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Split
applications and preprocessing modules. (#10339)
* Split `applications` and `preprocessing` modules. * Fix dependencies.
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| Original file line number | Diff line number | Diff line change |
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| # -*- coding: utf-8 -*- | ||
| """DenseNet models for Keras. | ||
| # Reference paper | ||
| - [Densely Connected Convolutional Networks] | ||
| (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award) | ||
| # Reference implementation | ||
| - [Torch DenseNets] | ||
| (https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua) | ||
| - [TensorNets] | ||
| (https://github.com/taehoonlee/tensornets/blob/master/tensornets/densenets.py) | ||
| """ | ||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
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| import os | ||
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| from .. import backend as K | ||
| from ..models import Model | ||
| from ..layers import Activation | ||
| from ..layers import AveragePooling2D | ||
| from ..layers import BatchNormalization | ||
| from ..layers import Concatenate | ||
| from ..layers import Conv2D | ||
| from ..layers import Dense | ||
| from ..layers import GlobalAveragePooling2D | ||
| from ..layers import GlobalMaxPooling2D | ||
| from ..layers import Input | ||
| from ..layers import MaxPooling2D | ||
| from ..layers import ZeroPadding2D | ||
| from ..utils.data_utils import get_file | ||
| from ..engine import get_source_inputs | ||
| from . import imagenet_utils | ||
| from .imagenet_utils import decode_predictions | ||
| from .imagenet_utils import _obtain_input_shape | ||
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| DENSENET121_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels.h5' | ||
| DENSENET121_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5' | ||
| DENSENET169_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels.h5' | ||
| DENSENET169_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5' | ||
| DENSENET201_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels.h5' | ||
| DENSENET201_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5' | ||
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| def dense_block(x, blocks, name): | ||
| """A dense block. | ||
| # Arguments | ||
| x: input tensor. | ||
| blocks: integer, the number of building blocks. | ||
| name: string, block label. | ||
| # Returns | ||
| output tensor for the block. | ||
| """ | ||
| for i in range(blocks): | ||
| x = conv_block(x, 32, name=name + '_block' + str(i + 1)) | ||
| return x | ||
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| def transition_block(x, reduction, name): | ||
| """A transition block. | ||
| # Arguments | ||
| x: input tensor. | ||
| reduction: float, compression rate at transition layers. | ||
| name: string, block label. | ||
| # Returns | ||
| output tensor for the block. | ||
| """ | ||
| bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 | ||
| x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, | ||
| name=name + '_bn')(x) | ||
| x = Activation('relu', name=name + '_relu')(x) | ||
| x = Conv2D(int(K.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, | ||
| name=name + '_conv')(x) | ||
| x = AveragePooling2D(2, strides=2, name=name + '_pool')(x) | ||
| return x | ||
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| def conv_block(x, growth_rate, name): | ||
| """A building block for a dense block. | ||
| # Arguments | ||
| x: input tensor. | ||
| growth_rate: float, growth rate at dense layers. | ||
| name: string, block label. | ||
| # Returns | ||
| output tensor for the block. | ||
| """ | ||
| bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 | ||
| x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, | ||
| name=name + '_0_bn')(x) | ||
| x1 = Activation('relu', name=name + '_0_relu')(x1) | ||
| x1 = Conv2D(4 * growth_rate, 1, use_bias=False, | ||
| name=name + '_1_conv')(x1) | ||
| x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, | ||
| name=name + '_1_bn')(x1) | ||
| x1 = Activation('relu', name=name + '_1_relu')(x1) | ||
| x1 = Conv2D(growth_rate, 3, padding='same', use_bias=False, | ||
| name=name + '_2_conv')(x1) | ||
| x = Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) | ||
| return x | ||
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| def DenseNet(blocks, | ||
| include_top=True, | ||
| weights='imagenet', | ||
| input_tensor=None, | ||
| input_shape=None, | ||
| pooling=None, | ||
| classes=1000): | ||
| """Instantiates the DenseNet architecture. | ||
| Optionally loads weights pre-trained on ImageNet. | ||
| Note that the data format convention used by the model is | ||
| the one specified in your Keras config at `~/.keras/keras.json`. | ||
| When using TensorFlow, for best performance you should | ||
| set `"image_data_format": "channels_last"` in the config. | ||
| # Arguments | ||
| blocks: numbers of building blocks for the four dense layers. | ||
| include_top: whether to include the fully-connected | ||
| layer at the top of the network. | ||
| weights: one of `None` (random initialization), | ||
| 'imagenet' (pre-training on ImageNet), | ||
| or the path to the weights file to be loaded. | ||
| input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) | ||
| to use as image input for the model. | ||
| input_shape: optional shape tuple, only to be specified | ||
| if `include_top` is False (otherwise the input shape | ||
| has to be `(224, 224, 3)` (with `channels_last` data format) | ||
| or `(3, 224, 224)` (with `channels_first` data format). | ||
| It should have exactly 3 inputs channels. | ||
| pooling: optional pooling mode for feature extraction | ||
| when `include_top` is `False`. | ||
| - `None` means that the output of the model will be | ||
| the 4D tensor output of the | ||
| last convolutional layer. | ||
| - `avg` means that global average pooling | ||
| will be applied to the output of the | ||
| last convolutional layer, and thus | ||
| the output of the model will be a 2D tensor. | ||
| - `max` means that global max pooling will | ||
| be applied. | ||
| classes: optional number of classes to classify images | ||
| into, only to be specified if `include_top` is True, and | ||
| if no `weights` argument is specified. | ||
| # Returns | ||
| A Keras model instance. | ||
| # Raises | ||
| ValueError: in case of invalid argument for `weights`, | ||
| or invalid input shape. | ||
| """ | ||
| if not (weights in {'imagenet', None} or os.path.exists(weights)): | ||
| raise ValueError('The `weights` argument should be either ' | ||
| '`None` (random initialization), `imagenet` ' | ||
| '(pre-training on ImageNet), ' | ||
| 'or the path to the weights file to be loaded.') | ||
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| if weights == 'imagenet' and include_top and classes != 1000: | ||
| raise ValueError('If using `weights` as imagenet with `include_top`' | ||
| ' as true, `classes` should be 1000') | ||
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| # Determine proper input shape | ||
| input_shape = _obtain_input_shape(input_shape, | ||
| default_size=224, | ||
| min_size=221, | ||
| data_format=K.image_data_format(), | ||
| require_flatten=include_top, | ||
| weights=weights) | ||
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| 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 | ||
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| bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 | ||
|
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| x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) | ||
| x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) | ||
| x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, | ||
| name='conv1/bn')(x) | ||
| x = Activation('relu', name='conv1/relu')(x) | ||
| x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x) | ||
| x = MaxPooling2D(3, strides=2, name='pool1')(x) | ||
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| x = dense_block(x, blocks[0], name='conv2') | ||
| x = transition_block(x, 0.5, name='pool2') | ||
| x = dense_block(x, blocks[1], name='conv3') | ||
| x = transition_block(x, 0.5, name='pool3') | ||
| x = dense_block(x, blocks[2], name='conv4') | ||
| x = transition_block(x, 0.5, name='pool4') | ||
| x = dense_block(x, blocks[3], name='conv5') | ||
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| x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, | ||
| name='bn')(x) | ||
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| if include_top: | ||
| x = GlobalAveragePooling2D(name='avg_pool')(x) | ||
| x = Dense(classes, activation='softmax', name='fc1000')(x) | ||
| else: | ||
| if pooling == 'avg': | ||
| x = GlobalAveragePooling2D(name='avg_pool')(x) | ||
| elif pooling == 'max': | ||
| x = GlobalMaxPooling2D(name='max_pool')(x) | ||
|
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| # 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 | ||
|
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| # Create model. | ||
| if blocks == [6, 12, 24, 16]: | ||
| model = Model(inputs, x, name='densenet121') | ||
| elif blocks == [6, 12, 32, 32]: | ||
| model = Model(inputs, x, name='densenet169') | ||
| elif blocks == [6, 12, 48, 32]: | ||
| model = Model(inputs, x, name='densenet201') | ||
| else: | ||
| model = Model(inputs, x, name='densenet') | ||
|
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| # Load weights. | ||
| if weights == 'imagenet': | ||
| if include_top: | ||
| if blocks == [6, 12, 24, 16]: | ||
| weights_path = get_file( | ||
| 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', | ||
| DENSENET121_WEIGHT_PATH, | ||
| cache_subdir='models', | ||
| file_hash='0962ca643bae20f9b6771cb844dca3b0') | ||
| elif blocks == [6, 12, 32, 32]: | ||
| weights_path = get_file( | ||
| 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', | ||
| DENSENET169_WEIGHT_PATH, | ||
| cache_subdir='models', | ||
| file_hash='bcf9965cf5064a5f9eb6d7dc69386f43') | ||
| elif blocks == [6, 12, 48, 32]: | ||
| weights_path = get_file( | ||
| 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', | ||
| DENSENET201_WEIGHT_PATH, | ||
| cache_subdir='models', | ||
| file_hash='7bb75edd58cb43163be7e0005fbe95ef') | ||
| else: | ||
| if blocks == [6, 12, 24, 16]: | ||
| weights_path = get_file( | ||
| 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', | ||
| DENSENET121_WEIGHT_PATH_NO_TOP, | ||
| cache_subdir='models', | ||
| file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3') | ||
| elif blocks == [6, 12, 32, 32]: | ||
| weights_path = get_file( | ||
| 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5', | ||
| DENSENET169_WEIGHT_PATH_NO_TOP, | ||
| cache_subdir='models', | ||
| file_hash='50662582284e4cf834ce40ab4dfa58c6') | ||
| elif blocks == [6, 12, 48, 32]: | ||
| weights_path = get_file( | ||
| 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5', | ||
| DENSENET201_WEIGHT_PATH_NO_TOP, | ||
| cache_subdir='models', | ||
| file_hash='1c2de60ee40562448dbac34a0737e798') | ||
| model.load_weights(weights_path) | ||
| elif weights is not None: | ||
| model.load_weights(weights) | ||
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| return model | ||
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| def DenseNet121(include_top=True, | ||
| weights='imagenet', | ||
| input_tensor=None, | ||
| input_shape=None, | ||
| pooling=None, | ||
| classes=1000): | ||
| return DenseNet([6, 12, 24, 16], | ||
| include_top, weights, | ||
| input_tensor, input_shape, | ||
| pooling, classes) | ||
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| def DenseNet169(include_top=True, | ||
| weights='imagenet', | ||
| input_tensor=None, | ||
| input_shape=None, | ||
| pooling=None, | ||
| classes=1000): | ||
| return DenseNet([6, 12, 32, 32], | ||
| include_top, weights, | ||
| input_tensor, input_shape, | ||
| pooling, classes) | ||
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| def DenseNet201(include_top=True, | ||
| weights='imagenet', | ||
| input_tensor=None, | ||
| input_shape=None, | ||
| pooling=None, | ||
| classes=1000): | ||
| return DenseNet([6, 12, 48, 32], | ||
| include_top, weights, | ||
| input_tensor, input_shape, | ||
| pooling, classes) | ||
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| def preprocess_input(x, data_format=None): | ||
| """Preprocesses a numpy array encoding a batch of images. | ||
| # Arguments | ||
| x: a 3D or 4D numpy array consists of RGB values within [0, 255]. | ||
| data_format: data format of the image tensor. | ||
| # Returns | ||
| Preprocessed array. | ||
| """ | ||
| return imagenet_utils.preprocess_input(x, data_format, mode='torch') | ||
|
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| from keras_applications import densenet | ||
|
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| setattr(DenseNet121, '__doc__', DenseNet.__doc__) | ||
| setattr(DenseNet169, '__doc__', DenseNet.__doc__) | ||
| setattr(DenseNet201, '__doc__', DenseNet.__doc__) | ||
| DenseNet121 = densenet.DenseNet121 | ||
| DenseNet169 = densenet.DenseNet169 | ||
| DenseNet201 = densenet.DenseNet201 | ||
| decode_predictions = densenet.decode_predictions | ||
| preprocess_input = densenet.preprocess_input |
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