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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 |