Python engine.Layer方法代碼示例

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def reset_states(self):
		assert self.stateful, 'Layer must be stateful.'
		input_shape = self.input_spec[0].shape

		if not input_shape[0]:
			raise Exception('If a RNN is stateful, a complete ' +
							'input_shape must be provided (including batch size).')

		if hasattr(self, 'states'):
			K.set_value(self.states[0],
			            np.zeros((input_shape[0], self.hidden_recurrent_dim)))
			K.set_value(self.states[1],
			            np.zeros((input_shape[0], self.input_dim)))
			K.set_value(self.states[2],
			            np.zeros((input_shape[0], self.hidden_dim)))
		else:
			self.states = [K.zeros((input_shape[0], self.hidden_recurrent_dim)),
							K.zeros((input_shape[0], self.input_dim)),
							K.zeros((input_shape[0], self.hidden_dim))] 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def reset_states(self):
		assert self.stateful, 'Layer must be stateful.'
		input_shape = self.input_spec[0].shape
		if not input_shape[0]:
			raise ValueError('If a RNN is stateful, it needs to know '
			                 'its batch size. Specify the batch size '
			                 'of your input tensors: \n'
			                 '- If using a Sequential model, '
			                 'specify the batch size by passing '
			                 'a `batch_input_shape` '
			                 'argument to your first layer.\n'
			                 '- If using the functional API, specify '
			                 'the time dimension by passing a '
			                 '`batch_shape` argument to your Input layer.')
		if hasattr(self, 'states'):
			K.set_value(self.states[0],
			            np.zeros((input_shape[0], self.input_dim)))
			K.set_value(self.states[1],
			            np.zeros((input_shape[0], self.output_dim)))
		else:
			self.states = [K.zeros((input_shape[0], self.input_dim)),
							K.zeros((input_shape[0], self.output_dim))] 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def emit_Slice(self, IR_node, in_scope=False):
        # It arouses some problems:
        # it can be implemented by Lambda Layer
        # https://github.com/keras-team/keras/issues/890

        self.used_layers.add(IR_node.type)

        extra_str = ""
        if IR_node.get_attr('strides'):
            extra_str += "strides={}".format(IR_node.get_attr('strides'))
        if IR_node.get_attr('begin_mask'):
            extra_str += ", begin_mask={}".format(IR_node.get_attr('begin_mask'))
        if IR_node.get_attr('end_mask'):
            extra_str += ", end_mask={}".format(IR_node.get_attr('end_mask'))
        if IR_node.get_attr('shrink_axis_mask'):
            extra_str += ", shrink_axis_mask={}".format(IR_node.get_attr('shrink_axis_mask'))

        code = "{:<15} = __slice({}, {}, {}, {})".format(
            IR_node.variable_name,
            self.parent_variable_name(IR_node),
            IR_node.get_attr('starts'),
            IR_node.get_attr('ends'),
            extra_str)
        return code 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def _emit_h_zero(self, IR_node):
        if not self.layers_codes.get(IR_node.pattern, None):
            class_code = '''
class my_h_zero(keras.layers.Layer):
    def __init__(self, **kwargs):
        super(my_h_zero, self).__init__(**kwargs)
    
    def call(self, dummy):
        {:<15} = K.constant(np.full((1, {}), {}))

        return {}
            '''.format(IR_node.variable_name,
            IR_node.get_attr('fill_size'),
            IR_node.get_attr('fill_value'),
            IR_node.variable_name)
            self.layers_codes[IR_node.pattern] = class_code

        code = "{:<15} = my_h_zero()({})".format(IR_node.variable_name, self.parent_variable_name(IR_node))

        return code 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def _layer_Affine(self):
        self.add_body(0, '''
from keras.engine import Layer, InputSpec
from keras import initializers
from keras  import backend as K

class Affine(Layer):
    def __init__(self, scale, bias=None, **kwargs):
        super(Affine, self).__init__(**kwargs)
        self.gamma = scale
        self.beta = bias

    def call(self, inputs, training=None):
        input_shape = K.int_shape(inputs)
        # Prepare broadcasting shape.
        return self.gamma * inputs + self.beta

    def compute_output_shape(self, input_shape):
        return input_shape
        ''') 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def _layer_Shape(self):
        self.add_body(0, '''
def __shape(input):
    return Lambda(lambda x: tf.shape(x))(input)
        ''')

#     def _layer_Constant(self):
#         self.add_body(0, '''
# class my_constant(keras.layers.Layer):
#     def __init__(self, value, **kwargs):
#         super(my_constant, self).__init__(**kwargs)
#         self._value = value
#     # the input is dummy, just for creating keras graph.
#     def call(self, dummy):
#         res = K.constant(self._value)
#         self.output_shapes = K.int_shape(res)
#         return res
    
#     def compute_output_shape(self, input_shape):
#         return self.output_shapes
# ''') 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def build(self, input_shape: list):
        """
        Build the layer.

        :param input_shape: the shapes of the input tensors,
            for MatchingLayer we need tow input tensors.
        """
        # Used purely for shape validation.
        if not isinstance(input_shape, list) or len(input_shape) != 2:
            raise ValueError('A `MatchingLayer` layer should be called '
                             'on a list of 2 inputs.')
        self._shape1 = input_shape[0]
        self._shape2 = input_shape[1]
        for idx in 0, 2:
            if self._shape1[idx] != self._shape2[idx]:
                raise ValueError(
                    'Incompatible dimensions: '
                    f'{self._shape1[idx]} != {self._shape2[idx]}.'
                    f'Layer shapes: {self._shape1}, {self._shape2}.'
                ) 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def build(self, input_shape):
        # Used purely for shape validation.
        if not isinstance(input_shape, list) or len(input_shape) != 2:
            raise ValueError('A `Match` layer should be called '
                             'on a list of 2 inputs.')
        self.shape1 = input_shape[0]
        self.shape2 = input_shape[1]
        if self.shape1[0] != self.shape2[0]:
            raise ValueError(
                'Dimension incompatibility '
                '%s != %s. ' % (self.shape1[0], self.shape2[0]) +
                'Layer shapes: %s, %s' % (self.shape1, self.shape2))
        if self.shape1[2] != self.shape2[2]:
            raise ValueError(
                'Dimension incompatibility '
                '%s != %s. ' % (self.shape1[2], self.shape2[2]) +
                'Layer shapes: %s, %s' % (self.shape1, self.shape2)) 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def build(self,input_shape):
        # Used purely for shape validation.
        if not isinstance(input_shape, list) or len(input_shape) != 2:
            raise ValueError('A `Match` layer should be called '
                             'on a list of 2 inputs.')
        self.shape1 = input_shape[0]
        self.shape2 = input_shape[1]
        if self.shape1[0] != self.shape2[0]:
            raise ValueError(
                'Dimension incompatibility '
                '%s != %s. ' % (self.shape1[0], self.shape2[0]) +
                'Layer shapes: %s, %s' % (self.shape1, self.shape2)) # batch_size should equal
        if self.shape1[2] != self.shape2[2]:
            raise ValueError(
                'Dimension incompatibility '
                '%s != %s. ' % (self.shape1[2], self.shape2[2]) + # embeding_size should equal
                'Layer shapes: %s, %s' % (self.shape1, self.shape2))

        self.A = self.add_weight( name='A',
                               shape=(self.shape1[2], self.shape2[2]),
                                dtype=K.tf.float32,
                                initializer=K.tf.contrib.layers.xavier_initializer(),
                               trainable=True )
        super(MatchBilinear, self).build(input_shape)  # Be sure to call this somewhere! 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def call(self, x, mask=None):

        assert self.built, 'Layer must be built before being called'
        input_shape = K.int_shape(x)

        reduction_axes = list(range(len(input_shape)))
        del reduction_axes[self.axis]
        broadcast_shape = [1] * len(input_shape)
        broadcast_shape[self.axis] = input_shape[self.axis]

        if sorted(reduction_axes) == range(K.ndim(x))[:-1]:
            x_normed = K.batch_normalization(
                x, self.running_mean, self.running_std,
                self.beta, self.gamma,
                epsilon=self.epsilon)
        else:
            # need broadcasting
            broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape)
            broadcast_running_std = K.reshape(self.running_std, broadcast_shape)
            broadcast_beta = K.reshape(self.beta, broadcast_shape)
            broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
            x_normed = K.batch_normalization(
                x, broadcast_running_mean, broadcast_running_std,
                broadcast_beta, broadcast_gamma,
                epsilon=self.epsilon)

        return x_normed 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
    """Build a ResNet graph.
        architecture: Can be resnet50 or resnet101
        stage5: Boolean. If False, stage5 of the network is not created
        train_bn: Boolean. Train or freeze Batch Norm layers
    """
    assert architecture in ["resnet50", "resnet101"]
    # Stage 1
    x = KL.ZeroPadding2D((3, 3))(input_image)
    x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
    x = BatchNorm(name='bn_conv1')(x, training=train_bn)
    x = KL.Activation('relu')(x)
    C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
    # Stage 2
    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn)
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn)
    C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn)
    # Stage 3
    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn)
    C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn)
    # Stage 4
    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn)
    block_count = {"resnet50": 5, "resnet101": 22}[architecture]
    for i in range(block_count):
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn)
    C4 = x
    # Stage 5
    if stage5:
        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn)
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn)
        C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn)
    else:
        C5 = None
    return [C1, C2, C3, C4, C5]


############################################################
#  Proposal Layer
############################################################ 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def compute_output_shape(self, input_shape):
        return (None, self.proposal_count, 4)


############################################################
#  ROIAlign Layer
############################################################ 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def compute_mask(self, inputs, mask=None):
        return [None, None, None, None]


############################################################
#  Detection Layer
############################################################ 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def resnet_graph(input_image, architecture, stage5=False):
    assert architecture in ["resnet50", "resnet101"]
    # Stage 1
    x = KL.ZeroPadding2D((3, 3))(input_image)
    x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
    x = BatchNorm(axis=3, name='bn_conv1')(x)
    x = KL.Activation('relu')(x)
    C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
    # Stage 2
    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')
    C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
    # Stage 3
    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')
    C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
    # Stage 4
    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    block_count = {"resnet50": 5, "resnet101": 22}[architecture]
    for i in range(block_count):
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i))
    C4 = x
    # Stage 5
    if stage5:
        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
        C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
    else:
        C5 = None
    return [C1, C2, C3, C4, C5]


############################################################
#  Proposal Layer
############################################################ 

# 需要導入模塊: from keras import engine [as 別名]
# 或者: from keras.engine import Layer [as 別名]
def reset_states(self):
        assert self.stateful, 'Layer must be stateful.'
        input_shape = self.input_spec[0].shape
        if not input_shape[0]:
            raise Exception('If a RNN is stateful, a complete ' +
                            'input_shape must be provided (including batch size).')
        if hasattr(self, 'states'):
            K.set_value(self.states[0],
                        np.zeros((input_shape[0], self.output_dim)))
            K.set_value(self.states[1],
                        np.zeros((input_shape[0], self.output_dim)))
        else:
            self.states = [K.zeros((input_shape[0], self.output_dim)),
                           K.zeros((input_shape[0], self.output_dim))]