# 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 test_pool2d_op import ( avg_pool2D_forward_naive, max_pool2D_forward_naive, pool2D_forward_naive, ) import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.nn.functional import avg_pool2d, max_pool2d class TestPool2D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): self.places.append(fluid.CUDAPlace(0)) def check_avg_static_results(self, place): with fluid.program_guard(fluid.Program(), fluid.Program()): input = fluid.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 = fluid.Executor(place) fetches = exe.run( fluid.default_main_program(), 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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.program_guard(fluid.Program(), fluid.Program()): input = fluid.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 = fluid.Executor(place) fetches = exe.run( fluid.default_main_program(), 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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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) 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_max_dygraph_nhwc_results(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable( 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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(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 test_pool2d(self): 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_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) class TestPool2DError_API(unittest.TestCase): def test_error_api(self): def run1(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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 fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(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) if __name__ == '__main__': unittest.main()