我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用keras.layers.SeparableConv2D()。
def test_tiny_separable_conv_valid(self): np.random.seed(1988) input_dim = 16 input_shape = (input_dim, input_dim, 3) depth_multiplier = 1 kernel_height = 3 kernel_width = 3 num_kernels = 4 # Define a model model = Sequential() model.add(SeparableConv2D(filters = num_kernels, kernel_size=(kernel_height, kernel_width), padding = 'valid', strides = (1,1), depth_multiplier = depth_multiplier, input_shape = input_shape)) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Test the keras model self._test_keras_model(model)
def test_tiny_separable_conv_same_fancy(self): np.random.seed(1988) input_dim = 16 input_shape = (input_dim, input_dim, 3) depth_multiplier = 1 kernel_height = 3 kernel_width = 3 num_kernels = 4 # Define a model model = Sequential() model.add(SeparableConv2D(filters = num_kernels, kernel_size=(kernel_height, kernel_width), padding = 'same', strides = (2,2), activation='relu', depth_multiplier = depth_multiplier, input_shape = input_shape)) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Test the keras model self._test_keras_model(model)
def test_tiny_separable_conv_valid_depth_multiplier(self): np.random.seed(1988) input_dim = 16 input_shape = (input_dim, input_dim, 3) depth_multiplier = 5 kernel_height = 3 kernel_width = 3 num_kernels = 40 # Define a model model = Sequential() model.add(SeparableConv2D(filters = num_kernels, kernel_size=(kernel_height, kernel_width), padding = 'valid', strides = (1,1), depth_multiplier = depth_multiplier, input_shape = input_shape)) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Test the keras model self._test_keras_model(model)
def test_tiny_separable_conv_same_fancy_depth_multiplier( self, model_precision=_MLMODEL_FULL_PRECISION): np.random.seed(1988) input_dim = 16 input_shape = (input_dim, input_dim, 3) depth_multiplier = 2 kernel_height = 3 kernel_width = 3 num_kernels = 40 # Define a model model = Sequential() model.add(SeparableConv2D(filters = num_kernels, kernel_size=(kernel_height, kernel_width), padding = 'same', strides = (2,2), activation='relu', depth_multiplier = depth_multiplier, input_shape = input_shape)) # Set some random weights model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Test the keras model self._test_keras_model(model, model_precision=model_precision)
def lin_interpolation_2d(inp, dim): num_rows, num_cols, num_filters = K.int_shape(inp)[1:] conv = SeparableConv2D(num_filters, (num_rows, num_cols), use_bias=False) x = conv(inp) w = conv.get_weights() w[0].fill(0) w[1].fill(0) linspace = linspace_2d(num_rows, num_cols, dim=dim) for i in range(num_filters): w[0][:,:, i, 0] = linspace[:,:] w[1][0, 0, i, i] = 1. conv.set_weights(w) conv.trainable = False x = Lambda(lambda x: K.squeeze(x, axis=1))(x) x = Lambda(lambda x: K.squeeze(x, axis=1))(x) x = Lambda(lambda x: K.expand_dims(x, axis=-1))(x) return x
def _separable_conv_block(ip, filters, kernel_size=(3, 3), strides=(1, 1), weight_decay=5e-5, id=None): '''Adds 2 blocks of [relu-separable conv-batchnorm] # Arguments: ip: input tensor filters: number of output filters per layer kernel_size: kernel size of separable convolutions strides: strided convolution for downsampling weight_decay: l2 regularization weight id: string id # Returns: a Keras tensor ''' channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 with K.name_scope('separable_conv_block_%s' % id): x = Activation('relu')(ip) x = SeparableConv2D(filters, kernel_size, strides=strides, name='separable_conv_1_%s' % id, padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON, name="separable_conv_1_bn_%s" % (id))(x) x = Activation('relu')(x) x = SeparableConv2D(filters, kernel_size, name='separable_conv_2_%s' % id, padding='same', use_bias=False, kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(x) x = BatchNormalization(axis=channel_dim, momentum=_BN_DECAY, epsilon=_BN_EPSILON, name="separable_conv_2_bn_%s" % (id))(x) return x
def test_tiny_xception(self, model_precision=_MLMODEL_FULL_PRECISION): img_input = Input(shape=(32,32,3)) x = Conv2D(2, (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(4, (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(8, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = SeparableConv2D(8, (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(8, (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 = add([x, residual]) residual = Conv2D(16, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) model = Model(inputs=[img_input], outputs=[residual]) self._test_keras_model(model, delta=1e-2, model_precision=model_precision)
def separable_act_conv_bn(x, filters, size, strides=(1, 1), padding='same', name=None): if name is not None: conv_name = name + '_conv' act_name = name + '_act' else: conv_name = None act_name = None x = Activation('relu', name=act_name)(x) x = SeparableConv2D(filters, size, strides=strides, padding=padding, use_bias=False, name=conv_name)(x) x = BatchNormalization(axis=-1, scale=False, name=name)(x) return x
