# 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. import unittest import numpy as np import paddle.fluid.core as core from op_test import OpTest def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0, ceil_mode=False): N, C, H, W = x.shape if global_pool == 1: ksize = [H, W] 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): 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): N, C, H, W = x.shape if global_pool == 1: ksize = [H, W] 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): 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.sum(x_masked, axis=(2, 3)) / ( (r_end - r_start) * (c_end - c_start)) return out class TestPool2d_Op(OpTest): def setUp(self): self.op_type = "pool2d" self.use_cudnn = False self.use_mkldnn = 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() 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).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 } 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] self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [0, 0] def init_kernel_type(self): pass 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 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-------------------- class TestCUDNNCase1(TestPool2d_Op): def init_kernel_type(self): self.use_cudnn = True class TestFP16CUDNNCase1(TestPool2d_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 if __name__ == '__main__': unittest.main()