304 lines
11 KiB
Python
304 lines
11 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=invalid-name
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# pylint: disable=unused-import
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"""ResNet50 model for Keras.
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# Reference:
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- [Deep Residual Learning for Image
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Recognition](https://arxiv.org/abs/1512.03385)
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Adapted from code contributed by BigMoyan.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import layers
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from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape
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from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
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from tensorflow.python.keras.applications.imagenet_utils import preprocess_input
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from tensorflow.python.keras.layers import Activation
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from tensorflow.python.keras.layers import AveragePooling2D
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from tensorflow.python.keras.layers import BatchNormalization
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from tensorflow.python.keras.layers import Conv2D
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from tensorflow.python.keras.layers import Dense
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from tensorflow.python.keras.layers import Flatten
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from tensorflow.python.keras.layers import GlobalAveragePooling2D
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from tensorflow.python.keras.layers import GlobalMaxPooling2D
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from tensorflow.python.keras.layers import Input
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from tensorflow.python.keras.layers import MaxPooling2D
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from tensorflow.python.keras.layers import ZeroPadding2D
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from tensorflow.python.keras.models import Model
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.keras.utils.data_utils import get_file
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.util.tf_export import tf_export
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WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
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WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
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def identity_block(input_tensor, kernel_size, filters, stage, block):
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"""The identity block is the block that has no conv layer at shortcut.
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Arguments:
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input_tensor: input tensor
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kernel_size: default 3, the kernel size of middle conv layer at main path
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filters: list of integers, the filters of 3 conv layer at main path
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stage: integer, current stage label, used for generating layer names
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block: 'a','b'..., current block label, used for generating layer names
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Returns:
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Output tensor for the block.
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"""
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filters1, filters2, filters3 = filters
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if K.image_data_format() == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
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x = Activation('relu')(x)
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x = Conv2D(
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filters2, kernel_size, padding='same', name=conv_name_base + '2b')(
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x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
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x = Activation('relu')(x)
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x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
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x = layers.add([x, input_tensor])
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x = Activation('relu')(x)
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return x
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def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2,
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2)):
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"""A block that has a conv layer at shortcut.
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Arguments:
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input_tensor: input tensor
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kernel_size: default 3, the kernel size of middle conv layer at main path
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filters: list of integers, the filters of 3 conv layer at main path
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stage: integer, current stage label, used for generating layer names
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block: 'a','b'..., current block label, used for generating layer names
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strides: Strides for the first conv layer in the block.
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Returns:
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Output tensor for the block.
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Note that from stage 3,
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the first conv layer at main path is with strides=(2, 2)
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And the shortcut should have strides=(2, 2) as well
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"""
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filters1, filters2, filters3 = filters
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if K.image_data_format() == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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x = Conv2D(
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filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(
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input_tensor)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
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x = Activation('relu')(x)
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x = Conv2D(
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filters2, kernel_size, padding='same', name=conv_name_base + '2b')(
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x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
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x = Activation('relu')(x)
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x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
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x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
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shortcut = Conv2D(
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filters3, (1, 1), strides=strides, name=conv_name_base + '1')(
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input_tensor)
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shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
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x = layers.add([x, shortcut])
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x = Activation('relu')(x)
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return x
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@tf_export('keras.applications.ResNet50',
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'keras.applications.resnet50.ResNet50')
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def ResNet50(include_top=True,
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weights='imagenet',
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000):
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"""Instantiates the ResNet50 architecture.
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Optionally loads 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_data_format='channels_last'` 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 data format
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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 fully-connected
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layer at the top of the network.
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weights: one of `None` (random initialization),
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'imagenet' (pre-training on ImageNet),
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or the path to the weights file to be loaded.
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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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input_shape: optional shape tuple, only to be specified
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if `include_top` is False (otherwise the input shape
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has to be `(224, 224, 3)` (with `channels_last` data format)
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or `(3, 224, 224)` (with `channels_first` data format).
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 197.
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E.g. `(200, 200, 3)` would be one valid value.
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pooling: Optional pooling mode for feature extraction
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when `include_top` is `False`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional layer.
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- `avg` means that global average pooling
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will be applied to the output of the
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last convolutional layer, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will
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be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified.
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Returns:
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A Keras model instance.
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Raises:
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ValueError: in case of invalid argument for `weights`,
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or invalid input shape.
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"""
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if not (weights in {'imagenet', None} or os.path.exists(weights)):
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raise ValueError('The `weights` argument should be either '
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'`None` (random initialization), `imagenet` '
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'(pre-training on ImageNet), '
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'or the path to the weights file to be loaded.')
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if weights == 'imagenet' and include_top and classes != 1000:
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raise ValueError('If using `weights` as imagenet with `include_top`'
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' as true, `classes` should be 1000')
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# Determine proper input shape
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input_shape = _obtain_input_shape(
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input_shape,
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default_size=224,
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min_size=197,
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data_format=K.image_data_format(),
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require_flatten=include_top,
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weights=weights)
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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, shape=input_shape)
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else:
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img_input = input_tensor
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if K.image_data_format() == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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x = Conv2D(
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64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input)
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x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
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x = Activation('relu')(x)
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x = MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
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x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
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x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
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x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
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x = AveragePooling2D((7, 7), name='avg_pool')(x)
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if include_top:
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x = Flatten()(x)
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x = Dense(classes, activation='softmax', name='fc1000')(x)
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else:
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if pooling == 'avg':
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x = GlobalAveragePooling2D()(x)
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elif pooling == 'max':
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x = GlobalMaxPooling2D()(x)
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# Ensure that the model takes into account
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# any potential predecessors of `input_tensor`.
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if input_tensor is not None:
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inputs = layer_utils.get_source_inputs(input_tensor)
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else:
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inputs = img_input
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# Create model.
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model = Model(inputs, x, name='resnet50')
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# load weights
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if weights == 'imagenet':
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if include_top:
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weights_path = get_file(
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'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
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WEIGHTS_PATH,
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cache_subdir='models',
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md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
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else:
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weights_path = get_file(
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'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
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WEIGHTS_PATH_NO_TOP,
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cache_subdir='models',
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md5_hash='a268eb855778b3df3c7506639542a6af')
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model.load_weights(weights_path)
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elif weights is not None:
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model.load_weights(weights)
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return model
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