Python keras.layers.SeparableConv2D() Examples

The following are 22 code examples of keras.layers.SeparableConv2D(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.layers , or try the search function

Example #1

Source File: xception.py From Keras-FasterRCNN with MIT License

6 votes

def classifier_layers(x, input_shape, trainable=False):

    # compile times on theano tend to be very high, so we use smaller ROI pooling regions to workaround
    # (hence a smaller stride in the region that follows the ROI pool)
    x = TimeDistributed(SeparableConv2D(1536, (3, 3),
                                        padding='same',
                                        use_bias=False),
                        name='block14_sepconv1')(x)
    x = TimeDistributed(BatchNormalization(), name='block14_sepconv1_bn')(x)
    x = Activation('relu', name='block14_sepconv1_act')(x)

    x = TimeDistributed(SeparableConv2D(2048, (3, 3),
                                        padding='same',
                                        use_bias=False),
                        name='block14_sepconv2')(x)
    x = TimeDistributed(BatchNormalization(), name='block14_sepconv2_bn')(x)
    x = Activation('relu', name='block14_sepconv2_act')(x)

    TimeDistributed(GlobalAveragePooling2D(), name='avg_pool')(x)

    return x

Example #2

Source File: test_tf_keras_layers.py From tf-coreml with Apache License 2.0

6 votes

def test_tiny_separable_conv_same_fancy_depth_multiplier(self):
      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)

Example #3

Source File: test_tf_keras_layers.py From tf-coreml with Apache License 2.0

6 votes

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)

Example #4

Source File: layers.py From pose-regression with MIT License

6 votes

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

Example #5

Source File: model_chunks.py From pirateAI with MIT License

5 votes

def a3c_sepconv(x, params):
    """
    Feed forward model used in a3c paper but with seperable convolutions
    :param x: input tensor
    :param params: {dict} hyperparams (sub-selection)
    :return: output tensor
    :raises ValueError: could not find parameter
    """
    x = layers.SeparableConv2D(filters=16, kernel_size=8, strides=4, activation='relu')(x)
    x = layers.SeparableConv2D(filters=32, kernel_size=4, strides=2, activation='relu')(x)
    return x

Example #6