[Codes] Update the function of calculating Params and Flops.

datas/docs/Calculate_Flops.md

@@ -0,0 +1,30 @@
+获取模型Flops&Param
+===========================
+
+[![BILIBILI](https://raw.githubusercontent.com/Fafa-DL/readme-data/main/Bilibili.png)](https://space.bilibili.com/46880349)
+- 提供 `tools/vis_lr.py` 工具来可视化学习率。
+
+**命令行**：
+
+```bash
+python tools/vis_lr.py \
+    ${CONFIG_FILE} \
+    [--shape ${Shape}] \
+```
+
+**所有参数的说明**：
+
+- `config` : 模型配置文件的路径。
+- `--shape` : 输入图片尺寸，默认224
+
+
+**示例Step**：
+
+```bash
+python tools/get_flops.py models/mobilenet/mobilenet_v3_small.py
+```
+
+**注意**：
+
+- 官方给出的参数量与浮点运算量是基于ImageNet，也就是说默认分类数为`1000`，所以当你评估自己模型时请在配置文件中将`num_classes`修改为对应数量，因为将很大程度上影响结果
+- 如果你有新增任何`基类`卷积/池化/采样功能，请在`utils/flops_counter.py/get_modules_mapping()`进行增加注册

tools/get_flops.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import sys
+sys.path.insert(0,os.getcwd())
+
+from utils.train_utils import file2dict
+from utils.flops_counter import get_model_complexity_info
+from models.build import BuildNet
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Get model flops and params')
+    parser.add_argument('config', help='config file path')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[224, 224],
+        help='input image size')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+
+    args = parse_args()
+
+    if len(args.shape) == 1:
+        input_shape = (3, args.shape[0], args.shape[0])
+    elif len(args.shape) == 2:
+        input_shape = (3, ) + tuple(args.shape)
+    else:
+        raise ValueError('invalid input shape')
+
+    model_cfg,train_pipeline,val_pipeline,data_cfg,lr_config,optimizer_cfg = file2dict(args.config)
+    model = BuildNet(model_cfg)
+    model.eval()
+
+    if hasattr(model, 'extract_feat'):
+        model.forward = model.extract_feat
+    else:
+        raise NotImplementedError(
+            'FLOPs counter is currently not currently supported with {}'.
+            format(model.__class__.__name__))
+
+    flops, params = get_model_complexity_info(model, input_shape)
+    split_line = '=' * 30
+    print(f'{split_line}\nInput shape: {input_shape}\n'
+          f'Flops: {flops}\nParams: {params}\n{split_line}')
+    print('!!!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.')
+
+
+if __name__ == '__main__':
+    main()