# 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 import paddle.fluid as fluid from paddle.fluid import Program, program_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_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False, data_type=np.float64): N, C, H, W = x.shape if global_pool == 1: ksize = [H, W] if adaptive: H_out, W_out = ksize else: H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1 ) // strides[0] + 1 if ceil_mode else ( H - ksize[0] + 2 * paddings[0]) // strides[0] + 1 W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1 ) // strides[1] + 1 if ceil_mode else ( W - ksize[1] + 2 * paddings[1]) // strides[1] + 1 out = np.zeros((N, C, H_out, W_out)) for i in range(H_out): for j in range(W_out): if adaptive: r_start = adaptive_start_index(i, H, ksize[0]) r_end = adaptive_end_index(i, H, ksize[0]) c_start = adaptive_start_index(j, W, ksize[1]) c_end = adaptive_end_index(j, W, ksize[1]) else: r_start = np.max((i * strides[0] - paddings[0], 0)) r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) c_start = np.max((j * strides[1] - paddings[1], 0)) c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) x_masked = x[:, :, r_start:r_end, c_start:c_end] out[:, :, i, j] = np.max(x_masked, axis=(2, 3)) return out def avg_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False, data_type=np.float64): N, C, H, W = x.shape if global_pool == 1: ksize = [H, W] if adaptive: H_out, W_out = ksize else: H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1 ) // strides[0] + 1 if ceil_mode else ( H - ksize[0] + 2 * paddings[0]) // strides[0] + 1 W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1 ) // strides[1] + 1 if ceil_mode else ( W - ksize[1] + 2 * paddings[1]) // strides[1] + 1 out = np.zeros((N, C, H_out, W_out)) for i in range(H_out): for j in range(W_out): if adaptive: r_start = adaptive_start_index(i, H, ksize[0]) r_end = adaptive_end_index(i, H, ksize[0]) c_start = adaptive_start_index(j, W, ksize[1]) c_end = adaptive_end_index(j, W, ksize[1]) else: r_start = i * strides[0] - paddings[0] r_end = i * strides[0] + ksize[0] - paddings[0] c_start = j * strides[1] - paddings[1] c_end = j * strides[1] + ksize[1] - paddings[1] field_size = (r_end - r_start) * (c_end - c_start) r_start = np.max((r_start, 0)) r_end = np.min((r_end, H)) c_start = np.max((c_start, 0)) c_end = np.min((c_end, W)) x_masked = x[:, :, r_start:r_end, c_start:c_end] if (exclusive or adaptive): field_size = (r_end - r_start) * (c_end - c_start) if data_type == np.int8 or data_type == np.uint8: out[:, :, i, j] = (np.rint( np.sum(x_masked, axis=(2, 3)) / field_size)).astype(data_type) else: out[:, :, i, j] = (np.sum(x_masked, axis=(2, 3)) / field_size).astype(data_type) return out def pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False, data_format='NCHW', 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] 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 == "NCHW": input_data_shape = x.shape[2:4] elif data_format == "NHWC": input_data_shape = x.shape[1:3] paddings = _get_padding_with_SAME(input_data_shape, ksize, strides) assert len(paddings) == 2 or len(paddings) == 4 is_sys = True if len(paddings) == 2 else False N = x.shape[0] C, H, W = [x.shape[1], x.shape[2], x.shape[3]] if data_format == 'NCHW' \ else [x.shape[3], x.shape[1], x.shape[2]] if global_pool == 1: ksize = [H, W] paddings = [0 for _ in range(len(paddings))] pad_h_up = paddings[0] if is_sys else paddings[0] pad_h_down = paddings[0] if is_sys else paddings[1] pad_w_left = paddings[1] if is_sys else paddings[2] pad_w_right = paddings[1] if is_sys else paddings[3] if adaptive: H_out, W_out = ksize else: H_out = (H - ksize[0] + pad_h_up + pad_h_down + strides[0] - 1) // strides[0] + 1 \ if ceil_mode else (H - ksize[0] + pad_h_up + pad_h_down) // strides[0] + 1 W_out = (W - ksize[1] + pad_w_left + pad_w_right + strides[1] - 1) // strides[1] + 1 \ if ceil_mode else (W - ksize[1] + pad_w_left + pad_w_right) // strides[1] + 1 out = np.zeros((N, C, H_out, W_out)) if data_format=='NCHW' \ else np.zeros((N, H_out, W_out, C)) for i in range(H_out): if adaptive: in_h_start = adaptive_start_index(i, H, ksize[0]) in_h_end = adaptive_end_index(i, H, ksize[0]) else: in_h_start = np.max((i * strides[0] - pad_h_up, 0)) in_h_end = np.min((i * strides[0] + ksize[0] - pad_h_up, H)) for j in range(W_out): if adaptive: in_w_start = adaptive_start_index(j, W, ksize[1]) in_w_end = adaptive_end_index(j, W, ksize[1]) else: in_h_start = i * strides[0] - pad_h_up in_w_start = j * strides[1] - pad_w_left in_h_end = i * strides[0] + ksize[0] - pad_h_up in_w_end = j * strides[1] + ksize[1] - pad_w_left field_size = (in_h_end - in_h_start) * (in_w_end - in_w_start) in_h_start = np.max((in_h_start, 0)) in_w_start = np.max((in_w_start, 0)) in_h_end = np.min((in_h_end, H)) in_w_end = np.min((in_w_end, W)) if data_format == 'NCHW': x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end] if pool_type == 'avg': if (exclusive or adaptive): field_size = (in_h_end - in_h_start) * ( in_w_end - in_w_start) # if (exclusive or adaptive) else (ksize[0] * ksize[1]) out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size elif pool_type == 'max': out[:, :, i, j] = np.max(x_masked, axis=(2, 3)) elif data_format == 'NHWC': x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :] if pool_type == 'avg': if (exclusive or adaptive): field_size = (in_h_end - in_h_start) * ( in_w_end - in_w_start) out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size elif pool_type == 'max': out[:, i, j, :] = np.max(x_masked, axis=(1, 2)) return out class TestPool2D_Op(OpTest): def setUp(self): self.op_type = "pool2d" self.use_cudnn = False self.init_kernel_type() self.use_mkldnn = False self.init_data_type() 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() input = np.random.random(self.shape).astype(self.dtype) output = pool2D_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, 'use_mkldnn': self.use_mkldnn, '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): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.has_cudnn(): place = core.CUDAPlace(0) self.check_output_with_place( place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)) else: self.check_output(check_dygraph=(self.use_mkldnn == False)) def test_check_grad(self): if self.dtype == np.float16: return # TODO(wangzhongpu): support mkldnn op in dygraph mode 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=0.07, check_dygraph=(self.use_mkldnn == False)) elif self.pool_type != "max": self.check_grad( set(['X']), 'Out', max_relative_error=0.07, check_dygraph=(self.use_mkldnn == False)) def init_data_format(self): self.data_format = "NCHW" def init_shape(self): self.shape = [2, 3, 5, 5] def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] def init_paddings(self): self.paddings = [0, 0] self.padding_algorithm = "EXPLICIT" def init_kernel_type(self): self.use_cudnn = False def init_data_type(self): self.dtype = np.float64 def init_pool_type(self): self.pool_type = "avg" self.pool2D_forward_naive = avg_pool2D_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(TestPool2D_Op): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] def init_paddings(self): self.paddings = [0, 0] def init_pool_type(self): self.pool_type = "avg" self.pool2D_forward_naive = avg_pool2D_forward_naive def init_global_pool(self): self.global_pool = False def init_shape(self): self.shape = [2, 3, 7, 7] class TestCase2(TestPool2D_Op): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] def init_paddings(self): self.paddings = [1, 1] def init_pool_type(self): self.pool_type = "avg" self.pool2D_forward_naive = avg_pool2D_forward_naive def init_global_pool(self): self.global_pool = False def init_shape(self): self.shape = [2, 3, 7, 7] class TestCase3(TestPool2D_Op): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive class TestCase4(TestCase1): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive class TestCase5(TestCase2): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive #--------------------test pool2d 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(TestPool2D_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) #--------------------test pool2d cudnn_fp16-------------------- def create_test_cudnn_fp16_class(parent, check_grad=True): @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): # TODO(wangzhongpu): support mkldnn op in dygraph mode 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, check_dygraph=(self.use_mkldnn == False)) def test_check_grad(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode place = core.CUDAPlace(0) if core.is_float16_supported( place) and self.pool_type != "max" and check_grad: self.check_grad_with_place( place, set(['X']), 'Out', max_relative_error=0.07, check_dygraph=(self.use_mkldnn == False)) cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op") TestCUDNNFp16Case.__name__ = cls_name globals()[cls_name] = TestCUDNNFp16Case create_test_cudnn_fp16_class(TestPool2D_Op) create_test_cudnn_fp16_class(TestCase1, check_grad=False) create_test_cudnn_fp16_class(TestCase2) create_test_cudnn_fp16_class(TestCase3) create_test_cudnn_fp16_class(TestCase4) create_test_cudnn_fp16_class(TestCase5) #--------------------test pool2d use 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 TestPool2DUseCeilCase(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") TestPool2DUseCeilCase.__name__ = cls_name globals()[cls_name] = TestPool2DUseCeilCase create_test_cudnn_use_ceil_class(TestPool2D_Op) create_test_cudnn_use_ceil_class(TestCase1) def create_test_use_ceil_class(parent): class TestPool2DUseCeilCase(parent): def init_ceil_mode(self): self.ceil_mode = True cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast") TestPool2DUseCeilCase.__name__ = cls_name globals()[cls_name] = TestPool2DUseCeilCase create_test_use_ceil_class(TestCase1) create_test_use_ceil_class(TestCase2) class TestAvgInclude(TestCase2): def init_exclusive(self): self.exclusive = False 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 = [8, 3, 6, 6] def init_test_case(self): self.ksize = [2, 3] self.strides = [1, 1] self.paddings = [0, 0, 0, 0] #-------test pool2d with asymmetric padding----- class TestPool2D_AsyPadding(TestPool2D_Op): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 0, 1, 2] def init_shape(self): self.shape = [2, 3, 5, 5] class TestCase1_AsyPadding(TestCase1): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 0, 1, 0] def init_shape(self): self.shape = [2, 3, 7, 7] class TestCase2_AsyPadding(TestCase2): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 2, 1, 2] def init_shape(self): self.shape = [2, 3, 7, 7] class TestCase3_AsyPadding(TestCase3): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 0, 1, 2] def init_shape(self): self.shape = [2, 3, 5, 5] class TestCase4_AsyPadding(TestCase4): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 0, 1, 0] def init_shape(self): self.shape = [2, 3, 7, 7] class TestCase5_AsyPadding((TestCase5)): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [2, 2, 1, 2] def init_shape(self): self.shape = [2, 3, 7, 7] create_test_cudnn_class(TestPool2D_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(TestPool2D_AsyPadding) create_test_cudnn_fp16_class(TestCase1_AsyPadding, check_grad=False) 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(TestPool2D_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_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 2, 1, 2] def init_shape(self): self.shape = [2, 3, 7, 7] class TestCUDNNAvgInclude_AsyPadding(TestCase2): def init_kernel_type(self): self.use_cudnn = True def init_exclusive(self): self.exclusive = False def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [2, 1, 1, 1] def init_shape(self): self.shape = [2, 3, 7, 7] class TestAvgPoolAdaptive_AsyPadding(TestCase1): def init_adaptive(self): self.adaptive = True def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 1, 0, 2] def init_shape(self): self.shape = [2, 3, 7, 7] #----------- test channel_last -------------- class TestPool2D_channel_last(TestPool2D_Op): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 5, 5, 3] class TestCase1_channel_last(TestCase1): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCase2_channel_last(TestCase2): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCase3_channel_last(TestCase3): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 5, 5, 3] class TestCase4_channel_last(TestCase4): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCase5_channel_last(TestCase5): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] create_test_cudnn_class(TestPool2D_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_fp16_class(TestPool2D_channel_last) create_test_cudnn_fp16_class(TestCase1_channel_last, check_grad=False) create_test_cudnn_fp16_class(TestCase2_channel_last) create_test_cudnn_fp16_class(TestCase3_channel_last) create_test_cudnn_fp16_class(TestCase4_channel_last) create_test_cudnn_fp16_class(TestCase5_channel_last) create_test_cudnn_use_ceil_class(TestPool2D_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 = "NHWC" def init_shape(self): self.shape = [2, 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 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 class TestPool2D_AsyPadding_channel_last(TestPool2D_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 5, 5, 3] class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 5, 5, 3] class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] create_test_cudnn_class(TestPool2D_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_fp16_class(TestPool2D_AsyPadding_channel_last) create_test_cudnn_fp16_class( TestCase1_AsyPadding_channel_last, check_grad=False) create_test_cudnn_fp16_class(TestCase2_AsyPadding_channel_last) create_test_cudnn_fp16_class(TestCase3_AsyPadding_channel_last) create_test_cudnn_fp16_class(TestCase4_AsyPadding_channel_last) create_test_cudnn_fp16_class(TestCase5_AsyPadding_channel_last) create_test_cudnn_use_ceil_class(TestPool2D_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 = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestCUDNNAvgInclude_AsyPadding_channel_last( TestCUDNNAvgInclude_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] class TestAvgPoolAdaptive_AsyPadding_channel_last( TestAvgPoolAdaptive_AsyPadding): def init_data_format(self): self.data_format = "NHWC" def init_shape(self): self.shape = [2, 7, 7, 3] # test paddings: SAME VALID def create_test_padding_SAME_class(parent): class TestPaddingSMAECase(parent): def init_paddings(self): self.paddings = [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(TestPool2D_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(TestPool2D_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] 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(TestPool2D_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(TestPool2D_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] 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(TestPool2D_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(TestPool2D_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] 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(TestPool2D_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(TestPool2D_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) class TestCase1_strides(TestCase1): def init_test_case(self): self.ksize = [3, 3] self.strides = [1, 2] def init_shape(self): self.shape = [2, 3, 4, 5] create_test_cudnn_class(TestCase1_strides) create_test_padding_SAME_class(TestCase1_strides) create_test_cudnn_padding_SAME_class(TestCase1_strides) # ----- test API class TestPool2DAPI(unittest.TestCase): def test_api(self): x_NHWC = np.random.random([2, 5, 5, 3]).astype("float32") x_NCHW = np.random.random([2, 3, 5, 5]).astype("float32") input_NHWC = fluid.layers.data( name="input_NHWC", shape=[2, 5, 5, 3], append_batch_size=False, dtype="float32") input_NCHW = fluid.layers.data( name="input_NCHW", shape=[2, 3, 5, 5], append_batch_size=False, dtype="float32") input_NHWC_negetive = fluid.layers.data( name="input_NHWC_negetive", shape=[2, -1, 5, 3], append_batch_size=False, dtype="float32") input_NCHW_negetive = fluid.layers.data( name="input_NCHW_negetive", shape=[2, 3, -1, -1], append_batch_size=False, dtype="float32") ksize = [3, 3] out_1 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding=[1, 1], use_cudnn=False, data_format="NHWC") out_2 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="avg", pool_padding=[[0, 0], [1, 1], [1, 1], [0, 0]], use_cudnn=False, data_format="NHWC") out_3 = fluid.layers.pool2d( input=input_NCHW, pool_size=ksize, pool_type="avg", pool_padding=[[0, 0], [0, 0], [1, 1], [1, 1]], use_cudnn=False, data_format="NCHW") out_4 = fluid.layers.pool2d( input=input_NCHW, pool_size=ksize, pool_type="avg", pool_padding=[1, 2, 1, 0], use_cudnn=False, data_format="NCHW") # test VALID out_5 = fluid.layers.pool2d( input=input_NCHW, pool_size=ksize, pool_type="avg", pool_padding="VALID", use_cudnn=False, data_format="NCHW") out_6 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding="VALID", use_cudnn=False, data_format="NHWC") # test SAME out_7 = fluid.layers.pool2d( input=input_NCHW, pool_size=[4, 4], pool_type="avg", pool_padding="SAME", use_cudnn=False, data_format="NCHW") out_8 = fluid.layers.pool2d( input=input_NHWC, pool_size=[4, 4], pool_type="max", pool_padding="SAME", use_cudnn=False, data_format="NHWC") # test negetive out_9 = fluid.layers.pool2d( input=input_NHWC_negetive, pool_size=ksize, pool_type="avg", pool_padding=[0, 0], use_cudnn=False, data_format="NHWC") assert out_9.shape == (2, -1, 3, 3) out_10 = fluid.layers.pool2d( input=input_NCHW_negetive, pool_size=ksize, pool_type="avg", pool_padding=[0, 0], use_cudnn=False, data_format="NCHW") assert out_10.shape == (2, 3, -1, -1) 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_NHWC": x_NHWC, "input_NCHW": x_NCHW, "input_NHWC_negetive": x_NHWC, "input_NCHW_negetive": x_NCHW }, fetch_list=[ out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8 ]) assert np.allclose( res_1, pool2D_forward_naive( x=x_NHWC, ksize=ksize, pool_type="max", strides=[1, 1], paddings=[1, 1], data_format="NHWC")) assert np.allclose( res_2, pool2D_forward_naive( x=x_NHWC, ksize=ksize, pool_type="avg", strides=[1, 1], paddings=[1, 1, 1, 1], data_format="NHWC")) assert np.allclose( res_3, pool2D_forward_naive( x=x_NCHW, ksize=ksize, pool_type="avg", strides=[1, 1], paddings=[1, 1, 1, 1], data_format="NCHW"), rtol=0.07, atol=1e-05) assert np.allclose( res_4, pool2D_forward_naive( x=x_NCHW, ksize=ksize, pool_type="avg", strides=[1, 1], paddings=[1, 2, 1, 0], data_format="NCHW"), rtol=0.07, atol=1e-05) # VALID assert np.allclose( res_5, pool2D_forward_naive( x=x_NCHW, ksize=ksize, pool_type="avg", strides=[1, 1], paddings=[10, 20], # any ele is ok padding_algorithm="VALID", data_format="NCHW"), rtol=0.07, atol=1e-05) assert np.allclose( res_6, pool2D_forward_naive( x=x_NHWC, ksize=ksize, pool_type="max", strides=[1, 1], paddings=[10, 20], padding_algorithm="VALID", data_format="NHWC")) # SAME assert np.allclose( res_7, pool2D_forward_naive( x=x_NCHW, ksize=[4, 4], pool_type="avg", strides=[1, 1], paddings=[10, 20], padding_algorithm="SAME", data_format="NCHW"), rtol=0.07, atol=1e-05) assert np.allclose( res_8, pool2D_forward_naive( x=x_NHWC, ksize=[4, 4], pool_type="max", strides=[1, 1], paddings=[10, 20], padding_algorithm="SAME", data_format="NHWC")) class TestPool2DAPI_Error(unittest.TestCase): def test_api(self): input_NHWC = fluid.layers.data( name="input_NHWC", shape=[2, 5, 5, 3], append_batch_size=False, dtype="float32") ksize = [3, 3] # cudnn type error def run_1(): out_1 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding=[1, 1], use_cudnn=[0], data_format="NHWC") self.assertRaises(TypeError, run_1) # data_format value error def run_2(): out_2 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding=[1, 1], use_cudnn=False, data_format="NHWCC") self.assertRaises(ValueError, run_2) # padding str value error def run_3(): out_3 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding="VALIDSAME", use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_3) # padding str valid and ceil_mode value error def run_4(): out_4 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding="VALID", use_cudnn=False, ceil_mode=True, data_format="NHWC") self.assertRaises(ValueError, run_4) # padding with 8 ele. value error def run_5(): out_5 = fluid.layers.pool2d( input=input_NHWC, pool_size=ksize, pool_type="max", pool_padding=[[1, 1], [0, 0], [0, 0], [1, 1]], use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_5) class TestDygraphPool2DAPIError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): # the input of Pool2D must be Variable. data1 = np.random.random((3, 32, 32, 5)).astype('float32') pool2d = fluid.dygraph.Pool2D( pool_size=2, pool_type='max', pool_stride=1, global_pooling=False) self.assertRaises(TypeError, pool2d, data1) # the input dtype of Pool2D must be uint8 or int8 or float16 or float32 or float64 # uint8 and int8 only can be set on mkldnn # float16 only can be set on GPU place data2 = fluid.layers.data( name='x1', shape=[3, 32, 32, 5], dtype="int32") self.assertRaises(TypeError, pool2d, data2) def test_data_format_error(self): with program_guard(Program(), Program()): # the data_format must be 'NCHW' or 'NHWC' data1 = np.random.random((3, 32, 32, 5)).astype('float32') self.assertRaises( ValueError, fluid.dygraph.Pool2D, pool_size=2, pool_type='max', pool_stride=1, global_pooling=False, data_format='NWHC') class TestDygraphPool2DAPI(unittest.TestCase): def test_nhwc(self): with fluid.dygraph.guard(): data = np.random.random((3, 32, 32, 5)).astype('float32') x = fluid.dygraph.to_variable(data) pool2d = fluid.dygraph.Pool2D( pool_size=2, pool_type='max', pool_stride=1, pool_padding=[0, 0], global_pooling=False, data_format='NHWC') out1 = pool2d(x) out2 = pool2D_forward_naive( data, [2, 2], [1, 1], paddings=[0, 0], pool_type='max', data_format='NHWC') self.assertTrue(np.allclose(out1.numpy(), out2)) def test_lower_case(self): with fluid.dygraph.guard(): data = np.random.random((3, 32, 32, 5)).astype('float32') x = fluid.dygraph.to_variable(data) pool2d = fluid.dygraph.Pool2D( pool_size=2, pool_type='max', pool_stride=1, pool_padding=[0, 0], global_pooling=False, data_format='nhwc') out1 = pool2d(x) out2 = pool2D_forward_naive( data, [2, 2], [1, 1], paddings=[0, 0], pool_type='max', data_format='NHWC') self.assertTrue(np.allclose(out1.numpy(), out2)) def test_upper_case(self): with fluid.dygraph.guard(): data = np.random.random((3, 32, 32, 5)).astype('float32') x = fluid.dygraph.to_variable(data) pool2d = fluid.dygraph.Pool2D( pool_size=2, pool_type='MAX', pool_stride=1, pool_padding=[0, 0], global_pooling=False, data_format='nhwc') out1 = pool2d(x) out2 = pool2D_forward_naive( data, [2, 2], [1, 1], paddings=[0, 0], pool_type='max', data_format='NHWC') self.assertTrue(np.allclose(out1.numpy(), out2)) if __name__ == '__main__': unittest.main()