tensorlayer/layers/convolution/deformable_conv.py
def _tf_batch_map_coordinates(self, inputs, coords):
        """Batch version of tf_map_coordinates

        Only supports 2D feature maps

        Parameters
        ----------
        inputs : ``tf.Tensor``
            shape = (b*c, h, w)
        coords : ``tf.Tensor``
            shape = (b*c, h, w, n, 2)

        Returns
        -------
        ``tf.Tensor``
            A Tensor with the shape as (b*c, h, w, n)

        """
        input_shape = inputs.get_shape()
        coords_shape = coords.get_shape()
        batch_channel = tf.shape(inputs)[0]
        input_h = int(input_shape[1])
        input_w = int(input_shape[2])
        kernel_n = int(coords_shape[3])
        n_coords = input_h * input_w * kernel_n

        coords_lt = tf.cast(tf.floor(coords), 'int32')
        coords_rb = tf.cast(tf.ceil(coords), 'int32')
        coords_lb = tf.stack([coords_lt[:, :, :, :, 0], coords_rb[:, :, :, :, 1]], axis=-1)
        coords_rt = tf.stack([coords_rb[:, :, :, :, 0], coords_lt[:, :, :, :, 1]], axis=-1)

        idx = self._tf_repeat(tf.range(batch_channel), n_coords)

        vals_lt = self._get_vals_by_coords(inputs, coords_lt, idx, (batch_channel, input_h, input_w, kernel_n))
        vals_rb = self._get_vals_by_coords(inputs, coords_rb, idx, (batch_channel, input_h, input_w, kernel_n))
        vals_lb = self._get_vals_by_coords(inputs, coords_lb, idx, (batch_channel, input_h, input_w, kernel_n))
        vals_rt = self._get_vals_by_coords(inputs, coords_rt, idx, (batch_channel, input_h, input_w, kernel_n))

        coords_offset_lt = coords - tf.cast(coords_lt, 'float32')

        vals_t = vals_lt + (vals_rt - vals_lt) * coords_offset_lt[:, :, :, :, 0]
        vals_b = vals_lb + (vals_rb - vals_lb) * coords_offset_lt[:, :, :, :, 0]
        mapped_vals = vals_t + (vals_b - vals_t) * coords_offset_lt[:, :, :, :, 1]

        return mapped_vals
def _tf_batch_map_coordinates(self, inputs, coords):
        """Batch version of tf_map_coordinates

        Only supports 2D feature maps

        Parameters
        ----------
        inputs : ``tf.Tensor``
            shape = (b*c, h, w)
        coords : ``tf.Tensor``
            shape = (b*c, h, w, n, 2)

        Returns
        -------
        ``tf.Tensor``
            A Tensor with the shape as (b*c, h, w, n)

        """
        input_shape = inputs.get_shape()
        coords_shape = coords.get_shape()
        batch_channel = tf.shape(inputs)[0]
        input_h = int(input_shape[1])
        input_w = int(input_shape[2])
        kernel_n = int(coords_shape[3])
        n_coords = input_h * input_w * kernel_n

        coords_lt = tf.cast(tf.floor(coords), 'int32')
        coords_rb = tf.cast(tf.ceil(coords), 'int32')
        coords_lb = tf.stack([coords_lt[:, :, :, :, 0], coords_rb[:, :, :, :, 1]], axis=-1)
        coords_rt = tf.stack([coords_rb[:, :, :, :, 0], coords_lt[:, :, :, :, 1]], axis=-1)

        idx = self._tf_repeat(tf.range(batch_channel), n_coords)

        vals_lt = self._get_vals_by_coords(inputs, coords_lt, idx, (batch_channel, input_h, input_w, kernel_n))
        vals_rb = self._get_vals_by_coords(inputs, coords_rb, idx, (batch_channel, input_h, input_w, kernel_n))
        vals_lb = self._get_vals_by_coords(inputs, coords_lb, idx, (batch_channel, input_h, input_w, kernel_n))
        vals_rt = self._get_vals_by_coords(inputs, coords_rt, idx, (batch_channel, input_h, input_w, kernel_n))

        coords_offset_lt = coords - tf.cast(coords_lt, 'float32')

        vals_t = vals_lt + (vals_rt - vals_lt) * coords_offset_lt[:, :, :, :, 0]
        vals_b = vals_lb + (vals_rb - vals_lb) * coords_offset_lt[:, :, :, :, 0]
        mapped_vals = vals_t + (vals_b - vals_t) * coords_offset_lt[:, :, :, :, 1]

        return mapped_vals
def _tf_batch_map_coordinates ( self , inputs , coords ) : input_shape = inputs . get_shape ( ) coords_shape = coords . get_shape ( ) batch_channel = tf . shape ( inputs ) [ 0 ] input_h = int ( input_shape [ 1 ] ) input_w = int ( input_shape [ 2 ] ) kernel_n = int ( coords_shape [ 3 ] ) n_coords = input_h * input_w * kernel_n coords_lt = tf . cast ( tf . floor ( coords ) , 'int32' ) coords_rb = tf . cast ( tf . ceil ( coords ) , 'int32' ) coords_lb = tf . stack ( [ coords_lt [ : , : , : , : , 0 ] , coords_rb [ : , : , : , : , 1 ] ] , axis = - 1 ) coords_rt = tf . stack ( [ coords_rb [ : , : , : , : , 0 ] , coords_lt [ : , : , : , : , 1 ] ] , axis = - 1 ) idx = self . _tf_repeat ( tf . range ( batch_channel ) , n_coords ) vals_lt = self . _get_vals_by_coords ( inputs , coords_lt , idx , ( batch_channel , input_h , input_w , kernel_n ) ) vals_rb = self . _get_vals_by_coords ( inputs , coords_rb , idx , ( batch_channel , input_h , input_w , kernel_n ) ) vals_lb = self . _get_vals_by_coords ( inputs , coords_lb , idx , ( batch_channel , input_h , input_w , kernel_n ) ) vals_rt = self . _get_vals_by_coords ( inputs , coords_rt , idx , ( batch_channel , input_h , input_w , kernel_n ) ) coords_offset_lt = coords - tf . cast ( coords_lt , 'float32' ) vals_t = vals_lt + ( vals_rt - vals_lt ) * coords_offset_lt [ : , : , : , : , 0 ] vals_b = vals_lb + ( vals_rb - vals_lb ) * coords_offset_lt [ : , : , : , : , 0 ] mapped_vals = vals_t + ( vals_b - vals_t ) * coords_offset_lt [ : , : , : , : , 1 ] return mapped_vals
Batch version of tf_map_coordinates

        Only supports 2D feature maps

        Parameters
        ----------
        inputs : ``tf.Tensor``
            shape = (b*c, h, w)
        coords : ``tf.Tensor``
            shape = (b*c, h, w, n, 2)

        Returns
        -------
        ``tf.Tensor``
            A Tensor with the shape as (b*c, h, w, n)
Batch version of tf_map_coordinates