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72 lines
2.3 KiB
72 lines
2.3 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import paddle |
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class SegPostProcessor(paddle.nn.Layer): |
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def forward(self, net_outputs): |
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# label_map [NHW], score_map [NHWC] |
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logit = net_outputs[0] |
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outputs = paddle.argmax(logit, axis=1, keepdim=False, dtype='int32'), \ |
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paddle.transpose(paddle.nn.functional.softmax(logit, axis=1), perm=[0, 2, 3, 1]) |
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return outputs |
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class ResPostProcessor(paddle.nn.Layer): |
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def __init__(self, out_key=None): |
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super(ResPostProcessor, self).__init__() |
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self.out_key = out_key |
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def forward(self, net_outputs): |
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if self.out_key is not None: |
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net_outputs = net_outputs[self.out_key] |
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outputs = paddle.transpose(net_outputs, perm=[0, 2, 3, 1]) |
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return outputs |
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class InferSegNet(paddle.nn.Layer): |
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def __init__(self, net): |
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super(InferSegNet, self).__init__() |
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self.net = net |
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self.postprocessor = SegPostProcessor() |
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def forward(self, x): |
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net_outputs = self.net(x) |
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outputs = self.postprocessor(net_outputs) |
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return outputs |
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class InferCDNet(paddle.nn.Layer): |
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def __init__(self, net): |
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super(InferCDNet, self).__init__() |
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self.net = net |
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self.postprocessor = SegPostProcessor() |
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def forward(self, x1, x2): |
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net_outputs = self.net(x1, x2) |
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outputs = self.postprocessor(net_outputs) |
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return outputs |
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class InferResNet(paddle.nn.Layer): |
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def __init__(self, net, out_key=None): |
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super(InferResNet, self).__init__() |
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self.net = net |
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self.postprocessor = ResPostProcessor(out_key=out_key) |
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def forward(self, x): |
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net_outputs = self.net(x) |
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outputs = self.postprocessor(net_outputs) |
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return outputs
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