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Merge branch 'doc/update_step_by_step' into 'Kn_dev_v0.21.1'

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doc: update stdc_step_by_step.md

See merge request eric_wu/mmsegmentation!10
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EricWu 2022-04-15 17:22:15 +08:00
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@ -205,6 +205,16 @@ Summary:
+------+-------+-------+
```
**NOTE: The training process might take some time, depending on your computation resource. If you just want to take a quick look at the deployment flow, you can download our pretrained model so you can skip Step 1, 2, and 3:**
```
# If you don't want to train your own model:
mkdir -p work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes
pushd work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes
wget https://github.com/kneron/Model_Zoo/raw/main/mmsegmentation/stdc_1/latest.zip
unzip latest.zip
popd
```
# Step 4: Export ONNX and Verify
## Step 4-1: Export ONNX
@ -212,29 +222,61 @@ Summary:
`tools/pytorch2onnx_kneron.py` is a script provided by kneron-mmsegmentation to help users to convert our trained pytorch model to ONNX:
```shell
python tools/pytorch2onnx_kneron.py \
work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
--checkpoint work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth \
--output-file work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx
--output-file work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx \
--verify
```
* `kn_stdc1_in1k-pre_512x1024_80k_cityscapes/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py` can be your training config.
* `kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth` can be your model checkpoint.
* `kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx` can be any other path. Here for convenience, the ONNX file is placed in the same folder of our pytorch checkpoint.
* `configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py` can be your training config.
* `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth` can be your model checkpoint.
* `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx` can be any other path. Here for convenience, the ONNX file is placed in the same folder of our pytorch checkpoint.
## Step 4-2: Verify ONNX
`tools/deploy_test_kneron.py` is a script provided by kneron-mmsegmentation to help users to verify if our exported ONNX generates similar outputs with what our PyTorch model does:
```shell
python tools/deploy_test_kneron.py \
work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py \
work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx \
--eval mIoU
```
* `kn_stdc1_in1k-pre_512x1024_80k_cityscapes/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py` can be your training config.
* `kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth` can be your exported ONNX file.
* `configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py` can be your training config.
* `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth` can be your exported ONNX file.
The expected result of the command above should be something similar to the following text (the numbers may slightly differ):
```
...
+---------------+-------+-------+
| Class | IoU | Acc |
+---------------+-------+-------+
| road | 97.52 | 98.62 |
| sidewalk | 80.59 | 88.69 |
| building | 89.59 | 95.38 |
| wall | 58.02 | 66.85 |
| fence | 55.37 | 69.76 |
| pole | 44.4 | 52.28 |
| traffic light | 50.23 | 60.07 |
| traffic sign | 62.58 | 70.25 |
| vegetation | 89.0 | 95.27 |
| terrain | 60.47 | 72.27 |
| sky | 90.56 | 97.07 |
| person | 70.7 | 84.88 |
| rider | 48.66 | 61.37 |
| car | 91.58 | 95.98 |
| truck | 73.92 | 82.66 |
| bus | 79.92 | 85.95 |
| train | 66.26 | 75.92 |
| motorcycle | 48.88 | 57.91 |
| bicycle | 66.9 | 82.0 |
+---------------+-------+-------+
Summary:
+------+-------+-------+
| aAcc | mIoU | mAcc |
+------+-------+-------+
| 94.4 | 69.75 | 78.59 |
+------+-------+-------+
```
Note that the ONNX results may differ from the PyTorch results due to some implementation differences between PyTorch and ONNXRuntime.