# 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_places from test_pool2d_op import ( avg_pool2D_forward_naive, max_pool2D_forward_naive, pool2D_forward_naive, ) import paddle from paddle import base from paddle.nn.functional import avg_pool2d, lp_pool2d, max_pool2d class TestPool2D_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], dtype="float32" ) result = avg_pool2d(input, kernel_size=2, stride=2, padding=0) input_np = np.random.random([2, 3, 32, 32]).astype("float32") result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='avg', ) exe = base.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]).astype("float32") input = paddle.to_tensor(input_np) result = avg_pool2d(input, kernel_size=2, stride=2, padding=0) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='avg', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0 ) result = avg_pool2d_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]).astype("float32") input = paddle.to_tensor(input_np) result = avg_pool2d( input, kernel_size=2, stride=2, padding=1, ceil_mode=False ) result_np = avg_pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[1, 1], ceil_mode=False, exclusive=False, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=1, ceil_mode=False ) result = avg_pool2d_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]).astype("float32") input = paddle.to_tensor(input_np) result = avg_pool2d( input, kernel_size=2, stride=2, padding=0, ceil_mode=True ) result_np = avg_pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], ceil_mode=True, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0, ceil_mode=True ) result = avg_pool2d_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], dtype="float32" ) result = max_pool2d(input, kernel_size=2, stride=2, padding=0) input_np = np.random.random([2, 3, 32, 32]).astype("float32") result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='max', ) exe = base.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]).astype("float32") input = paddle.to_tensor(input_np) result = max_pool2d( input, kernel_size=2, stride=2, padding=0, return_mask=False ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[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_pool2d( input=input, kernel_size=2, stride=2, padding=0, return_indices=False, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0 ) result = max_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) # test param_one_alias(["x", "input"]) result = max_pool2d_dg(input=input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) # test param_one_alias(["return_mask", "return_indices"]) max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0, return_indices=False ) result = max_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_max_dygraph_nhwc_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(np.transpose(input_np, [0, 2, 3, 1])) result = max_pool2d( input, kernel_size=2, stride=2, padding=0, return_mask=False, data_format="NHWC", ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='max', ) np.testing.assert_allclose( np.transpose(result.numpy(), [0, 3, 1, 2]), 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]).astype("float32") input = paddle.to_tensor(input_np) result = max_pool2d( input, kernel_size=2, stride=2, padding=1, ceil_mode=False ) result_np = max_pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[1, 1], ceil_mode=False, exclusive=False, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=1, ceil_mode=False ) result = max_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), 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]).astype("float32") input = paddle.to_tensor(input_np) result = max_pool2d( input, kernel_size=2, stride=2, padding=0, ceil_mode=True ) result_np = max_pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], ceil_mode=True, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0, ceil_mode=True ) result = max_pool2d_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]).astype("float32") input = paddle.to_tensor(input_np) result, indices = max_pool2d( input, kernel_size=2, stride=None, padding="SAME", return_mask=True, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='max', padding_algorithm="SAME", ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0 ) result = max_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_avg_dygraph_stride_is_none(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) result = avg_pool2d( input, kernel_size=2, stride=None, padding="SAME" ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='avg', padding_algorithm="SAME", ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0 ) result = avg_pool2d_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]).astype("float32") input = paddle.to_tensor(input_np) padding = [[0, 0], [0, 0], [0, 0], [0, 0]] result = max_pool2d( input, kernel_size=2, stride=2, padding=padding, return_mask=False, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='max', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0 ) result = max_pool2d_dg(input) 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]).astype("float32") input = paddle.to_tensor(input_np) padding = [[0, 0], [0, 0], [0, 0], [0, 0]] result = avg_pool2d( input, kernel_size=2, stride=2, padding=padding, divisor_override=4, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='avg', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0 ) result = avg_pool2d_dg(input) 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([2, 3, 33, 33]).astype("float32") input = paddle.to_tensor(input_np) result, _ = max_pool2d( input, kernel_size=5, stride=5, padding=0, ceil_mode=True, return_mask=True, ) result_np = pool2D_forward_naive( input_np, ksize=[5, 5], strides=[5, 5], paddings=[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 check_lp_static_results(self, place): with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): input = paddle.static.data( name="input", shape=[2, 3, 128, 128], dtype="float32" ) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=4, stride=4, ceil_mode=True, ) input_np = np.random.random([2, 3, 128, 128]).astype("float32") result_np = pool2D_forward_naive( input_np, ksize=[4, 4], paddings=[0, 0], strides=[4, 4], ceil_mode=True, norm_type=norm_type, pool_type='lp', ) exe = base.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_lp_dygraph_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=2, stride=1, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], paddings=[0, 0], strides=[1, 1], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) # test input alias result = lp_pool2d( input=input, norm_type=norm_type, kernel_size=2, stride=1, ceil_mode=False, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) # test 5th positional argument with bool result = lp_pool2d( input, norm_type, 2, 1, False, ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=2, stride=1, ceil_mode=False, ) result = lp_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.LPPool2d( norm_type, 2, 1, False, ) result = lp_pool2d_dg(input=input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_lp_dygraph_results_norm_type_is_inf(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) norm_type = np.inf result = lp_pool2d( input, norm_type, kernel_size=[2, 4], stride=2, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 4], paddings=[0, 0], strides=[2, 2], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=[2, 4], stride=2, ceil_mode=False, ) result = lp_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_lp_dygraph_results_norm_type_is_negative_inf(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) norm_type = -np.inf result = lp_pool2d( input, norm_type, kernel_size=2, stride=2, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], paddings=[0, 0], strides=[2, 2], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=2, stride=2, ceil_mode=False, ) result = lp_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_lp_dygraph_ceilmode_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=5, stride=3, ceil_mode=True, ) result_np = pool2D_forward_naive( input_np, ksize=[5, 5], paddings=[0, 0], strides=[3, 3], ceil_mode=True, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=5, stride=3, ceil_mode=True, ) result = lp_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_lp_dygraph_nhwc_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(np.transpose(input_np, [0, 2, 3, 1])) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=2, stride=2, ceil_mode=False, data_format="NHWC", ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], paddings=[0, 0], strides=[2, 2], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose( np.transpose(result.numpy(), [0, 3, 1, 2]), result_np, rtol=1e-05, ) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=2, stride=[2, 2], ceil_mode=False, data_format="NHWC", ) result = lp_pool2d_dg(input) np.testing.assert_allclose( np.transpose(result.numpy(), [0, 3, 1, 2]), result_np, rtol=1e-05, ) def check_lp_dygraph_stride_is_none(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=2, stride=None, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, paddings=[0, 0], ksize=[2, 2], strides=[2, 2], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=2, stride=None, ceil_mode=False, ) result = lp_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_lp_float16_static(self, place): if isinstance(place, (base.CUDAPlace, base.CustomPlace)): with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): input = paddle.static.data( name="input", shape=[2, 3, 64, 64], dtype="float16" ) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=4, stride=[2, 4], ceil_mode=True, ) input_np = np.random.random([2, 3, 64, 64]).astype("float16") result_np = pool2D_forward_naive( input_np, ksize=[4, 4], paddings=[0, 0], strides=[2, 4], ceil_mode=True, norm_type=norm_type, pool_type='lp', ) exe = base.Executor(place) fetches = exe.run( feed={"input": input_np}, fetch_list=[result], ) np.testing.assert_allclose( fetches[0], result_np.astype(np.float16), rtol=1e-03 ) def check_lp_float64_static(self, place): with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): input = paddle.static.data( name="input", shape=[2, 3, 64, 64], dtype="float64" ) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=5, stride=3, ceil_mode=True, ) input_np = np.random.random([2, 3, 64, 64]).astype("float64") result_np = pool2D_forward_naive( input_np, ksize=[5, 5], paddings=[0, 0], strides=[3, 3], ceil_mode=True, norm_type=norm_type, pool_type='lp', ) exe = base.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_lp_dygraph_float16(self, place): if isinstance(place, (base.CUDAPlace, base.CustomPlace)): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float16") input = paddle.to_tensor(input_np) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=3, stride=2, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, ksize=[3, 3], paddings=[0, 0], strides=[2, 2], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose( result.numpy(), result_np, rtol=1e-03 ) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=3, stride=2, ceil_mode=False, ) result = lp_pool2d_dg(input) np.testing.assert_allclose( result.numpy(), result_np.astype(np.float16), rtol=1e-03 ) def check_lp_dygraph_float64(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float64") input = paddle.to_tensor(input_np) norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=5, stride=3, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, ksize=[5, 5], paddings=[0, 0], strides=[3, 3], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) lp_pool2d_dg = paddle.nn.layer.LPPool2D( norm_type=norm_type, kernel_size=5, stride=3, ceil_mode=False, ) result = lp_pool2d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def test_pool2d_static(self): paddle.enable_static() for place in self.places: self.check_max_static_results(place) self.check_avg_static_results(place) self.check_lp_static_results(place) self.check_lp_float64_static(place) self.check_lp_float16_static(place) paddle.disable_static() def test_torch_compatible(self): paddle.set_flags({'FLAGS_use_accuracy_compatible_kernel': 1}) paddle.enable_static() for place in self.places: self.check_max_static_results(place) paddle.disable_static() def test_pool2d(self): for place in self.places: self.check_max_dygraph_results(place) self.check_avg_dygraph_results(place) self.check_max_dygraph_stride_is_none(place) self.check_avg_dygraph_stride_is_none(place) self.check_max_dygraph_padding(place) self.check_avg_divisor(place) self.check_max_dygraph_padding_results(place) self.check_max_dygraph_ceilmode_results(place) self.check_max_dygraph_nhwc_results(place) self.check_max_pool_return_mask_ceil(place) self.check_lp_dygraph_results(place) self.check_lp_dygraph_stride_is_none(place) self.check_lp_dygraph_ceilmode_results(place) self.check_lp_dygraph_nhwc_results(place) self.check_lp_dygraph_results_norm_type_is_inf(place) self.check_lp_dygraph_results_norm_type_is_negative_inf(place) self.check_lp_dygraph_float64(place) self.check_lp_dygraph_float16(place) class TestPool2DError_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]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = [[0, 1], [0, 0], [0, 0], [0, 0]] res_pd = max_pool2d( 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]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = [[0, 1], [0, 0], [0, 0], [0, 0]] res_pd = max_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, data_format='NHWC', ) self.assertRaises(ValueError, run2) def run3(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "padding" res_pd = max_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, data_format='NHWC', ) self.assertRaises(ValueError, run3) def run3_avg(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "padding" res_pd = avg_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, data_format='NHWC', ) self.assertRaises(ValueError, run3_avg) def run4(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "VALID" res_pd = max_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, data_format='NHWC', ) self.assertRaises(ValueError, run4) def run4_avg(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "VALID" res_pd = avg_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, data_format='NHWC', ) self.assertRaises(ValueError, run4_avg) def run5(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "padding" res_pd = avg_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, data_format='NHWC', ) self.assertRaises(ValueError, run5) def run6(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "VALID" res_pd = avg_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, data_format='NHWC', ) self.assertRaises(ValueError, run6) def run7(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "VALID" res_pd = avg_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=False, data_format='NNNN', ) self.assertRaises(ValueError, run7) def run8(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) padding = "VALID" res_pd = max_pool2d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=False, data_format='NNNN', ) self.assertRaises(ValueError, run8) def run9(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) res_pd = max_pool2d( input_pd, kernel_size=2, stride=2, padding=0, ceil_mode=False, data_format='NHWC', return_mask=True, ) self.assertRaises(ValueError, run9) def run_kernel_out_of_range(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) res_pd = avg_pool2d( input_pd, kernel_size=[-1, 2], stride=2, padding=0, ceil_mode=False, data_format='NHWC', ) self.assertRaises(ValueError, run_kernel_out_of_range) def run_stride_out_of_range(): with base.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = paddle.to_tensor(input_np) res_pd = avg_pool2d( input_pd, kernel_size=3, stride=[0, 2], padding=0, ceil_mode=False, data_format='NHWC', ) self.assertRaises(ValueError, run_stride_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]), dtype='float32' ) out = max_pool2d( 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]), dtype='float32' ) out = max_pool2d( x, 1, stride=(0, 0), return_mask=False, data_format='NHWC' ) self.assertRaises(ValueError, run_zero_tuple_stride) def run_zero_norm_type(): with base.dygraph.guard(): array = np.array([1], dtype=np.float32) x = paddle.to_tensor( np.reshape(array, [1, 1, 1, 1]), dtype='float32' ) out = lp_pool2d(x, 0, 2) self.assertRaises(ValueError, run_zero_norm_type) class TestPool2D_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, 0]).astype("float32") input = paddle.to_tensor(input_np) input.stop_gradient = False result = avg_pool2d(input, kernel_size=2, stride=2, padding=0) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[0, 0], pool_type='avg', ) 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, 0]).astype("float32") input = paddle.to_tensor(input_np) input.stop_gradient = False result = max_pool2d( input, kernel_size=2, stride=2, padding=0, return_mask=False ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], strides=[2, 2], paddings=[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 check_lp_dygraph_results(self, place): with base.dygraph.guard(place): input_np = np.random.random([2, 0, 32, 32]).astype("float32") input = paddle.to_tensor(input_np) input.stop_gradient = False norm_type = 2 result = lp_pool2d( input, norm_type, kernel_size=2, stride=1, ceil_mode=False, ) result_np = pool2D_forward_naive( input_np, ksize=[2, 2], paddings=[0, 0], strides=[1, 1], ceil_mode=False, norm_type=norm_type, pool_type='lp', ) 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_pool2d(self): for place in self.places: self.check_max_dygraph_results(place) self.check_avg_dygraph_results(place) self.check_lp_dygraph_results(place) if __name__ == '__main__': unittest.main()