168 lines
7.3 KiB
Python
168 lines
7.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
'''VGG19 model for Keras.
|
|
|
|
# Reference:
|
|
|
|
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
|
|
|
|
'''
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
import warnings
|
|
|
|
from keras.models import Model
|
|
from keras.layers import Flatten, Dense, Input
|
|
from keras.layers import Convolution2D, MaxPooling2D
|
|
from keras.preprocessing import image
|
|
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
|
|
|
|
|
|
TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels.h5'
|
|
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'
|
|
TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels_notop.h5'
|
|
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'
|
|
|
|
|
|
def VGG19(include_top=True, weights='imagenet',
|
|
input_tensor=None):
|
|
'''Instantiate the VGG19 architecture,
|
|
optionally loading weights pre-trained
|
|
on ImageNet. Note that when using TensorFlow,
|
|
for best performance you should set
|
|
`image_dim_ordering="tf"` in your Keras config
|
|
at ~/.keras/keras.json.
|
|
|
|
The model and the weights are compatible with both
|
|
TensorFlow and Theano. The dimension ordering
|
|
convention used by the model is the one
|
|
specified in your Keras config file.
|
|
|
|
# Arguments
|
|
include_top: whether to include the 3 fully-connected
|
|
layers at the top of the network.
|
|
weights: one of `None` (random initialization)
|
|
or "imagenet" (pre-training on ImageNet).
|
|
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
|
|
to use as image input for the model.
|
|
|
|
# Returns
|
|
A Keras model instance.
|
|
'''
|
|
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
|
|
if K.image_dim_ordering() == 'th':
|
|
if include_top:
|
|
input_shape = (3, 224, 224)
|
|
else:
|
|
input_shape = (3, None, None)
|
|
else:
|
|
if include_top:
|
|
input_shape = (224, 224, 3)
|
|
else:
|
|
input_shape = (None, None, 3)
|
|
|
|
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)
|
|
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 = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv4')(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 = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv4')(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 = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv4')(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 = Dense(4096, activation='relu', name='fc2')(x)
|
|
x = Dense(1000, activation='softmax', name='predictions')(x)
|
|
|
|
# Create model
|
|
model = Model(img_input, x)
|
|
|
|
# load weights
|
|
if weights == 'imagenet':
|
|
print('K.image_dim_ordering:', K.image_dim_ordering())
|
|
if K.image_dim_ordering() == 'th':
|
|
if include_top:
|
|
weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels.h5',
|
|
TH_WEIGHTS_PATH,
|
|
cache_subdir='models')
|
|
else:
|
|
weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels_notop.h5',
|
|
TH_WEIGHTS_PATH_NO_TOP,
|
|
cache_subdir='models')
|
|
model.load_weights(weights_path)
|
|
if K.backend() == 'tensorflow':
|
|
warnings.warn('You are using the TensorFlow backend, yet you '
|
|
'are using the Theano '
|
|
'image dimension ordering convention '
|
|
'(`image_dim_ordering="th"`). '
|
|
'For best performance, set '
|
|
'`image_dim_ordering="tf"` in '
|
|
'your Keras config '
|
|
'at ~/.keras/keras.json.')
|
|
convert_all_kernels_in_model(model)
|
|
else:
|
|
if include_top:
|
|
weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',
|
|
TF_WEIGHTS_PATH,
|
|
cache_subdir='models')
|
|
else:
|
|
weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
|
|
TF_WEIGHTS_PATH_NO_TOP,
|
|
cache_subdir='models')
|
|
model.load_weights(weights_path)
|
|
if K.backend() == 'theano':
|
|
convert_all_kernels_in_model(model)
|
|
return model
|
|
|
|
|
|
if __name__ == '__main__':
|
|
model = VGG19(include_top=True, weights='imagenet')
|
|
|
|
img_path = 'cat.jpg'
|
|
img = image.load_img(img_path, target_size=(224, 224))
|
|
x = image.img_to_array(img)
|
|
x = np.expand_dims(x, axis=0)
|
|
x = preprocess_input(x)
|
|
print('Input image shape:', x.shape)
|
|
|
|
preds = model.predict(x)
|
|
print('Predicted:', decode_predictions(preds))
|