try swin
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		@@ -786,6 +786,7 @@ class WindowAttention(nn.Module):
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            x: input features with shape of (num_windows*B, N, C)
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					            x: input features with shape of (num_windows*B, N, C)
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            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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					            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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        """
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					        """
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					        print(x.shape)
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        B_, N, C = x.shape
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					        B_, N, C = x.shape
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        qkv_bias = None
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					        qkv_bias = None
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        if self.q_bias is not None:
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					        if self.q_bias is not None:
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@@ -862,7 +863,7 @@ 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=1, pretrained_window_size=[5,5])
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					        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(7), num_heads=10, 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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