try swin
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parent
6ec566505f
commit
63ccb4ec75
26
main.py
26
main.py
@ -716,17 +716,19 @@ if __name__ == "__main__":
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model.load_model(save_path)
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model.load_model(save_path)
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model.evaluate('test')
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model.evaluate('test')
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else:
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else:
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while True:
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try:
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model = Main(args, logger)
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model = Main(args, logger)
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model.fit()
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model.fit()
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except Exception as e:
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# while True:
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print(e)
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# try:
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traceback.print_exc()
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# model = Main(args, logger)
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try:
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# model.fit()
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del model
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# except Exception as e:
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except Exception:
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# print(e)
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pass
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# traceback.print_exc()
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time.sleep(30)
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# try:
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continue
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# del model
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break
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# except Exception:
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# pass
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# time.sleep(30)
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# continue
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# break
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@ -862,7 +862,7 @@ class PoolFormerBlock(nn.Module):
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self.norm1 = norm_layer(dim)
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self.norm1 = norm_layer(dim)
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#self.token_mixer = Pooling(pool_size=pool_size)
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#self.token_mixer = Pooling(pool_size=pool_size)
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# self.token_mixer = FNetBlock()
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# self.token_mixer = FNetBlock()
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self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(7), num_heads=3, pretrained_window_size=[5,5])
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self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(7), num_heads=1, pretrained_window_size=[5,5])
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self.norm2 = norm_layer(dim)
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
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