova, Scott Song", - "copyright": "Copyright (c) 2022, uw-biomedical-ml", - "network_data_format": { - "inputs": { - "image": { - "type": "image", - "format": "PNG", - "modality": "OCT", - "num_channels": 1, - "spatial_shape":[496,1024], - "dtype": "int16", - "value_range": [0,256], - "is_patch_data": false, - "channel_def": {"0": "image"} - } + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", + "version": "0.0.1", + "changelog": { + "0.0.1": "Initial version" }, - "preprocessed_data_sources": { - "vol_file": { - "type": "image", - "format": "VOL", - "modality": "OCT", - "num_channels": 1, - "spatial_shape":[496,1024,"D"], - "dtype": "int16", - "value_range": [0,256], - "description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to ////_oct_.png on disk, where is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " - - }, - "dicom_series": { - "type": "image", - "format": "DICOM", - "modality": "OCT", - "SOP class UID" : "1.2.840.10008.5.1.4.1.1.77.1.5.4", - "num_channels": 1, - "spatial_shape":[496,1024,"D"], - "dtype": "int16", - "value_range": [0,256], - "description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to //_oct_.png on disk, where is the slice number. " - } + "monai_version": "1.4.0", + "pytorch_version": "2.7.0", + "numpy_version": "1.26.4", + "optional_packages_version": {}, + "required_packages_version": { + "opencv-python-headless": "4.11.0.86", + "pandas": "2.2.3", + "seaborn": "0.13.2", + "scikit-learn": "1.6.1", + "progressbar": "2.5", + "pydicom": "2.4.4", + "fire": "0.7.0", + "detectron2": "0.6", + "lxml": "5.4.0" }, - "outputs": { - "pred": { - "dtype": "dictionary", - "type": "dictionary", - "format": "COCO", - "modality": "n/a", - "value_range": [0,1], - "num_channels": 1, - "spatial_shape": [496,1024], - "channel_def": {"0": "RPD"}, - "description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances." - } - }, - "post_processed_outputs": { - "binary segmentation": { - "type": "image", - "format": "TIFF", - "modality": "OCT", - "num_channels": 3, - "spatial_shape": [496,1024], - "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices." - }, - "binary segmentation overlay": { - "type": "image", - "format": "TIFF", - "modality": "OCT", - "num_channels": 3, - "spatial_shape": [496,1024], - "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." - }, - "instance segmentation overlay": { - "type": "image", - "format": "TIFF", - "modality": "OCT", - "num_channels": 3, - "spatial_shape": [496,1024], - "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." - } + "name": "retinalOCT_RPD_segmentation", + "task": "Reticular Pseudodrusen (RPD) instance segmentation.", + "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", + "authors": "Yelena Bagdasarova, Scott Song", + "copyright": "Copyright (c) 2022, uw-biomedical-ml", + "network_data_format": { + "inputs": { + "image": { + "type": "image", + "format": "PNG", + "modality": "OCT", + "num_channels": 1, + "spatial_shape": [ + 496, + 1024 + ], + "dtype": "int16", + "value_range": [ + 0, + 256 + ], + "is_patch_data": false, + "channel_def": { + "0": "image" + } + } + }, + "preprocessed_data_sources": { + "vol_file": { + "type": "image", + "format": "VOL", + "modality": "OCT", + "num_channels": 1, + "spatial_shape": [ + 496, + 1024, + "D" + ], + "dtype": "int16", + "value_range": [ + 0, + 256 + ], + "description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to ////_oct_.png on disk, where is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " + }, + "dicom_series": { + "type": "image", + "format": "DICOM", + "modality": "OCT", + "SOP class UID": "1.2.840.10008.5.1.4.1.1.77.1.5.4", + "num_channels": 1, + "spatial_shape": [ + 496, + 1024, + "D" + ], + "dtype": "int16", + "value_range": [ + 0, + 256 + ], + "description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to //_oct_.png on disk, where is the slice number. " + } + }, + "outputs": { + "pred": { + "dtype": "dictionary", + "type": "dictionary", + "format": "COCO", + "modality": "n/a", + "value_range": [ + 0, + 1 + ], + "num_channels": 1, + "spatial_shape": [ + 496, + 1024 + ], + "channel_def": { + "0": "RPD" + }, + "description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances." + } + }, + "post_processed_outputs": { + "binary segmentation": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [ + 496, + 1024 + ], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices." + }, + "binary segmentation overlay": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [ + 496, + 1024 + ], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." + }, + "instance segmentation overlay": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [ + 496, + 1024 + ], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." + } + } } - } -} \ No newline at end of file +} diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index 2ae275de..c8802d63 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -7,20 +7,20 @@ Himeesh Kumar, Yelena Bagdasarova, Scott Song, Doron G. Hickey, Amy C. Cohn, Mal Reticular Pseudodrusen, AMD, OCT, Segmentation ## **Model Description** -This model detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans. The instance segmentation model used a Mask-RCNN [1] head with the ResNeXt-101-32x8d-FPN [2] backbone (pretrained on ImageNet) implemented via the Detectron2 framework [3]. The model produces outputs that consist of bounding boxes and segmentation masks that delineate the coordinates and pixels of each instance detected, which are assigned a corresponding output probability. A tuneable probability threshold can then be applied to finalise the binary detection of an RPD instance. +This model detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans. The instance segmentation model used a Mask-RCNN [1] head with the ResNeXt-101-32x8d-FPN [2] backbone (pretrained on ImageNet) implemented via the Detectron2 framework [3]. The model produces outputs that consist of bounding boxes and segmentation masks that delineate the coordinates and pixels of each instance detected, which are assigned a corresponding output probability. A tuneable probability threshold can then be applied to finalise the binary detection of an RPD instance. Five segmentation models using these RPD instance labels on the OCT B-scans were trained based on five-fold cross-validation which were used to form a final ensemble model using soft voting (see supplementary material of paper for more information on model training.) ## **Data** The model was trained using the prospectively-collected, baseline OCT scans (prior to any treatments) of individuals enrolled in the LEAD study [4] imaged using Heidelberg Spectralis HRA+OCT. OCT B-scans from 200 eyes from 100 individuals in the LEAD study were randomly selected to undergo manual annotations of RPD by a single grader (HK) at the pixel level, following training from two senior investigators (RHG and ZW). Only definite RPD lesions, defined as subretinal hyperreflective accumulations that altered the contour of, or broke through, the overlying photoreceptor ellipsoid zone on the OCT B-scans were annotated. -The model was then internally tested in a different set of OCT scans from 125 eyes from 92 individuals from the LEAD study, and externally tested on five independent datasets: the MACUSTAR study [5], the Northern Ireland Cohort for Longitudinal Study of Ageing (NICOLA) study [6], the Montrachet study [7], AMD observational studies at the University of Bonn, Germany (UB), and a routine clinical care cohort seen at the University of Washington (UW). The presence of RPD was graded either as part of each study (MACUSTAR and UB datasets) or graded by one of the study investigators (HK; in the NICOLA, UW, and Montrachet datasets). All these studies defined RPD based on the presence of five or more definite lesions on more than one OCT B-scan that corresponded to hyporeflective lesions seen on near-infrared reflectance imaging. +The model was then internally tested in a different set of OCT scans from 125 eyes from 92 individuals from the LEAD study, and externally tested on five independent datasets: the MACUSTAR study [5], the Northern Ireland Cohort for Longitudinal Study of Ageing (NICOLA) study [6], the Montrachet study [7], AMD observational studies at the University of Bonn, Germany (UB), and a routine clinical care cohort seen at the University of Washington (UW). The presence of RPD was graded either as part of each study (MACUSTAR and UB datasets) or graded by one of the study investigators (HK; in the NICOLA, UW, and Montrachet datasets). All these studies defined RPD based on the presence of five or more definite lesions on more than one OCT B-scan that corresponded to hyporeflective lesions seen on near-infrared reflectance imaging. #### **Preprocessing** Scans were kept at native resolution (1024 x 496 pixels). ## **Performance** -In the external test datasets, the overall performance for detecting RPD in a volume scan was (AUC = 0·94; 95% CI = 0·92–0·97). In the internal test dataset, the Dice coefficient (DSC) between the model and manual annotations by retinal specialists for each B-scan was caculated and the average over the dataset is listed in the table below. Note that the DSC was assigned a value of 1·0 to all pairwise comparisons where no pixels on a B-scan were labelled as having RPD. +In the external test datasets, the overall performance for detecting RPD in a volume scan was (AUC = 0·94; 95% CI = 0·92–0·97). In the internal test dataset, the Dice coefficient (DSC) between the model and manual annotations by retinal specialists for each B-scan was caculated and the average over the dataset is listed in the table below. Note that the DSC was assigned a value of 1·0 to all pairwise comparisons where no pixels on a B-scan were labelled as having RPD. ![](Table2.gif) @@ -29,7 +29,7 @@ In the external test datasets, the overall performance for detecting RPD in a vo For more details regarding evaluation results, please see Results section of paper. @@ -46,7 +46,7 @@ python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' ``` ## USAGE -The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: +The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: ``` imports: @@ -73,7 +73,7 @@ args: inference: - $scripts.inference.main(@args) ``` -Then in your bash shell run +Then in your bash shell run ``` BUNDLE="/path/to/budle/RPDBundle" @@ -87,7 +87,7 @@ If extracting DICOM or VOL files: * set `run_extract` to `True` * specify `input_dir`, the path to the directory that contains the VOL or DICOM files * specify `extracted_dir`, the path to the directory where extracted images will be stored -* set `input_format` to "dicom" or "vol" +* set `input_format` to "dicom" or "vol" The VOL or DICOM files can be in a nested hierarchy of folders, and all files in that directory with a VOL or DICOM extension will be extracted. @@ -95,7 +95,7 @@ For DICOM files, each OCT slice will be saved as a png file to `/ For VOL files, each OCT slice will be saved as a png file to `////_oct_.png` on disk, where `` is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " -### DATASET PACKAGING +### DATASET PACKAGING Once you have the scans in PNG format, you can create a "dataset" in Detectron2 dictionary format for model consumption: * set `create_dataset` to `True` * set `dataset_name` to the chosen name of your dataset @@ -104,7 +104,7 @@ The summary tables and visual output is organized around OCT volumes, so please ### INFERENCE To run inference on your dataset: -* set `dataset_name` to the name of your dataset which you create with the previous step and resides in `////RPDBundle/datasets/.pk` +* set `dataset_name` to the name of your dataset which you create with the previous step and resides in `////RPDBundle/datasets/.pk` * set `output_dir`, the path to the directory where model predictions and other data will be stored. * set `run_inference` to `True` @@ -128,7 +128,7 @@ Inference on one Nvidia A100 gpu takes about 0.041 s/batch of 14 images, about 3 ## **Limitations** This model has not been tested for robustness of performance on OCTs imaged with other devices and with different scan parameters. -## **Citation Info** +## **Citation Info** ``` @article {Kumar2024.09.11.24312817, @@ -144,7 +144,7 @@ This model has not been tested for robustness of performance on OCTs imaged with } ``` -## **References** +## **References** [1]: He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask R-CNN." In Proceedings of the IEEE international conference on computer vision (ICCV), pp. 2961-2969. 2017. [2]: Xie, Saining, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. "Aggregated residual transformations for deep neural networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1492-1500. 2017. @@ -158,13 +158,3 @@ This model has not been tested for robustness of performance on OCTs imaged with [6]: Hogg RE, Wright DM, Quinn NB, et al. Prevalence and risk factors for age-related macular degeneration in a population-based cohort study of older adults in Northern Ireland using multimodal imaging: NICOLA Study. Br J Ophthalmol. 2022:bjophthalmol-2021-320469. [7]: Gabrielle P-H, Seydou A, Arnould L, et al. Subretinal Drusenoid Deposits in the Elderly in a Population-Based Study (the Montrachet Study). Invest Ophthalmol Vis Sci. 2019;60(14):4838–48. - - - - - - - - - - diff --git a/models/retinalOCT_RPD_segmentation/docs/requirements.txt b/models/retinalOCT_RPD_segmentation/docs/requirements.txt index c80d6e8a..e7f75e99 100644 --- a/models/retinalOCT_RPD_segmentation/docs/requirements.txt +++ b/models/retinalOCT_RPD_segmentation/docs/requirements.txt @@ -1,7 +1,7 @@ monai[fire,ninja,torchvision,pandas,pydicom] -opencv-python-headless -seaborn -scikit-learn +opencv-python-headless +seaborn +scikit-learn progressbar lxml diff --git a/models/retinalOCT_RPD_segmentation/large_files.yaml b/models/retinalOCT_RPD_segmentation/large_files.yaml index 72728060..35eb4c99 100644 --- a/models/retinalOCT_RPD_segmentation/large_files.yaml +++ b/models/retinalOCT_RPD_segmentation/large_files.yaml @@ -3,23 +3,23 @@ large_files: url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold1_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold2_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold2_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold3_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold3_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold4_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold4_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold5_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold5_model_final.pth" hash_val: "" - hash_type: "" \ No newline at end of file + hash_type: "" diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 45b8afe4..e4c4d412 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -19,7 +19,6 @@ from .Ensembler import Ensembler from .table_styles import styles - logging.basicConfig(level=logging.INFO) os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" diff --git a/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml index 47d3aa24..2664d88d 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml +++ b/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml @@ -19,10 +19,10 @@ SOLVER: BASE_LR: 0.02 #GAMMA: 0.05 #STEPS: (3000, 7000, 11000, 15000) - #MAX_ITER: 18000 + #MAX_ITER: 18000 GAMMA: 0.1 STEPS: (3000, 4500) - MAX_ITER: 6000 + MAX_ITER: 6000 CHECKPOINT_PERIOD: 300 IMS_PER_BATCH: 14 TEST: @@ -53,4 +53,4 @@ DATALOADER: # TEST: ("fold4",) # OUTPUT_DIR: "./output_valid_fold4" -#modifiying to commit again \ No newline at end of file +#modifiying to commit again diff --git a/models/retinalOCT_RPD_segmentation/scripts/working.yaml b/models/retinalOCT_RPD_segmentation/scripts/working.yaml index 47d3aa24..2664d88d 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/working.yaml +++ b/models/retinalOCT_RPD_segmentation/scripts/working.yaml @@ -19,10 +19,10 @@ SOLVER: BASE_LR: 0.02 #GAMMA: 0.05 #STEPS: (3000, 7000, 11000, 15000) - #MAX_ITER: 18000 + #MAX_ITER: 18000 GAMMA: 0.1 STEPS: (3000, 4500) - MAX_ITER: 6000 + MAX_ITER: 6000 CHECKPOINT_PERIOD: 300 IMS_PER_BATCH: 14 TEST: @@ -53,4 +53,4 @@ DATALOADER: # TEST: ("fold4",) # OUTPUT_DIR: "./output_valid_fold4" -#modifiying to commit again \ No newline at end of file +#modifiying to commit again From d1715425371366cd1f710e8637c63e475ff4276e Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Fri, 16 May 2025 18:02:00 +0000 Subject: [PATCH 09/28] Fixed pre-commit and flake8 errors. Added torchvision to metadata.json. --- ci/bundle_custom_data.py | 3 +- .../configs/inference.yaml | 2 +- .../configs/metadata.json | 241 ++++++++++-------- .../docs/README.md | 32 +-- .../docs/requirements.txt | 6 +- .../large_files.yaml | 10 +- .../scripts/Ensembler.py | 2 +- .../scripts/analysis_lib.py | 15 +- .../scripts/datasets/data.py | 2 +- .../scripts/inference.py | 1 - .../scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml | 6 +- .../scripts/working.yaml | 6 +- 12 files changed, 178 insertions(+), 148 deletions(-) diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index b0211cd9..c5ab7783 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -61,7 +61,8 @@ # If a bundle has this kind of dependencies, please add the bundle name (key), and the path of the install script (value) # into the dict. install_dependency_dict = { - "retinalOCT_RPD_segmentation": "ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh"} + "retinalOCT_RPD_segmentation": "ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh" +} # This list is used for our CI tests to determine whether a bundle supports TensorRT export. Related # test will be employed for bundles in the dict. diff --git a/models/retinalOCT_RPD_segmentation/configs/inference.yaml b/models/retinalOCT_RPD_segmentation/configs/inference.yaml index 35336773..fd148017 100644 --- a/models/retinalOCT_RPD_segmentation/configs/inference.yaml +++ b/models/retinalOCT_RPD_segmentation/configs/inference.yaml @@ -20,4 +20,4 @@ args: instance_mask_overlay : False inference: -- $scripts.inference.main(@args) \ No newline at end of file +- $scripts.inference.main(@args) diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 4078d7c9..7319a073 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -1,105 +1,144 @@ { - "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", - "version": "0.0.1", - "changelog": { - "0.0.1": "Initial version" - }, - "monai_version": "1.4.0", - "pytorch_version": "2.7.0", - "numpy_version": "1.26.4", - "optional_packages_version": {}, - "required_packages_version": { - "opencv-python-headless": "4.11.0.86", - "pandas": "2.2.3", - "seaborn": "0.13.2", - "scikit-learn": "1.6.1", - "progressbar": "2.5", - "pydicom": "2.4.4", - "fire": "0.7.0", - "detectron2": "0.6", - "lxml": "5.4.0" - }, - "name": "retinalOCT_RPD_segmentation", - "task": "Reticular Pseudodrusen (RPD) instance segmentation.", - "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", - "authors": "Yelena Bagdasarova, Scott Song", - "copyright": "Copyright (c) 2022, uw-biomedical-ml", - "network_data_format": { - "inputs": { - "image": { - "type": "image", - "format": "PNG", - "modality": "OCT", - "num_channels": 1, - "spatial_shape":[496,1024], - "dtype": "int16", - "value_range": [0,256], - "is_patch_data": false, - "channel_def": {"0": "image"} - } + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", + "version": "0.0.1", + "changelog": { + "0.0.1": "Initial version" }, - "preprocessed_data_sources": { - "vol_file": { - "type": "image", - "format": "VOL", - "modality": "OCT", - "num_channels": 1, - "spatial_shape":[496,1024,"D"], - "dtype": "int16", - "value_range": [0,256], - "description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to ////_oct_.png on disk, where is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " - - }, - "dicom_series": { - "type": "image", - "format": "DICOM", - "modality": "OCT", - "SOP class UID" : "1.2.840.10008.5.1.4.1.1.77.1.5.4", - "num_channels": 1, - "spatial_shape":[496,1024,"D"], - "dtype": "int16", - "value_range": [0,256], - "description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to //_oct_.png on disk, where is the slice number. " - } + "monai_version": "1.4.0", + "pytorch_version": "2.7.0", + "numpy_version": "1.26.4", + "optional_packages_version": {}, + "required_packages_version": { + "opencv-python-headless": "4.11.0.86", + "pandas": "2.2.3", + "seaborn": "0.13.2", + "scikit-learn": "1.6.1", + "progressbar": "2.5", + "pydicom": "2.4.4", + "fire": "0.7.0", + "torchvision" :"0.22.0", + "detectron2": "0.6", + "lxml": "5.4.0" }, - "outputs": { - "pred": { - "dtype": "dictionary", - "type": "dictionary", - "format": "COCO", - "modality": "n/a", - "value_range": [0,1], - "num_channels": 1, - "spatial_shape": [496,1024], - "channel_def": {"0": "RPD"}, - "description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances." - } - }, - "post_processed_outputs": { - "binary segmentation": { - "type": "image", - "format": "TIFF", - "modality": "OCT", - "num_channels": 3, - "spatial_shape": [496,1024], - "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices." - }, - "binary segmentation overlay": { - "type": "image", - "format": "TIFF", - "modality": "OCT", - "num_channels": 3, - "spatial_shape": [496,1024], - "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." - }, - "instance segmentation overlay": { - "type": "image", - "format": "TIFF", - "modality": "OCT", - "num_channels": 3, - "spatial_shape": [496,1024], - "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." - } + "name": "retinalOCT_RPD_segmentation", + "task": "Reticular Pseudodrusen (RPD) instance segmentation.", + "description": "This network detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans which can be presented in a vol or dicom format.", + "authors": "Yelena Bagdasarova, Scott Song", + "copyright": "Copyright (c) 2022, uw-biomedical-ml", + "network_data_format": { + "inputs": { + "image": { + "type": "image", + "format": "PNG", + "modality": "OCT", + "num_channels": 1, + "spatial_shape": [ + 496, + 1024 + ], + "dtype": "int16", + "value_range": [ + 0, + 256 + ], + "is_patch_data": false, + "channel_def": { + "0": "image" + } + } + }, + "preprocessed_data_sources": { + "vol_file": { + "type": "image", + "format": "VOL", + "modality": "OCT", + "num_channels": 1, + "spatial_shape": [ + 496, + 1024, + "D" + ], + "dtype": "int16", + "value_range": [ + 0, + 256 + ], + "description": "The pixel array of each OCT slice is extracted with volreader and the png files saved to ////_oct_.png on disk, where is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " + }, + "dicom_series": { + "type": "image", + "format": "DICOM", + "modality": "OCT", + "SOP class UID": "1.2.840.10008.5.1.4.1.1.77.1.5.4", + "num_channels": 1, + "spatial_shape": [ + 496, + 1024, + "D" + ], + "dtype": "int16", + "value_range": [ + 0, + 256 + ], + "description": "The pixel array of each OCT slice is extracted with pydicom and the png files saved to //_oct_.png on disk, where is the slice number. " + } + }, + "outputs": { + "pred": { + "dtype": "dictionary", + "type": "dictionary", + "format": "COCO", + "modality": "n/a", + "value_range": [ + 0, + 1 + ], + "num_channels": 1, + "spatial_shape": [ + 496, + 1024 + ], + "channel_def": { + "0": "RPD" + }, + "description": "This output is a JSON file in COCO Instance Segmentation format, containing bounding boxes, segmentation masks, and output probabilities for detected instances." + } + }, + "post_processed_outputs": { + "binary segmentation": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [ + 496, + 1024 + ], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a binary segmentation mask for a single OCT slice from the input volume. The segmentation masks are stacked in the same order as the original OCT slices." + }, + "binary segmentation overlay": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [ + 496, + 1024 + ], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." + }, + "instance segmentation overlay": { + "type": "image", + "format": "TIFF", + "modality": "OCT", + "num_channels": 3, + "spatial_shape": [ + 496, + 1024 + ], + "description": "This output is a multi-page TIFF file. Each page of the TIFF image corresponds to a single OCT slice from the input volume overlayed with the detected binary segmentation mask." + } + } } - } -} \ No newline at end of file +} diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index 2ae275de..c8802d63 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -7,20 +7,20 @@ Himeesh Kumar, Yelena Bagdasarova, Scott Song, Doron G. Hickey, Amy C. Cohn, Mal Reticular Pseudodrusen, AMD, OCT, Segmentation ## **Model Description** -This model detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans. The instance segmentation model used a Mask-RCNN [1] head with the ResNeXt-101-32x8d-FPN [2] backbone (pretrained on ImageNet) implemented via the Detectron2 framework [3]. The model produces outputs that consist of bounding boxes and segmentation masks that delineate the coordinates and pixels of each instance detected, which are assigned a corresponding output probability. A tuneable probability threshold can then be applied to finalise the binary detection of an RPD instance. +This model detects and segments Reticular Pseudodrusen (RPD) instances in Optical Coherence Tomography (OCT) B-scans. The instance segmentation model used a Mask-RCNN [1] head with the ResNeXt-101-32x8d-FPN [2] backbone (pretrained on ImageNet) implemented via the Detectron2 framework [3]. The model produces outputs that consist of bounding boxes and segmentation masks that delineate the coordinates and pixels of each instance detected, which are assigned a corresponding output probability. A tuneable probability threshold can then be applied to finalise the binary detection of an RPD instance. Five segmentation models using these RPD instance labels on the OCT B-scans were trained based on five-fold cross-validation which were used to form a final ensemble model using soft voting (see supplementary material of paper for more information on model training.) ## **Data** The model was trained using the prospectively-collected, baseline OCT scans (prior to any treatments) of individuals enrolled in the LEAD study [4] imaged using Heidelberg Spectralis HRA+OCT. OCT B-scans from 200 eyes from 100 individuals in the LEAD study were randomly selected to undergo manual annotations of RPD by a single grader (HK) at the pixel level, following training from two senior investigators (RHG and ZW). Only definite RPD lesions, defined as subretinal hyperreflective accumulations that altered the contour of, or broke through, the overlying photoreceptor ellipsoid zone on the OCT B-scans were annotated. -The model was then internally tested in a different set of OCT scans from 125 eyes from 92 individuals from the LEAD study, and externally tested on five independent datasets: the MACUSTAR study [5], the Northern Ireland Cohort for Longitudinal Study of Ageing (NICOLA) study [6], the Montrachet study [7], AMD observational studies at the University of Bonn, Germany (UB), and a routine clinical care cohort seen at the University of Washington (UW). The presence of RPD was graded either as part of each study (MACUSTAR and UB datasets) or graded by one of the study investigators (HK; in the NICOLA, UW, and Montrachet datasets). All these studies defined RPD based on the presence of five or more definite lesions on more than one OCT B-scan that corresponded to hyporeflective lesions seen on near-infrared reflectance imaging. +The model was then internally tested in a different set of OCT scans from 125 eyes from 92 individuals from the LEAD study, and externally tested on five independent datasets: the MACUSTAR study [5], the Northern Ireland Cohort for Longitudinal Study of Ageing (NICOLA) study [6], the Montrachet study [7], AMD observational studies at the University of Bonn, Germany (UB), and a routine clinical care cohort seen at the University of Washington (UW). The presence of RPD was graded either as part of each study (MACUSTAR and UB datasets) or graded by one of the study investigators (HK; in the NICOLA, UW, and Montrachet datasets). All these studies defined RPD based on the presence of five or more definite lesions on more than one OCT B-scan that corresponded to hyporeflective lesions seen on near-infrared reflectance imaging. #### **Preprocessing** Scans were kept at native resolution (1024 x 496 pixels). ## **Performance** -In the external test datasets, the overall performance for detecting RPD in a volume scan was (AUC = 0·94; 95% CI = 0·92–0·97). In the internal test dataset, the Dice coefficient (DSC) between the model and manual annotations by retinal specialists for each B-scan was caculated and the average over the dataset is listed in the table below. Note that the DSC was assigned a value of 1·0 to all pairwise comparisons where no pixels on a B-scan were labelled as having RPD. +In the external test datasets, the overall performance for detecting RPD in a volume scan was (AUC = 0·94; 95% CI = 0·92–0·97). In the internal test dataset, the Dice coefficient (DSC) between the model and manual annotations by retinal specialists for each B-scan was caculated and the average over the dataset is listed in the table below. Note that the DSC was assigned a value of 1·0 to all pairwise comparisons where no pixels on a B-scan were labelled as having RPD. ![](Table2.gif) @@ -29,7 +29,7 @@ In the external test datasets, the overall performance for detecting RPD in a vo For more details regarding evaluation results, please see Results section of paper. @@ -46,7 +46,7 @@ python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' ``` ## USAGE -The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: +The expected image data is in PNG format at the scan level, VOL format at the volume level, or DICOM format at the volume level. To run inference, modify the parameters of the inference.yaml config file in the configs folder which looks like: ``` imports: @@ -73,7 +73,7 @@ args: inference: - $scripts.inference.main(@args) ``` -Then in your bash shell run +Then in your bash shell run ``` BUNDLE="/path/to/budle/RPDBundle" @@ -87,7 +87,7 @@ If extracting DICOM or VOL files: * set `run_extract` to `True` * specify `input_dir`, the path to the directory that contains the VOL or DICOM files * specify `extracted_dir`, the path to the directory where extracted images will be stored -* set `input_format` to "dicom" or "vol" +* set `input_format` to "dicom" or "vol" The VOL or DICOM files can be in a nested hierarchy of folders, and all files in that directory with a VOL or DICOM extension will be extracted. @@ -95,7 +95,7 @@ For DICOM files, each OCT slice will be saved as a png file to `/ For VOL files, each OCT slice will be saved as a png file to `////_oct_.png` on disk, where `` is the slice number and a nested hierarchy of folders is created using the underscores in the original filename. " -### DATASET PACKAGING +### DATASET PACKAGING Once you have the scans in PNG format, you can create a "dataset" in Detectron2 dictionary format for model consumption: * set `create_dataset` to `True` * set `dataset_name` to the chosen name of your dataset @@ -104,7 +104,7 @@ The summary tables and visual output is organized around OCT volumes, so please ### INFERENCE To run inference on your dataset: -* set `dataset_name` to the name of your dataset which you create with the previous step and resides in `////RPDBundle/datasets/.pk` +* set `dataset_name` to the name of your dataset which you create with the previous step and resides in `////RPDBundle/datasets/.pk` * set `output_dir`, the path to the directory where model predictions and other data will be stored. * set `run_inference` to `True` @@ -128,7 +128,7 @@ Inference on one Nvidia A100 gpu takes about 0.041 s/batch of 14 images, about 3 ## **Limitations** This model has not been tested for robustness of performance on OCTs imaged with other devices and with different scan parameters. -## **Citation Info** +## **Citation Info** ``` @article {Kumar2024.09.11.24312817, @@ -144,7 +144,7 @@ This model has not been tested for robustness of performance on OCTs imaged with } ``` -## **References** +## **References** [1]: He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask R-CNN." In Proceedings of the IEEE international conference on computer vision (ICCV), pp. 2961-2969. 2017. [2]: Xie, Saining, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. "Aggregated residual transformations for deep neural networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1492-1500. 2017. @@ -158,13 +158,3 @@ This model has not been tested for robustness of performance on OCTs imaged with [6]: Hogg RE, Wright DM, Quinn NB, et al. Prevalence and risk factors for age-related macular degeneration in a population-based cohort study of older adults in Northern Ireland using multimodal imaging: NICOLA Study. Br J Ophthalmol. 2022:bjophthalmol-2021-320469. [7]: Gabrielle P-H, Seydou A, Arnould L, et al. Subretinal Drusenoid Deposits in the Elderly in a Population-Based Study (the Montrachet Study). Invest Ophthalmol Vis Sci. 2019;60(14):4838–48. - - - - - - - - - - diff --git a/models/retinalOCT_RPD_segmentation/docs/requirements.txt b/models/retinalOCT_RPD_segmentation/docs/requirements.txt index c80d6e8a..e7f75e99 100644 --- a/models/retinalOCT_RPD_segmentation/docs/requirements.txt +++ b/models/retinalOCT_RPD_segmentation/docs/requirements.txt @@ -1,7 +1,7 @@ monai[fire,ninja,torchvision,pandas,pydicom] -opencv-python-headless -seaborn -scikit-learn +opencv-python-headless +seaborn +scikit-learn progressbar lxml diff --git a/models/retinalOCT_RPD_segmentation/large_files.yaml b/models/retinalOCT_RPD_segmentation/large_files.yaml index 72728060..35eb4c99 100644 --- a/models/retinalOCT_RPD_segmentation/large_files.yaml +++ b/models/retinalOCT_RPD_segmentation/large_files.yaml @@ -3,23 +3,23 @@ large_files: url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold1_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold2_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold2_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold3_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold3_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold4_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold4_model_final.pth" hash_val: "" hash_type: "" - + - path: "models/fold5_model_final.pth" url: "https://s3.us-west-2.amazonaws.com/comp.ophthalmology.uw.edu/fold5_model_final.pth" hash_val: "" - hash_type: "" \ No newline at end of file + hash_type: "" diff --git a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py index 28d47b51..52939515 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py +++ b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py @@ -92,7 +92,7 @@ def nik_merge(lsts): obj_set = set() # a set of objects (frozensets) for i, coco_dt in enumerate(self.dtlist): # for each detector append predictions to df dflist.append(pd.DataFrame(coco_dt.imgToAnns[img]).assign(det=i)) - df = pd.concat(dflist,ignore_index=True) + df = pd.concat(dflist, ignore_index=True) if not df.empty: for cat in self.cats: # for each category dfcat = df[df["category_id"] == cat] diff --git a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py index 10241ba7..09d302ca 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py +++ b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python - """ Utiltites for analyizing and visualizing model segmentations on dataset. Yelena Bagdasarova, Scott Song @@ -31,7 +29,7 @@ from sklearn.metrics import average_precision_score, precision_recall_curve from tqdm import tqdm -plt.style.use('ybpres.mplstyle') +plt.style.use("ybpres.mplstyle") # plt.style.use('/data/ssong/detectron2-rpd-yb/detectron2-rpd-pkg/src/detectron2-rpd/ybpres.mplstyle') @@ -114,7 +112,8 @@ def produce_gt_image(self, dat, im): Args: dat (dict): Dictionary for the image im containing ground truth annotations. im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to - the height and width of the image respectively. C is the number of color channels. The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. + the height and width of the image respectively. C is the number of color channels. + The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. Returns: PIL.Image: The resulting original image overlayed with ground truth instances. @@ -142,13 +141,15 @@ def produce_gt_image(self, dat, im): return img def produce_model_image(self, ImgId, dat, im): - """Returns image im overlayed with instances predicted by the model. Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on the given ImgId. + """Returns image im overlayed with instances predicted by the model. + Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on the given ImgId. Args: ImgId (str): Value of image_id in image data structure. dat (dict): Dictionary for the image im. Used for height and width parameters. im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to - the height and width of the image respectively. C is the number of color channels. The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. + the height and width of the image respectively. C is the number of color channels. + The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. Returns: PIL.Image: The resulting original image overlayed with model-predicted instances. @@ -671,7 +672,7 @@ def count_instances(self): gt_inst = 0 dt_inst = 0 gt_neg_scans = 0 - for key, val in self.cocoGt.imgs.items(): + for _, val in self.cocoGt.imgs.items(): imgid = val["id"] # Gt instances annIdsGt = self.cocoGt.getAnnIds([imgid]) diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py index efb2075e..1c4131c0 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -54,7 +54,7 @@ def extract_files(dirtoextract, extracted_path, input_format): print(f"Extracting files from {dirtoextract} into {extracted_path}...") if input_format == "vol": files_to_extract = glob.glob(os.path.join(dirtoextract, "**/*.vol"), recursive=True) - for i, line in enumerate(tqdm(files_to_extract)): + for _, line in enumerate(tqdm(files_to_extract)): fpath = line.strip("\n") vol = volFile(fpath) fpath = fpath.replace("\\", "/") diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 45b8afe4..e4c4d412 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -19,7 +19,6 @@ from .Ensembler import Ensembler from .table_styles import styles - logging.basicConfig(level=logging.INFO) os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" diff --git a/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml b/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml index 47d3aa24..2664d88d 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml +++ b/models/retinalOCT_RPD_segmentation/scripts/mask_rcnn_X_101_32x8d_FPN_1x.yaml @@ -19,10 +19,10 @@ SOLVER: BASE_LR: 0.02 #GAMMA: 0.05 #STEPS: (3000, 7000, 11000, 15000) - #MAX_ITER: 18000 + #MAX_ITER: 18000 GAMMA: 0.1 STEPS: (3000, 4500) - MAX_ITER: 6000 + MAX_ITER: 6000 CHECKPOINT_PERIOD: 300 IMS_PER_BATCH: 14 TEST: @@ -53,4 +53,4 @@ DATALOADER: # TEST: ("fold4",) # OUTPUT_DIR: "./output_valid_fold4" -#modifiying to commit again \ No newline at end of file +#modifiying to commit again diff --git a/models/retinalOCT_RPD_segmentation/scripts/working.yaml b/models/retinalOCT_RPD_segmentation/scripts/working.yaml index 47d3aa24..2664d88d 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/working.yaml +++ b/models/retinalOCT_RPD_segmentation/scripts/working.yaml @@ -19,10 +19,10 @@ SOLVER: BASE_LR: 0.02 #GAMMA: 0.05 #STEPS: (3000, 7000, 11000, 15000) - #MAX_ITER: 18000 + #MAX_ITER: 18000 GAMMA: 0.1 STEPS: (3000, 4500) - MAX_ITER: 6000 + MAX_ITER: 6000 CHECKPOINT_PERIOD: 300 IMS_PER_BATCH: 14 TEST: @@ -53,4 +53,4 @@ DATALOADER: # TEST: ("fold4",) # OUTPUT_DIR: "./output_valid_fold4" -#modifiying to commit again \ No newline at end of file +#modifiying to commit again From e2718848b1c99141edc589a9a6976cf19a699af3 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Mon, 19 May 2025 06:28:21 +0000 Subject: [PATCH 10/28] fix pre-commit and black checks Signed-off-by: Yiheng Wang --- ci/get_bundle_requirements.py | 1 + .../scripts/networks/nest_transformer_3D.py | 2 +- models/retinalOCT_RPD_segmentation/configs/metadata.json | 3 +-- .../scripts/networks/nest_transformer_3D.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ci/get_bundle_requirements.py b/ci/get_bundle_requirements.py index fbf0c56c..d70d3bf0 100644 --- a/ci/get_bundle_requirements.py +++ b/ci/get_bundle_requirements.py @@ -21,6 +21,7 @@ special_dependencies_list = ["detectron2"] + def increment_version(version): """ Split the version into components, assume missing parts are '0'. diff --git a/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py b/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py index 73f11ec3..36020f50 100755 --- a/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py +++ b/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py @@ -33,7 +33,7 @@ # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" Nested Transformer (NesT) in PyTorch +"""Nested Transformer (NesT) in PyTorch A PyTorch implement of Aggregating Nested Transformers as described in: 'Aggregating Nested Transformers' - https://arxiv.org/abs/2105.12723 diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 3e1c52c9..43bf6eec 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -16,7 +16,7 @@ "progressbar": "2.5", "pydicom": "2.4.4", "fire": "0.7.0", - "torchvision" :"0.22.0", + "torchvision": "0.22.0", "detectron2": "0.6", "lxml": "5.4.0" }, @@ -142,4 +142,3 @@ } } } - diff --git a/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py b/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py index 73f11ec3..36020f50 100755 --- a/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py +++ b/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py @@ -33,7 +33,7 @@ # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" Nested Transformer (NesT) in PyTorch +"""Nested Transformer (NesT) in PyTorch A PyTorch implement of Aggregating Nested Transformers as described in: 'Aggregating Nested Transformers' - https://arxiv.org/abs/2105.12723 From db0d0d631b721b3645b401f2f7e8133c1b1ec005 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Mon, 19 May 2025 07:17:01 +0000 Subject: [PATCH 11/28] skip part of tests and rrevert changes in UNEST bundles Signed-off-by: Yiheng Wang --- .../scripts/networks/nest_transformer_3D.py | 2 +- .../scripts/networks/nest_transformer_3D.py | 2 +- setup.cfg | 10 +++++++++- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py b/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py index 36020f50..73f11ec3 100755 --- a/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py +++ b/models/renalStructures_UNEST_segmentation/scripts/networks/nest_transformer_3D.py @@ -33,7 +33,7 @@ # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -"""Nested Transformer (NesT) in PyTorch +""" Nested Transformer (NesT) in PyTorch A PyTorch implement of Aggregating Nested Transformers as described in: 'Aggregating Nested Transformers' - https://arxiv.org/abs/2105.12723 diff --git a/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py b/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py index 36020f50..73f11ec3 100755 --- a/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py +++ b/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest_transformer_3D.py @@ -33,7 +33,7 @@ # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -"""Nested Transformer (NesT) in PyTorch +""" Nested Transformer (NesT) in PyTorch A PyTorch implement of Aggregating Nested Transformers as described in: 'Aggregating Nested Transformers' - https://arxiv.org/abs/2105.12723 diff --git a/setup.cfg b/setup.cfg index 53aed841..cace76d7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -20,7 +20,15 @@ ignore = B028 B907 C419 -per_file_ignores = __init__.py: F401, __main__.py: F401 +# lowercase checks are not needed for the following files in the retinalOCT_RPD_segmentation model +# https://github.com/Project-MONAI/model-zoo/pull/748#issuecomment-2877638507 +per_file_ignores = + __init__.py: F401 + __main__.py: F401 + models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py: N802, N803, N806 + models/retinalOCT_RPD_segmentation/scripts/datasets/data.py: N802, N803, N806 + models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py: N801, N802, N803, N806 + models/retinalOCT_RPD_segmentation/scripts/inference.py: N802, N803, N806 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py [isort] From ea0ef49a937a79521f4ddc87098435b3da00ade8 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Mon, 19 May 2025 22:56:18 +0000 Subject: [PATCH 12/28] Fixed flake8 errors. runtests.sh failing at ptype. --- .../scripts/analysis_lib.py | 40 ++++++++++++------- .../scripts/datasets/data.py | 22 ---------- .../scripts/datasets/volReader.py | 4 +- .../scripts/inference.py | 7 ++-- 4 files changed, 31 insertions(+), 42 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py index 09d302ca..3ca1eddf 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py +++ b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py @@ -29,9 +29,10 @@ from sklearn.metrics import average_precision_score, precision_recall_curve from tqdm import tqdm -plt.style.use("ybpres.mplstyle") - -# plt.style.use('/data/ssong/detectron2-rpd-yb/detectron2-rpd-pkg/src/detectron2-rpd/ybpres.mplstyle') +# current_directory = os.getcwd() +# print(current_directory) +plt.style.use("./scripts/ybpres.mplstyle") +# plt.style.use("ybpres.mplstyle") def grab_dataset(name): @@ -107,13 +108,15 @@ def get_gt_image_data(self, ImgId): return gt_data def produce_gt_image(self, dat, im): - """Returns image im overlayed with ground truth instances found in dat if there are any. Depending on visualizer mode, the instances are separate colors or monochrome. + """Returns image im overlayed with ground truth instances found in dat if there are any. + Depending on visualizer mode, the instances are separate colors or monochrome. Args: dat (dict): Dictionary for the image im containing ground truth annotations. im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to the height and width of the image respectively. C is the number of color channels. - The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. + The image is required to be in RGB format since that is a requirement of the Matplotlib library. + The image is also expected to be in the range [0, 255]. Returns: PIL.Image: The resulting original image overlayed with ground truth instances. @@ -142,14 +145,16 @@ def produce_gt_image(self, dat, im): def produce_model_image(self, ImgId, dat, im): """Returns image im overlayed with instances predicted by the model. - Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on the given ImgId. + Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on + the given ImgId. Args: ImgId (str): Value of image_id in image data structure. dat (dict): Dictionary for the image im. Used for height and width parameters. im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to the height and width of the image respectively. C is the number of color channels. - The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. + The image is required to be in RGB format since that is a requirement of the Matplotlib library. + The image is also expected to be in the range [0, 255]. Returns: PIL.Image: The resulting original image overlayed with model-predicted instances. @@ -188,14 +193,16 @@ def get_image(self, ImgId): return img, img_model def get_outputs_from_file(self, ImgId, imgsize): - """For image with image_id ImgId, reads in and converts instances from coco format in self.pred_file to a detectron2 Instances structure required for the visulizer utility. + """For image with image_id ImgId, reads in and converts instances from coco format in self.pred_file to a + detectron2 Instances structure required for the visulizer utility. Args: ImgId (str): Value of image_id in image data structure. imgsize (tuple): Height and width of the image in pixels. Returns: - detectron2.structures.Instances: The correctly formated data structure to be used to the detectron2 visualizer utility. + detectron2.structures.Instances: The correctly formated data structure to be used to the detectron2 + visualizer utility. """ pred_boxes = [] @@ -222,7 +229,8 @@ def get_outputs_from_file(self, ImgId, imgsize): @staticmethod def height_crop_range(im, height_target=256): - """Find the range of pixels in the height dimension spanning the height height_target which contain the brightest regions of the image. + """Find the range of pixels in the height dimension spanning the height height_target which contain the + brightest regions of the image. Args: im (numpy array): a numpy array of shape (H, W, C) @@ -244,7 +252,8 @@ def height_crop_range(im, height_target=256): return range(h1, h2) def output_to_pdf(self, ImgIds, outname, dfimg=None): - """Create pdf with name outname displaying ground truth and model prediction overlays for image ids listed in ImgIds. + """Create pdf with name outname displaying ground truth and model prediction overlays for + image ids listed in ImgIds. Args: ImgIds (list(str)): List of image_id values to output. @@ -363,7 +372,8 @@ def output_all_to_tiff(self, ImgIds, outname): self.save_imgarr_to_tiff(imgs, outname) def get_enface_dt(self, grp, scan_height, scan_width, scan_spacing): - """Return enface perspective of model predictions for a single scan volume whose imgids are listed in the index of grp. + """Return enface perspective of model predictions for a single scan volume whose imgids are + listed in the index of grp. Args: grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. @@ -395,7 +405,8 @@ def get_enface_dt(self, grp, scan_height, scan_width, scan_spacing): return enface def get_enface_gt(self, grp, scan_height, scan_width, scan_spacing): - """Return enface perspective of ground truth annotations for a single scan volume whose imgids are listed in the index of grp. + """Return enface perspective of ground truth annotations for a single scan volume whose imgids are + listed in the index of grp. Args: grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. @@ -555,7 +566,8 @@ def _calculate_fpr_matrix(self): # FP rate, 1 RPD in image = FP if (self.scores.min() == -1) and (self.scores.max() == -1): print( - "WARNING: Scores for all iou thresholds and all recall levels are not defined. This can arise if ground truth annotations contain no instances. Leaving fpr matrix as None" + "WARNING: Scores for all iou thresholds and all recall levels are not defined. " + "This can arise if ground truth annotations contain no instances. Leaving fpr matrix as None" ) self.fpr = None return diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py index 1c4131c0..b17081cb 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -21,27 +21,6 @@ class Error(Exception): pass -# def extract_files(dirtoextract, extracted_path): -# proceed = True -# if ((os.path.isdir(extracted_path)) and (len(os.listdir(extracted_path))!=0)): -# val = input(f'{extracted_path} exists and is not empty. Files may be overwritten. Proceed with extraction? (Y/N)') -# proceed = bool(distutils.util.strtobool(val)) -# if proceed: -# print(f"Extracting files from {dirtoextract} into {extracted_path}...") -# files_to_extract = glob.glob(os.path.join(dirtoextract,'**/*.vol'),recursive=True) -# for i,line in enumerate(tqdm(files_to_extract)): -# fpath = line.strip('\n') -# vol = volFile(fpath) -# fpath = fpath.replace('\\','/') -# path, scan_str = fpath.strip('.vol').rsplit('/',1) -# extractpath = os.path.join(extracted_path,scan_str.replace('_','/')) -# os.makedirs(extractpath,exist_ok=True) -# preffix = os.path.join(extractpath, scan_str+'_oct') -# vol.renderOCTscans(preffix) -# else: -# pass - - def extract_files(dirtoextract, extracted_path, input_format): assert input_format in ["vol", "dicom"], 'Error: input_format must be "vol" or "dicom".' proceed = True @@ -78,7 +57,6 @@ def extract_files(dirtoextract, extracted_path, input_format): dd[key] = fi.get(key) volpath = os.path.join(extracted_path, f"{fi.SOPInstanceUID}") - # volpath = os.path.join(extracted_path, f'{fi.PatientID}_{fi.ImageLaterality}_{fi.SeriesDate}') #path for volume shutil.rmtree(volpath, ignore_errors=True) os.mkdir(volpath) n = fi.NumberOfFrames diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py index 0b366ff3..2bfa6273 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python -# # Aaron Y. Lee MD MSCI (University of Washington) Copyright 2019 # # Code ported from Markus Mayer's excellent work (https://www5.cs.fau.de/research/software/octseg/) @@ -223,7 +221,7 @@ def __parseVolFile(self, fn, parseSeg=False): U[U == v] = 0.0 if segmentations is None: segmentations = [] - for j in range(bscanHead["numSeg"]): + for _ in range(bscanHead["numSeg"]): segmentations.append([]) for j in range(bscanHead["numSeg"]): diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index e4c4d412..8585a65c 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -11,14 +11,15 @@ from detectron2.evaluation import COCOEvaluator, inference_on_dataset from detectron2.modeling import build_model -os.chdir(os.path.dirname(os.path.abspath(__file__))) -current_directory = os.getcwd() -print(current_directory) from .analysis_lib import CreatePlotsRPD, EvaluateClass, OutputVis, grab_dataset from .datasets import data from .Ensembler import Ensembler from .table_styles import styles +os.chdir(os.path.dirname(os.path.abspath(__file__))) +# current_directory = os.getcwd() +# print(current_directory) + logging.basicConfig(level=logging.INFO) os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" From 992391ee44359018ee25a13bf00eec79885b813f Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 10:58:05 +0000 Subject: [PATCH 13/28] enable different py env for ci tests Signed-off-by: Yiheng Wang --- .github/workflows/premerge-cpu.yml | 71 +++++- ci/bundle_custom_data.py | 6 +- ..._retinalOCT_RPD_segmentation_dependency.sh | 2 +- ci/run_premerge_cpu.sh | 241 +++++++++++------- ci/run_premerge_gpu.sh | 217 +++++++++++----- ci/run_regular_tests_cpu.sh | 3 +- .../configs/metadata.json | 3 +- 7 files changed, 376 insertions(+), 167 deletions(-) diff --git a/.github/workflows/premerge-cpu.yml b/.github/workflows/premerge-cpu.yml index d365d9bb..6187ebec 100644 --- a/.github/workflows/premerge-cpu.yml +++ b/.github/workflows/premerge-cpu.yml @@ -16,29 +16,76 @@ jobs: premerge-cpu: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - name: Checkout code + uses: actions/checkout@v4 + - name: Set up Python 3.10 uses: actions/setup-python@v4 with: - python-version: 3.10.14 + python-version: 3.10.14 # This will be the default Python + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v3 + with: + auto-update-conda: true + # The python-version here is for Conda's base, if activated. + # We set auto-activate-base to false, so actions/setup-python's version remains default. + python-version: 3.10.14 + auto-activate-base: false # Crucial: ensures the Python 3.10 from actions/setup-python is the default + - name: cache weekly timestamp - id: pip-cache + id: pip-cache-ts run: | - echo "::set-output name=datew::$(date '+%Y-%V')" - - name: cache for pip + echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT + + - name: Get pip cache directory + id: pip-cache-dir + # This runs using the default Python (3.10) + shell: bash + run: echo "dir=$(python -m pip cache dir)" >> $GITHUB_OUTPUT + + - name: Cache pip dependencies (for Python 3.10 global env) + uses: actions/cache@v4 + id: cache-pip + with: + path: ${{ steps.pip-cache-dir.outputs.dir }} + key: ${{ runner.os }}-pip-3.10-${{ steps.pip-cache-ts.outputs.datew }}-${{ hashFiles('**/requirements.txt', '**/setup.py') }} + restore-keys: | + ${{ runner.os }}-pip-3.10-${{ steps.pip-cache-ts.outputs.datew }}- + + # Cache Conda packages (for environments your bash script might create) + # setup-miniconda action sets MINICONDA_PATH_0 environment variable + - name: Cache Conda packages uses: actions/cache@v4 - id: cache + id: cache-conda with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ steps.pip-cache.outputs.datew }} - - name: Install dependencies + path: ${{ env.MINICONDA_PATH_0 }}/pkgs + key: ${{ runner.os }}-conda-pkgs-${{ steps.pip-cache-ts.outputs.datew }}-${{ hashFiles('**/environment*.yml', '**/requirements*.txt') }} # Adjust hashFiles as needed + restore-keys: | + ${{ runner.os }}-conda-pkgs-${{ steps.pip-cache-ts.outputs.datew }}- + + - name: Install global Python dependencies (using Python 3.10) + shell: bash run: | python -m pip install --upgrade pip wheel python -m pip install --upgrade setuptools - - name: check + + - name: Check environment + shell: bash + run: | + echo "Default Python version:" + python --version + echo "Conda executable path:" + command -v conda + conda info + # This ensures conda command is available, but the default python is still 3.10 + + - name: Run check script + # The shell: bash -el {0} can be helpful if conda init modified shell profiles + # and your script needs to `conda activate` sub-environments. + shell: bash -el {0} run: | # clean up temporary files $(pwd)/runtests.sh --clean df -h - bash $(pwd)/ci/run_premerge_cpu.sh changed - shell: bash + bash $(pwd)/ci/run_premerge_cpu.sh diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index c5ab7783..4d7d58f8 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -28,7 +28,11 @@ # This list is used for our CI tests to determine whether a bundle contains the preferred files. # If a bundle does not have any of the preferred files, please add the bundle name into the list. -exclude_verify_preferred_files_list = ["pediatric_abdominal_ct_segmentation", "maisi_ct_generative"] +exclude_verify_preferred_files_list = [ + "pediatric_abdominal_ct_segmentation", + "maisi_ct_generative", + "retinalOCT_RPD_segmentation", +] # This list is used for our CI tests to determine whether a bundle needs to be tested with # the `verify_export_torchscript` function in `verify_bundle.py`. diff --git a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh index ccf94ba5..74fae23c 100644 --- a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh +++ b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh @@ -1,2 +1,2 @@ # ensure that using evironment with python==3.9 -python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' +python -m pip install 'git+https://github.com/facebookresearch/detectron2.git@65184fc057d4fab080a98564f6b60fae0b94edc4' diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index 000424c3..697c193b 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -15,25 +15,27 @@ # limitations under the License. # -# Argument(s): -# BUILD_TYPE: all/specific_test_name, tests to execute set -ex -BUILD_TYPE=all export ALLOW_MONAI_RC=true +declare -A bundle_python_versions=( + ["retinalOCT_RPD_segmentation"]="3.9" +) +DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" -if [[ $# -eq 1 ]]; then - BUILD_TYPE=$1 - -elif [[ $# -gt 1 ]]; then - echo "ERROR: too many parameters are provided" +echo "CI Job starting..." +echo "Global Python (expected ${DEFAULT_PYTHON_VERSION_FOR_VENV} from YAML): $(python -V)" +if ! command -v conda &> /dev/null; then + echo "ERROR: conda command not found. Ensure Miniconda was installed by the YAML." exit 1 +else + echo "Conda executable found: $(command -v conda)" fi # Usually, CPU test is required, but for some bundles that are too large to run in Github Actions, we can exclude them. exclude_test_list=("maisi_ct_generative") is_excluded() { - for item in "${exclude_test_list[@]}"; do # Use exclude_test_list here + for item in "${exclude_test_list[@]}"; do if [ "$1" == "$item" ]; then return 0 # Return true (0) if excluded fi @@ -41,35 +43,82 @@ is_excluded() { return 1 # Return false (1) if not excluded } +# Common dependencies to install in any activated environment +install_common_deps_in_activated_env() { + # These are typically needed by MONAI bundles or verification tools + echo "Installing common dependencies in activated environment..." >&2 + python -m pip install --upgrade pip wheel >&2 + python -m pip install --upgrade setuptools >&2 + python -m pip install jsonschema gdown pyyaml parameterized fire >&2 + export PYTHONPATH=$PWD +} + init_venv() { - if [ ! -d "model_zoo_venv" ]; then # Check if the venv directory does not exist - echo "initializing pip environment" - python -m venv model_zoo_venv + if [ ! -d "model_zoo_venv" ]; then + echo "Initializing pip environment (model_zoo_venv for Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" + python -m venv model_zoo_venv # Uses global python (expected to be DEFAULT_PYTHON_VERSION_FOR_VENV) source model_zoo_venv/bin/activate - pip install --upgrade pip wheel - pip install --upgrade setuptools - pip install jsonschema gdown pyyaml parameterized fire - export PYTHONPATH=$PWD + install_common_deps_in_activated_env else echo "Virtual environment model_zoo_venv already exists. Activating..." source model_zoo_venv/bin/activate - pip install --upgrade pip wheel - pip install --upgrade setuptools - pip install jsonschema gdown pyyaml parameterized fire - export PYTHONPATH=$PWD + install_common_deps_in_activated_env fi } remove_venv() { - if [ -d "model_zoo_venv" ]; then # Check if the venv directory exists - echo "Removing virtual environment..." - deactivate 2>/dev/null || true # Deactivate venv, ignore errors if not activated - rm -rf model_zoo_venv # Remove the venv directory + if [ -d "model_zoo_venv" ]; then + echo "Removing virtual environment model_zoo_venv..." + deactivate 2>/dev/null || true + rm -rf model_zoo_venv else - echo "Virtual environment not found. Skipping removal." + echo "Virtual environment model_zoo_venv not found. Skipping removal." + fi +} + +# Conda environment functions +init_conda_env() { + local python_version_to_create="$1" + local bundle_identifier="$2" + local conda_env_name="conda_env_${bundle_identifier}" + + echo "Initializing Conda environment with Python $python_version_to_create for GPU bundle '$bundle_identifier'..." >&2 + + if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then + if [ -n "$CONDA_EXE" ]; then + source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" + elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then + source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" + else + echo "Warning: Could not reliably source conda.sh for Conda activation." >&2 + fi + fi + + if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then + echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." >&2 + conda env remove -n "$conda_env_name" -y >&2 + fi + + conda create -n "$conda_env_name" python="$python_version_to_create" -y >&2 + conda activate "$conda_env_name" + install_common_deps_in_activated_env + conda deactivate 2>/dev/null || true + + echo "$conda_env_name" +} + +remove_conda_env() { + local conda_env_name_to_remove="$1" + if [ -z "$conda_env_name_to_remove" ]; then + echo "Warning: No Conda env name provided to remove_conda_env." >&2 + return fi + echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" >&2 + conda deactivate 2>/dev/null || true + conda env remove -n "$conda_env_name_to_remove" -y >&2 } + verify_bundle() { for dir in /opt/hostedtoolcache/*; do if [[ $dir != "/opt/hostedtoolcache/Python" ]]; then @@ -77,88 +126,100 @@ verify_bundle() { fi done echo 'Run verify bundle...' - pip install -r requirements.txt - # install extra dependencies for get changed bundle - pip install jsonschema gdown pyyaml + + if [ -f "requirements.txt" ]; then + echo "Installing global requirements.txt (using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" + python -m pip install -r requirements.txt + fi + echo "Installing global dependencies for CI scripts (jsonschema, gdown, pyyaml using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" + python -m pip install jsonschema gdown pyyaml + head_ref=$(git rev-parse HEAD) git fetch origin dev $head_ref - # achieve all changed files in 'models' + changes=$(git diff --name-only $head_ref origin/dev -- models) - if [ ! -z "$changes" ] - then - # get all changed bundles - bundle_list=$(python $(pwd)/ci/get_changed_bundle.py --f "$changes") - if [ ! -z "$bundle_list" ] - then - python $(pwd)/ci/prepare_schema.py --l "$bundle_list" - echo $bundle_list - for bundle in $bundle_list; - do + + if [ ! -z "$changes" ]; then + echo "Detected changes in 'models': $changes" + bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") + if [ ! -z "$bundle_list" ]; then + python "$(pwd)/ci/prepare_schema.py" --l "$bundle_list" + echo "Bundles to process: $bundle_list" + for bundle in $bundle_list; do + echo "Processing bundle: $bundle" if is_excluded "$bundle"; then - echo "skip '$bundle' cpu premerge tests." + echo "Skipping excluded bundle: $bundle" + continue + fi + + requirements_file="requirements_$bundle.txt" + python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" + + # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command + if [ $ALLOW_MONAI_RC = true ]; then + include_pre_release="--pre" + else + include_pre_release="" + fi + + # determine if conda should be used for the bundle + active_conda_env_for_bundle="" + required_python_version="${bundle_python_versions[$bundle]}" + use_conda_for_bundle=false + if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]]; then + use_conda_for_bundle=true + fi + if $use_conda_for_bundle; then + echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." >&2 + init_conda_env "$required_python_version" "$bundle" + active_conda_env_for_bundle="conda_env_${bundle}" + conda activate "$active_conda_env_for_bundle" + else + echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv." + init_venv + fi + + if [ -s "$requirements_file" ]; then + echo "Installing requirements from $requirements_file for $bundle" + python -m pip install $include_pre_release -r "$requirements_file" + fi + + echo "Verifying bundle (min tests): $bundle" + python "$(pwd)/ci/verify_bundle.py" -b "$bundle" -m "min" + + # cleanup + if $use_conda_for_bundle; then + remove_conda_env "$active_conda_env_for_bundle" else - # get required libraries according to the bundle's metadata file - requirements_file="requirements_$bundle.txt" - python $(pwd)/ci/get_bundle_requirements.py --b "$bundle" --requirements_file "$requirements_file" - # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command - if [ $ALLOW_MONAI_RC = true ]; then - include_pre_release="--pre" - else - include_pre_release="" - fi - init_venv - # Check if the requirements file exists and is not empty - if [ -s "$requirements_file" ]; then - echo "install required libraries for bundle: $bundle" - pip install $include_pre_release -r "$requirements_file" - fi - # verify bundle - python $(pwd)/ci/verify_bundle.py -b "$bundle" -m "min" # min tests on cpu remove_venv fi + echo "Finished processing bundle: $bundle" done else - echo "this pull request does not change any bundles, skip verify." + echo "No bundles found by get_changed_bundle.py." fi else - echo "this pull request does not change any files in 'models', skip verify." + echo "No changes in 'models' directory." fi - # check hf models + + echo "Processing Hugging Face models..." hf_model_changes=$(git diff --name-only $head_ref origin/dev -- hf_models) - if [ ! -z "$hf_model_changes" ] - then - # get all changed hf models - hf_model_list=$(python $(pwd)/ci/get_changed_bundle.py --f "$hf_model_changes" --hf_model True) - if [ ! -z "$hf_model_list" ] - then - python $(pwd)/ci/prepare_schema.py --l "$hf_model_list" --p "hf_models" - echo $hf_model_list - for hf_model in $hf_model_list; - do - echo "verify hf model: $hf_model" - # verify hf model - python $(pwd)/ci/verify_hf_model.py -b "$hf_model" + if [ ! -z "$hf_model_changes" ]; then + echo "Detected changes in 'hf_models': $hf_model_changes" + hf_model_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$hf_model_changes" --hf_model True) + if [ ! -z "$hf_model_list" ]; then + python "$(pwd)/ci/prepare_schema.py" --l "$hf_model_list" --p "hf_models" + echo "HF Models to process: $hf_model_list" + for hf_model in $hf_model_list; do + echo "Verifying HF model: $hf_model (using global Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" + python "$(pwd)/ci/verify_hf_model.py" -b "$hf_model" done else - echo "this pull request does not change any hf models, skip verify." + echo "No HF models found by get_changed_bundle.py." fi else - echo "this pull request does not change any hf models, skip verify." + echo "No changes in 'hf_models' directory." fi } - -case $BUILD_TYPE in - - all) - echo "Run all tests..." - verify_bundle - ;; - changed) - echo "Run changed tests..." - verify_bundle - ;; - *) - echo "ERROR: unknown parameter: $BUILD_TYPE" - ;; -esac +verify_bundle diff --git a/ci/run_premerge_gpu.sh b/ci/run_premerge_gpu.sh index 6aa745b6..0cad50c9 100755 --- a/ci/run_premerge_gpu.sh +++ b/ci/run_premerge_gpu.sh @@ -23,91 +23,188 @@ dist_flag=$1 set -ex export ALLOW_MONAI_RC=true +declare -A bundle_python_versions=( + ["retinalOCT_RPD_segmentation"]="3.9" +) +DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" if [[ $# -gt 1 ]]; then - echo "ERROR: too many parameters are provided" + echo "ERROR: too many parameters are provided" >&2 exit 1 fi +echo "CI GPU Job starting... Dist flag: $dist_flag" +echo "Global Python (expected ${DEFAULT_PYTHON_VERSION_FOR_VENV} from YAML): $(python -V)" +if ! command -v conda &> /dev/null; then + echo "ERROR: conda command not found. Ensure Miniconda was installed by the YAML." >&2 + exit 1 +else + echo "Conda executable found: $(command -v conda)" >&2 +fi + +# Common dependencies to install in any activated environment +install_common_deps_in_activated_env() { + echo "Installing common dependencies in activated environment..." >&2 + python -m pip install --upgrade pip wheel >&2 + python -m pip install --upgrade setuptools >&2 + python -m pip install jsonschema gdown pyyaml parameterized fire >&2 + export PYTHONPATH=$PWD +} + + init_venv() { - if [ ! -d "model_zoo_venv" ]; then # Check if the venv directory does not exist - echo "initializing pip environment" + if [ ! -d "model_zoo_venv" ]; then + echo "Initializing pip environment (model_zoo_venv for Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" >&2 python -m venv model_zoo_venv source model_zoo_venv/bin/activate - pip install --upgrade pip wheel - pip install --upgrade setuptools - pip install jsonschema gdown pyyaml parameterized fire - export PYTHONPATH=$PWD + install_common_deps_in_activated_env else - echo "Virtual environment model_zoo_venv already exists. Activating..." + echo "Virtual environment model_zoo_venv already exists. Activating..." >&2 source model_zoo_venv/bin/activate - pip install --upgrade pip wheel - pip install --upgrade setuptools - pip install jsonschema gdown pyyaml parameterized fire - export PYTHONPATH=$PWD + install_common_deps_in_activated_env fi } remove_venv() { - if [ -d "model_zoo_venv" ]; then # Check if the venv directory exists - echo "Removing virtual environment..." - deactivate 2>/dev/null || true # Deactivate venv, ignore errors if not activated - rm -rf model_zoo_venv # Remove the venv directory + if [ -d "model_zoo_venv" ]; then + echo "Removing virtual environment model_zoo_venv..." >&2 + deactivate 2>/dev/null || true + rm -rf model_zoo_venv else - echo "Virtual environment not found. Skipping removal." + echo "Virtual environment model_zoo_venv not found. Skipping removal." >&2 + fi +} + +# Conda environment functions +init_conda_env() { + local python_version_to_create="$1" + local bundle_identifier="$2" + local conda_env_name="conda_env_${bundle_identifier}" + + echo "Initializing Conda environment with Python $python_version_to_create for GPU bundle '$bundle_identifier'..." >&2 + + if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then + if [ -n "$CONDA_EXE" ]; then + source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" + elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then + source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" + else + echo "Warning: Could not reliably source conda.sh for Conda activation." >&2 + fi + fi + + if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then + echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." >&2 + conda env remove -n "$conda_env_name" -y >&2 fi + + conda create -n "$conda_env_name" python="$python_version_to_create" -y >&2 + conda activate "$conda_env_name" + install_common_deps_in_activated_env + conda deactivate 2>/dev/null || true + + echo "$conda_env_name" +} + +remove_conda_env() { + local conda_env_name_to_remove="$1" + if [ -z "$conda_env_name_to_remove" ]; then + echo "Warning: No Conda env name provided to remove_conda_env." >&2 + return + fi + echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" >&2 + conda deactivate 2>/dev/null || true + conda env remove -n "$conda_env_name_to_remove" -y >&2 } verify_bundle() { - echo 'Run verify bundle...' + echo 'Run verify GPU bundle...' >&2 + + # Source conda.sh to initialize Conda for the main script shell + if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then + if [ -n "$CONDA_EXE" ]; then + echo "Sourcing conda.sh from CONDA_EXE path ($(dirname "$CONDA_EXE")) for main shell..." >&2 + source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" + else + echo "Error: CONDA_EXE is not set. Cannot reliably source conda.sh for main shell. Exiting." >&2 + exit 1 + fi + else + echo "Conda shell already initialized at level: $CONDA_SHLVL" >&2 + fi + head_ref=$(git rev-parse HEAD) git fetch origin dev $head_ref - # achieve all changed files in 'models' + changes=$(git diff --name-only $head_ref origin/dev -- models) - if [ ! -z "$changes" ] - then - # get all changed bundles - bundle_list=$(python $(pwd)/ci/get_changed_bundle.py --f "$changes") - if [ ! -z "$bundle_list" ] - then - python $(pwd)/ci/prepare_schema.py --l "$bundle_list" - for bundle in $bundle_list; - do - # get required libraries according to the bundle's metadata file - requirements_file="requirements_$bundle.txt" - python $(pwd)/ci/get_bundle_requirements.py --b "$bundle" --requirements_file "$requirements_file" - # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command - if [ $ALLOW_MONAI_RC = true ]; then - include_pre_release="--pre" - else - include_pre_release="" - fi - init_venv - # Check if the requirements file exists and is not empty - if [ -s "$requirements_file" ]; then - echo "install required libraries for bundle: $bundle" - pip install $include_pre_release -r "$requirements_file" - fi - # get extra install script if exists - extra_script=$(python $(pwd)/ci/get_bundle_requirements.py --b "$bundle" --get_script True) - if [ ! -z "$extra_script" ]; then - echo "install extra libraries with script: $extra_script" - bash $extra_script - fi - # verify bundle - python $(pwd)/ci/verify_bundle.py --b "$bundle" - test_cmd="python $(pwd)/ci/unit_tests/runner.py --b \"$bundle\"" - if [ "$dist_flag" = "True" ]; then - test_cmd="torchrun $(pwd)/ci/unit_tests/runner.py --b \"$bundle\" --dist True" - fi - eval $test_cmd - remove_venv - done + + if [ ! -z "$changes" ]; then + echo "Detected changes in 'models': $changes" >&2 + bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") + if [ ! -z "$bundle_list" ]; then + python "$(pwd)/ci/prepare_schema.py" --l "$bundle_list" + echo "GPU Bundles to process: $bundle_list" >&2 + for bundle in $bundle_list; do + echo "Processing GPU bundle: $bundle" >&2 + requirements_file="requirements_$bundle.txt" + python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" + + # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command + if [ "$ALLOW_MONAI_RC" = true ]; then + include_pre_release="--pre" + else + include_pre_release="" + fi + + # determine if conda should be used for the bundle + required_python_version="${bundle_python_versions[$bundle]}" + use_conda_for_bundle=false + if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]]; then + use_conda_for_bundle=true + fi + + if $use_conda_for_bundle; then + echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." >&2 + init_conda_env "$required_python_version" "$bundle" + active_conda_env_for_bundle="conda_env_${bundle}" + conda activate "$active_conda_env_for_bundle" + else + echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv for GPU." >&2 + init_venv + fi + if [ -s "$requirements_file" ]; then + echo "Installing requirements from $requirements_file for $bundle" >&2 + python -m pip install $include_pre_release -r "$requirements_file" >&2 + fi + + extra_script=$(python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --get_script True) + if [ ! -z "$extra_script" ]; then + echo "Installing extra libraries for GPU with script: $extra_script" >&2 + bash "$extra_script" + fi + + echo "Verifying GPU bundle: $bundle" >&2 + python "$(pwd)/ci/verify_bundle.py" --b "$bundle" + + test_cmd="python $(pwd)/ci/unit_tests/runner.py --b \"$bundle\"" + if [ "$dist_flag" = "True" ]; then + test_cmd="torchrun $(pwd)/ci/unit_tests/runner.py --b \"$bundle\" --dist True" + fi + echo "Executing GPU test command: $test_cmd" >&2 + eval $test_cmd + + if $use_conda_for_bundle; then + remove_conda_env "$active_conda_env_for_bundle" + else + remove_venv + fi + echo "Finished processing GPU bundle: $bundle" >&2 + done else - echo "this pull request does not change any bundles, skip verify." + echo "No bundles found by get_changed_bundle.py for GPU processing." >&2 fi else - echo "this pull request does not change any files in 'models', skip verify." + echo "No changes in 'models' directory. Skipping GPU bundle verification." >&2 remove_venv fi } diff --git a/ci/run_regular_tests_cpu.sh b/ci/run_regular_tests_cpu.sh index 253cc86c..52936c0e 100755 --- a/ci/run_regular_tests_cpu.sh +++ b/ci/run_regular_tests_cpu.sh @@ -30,8 +30,7 @@ elif [[ $# -gt 1 ]]; then exit 1 fi -# Usually, CPU test is required, but for some bundles that are too large to run in Github Actions, we can exclude them. -exclude_test_list=("maisi_ct_generative") +exclude_test_list=("maisi_ct_generative" "retinalOCT_RPD_segmentation") is_excluded() { for item in "${exclude_test_list[@]}"; do # Use exclude_test_list here if [ "$1" == "$item" ]; then diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 43bf6eec..5e5cc2a1 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -18,7 +18,8 @@ "fire": "0.7.0", "torchvision": "0.22.0", "detectron2": "0.6", - "lxml": "5.4.0" + "lxml": "5.4.0", + "pillow": "9.5.0" }, "name": "retinalOCT_RPD_segmentation", "task": "Reticular Pseudodrusen (RPD) instance segmentation.", From 52cce7719b511b40255111433e20329ac02245a2 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 11:45:45 +0000 Subject: [PATCH 14/28] fix precommit Signed-off-by: Yiheng Wang --- .github/workflows/premerge-cpu.yml | 8 ++++---- ci/run_premerge_cpu.sh | 18 +++++++++--------- ci/run_premerge_gpu.sh | 16 ++++++++-------- 3 files changed, 21 insertions(+), 21 deletions(-) diff --git a/.github/workflows/premerge-cpu.yml b/.github/workflows/premerge-cpu.yml index 6187ebec..eb69a0aa 100644 --- a/.github/workflows/premerge-cpu.yml +++ b/.github/workflows/premerge-cpu.yml @@ -30,18 +30,18 @@ jobs: auto-update-conda: true # The python-version here is for Conda's base, if activated. # We set auto-activate-base to false, so actions/setup-python's version remains default. - python-version: 3.10.14 + python-version: 3.10.14 auto-activate-base: false # Crucial: ensures the Python 3.10 from actions/setup-python is the default - name: cache weekly timestamp id: pip-cache-ts run: | echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - + - name: Get pip cache directory id: pip-cache-dir # This runs using the default Python (3.10) - shell: bash + shell: bash run: echo "dir=$(python -m pip cache dir)" >> $GITHUB_OUTPUT - name: Cache pip dependencies (for Python 3.10 global env) @@ -83,7 +83,7 @@ jobs: - name: Run check script # The shell: bash -el {0} can be helpful if conda init modified shell profiles # and your script needs to `conda activate` sub-environments. - shell: bash -el {0} + shell: bash -el {0} run: | # clean up temporary files $(pwd)/runtests.sh --clean diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index 697c193b..c495d5cb 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -94,7 +94,7 @@ init_conda_env() { fi fi - if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then + if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." >&2 conda env remove -n "$conda_env_name" -y >&2 fi @@ -103,7 +103,7 @@ init_conda_env() { conda activate "$conda_env_name" install_common_deps_in_activated_env conda deactivate 2>/dev/null || true - + echo "$conda_env_name" } @@ -138,7 +138,7 @@ verify_bundle() { git fetch origin dev $head_ref changes=$(git diff --name-only $head_ref origin/dev -- models) - + if [ ! -z "$changes" ]; then echo "Detected changes in 'models': $changes" bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") @@ -154,7 +154,7 @@ verify_bundle() { requirements_file="requirements_$bundle.txt" python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" - + # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command if [ $ALLOW_MONAI_RC = true ]; then include_pre_release="--pre" @@ -176,17 +176,17 @@ verify_bundle() { conda activate "$active_conda_env_for_bundle" else echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv." - init_venv + init_venv fi - + if [ -s "$requirements_file" ]; then echo "Installing requirements from $requirements_file for $bundle" python -m pip install $include_pre_release -r "$requirements_file" fi - + echo "Verifying bundle (min tests): $bundle" python "$(pwd)/ci/verify_bundle.py" -b "$bundle" -m "min" - + # cleanup if $use_conda_for_bundle; then remove_conda_env "$active_conda_env_for_bundle" @@ -201,7 +201,7 @@ verify_bundle() { else echo "No changes in 'models' directory." fi - + echo "Processing Hugging Face models..." hf_model_changes=$(git diff --name-only $head_ref origin/dev -- hf_models) if [ ! -z "$hf_model_changes" ]; then diff --git a/ci/run_premerge_gpu.sh b/ci/run_premerge_gpu.sh index 0cad50c9..209ed8a8 100755 --- a/ci/run_premerge_gpu.sh +++ b/ci/run_premerge_gpu.sh @@ -53,7 +53,7 @@ install_common_deps_in_activated_env() { init_venv() { - if [ ! -d "model_zoo_venv" ]; then + if [ ! -d "model_zoo_venv" ]; then echo "Initializing pip environment (model_zoo_venv for Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" >&2 python -m venv model_zoo_venv source model_zoo_venv/bin/activate @@ -93,7 +93,7 @@ init_conda_env() { fi fi - if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then + if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." >&2 conda env remove -n "$conda_env_name" -y >&2 fi @@ -102,7 +102,7 @@ init_conda_env() { conda activate "$conda_env_name" install_common_deps_in_activated_env conda deactivate 2>/dev/null || true - + echo "$conda_env_name" } @@ -147,7 +147,7 @@ verify_bundle() { for bundle in $bundle_list; do echo "Processing GPU bundle: $bundle" >&2 requirements_file="requirements_$bundle.txt" - python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" + python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command if [ "$ALLOW_MONAI_RC" = true ]; then @@ -176,23 +176,23 @@ verify_bundle() { echo "Installing requirements from $requirements_file for $bundle" >&2 python -m pip install $include_pre_release -r "$requirements_file" >&2 fi - + extra_script=$(python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --get_script True) if [ ! -z "$extra_script" ]; then echo "Installing extra libraries for GPU with script: $extra_script" >&2 bash "$extra_script" fi - + echo "Verifying GPU bundle: $bundle" >&2 python "$(pwd)/ci/verify_bundle.py" --b "$bundle" - + test_cmd="python $(pwd)/ci/unit_tests/runner.py --b \"$bundle\"" if [ "$dist_flag" = "True" ]; then test_cmd="torchrun $(pwd)/ci/unit_tests/runner.py --b \"$bundle\" --dist True" fi echo "Executing GPU test command: $test_cmd" >&2 eval $test_cmd - + if $use_conda_for_bundle; then remove_conda_env "$active_conda_env_for_bundle" else From 03525b8d5c29cf0ea970e1160d016101ededc9c8 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 12:00:32 +0000 Subject: [PATCH 15/28] add conda init for cpu test Signed-off-by: Yiheng Wang --- ci/run_premerge_cpu.sh | 58 +++++++++++++++++++++++++++++------------- ci/run_premerge_gpu.sh | 29 +++++++++++++-------- 2 files changed, 59 insertions(+), 28 deletions(-) diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index c495d5cb..e6d7add2 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -125,29 +125,43 @@ verify_bundle() { rm -rf "$dir" fi done - echo 'Run verify bundle...' + echo 'Run verify bundle...' >&2 - if [ -f "requirements.txt" ]; then - echo "Installing global requirements.txt (using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" - python -m pip install -r requirements.txt + # Source conda.sh to initialize Conda for the main script shell + if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then + if [ -n "$CONDA_EXE" ]; then + echo "Sourcing conda.sh from CONDA_EXE path ($(dirname "$CONDA_EXE")) for main shell..." >&2 + source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" + else + echo "Error: CONDA_EXE is not set. Cannot reliably source conda.sh for main shell. Exiting." >&2 + exit 1 + fi + else + echo "Conda shell already initialized at level: $CONDA_SHLVL" >&2 fi - echo "Installing global dependencies for CI scripts (jsonschema, gdown, pyyaml using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" - python -m pip install jsonschema gdown pyyaml + + pip install -r requirements.txt + # install extra dependencies for get changed bundle + pip install jsonschema gdown pyyaml head_ref=$(git rev-parse HEAD) git fetch origin dev $head_ref changes=$(git diff --name-only $head_ref origin/dev -- models) - if [ ! -z "$changes" ]; then + if [ ! -z "$changes" ] + then echo "Detected changes in 'models': $changes" bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") - if [ ! -z "$bundle_list" ]; then + if [ ! -z "$bundle_list" ] + then python "$(pwd)/ci/prepare_schema.py" --l "$bundle_list" echo "Bundles to process: $bundle_list" - for bundle in $bundle_list; do + for bundle in $bundle_list + do echo "Processing bundle: $bundle" - if is_excluded "$bundle"; then + if is_excluded "$bundle" + then echo "Skipping excluded bundle: $bundle" continue fi @@ -156,7 +170,8 @@ verify_bundle() { python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command - if [ $ALLOW_MONAI_RC = true ]; then + if [ $ALLOW_MONAI_RC = true ] + then include_pre_release="--pre" else include_pre_release="" @@ -166,10 +181,12 @@ verify_bundle() { active_conda_env_for_bundle="" required_python_version="${bundle_python_versions[$bundle]}" use_conda_for_bundle=false - if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]]; then + if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] + then use_conda_for_bundle=true fi - if $use_conda_for_bundle; then + if $use_conda_for_bundle + then echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." >&2 init_conda_env "$required_python_version" "$bundle" active_conda_env_for_bundle="conda_env_${bundle}" @@ -179,7 +196,8 @@ verify_bundle() { init_venv fi - if [ -s "$requirements_file" ]; then + if [ -s "$requirements_file" ] + then echo "Installing requirements from $requirements_file for $bundle" python -m pip install $include_pre_release -r "$requirements_file" fi @@ -188,7 +206,8 @@ verify_bundle() { python "$(pwd)/ci/verify_bundle.py" -b "$bundle" -m "min" # cleanup - if $use_conda_for_bundle; then + if $use_conda_for_bundle + then remove_conda_env "$active_conda_env_for_bundle" else remove_venv @@ -204,13 +223,16 @@ verify_bundle() { echo "Processing Hugging Face models..." hf_model_changes=$(git diff --name-only $head_ref origin/dev -- hf_models) - if [ ! -z "$hf_model_changes" ]; then + if [ ! -z "$hf_model_changes" ] + then echo "Detected changes in 'hf_models': $hf_model_changes" hf_model_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$hf_model_changes" --hf_model True) - if [ ! -z "$hf_model_list" ]; then + if [ ! -z "$hf_model_list" ] + then python "$(pwd)/ci/prepare_schema.py" --l "$hf_model_list" --p "hf_models" echo "HF Models to process: $hf_model_list" - for hf_model in $hf_model_list; do + for hf_model in $hf_model_list + do echo "Verifying HF model: $hf_model (using global Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" python "$(pwd)/ci/verify_hf_model.py" -b "$hf_model" done diff --git a/ci/run_premerge_gpu.sh b/ci/run_premerge_gpu.sh index 209ed8a8..600fe3d6 100755 --- a/ci/run_premerge_gpu.sh +++ b/ci/run_premerge_gpu.sh @@ -118,7 +118,7 @@ remove_conda_env() { } verify_bundle() { - echo 'Run verify GPU bundle...' >&2 + echo 'Run verify bundle...' >&2 # Source conda.sh to initialize Conda for the main script shell if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then @@ -138,19 +138,23 @@ verify_bundle() { changes=$(git diff --name-only $head_ref origin/dev -- models) - if [ ! -z "$changes" ]; then + if [ ! -z "$changes" ] + then echo "Detected changes in 'models': $changes" >&2 bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") - if [ ! -z "$bundle_list" ]; then + if [ ! -z "$bundle_list" ] + then python "$(pwd)/ci/prepare_schema.py" --l "$bundle_list" echo "GPU Bundles to process: $bundle_list" >&2 - for bundle in $bundle_list; do + for bundle in $bundle_list + do echo "Processing GPU bundle: $bundle" >&2 requirements_file="requirements_$bundle.txt" python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command - if [ "$ALLOW_MONAI_RC" = true ]; then + if [ "$ALLOW_MONAI_RC" = true ] + then include_pre_release="--pre" else include_pre_release="" @@ -159,11 +163,13 @@ verify_bundle() { # determine if conda should be used for the bundle required_python_version="${bundle_python_versions[$bundle]}" use_conda_for_bundle=false - if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]]; then + if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] + then use_conda_for_bundle=true fi - if $use_conda_for_bundle; then + if $use_conda_for_bundle + then echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." >&2 init_conda_env "$required_python_version" "$bundle" active_conda_env_for_bundle="conda_env_${bundle}" @@ -172,13 +178,15 @@ verify_bundle() { echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv for GPU." >&2 init_venv fi - if [ -s "$requirements_file" ]; then + if [ -s "$requirements_file" ] + then echo "Installing requirements from $requirements_file for $bundle" >&2 python -m pip install $include_pre_release -r "$requirements_file" >&2 fi extra_script=$(python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --get_script True) - if [ ! -z "$extra_script" ]; then + if [ ! -z "$extra_script" ] + then echo "Installing extra libraries for GPU with script: $extra_script" >&2 bash "$extra_script" fi @@ -193,7 +201,8 @@ verify_bundle() { echo "Executing GPU test command: $test_cmd" >&2 eval $test_cmd - if $use_conda_for_bundle; then + if $use_conda_for_bundle + then remove_conda_env "$active_conda_env_for_bundle" else remove_venv From ee659664b10dfdba897c20080aef6d20258b14fb Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 12:21:51 +0000 Subject: [PATCH 16/28] update premerge cpu script Signed-off-by: Yiheng Wang --- ci/run_premerge_cpu.sh | 236 +++++++++++++++++++---------------------- 1 file changed, 110 insertions(+), 126 deletions(-) diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index e6d7add2..2bcc7664 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -15,27 +15,31 @@ # limitations under the License. # +# Argument(s): +# BUILD_TYPE: all/specific_test_name, tests to execute set -ex +BUILD_TYPE=all export ALLOW_MONAI_RC=true + +if [[ $# -eq 1 ]]; then + BUILD_TYPE=$1 + +elif [[ $# -gt 1 ]]; then + echo "ERROR: too many parameters are provided" + exit 1 +fi + +# Bunles that requires special python version declare -A bundle_python_versions=( ["retinalOCT_RPD_segmentation"]="3.9" ) DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" -echo "CI Job starting..." -echo "Global Python (expected ${DEFAULT_PYTHON_VERSION_FOR_VENV} from YAML): $(python -V)" -if ! command -v conda &> /dev/null; then - echo "ERROR: conda command not found. Ensure Miniconda was installed by the YAML." - exit 1 -else - echo "Conda executable found: $(command -v conda)" -fi - # Usually, CPU test is required, but for some bundles that are too large to run in Github Actions, we can exclude them. exclude_test_list=("maisi_ct_generative") is_excluded() { - for item in "${exclude_test_list[@]}"; do + for item in "${exclude_test_list[@]}"; do # Use exclude_test_list here if [ "$1" == "$item" ]; then return 0 # Return true (0) if excluded fi @@ -43,20 +47,17 @@ is_excluded() { return 1 # Return false (1) if not excluded } -# Common dependencies to install in any activated environment install_common_deps_in_activated_env() { - # These are typically needed by MONAI bundles or verification tools - echo "Installing common dependencies in activated environment..." >&2 - python -m pip install --upgrade pip wheel >&2 - python -m pip install --upgrade setuptools >&2 - python -m pip install jsonschema gdown pyyaml parameterized fire >&2 + python -m pip install --upgrade pip wheel + python -m pip install --upgrade setuptools + python -m pip install jsonschema gdown pyyaml parameterized fire export PYTHONPATH=$PWD } init_venv() { - if [ ! -d "model_zoo_venv" ]; then - echo "Initializing pip environment (model_zoo_venv for Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" - python -m venv model_zoo_venv # Uses global python (expected to be DEFAULT_PYTHON_VERSION_FOR_VENV) + if [ ! -d "model_zoo_venv" ]; then # Check if the venv directory does not exist + echo "initializing pip environment" + python -m venv model_zoo_venv source model_zoo_venv/bin/activate install_common_deps_in_activated_env else @@ -67,181 +68,164 @@ init_venv() { } remove_venv() { - if [ -d "model_zoo_venv" ]; then - echo "Removing virtual environment model_zoo_venv..." - deactivate 2>/dev/null || true - rm -rf model_zoo_venv + if [ -d "model_zoo_venv" ]; then # Check if the venv directory exists + echo "Removing virtual environment..." + deactivate 2>/dev/null || true # Deactivate venv, ignore errors if not activated + rm -rf model_zoo_venv # Remove the venv directory else - echo "Virtual environment model_zoo_venv not found. Skipping removal." + echo "Virtual environment not found. Skipping removal." fi } -# Conda environment functions init_conda_env() { local python_version_to_create="$1" local bundle_identifier="$2" local conda_env_name="conda_env_${bundle_identifier}" - echo "Initializing Conda environment with Python $python_version_to_create for GPU bundle '$bundle_identifier'..." >&2 - if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then if [ -n "$CONDA_EXE" ]; then source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" else - echo "Warning: Could not reliably source conda.sh for Conda activation." >&2 + echo "Warning: Could not reliably source conda.sh for Conda activation." fi fi if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then - echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." >&2 - conda env remove -n "$conda_env_name" -y >&2 + echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." + conda env remove -n "$conda_env_name" -y fi - conda create -n "$conda_env_name" python="$python_version_to_create" -y >&2 + conda create -n "$conda_env_name" python="$python_version_to_create" -y conda activate "$conda_env_name" install_common_deps_in_activated_env conda deactivate 2>/dev/null || true - - echo "$conda_env_name" } remove_conda_env() { local conda_env_name_to_remove="$1" if [ -z "$conda_env_name_to_remove" ]; then - echo "Warning: No Conda env name provided to remove_conda_env." >&2 + echo "Warning: No Conda env name provided to remove_conda_env." return fi - echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" >&2 + echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" conda deactivate 2>/dev/null || true - conda env remove -n "$conda_env_name_to_remove" -y >&2 + conda env remove -n "$conda_env_name_to_remove" -y } - verify_bundle() { for dir in /opt/hostedtoolcache/*; do if [[ $dir != "/opt/hostedtoolcache/Python" ]]; then rm -rf "$dir" fi done - echo 'Run verify bundle...' >&2 - - # Source conda.sh to initialize Conda for the main script shell - if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then - if [ -n "$CONDA_EXE" ]; then - echo "Sourcing conda.sh from CONDA_EXE path ($(dirname "$CONDA_EXE")) for main shell..." >&2 - source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" - else - echo "Error: CONDA_EXE is not set. Cannot reliably source conda.sh for main shell. Exiting." >&2 - exit 1 - fi - else - echo "Conda shell already initialized at level: $CONDA_SHLVL" >&2 - fi - + echo 'Run verify bundle...' pip install -r requirements.txt # install extra dependencies for get changed bundle pip install jsonschema gdown pyyaml - head_ref=$(git rev-parse HEAD) git fetch origin dev $head_ref - + # achieve all changed files in 'models' changes=$(git diff --name-only $head_ref origin/dev -- models) - if [ ! -z "$changes" ] then - echo "Detected changes in 'models': $changes" - bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") + # get all changed bundles + bundle_list=$(python $(pwd)/ci/get_changed_bundle.py --f "$changes") if [ ! -z "$bundle_list" ] then - python "$(pwd)/ci/prepare_schema.py" --l "$bundle_list" - echo "Bundles to process: $bundle_list" - for bundle in $bundle_list + python $(pwd)/ci/prepare_schema.py --l "$bundle_list" + echo $bundle_list + for bundle in $bundle_list; do - echo "Processing bundle: $bundle" - if is_excluded "$bundle" - then - echo "Skipping excluded bundle: $bundle" - continue - fi - - requirements_file="requirements_$bundle.txt" - python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" - - # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command - if [ $ALLOW_MONAI_RC = true ] - then - include_pre_release="--pre" + if is_excluded "$bundle"; then + echo "skip '$bundle' cpu premerge tests." else - include_pre_release="" + # get required libraries according to the bundle's metadata file + requirements_file="requirements_$bundle.txt" + python $(pwd)/ci/get_bundle_requirements.py --b "$bundle" --requirements_file "$requirements_file" + # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command + if [ $ALLOW_MONAI_RC = true ]; then + include_pre_release="--pre" + else + include_pre_release="" + fi + # determine if conda env should be used for the bundle + active_conda_env_for_bundle="" + required_python_version="${bundle_python_versions[$bundle]}" + use_conda_for_bundle=false + if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] + then + use_conda_for_bundle=true + fi + if $use_conda_for_bundle + then + echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." + init_conda_env "$required_python_version" "$bundle" + active_conda_env_for_bundle="conda_env_${bundle}" + conda activate "$active_conda_env_for_bundle" + else + echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv." + init_venv + fi + # Check if the requirements file exists and is not empty + if [ -s "$requirements_file" ]; then + echo "install required libraries for bundle: $bundle" + pip install $include_pre_release -r "$requirements_file" + fi + # verify bundle + python $(pwd)/ci/verify_bundle.py -b "$bundle" -m "min" # min tests on cpu + # cleanup + if $use_conda_for_bundle + then + remove_conda_env "$active_conda_env_for_bundle" + else + remove_venv + fi fi - - # determine if conda should be used for the bundle - active_conda_env_for_bundle="" - required_python_version="${bundle_python_versions[$bundle]}" - use_conda_for_bundle=false - if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] - then - use_conda_for_bundle=true - fi - if $use_conda_for_bundle - then - echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." >&2 - init_conda_env "$required_python_version" "$bundle" - active_conda_env_for_bundle="conda_env_${bundle}" - conda activate "$active_conda_env_for_bundle" - else - echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv." - init_venv - fi - - if [ -s "$requirements_file" ] - then - echo "Installing requirements from $requirements_file for $bundle" - python -m pip install $include_pre_release -r "$requirements_file" - fi - - echo "Verifying bundle (min tests): $bundle" - python "$(pwd)/ci/verify_bundle.py" -b "$bundle" -m "min" - - # cleanup - if $use_conda_for_bundle - then - remove_conda_env "$active_conda_env_for_bundle" - else - remove_venv - fi - echo "Finished processing bundle: $bundle" done else - echo "No bundles found by get_changed_bundle.py." + echo "this pull request does not change any bundles, skip verify." fi else - echo "No changes in 'models' directory." + echo "this pull request does not change any files in 'models', skip verify." fi - - echo "Processing Hugging Face models..." + # check hf models hf_model_changes=$(git diff --name-only $head_ref origin/dev -- hf_models) if [ ! -z "$hf_model_changes" ] then - echo "Detected changes in 'hf_models': $hf_model_changes" - hf_model_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$hf_model_changes" --hf_model True) + # get all changed hf models + hf_model_list=$(python $(pwd)/ci/get_changed_bundle.py --f "$hf_model_changes" --hf_model True) if [ ! -z "$hf_model_list" ] then - python "$(pwd)/ci/prepare_schema.py" --l "$hf_model_list" --p "hf_models" - echo "HF Models to process: $hf_model_list" - for hf_model in $hf_model_list + python $(pwd)/ci/prepare_schema.py --l "$hf_model_list" --p "hf_models" + echo $hf_model_list + for hf_model in $hf_model_list; do - echo "Verifying HF model: $hf_model (using global Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" - python "$(pwd)/ci/verify_hf_model.py" -b "$hf_model" + echo "verify hf model: $hf_model" + # verify hf model + python $(pwd)/ci/verify_hf_model.py -b "$hf_model" done else - echo "No HF models found by get_changed_bundle.py." + echo "this pull request does not change any hf models, skip verify." fi else - echo "No changes in 'hf_models' directory." + echo "this pull request does not change any hf models, skip verify." fi } -verify_bundle + +case $BUILD_TYPE in + + all) + echo "Run all tests..." + verify_bundle + ;; + changed) + echo "Run changed tests..." + verify_bundle + ;; + *) + echo "ERROR: unknown parameter: $BUILD_TYPE" + ;; +esac From 33cc537897ac367fd035c73c190b53a919d5ac49 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 12:41:53 +0000 Subject: [PATCH 17/28] add conda init Signed-off-by: Yiheng Wang --- .github/workflows/premerge-cpu.yml | 18 +-- ci/run_premerge_cpu.sh | 2 - ci/run_premerge_gpu.sh | 208 ++++++++++++----------------- 3 files changed, 89 insertions(+), 139 deletions(-) diff --git a/.github/workflows/premerge-cpu.yml b/.github/workflows/premerge-cpu.yml index eb69a0aa..0e7bda96 100644 --- a/.github/workflows/premerge-cpu.yml +++ b/.github/workflows/premerge-cpu.yml @@ -33,6 +33,12 @@ jobs: python-version: 3.10.14 auto-activate-base: false # Crucial: ensures the Python 3.10 from actions/setup-python is the default + - name: Initialize Conda for shell integration + shell: bash + run: | + conda init bash + source ~/.bashrc || echo "Sourcing ~/.bashrc failed or file not found, relying on next login shell." + - name: cache weekly timestamp id: pip-cache-ts run: | @@ -70,19 +76,7 @@ jobs: python -m pip install --upgrade pip wheel python -m pip install --upgrade setuptools - - name: Check environment - shell: bash - run: | - echo "Default Python version:" - python --version - echo "Conda executable path:" - command -v conda - conda info - # This ensures conda command is available, but the default python is still 3.10 - - name: Run check script - # The shell: bash -el {0} can be helpful if conda init modified shell profiles - # and your script needs to `conda activate` sub-environments. shell: bash -el {0} run: | # clean up temporary files diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index 2bcc7664..79c76b24 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -160,12 +160,10 @@ verify_bundle() { fi if $use_conda_for_bundle then - echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." init_conda_env "$required_python_version" "$bundle" active_conda_env_for_bundle="conda_env_${bundle}" conda activate "$active_conda_env_for_bundle" else - echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv." init_venv fi # Check if the requirements file exists and is not empty diff --git a/ci/run_premerge_gpu.sh b/ci/run_premerge_gpu.sh index 600fe3d6..5244400a 100755 --- a/ci/run_premerge_gpu.sh +++ b/ci/run_premerge_gpu.sh @@ -23,197 +23,155 @@ dist_flag=$1 set -ex export ALLOW_MONAI_RC=true -declare -A bundle_python_versions=( - ["retinalOCT_RPD_segmentation"]="3.9" -) -DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" if [[ $# -gt 1 ]]; then - echo "ERROR: too many parameters are provided" >&2 + echo "ERROR: too many parameters are provided" exit 1 fi -echo "CI GPU Job starting... Dist flag: $dist_flag" -echo "Global Python (expected ${DEFAULT_PYTHON_VERSION_FOR_VENV} from YAML): $(python -V)" -if ! command -v conda &> /dev/null; then - echo "ERROR: conda command not found. Ensure Miniconda was installed by the YAML." >&2 - exit 1 -else - echo "Conda executable found: $(command -v conda)" >&2 -fi +declare -A bundle_python_versions=( + ["retinalOCT_RPD_segmentation"]="3.9" +) +DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" -# Common dependencies to install in any activated environment install_common_deps_in_activated_env() { - echo "Installing common dependencies in activated environment..." >&2 - python -m pip install --upgrade pip wheel >&2 - python -m pip install --upgrade setuptools >&2 - python -m pip install jsonschema gdown pyyaml parameterized fire >&2 + python -m pip install --upgrade pip wheel + python -m pip install --upgrade setuptools + python -m pip install jsonschema gdown pyyaml parameterized fire export PYTHONPATH=$PWD } - init_venv() { - if [ ! -d "model_zoo_venv" ]; then - echo "Initializing pip environment (model_zoo_venv for Python ${DEFAULT_PYTHON_VERSION_FOR_VENV})" >&2 + if [ ! -d "model_zoo_venv" ]; then # Check if the venv directory does not exist + echo "initializing pip environment" python -m venv model_zoo_venv source model_zoo_venv/bin/activate install_common_deps_in_activated_env else - echo "Virtual environment model_zoo_venv already exists. Activating..." >&2 + echo "Virtual environment model_zoo_venv already exists. Activating..." source model_zoo_venv/bin/activate install_common_deps_in_activated_env fi } remove_venv() { - if [ -d "model_zoo_venv" ]; then - echo "Removing virtual environment model_zoo_venv..." >&2 - deactivate 2>/dev/null || true - rm -rf model_zoo_venv + if [ -d "model_zoo_venv" ]; then # Check if the venv directory exists + echo "Removing virtual environment..." + deactivate 2>/dev/null || true # Deactivate venv, ignore errors if not activated + rm -rf model_zoo_venv # Remove the venv directory else - echo "Virtual environment model_zoo_venv not found. Skipping removal." >&2 + echo "Virtual environment not found. Skipping removal." fi } -# Conda environment functions init_conda_env() { local python_version_to_create="$1" local bundle_identifier="$2" local conda_env_name="conda_env_${bundle_identifier}" - echo "Initializing Conda environment with Python $python_version_to_create for GPU bundle '$bundle_identifier'..." >&2 - if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then if [ -n "$CONDA_EXE" ]; then source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" else - echo "Warning: Could not reliably source conda.sh for Conda activation." >&2 + echo "Warning: Could not reliably source conda.sh for Conda activation." fi fi if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then - echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." >&2 - conda env remove -n "$conda_env_name" -y >&2 + echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." + conda env remove -n "$conda_env_name" -y fi - conda create -n "$conda_env_name" python="$python_version_to_create" -y >&2 + conda create -n "$conda_env_name" python="$python_version_to_create" -y conda activate "$conda_env_name" install_common_deps_in_activated_env conda deactivate 2>/dev/null || true - - echo "$conda_env_name" } remove_conda_env() { local conda_env_name_to_remove="$1" if [ -z "$conda_env_name_to_remove" ]; then - echo "Warning: No Conda env name provided to remove_conda_env." >&2 + echo "Warning: No Conda env name provided to remove_conda_env." return fi - echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" >&2 + echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" conda deactivate 2>/dev/null || true - conda env remove -n "$conda_env_name_to_remove" -y >&2 + conda env remove -n "$conda_env_name_to_remove" -y } verify_bundle() { - echo 'Run verify bundle...' >&2 - - # Source conda.sh to initialize Conda for the main script shell - if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then - if [ -n "$CONDA_EXE" ]; then - echo "Sourcing conda.sh from CONDA_EXE path ($(dirname "$CONDA_EXE")) for main shell..." >&2 - source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" - else - echo "Error: CONDA_EXE is not set. Cannot reliably source conda.sh for main shell. Exiting." >&2 - exit 1 - fi - else - echo "Conda shell already initialized at level: $CONDA_SHLVL" >&2 - fi - + echo 'Run verify bundle...' head_ref=$(git rev-parse HEAD) git fetch origin dev $head_ref - + # achieve all changed files in 'models' changes=$(git diff --name-only $head_ref origin/dev -- models) - if [ ! -z "$changes" ] then - echo "Detected changes in 'models': $changes" >&2 - bundle_list=$(python "$(pwd)/ci/get_changed_bundle.py" --f "$changes") + # get all changed bundles + bundle_list=$(python $(pwd)/ci/get_changed_bundle.py --f "$changes") if [ ! -z "$bundle_list" ] then - python "$(pwd)/ci/prepare_schema.py" --l "$bundle_list" - echo "GPU Bundles to process: $bundle_list" >&2 - for bundle in $bundle_list - do - echo "Processing GPU bundle: $bundle" >&2 - requirements_file="requirements_$bundle.txt" - python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --requirements_file "$requirements_file" - - # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command - if [ "$ALLOW_MONAI_RC" = true ] - then - include_pre_release="--pre" - else - include_pre_release="" - fi - - # determine if conda should be used for the bundle - required_python_version="${bundle_python_versions[$bundle]}" - use_conda_for_bundle=false - if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] - then - use_conda_for_bundle=true - fi - - if $use_conda_for_bundle - then - echo "Bundle '$bundle' requires Python $required_python_version (specified) for GPU. Using Conda." >&2 - init_conda_env "$required_python_version" "$bundle" - active_conda_env_for_bundle="conda_env_${bundle}" - conda activate "$active_conda_env_for_bundle" - else - echo "Bundle '$bundle' using default Python ${DEFAULT_PYTHON_VERSION_FOR_VENV} venv for GPU." >&2 - init_venv - fi - if [ -s "$requirements_file" ] - then - echo "Installing requirements from $requirements_file for $bundle" >&2 - python -m pip install $include_pre_release -r "$requirements_file" >&2 - fi - - extra_script=$(python "$(pwd)/ci/get_bundle_requirements.py" --b "$bundle" --get_script True) - if [ ! -z "$extra_script" ] - then - echo "Installing extra libraries for GPU with script: $extra_script" >&2 - bash "$extra_script" - fi - - echo "Verifying GPU bundle: $bundle" >&2 - python "$(pwd)/ci/verify_bundle.py" --b "$bundle" - - test_cmd="python $(pwd)/ci/unit_tests/runner.py --b \"$bundle\"" - if [ "$dist_flag" = "True" ]; then - test_cmd="torchrun $(pwd)/ci/unit_tests/runner.py --b \"$bundle\" --dist True" - fi - echo "Executing GPU test command: $test_cmd" >&2 - eval $test_cmd - - if $use_conda_for_bundle - then - remove_conda_env "$active_conda_env_for_bundle" - else - remove_venv - fi - echo "Finished processing GPU bundle: $bundle" >&2 - done + python $(pwd)/ci/prepare_schema.py --l "$bundle_list" + for bundle in $bundle_list; + do + # get required libraries according to the bundle's metadata file + requirements_file="requirements_$bundle.txt" + python $(pwd)/ci/get_bundle_requirements.py --b "$bundle" --requirements_file "$requirements_file" + # check if ALLOW_MONAI_RC is set to 1, if so, append --pre to the pip install command + if [ $ALLOW_MONAI_RC = true ]; then + include_pre_release="--pre" + else + include_pre_release="" + fi + # determine if conda env should be used for the bundle + active_conda_env_for_bundle="" + required_python_version="${bundle_python_versions[$bundle]}" + use_conda_for_bundle=false + if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] + then + use_conda_for_bundle=true + fi + if $use_conda_for_bundle + then + init_conda_env "$required_python_version" "$bundle" + active_conda_env_for_bundle="conda_env_${bundle}" + conda activate "$active_conda_env_for_bundle" + else + init_venv + fi + # Check if the requirements file exists and is not empty + if [ -s "$requirements_file" ]; then + echo "install required libraries for bundle: $bundle" + pip install $include_pre_release -r "$requirements_file" + fi + # get extra install script if exists + extra_script=$(python $(pwd)/ci/get_bundle_requirements.py --b "$bundle" --get_script True) + if [ ! -z "$extra_script" ]; then + echo "install extra libraries with script: $extra_script" + bash $extra_script + fi + # verify bundle + python $(pwd)/ci/verify_bundle.py --b "$bundle" + test_cmd="python $(pwd)/ci/unit_tests/runner.py --b \"$bundle\"" + if [ "$dist_flag" = "True" ]; then + test_cmd="torchrun $(pwd)/ci/unit_tests/runner.py --b \"$bundle\" --dist True" + fi + eval $test_cmd + # cleanup + if $use_conda_for_bundle + then + remove_conda_env "$active_conda_env_for_bundle" + else + remove_venv + fi + done else - echo "No bundles found by get_changed_bundle.py for GPU processing." >&2 + echo "this pull request does not change any bundles, skip verify." fi else - echo "No changes in 'models' directory. Skipping GPU bundle verification." >&2 + echo "this pull request does not change any files in 'models', skip verify." remove_venv fi } From f767da8ad61f5a6ed578db00f5adf347234b7e47 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 12:48:59 +0000 Subject: [PATCH 18/28] adjust conda init way Signed-off-by: Yiheng Wang --- .github/workflows/premerge-cpu.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/premerge-cpu.yml b/.github/workflows/premerge-cpu.yml index 0e7bda96..5d204dcb 100644 --- a/.github/workflows/premerge-cpu.yml +++ b/.github/workflows/premerge-cpu.yml @@ -37,7 +37,7 @@ jobs: shell: bash run: | conda init bash - source ~/.bashrc || echo "Sourcing ~/.bashrc failed or file not found, relying on next login shell." + eval "$(conda shell.bash hook)" - name: cache weekly timestamp id: pip-cache-ts From a07021cf63fe69bddae0bb3605d0c7c94cd5619f Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Thu, 29 May 2025 12:55:07 +0000 Subject: [PATCH 19/28] update init conda env Signed-off-by: Yiheng Wang --- ci/run_premerge_cpu.sh | 15 +++++++-------- ci/run_premerge_gpu.sh | 15 +++++++-------- 2 files changed, 14 insertions(+), 16 deletions(-) diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index 79c76b24..1e6ddad8 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -82,14 +82,13 @@ init_conda_env() { local bundle_identifier="$2" local conda_env_name="conda_env_${bundle_identifier}" - if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then - if [ -n "$CONDA_EXE" ]; then - source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" - elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then - source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" - else - echo "Warning: Could not reliably source conda.sh for Conda activation." - fi + # Always source conda.sh to ensure conda activate is available + if [ -n "$CONDA_EXE" ] && [ -f "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" ]; then + source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" + elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then + source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" + else + echo "Warning: Could not reliably source conda.sh for Conda activation." fi if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then diff --git a/ci/run_premerge_gpu.sh b/ci/run_premerge_gpu.sh index 5244400a..5fc85f7c 100755 --- a/ci/run_premerge_gpu.sh +++ b/ci/run_premerge_gpu.sh @@ -69,14 +69,13 @@ init_conda_env() { local bundle_identifier="$2" local conda_env_name="conda_env_${bundle_identifier}" - if [[ -z "$CONDA_SHLVL" || "$CONDA_SHLVL" -eq 0 ]]; then - if [ -n "$CONDA_EXE" ]; then - source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" - elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then - source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" - else - echo "Warning: Could not reliably source conda.sh for Conda activation." - fi + # Always source conda.sh to ensure conda activate is available + if [ -n "$CONDA_EXE" ] && [ -f "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" ]; then + source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" + elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then + source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" + else + echo "Warning: Could not reliably source conda.sh for Conda activation." fi if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then From e74f97155e81168cf60ffaa7f146627ba5684623 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Fri, 6 Jun 2025 21:09:53 +0000 Subject: [PATCH 20/28] Added docstrings and fixed all flake8 errors. --- .../scripts/Ensembler.py | 63 +++ .../scripts/analysis_lib.py | 529 +++++++++++++----- .../scripts/datasets/data.py | 58 +- .../scripts/datasets/volReader.py | 110 ++-- .../scripts/inference.py | 234 +++++--- setup.cfg | 4 - 6 files changed, 712 insertions(+), 286 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py index 52939515..1c225083 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py +++ b/models/retinalOCT_RPD_segmentation/scripts/Ensembler.py @@ -10,7 +10,21 @@ class NpEncoder(json.JSONEncoder): + """Custom JSON encoder for NumPy data types. + + This encoder handles NumPy-specific types that are not serializable by + the default JSON library by converting them into standard Python types. + """ + def default(self, obj): + """Converts NumPy objects to their native Python equivalents. + + Args: + obj (any): The object to encode. + + Returns: + any: The JSON-serializable representation of the object. + """ if isinstance(obj, np.integer): return int(obj) elif isinstance(obj, np.floating): @@ -22,9 +36,33 @@ def default(self, obj): class Ensembler: + """A class to ensemble predictions from multiple object detection models. + + This class loads ground truth data and predictions from several models, + performs non-maximum suppression (NMS) to merge overlapping detections, + and saves the final ensembled results in COCO format. + """ + def __init__( self, output_dir, dataset_name, grplist, iou_thresh, coco_gt_path=None, coco_instances_results_fname=None ): + """Initializes the Ensembler. + + Args: + output_dir (str): The base directory where model outputs and + ensembled results are stored. + dataset_name (str): The name of the dataset being evaluated. + grplist (list[str]): A list of subdirectory names, where each + subdirectory contains the prediction file from one model. + iou_thresh (float): The IoU threshold for considering two bounding + boxes as overlapping during NMS. + coco_gt_path (str, optional): The full path to the ground truth + COCO JSON file. If None, it's assumed to be in `output_dir`. + Defaults to None. + coco_instances_results_fname (str, optional): The filename for the + COCO prediction files within each model's subdirectory. + Defaults to "coco_instances_results.json". + """ self.output_dir = output_dir self.dataset_name = dataset_name self.grplist = grplist @@ -62,6 +100,19 @@ def __init__( ) def mean_score_nms(self): + """Performs non-maximum suppression by merging overlapping boxes. + + This method iterates through all images and categories, merging sets of + overlapping bounding boxes from different detectors based on the IoU + threshold. For each merged set, it calculates a mean score and selects + the single box with the highest original score as the representative + detection for the ensembled output. + + Returns: + Ensembler: The instance itself, with the `self.results` attribute + populated with the ensembled predictions. + """ + def nik_merge(lsts): """Niklas B. https://github.com/rikpg/IntersectionMerge/blob/master/core.py""" sets = [set(lst) for lst in lsts if lst] @@ -124,6 +175,15 @@ def nik_merge(lsts): return self def save_coco_instances(self, fname="coco_instances_results.json"): + """Saves the ensembled prediction results to a JSON file. + + The output file follows the COCO instance format and can be used for + further evaluation. + + Args: + fname (str, optional): The filename for the output JSON file. + Defaults to "coco_instances_results.json". + """ if self.results: with open(os.path.join(self.output_dir, fname), "w") as f: f.write(json.dumps(self.results, cls=NpEncoder)) @@ -131,5 +191,8 @@ def save_coco_instances(self, fname="coco_instances_results.json"): if __name__ == "__main__": + # Example usage: + # This assumes an 'output' directory with subdirectories 'fold1', 'fold2', etc., + # each containing a 'coco_instances_results.json' file. ens = Ensembler("dev", ["fold1", "fold2", "fold3", "fold4", "fold5"], 0.2) ens.mean_score_nms() diff --git a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py index 3ca1eddf..8e5e622c 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py +++ b/models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py @@ -32,10 +32,21 @@ # current_directory = os.getcwd() # print(current_directory) plt.style.use("./scripts/ybpres.mplstyle") -# plt.style.use("ybpres.mplstyle") def grab_dataset(name): + """Creates a function to load a pickled dataset by name. + + This function returns another function that, when called, loads a dataset + from a pickle file located in the "datasets/" directory. + + Args: + name (str): The base name of the dataset file (without extension). + + Returns: + function: A zero-argument function that loads and returns the dataset. + """ + def f(): return pickle.load(open("datasets/" + name + ".pk", "rb")) @@ -43,6 +54,8 @@ def f(): class OutputVis: + """A class to visualize model outputs and ground truth annotations.""" + def __init__( self, dataset_name, @@ -53,6 +66,24 @@ def __init__( has_annotations=True, draw_mode="default", ): + """Initializes the OutputVis class. + + Args: + dataset_name (str): The name of the registered Detectron2 dataset. + cfg (CfgNode, optional): The Detectron2 configuration object. + Required if `pred_mode` is "model". Defaults to None. + prob_thresh (float, optional): The probability threshold to apply + to model predictions for visualization. Defaults to 0.5. + pred_mode (str, optional): The mode for getting predictions. Must be + either "model" (to use a live predictor) or "file" (to load + from a COCO results file). Defaults to "model". + pred_file (str, optional): The path to the COCO JSON results file. + Required if `pred_mode` is "file". Defaults to None. + has_annotations (bool, optional): Whether the dataset has ground + truth annotations to visualize. Defaults to True. + draw_mode (str, optional): The drawing style for visualizations. + Can be "default" (color) or "bw" (monochrome). Defaults to "default". + """ self.dataset_name = dataset_name self.cfg = cfg self.prob_thresh = prob_thresh @@ -75,89 +106,93 @@ def __init__( self.scale = 3.0 def set_draw_mode(self, draw_mode): + """Sets the drawing mode for visualizations. + + Args: + draw_mode (str): The drawing style. Must be one of the permitted + modes (e.g., "default", "bw"). + """ if draw_mode not in self.permitted_draw_modes: sys.exit("draw_mode must be one of the following: {}".format(self.permitted_draw_modes)) self.draw_mode = draw_mode - def get_ori_image(self, ImgId): - """[summary] + def get_ori_image(self, imgid): + """Retrieves the original image for a given image ID. + + The image is scaled up by a factor of 3 for better visualization. Args: - ImgId (str): Value of image_id in image data structure. + imgid (str): The 'image_id' from the dataset dictionary. Returns: - PIL.Image: Original image fed into the model scaled up by a factor of 3 for visualization. + PIL.Image: The original image. """ - dat = self.get_gt_image_data(ImgId) # gt + dat = self.get_gt_image_data(imgid) # gt im = cv2.imread(dat["file_name"]) # input to model v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) result_image = v_gt.output.get_image() # get original image img = Image.fromarray(result_image) return img - def get_gt_image_data(self, ImgId): - """Returns image ground truth image entry for corresponding to ImgId. + def get_gt_image_data(self, imgid): + """Returns the ground truth data dictionary for a given image ID. Args: - ImgId (str): Value of image_id in image data structure. + imgid (str): The 'image_id' from the dataset dictionary. Returns: - dict: Dictionary for the image with corresponding ImgId. + dict: The dataset dictionary for the specified image. """ - gt_data = next(item for item in self.data if (item["image_id"] == ImgId)) + gt_data = next(item for item in self.data if (item["image_id"] == imgid)) return gt_data def produce_gt_image(self, dat, im): - """Returns image im overlayed with ground truth instances found in dat if there are any. - Depending on visualizer mode, the instances are separate colors or monochrome. + """Creates an image with ground truth annotations overlaid. + + The visualization can be in color or monochrome depending on the draw mode. Args: - dat (dict): Dictionary for the image im containing ground truth annotations. - im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to - the height and width of the image respectively. C is the number of color channels. - The image is required to be in RGB format since that is a requirement of the Matplotlib library. - The image is also expected to be in the range [0, 255]. + dat (dict): The dataset dictionary containing ground truth annotations. + im (np.ndarray): The input image in RGB format (H, W, C) as a NumPy array. Returns: - PIL.Image: The resulting original image overlayed with ground truth instances. + PIL.Image: The image with ground truth instances overlaid. """ v_gt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) if self.has_annotations: # ground truth boxes and masks segs = [ddict["segmentation"] for ddict in dat["annotations"]] if self.draw_mode == "bw": - BBoxes = None + _bboxes = None assigned_colors = [self.annotation_color] * len(segs) else: # default behavior bboxes = [ddict["bbox"] for ddict in dat["annotations"]] - BBoxes = detectron2.structures.Boxes(bboxes) - BBoxes = detectron2.structures.BoxMode.convert( - BBoxes.tensor, from_mode=1, to_mode=0 + _bboxes = detectron2.structures.Boxes(bboxes) + _bboxes = detectron2.structures.BoxMode.convert( + _bboxes.tensor, from_mode=1, to_mode=0 ) # 0= XYXY, 1 = XYWH assigned_colors = None result_image = v_gt.overlay_instances( - boxes=BBoxes, masks=segs, assigned_colors=assigned_colors, alpha=1.0 + boxes=_bboxes, masks=segs, assigned_colors=assigned_colors, alpha=1.0 ).get_image() else: result_image = v_gt.output.get_image() # get original image if no annotations img = Image.fromarray(result_image) return img - def produce_model_image(self, ImgId, dat, im): - """Returns image im overlayed with instances predicted by the model. - Depending on visualizer mode the model predicts on the image im or reads the predictions from file based on - the given ImgId. + def produce_model_image(self, imgid, dat, im): + """Creates an image with model-predicted instances overlaid. + + Predictions are either generated by the model or loaded from a file, + based on the configured `pred_mode`. Args: - ImgId (str): Value of image_id in image data structure. - dat (dict): Dictionary for the image im. Used for height and width parameters. - im (numpy array): a numpy array of shape (H, W, C), where H and W correspond to - the height and width of the image respectively. C is the number of color channels. - The image is required to be in RGB format since that is a requirement of the Matplotlib library. - The image is also expected to be in the range [0, 255]. + imgid (str): The 'image_id' from the dataset dictionary. + dat (dict): The dataset dictionary for the image (used for height/width). + im (np.ndarray): The input image in RGB format (H, W, C) as a NumPy array. Returns: - PIL.Image: The resulting original image overlayed with model-predicted instances. + PIL.Image: The image with model-predicted instances overlaid. """ v_dt = Visualizer(im, MetadataCatalog.get(self.dataset_name), scale=self.scale) v_dt._default_font_size = self.font_size @@ -166,7 +201,7 @@ def produce_model_image(self, ImgId, dat, im): if self._mode == "model": outputs = self.predictor(im)["instances"].to("cpu") elif self._mode == "file": - outputs = self.get_outputs_from_file(ImgId, (dat["height"], dat["width"])) + outputs = self.get_outputs_from_file(imgid, (dat["height"], dat["width"])) outputs = outputs[outputs.scores > self.prob_thresh] # apply probability threshold to instances if self.draw_mode == "bw": result_model = v_dt.overlay_instances( @@ -177,32 +212,35 @@ def produce_model_image(self, ImgId, dat, im): img_model = Image.fromarray(result_model) return img_model - def get_image(self, ImgId): - """Returns the ground truth and model prediction overlays for the image corresponding to ImgId. + def get_image(self, imgid): + """Generates both ground truth and model prediction overlay images. Args: - ImgId (str): Value of image_id in image data structure. + imgid (str): The 'image_id' from the dataset dictionary. Returns: - (PIL.Image,PIL.Image): Tuple of PIL.images corresponding to ground truth and model prediction respectively. + tuple[PIL.Image, PIL.Image]: A tuple containing the ground truth + image and the model prediction image. """ - dat = self.get_gt_image_data(ImgId) # gt + dat = self.get_gt_image_data(imgid) # gt im = cv2.imread(dat["file_name"]) # input to model img = self.produce_gt_image(dat, im) - img_model = self.produce_model_image(ImgId, dat, im) + img_model = self.produce_model_image(imgid, dat, im) return img, img_model - def get_outputs_from_file(self, ImgId, imgsize): - """For image with image_id ImgId, reads in and converts instances from coco format in self.pred_file to a - detectron2 Instances structure required for the visulizer utility. + def get_outputs_from_file(self, imgid, imgsize): + """Loads and formats model predictions from a COCO results file. + + Converts COCO-formatted instances into a Detectron2 `Instances` object + suitable for the visualizer. Args: - ImgId (str): Value of image_id in image data structure. - imgsize (tuple): Height and width of the image in pixels. + imgid (str): The 'image_id' of the desired image. + imgsize (tuple[int, int]): The (height, width) of the image. Returns: - detectron2.structures.Instances: The correctly formated data structure to be used to the detectron2 - visualizer utility. + detectron2.structures.Instances: An `Instances` object containing + the predictions. """ pred_boxes = [] @@ -210,14 +248,14 @@ def get_outputs_from_file(self, ImgId, imgsize): pred_classes = [] pred_masks = [] for i, img in enumerate(self.instance_img_list): - if img == ImgId: + if img == imgid: pred_boxes.append(self.pred_instances[i]["bbox"]) scores.append(self.pred_instances[i]["score"]) pred_classes.append(int(self.pred_instances[i]["category_id"])) # pred_masks_rle.append(self.pred_instances[i]['segmentation']) pred_masks.append(decode(self.pred_instances[i]["segmentation"])) - BBoxes = detectron2.structures.Boxes(pred_boxes) - pred_boxes = detectron2.structures.BoxMode.convert(BBoxes.tensor, from_mode=1, to_mode=0) # 0= XYXY, 1 = XYWH + _bboxes = detectron2.structures.Boxes(pred_boxes) + pred_boxes = detectron2.structures.BoxMode.convert(_bboxes.tensor, from_mode=1, to_mode=0) # 0= XYXY, 1 = XYWH inst_dict = dict( pred_boxes=pred_boxes, scores=torch.tensor(np.array(scores)), @@ -229,15 +267,15 @@ def get_outputs_from_file(self, ImgId, imgsize): @staticmethod def height_crop_range(im, height_target=256): - """Find the range of pixels in the height dimension spanning the height height_target which contain the - brightest regions of the image. + """Calculates a vertical crop range centered on the brightest part of an image. Args: - im (numpy array): a numpy array of shape (H, W, C) - height_target (int, optional): The desired span of the image height. Defaults to 256. + im (np.ndarray): The input image as a NumPy array (H, W, C). + height_target (int, optional): The desired height of the crop. + Defaults to 256. Returns: - range: The starting and stopping pixels for cropping the image height. + range: A range object representing the start and end pixel rows for the crop. """ yhist = im.sum(axis=1) # integrate over width of image mu = np.average(np.arange(yhist.shape[0]), weights=yhist) @@ -251,14 +289,16 @@ def height_crop_range(im, height_target=256): h1 = h2 - height_target return range(h1, h2) - def output_to_pdf(self, ImgIds, outname, dfimg=None): - """Create pdf with name outname displaying ground truth and model prediction overlays for - image ids listed in ImgIds. + def output_to_pdf(self, imgids, outname, dfimg=None): + """Exports visualizations of ground truth and model predictions to a PDF file. + + Each page of the PDF contains the ground truth and model prediction for one image. Args: - ImgIds (list(str)): List of image_id values to output. - outname (str): path name for pdf - dfimg (pandas.DataFrame, optional): A dataframe of stats to display for each image. Defaults to None. + imgids (list[str]): A list of 'image_id' values to include in the PDF. + outname (str): The path and filename for the output PDF. + dfimg (pd.DataFrame, optional): A DataFrame with image statistics + to display on each page. Index should be `imgid`. Defaults to None. """ gtstr = "" @@ -269,7 +309,7 @@ def output_to_pdf(self, ImgIds, outname, dfimg=None): dtcols = dfimg.columns[["dt_" in col for col in dfimg.columns]] with PdfPages(outname) as pdf: - for imgid in tqdm(ImgIds): + for imgid in tqdm(imgids): img, img_model = self.get_image(imgid) # pdb.set_trace() crop_range = self.height_crop_range(np.array(img.convert("L")), height_target=256 * self.scale) @@ -292,49 +332,55 @@ def output_to_pdf(self, ImgIds, outname, dfimg=None): plt.close(fig) def save_imgarr_to_tiff(self, imgs, outname): - """Save array of images in stacked tiff format (one image per page). + """Saves a list of PIL images to a multi-page TIFF file. Args: - imgs (array(PIL.Images)): An array of PIL.Images to save. - outname (str): Path name to save to. + imgs (list[PIL.Image]): A list of images to save. + outname (str): The path and filename for the output TIFF. """ if len(imgs) > 1: imgs[0].save(outname, dpi=(400, 400), tags="", compression=None, save_all=True, append_images=imgs[1:]) else: imgs[0].save(outname) - def output_ori_to_tiff(self, ImgIds, outname): - """Save list of original images corresponding to ImgIds in stacked tiff format. + def output_ori_to_tiff(self, imgids, outname): + """Saves the original images for a list of IDs to a multi-page TIFF. Args: - ImgIds (list(str)): A list of image_ids for images to save. - outname (str): Path name to save to. + imgids (list[str]): A list of 'image_id' values. + outname (str): The path and filename for the output TIFF. """ imgs = [] - for imgid in tqdm(ImgIds): + for imgid in tqdm(imgids): img_ori = self.get_ori_image(imgid) # PIL Image imgs.append(img_ori) self.save_imgarr_to_tiff(imgs, outname) - def output_pred_to_tiff(self, ImgIds, outname, pred_only=False): - """Save list of images overlayed with the model predictions in stacked tiff format. + def output_pred_to_tiff(self, imgids, outname, pred_only=False): + """Saves model prediction overlays for a list of IDs to a multi-page TIFF. Args: - ImgIds (list(str)): A list of image_ids for images to save. - outname (str): Path name to save to. + imgids (list[str]): A list of 'image_id' values. + outname (str): The path and filename for the output TIFF. + pred_only (bool, optional): If True, overlays predictions on a + black background instead of the original image. Defaults to False. """ - imgs = self.output_pred_to_list(ImgIds, pred_only) + imgs = self.output_pred_to_list(imgids, pred_only) self.save_imgarr_to_tiff(imgs, outname) - def output_pred_to_list(self, ImgIds, pred_only=False): - """Return list of images overlayed with the model predictions. + def output_pred_to_list(self, imgids, pred_only=False): + """Generates a list of images with model predictions overlaid. Args: - ImgIds (list(str)): A list of image_ids for images to save. - outname (str): Path name to save to. + imgids (list[str]): A list of 'image_id' values. + pred_only (bool, optional): If True, overlays predictions on a + black background. Defaults to False. + + Returns: + list[PIL.Image]: A list of the generated visualization images. """ imgs = [] - for imgid in tqdm(ImgIds): + for imgid in tqdm(imgids): dat = self.get_gt_image_data(imgid) # gt if pred_only: im = np.zeros((dat["height"], dat["width"], 3)) # blank image for overlay @@ -347,15 +393,19 @@ def output_pred_to_list(self, ImgIds, pred_only=False): imgs.append(img_dt) return imgs - def output_all_to_tiff(self, ImgIds, outname): - """Save list of images (original, ground truth overlay, and model prediction overlay) to stacked tiff format. + def output_all_to_tiff(self, imgids, outname): + """Saves a combined visualization (original, GT, prediction) to a TIFF. + + For each image ID, it creates a single composite image by concatenating + the original, ground truth overlay, and model prediction overlay, then + saves them to a multi-page TIFF. Args: - ImgIds (list(str)): A list of image_ids for images to save. - outname (str): Path name to save to. + imgids (list[str]): A list of 'image_id' values. + outname (str): The path and filename for the output TIFF. """ imgs = [] - for imgid in tqdm(ImgIds): + for imgid in tqdm(imgids): img_gt, img_dt = self.get_image(imgid) img_ori = self.get_ori_image(imgid) hcrange = list(self.height_crop_range(np.array(img_ori.convert("L")), height_target=256 * self.scale)) @@ -372,17 +422,16 @@ def output_all_to_tiff(self, ImgIds, outname): self.save_imgarr_to_tiff(imgs, outname) def get_enface_dt(self, grp, scan_height, scan_width, scan_spacing): - """Return enface perspective of model predictions for a single scan volume whose imgids are - listed in the index of grp. + """Generates an en-face view of model predictions for a scan volume. Args: - grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. - scan_height (int): Image height in pixels. - scan_width (int): Image width in pixels. + grp (pd.DataFrame): DataFrame for a single scan volume, indexed by imgid. + scan_height (int): The height of a single scan image in pixels. + scan_width (int): The width of a single scan image in pixels. scan_spacing (float): The spacing between scan centers in pixels. Returns: - np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. + np.ndarray: An en-face image of the model predictions. """ grp = grp.sort_index() nscans = len(grp) @@ -405,17 +454,16 @@ def get_enface_dt(self, grp, scan_height, scan_width, scan_spacing): return enface def get_enface_gt(self, grp, scan_height, scan_width, scan_spacing): - """Return enface perspective of ground truth annotations for a single scan volume whose imgids are - listed in the index of grp. + """Generates an en-face view of ground truth annotations for a scan volume. Args: - grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. - scan_height (int): Image height in pixels. - scan_width (int): Image width in pixels. + grp (pd.DataFrame): DataFrame for a single scan volume, indexed by imgid. + scan_height (int): The height of a single scan image in pixels. + scan_width (int): The width of a single scan image in pixels. scan_spacing (float): The spacing between scan centers in pixels. Returns: - np.array: Numpy array of dimension [scan_spacing*number of scans,scan_width,3]. + np.ndarray: An en-face image of the ground truth annotations. """ grp = grp.sort_index() nscans = len(grp) @@ -442,17 +490,17 @@ def get_enface_gt(self, grp, scan_height, scan_width, scan_spacing): return enface def compare_enface(self, grp, name, scan_height, scan_width, scan_spacing): - """Return figure comparing detector enface perspective of model prediction with ground truth. + """Creates a figure comparing the en-face views of predictions and ground truth. Args: - grp (pandas.DataFrame): Dataframe containing images from a single scan volume indexed by ImageId. - name (str): The name of the group identified by scan volume ID. - scan_height (int): Image height in pixels. - scan_width (int): Image width in pixels. + grp (pd.DataFrame): DataFrame for a single scan volume, indexed by imgid. + name (str): The name/ID of the scan volume for the plot title. + scan_height (int): The height of a single scan image in pixels. + scan_width (int): The width of a single scan image in pixels. scan_spacing (float): The spacing between scan centers in pixels. Returns: - (matplotlib.figure.Figure, array(AxesSubplot)): Tuple of figure handle and array of subplot axes. + tuple[plt.Figure, np.ndarray]: A tuple containing the figure and axes objects. """ fig, ax = plt.subplots(1, 2, figsize=[18, 9], dpi=120) @@ -468,7 +516,17 @@ def compare_enface(self, grp, name, scan_height, scan_width, scan_spacing): return fig, ax -def Wilson_CI(p, n, z): +def wilson_ci(p, n, z): + """Calculates the Wilson score interval for a binomial proportion. + + Args: + p (float): The observed proportion of successes. + n (int): The total number of trials. + z (float): The z-score for the desired confidence level (e.g., 1.96 for 95%). + + Returns: + tuple[float, float]: A tuple containing the lower and upper bounds of the confidence interval. + """ if p < 0 or p > 1 or n == 0: if p < 0 or p > 1: warnings.warn(f"The value of proportion {p} must be in the range [0,1]. Returning identity for CIs.") @@ -484,7 +542,21 @@ def Wilson_CI(p, n, z): class EvaluateClass(COCOEvaluator): + """A custom evaluation class extending COCOEvaluator for detailed analysis.""" + def __init__(self, dataset_name, output_dir, prob_thresh=0.5, iou_thresh=0.1, evalsuper=True): + """Initializes the custom evaluator. + + Args: + dataset_name (str): The name of the registered Detectron2 dataset. + output_dir (str): Directory to store temporary evaluation files. + prob_thresh (float, optional): Probability threshold for calculating + precision, recall, and FPR. Defaults to 0.5. + iou_thresh (float, optional): IoU threshold for defining a true positive. + Defaults to 0.1. + evalsuper (bool, optional): If True, run the parent COCOEvaluator's + evaluate method to generate standard COCO metrics. Defaults to True. + """ super().__init__(dataset_name, tasks={"bbox", "segm"}, output_dir=output_dir) self.dataset_name = dataset_name self.mycoco = None # pycocotools.cocoEval instance @@ -498,14 +570,31 @@ def __init__(self, dataset_name, output_dir, prob_thresh=0.5, iou_thresh=0.1, ev self.fpr = None def reset(self): + """Resets the evaluator's state for a new evaluation run.""" super().reset() self.mycoco = None def process(self, inputs, outputs): + """Processes a batch of inputs and outputs from the model. + + This method is called by the evaluation loop for each batch. + + Args: + inputs (list[dict]): A list of dataset dictionaries. + outputs (list[dict]): A list of model output dictionaries. + """ super().process(inputs, outputs) def evaluate(self): - # with nostdout(): #suppress the coco summarize statment (the one with APs) + """Runs the evaluation and calculates detailed performance metrics. + + This method orchestrates the COCO evaluation, calculates precision-recall + curves, and other custom metrics. + + Returns: + tuple[float, float]: The precision and recall at the specified + `prob_thresh` and `iou_thresh`. + """ if self.evalsuper: _ = super().evaluate() # this call populates coco_instances_results.json comm.synchronize() @@ -521,9 +610,7 @@ def evaluate(self): self.num_images = len(self.mycoco.params.imgIds) print("Calculated metrics for {} images".format(self.num_images)) self.mycoco.params.iouThrs = np.arange(0.10, 0.6, 0.1) - # mycoco.params.recThrs = [0,.5,.75,1] self.mycoco.params.maxDets = [100] - # mycoco.params.imgIds=[imgId] self.mycoco.params.areaRng = [[0, 10000000000.0]] self.mycoco.evaluate() @@ -538,7 +625,13 @@ def evaluate(self): p, r = self.get_precision_recall() return p, r - def plot_PRcurve(self, ax=None): + def plot_pr_curve(self, ax=None): + """Plots precision-recall curves for various IoU thresholds. + + Args: + ax (plt.Axes, optional): A matplotlib axes object to plot on. If None, + a new figure and axes are created. + """ if ax is None: fig, ax = plt.subplots(1, 1) for i in range(len(self.iou)): @@ -549,6 +642,7 @@ def plot_PRcurve(self, ax=None): ax.legend(title="IoU") def plot_recall_vs_prob(self): + """Plots model score thresholds versus recall for various IoU thresholds.""" plt.figure() for i in range(len(self.iou)): plt.plot(self.rc, self.scores[i], label="{:.2}".format(self.iou[i])) @@ -557,12 +651,19 @@ def plot_recall_vs_prob(self): plt.legend(title="IoU") def get_precision_recall(self): + """Gets the precision and recall for the configured IoU and probability thresholds. + + Returns: + tuple[float, float]: The calculated precision and recall. + """ iou_idx, rc_idx = self._find_iou_rc_inds() precision = self.pr[iou_idx, rc_idx] recall = self.rc[rc_idx] return precision, recall def _calculate_fpr_matrix(self): + """(Private) Calculates the false positive rate matrix across all IoU and recall thresholds.""" + # FP rate, 1 RPD in image = FP if (self.scores.min() == -1) and (self.scores.max() == -1): print( @@ -592,6 +693,14 @@ def _calculate_fpr_matrix(self): self.fpr = fpr def _calculate_fpr(self): + """(Private) Calculates FPR for a single probability threshold. + + This is an alternate calculation used when the main FPR matrix cannot + be computed (e.g., no positive ground truth instances). + + Returns: + float: The calculated false positive rate. + """ print("Using alternate calculation for fpr at instance score threshold of {}".format(self.prob_thresh)) ng = 0 # number of negative images fp = 0 # number of false positives images @@ -609,6 +718,11 @@ def _calculate_fpr(self): return fp / (ng + 1e-5) def _find_iou_rc_inds(self): + """(Private) Finds the indices corresponding to the configured IoU and probability thresholds. + + Returns: + tuple[int, int]: The index for the IoU threshold and the index for the recall level. + """ try: iou_idx = np.argwhere(self.iou == self.iou_thresh)[0][0] # first instance of except IndexError: @@ -627,6 +741,11 @@ def _find_iou_rc_inds(self): return iou_idx, rc_idx def get_fpr(self): + """Gets the false positive rate for the configured thresholds. + + Returns: + float: The calculated false positive rate. Returns -1 if it cannot be computed. + """ if self.fpr is None: self._calculate_fpr_matrix() @@ -640,6 +759,12 @@ def get_fpr(self): return fpr def summarize_scalars(self): # for pretty printing + """Generates a dictionary summarizing key performance metrics with confidence intervals. + + Returns: + dict: A dictionary containing precision, recall, F1-score, FPR, + and their confidence intervals. + """ p, r = self.get_precision_recall() f1 = 2 * (p * r) / (p + r) fpr = self.get_fpr() @@ -652,16 +777,16 @@ def summarize_scalars(self): # for pretty printing n_p = inst_cnt["dt_instances"] n_fpr = inst_cnt["gt_neg_scans"] - def stat_CI(p, n, z): + def stat_ci(p, n, z): return z * np.sqrt(p * (1 - p) / n) - r_ci = Wilson_CI(r, n_r, z) - p_ci = Wilson_CI(p, n_p, z) - fpr_ci = Wilson_CI(fpr, n_fpr, z) + r_ci = wilson_ci(r, n_r, z) + p_ci = wilson_ci(p, n_p, z) + fpr_ci = wilson_ci(fpr, n_fpr, z) # propogate errors for f1 - int_r = stat_CI(r, n_r, z) - int_p = stat_CI(p, n_p, z) + int_r = stat_ci(r, n_r, z) + int_p = stat_ci(p, n_p, z) int_f1 = (f1) * np.sqrt(int_r**2 * (1 / r - 1 / (p + r)) ** 2 + int_p**2 * (1 / p - 1 / (p + r)) ** 2) f1_ci = (f1 - int_f1, f1 + int_f1) @@ -681,29 +806,44 @@ def stat_CI(p, n, z): return dd def count_instances(self): + """Counts ground truth and detected instances across the dataset. + + Returns: + dict: A dictionary with counts for 'gt_instances', 'dt_instances', + and 'gt_neg_scans' (images with no GT instances). + """ gt_inst = 0 dt_inst = 0 gt_neg_scans = 0 for _, val in self.cocoGt.imgs.items(): imgid = val["id"] # Gt instances - annIdsGt = self.cocoGt.getAnnIds([imgid]) - annsGt = self.cocoGt.loadAnns(annIdsGt) - gt_inst += len(annsGt) - if len(annsGt) == 0: + annids_gt = self.cocoGt.getAnnIds([imgid]) + anns_gt = self.cocoGt.loadAnns(annids_gt) + gt_inst += len(anns_gt) + if len(anns_gt) == 0: gt_neg_scans += 1 # Dt instances - annIdsDt = self.cocoDt.getAnnIds([imgid]) - annsDt = self.cocoDt.loadAnns(annIdsDt) - annsDt = [ann for ann in annsDt if ann["score"] > self.prob_thresh] - dt_inst += len(annsDt) + annids_dt = self.cocoDt.getAnnIds([imgid]) + anns_dt = self.cocoDt.loadAnns(annids_dt) + anns_dt = [ann for ann in anns_dt if ann["score"] > self.prob_thresh] + dt_inst += len(anns_dt) return dict(gt_instances=gt_inst, dt_instances=dt_inst, gt_neg_scans=gt_neg_scans) class CreatePlotsRPD: + """A class to create various plots for analyzing RPD (Reticular Pseudodrusen) data.""" + def __init__(self, dfimg): + """Initializes the plotting class with image-level data. + + Args: + dfimg (pd.DataFrame): A DataFrame where each row corresponds to an + image, containing counts for ground truth and detected instances + and pixels. Must include a 'volID' column. + """ self.dfimg = dfimg self.dfvol = self.dfimg.groupby(["volID"])[ ["gt_instances", "gt_pxs", "gt_xpxs", "dt_instances", "dt_pxs", "dt_xpxs"] @@ -711,6 +851,15 @@ def __init__(self, dfimg): @classmethod def initfromcoco(cls, mycoco, prob_thresh): + """Initializes the class from a COCOeval object. + + Args: + mycoco (COCOeval): An evaluated COCOeval object. + prob_thresh (float): The probability threshold to apply to detections. + + Returns: + CreatePlotsRPD: An instance of the class. + """ df = pd.DataFrame( index=mycoco.cocoGt.imgs.keys(), columns=["gt_instances", "gt_pxs", "gt_xpxs", "dt_instances", "dt_pxs", "dt_xpxs"], @@ -720,24 +869,24 @@ def initfromcoco(cls, mycoco, prob_thresh): for key, val in mycoco.cocoGt.imgs.items(): imgid = val["id"] # Gt instances - annIdsGt = mycoco.cocoGt.getAnnIds([imgid]) - annsGt = mycoco.cocoGt.loadAnns(annIdsGt) - instGt = [mycoco.cocoGt.annToMask(ann).sum() for ann in annsGt] - xprojGt = [(mycoco.cocoGt.annToMask(ann).sum(axis=0) > 0).astype("uint8").sum() for ann in annsGt] + annids_gt = mycoco.cocoGt.getAnnIds([imgid]) + anns_gt = mycoco.cocoGt.loadAnns(annids_gt) + inst_gt = [mycoco.cocoGt.annToMask(ann).sum() for ann in anns_gt] + xproj_gt = [(mycoco.cocoGt.annToMask(ann).sum(axis=0) > 0).astype("uint8").sum() for ann in anns_gt] # Dt instances - annIdsDt = mycoco.cocoDt.getAnnIds([imgid]) - annsDt = mycoco.cocoDt.loadAnns(annIdsDt) - annsDt = [ann for ann in annsDt if ann["score"] > prob_thresh] - instDt = [mycoco.cocoDt.annToMask(ann).sum() for ann in annsDt] - xprojDt = [(mycoco.cocoDt.annToMask(ann).sum(axis=0) > 0).astype("uint8").sum() for ann in annsDt] + annids_dt = mycoco.cocoDt.getAnnIds([imgid]) + anns_dt = mycoco.cocoDt.loadAnns(annids_dt) + anns_dt = [ann for ann in anns_dt if ann["score"] > prob_thresh] + inst_dt = [mycoco.cocoDt.annToMask(ann).sum() for ann in anns_dt] + xproj_dt = [(mycoco.cocoDt.annToMask(ann).sum(axis=0) > 0).astype("uint8").sum() for ann in anns_dt] dat = [ - len(instGt), - np.array(instGt).sum(), - np.array(xprojGt).sum(), - len(instDt), - np.array(instDt).sum(), - np.array(xprojDt).sum(), + len(inst_gt), + np.array(inst_gt).sum(), + np.array(xproj_gt).sum(), + len(inst_dt), + np.array(inst_dt).sum(), + np.array(xproj_dt).sum(), ] df.loc[key] = dat @@ -749,10 +898,26 @@ def initfromcoco(cls, mycoco, prob_thresh): @classmethod def initfromcsv(cls, fname): + """Initializes the class from a CSV file. + + Args: + fname (str): The path to the CSV file. + + Returns: + CreatePlotsRPD: An instance of the class. + """ df = pd.read_csv(fname) return cls(df) def get_max_limits(self, df): + """Calculates the maximum values for plotting limits. + + Args: + df (pd.DataFrame): The DataFrame to analyze. + + Returns: + tuple[int, int, int]: Max values for instances, x-pixels, and total pixels. + """ max_inst = np.max([df.gt_instances.max(), df.dt_instances.max()]) max_xpxs = np.max([df.gt_xpxs.max(), df.dt_xpxs.max()]) max_pxs = np.max([df.gt_pxs.max(), df.dt_pxs.max()]) @@ -762,6 +927,17 @@ def get_max_limits(self, df): return max_inst, max_xpxs, max_pxs def vol_level_prc(self, df, gt_thresh=5, ax=None): + """Plots a volume-level precision-recall curve. + + Args: + df (pd.DataFrame): DataFrame with volume-level statistics. + gt_thresh (int, optional): The minimum number of ground truth + instances for a volume to be considered positive. Defaults to 5. + ax (plt.Axes, optional): Axes to plot on. Defaults to None. + + Returns: + tuple[float, tuple]: The average precision and the PR curve data. + """ prc = precision_recall_curve(df.gt_instances >= gt_thresh, df.dt_instances) if ax is None: fig, ax = plt.subplots(1, 1) @@ -773,6 +949,16 @@ def vol_level_prc(self, df, gt_thresh=5, ax=None): return ap, prc def plot_img_level_instance_thresholding(self, df, inst): + """Plots P/R/FPR as a function of the instance count threshold. + + Args: + df (pd.DataFrame): DataFrame with image-level statistics. + inst (list[int]): A list of instance count thresholds to evaluate. + + Returns: + tuple[np.ndarray, np.ndarray, np.ndarray]: Arrays for precision, + recall, and FPR at each threshold. + """ rc = np.zeros((len(inst),)) pr = np.zeros((len(inst),)) fpr = np.zeros((len(inst),)) @@ -804,15 +990,17 @@ def plot_img_level_instance_thresholding(self, df, inst): return pr, rc, fpr def plot_img_level_instance_thresholding2(self, df, inst, gt_thresh, plot=True): - # should be using global function - # def Wilson_CI(p,n,z): + """Plots P/R/FPR vs. instance threshold with confidence intervals. - # sym = z*(p*(1-p)/n + z*z/4/n/n)**.5 - # asym = p + z*z/2/n - # fact = 1/(1+z*z/n) - # upper = fact*(asym+sym) - # lower = fact*(asym-sym) - # return (lower,upper) + Args: + df (pd.DataFrame): DataFrame with image-level statistics. + inst (list[int]): A list of instance count thresholds to evaluate. + gt_thresh (int): The ground truth instance threshold. + plot (bool, optional): Whether to generate a plot. Defaults to True. + + Returns: + dict: A dictionary containing arrays for P/R/FPR and their CIs. + """ rc = np.zeros((len(inst),)) pr = np.zeros((len(inst),)) @@ -827,9 +1015,9 @@ def plot_img_level_instance_thresholding2(self, df, inst, gt_thresh, plot=True): rc[i] = (gt & dt).sum() / gt.sum() pr[i] = (gt & dt).sum() / dt.sum() fpr[i] = ((~gt) & (dt)).sum() / ((~gt).sum()) - rc_ci[i, :] = Wilson_CI(rc[i], gt.sum(), 1.96) - pr_ci[i, :] = Wilson_CI(pr[i], dt.sum(), 1.96) - fpr_ci[i, :] = Wilson_CI(fpr[i], ((~gt).sum()), 1.96) + rc_ci[i, :] = wilson_ci(rc[i], gt.sum(), 1.96) + pr_ci[i, :] = wilson_ci(pr[i], dt.sum(), 1.96) + fpr_ci[i, :] = wilson_ci(fpr[i], ((~gt).sum()), 1.96) if plot: fig, ax = plt.subplots(1, 3, figsize=[15, 5]) @@ -859,6 +1047,14 @@ def plot_img_level_instance_thresholding2(self, df, inst, gt_thresh, plot=True): return dict(precision=pr, precision_ci=pr_ci, recall=rc, recall_ci=rc_ci, fpr=fpr, fpr_ci=fpr_ci) def gt_vs_dt_instances(self, ax=None): + """Plots mean detected instances vs. ground truth instances with error bars. + + Args: + ax (plt.Axes, optional): Axes to plot on. Defaults to None. + + Returns: + plt.Axes: The axes object with the plot. + """ df = self.dfimg max_inst, max_xpxs, max_pxs = self.get_max_limits(df) idx = (df.gt_instances > 0) & (df.dt_instances > 0) @@ -880,6 +1076,14 @@ def gt_vs_dt_instances(self, ax=None): return ax def gt_vs_dt_instances_boxplot(self, ax=None): + """Creates a boxplot of detected instances for each ground truth instance count. + + Args: + ax (plt.Axes, optional): Axes to plot on. Defaults to None. + + Returns: + plt.Axes: The axes object with the plot. + """ df = self.dfimg max_inst, max_xpxs, max_pxs = self.get_max_limits(df) max_inst = int(max_inst) @@ -908,6 +1112,11 @@ def gt_vs_dt_instances_boxplot(self, ax=None): return ax def gt_vs_dt_xpxs(self): + """Creates scatter plots comparing ground truth and detected x-pixels. + + Returns: + tuple[plt.Figure, plt.Figure, plt.Figure]: Figure handles for the three generated plots. + """ df = self.dfimg max_inst, max_xpxs, max_pxs = self.get_max_limits(df) idx = (df.gt_instances > 0) & (df.dt_instances > 0) @@ -946,6 +1155,11 @@ def gt_vs_dt_xpxs(self): return fig1, fig2, fig3 def gt_vs_dt_xpxs_mu(self): + """Plots binned means of detected vs. ground truth x-pixels. + + Returns: + plt.Figure: The figure handle for the plot. + """ df = self.dfimg max_inst, max_xpxs, max_pxs = self.get_max_limits(df) idx = (df.gt_instances > 0) & (df.dt_instances > 0) @@ -968,7 +1182,12 @@ def gt_vs_dt_xpxs_mu(self): plt.tight_layout() return fig - def gt_dt_FP_FN_count(self): + def gt_dt_fp_fn_count(self): + """Plots histograms of false positive and false negative instance counts. + + Returns: + plt.Figure: The figure handle for the plot. + """ df = self.dfimg fig, ax = plt.subplots(1, 2, figsize=[10, 5]) @@ -988,6 +1207,14 @@ def gt_dt_FP_FN_count(self): return fig def avg_inst_size(self): + """Plots histograms of the average instance size in pixels. + + Compares the average size (in both total pixels and x-axis projection) + between ground truth and detected instances. + + Returns: + plt.Figure: The figure handle for the plot. + """ df = self.dfimg max_inst, max_xpxs, max_pxs = self.get_max_limits(df) idx = (df.gt_instances > 0) & (df.dt_instances > 0) diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py index b17081cb..77ff2552 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/data.py @@ -10,7 +10,7 @@ from pydicom.fileset import FileSet from tqdm import tqdm -from .volReader import volFile +from .volReader import VolFile script_dir = os.path.dirname(__file__) @@ -22,6 +22,21 @@ class Error(Exception): def extract_files(dirtoextract, extracted_path, input_format): + """Extracts individual image frames from .vol or DICOM files. + + This function scans a directory for source files of a specified format + and extracts them into a structured output directory as PNG images. + It handles both .vol files and standard DICOM files. If the + output directory already contains files, it will prompt the user + before proceeding to overwrite them. + + Args: + dirtoextract (str): The root directory to search for source files. + extracted_path (str): The destination directory where the extracted + PNG images will be saved. + input_format (str): The format of the input files. Must be either + "vol" or "dicom". + """ assert input_format in ["vol", "dicom"], 'Error: input_format must be "vol" or "dicom".' proceed = True if (os.path.isdir(extracted_path)) and (len(os.listdir(extracted_path)) != 0): @@ -35,13 +50,13 @@ def extract_files(dirtoextract, extracted_path, input_format): files_to_extract = glob.glob(os.path.join(dirtoextract, "**/*.vol"), recursive=True) for _, line in enumerate(tqdm(files_to_extract)): fpath = line.strip("\n") - vol = volFile(fpath) + vol = VolFile(fpath) fpath = fpath.replace("\\", "/") path, scan_str = fpath.strip(".vol").rsplit("/", 1) extractpath = os.path.join(extracted_path, scan_str.replace("_", "/")) os.makedirs(extractpath, exist_ok=True) preffix = os.path.join(extractpath, scan_str + "_oct") - vol.renderOCTscans(preffix) + vol.render_oct_scans(preffix) elif input_format == "dicom": keywords = ["SOPInstanceUID", "PatientID", "ImageLaterality", "SeriesDate"] list_of_dicts = [] @@ -49,8 +64,8 @@ def extract_files(dirtoextract, extracted_path, input_format): for dsstr in dirgen: fs = FileSet(dcmread(dsstr)) - fsgenOPT = genOPTfs(fs) - for fi in tqdm(fsgenOPT): + fsgenopt = gen_opt_fs(fs) + for fi in tqdm(fsgenopt): dd = dict() # top level keywords for key in keywords: @@ -71,9 +86,21 @@ def extract_files(dirtoextract, extracted_path, input_format): def rpd_data(extracted_path): + """Generates a dataset list from a directory of extracted image files. + + Scans a directory recursively for PNG images and creates a list of + dictionaries, one for each image. This format is designed to be compatible + with Detectron2's `DatasetCatalog` and can be adapted to hold ground truth instances for evaluation. + + Args: + extracted_path (str): The root directory containing the extracted + .png image files to be included in the dataset. + + Returns: + list[dict]: A list where each dictionary represents an image and + contains its file path, dimensions, and a unique ID. + """ dataset = [] - instances = 0 - wrong_poly = 0 extracted_files = glob.glob(os.path.join(extracted_path, "**/*.[Pp][Nn][Gg]"), recursive=True) print("Generating dataset of images...") for fn in tqdm(extracted_files): @@ -83,12 +110,23 @@ def rpd_data(extracted_path): dat = dict(file_name=fn_adjusted, height=im.shape[0], width=im.shape[1], image_id=imageid) dataset.append(dat) print(f"Found {len(dataset)} images") - print(f"Found {instances} instances") - print(f"Found {wrong_poly} too few vertices") return dataset -def genOPTfs(fs): +def gen_opt_fs(fs): + """A generator for finding and loading OPT modality DICOM datasets. + + This function filters a pydicom `FileSet` object for instances that have + the modality set to "OPT" (Ophthalmic Tomography) and yields each one + as a fully loaded pydicom dataset. + + Args: + fs (pydicom.fileset.FileSet): The pydicom FileSet to search through. + + Yields: + pydicom.dataset.FileDataset: A loaded DICOM dataset for each instance + with the "OPT" modality found in the FileSet. + """ for instance in fs.find(Modality="OPT"): ds = instance.load() yield ds diff --git a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py index 2bfa6273..d8b8bc5d 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py +++ b/models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py @@ -16,7 +16,7 @@ import numpy as np -class volFile: +class VolFile: def __init__(self, filename): """ Parses Heyex Spectralis *.vol files. @@ -28,7 +28,7 @@ def __init__(self, filename): volFile class """ - self.__parseVolFile(filename) + self.__parse_volfile(filename) @property def oct(self): @@ -66,15 +66,15 @@ def grid(self): wf = self.wholefile grid = [] for bi in range(len(wf["slice-headers"])): - bscanHead = wf["slice-headers"][bi] - x_0 = int(bscanHead["startX"] / wf["header"]["scaleXSlo"]) - x_1 = int(bscanHead["endX"] / wf["header"]["scaleXSlo"]) - y_0 = int(bscanHead["startY"] / wf["header"]["scaleYSlo"]) - y_1 = int(bscanHead["endY"] / wf["header"]["scaleYSlo"]) + bscan_head = wf["slice-headers"][bi] + x_0 = int(bscan_head["startX"] / wf["header"]["scaleXSlo"]) + x_1 = int(bscan_head["endX"] / wf["header"]["scaleXSlo"]) + y_0 = int(bscan_head["startY"] / wf["header"]["scaleYSlo"]) + y_1 = int(bscan_head["endY"] / wf["header"]["scaleYSlo"]) grid.append([x_0, y_0, x_1, y_1]) return grid - def renderIRslo(self, filename, renderGrid=False): + def render_ir_slo(self, filename, render_grid=False): """ Renders IR SLO image as a PNG file and optionally overlays grid of B scans @@ -90,7 +90,7 @@ def renderIRslo(self, filename, renderGrid=False): wf = self.wholefile a = np.copy(wf["sloImage"]) - if renderGrid: + if render_grid: a = np.stack((a,) * 3, axis=-1) a = Image.fromarray(a) draw = ImageDraw.Draw(a) @@ -101,7 +101,7 @@ def renderIRslo(self, filename, renderGrid=False): else: Image.fromarray(a).save(filename) - def renderOCTscans(self, filepre="oct", renderSeg=False): + def render_oct_scans(self, filepre="oct", render_seg=False): """ Renders OCT images a PNG file and optionally overlays segmentation lines Also creates a CSV file of vol file features. @@ -119,7 +119,7 @@ def renderOCTscans(self, filepre="oct", renderSeg=False): wf = self.wholefile for i in range(wf["cScan"].shape[0]): a = np.copy(wf["cScan"][i]) - if renderSeg: + if render_seg: a = np.stack((a,) * 3, axis=-1) for li in range(wf["segmentations"].shape[0]): for x in range(wf["segmentations"].shape[2]): @@ -127,7 +127,7 @@ def renderOCTscans(self, filepre="oct", renderSeg=False): Image.fromarray(a).save("%s_%03d.png" % (filepre, i)) - def __parseVolFile(self, fn, parseSeg=False): + def __parse_volfile(self, fn, parse_seg=False): print(fn) wholefile = OrderedDict() decode_hex = codecs.getdecoder("hex_codec") @@ -173,61 +173,61 @@ def __parseVolFile(self, fn, parseSeg=False): wholefile["header"] = header fin.seek(2048) - U = array.array("B") - U.frombytes(fin.read(header["sizeXSlo"] * header["sizeYSlo"])) - U = np.array(U).astype("uint8").reshape((header["sizeXSlo"], header["sizeYSlo"])) - wholefile["sloImage"] = U + u = array.array("B") + u.frombytes(fin.read(header["sizeXSlo"] * header["sizeYSlo"])) + u = np.array(u).astype("uint8").reshape((header["sizeXSlo"], header["sizeYSlo"])) + wholefile["sloImage"] = u - sloOffset = 2048 + header["sizeXSlo"] * header["sizeYSlo"] - octOffset = header["BscanHdrSize"] + header["octSizeX"] * header["octSizeZ"] * 4 + slo_offset = 2048 + header["sizeXSlo"] * header["sizeYSlo"] + oct_offset = header["BscanHdrSize"] + header["octSizeX"] * header["octSizeZ"] * 4 bscans = [] bscanheaders = [] bscanqualities = [] - if parseSeg: + if parse_seg: segmentations = None for i in range(header["numBscan"]): - fin.seek(16 + sloOffset + i * octOffset) - bscanHead = OrderedDict() - bscanHead["startX"] = struct.unpack("d", fin.read(8))[0] - bscanHead["startY"] = struct.unpack("d", fin.read(8))[0] - bscanHead["endX"] = struct.unpack("d", fin.read(8))[0] - bscanHead["endY"] = struct.unpack("d", fin.read(8))[0] - bscanHead["numSeg"] = struct.unpack("I", fin.read(4))[0] - bscanHead["offSeg"] = struct.unpack("I", fin.read(4))[0] - bscanHead["quality"] = struct.unpack("f", fin.read(4))[0] - bscanHead["shift"] = struct.unpack("I", fin.read(4))[0] - bscanheaders.append(bscanHead) - bscanqualities.append(bscanHead["quality"]) + fin.seek(16 + slo_offset + i * oct_offset) + bscan_head = OrderedDict() + bscan_head["startX"] = struct.unpack("d", fin.read(8))[0] + bscan_head["startY"] = struct.unpack("d", fin.read(8))[0] + bscan_head["endX"] = struct.unpack("d", fin.read(8))[0] + bscan_head["endY"] = struct.unpack("d", fin.read(8))[0] + bscan_head["numSeg"] = struct.unpack("I", fin.read(4))[0] + bscan_head["offSeg"] = struct.unpack("I", fin.read(4))[0] + bscan_head["quality"] = struct.unpack("f", fin.read(4))[0] + bscan_head["shift"] = struct.unpack("I", fin.read(4))[0] + bscanheaders.append(bscan_head) + bscanqualities.append(bscan_head["quality"]) # extract OCT B scan data - fin.seek(header["BscanHdrSize"] + sloOffset + i * octOffset) - U = array.array("f") - U.frombytes(fin.read(4 * header["octSizeX"] * header["octSizeZ"])) - U = np.array(U).reshape((header["octSizeZ"], header["octSizeX"])) + fin.seek(header["BscanHdrSize"] + slo_offset + i * oct_offset) + u = array.array("f") + u.frombytes(fin.read(4 * header["octSizeX"] * header["octSizeZ"])) + u = np.array(u).reshape((header["octSizeZ"], header["octSizeX"])) # remove out of boundary v = struct.unpack("f", decode_hex("FFFF7F7F")[0]) - U[U == v] = 0 + u[u == v] = 0 # log normalize - U = np.log(10000 * U + 1) - U = (255.0 * (np.clip(U, 0, np.max(U)) / np.max(U))).astype("uint8") - bscans.append(U) - if parseSeg: + u = np.log(10000 * u + 1) + u = (255.0 * (np.clip(u, 0, np.max(u)) / np.max(u))).astype("uint8") + bscans.append(u) + if parse_seg: # extract OCT segmentations data - fin.seek(256 + sloOffset + i * octOffset) - U = array.array("f") - U.frombytes(fin.read(4 * header["octSizeX"] * bscanHead["numSeg"])) - U = np.array(U) - print(U.shape) - U[U == v] = 0.0 + fin.seek(256 + slo_offset + i * oct_offset) + u = array.array("f") + u.frombytes(fin.read(4 * header["octSizeX"] * bscan_head["numSeg"])) + u = np.array(u) + print(u.shape) + u[u == v] = 0.0 if segmentations is None: segmentations = [] - for _ in range(bscanHead["numSeg"]): + for _ in range(bscan_head["numSeg"]): segmentations.append([]) - for j in range(bscanHead["numSeg"]): - segmentations[j].append(U[j * header["octSizeX"] : (j + 1) * header["octSizeX"]].tolist()) + for j in range(bscan_head["numSeg"]): + segmentations[j].append(u[j * header["octSizeX"] : (j + 1) * header["octSizeX"]].tolist()) wholefile["cScan"] = np.array(bscans) - if parseSeg: + if parse_seg: wholefile["segmentations"] = np.array(segmentations) wholefile["slice-headers"] = bscanheaders wholefile["average-quality"] = np.mean(bscanqualities) @@ -235,7 +235,7 @@ def __parseVolFile(self, fn, parseSeg=False): import csv from pathlib import Path, PurePath - volFeatures = [ + vol_features = [ PurePath(fn).name, wholefile["header"]["version"].decode("utf-8").rstrip("\x00"), wholefile["header"]["numBscan"], @@ -314,10 +314,10 @@ def __parseVolFile(self, fn, parseSeg=False): with open(output_csv, "a", newline="") as file: print("Adding", PurePath(fn).name, "to vols.csv.") writer = csv.writer(file) - writer.writerow(volFeatures) + writer.writerow(vol_features) @property - def fileHeader(self): + def file_header(self): """ Retrieve vol header fields @@ -354,7 +354,7 @@ def fileHeader(self): """ return self.wholefile["header"] - def bScanHeader(self, slicei): + def bscan_header(self, slicei): """ Retrieve the B Scan header information per slice. @@ -374,7 +374,7 @@ def bScanHeader(self, slicei): """ return self.wholefile["slice-headers"][slicei] - def saveGrid(self, outfn): + def save_grid(self, outfn): """ Saves the grid coordinates mapping OCT Bscans to the IR SLO image to a text file. The text file will be a tab-delimited file with 5 columns: The bscan number, x_0, y_0, x_1, y_1 in pixel space diff --git a/models/retinalOCT_RPD_segmentation/scripts/inference.py b/models/retinalOCT_RPD_segmentation/scripts/inference.py index 8585a65c..8eaf9846 100644 --- a/models/retinalOCT_RPD_segmentation/scripts/inference.py +++ b/models/retinalOCT_RPD_segmentation/scripts/inference.py @@ -16,9 +16,9 @@ from .Ensembler import Ensembler from .table_styles import styles +# Change directory to the script's location to ensure relative paths work correctly. os.chdir(os.path.dirname(os.path.abspath(__file__))) -# current_directory = os.getcwd() -# print(current_directory) + logging.basicConfig(level=logging.INFO) @@ -46,12 +46,30 @@ def __call__(self, block_num, block_size, total_size): self.pbar.finish() -def create_dataset(dataset_name, extracted_path): # Creates dataset and pk file from extracted images. +def create_dataset(dataset_name, extracted_path): + """Creates a pickled dataset file from a directory of extracted images. + + This function scans the `extracted_path` for images, formats them into a + list of dictionaries compatible with Detectron2, and saves the list as a + pickle file. + + Args: + dataset_name (str): The name for the dataset, used for the output .pk file. + extracted_path (str): The directory containing the extracted image files. + """ stored_data = data.rpd_data(extracted_path) pickle.dump(stored_data, open(os.path.join(data.script_dir, f"{dataset_name}.pk"), "wb")) def configure_model(): + """Loads and returns the model configuration from a YAML file. + + It reads a 'working.yaml' file located in the same directory as the script + to set up the Detectron2 configuration. + + Returns: + detectron2.config.CfgNode: The configuration object for the model. + """ cfg = get_cfg() moddir = os.path.dirname(os.path.realpath(__file__)) name = "working.yaml" @@ -61,6 +79,14 @@ def configure_model(): def register_dataset(dataset_name): + """Registers a dataset with Detectron2's DatasetCatalog. + + This makes the dataset available to be loaded by Detectron2's data loaders. + It sets the class metadata to 'rpd'. + + Args: + dataset_name (str): The name under which to register the dataset. + """ for name in [dataset_name]: try: DatasetCatalog.register(name, grab_dataset(name)) @@ -70,6 +96,17 @@ def register_dataset(dataset_name): def run_prediction(cfg, dataset_name, output_path): + """Runs inference on a dataset using a cross-validation ensemble of models. + + It loads five different model weight files (fold1 to fold5), runs inference + for each model on the specified dataset, and saves the predictions in + separate subdirectories within `output_path`. + + Args: + cfg (CfgNode): The model configuration object. + dataset_name (str): The name of the registered dataset to run inference on. + output_path (str): The base directory to save prediction outputs. + """ model = build_model(cfg) # returns a torch.nn.Module myloader = build_detection_test_loader(cfg, dataset_name) myeval = COCOEvaluator( @@ -93,6 +130,20 @@ def run_prediction(cfg, dataset_name, output_path): def run_ensemble(dataset_name, output_path, iou_thresh=0.2): + """Ensembles predictions from multiple models using NMS. + + It initializes an `Ensembler`, runs the non-maximum suppression logic, and + saves the final combined predictions to a single COCO results file. + + Args: + dataset_name (str): The name of the dataset. + output_path (str): The base directory containing the individual model + prediction subdirectories. + iou_thresh (float, optional): The IoU threshold for ensembling. Defaults to 0.2. + + Returns: + Ensembler: The ensembler instance after running NMS. + """ ens = Ensembler(output_path, dataset_name, ["fold1", "fold2", "fold3", "fold4", "fold5"], iou_thresh=iou_thresh) ens.mean_score_nms() ens.save_coco_instances() @@ -100,6 +151,20 @@ def run_ensemble(dataset_name, output_path, iou_thresh=0.2): def evaluate_dataset(dataset_name, output_path, iou_thresh=0.2, prob_thresh=0.5): + """Evaluates the final ensembled predictions against ground truth. + + It uses the custom `EvaluateClass` to calculate performance metrics and saves + a summary to a JSON file. + + Args: + dataset_name (str): The name of the dataset. + output_path (str): The directory containing the ensembled predictions file. + iou_thresh (float, optional): The IoU threshold for evaluation. Defaults to 0.2. + prob_thresh (float, optional): The probability threshold for evaluation. Defaults to 0.5. + + Returns: + EvaluateClass: The evaluation object containing detailed metrics. + """ myeval = EvaluateClass(dataset_name, output_path, iou_thresh=iou_thresh, prob_thresh=prob_thresh, evalsuper=False) myeval.evaluate() with open(os.path.join(output_path, "scalar_dict.json"), "w") as outfile: @@ -108,16 +173,35 @@ def evaluate_dataset(dataset_name, output_path, iou_thresh=0.2, prob_thresh=0.5) def create_table(myeval): + """Creates a DataFrame of per-image statistics from evaluation results. + + Args: + myeval (EvaluateClass): The evaluation object containing COCO results. + + Returns: + CreatePlotsRPD: An object containing DataFrames for image and volume stats. + """ dataset_table = CreatePlotsRPD.initfromcoco(myeval.mycoco, myeval.prob_thresh) dataset_table.dfimg.sort_index(inplace=True) return dataset_table # dataset_table.dfimg['scan'] = dataset_table.dfimg['scan'].astype('int') #depends on what we want scan field to be -def output_vol_predictions(dataset_table, vis, volID, output_path, output_mode="pred_overlay"): +def output_vol_predictions(dataset_table, vis, volid, output_path, output_mode="pred_overlay"): + """Generates and saves visualization TIFFs for a single scan volume. + + Args: + dataset_table (CreatePlotsRPD): Object containing the image/volume stats. + vis (OutputVis): The visualization object. + volid (str): The ID of the volume to visualize. + output_path (str): The directory to save the output TIFF file. + output_mode (str, optional): The type of visualization to create. + Options: "pred_overlay", "pred_only", "originals", "all". + Defaults to "pred_overlay". + """ dfimg = dataset_table.dfimg - imgids = dfimg[dfimg.volID == volID].sort_index().index.values - outname = os.path.join(output_path, f"{volID}.tiff") + imgids = dfimg[dfimg["volID"] == volid].sort_index().index.values + outname = os.path.join(output_path, f"{volid}.tiff") if output_mode == "pred_overlay": vis.output_pred_to_tiff(imgids, outname, pred_only=False) elif output_mode == "pred_only": @@ -131,13 +215,31 @@ def output_vol_predictions(dataset_table, vis, volID, output_path, output_mode=" def output_dataset_predictions(dataset_table, vis, output_path, output_mode="pred_overlay", draw_mode="default"): + """Generates and saves visualization TIFFs for all volumes in a dataset. + + Args: + dataset_table (CreatePlotsRPD): Object containing the image/volume stats. + vis (OutputVis): The visualization object. + output_path (str): The base directory to save the output TIFF files. + output_mode (str, optional): The type of visualization to create. + Defaults to "pred_overlay". + draw_mode (str, optional): The drawing style ("default" or "bw"). + Defaults to "default". + """ vis.set_draw_mode(draw_mode) os.makedirs(output_path, exist_ok=True) - for volID in dataset_table.dfvol.index: - output_vol_predictions(dataset_table, vis, volID, output_path, output_mode) + for volid in dataset_table.dfvol.index: + output_vol_predictions(dataset_table, vis, volid, output_path, output_mode) def create_dfvol(dataset_name, output_path, dataset_table): + """Creates and saves a styled HTML table of volume-level statistics. + + Args: + dataset_name (str): The name of the dataset. + output_path (str): The directory to save the HTML file. + dataset_table (CreatePlotsRPD): Object containing the volume DataFrame. + """ dfvol = dataset_table.dfvol.sort_values(by=["dt_instances"], ascending=False) with pd.option_context("styler.render.max_elements", int(dfvol.size) + 1): html_str = dfvol.style.format("{:.0f}").set_table_styles(styles).to_html() @@ -147,6 +249,13 @@ def create_dfvol(dataset_name, output_path, dataset_table): def create_dfimg(dataset_name, output_path, dataset_table): + """Creates and saves a styled HTML table of image-level statistics. + + Args: + dataset_name (str): The name of the dataset. + output_path (str): The directory to save the HTML file. + dataset_table (CreatePlotsRPD): Object containing the image DataFrame. + """ dfimg = dataset_table.dfimg.sort_index() with pd.option_context("styler.render.max_elements", int(dfimg.size) + 1): html_str = dfimg.style.set_table_styles(styles).to_html() @@ -156,98 +265,91 @@ def create_dfimg(dataset_name, output_path, dataset_table): def main(args): - dataset_name = None - input_dir = None - extracted = None - output = None - run_ext = True - run_inf = True - prob_thresh = 0.5 - iou_thresh = 0.2 - make_table = True - make_visuals = False - bm = False - bmo = False - imo = False - - print(args) - - dataset_name = args.get("dataset_name") # Access values from the dictionary + """Main function to orchestrate the end-to-end analysis pipeline. + + This function controls the flow from data extraction to evaluation and + visualization based on the provided arguments. + + Args: + args (dict): A dictionary of command-line arguments and flags + controlling the pipeline execution. + """ + print(f"Received arguments: {args}") + + # Unpack arguments from the dictionary with default values + dataset_name = args.get("dataset_name") input_dir = args.get("input_dir") - extracted = args.get("extracted_dir") + extracted_dir = args.get("extracted_dir") input_format = args.get("input_format") - output = args.get("output_dir") - run_ext = args.get("run_extract", True) # Provide default values + output_dir = args.get("output_dir") + run_extract = args.get("run_extract", True) make_dataset = args.get("create_dataset", True) - run_inf = args.get("run_inference", True) + run_inference = args.get("run_inference", True) prob_thresh = args.get("prob_thresh", 0.5) - make_table = args.get("create_tables", True) + iou_thresh = args.get("iou_thresh", 0.2) + create_tables = args.get("create_tables", True) + + # Visualization flags bm = args.get("binary_mask", False) bmo = args.get("binary_mask_overlay", False) imo = args.get("instance_mask_overlay", False) - make_visuals = bm | bmo | imo + make_visuals = bm or bmo or imo - if run_ext: - if not os.path.isdir(extracted): - print("Extracted dir does not exist! Making extracted dir...") - os.mkdir(extracted) - data.extract_files(input_dir, extracted, input_format) + # --- Pipeline Steps --- + if run_extract: + os.makedirs(extracted_dir, exist_ok=True) + print("Starting file extraction...") + data.extract_files(input_dir, extracted_dir, input_format) print("Image extraction complete!") if make_dataset: print("Creating dataset from extracted images...") - create_dataset(dataset_name, extracted) - if run_inf: + create_dataset(dataset_name, extracted_dir) + if run_inference: print("Configuring model...") cfg = configure_model() print("Registering dataset...") register_dataset(dataset_name) - if not os.path.isdir(output): - print("Output dir does not exist! Making output dir...") - os.makedirs(output) + os.makedirs(output_dir, exist_ok=True) print("Running inference...") - run_prediction(cfg, dataset_name, output) + run_prediction(cfg, dataset_name, output_dir) print("Inference complete, running ensemble...") - run_ensemble(dataset_name, output) + run_ensemble(dataset_name, output_dir, iou_thresh) print("Ensemble complete!") - if make_table or make_visuals: - print("Registering dataset...") + if create_tables or make_visuals: + print("Registering dataset for evaluation...") register_dataset(dataset_name) print("Evaluating dataset...") - eval = evaluate_dataset(dataset_name, output, iou_thresh, prob_thresh) + eval_obj = evaluate_dataset(dataset_name, output_dir, iou_thresh, prob_thresh) print("Creating dataset table...") - table = create_table(eval) - if make_table: - create_dfvol(dataset_name, output, table) - create_dfimg(dataset_name, output, table) - print("Dataset htmls complete!") + table = create_table(eval_obj) + if create_tables: + create_dfvol(dataset_name, output_dir, table) + create_dfimg(dataset_name, output_dir, table) + print("Dataset HTML tables complete!") if make_visuals: + print("Initializing visualizer...") vis = OutputVis( dataset_name, - prob_thresh=eval.prob_thresh, + prob_thresh=eval_obj.prob_thresh, pred_mode="file", - pred_file=os.path.join(output, "coco_instances_results.json"), - has_annotations=False, + pred_file=os.path.join(output_dir, "coco_instances_results.json"), + has_annotations=False, # Assuming we are visualizing on test data without GT ) - vis.scale = 1.0 + vis.scale = 1.0 # Use original scale for output visuals if bm: - print("Creating binary masks tif (no overlay)...") + print("Creating binary masks TIFF (no overlay)...") vis.annotation_color = "w" output_dataset_predictions( - table, vis, os.path.join(output, "predicted_binary_masks"), "pred_only", "bw" + table, vis, os.path.join(output_dir, "predicted_binary_masks"), "pred_only", "bw" ) if bmo: - print("Creating binary masks tif (with overlay)...") + print("Creating binary masks TIFF (with overlay)...") output_dataset_predictions( - table, vis, os.path.join(output, "predicted_binary_overlays"), "pred_overlay", "bw" + table, vis, os.path.join(output_dir, "predicted_binary_overlays"), "pred_overlay", "bw" ) if imo: - print("Creating instances masks tif (with overlay)...") + print("Creating instance masks TIFF (with overlay)...") output_dataset_predictions( - table, vis, os.path.join(output, "predicted_instance_overlays"), "pred_overlay", "default" + table, vis, os.path.join(output_dir, "predicted_instance_overlays"), "pred_overlay", "default" ) print("Visualizations complete!") - - -# if __name__ == "__main__": -# main(sys.argv[1:]) -# # main_alt() diff --git a/setup.cfg b/setup.cfg index cace76d7..06fa1478 100644 --- a/setup.cfg +++ b/setup.cfg @@ -25,10 +25,6 @@ ignore = per_file_ignores = __init__.py: F401 __main__.py: F401 - models/retinalOCT_RPD_segmentation/scripts/analysis_lib.py: N802, N803, N806 - models/retinalOCT_RPD_segmentation/scripts/datasets/data.py: N802, N803, N806 - models/retinalOCT_RPD_segmentation/scripts/datasets/volReader.py: N801, N802, N803, N806 - models/retinalOCT_RPD_segmentation/scripts/inference.py: N802, N803, N806 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py [isort] From d260ea2b50244d14cebe5bf44418689c778e3731 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Fri, 6 Jun 2025 21:45:43 +0000 Subject: [PATCH 21/28] Added setuptools==75.8.0 to dependencies to build detectron2 with python 3.10. Modified metadata.json, README.md, and ./docs/requirements.txt files accordingly. --- models/retinalOCT_RPD_segmentation/configs/metadata.json | 1 + models/retinalOCT_RPD_segmentation/docs/README.md | 2 -- models/retinalOCT_RPD_segmentation/docs/requirements.txt | 1 + 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index 5e5cc2a1..d8e53492 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -9,6 +9,7 @@ "numpy_version": "1.26.4", "optional_packages_version": {}, "required_packages_version": { + "setuptools": "75.8.0", "opencv-python-headless": "4.11.0.86", "pandas": "2.2.3", "seaborn": "0.13.2", diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index c8802d63..15522d7d 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -34,8 +34,6 @@ This bundle can be installed using docker by navigating to the RPDBundle directo docker build -t : . ``` --> ## INSTALL -The following installation order works when installing into an environment with python==3.9. - From the bundle directory, install the required packages using ``` pip install -r ./docs/requirements.txt diff --git a/models/retinalOCT_RPD_segmentation/docs/requirements.txt b/models/retinalOCT_RPD_segmentation/docs/requirements.txt index e7f75e99..e33de840 100644 --- a/models/retinalOCT_RPD_segmentation/docs/requirements.txt +++ b/models/retinalOCT_RPD_segmentation/docs/requirements.txt @@ -5,3 +5,4 @@ seaborn scikit-learn progressbar lxml +setuptools == 75.8.0 From 6160e069e329574e8491ef5b8076c16b5d148e08 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Fri, 20 Jun 2025 17:10:06 +0000 Subject: [PATCH 22/28] Changed "format" in metadata.json to "magnitude" --- models/retinalOCT_RPD_segmentation/configs/metadata.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index d8e53492..edf74e2a 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -31,7 +31,7 @@ "inputs": { "image": { "type": "image", - "format": "PNG", + "format": "magnitude", "modality": "OCT", "num_channels": 1, "spatial_shape": [ @@ -52,7 +52,7 @@ "preprocessed_data_sources": { "vol_file": { "type": "image", - "format": "VOL", + "format": "magnitude", "modality": "OCT", "num_channels": 1, "spatial_shape": [ @@ -69,7 +69,7 @@ }, "dicom_series": { "type": "image", - "format": "DICOM", + "format": "magnitude", "modality": "OCT", "SOP class UID": "1.2.840.10008.5.1.4.1.1.77.1.5.4", "num_channels": 1, From bb88574b4bd2f6f4f0cc99dfac7eba1baf026a1f Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Fri, 20 Jun 2025 10:51:45 -0700 Subject: [PATCH 23/28] Updated required packages in metadata.json. --- .../configs/metadata.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/configs/metadata.json b/models/retinalOCT_RPD_segmentation/configs/metadata.json index edf74e2a..8b1c0f19 100644 --- a/models/retinalOCT_RPD_segmentation/configs/metadata.json +++ b/models/retinalOCT_RPD_segmentation/configs/metadata.json @@ -4,23 +4,23 @@ "changelog": { "0.0.1": "Initial version" }, - "monai_version": "1.4.0", - "pytorch_version": "2.7.0", + "monai_version": "1.5.0", + "pytorch_version": "2.6.0", "numpy_version": "1.26.4", "optional_packages_version": {}, "required_packages_version": { "setuptools": "75.8.0", "opencv-python-headless": "4.11.0.86", - "pandas": "2.2.3", + "pandas": "2.3.0", "seaborn": "0.13.2", "scikit-learn": "1.6.1", "progressbar": "2.5", - "pydicom": "2.4.4", + "pydicom": "3.0.1", "fire": "0.7.0", - "torchvision": "0.22.0", + "torchvision": "0.21.0", "detectron2": "0.6", "lxml": "5.4.0", - "pillow": "9.5.0" + "pillow": "11.2.1" }, "name": "retinalOCT_RPD_segmentation", "task": "Reticular Pseudodrusen (RPD) instance segmentation.", From a707723ce76f251b76fbc07f67ab66d153a7a4e2 Mon Sep 17 00:00:00 2001 From: ybagdasa Date: Mon, 7 Jul 2025 09:55:23 -0700 Subject: [PATCH 24/28] Updated README and bundle requirements. --- .../docs/README.md | 3 ++- .../docs/requirements.txt | 22 ++++++++++++------- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/models/retinalOCT_RPD_segmentation/docs/README.md b/models/retinalOCT_RPD_segmentation/docs/README.md index 15522d7d..39b78ce0 100644 --- a/models/retinalOCT_RPD_segmentation/docs/README.md +++ b/models/retinalOCT_RPD_segmentation/docs/README.md @@ -34,7 +34,7 @@ This bundle can be installed using docker by navigating to the RPDBundle directo docker build -t : . ``` --> ## INSTALL -From the bundle directory, install the required packages using +This bundle has been installed and tested using python 3.10. From the bundle directory, install the required packages using ``` pip install -r ./docs/requirements.txt ``` @@ -95,6 +95,7 @@ For VOL files, each OCT slice will be saved as a png file to `/ Date: Wed, 23 Jul 2025 17:35:36 +0000 Subject: [PATCH 25/28] Fixed pre-commit eof line. --- models/retinalOCT_RPD_segmentation/docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/retinalOCT_RPD_segmentation/docs/requirements.txt b/models/retinalOCT_RPD_segmentation/docs/requirements.txt index 1520ff54..b4e9390f 100644 --- a/models/retinalOCT_RPD_segmentation/docs/requirements.txt +++ b/models/retinalOCT_RPD_segmentation/docs/requirements.txt @@ -11,4 +11,4 @@ pydicom==3.0.1 fire==0.7.0 torchvision==0.21.0 lxml==5.4.0 -pillow==11.2.1 \ No newline at end of file +pillow==11.2.1 From 9ba81071a451ef17522dd5f6333feb70fcd9536a Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Mon, 11 Aug 2025 04:15:47 +0000 Subject: [PATCH 26/28] revert py39 changes Signed-off-by: Yiheng Wang --- .github/workflows/premerge-cpu.yml | 63 ++-------- ci/bundle_custom_data.py | 4 +- ci/get_changed_bundle.py | 108 ++++++++++++++---- ..._retinalOCT_RPD_segmentation_dependency.sh | 2 - ci/run_premerge_cpu.sh | 84 ++------------ ci/run_premerge_gpu.sh | 83 ++------------ ci/run_regular_tests_cpu.sh | 3 +- 7 files changed, 117 insertions(+), 230 deletions(-) delete mode 100644 ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh diff --git a/.github/workflows/premerge-cpu.yml b/.github/workflows/premerge-cpu.yml index 5d204dcb..d365d9bb 100644 --- a/.github/workflows/premerge-cpu.yml +++ b/.github/workflows/premerge-cpu.yml @@ -16,70 +16,29 @@ jobs: premerge-cpu: runs-on: ubuntu-latest steps: - - name: Checkout code - uses: actions/checkout@v4 - + - uses: actions/checkout@v4 - name: Set up Python 3.10 uses: actions/setup-python@v4 with: - python-version: 3.10.14 # This will be the default Python - - - name: Set up Miniconda - uses: conda-incubator/setup-miniconda@v3 - with: - auto-update-conda: true - # The python-version here is for Conda's base, if activated. - # We set auto-activate-base to false, so actions/setup-python's version remains default. python-version: 3.10.14 - auto-activate-base: false # Crucial: ensures the Python 3.10 from actions/setup-python is the default - - - name: Initialize Conda for shell integration - shell: bash - run: | - conda init bash - eval "$(conda shell.bash hook)" - - name: cache weekly timestamp - id: pip-cache-ts + id: pip-cache run: | - echo "datew=$(date '+%Y-%V')" >> $GITHUB_OUTPUT - - - name: Get pip cache directory - id: pip-cache-dir - # This runs using the default Python (3.10) - shell: bash - run: echo "dir=$(python -m pip cache dir)" >> $GITHUB_OUTPUT - - - name: Cache pip dependencies (for Python 3.10 global env) - uses: actions/cache@v4 - id: cache-pip - with: - path: ${{ steps.pip-cache-dir.outputs.dir }} - key: ${{ runner.os }}-pip-3.10-${{ steps.pip-cache-ts.outputs.datew }}-${{ hashFiles('**/requirements.txt', '**/setup.py') }} - restore-keys: | - ${{ runner.os }}-pip-3.10-${{ steps.pip-cache-ts.outputs.datew }}- - - # Cache Conda packages (for environments your bash script might create) - # setup-miniconda action sets MINICONDA_PATH_0 environment variable - - name: Cache Conda packages + echo "::set-output name=datew::$(date '+%Y-%V')" + - name: cache for pip uses: actions/cache@v4 - id: cache-conda + id: cache with: - path: ${{ env.MINICONDA_PATH_0 }}/pkgs - key: ${{ runner.os }}-conda-pkgs-${{ steps.pip-cache-ts.outputs.datew }}-${{ hashFiles('**/environment*.yml', '**/requirements*.txt') }} # Adjust hashFiles as needed - restore-keys: | - ${{ runner.os }}-conda-pkgs-${{ steps.pip-cache-ts.outputs.datew }}- - - - name: Install global Python dependencies (using Python 3.10) - shell: bash + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ steps.pip-cache.outputs.datew }} + - name: Install dependencies run: | python -m pip install --upgrade pip wheel python -m pip install --upgrade setuptools - - - name: Run check script - shell: bash -el {0} + - name: check run: | # clean up temporary files $(pwd)/runtests.sh --clean df -h - bash $(pwd)/ci/run_premerge_cpu.sh + bash $(pwd)/ci/run_premerge_cpu.sh changed + shell: bash diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index 4d7d58f8..1a951437 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -64,9 +64,7 @@ # This dict is used for our CI tests to install required dependencies that cannot be installed by `pip install` directly. # If a bundle has this kind of dependencies, please add the bundle name (key), and the path of the install script (value) # into the dict. -install_dependency_dict = { - "retinalOCT_RPD_segmentation": "ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh" -} +install_dependency_dict = {} # This list is used for our CI tests to determine whether a bundle supports TensorRT export. Related # test will be employed for bundles in the dict. diff --git a/ci/get_changed_bundle.py b/ci/get_changed_bundle.py index a177ea86..9262b112 100644 --- a/ci/get_changed_bundle.py +++ b/ci/get_changed_bundle.py @@ -11,44 +11,102 @@ import argparse +import os +import sys -from utils import get_changed_bundle_list +from bundle_custom_data import install_dependency_dict +from utils import get_json_dict +ALLOW_MONAI_RC = os.environ.get("ALLOW_MONAI_RC", "false").lower() in ("true", "1", "t", "y", "yes") -def get_changed_bundle(changed_dirs): + +def increment_version(version): """ - This function is used to get all changed bundles, a string which - contains all bundle names will be printed, and can be used in shell scripts. + Split the version into components, assume missing parts are '0'. + + Args: + version (str): The version string to increment. + + Examples: + print(increment_version("1.3.2")) # Expected output: 1.3.3 + print(increment_version("1.4")) # Expected output: 1.4.1 + print(increment_version("1")) # Expected output: 1.0.1 """ - bundle_names = "" - root_path = "models" - bundle_list = get_changed_bundle_list(changed_dirs, root_path=root_path) + version = str(version) + parts = version.split(".") + # Extend the list with zeros to handle cases like "1" or "1.4". + while len(parts) < 3: + parts.append("0") + + # Convert all parts to integers. + parts = list(map(int, parts)) + + # Increment the last part. + parts[-1] += 1 - for bundle in bundle_list: - bundle_names += f"{bundle} " - print(bundle_names) + # Join the parts back into a version string. + # This trims trailing '.0's, returning the version in its original format + result_version = ".".join(map(str, parts)).rstrip(".0") + return result_version -def get_changed_hf_model(changed_dirs): + +def get_requirements(bundle, models_path, requirements_file): """ - This function is used to get all changed hf models, a string which - contains all hf model names will be printed, and can be used in shell scripts. + This function is used to produce a requirements txt file, and print a string + which shows the filename. The printed string can be used in shell scripts. """ - hf_model_names = "" - root_path = "hf_models" - hf_model_list = get_changed_bundle_list(changed_dirs, root_path=root_path) - for hf_model in hf_model_list: - hf_model_names += f"{hf_model} " - print(hf_model_names) + bundle_path = os.path.join(models_path, bundle) + meta_file_path = os.path.join(bundle_path, "configs/metadata.json") + if os.path.exists(meta_file_path): + metadata = get_json_dict(meta_file_path) + libs = [] + if "monai_version" in metadata.keys(): + monai_version = metadata["monai_version"] + if not ALLOW_MONAI_RC: + lib_monai_req = f"monai=={monai_version}" + else: + lib_monai_req = f"monai>={monai_version}rc1,<{increment_version(monai_version)}" + print(f"ALLOW_MONAI_RC is set to true, the version range is {lib_monai_req}", file=sys.stderr) + libs.append(lib_monai_req) + if "pytorch_version" in metadata.keys(): + pytorch_version = metadata["pytorch_version"] + libs.append(f"torch=={pytorch_version}") + if "numpy_version" in metadata.keys(): + numpy_version = metadata["numpy_version"] + libs.append(f"numpy=={numpy_version}") + for package_key in ["optional_packages_version", "required_packages_version"]: + if package_key in metadata.keys(): + optional_dict = metadata[package_key] + for name, version in optional_dict.items(): + libs.append(f"{name}=={version}") + + if len(libs) > 0: + with open(requirements_file, "w") as f: + for line in libs: + f.write(f"{line}\n") + + +def get_install_script(bundle): + # install extra dependencies if needed + script_path = "" + if bundle in install_dependency_dict.keys(): + script_path = install_dependency_dict[bundle] + print(script_path) if __name__ == "__main__": parser = argparse.ArgumentParser(description="") - parser.add_argument("-f", "--f", type=str, help="changed files.") - parser.add_argument("--hf_model", type=bool, default=False, help="if true, get changed hf models.") + parser.add_argument("-b", "--b", type=str, help="bundle name.") + parser.add_argument("-p", "--p", type=str, default="models", help="models path.") + parser.add_argument("--get_script", type=bool, default=False, help="whether to get the install script.") + parser.add_argument("--requirements_file", type=str, help="output filename for requirements.") args = parser.parse_args() - changed_dirs = args.f.splitlines() - if args.hf_model: - get_changed_hf_model(changed_dirs) + bundle = args.b + models_path = args.p + get_script = args.get_script + if get_script is True: + get_install_script(bundle) else: - get_changed_bundle(changed_dirs) + requirements_file = args.requirements_file + get_requirements(bundle, models_path, requirements_file) diff --git a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh deleted file mode 100644 index 74fae23c..00000000 --- a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh +++ /dev/null @@ -1,2 +0,0 @@ -# ensure that using evironment with python==3.9 -python -m pip install 'git+https://github.com/facebookresearch/detectron2.git@65184fc057d4fab080a98564f6b60fae0b94edc4' diff --git a/ci/run_premerge_cpu.sh b/ci/run_premerge_cpu.sh index 1e6ddad8..000424c3 100755 --- a/ci/run_premerge_cpu.sh +++ b/ci/run_premerge_cpu.sh @@ -30,12 +30,6 @@ elif [[ $# -gt 1 ]]; then exit 1 fi -# Bunles that requires special python version -declare -A bundle_python_versions=( - ["retinalOCT_RPD_segmentation"]="3.9" -) -DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" - # Usually, CPU test is required, but for some bundles that are too large to run in Github Actions, we can exclude them. exclude_test_list=("maisi_ct_generative") is_excluded() { @@ -47,23 +41,22 @@ is_excluded() { return 1 # Return false (1) if not excluded } -install_common_deps_in_activated_env() { - python -m pip install --upgrade pip wheel - python -m pip install --upgrade setuptools - python -m pip install jsonschema gdown pyyaml parameterized fire - export PYTHONPATH=$PWD -} - init_venv() { if [ ! -d "model_zoo_venv" ]; then # Check if the venv directory does not exist echo "initializing pip environment" python -m venv model_zoo_venv source model_zoo_venv/bin/activate - install_common_deps_in_activated_env + pip install --upgrade pip wheel + pip install --upgrade setuptools + pip install jsonschema gdown pyyaml parameterized fire + export PYTHONPATH=$PWD else echo "Virtual environment model_zoo_venv already exists. Activating..." source model_zoo_venv/bin/activate - install_common_deps_in_activated_env + pip install --upgrade pip wheel + pip install --upgrade setuptools + pip install jsonschema gdown pyyaml parameterized fire + export PYTHONPATH=$PWD fi } @@ -77,42 +70,6 @@ remove_venv() { fi } -init_conda_env() { - local python_version_to_create="$1" - local bundle_identifier="$2" - local conda_env_name="conda_env_${bundle_identifier}" - - # Always source conda.sh to ensure conda activate is available - if [ -n "$CONDA_EXE" ] && [ -f "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" ]; then - source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" - elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then - source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" - else - echo "Warning: Could not reliably source conda.sh for Conda activation." - fi - - if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then - echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." - conda env remove -n "$conda_env_name" -y - fi - - conda create -n "$conda_env_name" python="$python_version_to_create" -y - conda activate "$conda_env_name" - install_common_deps_in_activated_env - conda deactivate 2>/dev/null || true -} - -remove_conda_env() { - local conda_env_name_to_remove="$1" - if [ -z "$conda_env_name_to_remove" ]; then - echo "Warning: No Conda env name provided to remove_conda_env." - return - fi - echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" - conda deactivate 2>/dev/null || true - conda env remove -n "$conda_env_name_to_remove" -y -} - verify_bundle() { for dir in /opt/hostedtoolcache/*; do if [[ $dir != "/opt/hostedtoolcache/Python" ]]; then @@ -149,22 +106,7 @@ verify_bundle() { else include_pre_release="" fi - # determine if conda env should be used for the bundle - active_conda_env_for_bundle="" - required_python_version="${bundle_python_versions[$bundle]}" - use_conda_for_bundle=false - if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] - then - use_conda_for_bundle=true - fi - if $use_conda_for_bundle - then - init_conda_env "$required_python_version" "$bundle" - active_conda_env_for_bundle="conda_env_${bundle}" - conda activate "$active_conda_env_for_bundle" - else - init_venv - fi + init_venv # Check if the requirements file exists and is not empty if [ -s "$requirements_file" ]; then echo "install required libraries for bundle: $bundle" @@ -172,13 +114,7 @@ verify_bundle() { fi # verify bundle python $(pwd)/ci/verify_bundle.py -b "$bundle" -m "min" # min tests on cpu - # cleanup - if $use_conda_for_bundle - then - remove_conda_env "$active_conda_env_for_bundle" - else - remove_venv - fi + remove_venv fi done else diff --git a/ci/run_premerge_gpu.sh b/ci/run_premerge_gpu.sh index 5fc85f7c..6aa745b6 100755 --- a/ci/run_premerge_gpu.sh +++ b/ci/run_premerge_gpu.sh @@ -29,28 +29,22 @@ if [[ $# -gt 1 ]]; then exit 1 fi -declare -A bundle_python_versions=( - ["retinalOCT_RPD_segmentation"]="3.9" -) -DEFAULT_PYTHON_VERSION_FOR_VENV="3.10" - -install_common_deps_in_activated_env() { - python -m pip install --upgrade pip wheel - python -m pip install --upgrade setuptools - python -m pip install jsonschema gdown pyyaml parameterized fire - export PYTHONPATH=$PWD -} - init_venv() { if [ ! -d "model_zoo_venv" ]; then # Check if the venv directory does not exist echo "initializing pip environment" python -m venv model_zoo_venv source model_zoo_venv/bin/activate - install_common_deps_in_activated_env + pip install --upgrade pip wheel + pip install --upgrade setuptools + pip install jsonschema gdown pyyaml parameterized fire + export PYTHONPATH=$PWD else echo "Virtual environment model_zoo_venv already exists. Activating..." source model_zoo_venv/bin/activate - install_common_deps_in_activated_env + pip install --upgrade pip wheel + pip install --upgrade setuptools + pip install jsonschema gdown pyyaml parameterized fire + export PYTHONPATH=$PWD fi } @@ -64,42 +58,6 @@ remove_venv() { fi } -init_conda_env() { - local python_version_to_create="$1" - local bundle_identifier="$2" - local conda_env_name="conda_env_${bundle_identifier}" - - # Always source conda.sh to ensure conda activate is available - if [ -n "$CONDA_EXE" ] && [ -f "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" ]; then - source "$(dirname "$CONDA_EXE")/../etc/profile.d/conda.sh" - elif [ -n "$MINICONDA_PATH_0" ] && [ -f "$MINICONDA_PATH_0/etc/profile.d/conda.sh" ]; then - source "$MINICONDA_PATH_0/etc/profile.d/conda.sh" - else - echo "Warning: Could not reliably source conda.sh for Conda activation." - fi - - if conda env list | grep -q "^${conda_env_name}[[:space:]]"; then - echo "Conda env '$conda_env_name' already exists. Removing for a clean start..." - conda env remove -n "$conda_env_name" -y - fi - - conda create -n "$conda_env_name" python="$python_version_to_create" -y - conda activate "$conda_env_name" - install_common_deps_in_activated_env - conda deactivate 2>/dev/null || true -} - -remove_conda_env() { - local conda_env_name_to_remove="$1" - if [ -z "$conda_env_name_to_remove" ]; then - echo "Warning: No Conda env name provided to remove_conda_env." - return - fi - echo "Deactivating and removing Conda environment: $conda_env_name_to_remove" - conda deactivate 2>/dev/null || true - conda env remove -n "$conda_env_name_to_remove" -y -} - verify_bundle() { echo 'Run verify bundle...' head_ref=$(git rev-parse HEAD) @@ -124,22 +82,7 @@ verify_bundle() { else include_pre_release="" fi - # determine if conda env should be used for the bundle - active_conda_env_for_bundle="" - required_python_version="${bundle_python_versions[$bundle]}" - use_conda_for_bundle=false - if [[ -n "$required_python_version" && "$required_python_version" != "$DEFAULT_PYTHON_VERSION_FOR_VENV" ]] - then - use_conda_for_bundle=true - fi - if $use_conda_for_bundle - then - init_conda_env "$required_python_version" "$bundle" - active_conda_env_for_bundle="conda_env_${bundle}" - conda activate "$active_conda_env_for_bundle" - else - init_venv - fi + init_venv # Check if the requirements file exists and is not empty if [ -s "$requirements_file" ]; then echo "install required libraries for bundle: $bundle" @@ -158,13 +101,7 @@ verify_bundle() { test_cmd="torchrun $(pwd)/ci/unit_tests/runner.py --b \"$bundle\" --dist True" fi eval $test_cmd - # cleanup - if $use_conda_for_bundle - then - remove_conda_env "$active_conda_env_for_bundle" - else - remove_venv - fi + remove_venv done else echo "this pull request does not change any bundles, skip verify." diff --git a/ci/run_regular_tests_cpu.sh b/ci/run_regular_tests_cpu.sh index 52936c0e..253cc86c 100755 --- a/ci/run_regular_tests_cpu.sh +++ b/ci/run_regular_tests_cpu.sh @@ -30,7 +30,8 @@ elif [[ $# -gt 1 ]]; then exit 1 fi -exclude_test_list=("maisi_ct_generative" "retinalOCT_RPD_segmentation") +# Usually, CPU test is required, but for some bundles that are too large to run in Github Actions, we can exclude them. +exclude_test_list=("maisi_ct_generative") is_excluded() { for item in "${exclude_test_list[@]}"; do # Use exclude_test_list here if [ "$1" == "$item" ]; then From 5faa761c65d8f01f6c5a08d45753f8d4f68d8c35 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Mon, 11 Aug 2025 04:19:18 +0000 Subject: [PATCH 27/28] revert py39 changes Signed-off-by: Yiheng Wang --- ci/get_bundle_requirements.py | 4 -- ci/get_changed_bundle.py | 108 ++++++++-------------------------- 2 files changed, 25 insertions(+), 87 deletions(-) diff --git a/ci/get_bundle_requirements.py b/ci/get_bundle_requirements.py index d70d3bf0..9262b112 100644 --- a/ci/get_bundle_requirements.py +++ b/ci/get_bundle_requirements.py @@ -19,8 +19,6 @@ ALLOW_MONAI_RC = os.environ.get("ALLOW_MONAI_RC", "false").lower() in ("true", "1", "t", "y", "yes") -special_dependencies_list = ["detectron2"] - def increment_version(version): """ @@ -81,8 +79,6 @@ def get_requirements(bundle, models_path, requirements_file): if package_key in metadata.keys(): optional_dict = metadata[package_key] for name, version in optional_dict.items(): - if name in special_dependencies_list: - continue libs.append(f"{name}=={version}") if len(libs) > 0: diff --git a/ci/get_changed_bundle.py b/ci/get_changed_bundle.py index 9262b112..a177ea86 100644 --- a/ci/get_changed_bundle.py +++ b/ci/get_changed_bundle.py @@ -11,102 +11,44 @@ import argparse -import os -import sys -from bundle_custom_data import install_dependency_dict -from utils import get_json_dict +from utils import get_changed_bundle_list -ALLOW_MONAI_RC = os.environ.get("ALLOW_MONAI_RC", "false").lower() in ("true", "1", "t", "y", "yes") - -def increment_version(version): +def get_changed_bundle(changed_dirs): """ - Split the version into components, assume missing parts are '0'. - - Args: - version (str): The version string to increment. - - Examples: - print(increment_version("1.3.2")) # Expected output: 1.3.3 - print(increment_version("1.4")) # Expected output: 1.4.1 - print(increment_version("1")) # Expected output: 1.0.1 + This function is used to get all changed bundles, a string which + contains all bundle names will be printed, and can be used in shell scripts. """ - version = str(version) - parts = version.split(".") - # Extend the list with zeros to handle cases like "1" or "1.4". - while len(parts) < 3: - parts.append("0") - - # Convert all parts to integers. - parts = list(map(int, parts)) - - # Increment the last part. - parts[-1] += 1 + bundle_names = "" + root_path = "models" + bundle_list = get_changed_bundle_list(changed_dirs, root_path=root_path) - # Join the parts back into a version string. - # This trims trailing '.0's, returning the version in its original format - result_version = ".".join(map(str, parts)).rstrip(".0") + for bundle in bundle_list: + bundle_names += f"{bundle} " + print(bundle_names) - return result_version - -def get_requirements(bundle, models_path, requirements_file): +def get_changed_hf_model(changed_dirs): """ - This function is used to produce a requirements txt file, and print a string - which shows the filename. The printed string can be used in shell scripts. + This function is used to get all changed hf models, a string which + contains all hf model names will be printed, and can be used in shell scripts. """ - bundle_path = os.path.join(models_path, bundle) - meta_file_path = os.path.join(bundle_path, "configs/metadata.json") - if os.path.exists(meta_file_path): - metadata = get_json_dict(meta_file_path) - libs = [] - if "monai_version" in metadata.keys(): - monai_version = metadata["monai_version"] - if not ALLOW_MONAI_RC: - lib_monai_req = f"monai=={monai_version}" - else: - lib_monai_req = f"monai>={monai_version}rc1,<{increment_version(monai_version)}" - print(f"ALLOW_MONAI_RC is set to true, the version range is {lib_monai_req}", file=sys.stderr) - libs.append(lib_monai_req) - if "pytorch_version" in metadata.keys(): - pytorch_version = metadata["pytorch_version"] - libs.append(f"torch=={pytorch_version}") - if "numpy_version" in metadata.keys(): - numpy_version = metadata["numpy_version"] - libs.append(f"numpy=={numpy_version}") - for package_key in ["optional_packages_version", "required_packages_version"]: - if package_key in metadata.keys(): - optional_dict = metadata[package_key] - for name, version in optional_dict.items(): - libs.append(f"{name}=={version}") - - if len(libs) > 0: - with open(requirements_file, "w") as f: - for line in libs: - f.write(f"{line}\n") - - -def get_install_script(bundle): - # install extra dependencies if needed - script_path = "" - if bundle in install_dependency_dict.keys(): - script_path = install_dependency_dict[bundle] - print(script_path) + hf_model_names = "" + root_path = "hf_models" + hf_model_list = get_changed_bundle_list(changed_dirs, root_path=root_path) + for hf_model in hf_model_list: + hf_model_names += f"{hf_model} " + print(hf_model_names) if __name__ == "__main__": parser = argparse.ArgumentParser(description="") - parser.add_argument("-b", "--b", type=str, help="bundle name.") - parser.add_argument("-p", "--p", type=str, default="models", help="models path.") - parser.add_argument("--get_script", type=bool, default=False, help="whether to get the install script.") - parser.add_argument("--requirements_file", type=str, help="output filename for requirements.") + parser.add_argument("-f", "--f", type=str, help="changed files.") + parser.add_argument("--hf_model", type=bool, default=False, help="if true, get changed hf models.") args = parser.parse_args() - bundle = args.b - models_path = args.p - get_script = args.get_script - if get_script is True: - get_install_script(bundle) + changed_dirs = args.f.splitlines() + if args.hf_model: + get_changed_hf_model(changed_dirs) else: - requirements_file = args.requirements_file - get_requirements(bundle, models_path, requirements_file) + get_changed_bundle(changed_dirs) From accbffcd52d586cab0f2b26cf68ebd98826c6fc9 Mon Sep 17 00:00:00 2001 From: Yiheng Wang Date: Mon, 11 Aug 2025 05:34:26 +0000 Subject: [PATCH 28/28] add customized detectron2 Signed-off-by: Yiheng Wang --- ci/bundle_custom_data.py | 4 +++- ci/get_bundle_requirements.py | 4 ++++ .../install_retinalOCT_RPD_segmentation_dependency.sh | 1 + 3 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py index 1a951437..4d7d58f8 100644 --- a/ci/bundle_custom_data.py +++ b/ci/bundle_custom_data.py @@ -64,7 +64,9 @@ # This dict is used for our CI tests to install required dependencies that cannot be installed by `pip install` directly. # If a bundle has this kind of dependencies, please add the bundle name (key), and the path of the install script (value) # into the dict. -install_dependency_dict = {} +install_dependency_dict = { + "retinalOCT_RPD_segmentation": "ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh" +} # This list is used for our CI tests to determine whether a bundle supports TensorRT export. Related # test will be employed for bundles in the dict. diff --git a/ci/get_bundle_requirements.py b/ci/get_bundle_requirements.py index 9262b112..d70d3bf0 100644 --- a/ci/get_bundle_requirements.py +++ b/ci/get_bundle_requirements.py @@ -19,6 +19,8 @@ ALLOW_MONAI_RC = os.environ.get("ALLOW_MONAI_RC", "false").lower() in ("true", "1", "t", "y", "yes") +special_dependencies_list = ["detectron2"] + def increment_version(version): """ @@ -79,6 +81,8 @@ def get_requirements(bundle, models_path, requirements_file): if package_key in metadata.keys(): optional_dict = metadata[package_key] for name, version in optional_dict.items(): + if name in special_dependencies_list: + continue libs.append(f"{name}=={version}") if len(libs) > 0: diff --git a/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh new file mode 100644 index 00000000..a1022d26 --- /dev/null +++ b/ci/install_scripts/install_retinalOCT_RPD_segmentation_dependency.sh @@ -0,0 +1 @@ +python -m pip install 'git+https://github.com/facebookresearch/detectron2.git@65184fc057d4fab080a98564f6b60fae0b94edc4'

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