# Copyright (c) 2018 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. from __future__ import print_function from __future__ import division import unittest import numpy as np import paddle.fluid.core as core from op_test import OpTest 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_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False): N, C, D, H, W = x.shape if global_pool == 1: ksize = [D, H, W] if adaptive: D_out, H_out, W_out = ksize else: D_out = (D - ksize[0] + 2 * paddings[0] + strides[0] - 1 ) // strides[0] + 1 if ceil_mode else ( H - ksize[0] + 2 * paddings[0]) // strides[0] + 1 H_out = (H - ksize[1] + 2 * paddings[1] + strides[1] - 1 ) // strides[1] + 1 if ceil_mode else ( W - ksize[1] + 2 * paddings[1]) // strides[1] + 1 W_out = (W - ksize[2] + 2 * paddings[2] + strides[2] - 1 ) // strides[2] + 1 if ceil_mode else ( W - ksize[2] + 2 * paddings[2]) // strides[2] + 1 out = np.zeros((N, C, D_out, H_out, W_out)) for k in range(D_out): if adaptive: d_start = adaptive_start_index(k, D, ksize[0]) d_end = adaptive_end_index(k, D, ksize[0]) else: d_start = np.max((k * strides[0] - paddings[0], 0)) d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) for i in range(H_out): if adaptive: h_start = adaptive_start_index(i, H, ksize[1]) h_end = adaptive_end_index(i, H, ksize[1]) else: h_start = np.max((i * strides[1] - paddings[1], 0)) h_end = np.min((i * strides[1] + ksize[1] - paddings[1], H)) for j in range(W_out): if adaptive: w_start = adaptive_start_index(j, W, ksize[2]) w_end = adaptive_end_index(j, W, ksize[2]) else: w_start = np.max((j * strides[2] - paddings[2], 0)) w_end = np.min((j * strides[2] + ksize[2] - paddings[2], W)) x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4)) return out def avg_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False): N, C, D, H, W = x.shape if global_pool == 1: ksize = [D, H, W] if adaptive: D_out, H_out, W_out = ksize else: D_out = (D - ksize[0] + 2 * paddings[0] + strides[0] - 1 ) // strides[0] + 1 if ceil_mode else ( H - ksize[0] + 2 * paddings[0]) // strides[0] + 1 H_out = (H - ksize[1] + 2 * paddings[1] + strides[1] - 1 ) // strides[1] + 1 if ceil_mode else ( W - ksize[1] + 2 * paddings[1]) // strides[1] + 1 W_out = (W - ksize[2] + 2 * paddings[2] + strides[2] - 1 ) // strides[2] + 1 if ceil_mode else ( W - ksize[2] + 2 * paddings[2]) // strides[2] + 1 out = np.zeros((N, C, D_out, H_out, W_out)) for k in range(D_out): if adaptive: d_start = adaptive_start_index(k, D, ksize[0]) d_end = adaptive_end_index(k, D, ksize[0]) else: d_start = np.max((k * strides[0] - paddings[0], 0)) d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) for i in range(H_out): if adaptive: h_start = adaptive_start_index(i, H, ksize[1]) h_end = adaptive_end_index(i, H, ksize[1]) else: h_start = np.max((i * strides[1] - paddings[1], 0)) h_end = np.min((i * strides[1] + ksize[1] - paddings[1], H)) for j in range(W_out): if adaptive: w_start = adaptive_start_index(j, W, ksize[2]) w_end = adaptive_end_index(j, W, ksize[2]) else: w_start = np.max((j * strides[2] - paddings[2], 0)) w_end = np.min((j * strides[2] + ksize[2] - paddings[2], W)) x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \ if (exclusive or adaptive) else ksize[0] * ksize[1] * ksize[2] out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / field_size return out class TestPool3d_Op(OpTest): def setUp(self): self.op_type = "pool3d" self.use_cudnn = False self.dtype = np.float32 self.init_test_case() self.init_global_pool() self.init_kernel_type() self.init_pool_type() self.init_ceil_mode() self.init_exclusive() self.init_adaptive() if self.global_pool: self.paddings = [0 for _ in range(len(self.paddings))] input = np.random.random(self.shape).astype(self.dtype) output = self.pool3D_forward_naive( input, self.ksize, self.strides, self.paddings, self.global_pool, self.ceil_mode, self.exclusive, self.adaptive).astype(self.dtype) self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)} self.attrs = { 'strides': self.strides, 'paddings': self.paddings, 'ksize': self.ksize, 'pooling_type': self.pool_type, 'global_pooling': self.global_pool, 'use_cudnn': self.use_cudnn, 'ceil_mode': self.ceil_mode, 'data_format': 'AnyLayout', # TODO(dzhwinter) : should be fix latter 'exclusive': self.exclusive, 'adaptive': self.adaptive } self.outputs = {'Out': output} def testcudnn(self): return core.is_compiled_with_cuda() and self.use_cudnn def test_check_output(self): if self.testcudnn(): place = core.CUDAPlace(0) self.check_output_with_place(place, atol=1e-5) else: self.check_output() def test_check_grad(self): if self.dtype == np.float16: return if self.testcudnn() and self.pool_type != "max": place = core.CUDAPlace(0) self.check_grad_with_place( place, set(['X']), 'Out', max_relative_error=0.07) elif self.pool_type != "max": self.check_grad(set(['X']), 'Out', max_relative_error=0.07) def init_test_case(self): self.shape = [2, 3, 5, 5, 5] self.ksize = [3, 3, 3] self.strides = [1, 1, 1] self.paddings = [0, 0, 0] def init_kernel_type(self): pass def init_pool_type(self): self.pool_type = "avg" self.pool3D_forward_naive = avg_pool3D_forward_naive def init_global_pool(self): self.global_pool = True def init_ceil_mode(self): self.ceil_mode = False def init_exclusive(self): self.exclusive = True def init_adaptive(self): self.adaptive = False class TestCase1(TestPool3d_Op): def init_test_case(self): self.shape = [2, 3, 7, 7, 7] self.ksize = [3, 3, 3] self.strides = [1, 1, 1] self.paddings = [0, 0, 0] def init_pool_type(self): self.pool_type = "avg" self.pool3D_forward_naive = avg_pool3D_forward_naive def init_global_pool(self): self.global_pool = False class TestCase2(TestPool3d_Op): def init_test_case(self): self.shape = [2, 3, 7, 7, 7] self.ksize = [3, 3, 3] self.strides = [1, 1, 1] self.paddings = [1, 1, 1] def init_pool_type(self): self.pool_type = "avg" self.pool3D_forward_naive = avg_pool3D_forward_naive def init_global_pool(self): self.global_pool = False class TestCase3(TestPool3d_Op): def init_pool_type(self): self.pool_type = "max" self.pool3D_forward_naive = max_pool3D_forward_naive class TestCase4(TestCase1): def init_pool_type(self): self.pool_type = "max" self.pool3D_forward_naive = max_pool3D_forward_naive class TestCase5(TestCase2): def init_pool_type(self): self.pool_type = "max" self.pool3D_forward_naive = max_pool3D_forward_naive #--------------------test pool3d-------------------- class TestCUDNNCase1(TestPool3d_Op): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase1(TestPool3d_Op): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=1e-3) class TestCUDNNCase2(TestCase1): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase2(TestCase1): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=1e-3) class TestCUDNNCase3(TestCase2): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase3(TestCase2): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=1e-3) class TestCUDNNCase4(TestCase3): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase4(TestCase3): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=1e-3) class TestCUDNNCase5(TestCase4): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase5(TestCase4): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=1e-3) class TestCUDNNCase6(TestCase5): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase6(TestCase5): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 def test_check_output(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=1e-3) class TestCeilModeCase1(TestCUDNNCase1): def init_ceil_mode(self): self.ceil_mode = True class TestCeilModeCase2(TestCUDNNCase2): def init_ceil_mode(self): self.ceil_mode = True class TestCeilModeCase3(TestCase1): def init_ceil_mode(self): self.ceil_mode = True class TestCeilModeCase4(TestCase2): def init_ceil_mode(self): self.ceil_mode = True class TestAvgInclude(TestCase2): def init_exclusive(self): self.exclusive = False class TestCUDNNAvgInclude(TestCUDNNCase3): def init_exclusive(self): self.exclusive = False class TestAvgPoolAdaptive(TestCase1): def init_adaptive(self): self.adaptive = True if __name__ == '__main__': unittest.main()