# 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 unittest import numpy as np from op_test import get_device_place, get_places, is_custom_device from test_pool3d_op import ( avg_pool3D_forward_naive, max_pool3D_forward_naive, pool3D_forward_naive, ) import paddle from paddle import base from paddle.nn.functional import avg_pool3d, max_pool3d class TestPool3D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = get_places() 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) 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) # test param_two_alias(["x", "input"], ["return_mask", "return_indices"]) result = max_pool3d( input=input, kernel_size=2, stride=2, padding=0, return_indices=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=0 ) result = max_pool3d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) # test param_one_alias(["x", "input"]) result = max_pool3d_dg(input=input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) # test param_one_alias(["return_mask", "return_indices"]) max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=None, padding=0, return_indices=False ) 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 check_max_pool_return_mask_ceil(self, place): with base.dygraph.guard(place): input_np = np.random.random([1, 2, 6, 33, 33]).astype("float32") input = paddle.to_tensor(input_np) result, _ = max_pool3d( input, kernel_size=5, stride=5, padding=0, ceil_mode=True, return_mask=True, ) result_np = pool3D_forward_naive( input_np, ksize=[5, 5, 5], strides=[5, 5, 5], paddings=[0, 0, 0], ceil_mode=True, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) self.assertEqual(result.shape, list(result_np.shape)) 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) self.check_max_pool_return_mask_ceil(place) def test_static_fp16_gpu(self): paddle.enable_static() if paddle.base.core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() 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]) def test_static_bf16_gpu(self): paddle.enable_static() if ( paddle.base.core.is_compiled_with_cuda() or is_custom_device() ) and paddle.base.core.is_bfloat16_supported(get_device_place()): place = get_device_place() 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) class TestPool3D_API_ZeroSize(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = get_places() def check_avg_dygraph_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 0, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) input.stop_gradient = False 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) loss = paddle.sum(result) loss.backward() np.testing.assert_allclose(input.grad.shape, input.shape) def check_max_dygraph_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 0, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) input.stop_gradient = False 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) loss = paddle.sum(result) loss.backward() np.testing.assert_allclose(input.grad.shape, input.shape) def test_pool3d(self): paddle.enable_static() for place in self.places: self.check_max_dygraph_results(place) self.check_avg_dygraph_results(place) if __name__ == '__main__': unittest.main()