Feature/add sincos pos embed

cyto_dl/nn/vits/blocks/patchify.py

@@ -6,7 +6,7 @@
 from einops.layers.torch import Rearrange, Reduce
 from timm.models.layers import trunc_normal_
 
-from cyto_dl.nn.vits.utils import take_indexes
+from cyto_dl.nn.vits.utils import get_positional_embedding, take_indexes
 
 
 def random_indexes(size: int, device):
@@ -27,6 +27,7 @@ def __init__(
         context_pixels: List[int] = [0, 0, 0],
         input_channels: int = 1,
         tasks: Optional[List[str]] = [],
+        learnable_pos_embedding: bool = True,
     ):
         """
         Parameters
@@ -45,12 +46,16 @@ def __init__(
             Number of input channels
         tasks: List[str]
             List of tasks to encode
+        learnable_pos_embedding: bool
+            If True, learnable positional embeddings are used. If False, fixed sin/cos positional embeddings. Empirically, fixed positional embeddings work better for brightfield images.
         """
         super().__init__()
         self.n_patches = np.asarray(n_patches)
         self.spatial_dims = spatial_dims
 
-        self.pos_embedding = torch.nn.Parameter(torch.zeros(np.prod(n_patches), 1, emb_dim))
+        self.pos_embedding = get_positional_embedding(
+            n_patches, emb_dim, learnable=learnable_pos_embedding, use_cls_token=False
+        )
 
         context_pixels = context_pixels[:spatial_dims]
         weight_size = np.asarray(patch_size) + np.round(np.array(context_pixels) * 2).astype(int)
@@ -112,7 +117,6 @@ def __init__(
         self._init_weight()
 
     def _init_weight(self):
-        trunc_normal_(self.pos_embedding, std=0.02)
         for task in self.task_embedding:
             trunc_normal_(self.task_embedding[task], std=0.02)
 

cyto_dl/nn/vits/cross_mae.py

@@ -1,14 +1,13 @@
 from typing import List, Optional
 
-import numpy as np
 import torch
 import torch.nn as nn
 from einops import rearrange
 from einops.layers.torch import Rearrange
 from timm.models.layers import trunc_normal_
 
 from cyto_dl.nn.vits.blocks import CrossAttentionBlock
-from cyto_dl.nn.vits.utils import take_indexes
+from cyto_dl.nn.vits.utils import get_positional_embedding, take_indexes
 
 
 class CrossMAE_Decoder(torch.nn.Module):
@@ -24,6 +23,7 @@ def __init__(
         emb_dim: Optional[int] = 192,
         num_layer: Optional[int] = 4,
         num_head: Optional[int] = 3,
+        learnable_pos_embedding: Optional[bool] = True,
     ) -> None:
         """
         Parameters
@@ -40,6 +40,8 @@ def __init__(
             Number of transformer layers
         num_head: int
             Number of heads in transformer
+        learnable_pos_embedding: bool
+            If True, learnable positional embeddings are used. If False, fixed sin/cos positional embeddings are used. Empirically, fixed positional embeddings work better for brightfield images.
         """
         super().__init__()
 
@@ -58,7 +60,10 @@ def __init__(
 
         self.projection = torch.nn.Linear(enc_dim, emb_dim)
         self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, emb_dim))
-        self.pos_embedding = torch.nn.Parameter(torch.zeros(np.prod(num_patches) + 1, 1, emb_dim))
+
+        self.pos_embedding = get_positional_embedding(
+            num_patches, emb_dim, learnable=learnable_pos_embedding
+        )
 
         self.head = torch.nn.Linear(emb_dim, torch.prod(torch.as_tensor(base_patch_size)))
         self.num_patches = torch.as_tensor(num_patches)
@@ -86,7 +91,6 @@ def __init__(
 
     def init_weight(self):
         trunc_normal_(self.mask_token, std=0.02)
-        trunc_normal_(self.pos_embedding, std=0.02)
 
     def forward(self, features, forward_indexes, backward_indexes):
         # HACK TODO allow usage of multiple intermediate feature weights, this works when decoder is 0 layers

cyto_dl/nn/vits/mae.py

@@ -12,7 +12,7 @@
 
 from cyto_dl.nn.vits.blocks import IntermediateWeigher, Patchify
 from cyto_dl.nn.vits.cross_mae import CrossMAE_Decoder
-from cyto_dl.nn.vits.utils import take_indexes
+from cyto_dl.nn.vits.utils import get_positional_embedding, take_indexes
 
 
 class MAE_Encoder(torch.nn.Module):
@@ -107,6 +107,7 @@ def __init__(
         emb_dim: Optional[int] = 192,
         num_layer: Optional[int] = 4,
         num_head: Optional[int] = 3,
+        learnable_pos_embedding: Optional[bool] = True,
     ) -> None:
         """
         Parameters
@@ -123,12 +124,17 @@ def __init__(
             Number of transformer layers
         num_head: int
             Number of heads in transformer
+        learnable_pos_embedding: bool
+            If True, learnable positional embeddings are used. If False, fixed sin/cos positional embeddings. Empirically, fixed positional embeddings work better for brightfield images.
         """
         super().__init__()
         self.projection_norm = nn.LayerNorm(emb_dim)
         self.projection = torch.nn.Linear(enc_dim, emb_dim)
         self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, emb_dim))
-        self.pos_embedding = torch.nn.Parameter(torch.zeros(np.prod(num_patches) + 1, 1, emb_dim))
+
+        self.pos_embedding = get_positional_embedding(
+            num_patches, emb_dim, learnable=learnable_pos_embedding
+        )
 
         self.transformer = torch.nn.Sequential(
             *[Block(emb_dim, num_head) for _ in range(num_layer)]
@@ -161,7 +167,6 @@ def __init__(
 
     def init_weight(self):
         trunc_normal_(self.mask_token, std=0.02)
-        trunc_normal_(self.pos_embedding, std=0.02)
 
     def forward(self, features, forward_indexes, backward_indexes):
         # project from encoder dimension to decoder dimension
@@ -221,6 +226,7 @@ def __init__(
         context_pixels: Optional[List[int]] = [0, 0, 0],
         input_channels: Optional[int] = 1,
         features_only: Optional[bool] = False,
+        learnable_pos_embedding: Optional[bool] = True,
     ) -> None:
         """
         Parameters
@@ -251,6 +257,8 @@ def __init__(
             Number of input channels
         features_only: bool
             Only use encoder to extract features
+        learnable_pos_embedding: bool
+            If True, learnable positional embeddings are used. If False, fixed sin/cos positional embeddings. Empirically, fixed positional embeddings work better for brightfield images.
         """
         super().__init__()
         assert spatial_dims in (2, 3), "Spatial dims must be 2 or 3"
@@ -291,6 +299,7 @@ def __init__(
             emb_dim=decoder_dim,
             num_layer=decoder_layer,
             num_head=decoder_head,
+            learnable_pos_embedding=learnable_pos_embedding,
         )
 
     def forward(self, img):

cyto_dl/nn/vits/utils.py

@@ -1,6 +1,42 @@
+from typing import Sequence
+
+import numpy as np
 import torch
-from einops import repeat
+from einops import rearrange, repeat
+from positional_encodings.torch_encodings import (
+    PositionalEncoding2D,
+    PositionalEncoding3D,
+)
+from timm.models.layers import trunc_normal_
 
 
 def take_indexes(sequences, indexes):
     return torch.gather(sequences, 0, repeat(indexes, "t b -> t b c", c=sequences.shape[-1]))
+
+
+def get_positional_embedding(
+    num_patches: Sequence[int], emb_dim: int, use_cls_token: bool = True, learnable: bool = True
+):
+    """Generate a positional embedding (with or without a cls token) for a given number of patches
+    and embedding dimension.
+
+    Can be either learnable or fixed.
+    """
+    if learnable:
+        pe = torch.nn.Parameter(torch.zeros(np.prod(num_patches) + int(use_cls_token), 1, emb_dim))
+        trunc_normal_(pe, std=0.02)
+        return pe
+    else:
+        test_tensor = torch.ones(1, *num_patches, emb_dim)
+        if len(num_patches) not in (2, 3):
+            raise ValueError("Only 2d and 3d positional encodings are supported")
+        if len(num_patches) == 2:
+            pe = PositionalEncoding2D(emb_dim)(test_tensor)
+            pe = rearrange(pe, "b y x c -> (y x) b c")
+        elif len(num_patches) == 3:
+            pe = PositionalEncoding3D(emb_dim)(test_tensor)
+            pe = rearrange(pe, "b z y x c -> (z y x) b c")
+        if use_cls_token:
+            cls_token = torch.zeros(1, 1, emb_dim)
+            pe = torch.cat([cls_token, pe], dim=0)
+        return torch.nn.Parameter(pe, requires_grad=False)

pyproject.toml

@@ -48,6 +48,7 @@ dependencies = [
     "bioio-ome-tiff",
     "bioio-tifffile",
     "online-stats>=2023",
+    "positional-encodings>=6.0.3",
 ]
 requires-python = ">=3.9,<3.11"