modify position

docs/zh_CN/feature_visiualization/get_started.md

@@ -6,7 +6,7 @@
 
 ## 二、准备工作
 
-首先我们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/modeling/architecture/)拷贝到当前目录下，并下载预训练模型[预训练模型](../../docs/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resnet.py从[模型库](../../../ppcls/modeling/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。
 
 ```bash
 wget The Link for Pretrained Model
@@ -53,18 +53,17 @@ python tools/feature_maps_visualization/fm_vis.py -i the image you want to test
 ```
 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 `./resnet50_vd/model`
 + `-p`：权重文件路径，如 `./ResNet50_pretrained/`
 + `--show`：是否展示图片，默认值 False
-+ `--save`：是否保存图片，默认值：True
 + `--save_path`：保存路径，如：`./tools/`
 + `--use_gpu`：是否使用 GPU 预测，默认值：True
 
 ## 四、结果
 输入图片：  
 
-![](../../tools/feature_maps_visualization/test.jpg)  
+![](../../../tools/feature_maps_visualization/test.jpg)  
 
 输出特征图：  
 
-![](../../tools/feature_maps_visualization/fm.jpg)
+![](../../../tools/feature_maps_visualization/fm.jpg)

tools/feature_maps_visualization/download_resnet50_pretrained.sh

@@ -0,0 +1,2 @@
+wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
+tar -xf ResNet50_pretrained.tar

tools/feature_maps_visualization/fm_vis.py

@@ -28,7 +28,6 @@ def parse_args():
     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("--save", type=str2bool, default=True)
     parser.add_argument("--save_path", type=str)
     parser.add_argument("--use_gpu", type=str2bool, default=True)
     
@@ -66,9 +65,7 @@ def main():
         place = fluid.CUDAPlace(gpu_id)
     else:
         place = fluid.CPUPlace()
-    fm = None
-    
-    print(args.pretrained_model)
+
     pre_weights_dict = fluid.load_program_state(args.pretrained_model)
     with fluid.dygraph.guard(place):
         net = ResNet50()
@@ -83,12 +80,10 @@ def main():
         net.set_dict(pre_weights_dict_new)
         net.eval()
         _, fm = net(data)
-        assert args.channel_num >= 0 and args.channel_num <= fm.shape[1], "the channel is out of the range"
+        assert args.channel_num >= 0 and args.channel_num <= fm.shape[1], "the channel is out of the range, should be in {} but got {}".format([0, fm.shape[1]], args.channel_num)
         fm = (np.squeeze(fm[0][args.channel_num].numpy())*255).astype(np.uint8)
-        print(fm)
     if fm is not None:
         if args.save:
-            print(args.save_path)
             cv2.imwrite(args.save_path, fm)
         if args.show:
             cv2.show(fm)

tools/feature_maps_visualization/resnet.py

@@ -0,0 +1,215 @@
+import numpy as np
+import argparse
+import ast
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+from paddle.fluid import framework
+
+import math
+import sys
+import time
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = BatchNorm(num_filters, 
+                                     act=act,
+                                     param_attr=ParamAttr(name=bn_name + '_scale'),
+                                     bias_attr=ParamAttr(bn_name + '_offset'),
+                                     moving_mean_name=bn_name + '_mean',
+                                     moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name+"_branch2a")
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name+"_branch2b")
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None,
+            name=name+"_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+        self.fm = None
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu',
+            name="conv1")
+        self.pool2d_max = Pool2D(
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+
+        self.bottleneck_block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                if layers in [101, 152] and block == 2:
+                    if i == 0:
+                        conv_name="res"+str(block+2)+"a"
+                    else:
+                        conv_name="res"+str(block+2)+"b"+str(i)
+                else:
+                    conv_name="res"+str(block+2)+chr(97+i)
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block]
+                        if i == 0 else num_filters[block] * 4,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut,
+                        name=conv_name))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = Linear(self.pool2d_avg_output,
+                      class_dim,
+                      param_attr=ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_0.w_0"),
+                      bias_attr=ParamAttr(name="fc_0.b_0"))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        self.fm = y
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y, self.fm
+
+
+def ResNet50(**args):
+    model = ResNet(layers=50, **args)
+    return model
+
+
+def ResNet101(**args):
+    model = ResNet(layers=101, **args)
+    return model
+
+
+def ResNet152(**args):
+    model = ResNet(layers=152, **args)
+    return model
+
+
+if __name__ == "__main__":
+    import numpy as np
+    place = fluid.CPUPlace()
+    with fluid.dygraph.guard(place): 
+        model = ResNet50()
+        img = np.random.uniform(0, 255, [1, 3, 224, 224]).astype('float32')
+        img = fluid.dygraph.to_variable(img)
+        res = model(img)
+        print(res.shape)