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157 lines
6.6 KiB
Python
157 lines
6.6 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/tps_spatial_transformer.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import nn, ParamAttr
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from paddle.nn import functional as F
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import numpy as np
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import itertools
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def grid_sample(input, grid, canvas=None):
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input.stop_gradient = False
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output = F.grid_sample(input, grid)
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if canvas is None:
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return output
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else:
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input_mask = paddle.ones(shape=input.shape)
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output_mask = F.grid_sample(input_mask, grid)
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padded_output = output * output_mask + canvas * (1 - output_mask)
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return padded_output
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# phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2
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def compute_partial_repr(input_points, control_points):
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N = input_points.shape[0]
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M = control_points.shape[0]
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pairwise_diff = paddle.reshape(
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input_points, shape=[N, 1, 2]) - paddle.reshape(
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control_points, shape=[1, M, 2])
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# original implementation, very slow
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# pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance
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pairwise_diff_square = pairwise_diff * pairwise_diff
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pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :,
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1]
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repr_matrix = 0.5 * pairwise_dist * paddle.log(pairwise_dist)
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# fix numerical error for 0 * log(0), substitute all nan with 0
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mask = repr_matrix != repr_matrix
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repr_matrix[mask] = 0
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return repr_matrix
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# output_ctrl_pts are specified, according to our task.
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def build_output_control_points(num_control_points, margins):
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margin_x, margin_y = margins
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num_ctrl_pts_per_side = num_control_points // 2
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ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side)
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ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y
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ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y)
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ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
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ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
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output_ctrl_pts_arr = np.concatenate(
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[ctrl_pts_top, ctrl_pts_bottom], axis=0)
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output_ctrl_pts = paddle.to_tensor(output_ctrl_pts_arr)
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return output_ctrl_pts
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class TPSSpatialTransformer(nn.Layer):
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def __init__(self,
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output_image_size=None,
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num_control_points=None,
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margins=None):
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super(TPSSpatialTransformer, self).__init__()
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self.output_image_size = output_image_size
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self.num_control_points = num_control_points
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self.margins = margins
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self.target_height, self.target_width = output_image_size
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target_control_points = build_output_control_points(num_control_points,
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margins)
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N = num_control_points
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# create padded kernel matrix
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forward_kernel = paddle.zeros(shape=[N + 3, N + 3])
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target_control_partial_repr = compute_partial_repr(
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target_control_points, target_control_points)
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target_control_partial_repr = paddle.cast(target_control_partial_repr,
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forward_kernel.dtype)
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forward_kernel[:N, :N] = target_control_partial_repr
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forward_kernel[:N, -3] = 1
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forward_kernel[-3, :N] = 1
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target_control_points = paddle.cast(target_control_points,
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forward_kernel.dtype)
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forward_kernel[:N, -2:] = target_control_points
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forward_kernel[-2:, :N] = paddle.transpose(
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target_control_points, perm=[1, 0])
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# compute inverse matrix
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inverse_kernel = paddle.inverse(forward_kernel)
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# create target cordinate matrix
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HW = self.target_height * self.target_width
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target_coordinate = list(
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itertools.product(
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range(self.target_height), range(self.target_width)))
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target_coordinate = paddle.to_tensor(target_coordinate) # HW x 2
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Y, X = paddle.split(
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target_coordinate, target_coordinate.shape[1], axis=1)
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Y = Y / (self.target_height - 1)
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X = X / (self.target_width - 1)
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target_coordinate = paddle.concat(
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[X, Y], axis=1) # convert from (y, x) to (x, y)
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target_coordinate_partial_repr = compute_partial_repr(
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target_coordinate, target_control_points)
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target_coordinate_repr = paddle.concat(
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[
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target_coordinate_partial_repr, paddle.ones(shape=[HW, 1]),
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target_coordinate
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],
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axis=1)
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# register precomputed matrices
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self.inverse_kernel = inverse_kernel
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self.padding_matrix = paddle.zeros(shape=[3, 2])
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self.target_coordinate_repr = target_coordinate_repr
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self.target_control_points = target_control_points
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def forward(self, input, source_control_points):
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assert source_control_points.ndimension() == 3
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assert source_control_points.shape[1] == self.num_control_points
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assert source_control_points.shape[2] == 2
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batch_size = paddle.shape(source_control_points)[0]
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self.padding_matrix = paddle.expand(
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self.padding_matrix, shape=[batch_size, 3, 2])
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Y = paddle.concat([source_control_points, self.padding_matrix], 1)
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mapping_matrix = paddle.matmul(self.inverse_kernel, Y)
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source_coordinate = paddle.matmul(self.target_coordinate_repr,
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mapping_matrix)
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grid = paddle.reshape(
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source_coordinate,
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shape=[-1, self.target_height, self.target_width, 2])
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grid = paddle.clip(grid, 0,
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1) # the source_control_points may be out of [0, 1].
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# the input to grid_sample is normalized [-1, 1], but what we get is [0, 1]
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grid = 2.0 * grid - 1.0
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output_maps = grid_sample(input, grid, canvas=None)
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return output_maps, source_coordinate
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