v1.4.4 refactor vit

hyperbox/networks/vit/vit.py

@@ -19,6 +19,7 @@
     'ViT',
     'ViT_S',
     'ViT_B',
+    'ViT_L',
     'ViT_H',
     'ViT_G',
     'ViT_10B',
@@ -58,10 +59,10 @@ def __init__(
         hidden_dim_list = keepPositiveList([int(r*hidden_dim) for r in search_ratio])
         hidden_dim_list = spaces.ValueSpace(hidden_dim_list, key=f"{suffix}_hidden_dim", mask=self.mask) if len(hidden_dim_list) > 1 else hidden_dim_list[0]
         self.net = nn.Sequential(
-            ops.Linear(dim, hidden_dim_list),
+            ops.Linear(dim, hidden_dim_list, bias=True),
             nn.GELU(),
             nn.Dropout(dropout),
-            ops.Linear(hidden_dim_list, dim),
+            ops.Linear(hidden_dim_list, dim, bias=True),
             nn.Dropout(dropout)
         )
 
@@ -108,10 +109,10 @@ def __init__(
             qkv_dim_list = spaces.ValueSpace(qkv_dim_list, key=f"{suffix}_inner_dim", mask=self.mask) # coupled with self.inner_dim_list
         else:
             qkv_dim_list = self.inner_dim_list * 3
-        self.to_qkv = ops.Linear(dim, qkv_dim_list, bias = False)
+        self.to_qkv = ops.Linear(dim, qkv_dim_list, bias=True)
 
         self.to_out = nn.Sequential(
-            ops.Linear(self.inner_dim_list, dim),
+            ops.Linear(self.inner_dim_list, dim, bias=True),
             nn.Dropout(dropout)
         )
 
@@ -177,21 +178,20 @@ def __init__(
         num_patches = (image_height // patch_height) * (image_width // patch_width)
         patch_dim = channels * patch_height * patch_width
 
-        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),
-            nn.Linear(patch_dim, dim),
-        )
-        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.patch_embeddings = nn.Conv2d(channels, dim, kernel_size=patch_size, stride=patch_size)
+        self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, dim))
         self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
         self.dropout = nn.Dropout(emb_dropout)
 
     def forward(self, x):
-        x = self.to_patch_embedding(x)
-        b, n, _ = x.shape
+        x = self.patch_embeddings(x)
+        x = x.flatten(2)
+        x = x.transpose(-1, -2)
+        b, n = x.shape[:2]
 
         cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
         x = torch.cat((cls_tokens, x), dim=1)
-        x += self.pos_embedding[:, :(n + 1)]
+        x += self.position_embeddings[:, :(n + 1)]
         x = self.dropout(x)
         return x
 
@@ -344,6 +344,22 @@ def forward(self, x):
     emb_dropout=0.1,
 )
 
+_vit_l = dict(
+    image_size=224,
+    patch_size=16,
+    num_classes=1000,
+    dim=1024,
+    depth=24,
+    heads=16,
+    dim_head=64,
+    mlp_dim=4096,
+    search_ratio=[0.5, 0.75, 1],
+    pool='cls',
+    channels=3,
+    dropout=0.1,
+    emb_dropout=0.1,
+)
+
 _vit_h = dict(
     image_size=224,
     patch_size=16,
@@ -395,6 +411,7 @@ def forward(self, x):
 ViT = partial(VisionTransformer, **_vit_b)
 ViT_S = partial(VisionTransformer, **_vit_s)
 ViT_B = partial(VisionTransformer, **_vit_b)
+ViT_L = partial(VisionTransformer, **_vit_l)
 ViT_H = partial(VisionTransformer, **_vit_h)
 ViT_G = partial(VisionTransformer, **_vit_g)
 ViT_10B = partial(VisionTransformer, **_vit_10b)

setup.py

@@ -9,7 +9,7 @@
 
 setup(
     name="hyperbox",  # you should change "src" to your project name
-    version="1.4.3",
+    version="1.4.4",
     description="Hyperbox: An easy-to-use NAS framework.",
     author="marsggbo",
     url="https://github.com/marsggbo/hyperbox",