fix: adapt to release 2.3

docs/zh_CN/others/feature_visiualization.md

@@ -6,43 +6,45 @@
 
 ## 二、准备工作
 
-首先需要选定研究的模型，本文设定ResNet50作为研究模型，将resnet.py从[模型库](../../../ppcls/arch/architecture/)拷贝到当前目录下，并下载预训练模型[预训练模型](../../zh_CN/models/models_intro), 复制resnet50的模型链接，使用下列命令下载并解压预训练模型。
+首先需要选定研究的模型，本文设定ResNet50作为研究模型，将模型组网代码[resnet.py](../../../ppcls/arch/backbone/legendary_models/resnet.py)拷贝到[目录](../../../ppcls/utils/feature_maps_visualization/)下，并下载[ResNet50预训练模型](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams)，或使用以下命令下载。
 
 ```bash
-wget The Link for Pretrained Model
-tar -xf Downloaded Pretrained Model
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams
 ```
 
-以resnet50为例：
-```bash
-wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
-tar -xf ResNet50_pretrained.tar
-```
+其他模型网络结构代码及预训练模型请自行下载：[模型库](../../../ppcls/arch/backbone/)，[预训练模型](../models/models_intro.md)。
 
 ## 三、修改模型
 
 找到我们所需要的特征图位置，设置self.fm将其fetch出来，本文以resnet50中的stem层之后的特征图为例。
 
-在fm_vis.py中修改模型的名字。
+在ResNet50的forward函数中指定要可视化的特征图
 
-在ResNet50的__init__函数中定义self.fm
 ```python
-self.fm = None
+    def forward(self, x):
+        with paddle.static.amp.fp16_guard():
+            if self.data_format == "NHWC":
+                x = paddle.transpose(x, [0, 2, 3, 1])
+                x.stop_gradient = True
+            x = self.stem(x)
+            fm = x
+            x = self.max_pool(x)
+            x = self.blocks(x)
+            x = self.avg_pool(x)
+            x = self.flatten(x)
+            x = self.fc(x)
+        return x, fm
 ```
-在ResNet50的forward函数中指定特征图
+
+然后修改代码[fm_vis.py](../../../ppcls/utils/feature_maps_visualization/fm_vis.py)，引入 `ResNet50`，实例化 `net` 对象：
+
 ```python
-def forward(self, inputs):
-    y = self.conv(inputs)
-    self.fm = y
-    y = self.pool2d_max(y)
-    for bottleneck_block in self.bottleneck_block_list:
-        y = bottleneck_block(y)
-    y = self.avg_pool(y)
-    y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
-    y = self.out(y)
-    return y, self.fm
+from resnet import ResNet50
+net = ResNet50()
 ```
-执行函数
+
+最后执行函数
+
 ```bash
 python tools/feature_maps_visualization/fm_vis.py -i the image you want to test \
                                                 -c channel_num -p pretrained model \
@@ -51,9 +53,10 @@ python tools/feature_maps_visualization/fm_vis.py -i the image you want to test
                                                 --save_path where to save \
                                                 --use_gpu whether to use gpu
 ```
+
 参数说明：
 + `-i`：待预测的图片文件路径，如 `./test.jpeg`
-+ `-c`：特征图维度，如 `./resnet50_vd/model`
++ `-c`：特征图维度，如 `5`
 + `-p`：权重文件路径，如 `./ResNet50_pretrained/`
 + `--interpolation`: 图像插值方式， 默认值 1
 + `--save_path`：保存路径，如：`./tools/`
@@ -63,7 +66,7 @@ python tools/feature_maps_visualization/fm_vis.py -i the image you want to test
 
 * 输入图片：  
 
-![](../../../docs/images/feature_maps/feature_visualization_input.jpg)
+![](../../images/feature_maps/feature_visualization_input.jpg)
 
 * 运行下面的特征图可视化脚本
 
@@ -75,10 +78,9 @@ python tools/feature_maps_visualization/fm_vis.py \
     --show=True \
     --interpolation=1 \
     --save_path="./output.png" \
-    --use_gpu=False \
-    --load_static_weights=True
+    --use_gpu=False
 ```
 
 * 输出特征图保存为`output.png`，如下所示。
 
-![](../../../docs/images/feature_maps/feature_visualization_output.jpg)
+![](../../images/feature_maps/feature_visualization_output.jpg)

ppcls/utils/feature_maps_visualization/download_resnet50_pretrained.sh

-wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
-tar -xf ResNet50_pretrained.tar
\ No newline at end of file

ppcls/utils/feature_maps_visualization/fm_vis.py

@@ -19,7 +19,7 @@ import os
 import sys
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../../..')))
 
 import paddle
 from paddle.distributed import ParallelEnv
@@ -33,18 +33,13 @@ def parse_args():
         return v.lower() in ("true", "t", "1")
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("-i", "--image_file", type=str)
+    parser.add_argument("-i", "--image_file", required=True, type=str)
     parser.add_argument("-c", "--channel_num", type=int)
     parser.add_argument("-p", "--pretrained_model", type=str)
     parser.add_argument("--show", type=str2bool, default=False)
     parser.add_argument("--interpolation", type=int, default=1)
     parser.add_argument("--save_path", type=str, default=None)
     parser.add_argument("--use_gpu", type=str2bool, default=True)
-    parser.add_argument(
-        "--load_static_weights",
-        type=str2bool,
-        default=False,
-        help='Whether to load the pretrained weights saved in static mode')
 
     return parser.parse_args()
 
@@ -79,7 +74,7 @@ def main():
     place = paddle.set_device(place)
 
     net = ResNet50()
-    load_dygraph_pretrain(net, args.pretrained_model, args.load_static_weights)
+    load_dygraph_pretrain(net, args.pretrained_model)
 
     img = cv2.imread(args.image_file, cv2.IMREAD_COLOR)
     data = preprocess(img, operators)