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@@ -6,14 +6,6 @@ try:
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except:
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from yolov3_basic import BasicConv, ResBlock
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-# IN1K pretrained weight
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-pretrained_urls = {
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- 's': None,
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- 'm': None,
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- 'l': None,
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- 'x': None,
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-}
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-
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# --------------------- Yolov3's Backbone -----------------------
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## Modified DarkNet
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@@ -93,10 +85,6 @@ class Yolov3Backbone(nn.Module):
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# Initialize all layers
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self.init_weights()
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- # Load imagenet pretrained weight
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- if cfg.use_pretrained:
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- self.load_pretrained()
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-
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def init_weights(self):
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"""Initialize the parameters."""
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for m in self.modules():
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@@ -105,31 +93,6 @@ class Yolov3Backbone(nn.Module):
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# reset the Conv2d initialization parameters
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m.reset_parameters()
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- def load_pretrained(self):
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- url = pretrained_urls[self.model_scale]
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- if url is not None:
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- print('Loading backbone pretrained weight from : {}'.format(url))
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- # checkpoint state dict
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- checkpoint = torch.hub.load_state_dict_from_url(
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- url=url, map_location="cpu", check_hash=True)
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- checkpoint_state_dict = checkpoint.pop("model")
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- # model state dict
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- model_state_dict = self.state_dict()
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- # check
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- for k in list(checkpoint_state_dict.keys()):
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- if k in model_state_dict:
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- shape_model = tuple(model_state_dict[k].shape)
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- shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
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- if shape_model != shape_checkpoint:
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- checkpoint_state_dict.pop(k)
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- else:
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- checkpoint_state_dict.pop(k)
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- print('Unused key: ', k)
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- # load the weight
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- self.load_state_dict(checkpoint_state_dict)
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- else:
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- print('No pretrained weight for model scale: {}.'.format(self.model_scale))
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-
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def forward(self, x):
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c1 = self.layer_1(x)
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c2 = self.layer_2(c1)
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yjh0410