# 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 import paddle import paddle.fluid as fluid import paddle.fluid.core as core import paddle.nn.functional as F from paddle.fluid.framework import _test_eager_guard def adaptive_start_index(index, input_size, output_size): return int(np.floor(index * input_size / output_size)) def adaptive_end_index(index, input_size, output_size): return int(np.ceil((index + 1) * input_size / output_size)) def max_pool1D_forward_naive( x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=False, adaptive=False, data_type=np.float64, ): N, C, L = x.shape if global_pool == 1: ksize = [L] if adaptive: L_out = ksize[0] else: L_out = ( (L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1 if ceil_mode else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1 ) out = np.zeros((N, C, L_out)) for i in range(L_out): if adaptive: r_start = adaptive_start_index(i, L, ksize[0]) r_end = adaptive_end_index(i, L, ksize[0]) else: r_start = np.max((i * strides[0] - paddings[0], 0)) r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L)) x_masked = x[:, :, r_start:r_end] out[:, :, i] = np.max(x_masked, axis=(2)) return out def avg_pool1D_forward_naive( x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=False, adaptive=False, data_type=np.float64, ): N, C, L = x.shape if global_pool == 1: ksize = [L] if adaptive: L_out = ksize[0] else: L_out = ( (L - ksize[0] + 2 * paddings[0] + strides[0] - 1) // strides[0] + 1 if ceil_mode else (L - ksize[0] + 2 * paddings[0]) // strides[0] + 1 ) out = np.zeros((N, C, L_out)) for i in range(L_out): if adaptive: r_start = adaptive_start_index(i, L, ksize[0]) r_end = adaptive_end_index(i, L, ksize[0]) else: r_start = np.max((i * strides[0] - paddings[0], 0)) r_end = np.min((i * strides[0] + ksize[0] - paddings[0], L)) x_masked = x[:, :, r_start:r_end] field_size = ( (r_end - r_start) if (exclusive or adaptive) else (ksize[0]) ) if data_type == np.int8 or data_type == np.uint8: out[:, :, i] = ( np.rint(np.sum(x_masked, axis=(2, 3)) / field_size) ).astype(data_type) else: out[:, :, i] = (np.sum(x_masked, axis=(2)) / field_size).astype( data_type ) return out class TestPool1D_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], dtype="float32") result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=0) input_np = np.random.random([2, 3, 32]).astype("float32") result_np = avg_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0], ceil_mode=False ) 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]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = F.avg_pool1d(input, kernel_size=2, stride=2, padding=[0]) result_np = avg_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0] ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool1d_dg = paddle.nn.layer.AvgPool1D( kernel_size=2, stride=None, padding=0 ) result = avg_pool1d_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]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = F.avg_pool1d( input, kernel_size=2, stride=2, padding=[1], exclusive=True ) result_np = avg_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[1], exclusive=False ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) avg_pool1d_dg = paddle.nn.AvgPool1D( kernel_size=2, stride=None, padding=1, exclusive=True ) result = avg_pool1d_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], dtype="float32") result = F.max_pool1d(input, kernel_size=2, stride=2, padding=[0]) input_np = np.random.random([2, 3, 32]).astype("float32") result_np = max_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0] ) 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]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = F.max_pool1d(input, kernel_size=2, stride=2, padding=0) result_np = max_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0] ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool1d_dg = paddle.nn.layer.MaxPool1D( kernel_size=2, stride=None, padding=0 ) result = max_pool1d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_max_dygraph_return_index_results(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result, index = F.max_pool1d( input, kernel_size=2, stride=2, padding=0, return_mask=True ) result_np = max_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0] ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) max_pool1d_dg = paddle.nn.layer.MaxPool1D( kernel_size=2, stride=None, padding=0 ) result = max_pool1d_dg(input) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_max_dygraph_padding_same(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = F.max_pool1d( input, kernel_size=2, stride=2, padding="SAME" ) result_np = max_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0] ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def check_avg_dygraph_padding_same(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = F.avg_pool1d( input, kernel_size=2, stride=2, padding="SAME" ) result_np = avg_pool1D_forward_naive( input_np, ksize=[2], strides=[2], paddings=[0] ) np.testing.assert_allclose(result.numpy(), result_np, rtol=1e-05) def test_pool1d(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_padding_same(place) self.check_avg_dygraph_padding_same(place) self.check_max_dygraph_return_index_results(place) def test_dygraph_api(self): with _test_eager_guard(): self.test_pool1d() 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]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = [[2]] res_pd = F.max_pool1d( 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 = [[2]] res_pd = F.max_pool1d( input_pd, kernel_size=2, stride=2, padding=padding ) self.assertRaises(ValueError, run2) def run3(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = "padding" res_pd = F.max_pool1d( input_pd, kernel_size=2, stride=2, padding=padding ) self.assertRaises(ValueError, run3) 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 = F.max_pool1d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, ) self.assertRaises(ValueError, run4) def run5(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = "VALID" res_pd = F.max_pool1d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, ) self.assertRaises(ValueError, run5) def run6(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = "VALID" res_pd = F.avg_pool1d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, ) self.assertRaises(ValueError, run6) def run7(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = "paddle" res_pd = F.avg_pool1d( input_pd, kernel_size=2, stride=2, padding=padding, ceil_mode=True, ) self.assertRaises(ValueError, run7) def run_kernel_out_of_range(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = 0 res_pd = F.avg_pool1d( input_pd, kernel_size=-1, stride=2, padding=padding, ceil_mode=True, ) 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]).astype( np.float32 ) input_pd = fluid.dygraph.to_variable(input_np) padding = 0 res_pd = F.avg_pool1d( input_pd, kernel_size=2, stride=0, padding=padding, ceil_mode=True, ) self.assertRaises(ValueError, run_stride_out_of_range) def test_dygraph_api(self): with _test_eager_guard(): self.test_error_api() if __name__ == '__main__': unittest.main()