Method build

tensorlayer/layers/normalization.py:726–770

(self, inputs_shape)

        )

    def build(self, inputs_shape):
        # shape = inputs.get_shape().as_list()
        if len(inputs_shape) != 4:
            raise Exception("This GroupNorm only supports 2D images.")

        if self.data_format == 'channels_last':
            channels = inputs_shape[-1]
            self.int_shape = tf.concat(
                [#tf.shape(input=self.inputs)[0:3],
                inputs_shape[0:3],
                tf.convert_to_tensor(value=[self.groups, channels // self.groups])], axis=0
            )
        elif self.data_format == 'channels_first':
            channels = inputs_shape[1]
            self.int_shape = tf.concat(
                [
                    # tf.shape(input=self.inputs)[0:1],
                    inputs_shape[0:1],
                    tf.convert_to_tensor(value=[self.groups, channels // self.groups]),
                    # tf.shape(input=self.inputs)[2:4]
                    inputs_shape[2:4],
                ],
                axis=0
            )
        else:
            raise ValueError("data_format must be 'channels_last' or 'channels_first'.")

        if self.groups > channels:
            raise ValueError('Invalid groups %d for %d channels.' % (self.groups, channels))
        if channels % self.groups != 0:
            raise ValueError('%d channels is not commensurate with %d groups.' % (channels, self.groups))

        if self.data_format == 'channels_last':
            # mean, var = tf.nn.moments(x, [1, 2, 4], keep_dims=True)
            self.gamma = self._get_weights("gamma", shape=channels, init=tl.initializers.ones())
            # self.gamma = tf.compat.v1.get_variable('gamma', channels, initializer=tf.compat.v1.initializers.ones())
            self.beta = self._get_weights("beta", shape=channels, init=tl.initializers.zeros())
            # self.beta = tf.compat.v1.get_variable('beta', channels, initializer=tf.compat.v1.initializers.zeros())
        elif self.data_format == 'channels_first':
            # mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True)
            self.gamma = self._get_weights("gamma", shape=[1, channels, 1, 1], init=tl.initializers.ones())
            # self.gamma = tf.compat.v1.get_variable('gamma', [1, channels, 1, 1], initializer=tf.compat.v1.initializers.ones())
            self.beta = self._get_weights("beta", shape=[1, channels, 1, 1], init=tl.initializers.zeros())
            # self.beta = tf.compat.v1.get_variable('beta', [1, channels, 1, 1], initializer=tf.compat.v1.initializers.zeros())
        # self.add_weights([self.gamma, self.bata])

    def forward(self, inputs):
        x = tf.reshape(inputs, self.int_shape)