# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import OpTest def AffineGrid(theta, grid_shape): n = grid_shape[0] h = grid_shape[1] w = grid_shape[2] h_idx = np.repeat( np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[:, :, np.newaxis] w_idx = np.repeat( np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[:, :, np.newaxis] grid = np.concatenate( [w_idx, h_idx, np.ones([h, w, 1])], axis=2) # h * w * 3 grid = np.repeat(grid[np.newaxis, :], n, axis=0) # n * h * w *3 ret = np.zeros([n, h * w, 2]) theta = theta.transpose([0, 2, 1]) for i in range(len(theta)): ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i]) return ret.reshape([n, h, w, 2]).astype("float64") def getGridPointValue(data, x, y): data_shape = data.shape N = data_shape[0] C = data_shape[1] in_H = data_shape[2] in_W = data_shape[3] out_H = x.shape[1] out_W = x.shape[2] #out = np.zeros(data_shape, dtype='float64') out = np.zeros([N, C, out_H, out_W], dtype='float64') for i in range(N): for j in range(out_H): for k in range(out_W): if y[i, j, k] < 0 or y[i, j, k] > in_H - 1 or x[ i, j, k] < 0 or x[i, j, k] > in_W - 1: out[i, :, j, k] = 0 else: out[i, :, j, k] = data[i, :, y[i, j, k], x[i, j, k]] return out def clip(x, min_n, max_n): return np.maximum(np.minimum(x, max_n), min_n) def unnormalizeAndClip(grid_slice, max_val, align_corners, padding_mode): if align_corners: grid_slice = 0.5 * ((grid_slice.astype('float64') + 1.0) * max_val) else: grid_slice = 0.5 * ( (grid_slice.astype('float64') + 1.0) * (max_val + 1)) - 0.5 if padding_mode == "border": grid_slice = clip(grid_slice, 0, max_val) elif padding_mode == "reflect": double_range = 2 * max_val if align_corners else (max_val + 1) * 2 grid_abs = np.abs(grid_slice) if align_corners else np.abs(grid_slice + 0.5) extra = grid_abs - np.floor(grid_abs / double_range) * double_range grid_slice = np.minimum(extra, double_range - extra) grid_slice = grid_slice if align_corners else clip(grid_slice - 0.5, 0, max_val) return grid_slice def GridSampler(data, grid, align_corners=True, mode="bilinear", padding_mode="zeros"): dims = data.shape N = dims[0] in_C = dims[1] in_H = dims[2] in_W = dims[3] out_H = grid.shape[1] out_W = grid.shape[2] x = grid[:, :, :, 0] y = grid[:, :, :, 1] y_max = in_H - 1 x_max = in_W - 1 x = unnormalizeAndClip(x, x_max, align_corners, padding_mode) y = unnormalizeAndClip(y, y_max, align_corners, padding_mode) if mode == "bilinear": x0 = np.floor(x).astype('int32') x1 = x0 + 1 y0 = np.floor(y).astype('int32') y1 = y0 + 1 wa = np.tile(((x1 - x) * (y1 - y)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)) wb = np.tile(((x1 - x) * (y - y0)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)) wc = np.tile(((x - x0) * (y1 - y)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)) wd = np.tile(((x - x0) * (y - y0)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)) va = getGridPointValue(data, x0, y0) vb = getGridPointValue(data, x0, y1) vc = getGridPointValue(data, x1, y0) vd = getGridPointValue(data, x1, y1) out = (wa * va + wb * vb + wc * vc + wd * vd).astype('float64') elif mode == "nearest": x = np.round(x).astype('int32') y = np.round(y).astype('int32') out = getGridPointValue(data, x, y) return out class TestGridSamplerOp(OpTest): def setUp(self): self.use_cudnn = False self.numeric_grad_delta = 0.0001 self.op_type = 'grid_sampler' self.align_corners = True self.padding_mode = "zeros" self.mode = "bilinear" self.initTestCase() x = np.random.randint(0, 255, self.x_shape).astype('float64') theta = np.zeros(self.theta_shape).astype('float64') for i in range(self.theta_shape[0]): for j in range(2): for k in range(3): theta[i, j, k] = np.random.rand(1)[0] grid = AffineGrid(theta, self.grid_shape) self.inputs = {'X': x, 'Grid': grid} self.attrs = { 'use_cudnn': self.use_cudnn, "align_corners": self.align_corners, "padding_mode": self.padding_mode, "mode": self.mode } # print("X: {}".format(x)) self.outputs = { 'Output': GridSampler(x, grid, self.align_corners, self.mode, self.padding_mode) } def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad( ['X', 'Grid'], 'Output', max_relative_error=0.01, numeric_grad_delta=self.numeric_grad_delta) def initTestCase(self): self.x_shape = (2, 3, 8, 8) self.grid_shape = (2, 7, 9, 2) self.theta_shape = (2, 2, 3) self.align_corners = True self.padding_mode = "zeros" self.mode = "bilinear" self.use_cudnn = True class Case1(TestGridSamplerOp): def initTestCase(self): self.x_shape = (2, 3, 5, 6) self.grid_shape = (2, 8, 9, 2) self.theta_shape = (2, 2, 3) self.align_corners = False self.padding_mode = "zeros" self.mode = "bilinear" class Case1(TestGridSamplerOp): def initTestCase(self): self.x_shape = (2, 3, 5, 6) self.grid_shape = (2, 8, 9, 2) self.theta_shape = (2, 2, 3) self.align_corners = False self.padding_mode = "border" self.mode = "bilinear" class Case2(TestGridSamplerOp): def initTestCase(self): self.x_shape = (2, 3, 5, 6) self.grid_shape = (2, 8, 9, 2) self.theta_shape = (2, 2, 3) self.align_corners = False self.padding_mode = "reflect" self.mode = "bilinear" class Case3(TestGridSamplerOp): def initTestCase(self): self.x_shape = (2, 3, 5, 6) self.grid_shape = (2, 8, 9, 2) self.theta_shape = (2, 2, 3) self.align_corners = True self.padding_mode = "reflect" self.mode = "bilinear" class Case4(TestGridSamplerOp): def initTestCase(self): self.x_shape = (2, 3, 5, 6) self.grid_shape = (2, 8, 9, 2) self.theta_shape = (2, 2, 3) self.align_corners = False self.padding_mode = "reflect" self.mode = "nearest" self.numeric_grad_delta = 0.0001 if __name__ == "__main__": unittest.main()