# 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_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False, exclusive=True, adaptive=False, data_type=np.float32): 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.float32): 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] field_size = ((r_end - r_start) * (c_end - c_start)) \ if (exclusive or adaptive) else (ksize[0] * ksize[1]) 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 class TestPool2D_Op(OpTest): def setUp(self): self.op_type = "pool2d" self.use_cudnn = False self.use_mkldnn = False self.init_data_type() 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.pool2D_forward_naive( input, self.ksize, self.strides, self.paddings, self.global_pool, self.ceil_mode, self.exclusive, self.adaptive, self.dtype)).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': 'AnyLayout', # TODO(dzhwinter) : should be fix latter 'exclusive': self.exclusive, 'adaptive': self.adaptive } 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', 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] self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [0, 0] def init_kernel_type(self): pass def init_data_type(self): self.dtype = np.float32 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.shape = [2, 3, 7, 7] self.ksize = [3, 3] self.strides = [1, 1] 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 class TestCase2(TestPool2D_Op): def init_test_case(self): self.shape = [2, 3, 7, 7] self.ksize = [3, 3] self.strides = [1, 1] 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 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): 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) def test_check_grad(self): 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) 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 if __name__ == '__main__': unittest.main()