def ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the ResNet50 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. # Arguments include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization) or 'imagenet' (pre-training on ImageNet). 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 197. 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 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 weights not in {'imagenet', None}: raise ValueError('The `weights` argument should be either ' '`None` (random initialization) or `imagenet` ' '(pre-training on ImageNet).') 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=48, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x = Conv2D( #64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input) 64, (7, 7), strides=(1, 1), padding='same', name='conv1')(img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = 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') #x = AveragePooling2D((7, 7), name='avg_pool')(x) #x = AveragePooling2D((2, 2), name='avg_pool')(x) x = AveragePooling2D((4, 4), name='avg_pool')(x) if include_top: x = Flatten()(x) x = Dense(classes, activation='softmax', name='fc1000')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='resnet50') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', md5_hash='a7b3fe01876f51b976af0dea6bc144eb') else: weights_path = get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='a268eb855778b3df3c7506639542a6af') model.load_weights(weights_path) if K.backend() == 'theano': layer_utils.convert_all_kernels_in_model(model) if include_top: maxpool = model.get_layer(name='avg_pool') shape = maxpool.output_shape[1:] dense = model.get_layer(name='fc1000') layer_utils.convert_dense_weights_data_format( dense, shape, 'channels_first') if K.image_data_format() == 'channels_first' and K.backend( ) == 'tensorflow': warnings.warn('You are using the TensorFlow backend, yet you ' 'are using the Theano ' 'image data format convention ' '(`image_data_format="channels_first"`). ' 'For best performance, set ' '`image_data_format="channels_last"` in ' 'your Keras config ' 'at ~/.keras/keras.json.') return model
def InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Inception v3 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. 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)` (with `channels_last` data format) or `(3, 299, 299)` (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 139. 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 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.') 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=139, data_format=K.image_data_format(), require_flatten=False, weights=weights) 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 if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = 3 x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid') x = conv2d_bn(x, 32, 3, 3, padding='valid') x = conv2d_bn(x, 64, 3, 3) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv2d_bn(x, 80, 1, 1, padding='valid') x = conv2d_bn(x, 192, 3, 3, padding='valid') x = MaxPooling2D((3, 3), strides=(2, 2))(x) # mixed 0, 1, 2: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 32, 1, 1) x = layers.concatenate( [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed0') # mixed 1: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1, 1) x = layers.concatenate( [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed1') # mixed 2: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1, 1) x = layers.concatenate( [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed2') # mixed 3: 17 x 17 x 768 branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid') branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn( branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid') branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) x = layers.concatenate( [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3') # mixed 4: 17 x 17 x 768 branch1x1 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(x, 128, 1, 1) branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) branch7x7dbl = conv2d_bn(x, 128, 1, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate( [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed4') # mixed 5, 6: 17 x 17 x 768 for i in range(2): branch1x1 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(x, 160, 1, 1) branch7x7 = conv2d_bn(branch7x7, 160, 1, 7) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) branch7x7dbl = conv2d_bn(x, 160, 1, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate( [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed' + str(5 + i)) # mixed 7: 17 x 17 x 768 branch1x1 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(branch7x7, 192, 1, 7) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) branch7x7dbl = conv2d_bn(x, 192, 1, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate( [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed7') # mixed 8: 8 x 8 x 1280 branch3x3 = conv2d_bn(x, 192, 1, 1) branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid') branch7x7x3 = conv2d_bn(x, 192, 1, 1) branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7) branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1) branch7x7x3 = conv2d_bn( branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid') branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) x = layers.concatenate( [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8') # mixed 9: 8 x 8 x 2048 for i in range(2): branch1x1 = conv2d_bn(x, 320, 1, 1) branch3x3 = conv2d_bn(x, 384, 1, 1) branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3) branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1) branch3x3 = layers.concatenate( [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i)) branch3x3dbl = conv2d_bn(x, 448, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3) branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3) branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1) branch3x3dbl = layers.concatenate( [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate( [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed' + str(9 + i)) if include_top: # Classification block x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(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 = Model(inputs, x, name='inception_v3') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'inception_v3_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', file_hash='9a0d58056eeedaa3f26cb7ebd46da564') else: weights_path = get_file( 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='bcbd6486424b2319ff4ef7d526e38f63') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000): """Instantiates the MobileNet architecture. To load a MobileNet model via `load_model`, import the custom objects `relu6` and pass them to the `custom_objects` parameter. E.g. model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6}) Arguments: 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. alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier) dropout: dropout rate 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. 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. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ 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 and default size. if input_shape is None: default_size = 224 else: if K.image_data_format() == 'channels_first': rows = input_shape[1] cols = input_shape[2] else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = _obtain_input_shape( input_shape, default_size=default_size, min_size=32, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) if K.image_data_format() == 'channels_last': row_axis, col_axis = (0, 1) else: row_axis, col_axis = (1, 2) rows = input_shape[row_axis] cols = input_shape[col_axis] if weights == 'imagenet': if depth_multiplier != 1: raise ValueError('If imagenet weights are being loaded, ' 'depth multiplier must be 1') if alpha not in [0.25, 0.50, 0.75, 1.0]: raise ValueError('If imagenet weights are being loaded, ' 'alpha can be one of' '`0.25`, `0.50`, `0.75` or `1.0` only.') if rows != cols or rows not in [128, 160, 192, 224]: if rows is None: rows = 224 logging.warning('MobileNet shape is undefined.' ' Weights for input shape (224, 224) will be loaded.') else: raise ValueError('If imagenet weights are being loaded, ' 'input must have a static square shape (one of ' '(128, 128), (160, 160), (192, 192), or (224, 224)).' ' Input shape provided = %s' % (input_shape,)) if K.image_data_format() != 'channels_last': logging.warning('The MobileNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None 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 x = _conv_block(img_input, 32, alpha, strides=(2, 2)) x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) x = _depthwise_conv_block( x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2) x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) x = _depthwise_conv_block( x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4) x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) x = _depthwise_conv_block( x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) x = _depthwise_conv_block( x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12) x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) if include_top: if K.image_data_format() == 'channels_first': shape = (int(1024 * alpha), 1, 1) else: shape = (1, 1, int(1024 * alpha)) x = GlobalAveragePooling2D()(x) x = Reshape(shape, name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x) x = Activation('softmax', name='act_softmax')(x) x = Reshape((classes,), name='reshape_2')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='mobilenet_%0.2f_%s' % (alpha, rows)) # load weights if weights == 'imagenet': if K.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_first" format ' 'are not available.') if alpha == 1.0: alpha_text = '1_0' elif alpha == 0.75: alpha_text = '7_5' elif alpha == 0.50: alpha_text = '5_0' else: alpha_text = '2_5' if include_top: model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') else: model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model
def resnet10_attention(include_top=True, weights=None, input_tensor=None, input_shape=None, pooling='avg', classes=8): #确定合适的输入 input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=197, data_format=K.image_data_format, require_flatten=include_top, weights=weights) 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 if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 #构建网络框架 #padding='same' 填充,padding='valid'不填充 x = Conv2D(64, (7, 7), strides=2, padding='same', name='conv1')(img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) #构建residual block x = conv_attention_block_10(x, 3, [64, 64], stage=2, block='a', strides=(1, 1)) x = conv_attention_block_10(x, 3, [64, 64], stage=2, block='b', strides=(1, 1)) x = conv_attention_block_10(x, 3, [128, 128], stage=3, block='a') x = conv_attention_block_10(x, 3, [128, 128], stage=3, block='b', strides=(1, 1)) x = conv_attention_block_10(x, 3, [256, 256], stage=4, block='a') x = conv_attention_block_10(x, 3, [256, 256], stage=4, block='b', strides=(1, 1)) x = conv_attention_block_10(x, 3, [512, 512], stage=5, block='a') x = conv_attention_block_10(x, 3, [512, 512], stage=5, block='b', strides=(1, 1)) x = AveragePooling2D((7, 7), name='avg_pool')(x) # x = Flatten()(x) # x = Dense(classes,activation='softmax', name='fc8')(x) if include_top: x = Flatten()(x) x = Dense(classes, activation='softmax', name='fc8')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = Model(inputs, x, name='resnet') if weights is not None: model.load_weights(weights) return model
def resnet50_attention(include_top=True, weights=None, input_tensor=None, input_shape=None, pooling=None, classes=5): """Instantiates the ResNet50 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. 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 197. 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 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. """ # Determine proper input shape input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=197, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_attention_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_attention_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_attention_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_attention_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') x = AveragePooling2D((7, 7), name='avg_pool')(x) if include_top: x = Flatten()(x) x = Dense(classes, activation='softmax', name='fc5')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(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 = Model(inputs, x, name='resnet50_attention') # load weights if weights is not None: model.load_weights(weights) return model
def ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the ResNet50 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. 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 197. 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 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.') 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=197, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x = Conv2D( 64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = 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') x = AveragePooling2D((7, 7), name='avg_pool')(x) if include_top: x = Flatten()(x) x = Dense(classes, activation='softmax', name='fc1000')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='resnet50') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', md5_hash='a7b3fe01876f51b976af0dea6bc144eb') else: weights_path = get_file( 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='a268eb855778b3df3c7506639542a6af') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
def build_model(input_shape=None): input_shape = _obtain_input_shape(input_shape, default_size=im_size, min_size=24, data_format=K.image_data_format(), require_flatten=False, weights='None') img_input = Input(shape=input_shape) reg = regularizers.l2(0.001) # first block x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1', kernel_regularizer=reg)(img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2', kernel_regularizer=reg)(x) x = BatchNormalization(name='block1_conv2_bn')(x) x = Activation('relu', name='block1_conv2_act')(x) residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False, name='residual_conv2d_1', kernel_regularizer=reg)(x) residual = BatchNormalization()(residual) # second block x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1', kernel_regularizer=reg)(x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block2_sepconv2', kernel_regularizer=reg)(x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) # second residual residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False, name='residual_conv2d_2', kernel_regularizer=reg)(x) residual = BatchNormalization()(residual) for i in range(2): residual = x prefix = 'block' + str(i + 3) x = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1', kernel_regularizer=reg)(x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2', kernel_regularizer=reg)(x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3', kernel_regularizer=reg)(x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = Conv2D(384, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) # output blocks - block 21 x = Activation('relu', name='block21_sepconv1_act')(x) x = SeparableConv2D(384, (3, 3), padding='same', use_bias=False, name='block21_sepconv1', kernel_regularizer=reg)(x) x = BatchNormalization(name='block21_sepconv1_bn')(x) x = Activation('relu', name='block21_sepconv2_act')(x) x = SeparableConv2D(384, (3, 3), padding='same', use_bias=False, name='block21_sepconv2', kernel_regularizer=reg)(x) x = BatchNormalization(name='block21_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) x = layers.add([x, residual]) # block 22 x = SeparableConv2D(512, (3, 3), padding='same', use_bias=False, name='block22_sepconv1', kernel_regularizer=reg)(x) x = BatchNormalization(name='block22_sepconv1_bn')(x) x = Activation('relu', name='block22_sepconv1_act')(x) x = SeparableConv2D(768, (3, 3), padding='same', use_bias=False, name='block22_sepconv2', kernel_regularizer=reg)(x) x = BatchNormalization(name='block22_sepconv2_bn')(x) x = Activation('relu', name='block22_sepconv2_act')(x) # model finish x = GlobalMaxPooling2D()(x) x = Flatten()(x) x = Dense(256, activation='relu', kernel_regularizer=reg)(x) x = Dense(64, activation='relu', kernel_regularizer=reg)(x) x = Dense(num_classes, activation='softmax')(x) model = Model(img_input, x, name='micro_xception_bn_v1') opt = optimizers.Adam(lr=0.0008, decay=0.001) model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) return model
def MobileNetV2(input_shape=None, alpha=1.0, depth_multiplier=1, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000, **kwargs): """Instantiates the MobileNetV2 architecture. # Arguments input_shape: optional shape tuple, to be specified if you would like to use a model with an input img resolution that is not (224, 224, 3). It should have exactly 3 inputs channels (224, 224, 3). You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. `(160, 160, 3)` would be one valid value. alpha: controls the width of the network. This is known as the width multiplier in the MobileNetV2 paper. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier) 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. 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 or invalid depth_multiplier, alpha, rows when weights='imagenet' """ #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 and default size. # If both input_shape and input_tensor are used, they should match if input_shape is not None and input_tensor is not None: try: is_input_t_tensor = backend.is_keras_tensor(input_tensor) except ValueError: try: is_input_t_tensor = backend.is_keras_tensor( layer_utils.get_source_inputs(input_tensor)) except ValueError: raise ValueError('input_tensor: ', input_tensor, 'is not type input_tensor') if is_input_t_tensor: if backend.image_data_format == 'channels_first': if backend.int_shape(input_tensor)[1] != input_shape[1]: raise ValueError( 'input_shape: ', input_shape, 'and input_tensor: ', input_tensor, 'do not meet the same shape requirements') else: if backend.int_shape(input_tensor)[2] != input_shape[1]: raise ValueError( 'input_shape: ', input_shape, 'and input_tensor: ', input_tensor, 'do not meet the same shape requirements') else: raise ValueError('input_tensor specified: ', input_tensor, 'is not a keras tensor') # If input_shape is None, infer shape from input_tensor if input_shape is None and input_tensor is not None: try: backend.is_keras_tensor(input_tensor) except ValueError: raise ValueError('input_tensor: ', input_tensor, 'is type: ', type(input_tensor), 'which is not a valid type') if input_shape is None and not backend.is_keras_tensor(input_tensor): default_size = 224 elif input_shape is None and backend.is_keras_tensor(input_tensor): if backend.image_data_format() == 'channels_first': rows = backend.int_shape(input_tensor)[2] cols = backend.int_shape(input_tensor)[3] else: rows = backend.int_shape(input_tensor)[1] cols = backend.int_shape(input_tensor)[2] if rows == cols and rows in [96, 128, 160, 192, 224]: default_size = rows else: default_size = 224 # If input_shape is None and no input_tensor elif input_shape is None: default_size = 224 # If input_shape is not None, assume default size else: if backend.image_data_format() == 'channels_first': rows = input_shape[1] cols = input_shape[2] else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [96, 128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = _obtain_input_shape(input_shape, default_size=default_size, min_size=32, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if backend.image_data_format() == 'channels_last': row_axis, col_axis = (0, 1) else: row_axis, col_axis = (1, 2) rows = input_shape[row_axis] cols = input_shape[col_axis] if weights == 'imagenet': if depth_multiplier != 1: raise ValueError('If imagenet weights are being loaded, ' 'depth multiplier must be 1') if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]: raise ValueError('If imagenet weights are being loaded, ' 'alpha can be one of `0.35`, `0.50`, `0.75`, ' '`1.0`, `1.3` or `1.4` only.') if rows != cols or rows not in [96, 128, 160, 192, 224]: if rows is None: rows = 224 warnings.warn('MobileNet shape is undefined.' ' Weights for input shape' '(224, 224) will be loaded.') else: raise ValueError('If imagenet weights are being loaded, ' 'input must have a static square shape' '(one of (96, 96), (128, 128), (160, 160),' '(192, 192), or (224, 224)).' 'Input shape provided = %s' % (input_shape, )) if backend.image_data_format() != 'channels_last': warnings.warn('The MobileNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') backend.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None if input_tensor is None: img_input = Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor first_block_filters = _make_divisible(32 * alpha, 8) x = ZeroPadding2D(padding=correct_pad(backend, img_input, 3), name='Conv1_pad')(img_input) x = Conv2D(first_block_filters, kernel_size=3, strides=(2, 2), padding='valid', use_bias=False, name='Conv1')(x) x = BatchNormalization(epsilon=1e-3, momentum=0.999, name='bn_Conv1')(x) x = ReLU(6., name='Conv1_relu')(x) x = _inverted_res_block(x, filters=16, alpha=alpha, stride=1, expansion=1, block_id=0) x = _inverted_res_block(x, filters=24, alpha=alpha, stride=2, expansion=6, block_id=1) x = _inverted_res_block(x, filters=24, alpha=alpha, stride=1, expansion=6, block_id=2) x = _inverted_res_block(x, filters=32, alpha=alpha, stride=2, expansion=6, block_id=3) x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=4) x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1, expansion=6, block_id=5) x = _inverted_res_block(x, filters=64, alpha=alpha, stride=2, expansion=6, block_id=6) x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=7) x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=8) x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1, expansion=6, block_id=9) x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=10) x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=11) x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1, expansion=6, block_id=12) x = _inverted_res_block(x, filters=160, alpha=alpha, stride=2, expansion=6, block_id=13) x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=14) x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1, expansion=6, block_id=15) x = _inverted_res_block(x, filters=320, alpha=alpha, stride=1, expansion=6, block_id=16) # no alpha applied to last conv as stated in the paper: # if the width multiplier is greater than 1 we # increase the number of output channels if alpha > 1.0: last_block_filters = _make_divisible(1280 * alpha, 8) else: last_block_filters = 1280 x = Conv2D(last_block_filters, kernel_size=1, use_bias=False, name='Conv_1')(x) x = BatchNormalization(epsilon=1e-3, momentum=0.999, name='Conv_1_bn')(x) x = ReLU(6., name='out_relu')(x) if include_top: x = GlobalAveragePooling2D()(x) x = Dense(classes, activation='softmax', use_bias=True, name='Logits')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) x = Flatten(name='custom')(x) ##DB # 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 = Model(inputs, x, name='mobilenetv2_%0.2f_%s' % (alpha, rows)) # Load weights. if weights == 'imagenet': if backend.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_first" format ' 'are not available.') if include_top: model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' + str(alpha) + '_' + str(rows) + '.h5') weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') else: model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' + str(alpha) + '_' + str(rows) + '_no_top' + '.h5') weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: backend.set_image_data_format(old_data_format) return model
def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Xception architecture. Optionally loads weights pre-trained on ImageNet. This model is available for TensorFlow only, and can only be used with inputs following the TensorFlow data format `(width, height, channels)`. You should set `image_data_format='channels_last'` in your Keras config located 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 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. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ 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') if K.image_data_format() != 'channels_last': logging.warning( 'The Xception model is only available for the ' 'input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height). ' 'You should set `image_data_format="channels_last"` in your Keras ' 'config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None # Determine proper input shape input_shape = _obtain_input_shape(input_shape, default_size=299, min_size=71, data_format=K.image_data_format(), require_flatten=False, weights=weights) 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 x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = BatchNormalization(name='block1_conv2_bn')(x) x = Activation('relu', name='block1_conv2_act')(x) residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) x = layers.add([x, residual]) residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block3_sepconv1_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) x = BatchNormalization(name='block3_sepconv1_bn')(x) x = Activation('relu', name='block3_sepconv2_act')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) x = BatchNormalization(name='block3_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) x = layers.add([x, residual]) residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block4_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) x = BatchNormalization(name='block4_sepconv1_bn')(x) x = Activation('relu', name='block4_sepconv2_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) x = BatchNormalization(name='block4_sepconv2_bn')(x) x = 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 = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu', name='block13_sepconv1_act')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) x = BatchNormalization(name='block13_sepconv1_bn')(x) x = Activation('relu', name='block13_sepconv2_act')(x) x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) x = BatchNormalization(name='block13_sepconv2_bn')(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) x = layers.add([x, residual]) x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) x = BatchNormalization(name='block14_sepconv1_bn')(x) x = Activation('relu', name='block14_sepconv1_act')(x) x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x) x = BatchNormalization(name='block14_sepconv2_bn')(x) x = Activation('relu', name='block14_sepconv2_act')(x) if include_top: x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) x = Flatten(name='custom')(x) ##DB # 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 = Model(inputs, x, name='xception') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models', file_hash='0a58e3b7378bc2990ea3b43d5981f1f6') else: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='b0042744bf5b25fce3cb969f33bebb97') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model
def build_model(input_shape=None): input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=24, data_format=K.image_data_format(), require_flatten=False, weights='None') img_input = Input(shape=input_shape) channel_axis = 3 reg = regularizers.l2(0.001) # layer 0 x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid', reg=reg) x = conv2d_bn(x, 32, 3, 3, padding='valid', reg=reg) x = conv2d_bn(x, 64, 3, 3, reg=reg) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv2d_bn(x, 80, 1, 1, padding='valid', reg=reg) x = conv2d_bn(x, 192, 3, 3, padding='valid', reg=reg) x = MaxPooling2D((3, 3), strides=(2, 2))(x) # mixed 0, 1, 2: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1, reg=reg) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5, reg=reg) branch3x3dbl = conv2d_bn(x, 64, 1, 1, reg=reg) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, reg=reg) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, reg=reg) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 32, 1, 1, reg=reg) x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed0') # mixed 2: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1, reg=reg) branch5x5 = conv2d_bn(x, 48, 1, 1, reg=reg) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5, reg=reg) branch3x3dbl = conv2d_bn(x, 64, 1, 1, reg=reg) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, reg=reg) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, reg=reg) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1, 1, reg=reg) x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed2') # mixed 5, 6: 17 x 17 x 768 for i in range(1): branch1x1 = conv2d_bn(x, 192, 1, 1, reg=reg) branch7x7 = conv2d_bn(x, 160, 1, 1, reg=reg) branch7x7 = conv2d_bn(branch7x7, 160, 1, 7, reg=reg) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1, reg=reg) branch7x7dbl = conv2d_bn(x, 160, 1, 1, reg=reg) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1, reg=reg) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7, reg=reg) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1, reg=reg) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7, reg=reg) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1, reg=reg) x = layers.concatenate( [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed' + str(5 + i)) x = GlobalMaxPooling2D()(x) x = Flatten()(x) x = Dense(384, activation='relu')(x) x = Dense(64, activation='relu')(x) x = Dense(num_classes, activation='softmax')(x) model = Model(img_input, x, name='micro_xception_v1') opt = optimizers.Adam(lr=0.001, decay=0.001) model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) return model
def InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Inception v3 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. 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)` (with `channels_last` data format) or `(3, 299, 299)` (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 139. 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 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.') 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=139, data_format=K.image_data_format(), require_flatten=False, weights=weights) 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 if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = 3 x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid') x = conv2d_bn(x, 32, 3, 3, padding='valid') x = conv2d_bn(x, 64, 3, 3) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv2d_bn(x, 80, 1, 1, padding='valid') x = conv2d_bn(x, 192, 3, 3, padding='valid') x = MaxPooling2D((3, 3), strides=(2, 2))(x) # mixed 0, 1, 2: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 32, 1, 1) x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed0') # mixed 1: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1, 1) x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed1') # mixed 2: 35 x 35 x 256 branch1x1 = conv2d_bn(x, 64, 1, 1) branch5x5 = conv2d_bn(x, 48, 1, 1) branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1, 1) x = layers.concatenate([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed2') # mixed 3: 17 x 17 x 768 branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid') branch3x3dbl = conv2d_bn(x, 64, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid') branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) x = layers.concatenate([branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3') # mixed 4: 17 x 17 x 768 branch1x1 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(x, 128, 1, 1) branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) branch7x7dbl = conv2d_bn(x, 128, 1, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed4') # mixed 5, 6: 17 x 17 x 768 for i in range(2): branch1x1 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(x, 160, 1, 1) branch7x7 = conv2d_bn(branch7x7, 160, 1, 7) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) branch7x7dbl = conv2d_bn(x, 160, 1, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7) branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate( [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed' + str(5 + i)) # mixed 7: 17 x 17 x 768 branch1x1 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(x, 192, 1, 1) branch7x7 = conv2d_bn(branch7x7, 192, 1, 7) branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) branch7x7dbl = conv2d_bn(x, 192, 1, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=channel_axis, name='mixed7') # mixed 8: 8 x 8 x 1280 branch3x3 = conv2d_bn(x, 192, 1, 1) branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid') branch7x7x3 = conv2d_bn(x, 192, 1, 1) branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7) branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1) branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid') branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) x = layers.concatenate([branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8') # mixed 9: 8 x 8 x 2048 for i in range(2): branch1x1 = conv2d_bn(x, 320, 1, 1) branch3x3 = conv2d_bn(x, 384, 1, 1) branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3) branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1) branch3x3 = layers.concatenate([branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i)) branch3x3dbl = conv2d_bn(x, 448, 1, 1) branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3) branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3) branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1) branch3x3dbl = layers.concatenate([branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis) branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) branch_pool = conv2d_bn(branch_pool, 192, 1, 1) x = layers.concatenate( [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed' + str(9 + i)) if include_top: # Classification block x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(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 = Model(inputs, x, name='inception_v3') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'inception_v3_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', file_hash='9a0d58056eeedaa3f26cb7ebd46da564') else: weights_path = get_file( 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='bcbd6486424b2319ff4ef7d526e38f63') model.load_weights(weights_path) 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): """Instantiates the DenseNet architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with TensorFlow, Theano, and CNTK. The data format convention used by the model is the one specified in your Keras config file. 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.') 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=221, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 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) 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 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) 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) # 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 = 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') # 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) return model
def NASNet(input_shape=None, penultimate_filters=4032, num_blocks=6, stem_block_filters=96, skip_reduction=True, filter_multiplier=2, include_top=True, weights=None, input_tensor=None, pooling=None, classes=1000, default_size=None): """Instantiates a NASNet model. Note that only TensorFlow is supported for now, therefore it only works with the data format `image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`. Arguments: input_shape: Optional shape tuple, the input shape is by default `(331, 331, 3)` for NASNetLarge and `(224, 224, 3)` for NASNetMobile. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value. penultimate_filters: Number of filters in the penultimate layer. NASNet models use the notation `NASNet (N @ P)`, where: - N is the number of blocks - P is the number of penultimate filters num_blocks: Number of repeated blocks of the NASNet model. NASNet models use the notation `NASNet (N @ P)`, where: - N is the number of blocks - P is the number of penultimate filters stem_block_filters: Number of filters in the initial stem block skip_reduction: Whether to skip the reduction step at the tail end of the network. Set to `False` for CIFAR models. filter_multiplier: Controls the width of the network. - If `filter_multiplier` < 1.0, proportionally decreases the number of filters in each layer. - If `filter_multiplier` > 1.0, proportionally increases the number of filters in each layer. - If `filter_multiplier` = 1, default number of filters from the paper are used at each layer. include_top: Whether to include the fully-connected layer at the top of the network. weights: `None` (random initialization) or `imagenet` (ImageNet weights) input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 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. default_size: Specifies the default image size of the model Returns: A Keras model instance. Raises: ValueError: In case of invalid argument for `weights`, invalid input shape or invalid `penultimate_filters` value. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ if K.backend() != 'tensorflow': raise RuntimeError('Only Tensorflow backend is currently supported, ' 'as other backends do not support ' 'separable convolution.') 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') if (isinstance(input_shape, tuple) and None in input_shape and weights == 'imagenet'): raise ValueError('When specifying the input shape of a NASNet' ' and loading `ImageNet` weights, ' 'the input_shape argument must be static ' '(no None entries). Got: `input_shape=' + str(input_shape) + '`.') if default_size is None: default_size = 331 # Determine proper input shape and default size. input_shape = _obtain_input_shape( input_shape, default_size=default_size, min_size=32, data_format=K.image_data_format(), require_flatten=False, weights=weights) if K.image_data_format() != 'channels_last': logging.warning('The NASNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None 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 if penultimate_filters % 24 != 0: raise ValueError( 'For NASNet-A models, the value of `penultimate_filters` ' 'needs to be divisible by 24. Current value: %d' % penultimate_filters) channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 filters = penultimate_filters // 24 if not skip_reduction: x = Conv2D( stem_block_filters, (3, 3), strides=(2, 2), padding='valid', use_bias=False, name='stem_conv1', kernel_initializer='he_normal')( img_input) else: x = Conv2D( stem_block_filters, (3, 3), strides=(1, 1), padding='same', use_bias=False, name='stem_conv1', kernel_initializer='he_normal')( img_input) x = BatchNormalization( axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')( x) p = None if not skip_reduction: # imagenet / mobile mode x, p = _reduction_a_cell( x, p, filters // (filter_multiplier**2), block_id='stem_1') x, p = _reduction_a_cell( x, p, filters // filter_multiplier, block_id='stem_2') for i in range(num_blocks): x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i)) x, p0 = _reduction_a_cell( x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks)) p = p0 if not skip_reduction else p for i in range(num_blocks): x, p = _normal_a_cell( x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1)) x, p0 = _reduction_a_cell( x, p, filters * filter_multiplier**2, block_id='reduce_%d' % (2 * num_blocks)) p = p0 if not skip_reduction else p for i in range(num_blocks): x, p = _normal_a_cell( x, p, filters * filter_multiplier**2, block_id='%d' % (2 * num_blocks + i + 1)) x = Activation('relu')(x) if include_top: x = GlobalAveragePooling2D()(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 model = Model(inputs, x, name='NASNet') # load weights if weights == 'imagenet': if default_size == 224: # mobile version if include_top: weight_path = NASNET_MOBILE_WEIGHT_PATH model_name = 'nasnet_mobile.h5' else: weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP model_name = 'nasnet_mobile_no_top.h5' weights_file = get_file(model_name, weight_path, cache_subdir='models') model.load_weights(weights_file) elif default_size == 331: # large version if include_top: weight_path = NASNET_LARGE_WEIGHT_PATH model_name = 'nasnet_large.h5' else: weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP model_name = 'nasnet_large_no_top.h5' weights_file = get_file(model_name, weight_path, cache_subdir='models') model.load_weights(weights_file) else: raise ValueError('ImageNet weights can only be loaded with NASNetLarge' ' or NASNetMobile') elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model
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 when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with TensorFlow, Theano, and CNTK. The data format convention used by the model is the one specified in your Keras config file. 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.') 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=128, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 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) 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 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) 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) # 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 = 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') # 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) return model
def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000): """Instantiates the MobileNet architecture. To load a MobileNet model via `load_model`, import the custom objects `relu6` and pass them to the `custom_objects` parameter. E.g. model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6}) Arguments: 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. alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier) dropout: dropout rate 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. 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. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ 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 and default size. if input_shape is None: default_size = 224 else: if K.image_data_format() == 'channels_first': rows = input_shape[1] cols = input_shape[2] else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = _obtain_input_shape(input_shape, default_size=default_size, min_size=32, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) if K.image_data_format() == 'channels_last': row_axis, col_axis = (0, 1) else: row_axis, col_axis = (1, 2) rows = input_shape[row_axis] cols = input_shape[col_axis] if weights == 'imagenet': if depth_multiplier != 1: raise ValueError('If imagenet weights are being loaded, ' 'depth multiplier must be 1') if alpha not in [0.25, 0.50, 0.75, 1.0]: raise ValueError('If imagenet weights are being loaded, ' 'alpha can be one of' '`0.25`, `0.50`, `0.75` or `1.0` only.') if rows != cols or rows not in [128, 160, 192, 224]: if rows is None: rows = 224 logging.warning( 'MobileNet shape is undefined.' ' Weights for input shape (224, 224) will be loaded.') else: raise ValueError( 'If imagenet weights are being loaded, ' 'input must have a static square shape (one of ' '(128, 128), (160, 160), (192, 192), or (224, 224)).' ' Input shape provided = %s' % (input_shape, )) if K.image_data_format() != 'channels_last': logging.warning( 'The MobileNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None 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 x = _conv_block(img_input, 32, alpha, strides=(2, 2)) x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2) x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4) x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12) x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) if include_top: if K.image_data_format() == 'channels_first': shape = (int(1024 * alpha), 1, 1) else: shape = (1, 1, int(1024 * alpha)) x = GlobalAveragePooling2D()(x) x = Reshape(shape, name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x) x = Activation('softmax', name='act_softmax')(x) x = Reshape((classes, ), name='reshape_2')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='mobilenet_%0.2f_%s' % (alpha, rows)) # load weights if weights == 'imagenet': if K.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_first" format ' 'are not available.') if alpha == 1.0: alpha_text = '1_0' elif alpha == 0.75: alpha_text = '7_5' elif alpha == 0.50: alpha_text = '5_0' else: alpha_text = '2_5' if include_top: model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') else: model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model
def NASNet(input_shape=None, penultimate_filters=4032, num_blocks=6, stem_block_filters=96, skip_reduction=True, filter_multiplier=2, include_top=True, weights=None, input_tensor=None, pooling=None, classes=1000, default_size=None): """Instantiates a NASNet model. Note that only TensorFlow is supported for now, therefore it only works with the data format `image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`. Arguments: input_shape: Optional shape tuple, the input shape is by default `(331, 331, 3)` for NASNetLarge and `(224, 224, 3)` for NASNetMobile. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value. penultimate_filters: Number of filters in the penultimate layer. NASNet models use the notation `NASNet (N @ P)`, where: - N is the number of blocks - P is the number of penultimate filters num_blocks: Number of repeated blocks of the NASNet model. NASNet models use the notation `NASNet (N @ P)`, where: - N is the number of blocks - P is the number of penultimate filters stem_block_filters: Number of filters in the initial stem block skip_reduction: Whether to skip the reduction step at the tail end of the network. Set to `False` for CIFAR models. filter_multiplier: Controls the width of the network. - If `filter_multiplier` < 1.0, proportionally decreases the number of filters in each layer. - If `filter_multiplier` > 1.0, proportionally increases the number of filters in each layer. - If `filter_multiplier` = 1, default number of filters from the paper are used at each layer. include_top: Whether to include the fully-connected layer at the top of the network. weights: `None` (random initialization) or `imagenet` (ImageNet weights) input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 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. default_size: Specifies the default image size of the model Returns: A Keras model instance. Raises: ValueError: In case of invalid argument for `weights`, invalid input shape or invalid `penultimate_filters` value. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ if K.backend() != 'tensorflow': raise RuntimeError('Only Tensorflow backend is currently supported, ' 'as other backends do not support ' 'separable convolution.') 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') if (isinstance(input_shape, tuple) and None in input_shape and weights == 'imagenet'): raise ValueError('When specifying the input shape of a NASNet' ' and loading `ImageNet` weights, ' 'the input_shape argument must be static ' '(no None entries). Got: `input_shape=' + str(input_shape) + '`.') if default_size is None: default_size = 331 # Determine proper input shape and default size. input_shape = _obtain_input_shape( input_shape, default_size=default_size, min_size=32, data_format=K.image_data_format(), require_flatten=False, weights=weights) if K.image_data_format() != 'channels_last': logging.warning('The NASNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None 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 if penultimate_filters % 24 != 0: raise ValueError( 'For NASNet-A models, the value of `penultimate_filters` ' 'needs to be divisible by 24. Current value: %d' % penultimate_filters) channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 filters = penultimate_filters // 24 if not skip_reduction: x = Conv2D( stem_block_filters, (3, 3), strides=(2, 2), padding='valid', use_bias=False, name='stem_conv1', kernel_initializer='he_normal')( img_input) else: x = Conv2D( stem_block_filters, (3, 3), strides=(1, 1), padding='same', use_bias=False, name='stem_conv1', kernel_initializer='he_normal')( img_input) x = BatchNormalization( axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')( x) p = None if not skip_reduction: # imagenet / mobile mode x, p = _reduction_a_cell( x, p, filters // (filter_multiplier**2), block_id='stem_1') x, p = _reduction_a_cell( x, p, filters // filter_multiplier, block_id='stem_2') for i in range(num_blocks): x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i)) x, p0 = _reduction_a_cell( x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks)) p = p0 if not skip_reduction else p for i in range(num_blocks): x, p = _normal_a_cell( x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1)) x, p0 = _reduction_a_cell( x, p, filters * filter_multiplier**2, block_id='reduce_%d' % (2 * num_blocks)) p = p0 if not skip_reduction else p for i in range(num_blocks): x, p = _normal_a_cell( x, p, filters * filter_multiplier**2, block_id='%d' % (2 * num_blocks + i + 1)) x = Activation('relu')(x) if include_top: x = GlobalAveragePooling2D()(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(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 model = Model(inputs, x, name='NASNet') # load weights if weights == 'imagenet': if default_size == 224: # mobile version if include_top: weight_path = NASNET_MOBILE_WEIGHT_PATH model_name = 'nasnet_mobile.h5' else: weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP model_name = 'nasnet_mobile_no_top.h5' weights_file = get_file(model_name, weight_path, cache_subdir='models') model.load_weights(weights_file) elif default_size == 331: # large version if include_top: weight_path = NASNET_LARGE_WEIGHT_PATH model_name = 'nasnet_large.h5' else: weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP model_name = 'nasnet_large_no_top.h5' weights_file = get_file(model_name, weight_path, cache_subdir='models') model.load_weights(weights_file) else: raise ValueError('ImageNet weights can only be loaded with NASNetLarge' ' or NASNetMobile') elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model
def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Xception architecture. Optionally loads weights pre-trained on ImageNet. This model is available for TensorFlow only, and can only be used with inputs following the TensorFlow data format `(width, height, channels)`. You should set `image_data_format='channels_last'` in your Keras config located 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 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. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ 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') if K.image_data_format() != 'channels_last': logging.warning( 'The Xception model is only available for the ' 'input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height). ' 'You should set `image_data_format="channels_last"` in your Keras ' 'config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None # Determine proper input shape input_shape = _obtain_input_shape( input_shape, default_size=299, min_size=71, data_format=K.image_data_format(), require_flatten=False, weights=weights) 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 x = Conv2D( 32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')( img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) x = BatchNormalization(name='block1_conv2_bn')(x) x = Activation('relu', name='block1_conv2_act')(x) residual = Conv2D( 128, (1, 1), strides=(2, 2), padding='same', use_bias=False)( x) residual = BatchNormalization()(residual) x = SeparableConv2D( 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')( x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D( 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')( x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block2_pool')( x) x = layers.add([x, residual]) residual = Conv2D( 256, (1, 1), strides=(2, 2), padding='same', use_bias=False)( x) residual = BatchNormalization()(residual) x = Activation('relu', name='block3_sepconv1_act')(x) x = SeparableConv2D( 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')( x) x = BatchNormalization(name='block3_sepconv1_bn')(x) x = Activation('relu', name='block3_sepconv2_act')(x) x = SeparableConv2D( 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')( x) x = BatchNormalization(name='block3_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block3_pool')( x) x = layers.add([x, residual]) residual = Conv2D( 728, (1, 1), strides=(2, 2), padding='same', use_bias=False)( x) residual = BatchNormalization()(residual) x = Activation('relu', name='block4_sepconv1_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')( x) x = BatchNormalization(name='block4_sepconv1_bn')(x) x = Activation('relu', name='block4_sepconv2_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')( x) x = BatchNormalization(name='block4_sepconv2_bn')(x) x = 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 = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')( x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')( x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')( x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = Conv2D( 1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)( x) residual = BatchNormalization()(residual) x = Activation('relu', name='block13_sepconv1_act')(x) x = SeparableConv2D( 728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')( x) x = BatchNormalization(name='block13_sepconv1_bn')(x) x = Activation('relu', name='block13_sepconv2_act')(x) x = SeparableConv2D( 1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')( x) x = BatchNormalization(name='block13_sepconv2_bn')(x) x = MaxPooling2D( (3, 3), strides=(2, 2), padding='same', name='block13_pool')( x) x = layers.add([x, residual]) x = SeparableConv2D( 1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')( x) x = BatchNormalization(name='block14_sepconv1_bn')(x) x = Activation('relu', name='block14_sepconv1_act')(x) x = SeparableConv2D( 2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')( x) x = BatchNormalization(name='block14_sepconv2_bn')(x) x = Activation('relu', name='block14_sepconv2_act')(x) if include_top: x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='xception') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels.h5', TF_WEIGHTS_PATH, cache_subdir='models', file_hash='0a58e3b7378bc2990ea3b43d5981f1f6') else: weights_path = get_file( 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='b0042744bf5b25fce3cb969f33bebb97') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model
def VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the VGG19 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. Arguments: include_top: whether to include the 3 fully-connected layers 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 48. 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 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.') 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=48, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 # Block 1 x = Conv2D( 64, (3, 3), activation='relu', padding='same', name='block1_conv1')( img_input) x = Conv2D( 64, (3, 3), activation='relu', padding='same', name='block1_conv2')( x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D( 128, (3, 3), activation='relu', padding='same', name='block2_conv1')( x) x = Conv2D( 128, (3, 3), activation='relu', padding='same', name='block2_conv2')( x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv1')( x) x = Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv2')( x) x = Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv3')( x) x = Conv2D( 256, (3, 3), activation='relu', padding='same', name='block3_conv4')( x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv1')( x) x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv2')( x) x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv3')( x) x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block4_conv4')( x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv1')( x) x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv2')( x) x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv3')( x) x = Conv2D( 512, (3, 3), activation='relu', padding='same', name='block5_conv4')( x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) if include_top: # Classification block x = Flatten(name='flatten')(x) x = Dense(4096, activation='relu', name='fc1')(x) x = Dense(4096, activation='relu', name='fc2')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='vgg19') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'vgg19_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', file_hash='cbe5617147190e668d6c5d5026f83318') else: weights_path = get_file( 'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='253f8cb515780f3b799900260a226db6') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
def VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the VGG16 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format convention used by the model is the one specified in your Keras config file. Arguments: include_top: whether to include the 3 fully-connected layers 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 input channels, and width and height should be no smaller than 48. 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 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.') 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=48, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) 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 # Block 1 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) if include_top: # Classification block x = Flatten(name='flatten')(x) x = Dense(4096, activation='relu', name='fc1')(x) x = Dense(4096, activation='relu', name='fc2')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 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 # Create model. model = Model(inputs, x, name='vgg16') # load weights if weights == 'imagenet': if include_top: weights_path = get_file( 'vgg16_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', file_hash='64373286793e3c8b2b4e3219cbf3544b') else: weights_path = get_file( 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', file_hash='6d6bbae143d832006294945121d1f1fc') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Inception-ResNet v2 architecture. Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set `"image_data_format": "channels_last"` in your Keras config at `~/.keras/keras.json`. The model and the weights are compatible with TensorFlow, Theano and CNTK backends. The data format convention used by the model is the one specified in your Keras config file. Note that the default input image size for this model is 299x299, instead of 224x224 as in the VGG16 and ResNet models. Also, the input preprocessing function is different (i.e., do not use `imagenet_utils.preprocess_input()` with this model. Use `preprocess_input()` defined in this module instead). 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)` (with `'channels_last'` data format) or `(3, 299, 299)` (with `'channels_first'` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 139. 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 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.') 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=139, data_format=K.image_data_format(), require_flatten=False, weights=weights) 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 # Stem block: 35 x 35 x 192 x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid') x = conv2d_bn(x, 32, 3, padding='valid') x = conv2d_bn(x, 64, 3) x = MaxPooling2D(3, strides=2)(x) x = conv2d_bn(x, 80, 1, padding='valid') x = conv2d_bn(x, 192, 3, padding='valid') x = MaxPooling2D(3, strides=2)(x) # Mixed 5b (Inception-A block): 35 x 35 x 320 branch_0 = conv2d_bn(x, 96, 1) branch_1 = conv2d_bn(x, 48, 1) branch_1 = conv2d_bn(branch_1, 64, 5) branch_2 = conv2d_bn(x, 64, 1) branch_2 = conv2d_bn(branch_2, 96, 3) branch_2 = conv2d_bn(branch_2, 96, 3) branch_pool = AveragePooling2D(3, strides=1, padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1) branches = [branch_0, branch_1, branch_2, branch_pool] channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 x = Concatenate(axis=channel_axis, name='mixed_5b')(branches) # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320 for block_idx in range(1, 11): x = inception_resnet_block( x, scale=0.17, block_type='block35', block_idx=block_idx) # Mixed 6a (Reduction-A block): 17 x 17 x 1088 branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 256, 3) branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid') branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_pool] x = Concatenate(axis=channel_axis, name='mixed_6a')(branches) # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088 for block_idx in range(1, 21): x = inception_resnet_block( x, scale=0.1, block_type='block17', block_idx=block_idx) # Mixed 7a (Reduction-B block): 8 x 8 x 2080 branch_0 = conv2d_bn(x, 256, 1) branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid') branch_2 = conv2d_bn(x, 256, 1) branch_2 = conv2d_bn(branch_2, 288, 3) branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid') branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_2, branch_pool] x = Concatenate(axis=channel_axis, name='mixed_7a')(branches) # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080 for block_idx in range(1, 10): x = inception_resnet_block( x, scale=0.2, block_type='block8', block_idx=block_idx) x = inception_resnet_block( x, scale=1., activation=None, block_type='block8', block_idx=10) # Final convolution block: 8 x 8 x 1536 x = conv2d_bn(x, 1536, 1, name='conv_7b') if include_top: # Classification block x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(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 = Model(inputs, x, name='inception_resnet_v2') # Load weights if weights == 'imagenet': if include_top: fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5' weights_path = get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir='models', file_hash='e693bd0210a403b3192acc6073ad2e96') else: fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5' weights_path = get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir='models', file_hash='d19885ff4a710c122648d3b5c3b684e4') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model