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vgg16.py
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import keras
from keras.models import *
from keras.layers import *
"""
Code taken from:
https://github.com/fchollet/deep-learning-models
"""
IMAGE_ORDERING = 'channels_last'
if IMAGE_ORDERING == 'channels_first':
pretrained_url = "https://github.com/fchollet/deep-learning-models/" \
"releases/download/v0.1/" \
"vgg16_weights_th_dim_ordering_th_kernels_notop.h5"
elif IMAGE_ORDERING == 'channels_last':
pretrained_url = "https://github.com/fchollet/deep-learning-models/" \
"releases/download/v0.1/" \
"vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5"
def get_vgg_encoder(input_height=224, input_width=224, pretrained='imagenet'):
# assert input_height % 32 == 0
# assert input_width % 32 == 0
if IMAGE_ORDERING == 'channels_first':
img_input = Input(shape=(3, input_height, input_width))
elif IMAGE_ORDERING == 'channels_last':
img_input = Input(shape=(input_height, input_width, 3))
x = Conv2D(64, (3, 3), activation='relu', padding='same',
name='block1_conv1', data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same',
name='block1_conv2', data_format=IMAGE_ORDERING)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool',
data_format=IMAGE_ORDERING)(x)
f1 = x
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same',
name='block2_conv1', data_format=IMAGE_ORDERING)(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same',
name='block2_conv2', data_format=IMAGE_ORDERING)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool',
data_format=IMAGE_ORDERING)(x)
f2 = x
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same',
name='block3_conv1', data_format=IMAGE_ORDERING)(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same',
name='block3_conv2', data_format=IMAGE_ORDERING)(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same',
name='block3_conv3', data_format=IMAGE_ORDERING)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool',
data_format=IMAGE_ORDERING)(x)
f3 = x
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same',
name='block4_conv1', data_format=IMAGE_ORDERING)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
name='block4_conv2', data_format=IMAGE_ORDERING)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
name='block4_conv3', data_format=IMAGE_ORDERING)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool',
data_format=IMAGE_ORDERING)(x)
f4 = x
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same',
name='block5_conv1', data_format=IMAGE_ORDERING)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
name='block5_conv2', data_format=IMAGE_ORDERING)(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
name='block5_conv3', data_format=IMAGE_ORDERING)(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool',
data_format=IMAGE_ORDERING)(x)
f5 = x
if pretrained == 'imagenet':
VGG_Weights_path = keras.utils.get_file(
pretrained_url.split("/")[-1], pretrained_url)
Model(img_input, x).load_weights(VGG_Weights_path)
return img_input, [f1, f2, f3, f4, f5]