# 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 import paddle def AffineGrid4D(theta, size, align_corners): n = size[0] w = size[3] h = size[2] h_factor = w_factor = 1 if not align_corners: h_factor = (h - 1) / float(h) w_factor = (w - 1) / float(w) h_idx = ( np.repeat(np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[ :, :, np.newaxis ] * h_factor ) w_idx = ( np.repeat(np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[ :, :, np.newaxis ] * w_factor ) grid = np.concatenate( [w_idx, h_idx, np.ones([h, w, 1])], axis=2 ) # h * w * 3 grid = np.repeat(grid[np.newaxis, :], size[0], 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("float32") def AffineGrid5D(theta, size, align_corners): n = size[0] d = size[2] h = size[3] w = size[4] d_factor = h_factor = w_factor = 1 if not align_corners: d_factor = (d - 1) / float(d) h_factor = (h - 1) / float(h) w_factor = (w - 1) / float(w) d_idx = ( np.repeat( np.repeat( np.linspace(-1, 1, d)[:, np.newaxis, np.newaxis], h, axis=1 ), w, axis=2, )[:, :, :, np.newaxis] * d_factor ) h_idx = ( np.repeat( np.repeat( np.linspace(-1, 1, h)[np.newaxis, :, np.newaxis], w, axis=2 ), d, axis=0, )[:, :, :, np.newaxis] * h_factor ) w_idx = ( np.repeat( np.repeat( np.linspace(-1, 1, w)[np.newaxis, np.newaxis, :], h, axis=1 ), d, axis=0, )[:, :, :, np.newaxis] * w_factor ) grid = np.concatenate( [w_idx, h_idx, d_idx, np.ones([d, h, w, 1])], axis=3 ) # d * h * w * 4 grid = np.repeat(grid[np.newaxis, :], size[0], axis=0) # n * d * h * w * 4 ret = np.zeros([n, d * h * w, 3]) theta = theta.transpose([0, 2, 1]) for i in range(len(theta)): ret[i] = np.dot(grid[i].reshape([d * h * w, 4]), theta[i]) return ret.reshape([n, d, h, w, 3]).astype("float32") class TestAffineGridOp(OpTest): def setUp(self): self.initTestCase() self.op_type = "affine_grid" self.python_api = paddle.nn.functional.vision.affine_grid theta = np.random.randint(1, 3, self.theta_shape).astype("float32") self.inputs = {'Theta': theta} self.attrs = { "use_cudnn": self.use_cudnn, "align_corners": self.align_corners, } if self.dynamic_shape: self.inputs['OutputShape'] = self.output_shape else: self.attrs['output_shape'] = self.output_shape if self.theta_shape[1] == 2 and self.theta_shape[2] == 3: self.outputs = { 'Output': AffineGrid4D( theta, self.output_shape, self.align_corners ) } else: self.outputs = { 'Output': AffineGrid5D( theta, self.output_shape, self.align_corners ) } def test_check_output(self): self.check_output(check_eager=True) def test_check_grad_normal(self): self.check_grad( ['Theta'], 'Output', no_grad_set=['OutputShape'], check_eager=True ) def initTestCase(self): self.theta_shape = (17, 2, 3) self.output_shape = np.array([17, 2, 5, 7]).astype("int32") self.dynamic_shape = False self.use_cudnn = False self.align_corners = True class TestAffineGridOpCase1(TestAffineGridOp): def initTestCase(self): self.theta_shape = (20, 2, 3) self.output_shape = np.array([20, 2, 5, 7]).astype("int32") self.dynamic_shape = True self.use_cudnn = True if paddle.fluid.core.is_compiled_with_rocm(): self.use_cudnn = ( False # ROCM platform do not have MIOPEN kernel for affine_grid ) self.align_corners = True class TestAffineGridOpCase2(TestAffineGridOp): def initTestCase(self): self.theta_shape = (20, 2, 3) self.output_shape = np.array([20, 2, 5, 7]).astype("int32") self.dynamic_shape = True self.use_cudnn = False self.align_corners = True class TestAffineGridOpCase3(TestAffineGridOp): def initTestCase(self): self.theta_shape = (20, 2, 3) self.output_shape = np.array([20, 2, 5, 7]).astype("int32") self.dynamic_shape = True self.use_cudnn = False self.align_corners = False class TestAffineGridOpCase4(TestAffineGridOp): def initTestCase(self): self.theta_shape = (25, 2, 3) self.output_shape = np.array([25, 2, 5, 6]).astype("int32") self.dynamic_shape = False self.use_cudnn = False self.align_corners = False class TestAffineGridOp5DCase1(TestAffineGridOp): def initTestCase(self): self.theta_shape = (20, 3, 4) self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32") self.dynamic_shape = True self.use_cudnn = False self.align_corners = False class TestAffineGridOp5DCase2(TestAffineGridOp): def initTestCase(self): self.theta_shape = (20, 3, 4) self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32") self.dynamic_shape = True self.use_cudnn = False self.align_corners = True class TestAffineGridOp5DCase3(TestAffineGridOp): def initTestCase(self): self.theta_shape = (20, 3, 4) self.output_shape = np.array([20, 1, 2, 5, 7]).astype("int32") self.dynamic_shape = True self.use_cudnn = False self.align_corners = False class TestAffineGridOp5DCase4(TestAffineGridOp): def initTestCase(self): self.theta_shape = (25, 3, 4) self.output_shape = np.array([25, 1, 2, 5, 6]).astype("int32") self.dynamic_shape = False self.use_cudnn = False self.align_corners = False if __name__ == '__main__': paddle.enable_static() unittest.main()