Remove padding calc from pack, minor fixes

timm/models/vision_transformer_packed.py

@@ -84,13 +84,21 @@ def add_image(self, tokens, pos_indices):
         self.total_len += seq_len
         self.num_images += 1
 
-    def to_tensors(self, max_len, max_packed, return_mask=True):
+    def to_tensors(self, max_seq_len, max_num_seq):
+        """
+        Args:
+            max_seq_len: maximum sequence length (pad to this)
+            max_num_seq: maximum # of sequences (images) packed into one sequence (across the batch)
+
+        Returns:
+            Tuple of tensors for packed batch of images
+        """
         assert self.total_len > 0
-        assert max_len >= self.total_len
+        assert max_seq_len >= self.total_len
         device = self.tokens[-1].device
         dim = self.tokens[-1].shape[-1]
-        pad_len = max_len - self.total_len
-        seq_pad = max(0, max_packed - len(self.seq_lens))
+        pad_len = max_seq_len - self.total_len
+        seq_pad = max(0, max_num_seq - len(self.seq_lens))
         seq_lens = self.seq_lens + [0] * seq_pad if seq_pad else self.seq_lens
         seq_lens = torch.tensor(seq_lens, dtype=torch.int64, device=device)
         if pad_len:
@@ -104,9 +112,6 @@ def to_tensors(self, max_len, max_packed, return_mask=True):
         tokens = torch.concat(tokens)
         pos_indices = torch.concat(pos_indices)
         seq_ids = torch.concat(seq_ids)
-        if return_mask:
-            mask = seq_ids != 0
-            return tokens, pos_indices, seq_ids, seq_lens, mask
         return tokens, pos_indices, seq_ids, seq_lens
 
 
@@ -173,7 +178,7 @@ def pack_images(
         max_packed = max(sequence.num_images, max_packed)
         next_pos += 1
 
-    tensors = [p.to_tensors(max_len=max_seq_len, max_packed=max_packed) for p in packed_sequences]
+    tensors = [p.to_tensors(max_seq_len=max_seq_len, max_num_seq=max_packed) for p in packed_sequences]
     o = [torch.stack(t) for t in zip(*tensors)]
     return tuple(o)
 
@@ -655,12 +660,12 @@ def init_weights(self, mode=''):
 
     @torch.jit.ignore
     def no_weight_decay(self):
-        return {'embeds.pos_embed', 'embeds.cls_token'}
+        return {'pos_embed_h', 'pos_embed_w'}
 
     @torch.jit.ignore
     def group_matcher(self, coarse=False):
         return dict(
-            stem=r'^embeds',  # stem and embed
+            stem=r'^embeds',  # stem and embed  # FIXME correct when design finalized
             blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
         )
 
@@ -675,7 +680,7 @@ def get_classifier(self):
     def reset_classifier(self, num_classes: int, global_pool=None):
         self.num_classes = num_classes
         if global_pool is not None:
-            assert global_pool in ('', 'avg', 'token')
+            assert global_pool in ('', 'avg', 'attn')
             self.global_pool = global_pool
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
 
@@ -693,7 +698,7 @@ def forward_features(
             tokens = tokens.unbind(0)
 
         if isinstance(tokens, (list, tuple)):
-            tokens, pos_indices, seq_ids, seq_lens, padding_mask = pack_images(
+            tokens, pos_indices, seq_ids, seq_lens = pack_images(
                 tokens,
                 self.patch_size,
                 max_grid_size=self.grid_size,