# Copyright 2015 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 """ResNet models for Keras. Reference: - [Deep Residual Learning for Image Recognition]( https://arxiv.org/abs/1512.03385) (CVPR 2015) """ import tensorflow.compat.v2 as tf from keras import backend from keras.applications import imagenet_utils from keras.engine import training from keras.layers import VersionAwareLayers from keras.utils import data_utils from keras.utils import layer_utils from tensorflow.python.util.tf_export import keras_export BASE_WEIGHTS_PATH = ( 'https://storage.googleapis.com/tensorflow/keras-applications/resnet/') WEIGHTS_HASHES = { 'resnet50': ('2cb95161c43110f7111970584f804107', '4d473c1dd8becc155b73f8504c6f6626'), 'resnet101': ('f1aeb4b969a6efcfb50fad2f0c20cfc5', '88cf7a10940856eca736dc7b7e228a21'), 'resnet152': ('100835be76be38e30d865e96f2aaae62', 'ee4c566cf9a93f14d82f913c2dc6dd0c'), 'resnet50v2': ('3ef43a0b657b3be2300d5770ece849e0', 'fac2f116257151a9d068a22e544a4917'), 'resnet101v2': ('6343647c601c52e1368623803854d971', 'c0ed64b8031c3730f411d2eb4eea35b5'), 'resnet152v2': ('a49b44d1979771252814e80f8ec446f9', 'ed17cf2e0169df9d443503ef94b23b33'), 'resnext50': ('67a5b30d522ed92f75a1f16eef299d1a', '62527c363bdd9ec598bed41947b379fc'), 'resnext101': ('34fb605428fcc7aa4d62f44404c11509', '0f678c91647380debd923963594981b3') } layers = None def ResNet(stack_fn, preact, use_bias, model_name='resnet', include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs): """Instantiates the ResNet, ResNetV2, and ResNeXt architecture. Args: stack_fn: a function that returns output tensor for the stacked residual blocks. preact: whether to use pre-activation or not (True for ResNetV2, False for ResNet and ResNeXt). use_bias: whether to use biases for convolutional layers or not (True for ResNet and ResNetV2, False for ResNeXt). model_name: string, model name. 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. 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. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. **kwargs: For backwards compatibility only. Returns: A `keras.Model` instance. """ global layers if 'layers' in kwargs: layers = kwargs.pop('layers') else: layers = VersionAwareLayers() if kwargs: raise ValueError('Unknown argument(s): %s' % (kwargs,)) if not (weights in {'imagenet', None} or tf.io.gfile.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.') 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)), name='conv1_pad')(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x) if not preact: 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)), name='pool1_pad')(x) x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x) x = stack_fn(x) if preact: x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='post_bn')(x) x = layers.Activation('relu', name='post_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. model = training.Model(inputs, x, name=model_name) # Load weights. if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES): if include_top: file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5' file_hash = WEIGHTS_HASHES[model_name][0] else: file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5' file_hash = WEIGHTS_HASHES[model_name][1] weights_path = data_utils.get_file( file_name, BASE_WEIGHTS_PATH + file_name, cache_subdir='models', file_hash=file_hash) model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None): """A residual block. Args: x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. conv_shortcut: default True, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 if conv_shortcut: shortcut = layers.Conv2D( 4 * filters, 1, strides=stride, name=name + '_0_conv')(x) shortcut = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut) else: shortcut = x x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x) x = layers.Activation('relu', name=name + '_1_relu')(x) x = layers.Conv2D( filters, kernel_size, padding='SAME', name=name + '_2_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x) x = layers.Activation('relu', name=name + '_2_relu')(x) x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x) x = layers.Add(name=name + '_add')([shortcut, x]) x = layers.Activation('relu', name=name + '_out')(x) return x def stack1(x, filters, blocks, stride1=2, name=None): """A set of stacked residual blocks. Args: x: input tensor. filters: integer, filters of the bottleneck layer in a block. blocks: integer, blocks in the stacked blocks. stride1: default 2, stride of the first layer in the first block. name: string, stack label. Returns: Output tensor for the stacked blocks. """ x = block1(x, filters, stride=stride1, name=name + '_block1') for i in range(2, blocks + 1): x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i)) return x def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None): """A residual block. Args: x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. conv_shortcut: default False, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 preact = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_preact_bn')(x) preact = layers.Activation('relu', name=name + '_preact_relu')(preact) if conv_shortcut: shortcut = layers.Conv2D( 4 * filters, 1, strides=stride, name=name + '_0_conv')(preact) else: shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x x = layers.Conv2D( filters, 1, strides=1, use_bias=False, name=name + '_1_conv')(preact) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x) x = layers.Activation('relu', name=name + '_1_relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x) x = layers.Conv2D( filters, kernel_size, strides=stride, use_bias=False, name=name + '_2_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x) x = layers.Activation('relu', name=name + '_2_relu')(x) x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x) x = layers.Add(name=name + '_out')([shortcut, x]) return x def stack2(x, filters, blocks, stride1=2, name=None): """A set of stacked residual blocks. Args: x: input tensor. filters: integer, filters of the bottleneck layer in a block. blocks: integer, blocks in the stacked blocks. stride1: default 2, stride of the first layer in the first block. name: string, stack label. Returns: Output tensor for the stacked blocks. """ x = block2(x, filters, conv_shortcut=True, name=name + '_block1') for i in range(2, blocks): x = block2(x, filters, name=name + '_block' + str(i)) x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks)) return x def block3(x, filters, kernel_size=3, stride=1, groups=32, conv_shortcut=True, name=None): """A residual block. Args: x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. groups: default 32, group size for grouped convolution. conv_shortcut: default True, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 if conv_shortcut: shortcut = layers.Conv2D( (64 // groups) * filters, 1, strides=stride, use_bias=False, name=name + '_0_conv')(x) shortcut = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut) else: shortcut = x x = layers.Conv2D(filters, 1, use_bias=False, name=name + '_1_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x) x = layers.Activation('relu', name=name + '_1_relu')(x) c = filters // groups x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x) x = layers.DepthwiseConv2D( kernel_size, strides=stride, depth_multiplier=c, use_bias=False, name=name + '_2_conv')(x) x_shape = backend.shape(x)[:-1] x = backend.reshape(x, backend.concatenate([x_shape, (groups, c, c)])) x = layers.Lambda( lambda x: sum(x[:, :, :, :, i] for i in range(c)), name=name + '_2_reduce')(x) x = backend.reshape(x, backend.concatenate([x_shape, (filters,)])) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x) x = layers.Activation('relu', name=name + '_2_relu')(x) x = layers.Conv2D( (64 // groups) * filters, 1, use_bias=False, name=name + '_3_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x) x = layers.Add(name=name + '_add')([shortcut, x]) x = layers.Activation('relu', name=name + '_out')(x) return x def stack3(x, filters, blocks, stride1=2, groups=32, name=None): """A set of stacked residual blocks. Args: x: input tensor. filters: integer, filters of the bottleneck layer in a block. blocks: integer, blocks in the stacked blocks. stride1: default 2, stride of the first layer in the first block. groups: default 32, group size for grouped convolution. name: string, stack label. Returns: Output tensor for the stacked blocks. """ x = block3(x, filters, stride=stride1, groups=groups, name=name + '_block1') for i in range(2, blocks + 1): x = block3( x, filters, groups=groups, conv_shortcut=False, name=name + '_block' + str(i)) return x @keras_export('keras.applications.resnet50.ResNet50', 'keras.applications.resnet.ResNet50', 'keras.applications.ResNet50') def ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet50 architecture.""" def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 4, name='conv3') x = stack1(x, 256, 6, name='conv4') return stack1(x, 512, 3, name='conv5') return ResNet(stack_fn, False, True, 'resnet50', include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs) @keras_export('keras.applications.resnet.ResNet101', 'keras.applications.ResNet101') def ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet101 architecture.""" def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 4, name='conv3') x = stack1(x, 256, 23, name='conv4') return stack1(x, 512, 3, name='conv5') return ResNet(stack_fn, False, True, 'resnet101', include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs) @keras_export('keras.applications.resnet.ResNet152', 'keras.applications.ResNet152') def ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet152 architecture.""" def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 8, name='conv3') x = stack1(x, 256, 36, name='conv4') return stack1(x, 512, 3, name='conv5') return ResNet(stack_fn, False, True, 'resnet152', include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs) @keras_export('keras.applications.resnet50.preprocess_input', 'keras.applications.resnet.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input( x, data_format=data_format, mode='caffe') @keras_export('keras.applications.resnet50.decode_predictions', 'keras.applications.resnet.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_CAFFE, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ DOC = """ Reference: - [Deep Residual Learning for Image Recognition]( https://arxiv.org/abs/1512.03385) (CVPR 2015) For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call `tf.keras.applications.resnet.preprocess_input` on your inputs before passing them to the model. `resnet.preprocess_input` will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. Args: 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. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. Returns: A Keras model instance. """ setattr(ResNet50, '__doc__', ResNet50.__doc__ + DOC) setattr(ResNet101, '__doc__', ResNet101.__doc__ + DOC) setattr(ResNet152, '__doc__', ResNet152.__doc__ + DOC)