wehub-resource-sync
443 lines
13 KiB
Python
443 lines
13 KiB
Python
# Copyright (c) 2022 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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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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def pixel_unshuffle_np(x, down_factor, data_format="NCHW"):
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'''Numpy implementation of pixel unshuffle'''
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if data_format == "NCHW":
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n, c, h, w = x.shape
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new_shape = (
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n,
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c,
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h // down_factor,
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down_factor,
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w // down_factor,
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down_factor,
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)
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npresult = np.reshape(x, new_shape)
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npresult = npresult.transpose(0, 1, 3, 5, 2, 4)
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oshape = [
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n,
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c * down_factor * down_factor,
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h // down_factor,
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w // down_factor,
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]
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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 = (
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n,
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h // down_factor,
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down_factor,
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w // down_factor,
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down_factor,
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c,
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)
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npresult = np.reshape(x, new_shape)
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npresult = npresult.transpose(0, 1, 3, 5, 2, 4)
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oshape = [
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n,
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h // down_factor,
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w // down_factor,
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c * down_factor * down_factor,
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]
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npresult = np.reshape(npresult, oshape)
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return npresult
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def pixel_unshuffle_wrapper(x, downscale_factor, data_format):
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return paddle.nn.functional.pixel_unshuffle(
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x, downscale_factor, data_format
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)
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class TestPixelUnshuffleOp(OpTest):
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'''TestPixelUnshuffleOp'''
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def setUp(self):
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'''setUp'''
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self.op_type = "pixel_unshuffle"
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self.python_api = pixel_unshuffle_wrapper
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self.init_dtype()
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self.init_shape()
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self.init_data_format()
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n, c, h, w = self.shape
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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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down_factor = 3
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x = np.random.random(shape).astype(self.dtype)
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npresult = pixel_unshuffle_np(x, down_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 = {
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"downscale_factor": down_factor,
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"data_format": self.format,
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}
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def init_shape(self):
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self.shape = [2, 1, 12, 12]
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def init_dtype(self):
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self.dtype = np.float64
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def init_data_format(self):
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'''init_data_format'''
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self.format = "NCHW"
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def test_check_output(self):
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'''test_check_output'''
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self.check_output()
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def test_check_grad(self):
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'''test_check_grad'''
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self.check_grad(["X"], "Out")
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class TestChannelLast(TestPixelUnshuffleOp):
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'''TestChannelLast'''
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def init_data_format(self):
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'''init_data_format'''
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self.format = "NHWC"
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class TestPixelUnshuffleFP16Op(TestPixelUnshuffleOp):
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def init_dtype(self):
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self.dtype = np.float16
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class TestPixelUnshuffleOp_ZeroSize(TestPixelUnshuffleOp):
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def init_shape(self):
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self.shape = [2, 0, 0, 12]
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestPixelUnshuffleBP16Op(OpTest):
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'''TestPixelUnshuffleBP16Op'''
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def setUp(self):
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self.op_type = "pixel_unshuffle"
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self.python_api = pixel_unshuffle_wrapper
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self.init_dtype()
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self.init_data_format()
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n, c, h, w = 2, 1, 12, 12
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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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down_factor = 3
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x = np.random.random(shape).astype(self.np_dtype)
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npresult = pixel_unshuffle_np(x, down_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 = {
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"downscale_factor": down_factor,
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"data_format": self.format,
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}
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self.place = get_device_place()
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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def init_dtype(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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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_with_place(self.place)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place,
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['X'],
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'Out',
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)
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class TestPixelUnshuffleAPI(unittest.TestCase):
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'''TestPixelUnshuffleAPI'''
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def setUp(self):
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'''setUp'''
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self.x_1_np = np.random.random([2, 1, 12, 12]).astype("float64")
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self.x_2_np = np.random.random([2, 12, 12, 1]).astype("float64")
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self.out_1_np = pixel_unshuffle_np(self.x_1_np, 3)
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self.out_2_np = pixel_unshuffle_np(self.x_2_np, 3, "NHWC")
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def test_static_graph_functional(self):
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'''test_static_graph_functional'''
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.enable_static()
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x_1 = paddle.static.data(
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name="x", shape=[2, 1, 12, 12], dtype="float64"
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)
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x_2 = paddle.static.data(
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name="x2", shape=[2, 12, 12, 1], dtype="float64"
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)
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out_1 = F.pixel_unshuffle(x_1, 3)
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out_2 = F.pixel_unshuffle(x_2, 3, "NHWC")
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exe = paddle.static.Executor(place=place)
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res_1, res_2 = exe.run(
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base.default_main_program(),
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feed={"x": self.x_1_np, "x2": self.x_2_np},
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fetch_list=[out_1, out_2],
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use_prune=True,
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)
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np.testing.assert_allclose(res_1, self.out_1_np, rtol=1e-05, atol=1)
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np.testing.assert_allclose(res_2, self.out_2_np, rtol=1e-05, atol=1)
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def test_static_param_alias(self):
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'''test_static_param_alias'''
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x_1 = paddle.static.data(
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name="x", shape=[2, 1, 12, 12], dtype="float64"
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)
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out_1 = F.pixel_unshuffle(x=x_1, downscale_factor=3)
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out_1_alias = F.pixel_unshuffle(input=x_1, downscale_factor=3)
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exe = paddle.static.Executor()
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res_1, res_1_alias = exe.run(
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main,
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feed={"x": self.x_1_np},
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fetch_list=[out_1, out_1_alias],
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)
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np.testing.assert_allclose(res_1, res_1_alias)
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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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'''test_static_graph_layer'''
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.enable_static()
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x_1 = paddle.static.data(
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name="x", shape=[2, 1, 12, 12], dtype="float64"
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)
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x_2 = paddle.static.data(
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name="x2", shape=[2, 12, 12, 1], dtype="float64"
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)
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# init instance
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ps_1 = paddle.nn.PixelUnshuffle(3)
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ps_2 = paddle.nn.PixelUnshuffle(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_unshuffle_np(self.x_1_np, 3)
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out_2_np = pixel_unshuffle_np(self.x_2_np, 3, "NHWC")
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exe = paddle.static.Executor(place=place)
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res_1, res_2 = exe.run(
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base.default_main_program(),
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feed={"x": self.x_1_np, "x2": self.x_2_np},
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fetch_list=[out_1, out_2],
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use_prune=True,
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)
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np.testing.assert_allclose(res_1, out_1_np)
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np.testing.assert_allclose(res_2, out_2_np)
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def run_dygraph(self, down_factor, data_format):
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'''run_dygraph'''
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n, c, h, w = 2, 1, 12, 12
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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_unshuffle_np(x, down_factor, data_format)
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for use_cuda in (
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[False, True]
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if (core.is_compiled_with_cuda() or is_custom_device())
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else [False]
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):
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place = get_device_place() if use_cuda else paddle.CPUPlace()
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paddle.disable_static(place=place)
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pixel_unshuffle = paddle.nn.PixelUnshuffle(
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down_factor, data_format=data_format
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)
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result = pixel_unshuffle(paddle.to_tensor(x))
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np.testing.assert_allclose(result.numpy(), npresult, rtol=1e-05)
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result_functional = F.pixel_unshuffle(
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paddle.to_tensor(x), 3, data_format
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)
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np.testing.assert_allclose(
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result_functional.numpy(), npresult, rtol=1e-05
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)
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pixel_unshuffle_str = f'downscale_factor={down_factor}'
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if data_format != 'NCHW':
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pixel_unshuffle_str += f', data_format={data_format}'
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self.assertEqual(pixel_unshuffle.extra_repr(), pixel_unshuffle_str)
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def test_dygraph1(self):
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'''test_dygraph1'''
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self.run_dygraph(3, "NCHW")
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def test_dygraph2(self):
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'''test_dygraph2'''
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self.run_dygraph(3, "NHWC")
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def test_dygraph_param_alias(self):
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'''test_dygraph_param_alias'''
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paddle.disable_static()
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x = paddle.to_tensor(self.x_1_np)
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out_1 = F.pixel_unshuffle(x=x, downscale_factor=3)
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out_1_alias = F.pixel_unshuffle(input=x, downscale_factor=3)
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np.testing.assert_allclose(out_1.numpy(), out_1_alias.numpy())
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class TestPixelUnshuffleError(unittest.TestCase):
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'''TestPixelUnshuffleError'''
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def test_error_functional(self):
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'''test_error_functional'''
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def error_input():
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with paddle.base.dygraph.guard():
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x = np.random.random([4, 12, 12]).astype("float64")
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pixel_unshuffle = F.pixel_unshuffle(paddle.to_tensor(x), 2)
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self.assertRaises(ValueError, error_input)
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def error_downscale_factor_1():
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with paddle.base.dygraph.guard():
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x = np.random.random([2, 1, 12, 12]).astype("float64")
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pixel_unshuffle = F.pixel_unshuffle(paddle.to_tensor(x), 3.33)
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self.assertRaises(TypeError, error_downscale_factor_1)
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def error_downscale_factor_2():
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with paddle.base.dygraph.guard():
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x = np.random.random([2, 1, 12, 12]).astype("float64")
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pixel_unshuffle = F.pixel_unshuffle(paddle.to_tensor(x), -1)
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self.assertRaises(ValueError, error_downscale_factor_2)
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def error_data_format():
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with paddle.base.dygraph.guard():
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x = np.random.random([2, 1, 12, 12]).astype("float64")
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pixel_unshuffle = F.pixel_unshuffle(
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paddle.to_tensor(x), 3, "WOW"
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)
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self.assertRaises(ValueError, error_data_format)
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def test_error_layer(self):
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'''test_error_layer'''
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def error_input_layer():
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with paddle.base.dygraph.guard():
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x = np.random.random([4, 12, 12]).astype("float64")
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ps = paddle.nn.PixelUnshuffle(2)
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ps(paddle.to_tensor(x))
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self.assertRaises(ValueError, error_input_layer)
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def error_downscale_factor_layer_1():
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with paddle.base.dygraph.guard():
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x = np.random.random([2, 1, 12, 12]).astype("float64")
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ps = paddle.nn.PixelUnshuffle(3.33)
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self.assertRaises(TypeError, error_downscale_factor_layer_1)
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def error_downscale_factor_layer_2():
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with paddle.base.dygraph.guard():
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x = np.random.random([2, 1, 12, 12]).astype("float64")
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ps = paddle.nn.PixelUnshuffle(-1)
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self.assertRaises(ValueError, error_downscale_factor_layer_2)
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def error_data_format_layer():
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with paddle.base.dygraph.guard():
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x = np.random.random([2, 1, 12, 12]).astype("float64")
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ps = paddle.nn.PixelUnshuffle(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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