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- # Copyright (c) 2020 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 sys
- import unittest
-
- import numpy as np
-
- sys.path.append("../deprecated/legacy_test")
- from test_pool3d_op import (
- avg_pool3D_forward_naive,
- max_pool3D_forward_naive,
- pool3D_forward_naive,
- )
-
- import paddle
- from paddle import base
- from paddle.base import core
- from paddle.nn.functional import avg_pool3d, max_pool3d
- from paddle.pir_utils import test_with_pir_api
-
-
- class TestPool3D_API(unittest.TestCase):
- def setUp(self):
- np.random.seed(123)
- self.places = [base.CPUPlace()]
- if core.is_compiled_with_cuda():
- self.places.append(base.CUDAPlace(0))
-
- @test_with_pir_api
- def check_avg_static_results(self, place):
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- input = paddle.static.data(
- name="input", shape=[2, 3, 32, 32, 32], dtype="float32"
- )
- result = avg_pool3d(input, kernel_size=2, stride=2, padding=0)
-
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='avg',
- )
-
- exe = paddle.static.Executor(place)
- fetches = exe.run(
- feed={"input": input_np},
- fetch_list=[result],
- )
- np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
-
- def check_avg_dygraph_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = avg_pool3d(input, kernel_size=2, stride=2, padding="SAME")
-
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='avg',
- padding_algorithm="SAME",
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- avg_pool3d_dg = paddle.nn.layer.AvgPool3D(
- kernel_size=2, stride=None, padding="SAME"
- )
- result = avg_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_avg_dygraph_padding_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = avg_pool3d(
- input,
- kernel_size=2,
- stride=2,
- padding=1,
- ceil_mode=False,
- exclusive=True,
- )
-
- result_np = avg_pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[1, 1, 1],
- ceil_mode=False,
- exclusive=False,
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- avg_pool3d_dg = paddle.nn.layer.AvgPool3D(
- kernel_size=2,
- stride=None,
- padding=1,
- ceil_mode=False,
- exclusive=True,
- )
- result = avg_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_avg_dygraph_ceilmode_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = avg_pool3d(
- input, kernel_size=2, stride=2, padding=0, ceil_mode=True
- )
-
- result_np = avg_pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- ceil_mode=True,
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- avg_pool3d_dg = paddle.nn.layer.AvgPool3D(
- kernel_size=2, stride=None, padding=0, ceil_mode=True
- )
- result = avg_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- @test_with_pir_api
- def check_max_static_results(self, place):
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- input = paddle.static.data(
- name="input", shape=[2, 3, 32, 32, 32], dtype="float32"
- )
- result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
-
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='max',
- )
-
- exe = paddle.static.Executor(place)
- fetches = exe.run(
- feed={"input": input_np},
- fetch_list=[result],
- )
- np.testing.assert_allclose(fetches[0], result_np, rtol=1e-05)
-
- def check_max_dygraph_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = max_pool3d(input, kernel_size=2, stride=2, padding=0)
-
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='max',
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
- max_pool3d_dg = paddle.nn.layer.MaxPool3D(
- kernel_size=2, stride=None, padding=0
- )
- result = max_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_max_dygraph_ndhwc_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(np.transpose(input_np, [0, 2, 3, 4, 1]))
- result = max_pool3d(
- input,
- kernel_size=2,
- stride=2,
- padding=0,
- data_format="NDHWC",
- return_mask=False,
- )
-
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='max',
- )
-
- np.testing.assert_allclose(
- np.transpose(result.numpy(), [0, 4, 1, 2, 3]),
- result_np,
- rtol=1e-05,
- )
-
- def check_max_dygraph_ceilmode_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = max_pool3d(
- input, kernel_size=2, stride=2, padding=0, ceil_mode=True
- )
-
- result_np = max_pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- ceil_mode=True,
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- max_pool3d_dg = paddle.nn.layer.MaxPool3D(
- kernel_size=2, stride=None, padding=0, ceil_mode=True
- )
- result = max_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_max_dygraph_padding_results(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result = max_pool3d(
- input, kernel_size=2, stride=2, padding=1, ceil_mode=False
- )
-
- result_np = max_pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[1, 1, 1],
- ceil_mode=False,
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- max_pool3d_dg = paddle.nn.layer.MaxPool3D(
- kernel_size=2, stride=None, padding=1, ceil_mode=False
- )
- result = max_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_max_dygraph_stride_is_none(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- result, indices = max_pool3d(
- input,
- kernel_size=2,
- stride=None,
- padding="SAME",
- return_mask=True,
- )
-
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='max',
- padding_algorithm="SAME",
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
- max_pool3d_dg = paddle.nn.layer.MaxPool3D(
- kernel_size=2, stride=2, padding=0
- )
- result = max_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_max_dygraph_padding(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- padding = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]
- result = max_pool3d(input, kernel_size=2, stride=2, padding=padding)
-
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='max',
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
- max_pool3d_dg = paddle.nn.layer.MaxPool3D(
- kernel_size=2, stride=2, padding=0
- )
- result = max_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- padding = [0, 0, 0, 0, 0, 0]
- result = max_pool3d(input, kernel_size=2, stride=2, padding=padding)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def check_avg_divisor(self, place):
- with base.dygraph.guard(place):
- input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
- input = paddle.to_tensor(input_np)
- padding = 0
- result = avg_pool3d(
- input,
- kernel_size=2,
- stride=2,
- padding=padding,
- divisor_override=8,
- )
-
- result_np = pool3D_forward_naive(
- input_np,
- ksize=[2, 2, 2],
- strides=[2, 2, 2],
- paddings=[0, 0, 0],
- pool_type='avg',
- )
-
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
- avg_pool3d_dg = paddle.nn.layer.AvgPool3D(
- kernel_size=2, stride=2, padding=0
- )
- result = avg_pool3d_dg(input)
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- padding = [0, 0, 0, 0, 0, 0]
- result = avg_pool3d(
- input,
- kernel_size=2,
- stride=2,
- padding=padding,
- divisor_override=8,
- )
- np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05)
-
- def test_pool3d(self):
- paddle.enable_static()
- for place in self.places:
- self.check_max_dygraph_results(place)
- self.check_avg_dygraph_results(place)
- self.check_max_static_results(place)
- self.check_avg_static_results(place)
- self.check_max_dygraph_stride_is_none(place)
- self.check_max_dygraph_padding(place)
- self.check_avg_divisor(place)
- self.check_max_dygraph_ndhwc_results(place)
- self.check_max_dygraph_ceilmode_results(place)
-
- @test_with_pir_api
- def test_static_fp16_gpu(self):
- paddle.enable_static()
- if paddle.base.core.is_compiled_with_cuda():
- place = paddle.CUDAPlace(0)
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- input = np.random.random([1, 2, 3, 32, 32]).astype("float16")
-
- x = paddle.static.data(
- name="x", shape=[1, 2, 3, 32, 32], dtype="float16"
- )
-
- m = paddle.nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
- y = m(x)
-
- exe = paddle.static.Executor(place)
- res = exe.run(
- feed={
- "x": input,
- },
- fetch_list=[y],
- )
-
- np.testing.assert_array_equal(res[0].shape, [1, 2, 1, 16, 16])
-
- @test_with_pir_api
- def test_static_bf16_gpu(self):
- paddle.enable_static()
- if (
- paddle.base.core.is_compiled_with_cuda()
- and paddle.base.core.is_bfloat16_supported(core.CUDAPlace(0))
- ):
- place = paddle.CUDAPlace(0)
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- input = np.random.random([1, 2, 3, 32, 32]).astype(np.uint16)
-
- x = paddle.static.data(
- name="x", shape=[1, 2, 3, 32, 32], dtype="bfloat16"
- )
-
- m = paddle.nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
- y = m(x)
-
- exe = paddle.static.Executor(place)
- res = exe.run(
- feed={
- "x": input,
- },
- fetch_list=[y],
- )
-
- np.testing.assert_array_equal(res[0].shape, [1, 2, 1, 16, 16])
-
-
- class TestPool3DError_API(unittest.TestCase):
- def test_error_api(self):
- def run1():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
- res_pd = avg_pool3d(
- input_pd, kernel_size=2, stride=2, padding=padding
- )
-
- self.assertRaises(ValueError, run1)
-
- def run2():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=padding,
- data_format='NCDHW',
- )
-
- self.assertRaises(ValueError, run2)
-
- def run3():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- padding = [[0, 1], [0, 0], [0, 0], [0, 0], [0, 0]]
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=padding,
- data_format='NDHWC',
- )
-
- self.assertRaises(ValueError, run3)
-
- def run4():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=0,
- data_format='NNNN',
- )
-
- self.assertRaises(ValueError, run4)
-
- def run5():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = max_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=0,
- data_format='NNNN',
- )
-
- self.assertRaises(ValueError, run5)
-
- def run6():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding="padding",
- data_format='NNNN',
- )
-
- self.assertRaises(ValueError, run6)
-
- def run7():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = max_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding="padding",
- data_format='NNNN',
- )
-
- self.assertRaises(ValueError, run7)
-
- def run8():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding="VALID",
- ceil_mode=True,
- data_format='NNNN',
- )
-
- self.assertRaises(ValueError, run8)
-
- def run9():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = max_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding="VALID",
- ceil_mode=True,
- data_format='NNNN',
- )
-
- self.assertRaises(ValueError, run9)
-
- def run10():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = max_pool3d(
- input_pd,
- kernel_size=2,
- stride=2,
- padding=0,
- data_format='NDHWC',
- return_mask=True,
- )
-
- self.assertRaises(ValueError, run10)
-
- def run_kernel_out_of_range():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=-1,
- stride=2,
- padding="VALID",
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run_kernel_out_of_range)
-
- def run_size_out_of_range():
- with base.dygraph.guard():
- input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype(
- np.float32
- )
- input_pd = paddle.to_tensor(input_np)
- res_pd = avg_pool3d(
- input_pd,
- kernel_size=2,
- stride=0,
- padding="VALID",
- ceil_mode=True,
- )
-
- self.assertRaises(ValueError, run_size_out_of_range)
-
- def run_zero_stride():
- with base.dygraph.guard():
- array = np.array([1], dtype=np.float32)
- x = paddle.to_tensor(
- np.reshape(array, [1, 1, 1, 1, 1]), dtype='float32'
- )
- out = max_pool3d(
- x, 1, stride=0, padding=1, return_mask=True, ceil_mode=True
- )
-
- self.assertRaises(ValueError, run_zero_stride)
-
- def run_zero_tuple_stride():
- with base.dygraph.guard():
- array = np.array([1], dtype=np.float32)
- x = paddle.to_tensor(
- np.reshape(array, [1, 1, 1, 1, 1]), dtype='float32'
- )
- out = max_pool3d(x, 1, stride=(0, 0, 0), ceil_mode=False)
-
- self.assertRaises(ValueError, run_zero_tuple_stride)
-
-
- if __name__ == '__main__':
- unittest.main()
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