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168 lines
7.3 KiB
Python
168 lines
7.3 KiB
Python
# -*- coding: utf-8 -*-
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'''VGG19 model for Keras.
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# Reference:
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- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
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'''
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from __future__ import print_function
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import numpy as np
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import warnings
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from keras.models import Model
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from keras.layers import Flatten, Dense, Input
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from keras.layers import Convolution2D, MaxPooling2D
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from keras.preprocessing import image
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from keras.utils.layer_utils import convert_all_kernels_in_model
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from keras.utils.data_utils import get_file
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from keras import backend as K
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TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_th_dim_ordering_th_kernels.h5'
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TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'
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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'
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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'
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def VGG19(include_top=True, weights='imagenet',
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input_tensor=None):
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'''Instantiate the VGG19 architecture,
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optionally loading weights pre-trained
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on ImageNet. Note that when using TensorFlow,
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for best performance you should set
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`image_dim_ordering="tf"` in your Keras config
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at ~/.keras/keras.json.
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The model and the weights are compatible with both
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TensorFlow and Theano. The dimension ordering
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convention used by the model is the one
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specified in your Keras config file.
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# Arguments
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include_top: whether to include the 3 fully-connected
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layers at the top of the network.
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weights: one of `None` (random initialization)
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or "imagenet" (pre-training on ImageNet).
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input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
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to use as image input for the model.
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# Returns
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A Keras model instance.
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'''
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if weights not in {'imagenet', None}:
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raise ValueError('The `weights` argument should be either '
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'`None` (random initialization) or `imagenet` '
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'(pre-training on ImageNet).')
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# Determine proper input shape
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if K.image_dim_ordering() == 'th':
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if include_top:
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input_shape = (3, 224, 224)
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else:
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input_shape = (3, None, None)
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else:
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if include_top:
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input_shape = (224, 224, 3)
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else:
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input_shape = (None, None, 3)
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if input_tensor is None:
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img_input = Input(shape=input_shape)
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else:
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if not K.is_keras_tensor(input_tensor):
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img_input = Input(tensor=input_tensor)
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else:
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img_input = input_tensor
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# Block 1
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x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
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x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
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# Block 2
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x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
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x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
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# Block 3
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x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
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x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
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x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
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x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv4')(x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
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# Block 4
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv4')(x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
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# Block 5
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)
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x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv4')(x)
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x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
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if include_top:
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# Classification block
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x = Flatten(name='flatten')(x)
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x = Dense(4096, activation='relu', name='fc1')(x)
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x = Dense(4096, activation='relu', name='fc2')(x)
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x = Dense(1000, activation='softmax', name='predictions')(x)
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# Create model
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model = Model(img_input, x)
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# load weights
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if weights == 'imagenet':
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print('K.image_dim_ordering:', K.image_dim_ordering())
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if K.image_dim_ordering() == 'th':
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if include_top:
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weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels.h5',
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TH_WEIGHTS_PATH,
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cache_subdir='models')
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else:
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weights_path = get_file('vgg19_weights_th_dim_ordering_th_kernels_notop.h5',
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TH_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models')
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model.load_weights(weights_path)
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if K.backend() == 'tensorflow':
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warnings.warn('You are using the TensorFlow backend, yet you '
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'are using the Theano '
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'image dimension ordering convention '
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'(`image_dim_ordering="th"`). '
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'For best performance, set '
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'`image_dim_ordering="tf"` in '
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'your Keras config '
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'at ~/.keras/keras.json.')
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convert_all_kernels_in_model(model)
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else:
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if include_top:
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weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',
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TF_WEIGHTS_PATH,
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cache_subdir='models')
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else:
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weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
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TF_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models')
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model.load_weights(weights_path)
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if K.backend() == 'theano':
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convert_all_kernels_in_model(model)
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return model
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if __name__ == '__main__':
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model = VGG19(include_top=True, weights='imagenet')
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img_path = 'cat.jpg'
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img = image.load_img(img_path, target_size=(224, 224))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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print('Input image shape:', x.shape)
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preds = model.predict(x)
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print('Predicted:', decode_predictions(preds))
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