You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
73 lines
2.3 KiB
73 lines
2.3 KiB
2 years ago
|
# 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
|
||
|
|
|
||
|
|
|
||
|
|
class SegPostProcessor(paddle.nn.Layer):
|
||
|
|
def forward(self, net_outputs):
|
||
|
|
# label_map [NHW], score_map [NHWC]
|
||
|
|
logit = net_outputs[0]
|
||
|
|
outputs = paddle.argmax(logit, axis=1, keepdim=False, dtype='int32'), \
|
||
|
|
paddle.transpose(paddle.nn.functional.softmax(logit, axis=1), perm=[0, 2, 3, 1])
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
|
||
|
|
class ResPostProcessor(paddle.nn.Layer):
|
||
|
|
def __init__(self, out_key=None):
|
||
|
|
super(ResPostProcessor, self).__init__()
|
||
|
|
self.out_key = out_key
|
||
|
|
|
||
|
|
def forward(self, net_outputs):
|
||
|
|
if self.out_key is not None:
|
||
|
|
net_outputs = net_outputs[self.out_key]
|
||
|
|
outputs = paddle.transpose(net_outputs, perm=[0, 2, 3, 1])
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
|
||
|
|
class InferSegNet(paddle.nn.Layer):
|
||
|
|
def __init__(self, net):
|
||
|
|
super(InferSegNet, self).__init__()
|
||
|
|
self.net = net
|
||
|
|
self.postprocessor = SegPostProcessor()
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
net_outputs = self.net(x)
|
||
|
|
outputs = self.postprocessor(net_outputs)
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
|
||
|
|
class InferCDNet(paddle.nn.Layer):
|
||
|
|
def __init__(self, net):
|
||
|
|
super(InferCDNet, self).__init__()
|
||
|
|
self.net = net
|
||
|
|
self.postprocessor = SegPostProcessor()
|
||
|
|
|
||
|
|
def forward(self, x1, x2):
|
||
|
|
net_outputs = self.net(x1, x2)
|
||
|
|
outputs = self.postprocessor(net_outputs)
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
|
||
|
|
class InferResNet(paddle.nn.Layer):
|
||
|
|
def __init__(self, net, out_key=None):
|
||
|
|
super(InferResNet, self).__init__()
|
||
|
|
self.net = net
|
||
|
|
self.postprocessor = ResPostProcessor(out_key=out_key)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
net_outputs = self.net(x)
|
||
|
|
outputs = self.postprocessor(net_outputs)
|
||
|
|
return outputs
|