cleanup levit

fix images

README.md

@@ -279,11 +279,10 @@ levit = LeViT(
     num_classes = 1000,
     stages = 3,             # number of stages
     dim = (256, 384, 512),  # dimensions at each stage
-    depth = 4,
+    depth = 4,              # transformer of depth 4 at each stage
     heads = (4, 6, 8),      # heads at each stage
     mlp_mult = 2,
-    dropout = 0.1,
-    emb_dropout = 0.1
+    dropout = 0.1
 )
 
 img = torch.randn(1, 3, 224, 224)

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.15.0',
+  version = '0.15.2',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

vit_pytorch/levit.py

@@ -81,7 +81,6 @@ class Attention(nn.Module):
     def apply_pos_bias(self, fmap):
         bias = self.pos_bias(self.pos_indices)
         bias = rearrange(bias, 'i j h -> () h i j')
-        print(bias.shape, fmap.shape)
         return fmap + bias
 
     def forward(self, x):
@@ -136,7 +135,6 @@ class LeViT(nn.Module):
         dim_key = 32,
         dim_value = 64,
         dropout = 0.,
-        emb_dropout = 0.,
         num_distill_classes = None
     ):
         super().__init__()
@@ -147,7 +145,7 @@ class LeViT(nn.Module):
 
         assert all(map(lambda t: len(t) == stages, (dims, depths, layer_heads))), 'dimensions, depths, and heads must be a tuple that is less than the designated number of stages'
 
-        self.to_patch_embedding = nn.Sequential(
+        self.conv_embedding = nn.Sequential(
             nn.Conv2d(3, 32, 3, stride = 2, padding = 1),
             nn.Conv2d(32, 64, 3, stride = 2, padding = 1),
             nn.Conv2d(64, 128, 3, stride = 2, padding = 1),
@@ -177,7 +175,7 @@ class LeViT(nn.Module):
         self.mlp_head = nn.Linear(dim, num_classes)
 
     def forward(self, img):
-        x = self.to_patch_embedding(img)
+        x = self.conv_embedding(img)
 
         x = self.backbone(x)