39 lines
983 B
Python
39 lines
983 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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from mmseg.models import EncoderDecoder
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from mmseg.models.decode_heads.decode_head import BaseDecodeHead
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from mmseg.registry import MODELS
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@MODELS.register_module(name='InferExampleHead')
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class ExampleDecodeHead(BaseDecodeHead):
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def __init__(self, num_classes=19, out_channels=None):
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super().__init__(
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3, 3, num_classes=num_classes, out_channels=out_channels)
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def forward(self, inputs):
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return self.cls_seg(inputs[0])
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@MODELS.register_module(name='InferExampleBackbone')
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class ExampleBackbone(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = nn.Conv2d(3, 3, 3)
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def init_weights(self, pretrained=None):
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pass
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def forward(self, x):
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return [self.conv(x)]
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@MODELS.register_module(name='InferExampleModel')
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class ExampleModel(EncoderDecoder):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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