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@ -536,6 +536,7 @@ class Mlp(nn.Module):
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self.drop2 = nn.Dropout(drop_probs[1])
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def forward(self, x):
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print(x)
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop1(x)
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@ -612,9 +613,8 @@ class MetaFormerBlock(nn.Module):
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if res_scale_init_value else nn.Identity()
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def forward(self, x):
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#B, C, H, W = x.shape
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#x = x.view(B, H, W, C)
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x = x.permute(0, 2, 3, 1)
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B, C, H, W = x.shape
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x = x.view(B, H, W, C)
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x = self.res_scale1(x) + \
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self.layer_scale1(
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self.drop_path1(
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@ -627,8 +627,7 @@ class MetaFormerBlock(nn.Module):
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self.mlp(self.norm2(x))
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)
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)
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#x = x.view(B, C, H, W)
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x = x.permute(0, 3, 1, 2)
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x = x.view(B, C, H, W)
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return x
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class MetaFormer(nn.Module):
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@ -814,31 +813,25 @@ class MetaFormer(nn.Module):
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x = self.norm(x)
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# (B, H, W, C) -> (B, C)
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x = self.head(x)
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#x=self.head(self.norm(x.mean([2, 3])))
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return x
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def forward_features(self, x):
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x = self.patch_embed(x)
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print('timm')
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#x = self.stages(x)
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'''
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for i, stage in enumerate(self.stages):
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x=stage(x)
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#print(x[0][0][0][0])
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'''
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x = self.stages(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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print('timm')
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return x
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def checkpoint_filter_fn(state_dict, model):
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import re
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out_dict = {}
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for k, v in state_dict.items():
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'''
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k = k.replace('proj', 'conv')
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k = re.sub(r'layer_scale_([0-9]+)', r'layer_scale\1.scale', k)
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k = k.replace('network.1', 'downsample_layers.1')
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@ -848,7 +841,6 @@ def checkpoint_filter_fn(state_dict, model):
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k = k.replace('network.4', 'network.2')
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k = k.replace('network.6', 'network.3')
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k = k.replace('network', 'stages')
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'''
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k = re.sub(r'downsample_layers.([0-9]+)', r'stages.\1.downsample', k)
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k = re.sub(r'([0-9]+).([0-9]+)', r'\1.blocks.\2', k)
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k = k.replace('stages.0.downsample', 'patch_embed')
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