''' This code is part of the Keras VGG-16 model ''' from __future__ import print_function from __future__ import absolute_import from keras.models import Model from keras.layers import Input from keras.layers import Convolution2D, MaxPooling2D from keras.layers.convolutional import AtrousConvolution2D from keras.utils.data_utils import get_file from keras import backend as K TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5' def dcn_vgg(input_tensor=None): input_shape = (3, None, None) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # conv_1 x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input) x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # conv_2 x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x) x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # conv_3 x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x) x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x) x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', border_mode='same')(x) # conv_4 x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x) x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x) x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x) x = MaxPooling2D((2, 2), strides=(1, 1), name='block4_pool', border_mode='same')(x) # conv_5 x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1', atrous_rate=(2, 2))(x) x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2', atrous_rate=(2, 2))(x) x = AtrousConvolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3', atrous_rate=(2, 2))(x) # Create model model = Model(img_input, x) # Load weights weights_path = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5', TH_WEIGHTS_PATH_NO_TOP, cache_subdir='models') model.load_weights(weights_path) return model