Support TSM-R50 Python API (wang-xinyu#488)

* add tensorrt temporal shift module and related pytorch implementations

* add .gitignore and getn weights script.

* rename get_wts.py script

* Add tsm-r50 demo.

* update readme

* remove useless codes

* update readme

* update readme

* remote video and .gitignore, update tutorial

* update readme and tutorial

* fix a few bugs and test on tensorrt 5.1

* update readme

Author: irvingzhang0512

lenet/lenet.py

@@ -27,7 +27,7 @@ def load_weights(file):
 
     weight_map = {}
     with open(file, "r") as f:
-        lines = f.readlines()
+        lines = [line.strip() for line in f]
     count = int(lines[0])
     assert count == len(lines) - 1
     for i in range(1, count + 1):

resnet/resnet50.py

@@ -29,7 +29,7 @@ def load_weights(file):
 
     weight_map = {}
     with open(file, "r") as f:
-        lines = f.readlines()
+        lines = [line.strip() for line in f]
     count = int(lines[0])
     assert count == len(lines) - 1
     for i in range(1, count + 1):
@@ -138,7 +138,7 @@ def bottleneck(network, weight_map, input, in_channels, out_channels, stride,
     return relu3
 
 
-def createLenetEngine(maxBatchSize, builder, config, dt):
+def create_engine(maxBatchSize, builder, config, dt):
     weight_map = load_weights(WEIGHT_PATH)
     network = builder.create_network()
 
@@ -233,7 +233,7 @@ def createLenetEngine(maxBatchSize, builder, config, dt):
 def APIToModel(maxBatchSize):
     builder = trt.Builder(TRT_LOGGER)
     config = builder.create_builder_config()
-    engine = createLenetEngine(maxBatchSize, builder, config, trt.float32)
+    engine = create_engine(maxBatchSize, builder, config, trt.float32)
     assert engine
     with open(ENGINE_PATH, "wb") as f:
         f.write(engine.serialize())

tsm/README.md

@@ -0,0 +1,66 @@
+# Temporal Shift Module
+
+TSM-R50 from "TSM: Temporal Shift Module for Efficient Video Understanding" <https://arxiv.org/abs/1811.08383>
+
+TSM is a widely used Action Recognition model. This TensorRT implementation is tested with TensorRT 5.1 and TensorRT 7.2.
+
+For the PyTorch implementation, you can refer to [open-mmlab/mmaction2](https://github.com/open-mmlab/mmaction2) or [mit-han-lab/temporal-shift-module](https://github.com/mit-han-lab/temporal-shift-module).
+
+More details about the shift module(which is the core of TSM) could to [test_shift.py](./test_shift.py).
+
+## Tutorial
+
++ An example could refer to [demo.sh](./demo.sh)
+  + Requirements: Successfully installed `torch>=1.3.0, torchvision`
+
++ Step 1: Train/Download TSM-R50 checkpoints from [offical Github repo](https://github.com/mit-han-lab/temporal-shift-module) or [MMAction2](https://github.com/open-mmlab/mmaction2)
+  + Supported settings: `num_segments`, `shift_div`, `num_classes`.
+  + Fixed settings: `backbone`(ResNet50), `shift_place`(blockres), `temporal_pool`(False).
+
++ Step 2: Convert PyTorch checkpoints to TensorRT weights.
+
+```shell
+python gen_wts.py /path/to/pytorch.pth --out-filename /path/to/tensorrt.wts
+```
+
++ Step 3: Modify configs in `tsm_r50.py`.
+
+```python
+BATCH_SIZE = 1
+NUM_SEGMENTS = 8
+INPUT_H = 224
+INPUT_W = 224
+OUTPUT_SIZE = 400
+SHIFT_DIV = 8
+```
+
++ Step 4: Inference with `tsm_r50.py`.
+
+```shell
+usage: tsm_r50.py [-h] [--tensorrt-weights TENSORRT_WEIGHTS] [--input-video INPUT_VIDEO] [--save-engine-path SAVE_ENGINE_PATH] [--load-engine-path LOAD_ENGINE_PATH] [--test-mmaction2] [--mmaction2-config MMACTION2_CONFIG] [--mmaction2-checkpoint MMACTION2_CHECKPOINT]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --tensorrt-weights TENSORRT_WEIGHTS
+                        Path to TensorRT weights, which is generated by gen_weights.py
+  --input-video INPUT_VIDEO
+                        Path to local video file
+  --save-engine-path SAVE_ENGINE_PATH
+                        Save engine to local file
+  --load-engine-path LOAD_ENGINE_PATH
+                        Saved engine file path
+  --test-mmaction2      Compare TensorRT results with MMAction2 Results
+  --mmaction2-config MMACTION2_CONFIG
+                        Path to MMAction2 config file
+  --mmaction2-checkpoint MMACTION2_CHECKPOINT
+                        Path to MMAction2 checkpoint url or file path
+```
+
+## TODO
+
++ [x] Python Shift module.
++ [x] Generate wts of official tsm and mmaction2 tsm.
++ [x] Python API Definition
++ [x] Test with mmaction2 demo
++ [x] Tutorial
++ [ ] C++ API Definition

tsm/demo.sh

@@ -0,0 +1,43 @@
+# Step 1: Get checkpoints from mmaction2
+# https://github.com/open-mmlab/mmaction2/tree/master/configs/recognition/tsm
+wget https://download.openmmlab.com/mmaction/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb/tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth
+
+# Step 2: Convert pytorch checkpoints to TensorRT weights
+python gen_wts.py tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth --out-filename ./tsm_r50_kinetics400_mmaction2.wts
+
+# Step 3: Skip this step since we use default settings.
+
+# Step 4: Inference
+# 1) Save local engine file to `./tsm_r50_kinetics400_mmaction2.trt`.
+python tsm_r50.py \
+    --tensorrt-weights ./tsm_r50_kinetics400_mmaction2.wts \
+    --save-engine-path ./tsm_r50_kinetics400_mmaction2.trt
+
+# 2) Predict the recognition result using a single video `demo.mp4`.
+#    Should print `Result class id 6`, aka `arm wrestling`
+# Download demo video
+wget https://raw.githubusercontent.com/open-mmlab/mmaction2/master/demo/demo.mp4
+# # use *.wts as input
+# python tsm_r50.py --tensorrt-weights ./tsm_r50_kinetics400_mmaction2.wts \
+#     --input-video ./demo.mp4
+# use engine file as input
+python tsm_r50.py --load-engine-path ./tsm_r50_kinetics400_mmaction2.trt \
+    --input-video ./demo.mp4
+
+# 3) Optional: Compare inference result with MMAction2 TSM-R50 model
+#    Have to install MMAction2 First, please refer to https://github.com/open-mmlab/mmaction2/blob/master/docs/install.md
+# pip3 install pytest-runner
+# pip3 install mmcv
+# pip3 install mmaction2
+# # use *.wts as input
+# python tsm_r50.py \
+#     --tensorrt-weights ./tsm_r50_kinetics400_mmaction2.wts \
+#     --test-mmaction2 \
+#     --mmaction2-config mmaction2_tsm_r50_config.py \
+#     --mmaction2-checkpoint tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth
+# # use TensorRT engine as input
+# python tsm_r50.py \
+#     --load-engine-path ./tsm_r50_kinetics400_mmaction2.trt \
+#     --test-mmaction2 \
+#     --mmaction2-config mmaction2_tsm_r50_config.py \
+#     --mmaction2-checkpoint tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth

tsm/gen_wts.py

@@ -0,0 +1,46 @@
+import argparse
+import struct
+
+import torch
+import numpy as np
+
+
+def write_one_weight(writer, name, weight):
+    assert isinstance(weight, np.ndarray)
+    values = weight.reshape(-1)
+    writer.write('{} {}'.format(name, len(values)))
+    for value in values:
+        writer.write(' ')
+        # float to bytes to hex_string
+        writer.write(struct.pack('>f', float(value)).hex())
+    writer.write('\n')
+
+
+def convert_name(name):
+    return name.replace("module.", "").replace("base_model.", "").\
+        replace("net.", "").replace("new_fc", "fc").replace("backbone.", "").\
+        replace("cls_head.fc_cls", "fc").replace(".conv.", ".").\
+        replace("conv1.bn", "bn1").replace("conv2.bn", "bn2").\
+        replace("conv3.bn", "bn3").replace("downsample.bn", "downsample.1").\
+        replace("downsample.weight", "downsample.0.weight")
+
+
+def main(args):
+    ckpt = torch.load(args.checkpoint)['state_dict']
+    ckpt = {k: v for k, v in ckpt.items() if 'num_batches_tracked' not in k}
+    with open(args.out_filename, "w") as f:
+        f.write(f"{len(ckpt)}\n")
+        for k, v in ckpt.items():
+            key = convert_name(k)
+            write_one_weight(f, key, v.cpu().numpy())
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint", type=str, help="Path to checkpoint file")
+    parser.add_argument("--out-filename",
+                        type=str,
+                        default="tsm_r50.wts",
+                        help="Path to converted wegiths file")
+    args = parser.parse_args()
+    main(args)

tsm/mmaction2_tsm_r50_config.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='Recognizer2D',
+    backbone=dict(
+        type='ResNetTSM',
+        pretrained='torchvision://resnet50',
+        depth=50,
+        norm_eval=False,
+        shift_div=8),
+    cls_head=dict(
+        type='TSMHead',
+        num_classes=400,
+        in_channels=2048,
+        spatial_type='avg',
+        consensus=dict(type='AvgConsensus', dim=1),
+        dropout_ratio=0.5,
+        init_std=0.001,
+        is_shift=True),
+    # model training and testing settings
+    train_cfg=None,
+    test_cfg=dict(average_clips='prob'))