wehub-resource-sync
227 lines
7.4 KiB
Python
227 lines
7.4 KiB
Python
# Copyright (c) 2025 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 get_places
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from test_pool1d_api import avg_pool1D_forward_naive
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from test_pool2d_api import avg_pool2D_forward_naive
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from test_pool3d_op import avg_pool3D_forward_naive
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import paddle
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class TestCompatAvgPool1DAPI(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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self.input_np = np.random.random([2, 3, 32]).astype("float32")
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def run_test_case(
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self,
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kernel_size,
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stride,
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padding,
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ceil_mode,
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count_include_pad,
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):
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for place in self.places:
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paddle.disable_static(place)
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input_pd = paddle.to_tensor(self.input_np)
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pool_layer = paddle.compat.nn.AvgPool1D(
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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ceil_mode=ceil_mode,
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count_include_pad=count_include_pad,
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)
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result_pd = pool_layer(input_pd)
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if isinstance(kernel_size, int):
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kernel_size = [kernel_size]
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if stride is None:
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stride = kernel_size
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if isinstance(stride, int):
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stride = [stride]
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if isinstance(padding, int):
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padding = [padding]
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result_np = avg_pool1D_forward_naive(
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self.input_np,
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kernel_size,
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stride,
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padding,
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ceil_mode=ceil_mode,
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exclusive=not count_include_pad,
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)
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np.testing.assert_allclose(result_pd.numpy(), result_np, rtol=1e-05)
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@unittest.skipIf(
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paddle.is_compiled_with_xpu(),
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"XPU Kernel has accuracy issue.",
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)
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def test_all_cases(self):
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self.run_test_case(2, 2, 0, False, True)
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self.run_test_case(3, 1, 1, False, True)
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self.run_test_case(3, 2, 1, True, False)
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self.run_test_case(3, None, 0, False, True)
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def test_errors(self):
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with self.assertRaises(TypeError):
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pool = paddle.compat.nn.AvgPool1D(2, exclusive=False, name="test")
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class TestCompatAvgPool2DAPI(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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self.input_np = np.random.random([2, 3, 32, 32]).astype("float32")
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def run_test_case(
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self,
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kernel_size,
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stride,
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padding,
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ceil_mode,
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count_include_pad,
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divisor_override,
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):
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for place in self.places:
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paddle.disable_static(place)
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input_pd = paddle.to_tensor(self.input_np)
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pool_layer = paddle.compat.nn.AvgPool2D(
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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ceil_mode=ceil_mode,
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count_include_pad=count_include_pad,
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divisor_override=divisor_override,
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)
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result_pd = pool_layer(input_pd)
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if isinstance(kernel_size, int):
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kernel_size = [kernel_size, kernel_size]
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if stride is None:
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stride = kernel_size
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if isinstance(stride, int):
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stride = [stride, stride]
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if isinstance(padding, int):
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padding = [padding, padding]
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result_np = avg_pool2D_forward_naive(
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self.input_np,
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kernel_size,
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stride,
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padding,
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ceil_mode=ceil_mode,
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exclusive=not count_include_pad,
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)
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if divisor_override is not None:
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result_np = (
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result_np
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* (kernel_size[0] * kernel_size[1])
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/ divisor_override
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)
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np.testing.assert_allclose(result_pd.numpy(), result_np, rtol=1e-05)
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@unittest.skipIf(
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paddle.is_compiled_with_xpu(),
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"XPU Kernel has accuracy issue.",
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)
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def test_all_cases(self):
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self.run_test_case(2, 2, 0, False, True, None)
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self.run_test_case([3, 3], [1, 1], [1, 1], False, True, None)
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self.run_test_case(3, 2, 1, True, False, None)
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self.run_test_case(3, None, 0, False, True, None)
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self.run_test_case(3, 2, 1, False, False, 5)
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def test_errors(self):
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with self.assertRaises(TypeError):
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pool = paddle.compat.nn.AvgPool2D(
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2, exclusive=True, data_format="NHWC", name="test"
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)
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class TestCompatAvgPool3DAPI(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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self.input_np = np.random.random([2, 3, 16, 16, 16]).astype("float32")
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def run_test_case(
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self,
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kernel_size,
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stride,
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padding,
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ceil_mode,
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count_include_pad,
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divisor_override,
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):
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for place in self.places:
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paddle.disable_static(place)
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input_pd = paddle.to_tensor(self.input_np)
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pool_layer = paddle.compat.nn.AvgPool3D(
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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ceil_mode=ceil_mode,
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count_include_pad=count_include_pad,
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divisor_override=divisor_override,
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)
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result_pd = pool_layer(input_pd)
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if isinstance(kernel_size, int):
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kernel_size = [kernel_size, kernel_size, kernel_size]
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if stride is None:
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stride = kernel_size
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if isinstance(stride, int):
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stride = [stride, stride, stride]
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if isinstance(padding, int):
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padding = [padding, padding, padding]
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result_np = avg_pool3D_forward_naive(
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self.input_np,
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kernel_size,
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stride,
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padding,
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ceil_mode=ceil_mode,
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exclusive=not count_include_pad,
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)
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if divisor_override is not None:
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result_np = (
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result_np
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* (kernel_size[0] * kernel_size[1] * kernel_size[2])
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/ divisor_override
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)
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np.testing.assert_allclose(result_pd.numpy(), result_np, rtol=1e-05)
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@unittest.skipIf(
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paddle.is_compiled_with_xpu(),
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"XPU Kernel has accuracy issue.",
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)
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def test_all_cases(self):
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self.run_test_case(2, 2, 0, False, True, None)
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self.run_test_case([3, 3, 3], [1, 1, 1], [1, 1, 1], False, True, None)
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self.run_test_case(3, 2, 1, True, False, None)
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self.run_test_case(3, None, 0, False, True, None)
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self.run_test_case(3, 2, 1, False, False, 5)
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def test_errors(self):
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with self.assertRaises(TypeError):
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pool = paddle.compat.nn.AvgPool3D(
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2, exclusive=True, data_format="NDHWC", name="test"
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)
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if __name__ == '__main__':
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unittest.main()
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