环境：tensorflow1.13
模型：使用vgg19作为例子
注意：本文档的结果是使用CPU模式跑出来的，因为显卡是30系，系统是win10，没法在tf1.13版本下使用GPU模式。所以如果读者使用GPU模式跑出来的结果与本文档有些许出入，应该是正常现象。
文档背景是为了在tensorflow1.x中使用预训练的vgg模型计算vgg loss。
模型下载
官方的预训练模型在tensorflow的model仓库中，完整路径是tensorflow/models/research/slim，请注意选择tf1.13分支：
https://github.com/tensorflow/models/tree/r1.13.0/research/slim
vgg19的模型下载地址：
http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz
解压后文件名是vgg_19.ckpt。
辅助函数
在下面的代码中，为了对比不同的模型使用方式是否带来一致的结果，写了一个可视化feature map的辅助函数来方便对比。下面的代码将直接使用，而不再赘述，读者如果想要尝试本文档代码，请自行粘贴过去。
def visualize_feature_map(feature_map,
col_nums=None,
gap_value=0.5,
gap_width=10,
gap_height=10):
"""
Visualize feature map in one image.
Parameters
----------
feature_map: numpy array, shape is (height, width, channel)
col_nums: number of feature map columns
gap_value: value for feature map gap
gap_width: width of gap
gap_height: height of gap
Returns
-------
image: image to show feature map
"""
eps = 1e-6
if feature_map.ndim == 4:
if feature_map.shape[0] == 1:
feature_map = np.squeeze(feature_map)
else:
raise ValueError("feature map must be 3 dims ndarray (height, "
"width, channel) or 4 dims ndarray whose shape "
"must be (1, height, width, channel)")
# compute col_nums (if not set) and row_nums
height, width, channel = feature_map.shape
if col_nums is None:
col_nums = int(round(np.sqrt(channel)))
row_nums = int(np.ceil(channel / col_nums))
# compute final image width and height
image_width = col_nums * (width + gap_width) - gap_width
image_height = row_nums * (height + gap_height) - gap_height
image = np.ones(shape=(image_height, image_width),
dtype=feature_map.dtype) * gap_value
cnt = 0
while cnt < channel:
row = cnt // col_nums
col = cnt % col_nums
row_beg = row * (height + gap_height)
row_end = row_beg + height
col_beg = col * (width + gap_width)
col_end = col_beg + width
image[row_beg:row_end, col_beg:col_end] = \
feature_map[:, :, cnt] / (np.std(feature_map[:, :, cnt]) + eps)
cnt += 1
return image
模型使用
有三种典型的方式使用官方模型：
-
使用官方提供的模型文件加载模型。
需要首先使用placeholder在计算图中定义模型，然后使用tf.train.Saver()的restore方法加载模型参数。这种方式要求我们新定义的模型的节点名和参数名必须与vgg_19.ckpt中保存的一致，这也是为什么建议直接使用官方的模型定义文件。 -
魔改官方的模型文件。
在卷积部分，官方的模型文件只提供了relu层的feature map，有时候我们可能需要conv层的feature map，这时候就需要魔改。魔改后的节点名和参数名仍然必须与vgg_19.ckpt中保存的一致。 -
使用NewCheckpointReader并自定义模型
使用pywrap_tensorflow.NewCheckpointReader(model_path)可以读取权重参数，然后自己重新定义模型结构并把权重参数赋值过去。采用这种办法可以灵活定义模型结构和节点名称，但是代码写起来比较麻烦。（官方定义的模型文件只能拿到relu层的feature map，拿不到conv层的，所以灵活性欠佳）
1. 使用官方提供的模型文件加载模型
官方的模型定义文件路径（网址）：
https://github.com/tensorflow/models/blob/r1.13.0/research/slim/nets/vgg.py
找到vgg19的定义如下：
def vgg_19(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_19',
fc_conv_padding='VALID',
global_pool=False):
"""
Oxford Net VGG 19-Layers version E Example.
Note: All the fully_connected layers have been transformed to conv2d
layers. To use in classification mode, resize input to 224x224.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes. If 0 or None, the logits
layer is omitted and the input features to the logits layer are
returned instead.
is_training: whether or not the model is being trained.
dropout_keep_prob: the probability that activations are kept in the
dropout layers during training.
spatial_squeeze: whether or not should squeeze the spatial dimensions
of the outputs. Useful to remove unnecessary dimensions for
classification.
scope: Optional scope for the variables.
fc_conv_padding: the type of padding to use for the fully connected
layer that is implemented as a convolutional layer. Use 'SAME'
padding if you are applying the network in a fully convolutional
manner and want to get a prediction map downsampled by a factor of
32 as an output. Otherwise, the output prediction map will be
(input / 32) - 6 in case of 'VALID' padding.
global_pool: Optional boolean flag. If True, the input to the
classification layer is avgpooled to size 1x1, for any input size.
(This is not part of the original VGG architecture.)
Returns:
net: the output of the logits layer (if num_classes is a non-zero
integer), or the non-dropped-out input to the logits layer (if
num_classes is 0 or None).
end_points: a dict of tensors with intermediate activations.
"""
with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope(
[slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3],
scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
# Use conv2d instead of fully_connected layers.
net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding,
scope='fc6')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout6')
net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
# Convert end_points_collection into a end_point dict.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if global_pool:
net = tf.reduce_mean(net, [1, 2], keep_dims=True,
name='global_pool')
end_points['global_pool'] = net
if num_classes:
net = slim.dropout(net, dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
return net, end_points
关于上述模型定义，有两个函数需要稍加解释：
-
slim.conv2d
slim.conv2d的定义在Pycharm中通过ctrl加单击的方式追进去后发现不对，真正的定义在下面路径的文件中：
D:\Program\anaconda3\envs\tf13\Lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py
（请注意D:\Program\anaconda3\envs\tf13是我电脑上tf1.13的环境路径，需要根据自己环境修改）其中第1117行的
def convolution2d是函数定义及实现，3327行的conv2d = convolution2d起了一个短名字。convolution2d的参数列表中有activation_fn=nn.relu，所以这个卷积默认带有relu作为激活函数。 -
slim.repeat
作用是把某个算子重复n次。函数实现跟slim.conv2d同一个文件，文件中的函数定义和部分解释如下：def repeat(inputs, repetitions, layer, *args, **kwargs): """Applies the same layer with the same arguments repeatedly. y = repeat(x, 3, conv2d, 64, [3, 3], scope='conv1') # It is equivalent to: x = conv2d(x, 64, [3, 3], scope='conv1/conv1_1') x = conv2d(x, 64, [3, 3], scope='conv1/conv1_2') y = conv2d(x, 64, [3, 3], scope='conv1/conv1_3') ...... """
使用脚本如下，注意其中vgg_19和visualize_feature_map两个函数在本文档上面已经出现过，并且比较长，所以下面的脚本中将其省略：
# -*- coding: utf-8 -*-
import os
import cv2
import tensorflow as tf
import numpy as np
os.environ['CUDA_VISIBLE_DEVICES'] = "/gpu:0"
slim = tf.contrib.slim
def vgg_19(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_19',
fc_conv_padding='VALID',
global_pool=False):
# 见本文档前面
pass
def visualize_feature_map(feature_map,
col_nums=None,
gap_value=0.5,
gap_width=10,
gap_height=10):
# 见本文档前面
pass
def main():
image_file = r'E:\images\lena512color.tiff'
model_path = r'E:\pretrained_model\tf1x\vgg_19.ckpt'
inputs_ = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3])
outputs, feature_map_dict = vgg_19(inputs_,
num_classes=0,
is_training=False,
global_pool=True)
# print trainable variables
for var in tf.trainable_variables():
print(var)
# load pretrained model
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, model_path)
# running test
inputs = cv2.imread(image_file)
inputs = np.expand_dims(inputs, axis=0)
out, feature_maps = sess.run([outputs, feature_map_dict],
feed_dict={
inputs_: inputs,
})
# print shape of feature maps
for key in feature_maps.keys():
print(key, feature_maps.get(key).shape)
feature_map = feature_maps.get('vgg_19/conv3/conv3_4')
feature_map = np.squeeze(feature_map)
image = visualize_feature_map(feature_map)
image = np.clip(image * 255, 0, 255).astype(np.uint8)
# cv2.imwrite('lena_feature_map_vgg_conv3_4.png', image)
# print statistics for feature map
for i in range(5):
mean_val = np.mean(feature_map[:, :, i])
std = np.std(feature_map[:, :, i])
min_val = np.min(feature_map[:, :, i])
max_val = np.max(feature_map[:, :, i])
print(i + 1, " min=%.4f, max=%.4f, mean=%.4f, std=%.4f" % (
min_val, max_val, mean_val, std))
# print part of final global feature vector
feature_vec = feature_maps.get('global_pool')
feature_vec = np.squeeze(feature_vec)
for i in range(10):
print(feature_vec[i])
if __name__ == '__main__':
main()
对上述脚本需要做如下说明：
-
vgg_19的参数设置需要多加注意。
如果只是做推理的话，is_training一定要设置成False；
我使用的目的是为了计算vgg loss，所以不需要全连接的部分，因此为了使全连接部分不要报错，把num_classes设置成0，并且global_pool设置为True。 -
使用placeholder创建计算图以后才能restore权重。
脚本在计算图和restore权重的中间部分(# print trainable variables部分)打印了权重变量，包括名称，shape，dtype。<tf.Variable 'vgg_19/conv1/conv1_1/weights:0' shape=(3, 3, 3, 64) dtype=float32_ref> <tf.Variable 'vgg_19/conv1/conv1_1/biases:0' shape=(64,) dtype=float32_ref> <tf.Variable 'vgg_19/conv1/conv1_2/weights:0' shape=(3, 3, 64, 64) dtype=float32_ref> <tf.Variable 'vgg_19/conv1/conv1_2/biases:0' shape=(64,) dtype=float32_ref> <tf.Variable 'vgg_19/conv2/conv2_1/weights:0' shape=(3, 3, 64, 128) dtype=float32_ref> <tf.Variable 'vgg_19/conv2/conv2_1/biases:0' shape=(128,) dtype=float32_ref> ...... -
vgg_19的输出有两个。
第一个很容易理解，就是网络推理的输出，但对于计算vgg loss而言没有用。
第二个输出以dict的形式保存了网络的feature map，dict的key是feature map的节点名，value是feature map的数值，这是计算vgg loss真正需要的东西。在# print shape of feature maps部分打印了feature map的name和shape，另外还把conv3_4画在一张图上，用于一些简单直观的测试和检查。vgg_19/conv1/conv1_1 (1, 512, 512, 64) vgg_19/conv1/conv1_2 (1, 512, 512, 64) vgg_19/pool1 (1, 256, 256, 64) vgg_19/conv2/conv2_1 (1, 256, 256, 128) vgg_19/conv2/conv2_2 (1, 256, 256, 128) vgg_19/pool2 (1, 128, 128, 128) ...... -
打印feature map的一些统计数值，可以检查并确认以下事实 ：
feature map只有relu，没有conv，因为feature map的最小值都是0.0；
vgg出现在BN之前，所以网络中没有BN，导致feature map的数值很大（如果有BN的话数值一般不会超过5），因此计算vgg loss时根据具体情况一般需要乘上一个很小的权重系数。1 min=0.0000, max=9201.5811, mean=386.3745, std=737.3252 2 min=0.0000, max=7389.5913, mean=1412.0540, std=616.6437 3 min=0.0000, max=3323.7239, mean=400.2662, std=522.4063 4 min=0.0000, max=4319.3765, mean=369.9904, std=644.4222 5 min=0.0000, max=8997.2305, mean=905.1512, std=1288.8953 ...... -
打印一部分最后的feature vector，用于魔改模型定义函数后检查正确性：
0.00055606366 0.0 0.0 0.15579844 0.0 1.0548652 0.0 0.0 0.05207316 0.29752082 ......
2. 魔改官方的模型文件
魔改后的模型和测试代码如下，同样visualize_feature_map需要从上面粘贴过来：
# -*- coding: utf-8 -*-
import os
import cv2
import tensorflow as tf
import numpy as np
slim = tf.contrib.slim
def vgg19(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_19',
fc_conv_padding='VALID',
global_pool=False):
with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope(
[slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
# conv blocks are modified as follows
net_config = [
[64, 2],
[128, 2],
[256, 4],
[512, 4],
[512, 4],
] # [filters, blocks]
net = inputs
relu_dict = {
}
for i, config in enumerate(net_config):
filters = config[0]
for j in range(config[1]):
conv_scope = 'conv%d/conv%d_%d' % (i + 1, i + 1, j + 1)
relu_name = 'conv%d/relu%d_%d' % (i + 1, i + 1, j + 1)
net = slim.conv2d(net, filters, [3, 3],
activation_fn=None,
scope=conv_scope)
net = tf.nn.relu(net, name=relu_name)
relu_dict[net.op.name] = net
net = slim.max_pool2d(net, [2, 2], scope='pool%d' % (i + 1))
# Use conv2d instead of fully_connected layers.
net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding,
scope='fc6')
net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
scope='dropout6')
net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
# Convert end_points_collection into a end_point dict.
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
if global_pool:
net = tf.reduce_mean(net, [1, 2], keep_dims=True,
name='global_pool')
end_points['global_pool'] = net
if num_classes:
net = slim.dropout(net, dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = slim.conv2d(net, num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
if spatial_squeeze:
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
end_points[sc.name + '/fc8'] = net
end_points.update(relu_dict)
return net, end_points
def visualize_feature_map(feature_map,
col_nums=None,
gap_value=0.5,
gap_width=10,
gap_height=10):
# 见本文档前面
pass
def main():
image_file = r'D:\data\test_images\lena512color.tiff'
model_path = r'E:\pretrained_model\tensorflow1.13\vgg_19.ckpt'
inputs_ = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3])
outputs, feature_map_dict = vgg19(inputs_,
num_classes=0,
is_training=False,
global_pool=True)
# check trainable variables
for var in tf.trainable_variables():
print(var)
# load pretrained model
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, model_path)
# running test
inputs = cv2.imread(image_file)
inputs = np.expand_dims(inputs, axis=0)
out, feature_maps = sess.run([outputs, feature_map_dict],
feed_dict={
inputs_: inputs,
})
# print shape of feature maps
for key in feature_maps.keys():
print(key, feature_maps.get(key).shape)
feature_map = feature_maps.get('vgg_19/conv3/relu3_4')
feature_map = np.squeeze(feature_map)
image = visualize_feature_map(feature_map)
image = np.clip(image * 255, 0, 255).astype(np.uint8)
cv2.imwrite('lena_feature_map_vgg_relu3_4--2.png', image)
# print statistics for relu3_4
for i in range(5):
mean_val = np.mean(feature_map[:, :, i])
std = np.std(feature_map[:, :, i])
min_val = np.min(feature_map[:, :, i])
max_val = np.max(feature_map[:, :, i])
print(i + 1, " min=%.4f, max=%.4f, mean=%.4f, std=%.4f" % (
min_val, max_val, mean_val, std))
# print statistics for conv3_4
print('\n')
feature_map = feature_maps.get('vgg_19/conv3/conv3_4')
feature_map = np.squeeze(feature_map)
for i in range(5):
mean_val = np.mean(feature_map[:, :, i])
std = np.std(feature_map[:, :, i])
min_val = np.min(feature_map[:, :, i])
max_val = np.max(feature_map[:, :, i])
print(i + 1, " min=%.4f, max=%.4f, mean=%.4f, std=%.4f" % (
min_val, max_val, mean_val, std))
feature_vec = feature_maps.get('global_pool')
feature_vec = np.squeeze(feature_vec)
for i in range(10):
print(feature_vec[i])
if __name__ == '__main__':
main()
说明如下：
-
改模型结构的目的：将conv和relu分离开来，方便使用conv层的结果来作为vgg loss的输入。
-
改模型定义函数的关键点：确保结构不能变；确保节点名不能变；由于原始代码不能直接收集relu层的feature map，所以需要自行定义一个dict进行收集。
-
conv3_4的feature map统计信息如下，可以看到min value已经出现负数，所以确实实现了与relu层的分离。
1 min=-2909.7209, max=9201.5811, mean=92.2542, std=991.5225 2 min=-431.0446, max=7389.5913, mean=1411.6982, std=617.5237 3 min=-1092.2075, max=3323.7239, mean=339.5828, std=582.6731 4 min=-2396.4478, max=4319.3765, mean=106.6278, std=852.0536 5 min=-3547.4551, max=8997.2305, mean=699.2141, std=1488.1344 -
feature_maps里面的内容多了relu部分，因为是在代码的最后update进去的，所以这部分在feature_maps的最后：
...... vgg_19/conv1/relu1_1 (1, 512, 512, 64) vgg_19/conv1/relu1_2 (1, 512, 512, 64) vgg_19/conv2/relu2_1 (1, 256, 256, 128) vgg_19/conv2/relu2_2 (1, 256, 256, 128) vgg_19/conv3/relu3_1 (1, 128, 128, 256) vgg_19/conv3/relu3_2 (1, 128, 128, 256) vgg_19/conv3/relu3_3 (1, 128, 128, 256) vgg_19/conv3/relu3_4 (1, 128, 128, 256) ...... -
其他输出变量已经检查，与第一种方式没有差异，说明魔改的结果是正确的。
3. 使用NewCheckpointReader并自定义模型
这种方法分两部分说明。
第一部分简单说明如何从预训练模型中拿到权重参数；第二部分详细说明如何将预训练权重系数赋值给新定义的模型，并进行测试。
下面是第一部分的代码：
# -*- coding: utf-8 -*-
from tensorflow.python import pywrap_tensorflow as wrap
def main():
model_path = r'E:\pretrained_model\tf1x\vgg_19.ckpt'
reader = wrap.NewCheckpointReader(model_path)
variables_shape = reader.get_variable_to_shape_map()
variables_dtype = reader.get_variable_to_dtype_map()
for key in variables_shape.keys():
print(key, variables_shape.get(key), variables_dtype.get(key))
print('\n')
print(reader.has_tensor("vgg_19/mean_rgb"))
rgb_mean = reader.get_tensor("vgg_19/mean_rgb")
print(rgb_mean)
if __name__ == '__main__':
main()
上述代码有几个需说明的地方：
- NewCheckpointReader用于加载预训练模型的权重
- get_variable_to_shape_map()和get_variable_to_dtype_map()可以查看权重参数的shape和dtype
- get_tensor()可以获得权重参数，返回的是
numpy数组
打印出来的结果如下，其中有两个比较巧妙的参数，global_step和vgg_19/mean_rgb，mean_rgb打印出来有具体数值：
global_step [] <dtype: 'int64'>
vgg_19/conv2/conv2_2/biases [128] <dtype: 'float32'>
vgg_19/conv2/conv2_2/weights [3, 3, 128, 128] <dtype: 'float32'>
vgg_19/conv1/conv1_1/biases [64] <dtype: 'float32'>
vgg_19/conv1/conv1_1/weights [3, 3, 3, 64] <dtype: 'float32'>
vgg_19/conv1/conv1_2/biases [64] <dtype: 'float32'>
vgg_19/conv1/conv1_2/weights [3, 3, 64, 64] <dtype: 'float32'>
......
vgg_19/mean_rgb [3] <dtype: 'float32'>
......
vgg_19/fc8/weights [1, 1, 4096, 1000] <dtype: 'float32'>
[123.68 116.78 103.94]
下面是第二部分的代码：
# -*- coding: utf-8 -*-
import os
import cv2
import tensorflow as tf
import numpy as np
from tensorflow.python import pywrap_tensorflow as wrap
os.environ['CUDA_VISIBLE_DEVICES'] = "/gpu:0"
slim = tf.contrib.slim
def vgg19(inputs,
scope_name='vgg_19'):
with tf.variable_scope(scope_name):
net_config = [
[64, 2],
[128, 2],
[256, 4],
[512, 4],
[512, 4],
] # [filters, blocks]
feature_maps = {
}
x = inputs
for i, config in enumerate(net_config):
filters = config[0]
for j in range(config[1]):
conv_name = 'conv%d_%d' % (i + 1, j + 1)
relu_name = 'relu%d_%d' % (i + 1, j + 1)
x = tf.layers.conv2d(x, filters, [3, 3],
padding='same',
name=conv_name)
feat_map_name = x.op.name.replace('/BiasAdd', '')
feature_maps[feat_map_name] = x
x = tf.nn.relu(x, name=relu_name)
feature_maps[x.op.name] = x
x = tf.layers.max_pooling2d(x, (2, 2), (2, 2),
name='pool%d' % (i + 1))
feat_map_name = x.op.name.replace('/MaxPool', '')
feature_maps[feat_map_name] = x
return x, feature_maps
def visualize_feature_map(feature_map,
col_nums=None,
gap_value=0.5,
gap_width=10,
gap_height=10):
# 见本文档前面
pass
def _get_pretrained_tensor_name(name):
block_num = int(name.split('/')[1][4:].split('_')[0])
name = name.replace('vgg_19', 'vgg_19/conv%d' % block_num)
name = name.replace('kernel', 'weights').replace('bias', 'biases')
return name
def main():
image_file = r'E:\images\lena512color.tiff'
model_path = r'E:\pretrained_model\tf1x\vgg_19.ckpt'
inputs_ = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 3])
outputs, feature_map_dict = vgg19(inputs_)
trainable_vars = tf.trainable_variables()
# use NewCheckpointReader to get weights
reader = wrap.NewCheckpointReader(model_path)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# print trainable variables before assignment
for var in trainable_vars:
print(var)
print(sess.run(var)[:, :, 0, 0])
break
# trainable variables assignment
print('\n')
for i, var in enumerate(trainable_vars):
name = _get_pretrained_tensor_name(var.op.name)
sess.run(var.assign(reader.get_tensor(name)))
# print trainable variables after assignment
for var in trainable_vars:
print(var)
name = _get_pretrained_tensor_name(var.op.name)
print(sess.run(var)[:, :, 0, 0])
print('pretrained weight:')
print(reader.get_tensor(name)[:, :, 0, 0])
break
# test case
inputs = cv2.imread(image_file)
inputs = np.expand_dims(inputs, axis=0)
out, feature_maps = sess.run([outputs, feature_map_dict],
feed_dict={
inputs_: inputs,
})
# print shape of feature maps
print('\n')
for key in feature_maps.keys():
print(key, feature_maps.get(key).shape)
feature_map = feature_maps.get('vgg_19/relu3_4')
feature_map = np.squeeze(feature_map)
image = visualize_feature_map(feature_map)
image = np.clip(image * 255, 0, 255).astype(np.uint8)
cv2.imwrite('lena_feature_map_vgg_conv3_4--2.png', image)
# print statistics for feature map
print('\n')
for i in range(5):
mean_val = np.mean(feature_map[:, :, i])
std = np.std(feature_map[:, :, i])
min_val = np.min(feature_map[:, :, i])
max_val = np.max(feature_map[:, :, i])
print(i + 1, " min=%.4f, max=%.4f, mean=%.4f, std=%.4f" % (
min_val, max_val, mean_val, std))
if __name__ == '__main__':
main()
比如针对vgg loss这个需求，通常我们不需要最后的全连接层，所以本着节省算力及显存的目的，上述新定义的模型将全连接部分给去掉了，并且feature map / variables 的名字也重新进行了定义，这种情况下就无法使用restore功能加载预训练参数，只能使用赋值的方式。
上述代码整体分为两个部分，一是权重参数赋值，二是跟之前一样的测试用例。
下面对赋值的流程进行说明：
- 使用placeholder创建计算图并获取
trainable_vars - 使用
NewCheckpointReader加载预训练模型的权重参数 - 创建Session并初始化全局变量
- 使用
var.assign()方法给权重参数赋值
上述代码打印出来的结果如下：
<tf.Variable 'vgg_19/conv1_1/kernel:0' shape=(3, 3, 3, 64) dtype=float32_ref>
[[ 0.04975817 -0.0374901 -0.04425776]
[ 0.03555809 0.08642714 0.05649987]
[-0.07783681 -0.03184588 -0.07609541]]
（sess.run(tf.global_variables_initializer())之后打印了kernel的一部分，为随机初始化的结果）
<tf.Variable 'vgg_19/conv1_1/kernel:0' shape=(3, 3, 3, 64) dtype=float32_ref>
[[ 0.39416704 0.37740308 -0.04594866]
[ 0.2671299 0.09986369 -0.34100872]
[-0.07573577 -0.2803425 -0.41602272]]
pretrained weight:
[[ 0.39416704 0.37740308 -0.04594866]
[ 0.2671299 0.09986369 -0.34100872]
[-0.07573577 -0.2803425 -0.41602272]]
（权重参数赋值之后，有一次打印了kernel的一部分，同时也打印了预训练模型中对应的部分，可以看到kernel被成功赋值）
vgg_19/conv1_1 (1, 512, 512, 64)
vgg_19/relu1_1 (1, 512, 512, 64)
vgg_19/conv1_2 (1, 512, 512, 64)
vgg_19/relu1_2 (1, 512, 512, 64)
vgg_19/pool1 (1, 256, 256, 64)
......
vgg_19/conv5_4 (1, 32, 32, 512)
vgg_19/relu5_4 (1, 32, 32, 512)
vgg_19/pool5 (1, 16, 16, 512)
（检查featuremap的名字和shape）
1 min=0.0000, max=9201.5811, mean=386.3745, std=737.3252
2 min=0.0000, max=7389.5913, mean=1412.0540, std=616.6437
3 min=0.0000, max=3323.7239, mean=400.2662, std=522.4063
4 min=0.0000, max=4319.3765, mean=369.9904, std=644.4222
5 min=0.0000, max=8997.2305, mean=905.1512, std=1288.8953
（打印 relu3_4，并与之前的两种方法对比数值，结果是一样的，说明整体流程没什么问题）
最后来看一下代码中保存的那张feature map图：
