StyleTransfer/vgg.py at master · 8000net/StyleTransfer · GitHub

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from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D, Input
from keras.utils.data_utils import get_file
import keras.backend as K
import h5py
import numpy as np
import tensorflow as tf

WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5'

MEAN_PIXEL = np.array([103.939, 116.779, 123.68])

weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
                        WEIGHTS_PATH_NO_TOP,
                        cache_subdir='models',
                        file_hash='253f8cb515780f3b799900260a226db6')
WEIGHTS_FILE = h5py.File(weights_path)

def vgg_layers(img_input, input_shape):
    # Block 1
    img_input = Input(tensor=img_input, shape=input_shape)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    return x


def load_weights(model):
    f = WEIGHTS_FILE
    layer_names = [name for name in f.attrs['layer_names']]

    for layer in model.layers:
        b_name = layer.name.encode()
        if b_name in layer_names:
            g = f[b_name]
            weights = [g[name] for name in g.attrs['weight_names']]
            layer.set_weights(weights)
            layer.trainable = False


def VGG19(img_input, input_shape):
    """
    VGG19, but can take input_tensor, and load weights on VGG layers only
    """
    model = Model(img_input, vgg_layers(img_input, input_shape), name='vgg19')
    load_weights(model)
    return model


def preprocess_input(x):
    # Convert 'RGB' -> 'BGR'
    if type(x) is np.ndarray:
        x = x[..., ::-1]
    else:
        x = tf.reverse(x, [-1])

    return x - MEAN_PIXEL