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Update vit.py to include LayerNorm in the MLP head which is missing #317

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Update vit.py to include LayerNorm in the MLP head which is missing #317

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14 changes: 7 additions & 7 deletions vit_pytorch/vit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ def forward(self, x):
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.norm = nn.LayerNorm(dim)

self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
Expand All @@ -77,8 +77,7 @@ def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x

return self.norm(x)
return x

class ViT(nn.Module):
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
Expand All @@ -90,7 +89,7 @@ def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, ml

num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling).'

self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
Expand All @@ -106,9 +105,11 @@ def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, ml
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

self.pool = pool
self.to_latent = nn.Identity()

self.mlp_head = nn.Linear(dim, num_classes)
self.mlp_head = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, num_classes)
)

def forward(self, img):
x = self.to_patch_embedding(img)
Expand All @@ -123,5 +124,4 @@ def forward(self, img):

x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]

x = self.to_latent(x)
return self.mlp_head(x)