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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: Export ONNX and Verify
## Step 4-1: Export ONNX ## 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: `tools/pytorch2onnx_kneron.py` is a script provided by kneron-mmsegmentation to help users to convert our trained pytorch model to ONNX:
```shell ```shell
python tools/pytorch2onnx_kneron.py \ 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 \ --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. * `configs/stdc/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. * `work_dirs/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. * `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 ## 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: `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 ```shell
python tools/deploy_test_kneron.py \ 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 \ work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.onnx \
--eval mIoU --eval mIoU
``` ```
* `kn_stdc1_in1k-pre_512x1024_80k_cityscapes/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py` can be your training config. * `configs/stdc/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. * `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): 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. Note that the ONNX results may differ from the PyTorch results due to some implementation differences between PyTorch and ONNXRuntime.