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24 lines
877 B
24 lines
877 B
3 years ago
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from paddle.nn import Conv2D
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from ppcls.arch.backbone.legendary_models.resnet import ResNet50, MODEL_URLS, _load_pretrained
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__all__ = ["ResNet50_last_stage_stride1"]
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def ResNet50_last_stage_stride1(pretrained=False, use_ssld=False, **kwargs):
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def replace_function(conv, pattern):
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new_conv = Conv2D(
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in_channels=conv._in_channels,
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out_channels=conv._out_channels,
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kernel_size=conv._kernel_size,
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stride=1,
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padding=conv._padding,
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groups=conv._groups,
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bias_attr=conv._bias_attr)
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return new_conv
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pattern = ["blocks[13].conv1.conv", "blocks[13].short.conv"]
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model = ResNet50(pretrained=False, use_ssld=use_ssld, **kwargs)
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model.upgrade_sublayer(pattern, replace_function)
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_load_pretrained(pretrained, model, MODEL_URLS["ResNet50"], use_ssld)
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return model
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