fix bug

@@ -22,17 +22,18 @@ model:
     num_classes: 5
     num_classes: 5
     embed_dim: 8
     embed_dim: 8
     depth: 12
     depth: 12
-    num_heads: 12
+    num_heads: 2
     mlp_ratio: 4.0
     mlp_ratio: 4.0
     qkv_bias: true
     qkv_bias: true
-    qk_scale: none
-    representation_size: none
+    qk_scale: null
+    representation_size: null
     distilled: false 
     distilled: false 
     drop_ratio: 0.
     drop_ratio: 0.
     attn_drop_ratio: 0.
     attn_drop_ratio: 0.
     drop_path_ratio: 0.
     drop_path_ratio: 0.
-    norm_layer: none
-    act_layer: none
+    norm_layer: null
+    act_layer: null
+    input_length: 29
 
 
 solver:
 solver:
   name: 'VITSolver'
   name: 'VITSolver'

@@ -2,6 +2,7 @@ import copy
 from utils import MODEL_REGISTRY
 from utils import MODEL_REGISTRY
 
 
 from .mlp import MLPModel
 from .mlp import MLPModel
+from .vit import VisionTransformer
 
 
 
 
 def build_model(cfg):
 def build_model(cfg):

@@ -182,7 +182,7 @@ class VisionTransformer(nn.Module):
                  embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True,
                  embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True,
                  qk_scale=None, representation_size=None, distilled=False, drop_ratio=0.,
                  qk_scale=None, representation_size=None, distilled=False, drop_ratio=0.,
                  attn_drop_ratio=0., drop_path_ratio=0., embed_layer=PatchEmbed, norm_layer=None,
                  attn_drop_ratio=0., drop_path_ratio=0., embed_layer=PatchEmbed, norm_layer=None,
-                 act_layer=None):
+                 act_layer=None, input_length=29):
         """
         """
         Args:
         Args:
             img_size (int, tuple): input image size
             img_size (int, tuple): input image size
@@ -215,7 +215,8 @@ class VisionTransformer(nn.Module):
 
 
         self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
         self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
         self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
         self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        # self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, input_length + self.num_tokens, embed_dim))
         self.pos_drop = nn.Dropout(p=drop_ratio)
         self.pos_drop = nn.Dropout(p=drop_ratio)
 
 
         dpr = [x.item() for x in torch.linspace(0, drop_path_ratio, depth)]  # stochastic depth decay rule
         dpr = [x.item() for x in torch.linspace(0, drop_path_ratio, depth)]  # stochastic depth decay rule

@@ -2,6 +2,7 @@ import copy
 
 
 from utils.registery import SOLVER_REGISTRY
 from utils.registery import SOLVER_REGISTRY
 from .mlp_solver import MLPSolver
 from .mlp_solver import MLPSolver
+from .vit_solver import VITSolver
 
 
 
 
 def build_solver(cfg):
 def build_solver(cfg):