# 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 import paddle.fluid.core as core from op_test import OpTest import paddle.fluid as fluid 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 pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False, data_format='NCDHW', pool_type='max', padding_algorithm="EXPLICIT"): # update paddings def _get_padding_with_SAME(input_shape, pool_size, pool_stride): padding = [] for input_size, filter_size, stride_size in zip(input_shape, pool_size, pool_stride): out_size = int((input_size + stride_size - 1) / stride_size) pad_sum = np.max(( (out_size - 1) * stride_size + filter_size - input_size, 0)) pad_0 = int(pad_sum / 2) pad_1 = int(pad_sum - pad_0) padding.append(pad_0) padding.append(pad_1) return padding if isinstance(padding_algorithm, str): padding_algorithm = padding_algorithm.upper() if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]: raise ValueError("Unknown Attr(padding_algorithm): '%s'. " "It can only be 'SAME' or 'VALID'." % str(padding_algorithm)) if padding_algorithm == "VALID": paddings = [0, 0, 0, 0, 0, 0] if ceil_mode != False: raise ValueError( "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode)" " must be False. " "Received ceil_mode: True.") elif padding_algorithm == "SAME": input_data_shape = [] if data_format == "NCDHW": input_data_shape = x.shape[2:5] elif data_format == "NDHWC": input_data_shape = x.shape[1:4] paddings = _get_padding_with_SAME(input_data_shape, ksize, strides) assert len(paddings) == 3 or len(paddings) == 6 is_sys = True if len(paddings) == 3 else False N = x.shape[0] C,D, H, W = [x.shape[1], x.shape[2], x.shape[3], x.shape[4]] \ if data_format == 'NCDHW' else [x.shape[4], x.shape[1], x.shape[2],x.shape[3]] if global_pool == 1: ksize = [D, H, W] paddings = [0 for _ in range(len(paddings))] pad_d_forth = paddings[0] if is_sys else paddings[0] pad_d_back = paddings[0] if is_sys else paddings[1] pad_h_up = paddings[1] if is_sys else paddings[2] pad_h_down = paddings[1] if is_sys else paddings[3] pad_w_left = paddings[2] if is_sys else paddings[4] pad_w_right = paddings[2] if is_sys else paddings[5] if adaptive: D_out, H_out, W_out = ksize else: D_out = (D - ksize[0] + pad_d_forth+pad_d_back + strides[0] - 1) // strides[0] + 1 \ if ceil_mode else (D - ksize[0] + pad_d_forth+pad_d_back) // strides[0] + 1 H_out = (H - ksize[1] + pad_h_up + pad_h_down + strides[1] - 1) // strides[1] + 1 \ if ceil_mode else (H - ksize[1] + pad_h_up + pad_h_down) // strides[1] + 1 W_out = (W - ksize[2] + pad_w_left + pad_w_right + strides[2] - 1) // strides[2] + 1 \ if ceil_mode else (W - ksize[2] + pad_w_left + pad_w_right) // strides[2] + 1 out = np.zeros((N, C, D_out, H_out, W_out)) if data_format=='NCDHW' \ else np.zeros((N, D_out, H_out, W_out, C)) 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]) 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]) 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: d_start = k * strides[0] - pad_d_forth d_end = np.min((k * strides[0] + ksize[0] - pad_d_forth, D + pad_d_back)) h_start = i * strides[1] - pad_h_up h_end = np.min( (i * strides[1] + ksize[1] - pad_h_up, H + pad_h_down)) w_start = j * strides[2] - pad_w_left w_end = np.min((j * strides[2] + ksize[2] - pad_w_left, W + pad_w_right)) field_size = (d_end - d_start) * (h_end - h_start) * ( w_end - w_start) w_start = np.max((w_start, 0)) d_start = np.max((d_start, 0)) h_start = np.max((h_start, 0)) w_end = np.min((w_end, W)) d_end = np.min((d_end, D)) h_end = np.min((h_end, H)) if data_format == 'NCDHW': x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start: w_end] if pool_type == 'avg': if (exclusive or adaptive): field_size = (d_end - d_start) * ( h_end - h_start) * (w_end - w_start) out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / field_size elif pool_type == 'max': out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4)) elif data_format == 'NDHWC': x_masked = x[:, d_start:d_end, h_start:h_end, w_start: w_end, :] if pool_type == 'avg': if (exclusive or adaptive): field_size = (d_end - d_start) * ( h_end - h_start) * (w_end - w_start) out[:, k, i, j, :] = np.sum(x_masked, axis=(1, 2, 3)) / field_size elif pool_type == 'max': out[:, k, i, j, :] = np.max(x_masked, axis=(1, 2, 3)) return out def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False): out = pool3D_forward_naive( x=x, ksize=ksize, strides=strides, paddings=paddings, global_pool=global_pool, ceil_mode=ceil_mode, exclusive=exclusive, adaptive=adaptive, data_format='NCDHW', pool_type="max") return out def avg_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False): out = pool3D_forward_naive( x=x, ksize=ksize, strides=strides, paddings=paddings, global_pool=global_pool, ceil_mode=ceil_mode, exclusive=exclusive, adaptive=adaptive, data_format='NCDHW', pool_type="avg") return out class TestPool3D_Op(OpTest): def setUp(self): self.op_type = "pool3d" self.init_kernel_type() self.dtype = np.float64 self.init_test_case() self.padding_algorithm = "EXPLICIT" self.init_paddings() self.init_global_pool() self.init_kernel_type() self.init_pool_type() self.init_ceil_mode() self.init_exclusive() self.init_adaptive() self.init_data_format() self.init_shape() paddle.enable_static() input = np.random.random(self.shape).astype(self.dtype) output = pool3D_forward_naive( input, self.ksize, self.strides, self.paddings, self.global_pool, self.ceil_mode, self.exclusive, self.adaptive, self.data_format, self.pool_type, self.padding_algorithm).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': self.data_format, 'exclusive': self.exclusive, 'adaptive': self.adaptive, "padding_algorithm": self.padding_algorithm, } self.outputs = {'Out': output} def has_cudnn(self): return core.is_compiled_with_cuda() and self.use_cudnn def test_check_output(self): if self.has_cudnn(): 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.has_cudnn() and self.pool_type != "max": place = core.CUDAPlace(0) self.check_grad_with_place(place, set(['X']), 'Out') elif self.pool_type != "max": self.check_grad(set(['X']), 'Out') def init_data_format(self): self.data_format = "NCDHW" def init_shape(self): self.shape = [1, 3, 5, 6, 5] def init_test_case(self): self.ksize = [2, 3, 1] self.strides = [2, 2, 3] def init_paddings(self): self.paddings = [0, 0, 0] self.padding_algorithm = "EXPLICIT" def init_kernel_type(self): self.use_cudnn = False def init_pool_type(self): self.pool_type = "avg" 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_shape(self): self.shape = [1, 3, 7, 7, 7] def init_test_case(self): self.ksize = [3, 3, 3] self.strides = [1, 1, 1] def init_paddings(self): self.paddings = [0, 0, 0] def init_pool_type(self): self.pool_type = "avg" def init_global_pool(self): self.global_pool = False class TestCase2(TestPool3D_Op): def init_shape(self): self.shape = [1, 3, 6, 7, 7] def init_test_case(self): self.ksize = [3, 3, 4] self.strides = [1, 3, 2] def init_paddings(self): self.paddings = [1, 1, 1] def init_pool_type(self): self.pool_type = "avg" def init_global_pool(self): self.global_pool = False class TestCase3(TestPool3D_Op): def init_pool_type(self): self.pool_type = "max" class TestCase4(TestCase1): def init_pool_type(self): self.pool_type = "max" class TestCase5(TestCase2): def init_pool_type(self): self.pool_type = "max" #--------------------test pool3d cudnn-------------------- def create_test_cudnn_class(parent): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNCase(parent): def init_kernel_type(self): self.use_cudnn = True cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOp") TestCUDNNCase.__name__ = cls_name globals()[cls_name] = TestCUDNNCase create_test_cudnn_class(TestPool3D_Op) create_test_cudnn_class(TestCase1) create_test_cudnn_class(TestCase2) create_test_cudnn_class(TestCase3) create_test_cudnn_class(TestCase4) create_test_cudnn_class(TestCase5) def create_test_cudnn_fp16_class(parent): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNFp16Case(parent): 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) cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op") TestCUDNNFp16Case.__name__ = cls_name globals()[cls_name] = TestCUDNNFp16Case def create_test_fp16_class(parent): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestFp16Case(parent): def init_kernel_type(self): self.use_cudnn = False 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-2) cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op") TestFp16Case.__name__ = cls_name globals()[cls_name] = TestFp16Case create_test_cudnn_fp16_class(TestPool3D_Op) create_test_cudnn_fp16_class(TestCase1) create_test_cudnn_fp16_class(TestCase2) create_test_cudnn_fp16_class(TestCase3) create_test_cudnn_fp16_class(TestCase4) create_test_cudnn_fp16_class(TestCase5) create_test_fp16_class(TestPool3D_Op) create_test_fp16_class(TestCase1) create_test_fp16_class(TestCase2) create_test_fp16_class(TestCase3) create_test_fp16_class(TestCase4) create_test_fp16_class(TestCase5) # ---- test ceil mode ------ def create_test_cudnn_use_ceil_class(parent): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestPool3DUseCeilCase(parent): def init_kernel_type(self): self.use_cudnn = True def init_ceil_mode(self): self.ceil_mode = True cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOpCeilMode") TestPool3DUseCeilCase.__name__ = cls_name globals()[cls_name] = TestPool3DUseCeilCase create_test_cudnn_use_ceil_class(TestPool3D_Op) create_test_cudnn_use_ceil_class(TestCase1) def create_test_use_ceil_class(parent): class TestPool3DUseCeilCase(parent): def init_ceil_mode(self): self.ceil_mode = True cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast") TestPool3DUseCeilCase.__name__ = cls_name globals()[cls_name] = TestPool3DUseCeilCase create_test_use_ceil_class(TestCase1) create_test_use_ceil_class(TestCase2) class TestAvgInclude(TestCase2): def init_exclusive(self): self.exclusive = False @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNAvgInclude(TestCase2): def init_kernel_type(self): self.use_cudnn = True def init_exclusive(self): self.exclusive = False class TestAvgPoolAdaptive(TestCase1): def init_adaptive(self): self.adaptive = True class TestAvgPoolAdaptiveAsyOutSize(TestCase1): def init_adaptive(self): self.adaptive = True def init_shape(self): self.shape = [1, 3, 3, 4, 4] def init_test_case(self): self.ksize = [2, 2, 3] self.strides = [1, 1, 1] #-------test pool3d with asymmetric padding------ class TestPool3D_Op_AsyPadding(TestPool3D_Op): def init_test_case(self): self.ksize = [3, 4, 3] self.strides = [1, 1, 2] def init_paddings(self): self.paddings = [0, 0, 0, 2, 3, 0] def init_shape(self): self.shape = [1, 3, 5, 5, 6] class TestCase1_AsyPadding(TestCase1): def init_test_case(self): self.ksize = [3, 3, 4] self.strides = [1, 1, 2] def init_paddings(self): self.paddings = [1, 0, 2, 1, 2, 1] def init_shape(self): self.shape = [1, 3, 7, 7, 6] class TestCase2_AsyPadding(TestCase2): def init_test_case(self): self.ksize = [3, 3, 3] self.strides = [1, 1, 1] def init_paddings(self): self.paddings = [1, 2, 1, 1, 1, 0] def init_shape(self): self.shape = [1, 3, 7, 7, 7] class TestCase3_AsyPadding(TestCase3): def init_test_case(self): self.ksize = [3, 3, 3] self.strides = [1, 1, 1] def init_paddings(self): self.paddings = [1, 0, 0, 0, 1, 0] def init_shape(self): self.shape = [1, 3, 5, 5, 5] class TestCase4_AsyPadding(TestCase4): def init_test_case(self): self.ksize = [3, 3, 3] self.strides = [1, 1, 1] def init_paddings(self): self.paddings = [1, 0, 2, 1, 2, 1] def init_shape(self): self.shape = [1, 3, 7, 7, 7] class TestCase5_AsyPadding(TestCase5): def init_test_case(self): self.ksize = [3, 3, 3] self.strides = [1, 1, 1] def init_paddings(self): self.paddings = [1, 2, 1, 1, 1, 0] def init_shape(self): self.shape = [1, 3, 7, 7, 7] create_test_cudnn_class(TestPool3D_Op_AsyPadding) create_test_cudnn_class(TestCase1_AsyPadding) create_test_cudnn_class(TestCase2_AsyPadding) create_test_cudnn_class(TestCase3_AsyPadding) create_test_cudnn_class(TestCase4_AsyPadding) create_test_cudnn_class(TestCase5_AsyPadding) create_test_cudnn_fp16_class(TestPool3D_Op_AsyPadding) create_test_cudnn_fp16_class(TestCase1_AsyPadding) create_test_cudnn_fp16_class(TestCase2_AsyPadding) create_test_cudnn_fp16_class(TestCase3_AsyPadding) create_test_cudnn_fp16_class(TestCase4_AsyPadding) create_test_cudnn_fp16_class(TestCase5_AsyPadding) create_test_cudnn_use_ceil_class(TestPool3D_Op_AsyPadding) create_test_cudnn_use_ceil_class(TestCase1_AsyPadding) create_test_use_ceil_class(TestCase1_AsyPadding) create_test_use_ceil_class(TestCase2_AsyPadding) class TestAvgInclude_AsyPadding(TestCase2): def init_exclusive(self): self.exclusive = False def init_paddings(self): self.paddings = [2, 2, 1, 1, 0, 0] @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNAvgInclude_AsyPadding(TestCase2): def init_kernel_type(self): self.use_cudnn = True def init_exclusive(self): self.exclusive = False def init_paddings(self): self.paddings = [1, 0, 0, 0, 0, 0] def init_shape(self): self.shape = [1, 3, 5, 5, 5] class TestAvgPoolAdaptive_AsyPadding(TestCase1): def init_adaptive(self): self.adaptive = True def init_paddings(self): self.paddings = [1, 0, 2, 1, 2, 1] # ------------ test channel_last -------------- class TestPool3D_channel_last(TestPool3D_Op): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 5, 5, 6, 3] class TestCase1_channel_last(TestCase1): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 7, 7, 3] class TestCase2_channel_last(TestCase2): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 7, 5, 3] class TestCase3_channel_last(TestCase3): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 5, 6, 5, 3] class TestCase4_channel_last(TestCase4): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 6, 7, 3] class TestCase5_channel_last(TestCase5): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 7, 7, 3] create_test_cudnn_class(TestPool3D_channel_last) create_test_cudnn_class(TestCase1_channel_last) create_test_cudnn_class(TestCase2_channel_last) create_test_cudnn_class(TestCase3_channel_last) create_test_cudnn_class(TestCase4_channel_last) create_test_cudnn_class(TestCase5_channel_last) create_test_cudnn_use_ceil_class(TestPool3D_channel_last) create_test_cudnn_use_ceil_class(TestCase1_channel_last) create_test_use_ceil_class(TestCase1_channel_last) create_test_use_ceil_class(TestCase2_channel_last) class TestCase5_Max(TestCase2): def init_pool_type(self): self.pool_type = "max" def test_check_grad(self): if self.dtype == np.float16: return if self.has_cudnn() and self.pool_type == "max": place = core.CUDAPlace(0) self.check_grad_with_place( place, set(['X']), 'Out', max_relative_error=1.00) elif self.pool_type == "max": self.check_grad(set(['X']), 'Out', max_relative_error=1.00) class TestCase5_channel_last_Max(TestCase5_Max): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 7, 7, 3] create_test_cudnn_class(TestCase5_Max) create_test_cudnn_class(TestCase5_channel_last_Max) class TestAvgInclude_channel_last(TestCase2_channel_last): def init_exclusive(self): self.exclusive = False @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNAvgInclude_channel_last(TestCase2_channel_last): def init_kernel_type(self): self.use_cudnn = True def init_exclusive(self): self.exclusive = False class TestAvgPoolAdaptive_channel_last(TestCase1_channel_last): def init_adaptive(self): self.adaptive = True # --- asy padding class TestPool3D_Op_AsyPadding_channel_last(TestPool3D_Op_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 5, 5, 6, 3] class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 6, 8, 3] class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 6, 8, 7, 3] class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 5, 7, 5, 3] class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 6, 7, 7, 3] class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 8, 6, 3] create_test_cudnn_class(TestPool3D_Op_AsyPadding_channel_last) create_test_cudnn_class(TestCase1_AsyPadding_channel_last) create_test_cudnn_class(TestCase2_AsyPadding_channel_last) create_test_cudnn_class(TestCase3_AsyPadding_channel_last) create_test_cudnn_class(TestCase4_AsyPadding_channel_last) create_test_cudnn_class(TestCase5_AsyPadding_channel_last) create_test_cudnn_use_ceil_class(TestPool3D_Op_AsyPadding_channel_last) create_test_cudnn_use_ceil_class(TestCase1_AsyPadding_channel_last) create_test_use_ceil_class(TestCase1_AsyPadding_channel_last) create_test_use_ceil_class(TestCase2_AsyPadding_channel_last) class TestAvgInclude_AsyPadding_channel_last(TestAvgInclude_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNAvgInclude_AsyPadding_channel_last( TestCUDNNAvgInclude_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" class TestAvgPoolAdaptive_AsyPadding_channel_last( TestAvgPoolAdaptive_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" def init_shape(self): self.shape = [1, 7, 7, 7, 3] #test padding = SAME VALID def create_test_padding_SAME_class(parent): class TestPaddingSMAECase(parent): def init_paddings(self): self.paddings = [0, 0, 0] self.padding_algorithm = "SAME" cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp") TestPaddingSMAECase.__name__ = cls_name globals()[cls_name] = TestPaddingSMAECase create_test_padding_SAME_class(TestPool3D_Op) create_test_padding_SAME_class(TestCase1) create_test_padding_SAME_class(TestCase2) create_test_padding_SAME_class(TestCase3) create_test_padding_SAME_class(TestCase4) create_test_padding_SAME_class(TestCase5) create_test_padding_SAME_class(TestPool3D_channel_last) create_test_padding_SAME_class(TestCase1_channel_last) create_test_padding_SAME_class(TestCase2_channel_last) create_test_padding_SAME_class(TestCase3_channel_last) create_test_padding_SAME_class(TestCase4_channel_last) create_test_padding_SAME_class(TestCase5_channel_last) def create_test_cudnn_padding_SAME_class(parent): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNPaddingSMAECase(parent): def init_kernel_type(self): self.use_cudnn = True def init_paddings(self): self.paddings = [1, 1, 1] self.padding_algorithm = "SAME" cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingSAMEOp") TestCUDNNPaddingSMAECase.__name__ = cls_name globals()[cls_name] = TestCUDNNPaddingSMAECase create_test_cudnn_padding_SAME_class(TestPool3D_Op) create_test_cudnn_padding_SAME_class(TestCase1) create_test_cudnn_padding_SAME_class(TestCase2) create_test_cudnn_padding_SAME_class(TestCase3) create_test_cudnn_padding_SAME_class(TestCase4) create_test_cudnn_padding_SAME_class(TestCase5) create_test_cudnn_padding_SAME_class(TestPool3D_channel_last) create_test_cudnn_padding_SAME_class(TestCase1_channel_last) create_test_cudnn_padding_SAME_class(TestCase2_channel_last) create_test_cudnn_padding_SAME_class(TestCase3_channel_last) create_test_cudnn_padding_SAME_class(TestCase4_channel_last) create_test_cudnn_padding_SAME_class(TestCase5_channel_last) def create_test_padding_VALID_class(parent): class TestPaddingVALIDCase(parent): def init_paddings(self): self.paddings = [1, 1, 1] self.padding_algorithm = "VALID" cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp") TestPaddingVALIDCase.__name__ = cls_name globals()[cls_name] = TestPaddingVALIDCase create_test_padding_VALID_class(TestPool3D_Op) create_test_padding_VALID_class(TestCase1) create_test_padding_VALID_class(TestCase2) create_test_padding_VALID_class(TestCase3) create_test_padding_VALID_class(TestCase4) create_test_padding_VALID_class(TestCase5) create_test_padding_VALID_class(TestPool3D_channel_last) create_test_padding_VALID_class(TestCase1_channel_last) create_test_padding_VALID_class(TestCase2_channel_last) create_test_padding_VALID_class(TestCase3_channel_last) create_test_padding_VALID_class(TestCase4_channel_last) create_test_padding_VALID_class(TestCase5_channel_last) def create_test_cudnn_padding_VALID_class(parent): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNPaddingVALIDCase(parent): def init_kernel_type(self): self.use_cudnn = True def init_paddings(self): self.paddings = [1, 1, 1] self.padding_algorithm = "VALID" cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingVALIDOp") TestCUDNNPaddingVALIDCase.__name__ = cls_name globals()[cls_name] = TestCUDNNPaddingVALIDCase create_test_cudnn_padding_VALID_class(TestPool3D_Op) create_test_cudnn_padding_VALID_class(TestCase1) create_test_cudnn_padding_VALID_class(TestCase2) create_test_cudnn_padding_VALID_class(TestCase3) create_test_cudnn_padding_VALID_class(TestCase4) create_test_cudnn_padding_VALID_class(TestCase5) create_test_cudnn_padding_VALID_class(TestPool3D_channel_last) create_test_cudnn_padding_VALID_class(TestCase1_channel_last) create_test_cudnn_padding_VALID_class(TestCase2_channel_last) create_test_cudnn_padding_VALID_class(TestCase3_channel_last) create_test_cudnn_padding_VALID_class(TestCase4_channel_last) create_test_cudnn_padding_VALID_class(TestCase5_channel_last) #test API class TestPool3DAPI(unittest.TestCase): def test_api(self): x_NDHWC = np.random.random([2, 5, 5, 5, 3]).astype("float32") x_NCDHW = np.random.random([2, 3, 5, 5, 5]).astype("float32") input_NDHWC = fluid.layers.data( name="input_NDHWC", shape=[2, 5, 5, 5, 3], append_batch_size=False, dtype="float32") input_NCDHW = fluid.layers.data( name="input_NCDHW", shape=[2, 3, 5, 5, 5], append_batch_size=False, dtype="float32") ksize = [3, 3, 3] out_1 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="max", pool_padding=[1, 1, 1], use_cudnn=False, data_format="NDHWC") out_2 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="avg", pool_padding=[[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]], use_cudnn=False, data_format="NDHWC") out_3 = fluid.layers.pool3d( input=input_NCDHW, pool_size=ksize, pool_type="avg", pool_padding=[[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]], use_cudnn=False, data_format="NCDHW") out_4 = fluid.layers.pool3d( input=input_NCDHW, pool_size=ksize, pool_type="avg", pool_padding=[1, 2, 1, 0, 0, 1], use_cudnn=False, data_format="NCDHW") # test VALID out_5 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="avg", pool_padding="VALID", use_cudnn=False, data_format="NDHWC") out_6 = fluid.layers.pool3d( input=input_NCDHW, pool_size=ksize, pool_type="avg", pool_padding="VALID", use_cudnn=False, data_format="NCDHW") # test SAME out_7 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_stride=[1, 1, 2], pool_type="avg", pool_padding="SAME", use_cudnn=False, data_format="NDHWC") out_8 = fluid.layers.pool3d( input=input_NCDHW, pool_size=[4, 4, 4], pool_type="avg", pool_padding="SAME", use_cudnn=False, data_format="NCDHW") exe = fluid.Executor(place=fluid.CPUPlace()) [res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8] = exe.run( fluid.default_main_program(), feed={"input_NDHWC": x_NDHWC, "input_NCDHW": x_NCDHW}, fetch_list=[ out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8 ]) assert np.allclose( res_1, pool3D_forward_naive( x=x_NDHWC, ksize=ksize, pool_type="max", strides=[1, 1, 1], paddings=[1, 1, 1], data_format="NDHWC")) assert np.allclose( res_2, pool3D_forward_naive( x=x_NDHWC, ksize=ksize, pool_type="avg", strides=[1, 1, 1], paddings=[1, 1, 1, 1, 1, 1], data_format="NDHWC")) assert np.allclose( res_3, pool3D_forward_naive( x=x_NCDHW, ksize=ksize, pool_type="avg", strides=[1, 1, 1], paddings=[1, 1, 1, 1, 1, 1], data_format="NCDHW"), rtol=0.07, atol=1e-05) assert np.allclose( res_4, pool3D_forward_naive( x=x_NCDHW, ksize=ksize, pool_type="avg", strides=[1, 1, 1], paddings=[1, 2, 1, 0, 0, 1], data_format="NCDHW"), rtol=0.07, atol=1e-05) # VALID assert np.allclose( res_5, pool3D_forward_naive( x=x_NDHWC, ksize=ksize, pool_type="avg", strides=[1, 1, 1], paddings=[10, 20], padding_algorithm="VALID", data_format="NDHWC")) assert np.allclose( res_6, pool3D_forward_naive( x=x_NCDHW, ksize=ksize, pool_type="avg", strides=[1, 1, 1], paddings=[10, 20], padding_algorithm="VALID", data_format="NCDHW"), rtol=0.07, atol=1e-05) # SAME assert np.allclose( res_7, pool3D_forward_naive( x=x_NDHWC, ksize=ksize, pool_type="avg", strides=[1, 1, 2], paddings=[10, 20], padding_algorithm="SAME", data_format="NDHWC")) assert np.allclose( res_8, pool3D_forward_naive( x=x_NCDHW, ksize=[4, 4, 4], pool_type="avg", strides=[1, 1, 1], paddings=[10, 20], padding_algorithm="SAME", data_format="NCDHW"), rtol=0.07, atol=1e-05) class TestPool3DAPI_Error(unittest.TestCase): def test_api(self): input_NDHWC = fluid.layers.data( name="input_NDHWC", shape=[2, 5, 5, 5, 3], append_batch_size=False, dtype="float32") ksize = [3, 3, 3] # cudnn type error def run_1(): out_1 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="max", pool_padding=[1, 1, 1], use_cudnn=[0], data_format="NDHWC") self.assertRaises(TypeError, run_1) # data_format value error def run_2(): out_2 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="max", pool_padding=[1, 1, 1], use_cudnn=False, data_format="NDHWCC") self.assertRaises(ValueError, run_2) # padding str value error def run_3(): out_3 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="max", pool_padding="VALIDSAME", use_cudnn=False, data_format="NDHWC") self.assertRaises(ValueError, run_3) # padding str valid and ceil_mode value error def run_4(): out_4 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="max", pool_padding="VALID", use_cudnn=False, ceil_mode=True, data_format="NDHWC") self.assertRaises(ValueError, run_4) # padding with 8 ele. value error def run_5(): out_5 = fluid.layers.pool3d( input=input_NDHWC, pool_size=ksize, pool_type="max", pool_padding=[[1, 1], [0, 0], [0, 0], [1, 1], [1, 1]], use_cudnn=False, data_format="NDHWC") self.assertRaises(ValueError, run_5) if __name__ == '__main__': unittest.main()