# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=invalid-name """DenseNet models for Keras. Reference: - [Densely Connected Convolutional Networks]( https://arxiv.org/abs/1608.06993) (CVPR 2017) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.keras import backend from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.engine import training from tensorflow.python.keras.layers import VersionAwareLayers from tensorflow.python.keras.utils import data_utils from tensorflow.python.keras.utils import layer_utils from tensorflow.python.lib.io import file_io from tensorflow.python.util.tf_export import keras_export BASE_WEIGTHS_PATH = ('https://storage.googleapis.com/tensorflow/' 'keras-applications/densenet/') DENSENET121_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5') DENSENET121_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET169_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5') DENSENET169_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5') DENSENET201_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5') DENSENET201_WEIGHT_PATH_NO_TOP = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5') layers = VersionAwareLayers() 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 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 backend.image_data_format() == 'channels_last' else 1 x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')( x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D( int(backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')( x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x) return x 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 backend.image_data_format() == 'channels_last' else 1 x1 = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')( x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D( 4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')( x1) x1 = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')( x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D( growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')( x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x def DenseNet( blocks, include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax'): """Instantiates the DenseNet architecture. Reference: - [Densely Connected Convolutional Networks]( https://arxiv.org/abs/1608.06993) (CVPR 2017) 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`. Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your inputs before passing them to the model. 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, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. 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 block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, 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. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Returns: A `keras.Model` instance. Raises: ValueError: in case of invalid argument for `weights`, or invalid input shape. ValueError: if `classifier_activation` is not `softmax` or `None` when using a pretrained top layer. """ if not (weights in {'imagenet', None} or file_io.file_exists_v2(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.') 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') # Determine proper input shape input_shape = imagenet_utils.obtain_input_shape( input_shape, default_size=224, min_size=32, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')( x) x = layers.Activation('relu', name='conv1/relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x) x = layers.MaxPooling2D(3, strides=2, name='pool1')(x) 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') x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) x = layers.Activation('relu', name='relu')(x) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) imagenet_utils.validate_activation(classifier_activation, weights) x = layers.Dense(classes, activation=classifier_activation, name='predictions')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D(name='avg_pool')(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D(name='max_pool')(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. if blocks == [6, 12, 24, 16]: model = training.Model(inputs, x, name='densenet121') elif blocks == [6, 12, 32, 32]: model = training.Model(inputs, x, name='densenet169') elif blocks == [6, 12, 48, 32]: model = training.Model(inputs, x, name='densenet201') else: model = training.Model(inputs, x, name='densenet') # Load weights. if weights == 'imagenet': if include_top: if blocks == [6, 12, 24, 16]: weights_path = data_utils.get_file( 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', DENSENET121_WEIGHT_PATH, cache_subdir='models', file_hash='9d60b8095a5708f2dcce2bca79d332c7') elif blocks == [6, 12, 32, 32]: weights_path = data_utils.get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', DENSENET169_WEIGHT_PATH, cache_subdir='models', file_hash='d699b8f76981ab1b30698df4c175e90b') elif blocks == [6, 12, 48, 32]: weights_path = data_utils.get_file( 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', DENSENET201_WEIGHT_PATH, cache_subdir='models', file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807') else: if blocks == [6, 12, 24, 16]: weights_path = data_utils.get_file( 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET121_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='30ee3e1110167f948a6b9946edeeb738') elif blocks == [6, 12, 32, 32]: weights_path = data_utils.get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET169_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='b8c4d4c20dd625c148057b9ff1c1176b') elif blocks == [6, 12, 48, 32]: weights_path = data_utils.get_file( 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET201_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='c13680b51ded0fb44dff2d8f86ac8bb1') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model @keras_export('keras.applications.densenet.DenseNet121', 'keras.applications.DenseNet121') def DenseNet121(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Densenet121 architecture.""" return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor, input_shape, pooling, classes) @keras_export('keras.applications.densenet.DenseNet169', 'keras.applications.DenseNet169') def DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Densenet169 architecture.""" return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor, input_shape, pooling, classes) @keras_export('keras.applications.densenet.DenseNet201', 'keras.applications.DenseNet201') def DenseNet201(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Densenet201 architecture.""" return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor, input_shape, pooling, classes) @keras_export('keras.applications.densenet.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input( x, data_format=data_format, mode='torch') @keras_export('keras.applications.densenet.decode_predictions') def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ DOC = """ Reference: - [Densely Connected Convolutional Networks]( https://arxiv.org/abs/1608.06993) (CVPR 2017) 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`. Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your inputs before passing them to the model. Arguments: 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, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. 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 block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, 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. """ setattr(DenseNet121, '__doc__', DenseNet121.__doc__ + DOC) setattr(DenseNet169, '__doc__', DenseNet169.__doc__ + DOC) setattr(DenseNet201, '__doc__', DenseNet201.__doc__ + DOC)