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Apart from training/testing scripts, We provide lots of useful tools under the
`tools/` directory.
### Get the FLOPs and params (experimental)
We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.
```shell
python tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
```
You will get the result like this.
```none
==============================
Input shape: (3, 2048, 1024)
Flops: 1429.68 GMac
Params: 48.98 M
==============================
```
**Note**: This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers.
(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 1280, 800).
(2) Some operators are not counted into FLOPs like GN and custom operators.
### Publish a model
Before you upload a model to AWS, you may want to
(1) convert model weights to CPU tensors, (2) delete the optimizer states and
(3) compute the hash of the checkpoint file and append the hash id to the filename.
```shell
python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
```
E.g.,
```shell
python tools/publish_model.py work_dirs/pspnet/latest.pth psp_r50_hszhao_200ep.pth
```
The final output filename will be `psp_r50_512x1024_40ki_cityscapes-{hash id}.pth`.
### Convert to ONNX (experimental)
We provide a script to convert model to [ONNX](https://github.com/onnx/onnx) format. The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron). Besides, we also support comparing the output results between Pytorch and ONNX model.
```bash
python tools/pytorch2onnx.py \
${CONFIG_FILE} \
--checkpoint ${CHECKPOINT_FILE} \
--output-file ${ONNX_FILE} \
--input-img ${INPUT_IMG} \
--shape ${INPUT_SHAPE} \
--show \
--verify \
--dynamic-export \
--cfg-options \
model.test_cfg.mode="whole"
Description of arguments:
- `config` : The path of a model config file.
- `--checkpoint` : The path of a model checkpoint file.
- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`.
- `--input-img` : The path of an input image for conversion and visualize.
- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to img_scale of testpipeline.
- `--rescale-shape`: rescale shape of output, set this value to avoid OOM, only work on `slide` mode.
- `--show`: Determines whether to print the architecture of the exported model. If not specified, it will be set to `False`.
- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`.
- `--dynamic-export`: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to `False`.
- `--cfg-options`:Update config options.
**Note**: This tool is still experimental. Some customized operators are not supported for now.
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### Evaluate ONNX model with ONNXRuntime
We provide `tools/ort_test.py` to evaluate ONNX model with ONNXRuntime backend.
#### Prerequisite
- Install onnx and onnxruntime-gpu
```shell
pip install onnx onnxruntime-gpu
```
#### Usage
```python
python tools/ort_test.py \
${CONFIG_FILE} \
${ONNX_FILE} \
--out ${OUTPUT_FILE} \
--eval ${EVALUATION_METRICS} \
--show \
--show-dir ${SHOW_DIRECTORY} \
--options ${CFG_OPTIONS} \
--eval-options ${EVALUATION_OPTIONS} \
--opacity ${OPACITY} \
```
Description of all arguments
- `config`: The path of a model config file.
- `model`: The path of a ONNX model file.
- `--out`: The path of output result file in pickle format.
- `--format-only` : Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. If not specified, it will be set to `False`. Note that this argument is **mutually exclusive** with `--eval`.
- `--eval`: Evaluation metrics, which depends on the dataset, e.g., "mIoU" for generic datasets, and "cityscapes" for Cityscapes. Note that this argument is **mutually exclusive** with `--format-only`.
- `--show`: Show results flag.
- `--show-dir`: Directory where painted images will be saved
- `--options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file.
- `--eval-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function
- `--opacity`: Opacity of painted segmentation map. In (0, 1] range.
#### Results and Models
| Model | Config | Dataset | Metric | PyTorch | ONNXRuntime |
| :--------: | :--------------------------------------------: | :--------: | :----: | :-----: | :---------: |
| FCN | fcn_r50-d8_512x1024_40k_cityscapes.py | cityscapes | mIoU | 72.2 | 72.2 |
| PSPNet | pspnet_r50-d8_769x769_40k_cityscapes.py | cityscapes | mIoU | 78.2 | 78.1 |
| deeplabv3 | deeplabv3_r50-d8_769x769_40k_cityscapes.py | cityscapes | mIoU | 78.5 | 78.3 |
| deeplabv3+ | deeplabv3plus_r50-d8_769x769_40k_cityscapes.py | cityscapes | mIoU | 78.9 | 78.7 |
### Convert to TorchScript (experimental)
We also provide a script to convert model to [TorchScript](https://pytorch.org/docs/stable/jit.html) format. You can use the pytorch C++ API [LibTorch](https://pytorch.org/docs/stable/cpp_index.html) inference the trained model. The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron). Besides, we also support comparing the output results between Pytorch and TorchScript model.
```shell
python tools/pytorch2torchscript.py \
${CONFIG_FILE} \
--checkpoint ${CHECKPOINT_FILE} \
--output-file ${ONNX_FILE}
--shape ${INPUT_SHAPE}
--verify \
--show
Description of arguments:
- `config` : The path of a pytorch model config file.
- `--checkpoint` : The path of a pytorch model checkpoint file.
- `--output-file`: The path of output TorchScript model. If not specified, it will be set to `tmp.pt`.
- `--input-img` : The path of an input image for conversion and visualize.
- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `512 512`.
- `--show`: Determines whether to print the traced graph of the exported model. If not specified, it will be set to `False`.
- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`.
**Note**: It's only support PyTorch>=1.8.0 for now.
**Note**: This tool is still experimental. Some customized operators are not supported for now.
Examples:
- Convert the cityscapes PSPNet pytorch model.
```shell
python tools/pytorch2torchscript.py configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py \
--checkpoint checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth \
--output-file checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pt \
--shape 512 1024
```
## Miscellaneous
### Print the entire config
`tools/print_config.py` prints the whole config verbatim, expanding all its
imports.
```shell
python tools/print_config.py \
${CONFIG} \
--graph \
--options ${OPTIONS [OPTIONS...]} \
Description of arguments:
- `config` : The path of a pytorch model config file.
- `--graph` : Determines whether to print the models graph.
- `--options`: Custom options to replace the config file.
`tools/analyze_logs.py` plots loss/mIoU curves given a training log file. `pip install seaborn` first to install the dependency.
```shell
python tools/analyze_logs.py xxx.log.json [--keys ${KEYS}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]
```
Examples:
- Plot the mIoU, mAcc, aAcc metrics.
```shell
python tools/analyze_logs.py log.json --keys mIoU mAcc aAcc --legend mIoU mAcc aAcc
```
- Plot loss metric.
```shell
python tools/analyze_logs.py log.json --keys loss --legend loss
```