# 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 sys import unittest import numpy as np from get_test_cover_info import ( XPUOpTestWrapper, create_test_class, get_xpu_op_support_types, ) from op_test_xpu import XPUOpTest sys.path.append("../legacy_test") from test_pool2d_op import adaptive_end_index, adaptive_start_index import paddle paddle.enable_static() 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( f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. " "It can only be 'SAME' or 'VALID'." ) if padding_algorithm == "VALID": paddings = [0, 0, 0, 0] if ceil_mode is not 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 XPUTestPool2D_Op(XPUOpTestWrapper): def __init__(self): self.op_name = 'pool2d' self.use_dynamic_create_class = False class TestPool2D_Op(XPUOpTest): def setUp(self): self.op_type = "pool2d" self.dtype = self.in_type self.place = paddle.XPUPlace(0) self.use_cudnn = False self.init_kernel_type() self.use_onednn = False 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': XPUOpTest.np_dtype_to_base_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_onednn': self.use_onednn, 'data_format': self.data_format, 'exclusive': self.exclusive, 'adaptive': self.adaptive, "padding_algorithm": self.padding_algorithm, 'ceil_mode': self.ceil_mode, } self.outputs = {'Out': output} def test_check_output(self): self.check_output_with_place(self.place) def test_check_grad(self): self.check_grad_with_place(self.place, {'X'}, 'Out') 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_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 TestAvgPoolAdaptive(TestPool2D_Op): def init_adaptive(self): self.adaptive = True class TestAvgPoolAdaptiveAsyOutSize(TestPool2D_Op): 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] 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 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] 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 TestCaseCeil1(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] def init_ceil_mode(self): self.ceil_mode = True class TestCaseCeil2(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] def init_ceil_mode(self): self.ceil_mode = True class TestCaseCeil3(TestPool2D_Op): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive def init_ceil_mode(self): self.ceil_mode = True class TestCaseCeil4(TestCaseCeil1): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive def init_ceil_mode(self): self.ceil_mode = True class TestCaseCeil5(TestCaseCeil2): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive def init_ceil_mode(self): self.ceil_mode = True class TestCaseAdaptiveAvg(TestPool2D_Op): def init_test_case(self): self.ksize = [2, 2] self.strides = [2, 2] 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, 4, 8, 8] def init_adaptive_mode(self): self.adaptive = True class TestCaseAdaptiveMax(TestPool2D_Op): def init_test_case(self): self.ksize = [2, 2] self.strides = [2, 2] def init_paddings(self): self.paddings = [0, 0] def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive def init_global_pool(self): self.global_pool = False def init_shape(self): self.shape = [2, 4, 8, 8] def init_adaptive_mode(self): self.adaptive = True support_types = get_xpu_op_support_types('pool2d') for stype in support_types: create_test_class(globals(), XPUTestPool2D_Op, stype) if __name__ == '__main__': unittest.main()