# Copyright (c) 2019 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 eager_op_test import OpTest import paddle import paddle.fluid as fluid import paddle.fluid.core as core import paddle.nn.functional as F def pixel_shuffle_np(x, up_factor, data_format="NCHW"): if data_format == "NCHW": n, c, h, w = x.shape new_shape = ( n, c // (up_factor * up_factor), up_factor, up_factor, h, w, ) # reshape to (num,output_channel,upscale_factor,upscale_factor,h,w) npresult = np.reshape(x, new_shape) # transpose to (num,output_channel,h,upscale_factor,w,upscale_factor) npresult = npresult.transpose(0, 1, 4, 2, 5, 3) oshape = [n, c // (up_factor * up_factor), h * up_factor, w * up_factor] npresult = np.reshape(npresult, oshape) return npresult else: n, h, w, c = x.shape new_shape = ( n, h, w, c // (up_factor * up_factor), up_factor, up_factor, ) # reshape to (num,h,w,output_channel,upscale_factor,upscale_factor) npresult = np.reshape(x, new_shape) # transpose to (num,h,upscale_factor,w,upscale_factor,output_channel) npresult = npresult.transpose(0, 1, 4, 2, 5, 3) oshape = [n, h * up_factor, w * up_factor, c // (up_factor * up_factor)] npresult = np.reshape(npresult, oshape) return npresult class TestPixelShuffleOp(OpTest): def setUp(self): self.op_type = "pixel_shuffle" self.python_api = paddle.nn.functional.pixel_shuffle self.init_data_format() n, c, h, w = 2, 9, 4, 4 if self.format == "NCHW": shape = [n, c, h, w] if self.format == "NHWC": shape = [n, h, w, c] up_factor = 3 x = np.random.random(shape).astype("float64") npresult = pixel_shuffle_np(x, up_factor, self.format) self.inputs = {'X': x} self.outputs = {'Out': npresult} self.attrs = {'upscale_factor': up_factor, "data_format": self.format} def init_data_format(self): self.format = "NCHW" def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad( ['X'], 'Out', ) class TestChannelLast(TestPixelShuffleOp): def init_data_format(self): self.format = "NHWC" class TestPixelShuffleAPI(unittest.TestCase): def setUp(self): self.x_1_np = np.random.random([2, 9, 4, 4]).astype("float64") self.x_2_np = np.random.random([2, 4, 4, 9]).astype("float64") self.out_1_np = pixel_shuffle_np(self.x_1_np, 3) self.out_2_np = pixel_shuffle_np(self.x_2_np, 3, "NHWC") def test_static_graph_functional(self): for use_cuda in ( [False, True] if core.is_compiled_with_cuda() else [False] ): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() x_1 = paddle.fluid.data( name="x", shape=[2, 9, 4, 4], dtype="float64" ) x_2 = paddle.fluid.data( name="x2", shape=[2, 4, 4, 9], dtype="float64" ) out_1 = F.pixel_shuffle(x_1, 3) out_2 = F.pixel_shuffle(x_2, 3, "NHWC") exe = paddle.static.Executor(place=place) res_1 = exe.run( fluid.default_main_program(), feed={"x": self.x_1_np}, fetch_list=out_1, use_prune=True, ) res_2 = exe.run( fluid.default_main_program(), feed={"x2": self.x_2_np}, fetch_list=out_2, use_prune=True, ) assert np.allclose(res_1, self.out_1_np) assert np.allclose(res_2, self.out_2_np) def test_api_fp16(self): paddle.enable_static() with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): if core.is_compiled_with_cuda(): place = paddle.CUDAPlace(0) self.x_1_np = np.random.random([2, 9, 4, 4]).astype("float16") self.x_2_np = np.random.random([2, 4, 4, 9]).astype("float16") x_1 = paddle.fluid.data( name="x", shape=[2, 9, 4, 4], dtype="float16" ) x_2 = paddle.fluid.data( name="x2", shape=[2, 4, 4, 9], dtype="float16" ) # init instance ps_1 = paddle.nn.PixelShuffle(3) ps_2 = paddle.nn.PixelShuffle(3, "NHWC") out_1 = ps_1(x_1) out_2 = ps_2(x_2) out_1_np = pixel_shuffle_np(self.x_1_np, 3) out_2_np = pixel_shuffle_np(self.x_2_np, 3, "NHWC") exe = paddle.static.Executor(place=place) res_1 = exe.run( fluid.default_main_program(), feed={"x": self.x_1_np}, fetch_list=out_1, use_prune=True, ) res_2 = exe.run( fluid.default_main_program(), feed={"x2": self.x_2_np}, fetch_list=out_2, use_prune=True, ) assert np.allclose(res_1, out_1_np) assert np.allclose(res_2, out_2_np) # same test between layer and functional in this op. def test_static_graph_layer(self): for use_cuda in ( [False, True] if core.is_compiled_with_cuda() else [False] ): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() x_1 = paddle.fluid.data( name="x", shape=[2, 9, 4, 4], dtype="float64" ) x_2 = paddle.fluid.data( name="x2", shape=[2, 4, 4, 9], dtype="float64" ) # init instance ps_1 = paddle.nn.PixelShuffle(3) ps_2 = paddle.nn.PixelShuffle(3, "NHWC") out_1 = ps_1(x_1) out_2 = ps_2(x_2) out_1_np = pixel_shuffle_np(self.x_1_np, 3) out_2_np = pixel_shuffle_np(self.x_2_np, 3, "NHWC") exe = paddle.static.Executor(place=place) res_1 = exe.run( fluid.default_main_program(), feed={"x": self.x_1_np}, fetch_list=out_1, use_prune=True, ) res_2 = exe.run( fluid.default_main_program(), feed={"x2": self.x_2_np}, fetch_list=out_2, use_prune=True, ) assert np.allclose(res_1, out_1_np) assert np.allclose(res_2, out_2_np) def run_dygraph(self, up_factor, data_format): n, c, h, w = 2, 9, 4, 4 if data_format == "NCHW": shape = [n, c, h, w] if data_format == "NHWC": shape = [n, h, w, c] x = np.random.random(shape).astype("float64") npresult = pixel_shuffle_np(x, up_factor, data_format) for use_cuda in ( [False, True] if core.is_compiled_with_cuda() else [False] ): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.disable_static(place=place) pixel_shuffle = paddle.nn.PixelShuffle( up_factor, data_format=data_format ) result = pixel_shuffle(paddle.to_tensor(x)) np.testing.assert_allclose(result.numpy(), npresult, rtol=1e-05) result_functional = F.pixel_shuffle( paddle.to_tensor(x), 3, data_format ) np.testing.assert_allclose( result_functional.numpy(), npresult, rtol=1e-05 ) def test_dygraph1(self): self.run_dygraph(3, "NCHW") def test_dygraph2(self): self.run_dygraph(3, "NHWC") class TestPixelShuffleError(unittest.TestCase): def test_error_functional(self): def error_upscale_factor(): with paddle.fluid.dygraph.guard(): x = np.random.random([2, 9, 4, 4]).astype("float64") pixel_shuffle = F.pixel_shuffle(paddle.to_tensor(x), 3.33) self.assertRaises(TypeError, error_upscale_factor) def error_0_upscale_factor(): with paddle.fluid.dygraph.guard(): x = paddle.uniform([1, 1, 1, 1], dtype='float64') pixel_shuffle = F.pixel_shuffle(x, 0) self.assertRaises(ValueError, error_0_upscale_factor) def error_data_format(): with paddle.fluid.dygraph.guard(): x = np.random.random([2, 9, 4, 4]).astype("float64") pixel_shuffle = F.pixel_shuffle(paddle.to_tensor(x), 3, "WOW") self.assertRaises(ValueError, error_data_format) def test_error_layer(self): def error_upscale_factor_layer(): with paddle.fluid.dygraph.guard(): x = np.random.random([2, 9, 4, 4]).astype("float64") ps = paddle.nn.PixelShuffle(3.33) self.assertRaises(TypeError, error_upscale_factor_layer) def error_data_format_layer(): with paddle.fluid.dygraph.guard(): x = np.random.random([2, 9, 4, 4]).astype("float64") ps = paddle.nn.PixelShuffle(3, "MEOW") self.assertRaises(ValueError, error_data_format_layer) if __name__ == '__main__': paddle.enable_static() unittest.main()