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