add convert cmd test

Author: daisycx

.github/workflows/ci.yml

@@ -51,3 +51,15 @@ jobs:
       - uses: actions/checkout@v2
       - name: test tflite
         run: ./ci/pytest_tflite.sh
+
+  test-convert-cmd:
+    runs-on: ubuntu-latest
+    container:
+      image: enginesh233/mgeconvert_ci:v1.1
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - uses: actions/checkout@v2
+      - name: test convert cmd
+        run: ./ci/test_convert_cmd.sh

README.md

@@ -75,7 +75,9 @@ python3 -m pip install git+https://github.com/MegEngine/mgeconvert.git@v0.4.2 --
 > :warning: 如果需要转换``TracedModule``模型，请安装0.5.0以上版本
 
 
-- ``--targets`` 的可选值有 ``caffe``、``onnx``、``tflite``，可选值支持组合传入，比如 ``--targets=caffe,tflite`` 。
+- ``--targets`` 的可选值有 ``caffe``、``onnx``、``tflite`` 和 `all`。
+
+`all` 代表安装全部转换器。可选值支持组合传入，比如 ``--targets=caffe,tflite`` 。
 
 - ``tflite`` 转换器的schema默认使用r2.3版本，支持使用参数 ``tfversion`` 选择tflite schema的版本, 例如：
 
@@ -190,10 +192,10 @@ convert tracedmodule_to_tflite -i tracedmodule.tm -o out.tflite --quantize_file_
 convert tracedmodule_to_tflite -i tracedmodule.tm -o out.tflite --quantize_file_path quant_params.json --param_fake_quant
 ```
 
-- 如果QAT模型中没有QuantStub对输入数据进行量化处理，可以在转换时指定输入数据的量化类型、scale和zero_point量化参数 ：
+- 如果QAT模型中没有QuantStub对输入数据进行量化处理，可以在转换时指定输入数据的量化类型、scale和zero_point量化参数,如果有多个scale、zero point用逗号隔开：
 
 ```bash
-convert tracedmodule_to_tflite -i tracedmodule.tm -o out.tflite --input_data_type quint8 --input_scales 0.125 --input_zero_points 128 --require_quantize
+convert tracedmodule_to_tflite -i tracedmodule.tm -o out.tflite --input_data_type quint8 --input_scales 0.125,0.125 --input_zero_points 128,128 --require_quantize
 ```
 
 #### onnx模型转换
@@ -212,7 +214,7 @@ convert tracedmodule_to_onnx -i tracedmodule.tm -o out.onnx
 
 ### 2. python接口使用
 
-可参考wiki中的例子。
+可参考[wiki](https://github.com/MegEngine/mgeconvert/wiki/Mgeconvert-Python-Api-Doc)中的例子。
 
 ## 常见问题说明
 

bin/convert

@@ -196,11 +196,11 @@ def init(subparsers):
                 )
 
                 p.add_argument(
-                    "--input_scales", default=None, nargs="+", type=float,
+                    "--input_scales", default=None, nargs="+", type=str,
                 )
 
                 p.add_argument(
-                    "--input_zero_points", default=None, nargs="+", type=int,
+                    "--input_zero_points", default=None, nargs="+", type=str,
                 )
 
                 p.add_argument(
@@ -313,11 +313,11 @@ def init(subparsers):
                 )
 
                 p.add_argument(
-                    "--input_scales", default=None, nargs="+", type=float,
+                    "--input_scales", default=None, nargs="+", type=str,
                 )
 
                 p.add_argument(
-                    "--input_zero_points", default=None, nargs="+", type=int,
+                    "--input_zero_points", default=None, nargs="+", type=str,
                 )
 
                 p.add_argument(
@@ -347,7 +347,7 @@ def init(subparsers):
         def to_tflite(args):
             if "tracedmodule" in target:
                 mgeconvert.tracedmodule_to_tflite(
-                    mge_fpath=args.input,
+                    args.input,
                     output=args.output,
                     input_data_type=args.input_data_type,
                     input_scales=args.input_scales,
@@ -361,7 +361,7 @@ def init(subparsers):
                 )
             else:
                 mgeconvert.mge_to_tflite(
-                    mge_fpath=args.input,
+                    args.input,
                     output=args.output,
                     graph_name=args.graph_name,
                     mtk=args.mtk,
@@ -395,13 +395,13 @@ def init(subparsers):
                 p.add_argument(
                     "--input_scales",
                     default=None,
-                    type=Union[float, List[float]],
+                    type=str,
                     help="the scale of input data used for quantize model input",
                 )
                 p.add_argument(
                     "--input_zero_points",
                     default=None,
-                    type=Union[int, List[int]],
+                    type=str,
                     help="the zero point of input data used for quantize model input",
                 )
                 p.add_argument(

ci/test_convert_cmd.sh

@@ -0,0 +1,40 @@
+#!/bin/bash -e
+
+set -e
+
+python3 -m pip install -q -r ci/requires-test.txt
+
+apt install -y protobuf-compiler
+
+pip3 install . --user --install-option="--targets=all"
+
+export LD_LIBRARY_PATH=$HOME/.local/lib:$LD_LIBRARY_PATH:
+
+sudo python3 -m pip uninstall flatbuffers -y
+sudo python3 -m pip install tensorflow==2.5.0
+sudo -H python3 -m pip install -q megengine==1.6.0 -f https://megengine.org.cn/whl/mge.html
+pip3 install scikit-image==0.17.2
+
+
+python3 test/gen_models.py
+
+python3 bin/convert tracedmodule_to_tflite -i float_model.tm -o out.tflite
+python3 bin/convert mge_to_tflite -i float_model.mge -o out.tflite
+
+python3 bin/convert tracedmodule_to_tflite -i qat_model.tm -o out.tflite --require_quantize
+python3 bin/convert tracedmodule_to_tflite -i qat_model.tm -o out.tflite --quantize_file_path quant_params.json
+python3 bin/convert tracedmodule_to_tflite -i qat_model.tm -o out.tflite --quantize_file_path quant_params.json --param_fake_quant
+python3 bin/convert tracedmodule_to_tflite -i qat_model.tm -o out.tflite --input_data_type quint8 --input_scales 0.125 --input_zero_points 128 --require_quantize
+
+
+export USE_CAFFE_PROTO=1
+python3 bin/convert tracedmodule_to_caffe -i float_model.tm -c out.prototxt -b out.caffemodel
+python3 bin/convert mge_to_caffe -i float_model.mge -c out.prototxt -b out.caffemodel
+
+python3 bin/convert tracedmodule_to_caffe -i qat_model.tm -c out.prototxt -b out.caffemodel --quantize_file_path quant_params.json
+python3 bin/convert tracedmodule_to_caffe -i qat_model.tm -c out.prototxt -b out.caffemodel --quantize_file_path quant_params.json --param_fake_quant
+python3 bin/convert tracedmodule_to_caffe -i qat_model.tm -c out.prototxt -b out.caffemodel --quantize_file_path quant_params.json --input_data_type quint8 --input_scales 0.125 --input_zero_points 128
+
+
+python3 bin/convert tracedmodule_to_onnx -i float_model.tm -o out.onnx
+python3 bin/convert mge_to_onnx -i float_model.mge -o out.onnx

mgeconvert/frontend/tm_to_ir/tm_utils.py

@@ -101,12 +101,32 @@ def _update_inputs_qparams(
             )
         traced_module.graph.inputs[i + 1].qparams.dtype_meta = q_dtype_meta
     if input_scales is not None:
+        if isinstance(input_scales, str):
+            str_scales = input_scales.split(",")
+            input_scales = []
+            try:
+                for s in str_scales:
+                    input_scales.append(float(s))
+            except:
+                raise ValueError(
+                    "input scales({}) do not in correct format.".format(str_scales)
+                )
         if not isinstance(input_scales, Sequence):
             input_scales = (input_scales,)
         for i in range(len(traced_module.graph.inputs[1:])):
             scale = input_scales[i] if i < len(input_scales) else input_scales[-1]
             traced_module.graph.inputs[i + 1].qparams.scale = Tensor(float(scale))
     if input_zero_points is not None:
+        if isinstance(input_zero_points, str):
+            str_zp = input_zero_points.split(",")
+            input_zero_points = []
+            try:
+                for zp in str_zp:
+                    input_zero_points.append(float(zp))
+            except:
+                raise ValueError(
+                    "input zero points({}) do not in correct format.".format(str_zp)
+                )
         if not isinstance(input_zero_points, Sequence):
             input_zero_points = (input_zero_points,)
         for i in range(len(traced_module.graph.inputs[1:])):

test/gen_models.py

@@ -0,0 +1,24 @@
+from test.utils import ConvOpr, dump_mge_model
+
+import megengine as mge
+import numpy as np
+from megengine.core.tensor import dtype
+from megengine.quantization.quantize import quantize_qat
+from megengine.traced_module import trace_module
+
+if __name__ == "__main__":
+    net = ConvOpr("normal")
+    traced_module = trace_module(net, mge.tensor(net.data))
+    mge.save(traced_module, "float_model.tm")
+    dump_mge_model(net, net.data, "float_model")
+
+    qat_net = quantize_qat(net)
+    inp_dtype = dtype.qint8(16.0 / 128)
+    data = mge.tensor(np.random.random((1, 3, 224, 224))) * 16
+    data = data.astype(inp_dtype)
+    inp = mge.tensor(dtype.convert_from_qint8(data.numpy()))
+    inp.qparams.scale = mge.tensor(dtype.get_scale(inp_dtype))
+    inp.qparams.dtype_meta = dtype._builtin_quant_dtypes["qint8"]
+
+    qat_module = trace_module(qat_net, inp)
+    mge.save(qat_module, "qat_model.tm")