Exclude embeds module and mask attn functions from tracing

Author: rwightman

timm/models/vision_transformer_flex.py

@@ -27,6 +27,7 @@
 from timm.layers import AttentionPoolLatent, Mlp, to_2tuple, get_act_layer, get_norm_layer, LayerType, _assert
 from timm.models._builder import build_model_with_cfg
 from timm.models._features import feature_take_indices
+from timm.models._features_fx import register_notrace_function, register_notrace_module
 from timm.models._registry import register_model, generate_default_cfgs
 from timm.models._manipulate import checkpoint_seq, named_apply
 
@@ -55,6 +56,7 @@ def batch_patchify(
     return patches, (nh, nw)
 
 
+@register_notrace_module
 class FlexEmbeds(nn.Module):
     """ Na(Flex) Embedding module for Vision Transformers
 
@@ -216,18 +218,18 @@ def forward(self, x, patch_coord=None, patch_valid=None):
         naflex_grid_sizes: Optional[List[Tuple[int, int]]] = None
         grid_size: Optional[Tuple[int, int]] = None
 
+        B = x.shape[0]
         if self.is_linear:
             # Linear embedding path, works with NaFlex mode or standard 2D mode
-            B = x.shape[0]
-            if x.ndim == 3:
-                # pre-patchified NaFlex mode, input is expected to be (B, N, P*P*C) where N is num_patches
-                _assert(patch_coord is not None, 'patch_coord must not be None in NaFlex mode')
-
+            if patch_coord is not None:
+                _assert(x.ndim == 3, 'Expecting patchified input with ndim == 3')
+                # Pre-patchified NaFlex mode, input is expected to be (B, N, P*P*C) where N is num_patches
                 # Calculate the appropriate grid size from coords
                 max_y = patch_coord[:, :, 0].max(dim=1)[0] + 1
                 max_x = patch_coord[:, :, 1].max(dim=1)[0] + 1
                 naflex_grid_sizes = [(h.item(), w.item()) for h, w in zip(max_y, max_x)]
             else:
+                _assert(x.ndim == 4, 'Expecting 2D image input with input ndim == 4')
                 x, grid_size = batch_patchify(x, self.patch_size, pad=self.dynamic_img_pad)
 
             if self.norm_input is not None:
@@ -252,7 +254,7 @@ def forward(self, x, patch_coord=None, patch_valid=None):
         x = self.norm_proj(x)
 
         if self.pos_embed_type == 'learned':
-            if naflex_grid_sizes:
+            if naflex_grid_sizes is not None:
                 self._apply_learned_naflex_pos_embed(x, naflex_grid_sizes=naflex_grid_sizes)
             else:
                 self._apply_learned_pos_embed(x, grid_size=grid_size)
@@ -336,6 +338,7 @@ def _apply_learned_pos_embed(
         x.add_(pos_embed)
 
 
+@register_notrace_function
 def create_attention_mask(
         patch_valid: Optional[torch.Tensor],
         num_prefix_tokens: int = 0,
@@ -367,6 +370,8 @@ def create_attention_mask(
 
     return mask_float
 
+
+@register_notrace_function
 def create_attention_mask2(
     patch_valid: Optional[torch.Tensor],
     num_prefix_tokens: int = 0,
@@ -404,6 +409,7 @@ def create_attention_mask2(
     return mask_float
 
 
+@register_notrace_function
 def create_pool_mask(
         patch_valid: Optional[torch.Tensor],
         dtype: torch.dtype = torch.float32,