try modify swin
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								models.py
									
									
									
									
									
								
							
							
						
						
									
										10
									
								
								models.py
									
									
									
									
									
								
							@@ -532,9 +532,9 @@ class FouriER(torch.nn.Module):
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        self.patch_embed = PatchEmbed(in_chans=channels, patch_size=self.p.patch_size, 
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                                      embed_dim=self.p.embed_dim, stride=4, padding=2)
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        network = []
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        layers = [4, 4, 12, 4]
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        embed_dims = [self.p.embed_dim, 128, 320, 128]
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        mlp_ratios = [4, 4, 4, 4]
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        layers = [2, 2, 6, 2]
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        embed_dims = [self.p.embed_dim, 320, 256, 128]
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        mlp_ratios = [4, 4, 8, 12]
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        num_heads = [2, 4, 8, 16]
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        downsamples = [True, True, True, True]
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        pool_size=3
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@@ -558,7 +558,7 @@ class FouriER(torch.nn.Module):
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                                 use_layer_scale=use_layer_scale, 
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                                 layer_scale_init_value=layer_scale_init_value,
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                                 num_heads=num_heads[i], input_resolution=(image_h // (2**i), image_w // (2**i)),
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                                 window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2)
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                                 window_size=window_size, shift_size=0)
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            network.append(stage)
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            if i >= len(layers) - 1:
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                break
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@@ -949,7 +949,7 @@ class PoolFormerBlock(nn.Module):
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        self.window_size = window_size
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        self.shift_size = shift_size
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        self.input_resolution = input_resolution
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        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=num_heads)
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        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, attn_drop=0.2, proj_drop=0.1)
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        self.norm2 = norm_layer(dim)
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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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