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| # Copyright (c) 2022 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 paddle | |
| import paddle.nn as nn | |
| import paddle.nn.functional as F | |
| def avg_reduce_hw(x): | |
| # Reduce hw by avg | |
| # Return cat([avg_pool_0, avg_pool_1, ...]) | |
| if not isinstance(x, (list, tuple)): | |
| return F.adaptive_avg_pool2d(x, 1) | |
| elif len(x) == 1: | |
| return F.adaptive_avg_pool2d(x[0], 1) | |
| else: | |
| res = [] | |
| for xi in x: | |
| res.append(F.adaptive_avg_pool2d(xi, 1)) | |
| return paddle.concat(res, axis=1) | |
| def avg_max_reduce_hw_helper(x, is_training, use_concat=True): | |
| assert not isinstance(x, (list, tuple)) | |
| avg_pool = F.adaptive_avg_pool2d(x, 1) | |
| # TODO(pjc): when axis=[2, 3], the paddle.max api has bug for training. | |
| if is_training: | |
| max_pool = F.adaptive_max_pool2d(x, 1) | |
| else: | |
| max_pool = paddle.max(x, axis=[2, 3], keepdim=True) | |
| if use_concat: | |
| res = paddle.concat([avg_pool, max_pool], axis=1) | |
| else: | |
| res = [avg_pool, max_pool] | |
| return res | |
| def avg_max_reduce_hw(x, is_training): | |
| # Reduce hw by avg and max | |
| # Return cat([avg_pool_0, avg_pool_1, ..., max_pool_0, max_pool_1, ...]) | |
| if not isinstance(x, (list, tuple)): | |
| return avg_max_reduce_hw_helper(x, is_training) | |
| elif len(x) == 1: | |
| return avg_max_reduce_hw_helper(x[0], is_training) | |
| else: | |
| res_avg = [] | |
| res_max = [] | |
| for xi in x: | |
| avg, max = avg_max_reduce_hw_helper(xi, is_training, False) | |
| res_avg.append(avg) | |
| res_max.append(max) | |
| res = res_avg + res_max | |
| return paddle.concat(res, axis=1) | |
| def avg_reduce_channel(x): | |
| # Reduce channel by avg | |
| # Return cat([avg_ch_0, avg_ch_1, ...]) | |
| if not isinstance(x, (list, tuple)): | |
| return paddle.mean(x, axis=1, keepdim=True) | |
| elif len(x) == 1: | |
| return paddle.mean(x[0], axis=1, keepdim=True) | |
| else: | |
| res = [] | |
| for xi in x: | |
| res.append(paddle.mean(xi, axis=1, keepdim=True)) | |
| return paddle.concat(res, axis=1) | |
| def max_reduce_channel(x): | |
| # Reduce channel by max | |
| # Return cat([max_ch_0, max_ch_1, ...]) | |
| if not isinstance(x, (list, tuple)): | |
| return paddle.max(x, axis=1, keepdim=True) | |
| elif len(x) == 1: | |
| return paddle.max(x[0], axis=1, keepdim=True) | |
| else: | |
| res = [] | |
| for xi in x: | |
| res.append(paddle.max(xi, axis=1, keepdim=True)) | |
| return paddle.concat(res, axis=1) | |
| def avg_max_reduce_channel_helper(x, use_concat=True): | |
| # Reduce hw by avg and max, only support single input | |
| assert not isinstance(x, (list, tuple)) | |
| mean_value = paddle.mean(x, axis=1, keepdim=True) | |
| max_value = paddle.max(x, axis=1, keepdim=True) | |
| if use_concat: | |
| res = paddle.concat([mean_value, max_value], axis=1) | |
| else: | |
| res = [mean_value, max_value] | |
| return res | |
| def avg_max_reduce_channel(x): | |
| # Reduce hw by avg and max | |
| # Return cat([avg_ch_0, max_ch_0, avg_ch_1, max_ch_1, ...]) | |
| if not isinstance(x, (list, tuple)): | |
| return avg_max_reduce_channel_helper(x) | |
| elif len(x) == 1: | |
| return avg_max_reduce_channel_helper(x[0]) | |
| else: | |
| res = [] | |
| for xi in x: | |
| res.extend(avg_max_reduce_channel_helper(xi, False)) | |
| return paddle.concat(res, axis=1) | |
| def cat_avg_max_reduce_channel(x): | |
| # Reduce hw by cat+avg+max | |
| assert isinstance(x, (list, tuple)) and len(x) > 1 | |
| x = paddle.concat(x, axis=1) | |
| mean_value = paddle.mean(x, axis=1, keepdim=True) | |
| max_value = paddle.max(x, axis=1, keepdim=True) | |
| res = paddle.concat([mean_value, max_value], axis=1) | |
| return res |