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vgg_network.py
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vgg_network.py
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# Copyright (c) 2016-2017 Shafeen Tejani. Released under GPLv3.
import tensorflow as tf
import numpy as np
import scipy.io
class VGG:
LAYERS = (
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
'relu5_3', 'conv5_4', 'relu5_4'
)
def __init__(self, data_path):
self.data_path = data_path
self.data = scipy.io.loadmat(data_path)
mean = self.data['normalization'][0][0][0]
self.mean_pixel = np.mean(mean, axis=(0, 1))
self.weights = self.data['layers'][0]
def preprocess(self, image):
return image - self.mean_pixel
def unprocess(self, image):
return image + self.mean_pixel
def net(self, input_image):
net = {}
current_layer = input_image
for i, name in enumerate(self.LAYERS):
if _is_convolutional_layer(name):
kernels, bias = self.weights[i][0][0][0][0]
# matconvnet: weights are [width, height, in_channels, out_channels]
# tensorflow: weights are [height, width, in_channels, out_channels]
kernels = np.transpose(kernels, (1, 0, 2, 3))
bias = bias.reshape(-1)
current_layer = _conv_layer_from(current_layer, kernels, bias)
elif _is_relu_layer(name):
current_layer = tf.nn.relu(current_layer)
elif _is_pooling_layer(name):
current_layer = _pooling_layer_from(current_layer)
net[name] = current_layer
assert len(net) == len(self.LAYERS)
return net
def _is_convolutional_layer(name):
return name[:4] == 'conv'
def _is_relu_layer(name):
return name[:4] == 'relu'
def _is_pooling_layer(name):
return name[:4] == 'pool'
def _conv_layer_from(input, weights, bias):
conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
padding='SAME')
return tf.nn.bias_add(conv, bias)
def _pooling_layer_from(input):
return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
padding='SAME')