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[WIP] support get flops and parameters in dev-1.x (#1414)
* [Feature] support get_flops * [Fix] add the divisor * [Doc] add the get_flops doc * [Doc] update the get_flops doc * [Doc] update get FLOPs doc * [Fix] delete unnecessary args * [Fix] delete unnecessary code in get_flops * [Doc] update get flops doc * [Fix] remove unnecessary code * [Doc] add space between Chinese and English * [Doc] add English doc of get flops * Update docs/zh_cn/user_guides/useful_tools.md Co-authored-by: Tong Gao <[email protected]> * Update docs/zh_cn/user_guides/useful_tools.md Co-authored-by: Tong Gao <[email protected]> * Update docs/en/user_guides/useful_tools.md Co-authored-by: Tong Gao <[email protected]> * Update docs/en/user_guides/useful_tools.md Co-authored-by: Tong Gao <[email protected]> * Update docs/en/user_guides/useful_tools.md Co-authored-by: Tong Gao <[email protected]> * Update docs/en/user_guides/useful_tools.md Co-authored-by: Tong Gao <[email protected]> * [Docs] fix the lint * fix * fix docs Co-authored-by: Tong Gao <[email protected]>
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‎docs/en/user_guides/useful_tools.md

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In addition, based on this tool, users can also convert predictions obtained from other libraries into MMOCR-supported formats, then use MMOCR's built-in metrics to evaluate them.
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| ARGS | Type | Description |
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| ------------- | ----- | --------------------------------- |
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| config | str | (required) Path to the config. |
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| pkl_results | str | (required) The saved predictions. |
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| --cfg-options | float | Override configs. [Example](<>) |
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| ARGS | Type | Description |
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| ------------- | ----- | ------------------------------------------------------------------ |
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| config | str | (required) Path to the config. |
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| pkl_results | str | (required) The saved predictions. |
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| --cfg-options | float | Override configs. [Example](./config.md#command-line-modification) |
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### Calculate FLOPs and the Number of Parameters
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We provide a method to calculate the FLOPs and the number of parameters, first we install the dependencies using the following command.
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```shell
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pip install fvcore
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```
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The usage of the script to calculate FLOPs and the number of parameters is as follows.
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```shell
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python tools/analysis_tools/get_flops.py ${config} --shape ${IMAGE_SHAPE}
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```
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| ARGS | Type | Description |
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| ------- | ---- | ----------------------------------------------------------------------------------------- |
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| config | str | (required) Path to the config. |
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| --shape | int | Image size to use when calculating FLOPs, such as `--shape 320 320`. Default is `640 640` |
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For example, you can run the following command to get FLOPs and the number of parameters of `dbnet_resnet18_fpnc_100k_synthtext.py`:
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```shell
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python tools/analysis_tools/get_flops.py configs/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext.py --shape 1024 1024
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```
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The output is as follows:
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```shell
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input shape is (1, 3, 1024, 1024)
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| module | #parameters or shape | #flops |
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| :------------------------ | :------------------- | :------ |
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| model | 12.341M | 63.955G |
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| backbone | 11.177M | 38.159G |
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| backbone.conv1 | 9.408K | 2.466G |
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| backbone.conv1.weight | (64, 3, 7, 7) | |
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| backbone.bn1 | 0.128K | 83.886M |
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| backbone.bn1.weight | (64,) | |
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| backbone.bn1.bias | (64,) | |
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| backbone.layer1 | 0.148M | 9.748G |
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| backbone.layer1.0 | 73.984K | 4.874G |
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| backbone.layer1.1 | 73.984K | 4.874G |
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| backbone.layer2 | 0.526M | 8.642G |
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| backbone.layer2.0 | 0.23M | 3.79G |
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| backbone.layer2.1 | 0.295M | 4.853G |
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| backbone.layer3 | 2.1M | 8.616G |
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| backbone.layer3.0 | 0.919M | 3.774G |
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| backbone.layer3.1 | 1.181M | 4.842G |
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| backbone.layer4 | 8.394M | 8.603G |
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| backbone.layer4.0 | 3.673M | 3.766G |
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| backbone.layer4.1 | 4.721M | 4.837G |
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| neck | 0.836M | 14.887G |
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| neck.lateral_convs | 0.246M | 2.013G |
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| neck.lateral_convs.0.conv | 16.384K | 1.074G |
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| neck.lateral_convs.1.conv | 32.768K | 0.537G |
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| neck.lateral_convs.2.conv | 65.536K | 0.268G |
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| neck.lateral_convs.3.conv | 0.131M | 0.134G |
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| neck.smooth_convs | 0.59M | 12.835G |
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| neck.smooth_convs.0.conv | 0.147M | 9.664G |
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| neck.smooth_convs.1.conv | 0.147M | 2.416G |
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| neck.smooth_convs.2.conv | 0.147M | 0.604G |
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| neck.smooth_convs.3.conv | 0.147M | 0.151G |
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| det_head | 0.329M | 10.909G |
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| det_head.binarize | 0.164M | 10.909G |
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| det_head.binarize.0 | 0.147M | 9.664G |
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| det_head.binarize.1 | 0.128K | 20.972M |
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| det_head.binarize.3 | 16.448K | 1.074G |
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| det_head.binarize.4 | 0.128K | 83.886M |
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| det_head.binarize.6 | 0.257K | 67.109M |
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| det_head.threshold | 0.164M | |
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| det_head.threshold.0 | 0.147M | |
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| det_head.threshold.1 | 0.128K | |
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| det_head.threshold.3 | 16.448K | |
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| det_head.threshold.4 | 0.128K | |
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| det_head.threshold.6 | 0.257K | |
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!!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
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```

‎docs/zh_cn/user_guides/useful_tools.md

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此外，基于此工具，用户也可以将其他算法库获取的预测结果转换成 MMOCR 支持的格式，从而使用 MMOCR 内置的评估指标来对其他算法库的模型进行评测。
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| 参数 | 类型 | 说明 |
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| ------------- | ----- | ---------------------------------------- |
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| config | str | （必须）配置文件路径。 |
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| pkl_results | str | （必须）预先保存的预测结果文件。 |
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| --cfg-options | float | 用于覆写配置文件中的指定参数。[示例](<>) |
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| 参数 | 类型 | 说明 |
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| ------------- | ----- | ---------------------------------------------------------------- |
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| config | str | （必须）配置文件路径。 |
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| pkl_results | str | （必须）预先保存的预测结果文件。 |
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| --cfg-options | float | 用于覆写配置文件中的指定参数。[示例](./config.md#命令行修改配置) |
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### 计算 FLOPs 和参数量
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我们提供一个计算 FLOPs 和参数量的方法，首先我们使用以下命令安装依赖。
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```shell
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pip install fvcore
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```
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计算 FLOPs 和参数量的脚本使用方法如下：
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```shell
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python tools/analysis_tools/get_flops.py ${config} --shape ${IMAGE_SHAPE}
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```
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| 参数 | 类型 | 说明 |
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| ------- | ------ | ------------------------------------------------------------------ |
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| config | str | （必须) 配置文件路径。 |
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| --shape | int\*2 | 计算 FLOPs 使用的图片尺寸，如 `--shape 320 320`。 默认为 `640 640` |
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获取 `dbnet_resnet18_fpnc_100k_synthtext.py` FLOPs 和参数量的示例命令如下。
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```shell
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python tools/analysis_tools/get_flops.py configs/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext.py --shape 1024 1024
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```
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输出如下：
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```shell
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input shape is (1, 3, 1024, 1024)
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| module | #parameters or shape | #flops |
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| :------------------------ | :------------------- | :------ |
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| model | 12.341M | 63.955G |
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| backbone | 11.177M | 38.159G |
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| backbone.conv1 | 9.408K | 2.466G |
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| backbone.conv1.weight | (64, 3, 7, 7) | |
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| backbone.bn1 | 0.128K | 83.886M |
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| backbone.bn1.weight | (64,) | |
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| backbone.bn1.bias | (64,) | |
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| backbone.layer1 | 0.148M | 9.748G |
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| backbone.layer1.0 | 73.984K | 4.874G |
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| backbone.layer1.1 | 73.984K | 4.874G |
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| backbone.layer2 | 0.526M | 8.642G |
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| backbone.layer2.0 | 0.23M | 3.79G |
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| backbone.layer2.1 | 0.295M | 4.853G |
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| backbone.layer3 | 2.1M | 8.616G |
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| backbone.layer3.0 | 0.919M | 3.774G |
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| backbone.layer3.1 | 1.181M | 4.842G |
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| backbone.layer4 | 8.394M | 8.603G |
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| backbone.layer4.0 | 3.673M | 3.766G |
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| backbone.layer4.1 | 4.721M | 4.837G |
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| neck | 0.836M | 14.887G |
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| neck.lateral_convs | 0.246M | 2.013G |
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| neck.lateral_convs.0.conv | 16.384K | 1.074G |
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| neck.lateral_convs.1.conv | 32.768K | 0.537G |
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| neck.lateral_convs.2.conv | 65.536K | 0.268G |
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| neck.lateral_convs.3.conv | 0.131M | 0.134G |
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| neck.smooth_convs | 0.59M | 12.835G |
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| neck.smooth_convs.0.conv | 0.147M | 9.664G |
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| neck.smooth_convs.1.conv | 0.147M | 2.416G |
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| neck.smooth_convs.2.conv | 0.147M | 0.604G |
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| neck.smooth_convs.3.conv | 0.147M | 0.151G |
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| det_head | 0.329M | 10.909G |
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| det_head.binarize | 0.164M | 10.909G |
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| det_head.binarize.0 | 0.147M | 9.664G |
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| det_head.binarize.1 | 0.128K | 20.972M |
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| det_head.binarize.3 | 16.448K | 1.074G |
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| det_head.binarize.4 | 0.128K | 83.886M |
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| det_head.binarize.6 | 0.257K | 67.109M |
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| det_head.threshold | 0.164M | |
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| det_head.threshold.0 | 0.147M | |
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| det_head.threshold.1 | 0.128K | |
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| det_head.threshold.3 | 16.448K | |
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| det_head.threshold.4 | 0.128K | |
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| det_head.threshold.6 | 0.257K | |
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!!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
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```

‎tools/analysis_tools/get_flops.py

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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import torch
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from fvcore.nn import FlopCountAnalysis, flop_count_table
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from mmengine import Config
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from mmocr.registry import MODELS
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from mmocr.utils import register_all_modules
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register_all_modules()
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def parse_args():
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parser = argparse.ArgumentParser(description='Train a detector')
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parser.add_argument('config', help='train config file path')
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parser.add_argument(
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'--shape',
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type=int,
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nargs='+',
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default=[640, 640],
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help='input image size')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if len(args.shape) == 1:
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h = w = args.shape[0]
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elif len(args.shape) == 2:
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h, w = args.shape
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else:
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raise ValueError('invalid input shape, please use --shape h w')
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input_shape = (1, 3, h, w)
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cfg = Config.fromfile(args.config)
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model = MODELS.build(cfg.model)
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flops = FlopCountAnalysis(model, torch.ones(input_shape))
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# params = parameter_count_table(model)
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flops_data = flop_count_table(flops)
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print(flops_data)
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print('!!!Please be cautious if you use the results in papers. '
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'You may need to check if all ops are supported and verify that the '
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'flops computation is correct.')
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if __name__ == '__main__':
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main()

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