vgg16.py

# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import absolute_import

import warnings

from keras.models import Model
from keras.layers import Flatten, Dense, Input, Dropout
from keras.layers import Convolution2D, MaxPooling2D
from keras.engine.topology import get_source_inputs
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
from keras import backend as K
from keras.applications.imagenet_utils import decode_predictions, preprocess_input, _obtain_input_shape


PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/'
TF_WEIGHTS = 'vgg16_weights_tf_dim_ordering_tf_kernels.h5'
TF_WEIGHTS_NO_TOP = 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'


def VGG16(include_top=True, weights='imagenet',
          input_tensor=None, input_shape=None):
    if weights not in {'imagenet', None}:
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization) or `imagenet` '
                         '(pre-training on ImageNet).')
    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=48,
                                      dim_ordering=K.image_dim_ordering(),
                                      include_top=include_top)

    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
    # Block 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)

    # Block 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)

    # Block 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')(x)

    # Block 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=(2, 2), name='block4_pool')(x)

    # Block 5
    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)
    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)
    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

    if include_top:
        # Classification block
        x = Flatten(name='flatten')(x)
        x = Dense(4096, activation='relu', name='fc1')(x)
        x = Dropout(0.5)(x)
        x = Dense(4096, activation='relu', name='fc2')(x)
        x = Dropout(0.5)(x)
        x = Dense(1000, activation='softmax', name='predictions')(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = Model(inputs, x, name='vgg16')

    # load weights
    if weights == 'imagenet':
        if K.image_dim_ordering() == 'tf':
            wname = TF_WEIGHTS if include_top else TF_WEIGHTS_NO_TOP
            weights_path = get_file(wname, PATH+wname, cache_subdir='models')
            model.load_weights(weights_path)
    return model