PCCSNet_XOE

PCCSNet with XOE

This folder contains the codes for PCCSNet with XOE (both AOE and MOE).

Basic Setups

All of our codes are run on Python 3.7 and PyTorch 1.4.0 with CUDA 10.1 support.

Results & Pre-trained Models

Pre-trained models of PCCSNet with AOE or MOE are available here, which will give the following PCCSNet+AOE Arbitrary and PCCSNet+MOE Momentary results (min ADE / min FDE, k = 20) after evaluation.

Note that the results are ADE-Prioritized results.

Dataset	PCCSNet Traditional	PCCSNet Momentary	PCCSNet Arbitrary	PCCSNet+MOE Momentary	PCCSNet+AOE Arbitrary
ETH	0.28 / 0.54	0.34 / 0.65	0.33 / 0.60	0.31 / 0.57	0.31 / 0.56
HOTEL	0.11 / 0.19	0.14 / 0.25	0.20 / 0.36	0.13 / 0.21	0.22 / 0.41
UNIV	0.29 / 0.60	0.31 / 0.63	0.25 / 0.54	0.25 / 0.53	0.24 / 0.52
ZARA1	0.21 / 0.44	0.23 / 0.46	0.21 / 0.44	0.20 / 0.41	0.20 / 0.42
ZARA2	0.15 / 0.34	0.16 / 0.37	0.16 / 0.34	0.14 / 0.31	0.16 / 0.33
ETH/UCY Avg	0.21 / 0.42	0.24 / 0.47	0.23 / 0.46	0.20 / 0.41	0.23 / 0.45
SDD	8.62 / 16.16	9.19 / 17.71	8.80 / 16.91	8.40 / 16.08	7.91 / 15.14

Evaluation

To evaluate the pre-trained models, first download the pre-trained models and unzip the files into the saved_models folder. Then use the following commands:

For AOE:

For ETH/UCY:

python PCCSNet_MOE/main.py -d <DATASET_IDX> -o 5 -k 20 --flip_aug -aoe -ntc --add_behavior --add_intention --add_empirical

For SDD:

python PCCSNet_MOE/main.py -df SDD -d <DATASET_IDX> -o 5 -k 20 --flip_aug -aoe -ntc --add_behavior --add_intention --add_empirical --grid_size 40

For MOE:

For ETH/UCY:

python PCCSNet_MOE/main.py -d <DATASET_IDX> -o 5 -k 20 --flip_aug -moe -ntc

For SDD:

python PCCSNet_MOE/main.py -df SDD -d <DATASET_IDX> -o 5 -k 20 --flip_aug -moe -ntc --grid_size 40

where DATASET_IDX is the target dataset index (See notations for details). Note that it is possible to use multiple indexes for consecutive evaluations, for example:

python main.py -d 0 2 4 -o 5 -k 20 --flip_aug --rotate -ntc

Training from Scratch

For AOE:

To train models from scratch, use the command

python PCCSNet_MOE/main.py -d <DATASET_IDX> -train -k 20 --flip_aug -aoe -pm <PAST FEATURE WEIGHT> --add_behavior --add_intention --add_empirical

this is equivalent to

python PCCSNet_MOE/main.py -d <DATASET_IDX> -o 0 1 2 3 4 5 -k 20 --flip_aug -aoe -pm <PAST FEATURE WEIGHT> --add_behavior --add_intention --add_empirical

You can add --add_behavior, --add_intention, --add_empirical to the command to enable these modules in AOE.

For MOE:

To train models from scratch, use the command

python PCCSNet_MOE/main.py -d <DATASET_IDX> -train -k 20 --flip_aug -moe -pm <PAST FEATURE WEIGHT>

this is equivalent to

python PCCSNet_MOE/main.py -d <DATASET_IDX> -o 0 1 2 3 4 5 -k 20 --flip_aug -moe -pm <PAST FEATURE WEIGHT>

You can add -df SDD to the command if you wish to train on SDD data. Note that since the XOE output tends to have larger values compared to LSTM outputs, we added the PAST FEATURE WEIGHT pm, which is a constant that multiplies to the XOE output during clustering in order to deal with the unbalanced weight problem. For ETH/UCY data, pm is around 0.02; and for SDD data, pm is around 0.0025.

By default, the models will be automatically saved to saved_models folder. It is also possible to change it by add -sd SAVE_DIR to the command, in which case it will be automatically created if non-existent.

Notations

For simplicity, we adopted some notations for the operations mentioned above and in the paper.

For ETH/UCY dataset:

Dataset idx	Dataset Name
0	eth
1	hotel
2	univ
3	zara1
4	zara2

For SDD dataset:

Dataset idx	Dataset Name
0	SDD

For the operations:

Operation idx	Operation Name
0	Train Past Encoder
1	Train Future Encoder
2	Train Decoder
3	Train Classifier
4	Train Synthesizer
5	Evaluation
6	Test (Generate Prediction)
> 6	No Operations (Return)