Fix cooadtr merge

main.py

@@ -59,9 +59,10 @@ def main(args):
     np.random.seed(seed)
     random.seed(seed)
 
-    model = transformer_models.VisionTransformer_v3(
+    # model = transformer_models.VisionTransformer_v3(
+    model = transformer_models.CoVisionTransformer(
         args=args,
-        img_dim=args.enc_layers,  # VisionTransformer_v3
+        img_dim=args.enc_layers,
         patch_dim=args.patch_dim,
         out_dim=args.numclass,
         embedding_dim=args.embedding_dim,
@@ -78,18 +79,12 @@ def main(args):
     try:
         from ptflops import get_model_complexity_info
 
-        def input_constructor(*largs, **lkwargs):
-            return {
-                "sequence_input_rgb": torch.ones(()).new_empty(
-                    (1, args.enc_layers, args.dim_feature // 3 * 2)
-                ),
-                "sequence_input_flow": torch.ones(()).new_empty(
-                    (1, args.enc_layers, args.dim_feature // 3)
-                ),
-            }
+        # Warm up model
+        model.forward_steps(torch.randn(1, args.dim_feature, args.enc_layers))
+        model.call_mode = "forward_step"
 
         flops, params = get_model_complexity_info(
-            model, (0, 0), input_constructor=input_constructor, as_strings=False
+            model, (args.dim_feature,), as_strings=False
         )
         print(f"Model FLOPs: {flops}")
         print(f"Model params: {params}")
@@ -98,11 +93,12 @@ def input_constructor(*largs, **lkwargs):
         print(e)
         ...
 
+    model.call_mode = "forward"
     model.to(device)
 
     loss_need = [
         "labels_encoder",
-        "labels_decoder",
+        # "labels_decoder",
     ]
     criterion = utl.SetCriterion(
         num_classes=args.numclass, losses=loss_need, args=args
@@ -200,6 +196,7 @@ def input_constructor(*largs, **lkwargs):
     for epoch in range(args.start_epoch, args.epochs):
         if args.distributed:
             sampler_train.set_epoch(epoch)
+
         train_stats = train_one_epoch(
             model,
             criterion,

transformer_models/Transformer.py

@@ -1,5 +1,6 @@
 from torch import nn
 from .Attention import SelfAttention
+from continual_transformers import CoSiTransformerEncoder, CoReSiTransformerEncoder
 
 
 class Residual(nn.Module):
@@ -80,6 +81,46 @@ def forward(self, x):
         return self.net(x)
 
 
+def CoTransformerModel(
+    dim,
+    depth,
+    heads,
+    mlp_dim,
+    dropout_rate=0.1,
+    attn_dropout_rate=0.1,
+    sequence_len=64,
+):
+    assert depth in {1, 2}
+
+    if depth == 1:
+        return CoSiTransformerEncoder(
+            sequence_len=sequence_len,
+            embed_dim=dim,
+            num_heads=heads,
+            dropout=dropout_rate,
+            in_proj_bias=False,
+            query_index=-1,
+            ff_hidden_dim=mlp_dim,
+            ff_activation=nn.GELU(),
+            device=None,
+            dtype=None,
+        )
+
+    # depth == 2
+    return CoReSiTransformerEncoder(
+        sequence_len=sequence_len,
+        embed_dim=dim,
+        num_heads=heads,
+        dropout=dropout_rate,
+        in_proj_bias=False,
+        query_index=-1,
+        ff_hidden_dim=mlp_dim,
+        ff_activation=nn.GELU(),
+        device=None,
+        dtype=None,
+    )
+
+
 def _register_ptflops():
     try:
         from ptflops import flops_counter as fc