Support set_input_size() in EVA models

Author: rwightman

timm/layers/pos_embed_sincos.py

@@ -354,6 +354,7 @@ def __init__(
         self.dim = dim
         self.max_res = max_res
         self.temperature = temperature
+        self.linear_bands = linear_bands
         self.in_pixels = in_pixels
         self.feat_shape = feat_shape
         self.ref_feat_shape = ref_feat_shape
@@ -383,17 +384,7 @@ def __init__(
             self.pos_embed_cos = None
         else:
             # cache full sin/cos embeddings if shape provided up front
-            emb_sin, emb_cos = build_rotary_pos_embed(
-                feat_shape=feat_shape,
-                dim=dim,
-                max_res=max_res,
-                linear_bands=linear_bands,
-                in_pixels=in_pixels,
-                ref_feat_shape=self.ref_feat_shape,
-                grid_offset=self.grid_offset,
-                grid_indexing=self.grid_indexing,
-                temperature=self.temperature,
-            )
+            emb_sin, emb_cos = self._get_pos_embed_values(feat_shape)
             self.bands = None
             self.register_buffer(
                 'pos_embed_sin',
@@ -406,6 +397,29 @@ def __init__(
                 persistent=False,
             )
 
+    def _get_pos_embed_values(self, feat_shape: List[int]):
+        emb_sin, emb_cos = build_rotary_pos_embed(
+            feat_shape=feat_shape,
+            dim=self.dim,
+            max_res=self.max_res,
+            temperature=self.temperature,
+            linear_bands=self.linear_bands,
+            in_pixels=self.in_pixels,
+            ref_feat_shape=self.ref_feat_shape,
+            grid_offset=self.grid_offset,
+            grid_indexing=self.grid_indexing,
+        )
+        return emb_sin, emb_cos
+
+    def update_feat_shape(self, feat_shape: List[int]):
+        if self.feat_shape is not None and feat_shape != self.feat_shape:
+            # only update if feat_shape was set and different from previous value
+            assert self.pos_embed is not None
+            emb_sin, emb_cos = self._get_pos_embed_values(feat_shape)
+            self.pos_embed_sin = emb_sin.to(self.pos_embed_sin.device, self.pos_embed_sin.dtype)
+            self.pos_embed_cos = emb_cos.to(self.pos_embed_cos.device, self.pos_embed_cos.dtype)
+            self.feat_shape = feat_shape
+
     def get_embed(self, shape: Optional[List[int]] = None):
         if shape is not None and self.bands is not None:
             # rebuild embeddings every call, use if target shape changes
@@ -453,6 +467,7 @@ def __init__(
         self.max_res = max_res
         self.temperature = temperature
         self.in_pixels = in_pixels
+        self.linear_bands = linear_bands
         self.feat_shape = feat_shape
         self.ref_feat_shape = ref_feat_shape
         self.grid_offset = grid_offset
@@ -480,27 +495,40 @@ def __init__(
             self.pos_embed = None
         else:
             # cache full sin/cos embeddings if shape provided up front
-            embeds = build_rotary_pos_embed(
-                feat_shape=feat_shape,
-                dim=dim,
-                max_res=max_res,
-                linear_bands=linear_bands,
-                in_pixels=in_pixels,
-                ref_feat_shape=self.ref_feat_shape,
-                grid_offset=self.grid_offset,
-                grid_indexing=self.grid_indexing,
-                temperature=self.temperature,
-            )
             self.bands = None
             self.register_buffer(
                 'pos_embed',
-                torch.cat(embeds, -1),
+                self._get_pos_embed_values(feat_shape=feat_shape),
                 persistent=False,
             )
 
+    def _get_pos_embed_values(self, feat_shape: List[int]):
+        embeds = build_rotary_pos_embed(
+            feat_shape=feat_shape,
+            dim=self.dim,
+            max_res=self.max_res,
+            temperature=self.temperature,
+            linear_bands=self.linear_bands,
+            in_pixels=self.in_pixels,
+            ref_feat_shape=self.ref_feat_shape,
+            grid_offset=self.grid_offset,
+            grid_indexing=self.grid_indexing,
+        )
+        return torch.cat(embeds, -1)
+
+    def update_feat_shape(self, feat_shape: List[int]):
+        if self.feat_shape is not None and feat_shape != self.feat_shape:
+            # only update if feat_shape was set and different from previous value
+            assert self.pos_embed is not None
+            self.pos_embed = self._get_pos_embed_values(feat_shape).to(
+                device=self.pos_embed.device,
+                dtype=self.pos_embed.dtype,
+            )
+            self.feat_shape = feat_shape
+
     def get_embed(self, shape: Optional[List[int]] = None):
         if shape is not None and self.bands is not None:
-            # rebuild embeddings every call, use if target shape changes
+            # rebuild embeddings from cached bands every call, use if target shape changes
             embeds = build_rotary_pos_embed(
                 shape,
                 self.bands,
@@ -684,6 +712,7 @@ def __init__(
 
         head_dim = dim // num_heads
         assert head_dim % 4 == 0, f"head_dim must be divisible by 4, got {head_dim}"
+
         freqs = init_random_2d_freqs(
             head_dim,
             depth,
@@ -692,18 +721,32 @@ def __init__(
             rotate=True,
         )  # (2, depth, num_heads, head_dim//2)
         self.freqs = nn.Parameter(freqs)
+
         if feat_shape is not None:
             # cache pre-computed grid
-            t_x, t_y = get_mixed_grid(
-                feat_shape,
-                grid_indexing=grid_indexing,
-                device=self.freqs.device
-            )
+            t_x, t_y = self._get_grid_values(feat_shape)
             self.register_buffer('t_x', t_x, persistent=False)
             self.register_buffer('t_y', t_y, persistent=False)
         else:
             self.t_x = self.t_y = None
 
+    def _get_grid_values(self, feat_shape: Optional[List[int]]):
+        t_x, t_y = get_mixed_grid(
+            feat_shape,
+            grid_indexing=self.grid_indexing,
+            device=self.freqs.device
+        )
+        return t_x, t_y
+
+    def update_feat_shape(self, feat_shape: Optional[List[int]]):
+        if self.feat_shape is not None and feat_shape != self.feat_shape:
+            assert self.t_x is not None
+            assert self.t_y is not None
+            t_x, t_y = self._get_grid_values(feat_shape)
+            self.t_x = t_x.to(self.t_x.device, self.t_x.dtype)
+            self.t_y = t_y.to(self.t_y.device, self.t_y.dtype)
+            self.feat_shape = feat_shape
+
     def get_embed(self, shape: Optional[List[int]] = None) -> torch.Tensor:
         """Generate rotary embeddings for the given spatial shape.
 

timm/models/eva.py

@@ -723,6 +723,35 @@ def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None)
             self.global_pool = global_pool
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
 
+    def set_input_size(
+            self,
+            img_size: Optional[Tuple[int, int]] = None,
+            patch_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """Update the input image resolution and patch size.
+
+        Args:
+            img_size: New input resolution, if None current resolution is used.
+            patch_size: New patch size, if None existing patch size is used.
+        """
+        prev_grid_size = self.patch_embed.grid_size
+        self.patch_embed.set_input_size(img_size=img_size, patch_size=patch_size)
+
+        if self.pos_embed is not None:
+            num_prefix_tokens = 0 if self.no_embed_class else self.num_prefix_tokens
+            num_new_tokens = self.patch_embed.num_patches + num_prefix_tokens
+            if num_new_tokens != self.pos_embed.shape[1]:
+                self.pos_embed = nn.Parameter(resample_abs_pos_embed(
+                    self.pos_embed,
+                    new_size=self.patch_embed.grid_size,
+                    old_size=prev_grid_size,
+                    num_prefix_tokens=num_prefix_tokens,
+                    verbose=True,
+                ))
+
+        if self.rope is not None:
+            self.rope.update_feat_shape(self.patch_embed.grid_size)
+
     def _pos_embed(self, x) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         if self.dynamic_img_size:
             B, H, W, C = x.shape