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 conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): """A block that has a conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names strides: Strides for the first conv layer in the block. # Returns Output tensor for the block. Note that from stage 3, the first conv layer at main path is with strides=(2, 2) And the shortcut should have strides=(2, 2) as well """ filters1, filters2, filters3 = filters if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = layers.Conv2D(filters1, (1, 1), strides=strides, kernel_initializer='he_normal', name=conv_name_base + '2a')(input_tensor) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters2, kernel_size, padding='same', kernel_initializer='he_normal', name=conv_name_base + '2b')(x) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters3, (1, 1), kernel_initializer='he_normal', name=conv_name_base + '2c')(x) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = layers.Conv2D(filters3, (1, 1), strides=strides, kernel_initializer='he_normal', name=conv_name_base + '1')(input_tensor) shortcut = layers.BatchNormalization( axis=bn_axis, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) x = layers.Activation('relu')(x) return x
def identity_block(input_tensor, kernel_size, filters, stage, block): """The identity block is the block that has no conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names # Returns Output tensor for the block. """ filters1, filters2, filters3 = filters if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = layers.Conv2D(filters1, (1, 1), kernel_initializer=init, name=conv_name_base + '2a', kernel_regularizer=keras.regularizers.l2(0.01))( input_tensor) # L2正则化 x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a', gamma_initializer='zeros')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters2, kernel_size, padding='same', kernel_initializer=init, name=conv_name_base + '2b', kernel_regularizer=keras.regularizers.l2(0.01))( x) # L2正则化 x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b', gamma_initializer='zeros')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters3, (1, 1), kernel_initializer=init, name=conv_name_base + '2c', kernel_regularizer=keras.regularizers.l2(0.01))( x) # L2正则化 x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c', gamma_initializer='zeros')(x) x = layers.add([x, input_tensor]) x = layers.Activation('relu')(x) return x
def ResNet18(input_shape=None, classes=10, **kwargs): # Define the input as a tensor with shape input_shape x_input = Input(input_shape) if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 #stage 1 with tf.name_scope('stage1'): x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(x_input) x = layers.Conv2D(64, (7, 7), strides=(2, 2), padding='valid', kernel_initializer='he_normal', name='conv1')(x) x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = layers.Activation('relu')(x) print("stage1:" + str(x.shape)) #stage 2 with tf.name_scope('stage2'): x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x) x = identity_block(x, 3, [64, 64], stage=2, block='b') x = identity_block(x, 3, [64, 64], stage=2, block='c') print("stage2:" + str(x.shape)) #stage 3 with tf.name_scope('stage3'): x = conv_block(x, 3, [128, 128], stage=3, block='a') x = identity_block(x, 3, [128, 128], stage=3, block='d') print("stage3:" + str(x.shape)) #stage 4 with tf.name_scope('stage4'): x = conv_block(x, 3, [256, 256], stage=4, block='a') x = identity_block(x, 3, [256, 256], stage=4, block='c') print("stage4:" + str(x.shape)) #stage 5 with tf.name_scope('stage5'): x = conv_block(x, 3, [512, 512], stage=5, block='a') x = identity_block(x, 3, [512, 512], stage=5, block='c') print("stage5:" + str(x.shape)) #full-connected layer with tf.name_scope('fc'): x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='fc10')(x) # Create model. model = models.Model(x_input, x, name='resnet18') return model
def resnet_net(img_input, include_top, classes, pooling): if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input) x = layers.Conv2D(64, (7, 7), strides=(2, 2), padding='valid', kernel_initializer='he_normal', name='conv1')(x) x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = layers.Activation('relu')(x) x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='fc1000')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(x) else: warnings.warn('The output shape of `ResNet50(include_top=False)` ' 'has been changed since Keras 2.2.0.') 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 conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(1, 1), name=None): """Utility function to apply conv + BN. # Arguments x: input tensor. filters: filters in `Conv2D`. num_row: height of the convolution kernel. num_col: width of the convolution kernel. padding: padding mode in `Conv2D`. strides: strides in `Conv2D`. name: name of the ops; will become `name + '_conv'` for the convolution and `name + '_bn'` for the batch norm layer. # Returns Output tensor after applying `Conv2D` and `BatchNormalization`. """ if name is not None: bn_name = name + '_bn' conv_name = name + '_conv' else: bn_name = None conv_name = None if backend.image_data_format() == 'channels_first': bn_axis = 1 else: bn_axis = 3 x = layers.Conv2D(filters, (num_row, num_col), strides=strides, padding=padding, use_bias=False, name=conv_name)(x) x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) x = layers.Activation('relu', name=name)(x) return x
def ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet50 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`. # 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. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. """ global backend, layers, models, keras_utils backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs) 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.') 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 = _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 if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input) x = layers.Conv2D(64, (7, 7), strides=(2, 2), padding='valid', kernel_initializer='he_normal', name='conv1')(x) x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = layers.Activation('relu')(x) x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='fc1000')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(x) else: warnings.warn('The output shape of `ResNet50(include_top=False)` ' 'has been changed since Keras 2.2.0.') # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = keras_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = models.Model(inputs, x, name='resnet50') # Load weights. if weights == 'imagenet': if include_top: weights_path = keras_utils.get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', md5_hash='a7b3fe01876f51b976af0dea6bc144eb', cache_dir=os.path.join(os.path.dirname(__file__), '..')) else: weights_path = keras_utils.get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='a268eb855778b3df3c7506639542a6af', cache_dir=os.path.join(os.path.dirname(__file__), '..')) model.load_weights(weights_path) if backend.backend() == 'theano': keras_utils.convert_all_kernels_in_model(model) elif weights is not None: model.load_weights(weights) return model
def DenseNet(blocks, include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """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`. # 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. # Returns A Keras model instance. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. """ global backend, layers, models, keras_utils backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs) 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.') 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 = _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) x = layers.Dense(classes, activation='softmax', name='fc1000')(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`. from keras.engine.topology import get_source_inputs if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. if blocks == [6, 12, 24, 16]: model = models.Model(inputs, x, name='densenet121') elif blocks == [6, 12, 32, 32]: model = models.Model(inputs, x, name='densenet169') elif blocks == [6, 12, 48, 32]: model = models.Model(inputs, x, name='densenet201') else: model = models.Model(inputs, x, name='densenet') weights_path=[] # Load weights. if weights == 'imagenet': if include_top: if blocks == [6, 12, 24, 16]: weights_path = keras_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 = keras_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 = keras_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 = keras_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 = keras_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 = DENSENET201_WEIGHT_PATH_NO_TOP model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the Xception 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`. Note that the default input image size for this model is 299x299. # 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 `(299, 299, 3)`. It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. `(150, 150, 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. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs) 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.') 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 = _obtain_input_shape(input_shape, default_size=299, min_size=71, 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 channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1 x = layers.Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) x = layers.BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x) x = layers.Activation('relu', name='block1_conv1_act')(x) x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x) x = layers.Activation('relu', name='block1_conv2_act')(x) residual = layers.Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x) x = layers.Activation('relu', name='block2_sepconv2_act')(x) x = layers.SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) residual = layers.Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.Activation('relu', name='block3_sepconv1_act')(x) x = layers.SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x) x = layers.Activation('relu', name='block3_sepconv2_act')(x) x = layers.SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) x = layers.add([x, residual]) residual = layers.Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.Activation('relu', name='block4_sepconv1_act')(x) x = layers.SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x) x = layers.Activation('relu', name='block4_sepconv2_act')(x) x = layers.SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) x = layers.add([x, residual]) for i in range(8): residual = x prefix = 'block' + str(i + 5) x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x) x = layers.SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name=prefix + '_sepconv1_bn')(x) x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x) x = layers.SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name=prefix + '_sepconv2_bn')(x) x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x) x = layers.SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) x = layers.BatchNormalization(axis=channel_axis, name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = layers.Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = layers.BatchNormalization(axis=channel_axis)(residual) x = layers.Activation('relu', name='block13_sepconv1_act')(x) x = layers.SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block13_sepconv1_bn')(x) x = layers.Activation('relu', name='block13_sepconv2_act')(x) x = layers.SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block13_sepconv2_bn')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) x = layers.add([x, residual]) x = layers.SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) x = layers.BatchNormalization(axis=channel_axis, name='block14_sepconv1_bn')(x) x = layers.Activation('relu', name='block14_sepconv1_act')(x) x = layers.SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x) x = layers.BatchNormalization(axis=channel_axis, name='block14_sepconv2_bn')(x) x = layers.Activation('relu', name='block14_sepconv2_act')(x) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. from keras.engine.topology import get_source_inputs if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = models.Model(inputs, x, name='xception') # Load weights. if weights == 'imagenet': # if include_top: # weights_path = keras_utils.get_file( # 'xception_weights_tf_dim_ordering_tf_kernels.h5', # TF_WEIGHTS_PATH, # cache_subdir='models', # file_hash='0a58e3b7378bc2990ea3b43d5981f1f6') weights_path = WEIGHTS_PATH_NO_TOP model.load_weights(weights_path) if backend.backend() == 'theano': keras_utils.convert_all_kernels_in_model(model) elif weights is not None: model.load_weights(weights) return model