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try NHWC
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@ -63,7 +63,8 @@ class Stem(nn.Module):
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def forward(self, x):
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def forward(self, x):
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x = self.conv(x)
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x = self.conv(x)
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x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
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x = self.norm(x.permute(0, 2, 3, 1))
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# [B, H, W, C]
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return x
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return x
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class Downsampling(nn.Module):
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class Downsampling(nn.Module):
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@ -89,8 +90,8 @@ class Downsampling(nn.Module):
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)
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)
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def forward(self, x):
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def forward(self, x):
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x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
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x = self.norm(x).permute(0, 3, 1, 2)
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x = self.conv(x)
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x = self.conv(x).permute(0, 2, 3, 1)
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return x
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return x
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@ -396,7 +397,8 @@ class MetaFormerBlock(nn.Module):
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if res_scale_init_value else nn.Identity()
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if res_scale_init_value else nn.Identity()
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def forward(self, x):
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def forward(self, x):
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x = x.permute(0, 2, 3, 1)
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print(x.shape)
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#x = x.permute(0, 2, 3, 1)
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x = self.res_scale1(x) + \
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x = self.res_scale1(x) + \
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self.layer_scale1(
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self.layer_scale1(
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self.drop_path1(
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self.drop_path1(
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@ -409,7 +411,8 @@ class MetaFormerBlock(nn.Module):
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self.mlp(self.norm2(x))
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self.mlp(self.norm2(x))
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)
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)
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)
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)
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x = x.permute(0, 3, 1, 2)
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#x = x.permute(0, 3, 1, 2)
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return x
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return x
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class MetaFormer(nn.Module):
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class MetaFormer(nn.Module):
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