Merge pull request #198 from wqz960/PaddleClas_74

add feature maps visualization

docs/zh_CN/feature_visiualization/get_started.md

+# 特征图可视化指南
+
+## 一、概述
+
+特征图是输入图片在卷积网络中的特征表达，对特征图的研究可以有利于我们对于模型的理解与设计，所以基于动态图我们使用本工具来可视化特征图。
+
+## 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/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
+tar -xf Downloaded Pretrained Model
+```
+
+以resnet50为例：
+```bash
+wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
+tar -xf ResNet50_pretrained.tar
+```
+
+## 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__init__函数中定义self.fm
+```python
+self.fm = None
+```
+在ResNet50的forward函数中指定特征图
+```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.pool2d_avg(y)
+    y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+    y = self.out(y)
+    return y, self.fm
+```
+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 \
+                                                --show whether to show \
+                                                --interpolation interpolation method\
+                                                --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`
++ `-p`：权重文件路径，如 `./ResNet50_pretrained/`
++ `--show`：是否展示图片，默认值 False
++ `--interpolation`: 图像插值方式， 默认值 1
++ `--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)  
+
+输出特征图：  
+
+![](../../../tools/feature_maps_visualization/fm.jpg)

tools/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

tools/feature_maps_visualization/fm_vis.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from resnet import ResNet50 
+import paddle.fluid as fluid
+
+import numpy as np 
+import cv2 
+import utils
+import argparse
+
+def parse_args():
+    def str2bool(v):
+        return v.lower() in ("true", "t", "1")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--image_file", 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)
+    parser.add_argument("--use_gpu", type=str2bool, default=True)
+    
+    return parser.parse_args()
+
+def create_operators(interpolation=1):
+    size = 224
+    img_mean = [0.485, 0.456, 0.406]
+    img_std = [0.229, 0.224, 0.225]
+    img_scale = 1.0 / 255.0
+
+    decode_op = utils.DecodeImage()
+    resize_op = utils.ResizeImage(resize_short=256, interpolation=interpolation)
+    crop_op = utils.CropImage(size=(size, size))
+    normalize_op = utils.NormalizeImage(
+        scale=img_scale, mean=img_mean, std=img_std)
+    totensor_op = utils.ToTensor()
+
+    return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
+
+
+def preprocess(fname, ops):
+    data = open(fname, 'rb').read()
+    for op in ops:
+        data = op(data)
+
+    return data
+
+def main():
+    args = parse_args()
+    operators = create_operators(args.interpolation)
+    # assign the place
+    if args.use_gpu:
+        gpu_id = fluid.dygraph.parallel.Env().dev_id
+        place = fluid.CUDAPlace(gpu_id)
+    else:
+        place = fluid.CPUPlace()
+
+    #pre_weights_dict = fluid.load_program_state(args.pretrained_model)
+    with fluid.dygraph.guard(place):
+        net = ResNet50()
+        data = preprocess(args.image_file, operators)
+        data = np.expand_dims(data, axis=0)
+        data = fluid.dygraph.to_variable(data)
+        dy_weights_dict = net.state_dict()
+        pre_weights_dict_new = {}
+        for key in dy_weights_dict:
+            weights_name = dy_weights_dict[key].name
+            pre_weights_dict_new[key] = pre_weights_dict[weights_name]
+        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, 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)
+    if fm is not None:
+        if args.save:
+            cv2.imwrite(args.save_path, fm)
+        if args.show:
+            cv2.show(fm)
+            cv2.waitKey(0)
+        
+if __name__ == "__main__":
+    main()

tools/feature_maps_visualization/resnet.py

+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)

tools/feature_maps_visualization/utils.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import cv2
+import numpy as np
+
+
+class DecodeImage(object):
+    def __init__(self, to_rgb=True):
+        self.to_rgb = to_rgb
+
+    def __call__(self, img):
+        data = np.frombuffer(img, dtype='uint8')
+        img = cv2.imdecode(data, 1)
+        if self.to_rgb:
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+                img.shape)
+            img = img[:, :, ::-1]
+
+        return img
+
+
+class ResizeImage(object):
+    def __init__(self, resize_short=None, interpolation=1):
+        self.resize_short = resize_short
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        img_h, img_w = img.shape[:2]
+        percent = float(self.resize_short) / min(img_w, img_h)
+        w = int(round(img_w * percent))
+        h = int(round(img_h * percent))
+        return cv2.resize(img, (w, h), interpolation=self.interpolation)
+
+
+class CropImage(object):
+    def __init__(self, size):
+        if type(size) is int:
+            self.size = (size, size)
+        else:
+            self.size = size
+
+    def __call__(self, img):
+        w, h = self.size
+        img_h, img_w = img.shape[:2]
+        w_start = (img_w - w) // 2
+        h_start = (img_h - h) // 2
+
+        w_end = w_start + w
+        h_end = h_start + h
+        return img[h_start:h_end, w_start:w_end, :]
+
+
+class NormalizeImage(object):
+    def __init__(self, scale=None, mean=None, std=None):
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, img):
+        return (img.astype('float32') * self.scale - self.mean) / self.std
+
+
+class ToTensor(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img):
+        img = img.transpose((2, 0, 1))
+        return img