Add SigLIP weights

tests/test_vit.py

@@ -1,3 +1,4 @@
+import pytest
 import timm
 import torch
 
@@ -16,12 +17,19 @@ def test_resize_pe():
     m(torch.randn(1, 3, 256, 256))
 
 
-def test_from_pretrained():
-    m = ViT.from_config("Ti_16", 224, True).eval()
+@pytest.mark.parametrize(
+    "config,timm_name",
+    [
+        (dict(variant="Ti_16", img_size=224, weights="augreg"), "vit_tiny_patch16_224.augreg_in21k"),
+        (dict(variant="B_16", img_size=224, weights="siglip"), "vit_base_patch16_siglip_224"),
+    ],
+)
+def test_from_pretrained(config, timm_name):
+    m = ViT.from_config(**config).eval()
     x = torch.randn(1, 3, 224, 224)
     out = m(x)
 
-    m_timm = timm.create_model("vit_tiny_patch16_224.augreg_in21k", pretrained=True, num_classes=0).eval()
+    m_timm = timm.create_model(timm_name, pretrained=True, num_classes=0).eval()
     out_timm = m_timm(x)
 
     torch.testing.assert_close(out, out_timm, rtol=2e-5, atol=2e-5)

vision_toolbox/backbones/deit.py

@@ -28,8 +28,8 @@ def __init__(
     ) -> None:
         # fmt: off
         super().__init__(
-            d_model, depth, n_heads, patch_size, img_size, True, bias, mlp_ratio,
-            dropout, layer_scale_init, stochastic_depth, norm_eps
+            d_model, depth, n_heads, patch_size, img_size, True, "cls_token", bias,
+            mlp_ratio, dropout, layer_scale_init, stochastic_depth, norm_eps,
         )
         # fmt: on
         self.dist_token = nn.Parameter(torch.zeros(1, 1, d_model))
@@ -133,7 +133,7 @@ def __init__(
     ):
         # fmt: off
         super().__init__(
-            d_model, depth, n_heads, patch_size, img_size, cls_token, bias,
+            d_model, depth, n_heads, patch_size, img_size, cls_token, "cls_token", bias,
             mlp_ratio, dropout, layer_scale_init, stochastic_depth, norm_eps,
         )
         # fmt: on

vision_toolbox/backbones/mlp_mixer.py

@@ -10,7 +10,7 @@
 from torch import Tensor, nn
 
 from ..utils import torch_hub_download
-from .vit import MLP
+from .vit import MLP, load_flax_conv2d, load_flax_linear, load_flax_ln
 
 
 class MixerBlock(nn.Module):
@@ -84,33 +84,17 @@ def from_config(variant: str, patch_size: int, img_size: int, pretrained: bool =
         return m
 
     @torch.no_grad()
-    def load_jax_weights(self, path: str) -> MLPMixer:
-        jax_weights: Mapping[str, np.ndarray] = np.load(path)
+    def load_jax_weights(self, path: str) -> None:
+        jax_weights = {k: torch.from_numpy(v) for k, v in np.load(path).items()}
 
-        def get_w(key: str) -> Tensor:
-            return torch.from_numpy(jax_weights[key])
-
-        self.patch_embed.weight.copy_(get_w("stem/kernel").permute(3, 2, 0, 1))
-        self.patch_embed.bias.copy_(get_w("stem/bias"))
+        load_flax_conv2d(self.patch_embed, jax_weights, "stem")
+        load_flax_ln(self.norm, jax_weights, "pre_head_layer_norm")
 
         for i, layer in enumerate(self.layers):
-            layer: MixerBlock
-            prefix = f"MixerBlock_{i}/"
-
-            layer.norm1.weight.copy_(get_w(prefix + "LayerNorm_0/scale"))
-            layer.norm1.bias.copy_(get_w(prefix + "LayerNorm_0/bias"))
-            layer.token_mixing.linear1.weight.copy_(get_w(prefix + "token_mixing/Dense_0/kernel").T)
-            layer.token_mixing.linear1.bias.copy_(get_w(prefix + "token_mixing/Dense_0/bias"))
-            layer.token_mixing.linear2.weight.copy_(get_w(prefix + "token_mixing/Dense_1/kernel").T)
-            layer.token_mixing.linear2.bias.copy_(get_w(prefix + "token_mixing/Dense_1/bias"))
-
-            layer.norm2.weight.copy_(get_w(prefix + "LayerNorm_1/scale"))
-            layer.norm2.bias.copy_(get_w(prefix + "LayerNorm_1/bias"))
-            layer.channel_mixing.linear1.weight.copy_(get_w(prefix + "channel_mixing/Dense_0/kernel").T)
-            layer.channel_mixing.linear1.bias.copy_(get_w(prefix + "channel_mixing/Dense_0/bias"))
-            layer.channel_mixing.linear2.weight.copy_(get_w(prefix + "channel_mixing/Dense_1/kernel").T)
-            layer.channel_mixing.linear2.bias.copy_(get_w(prefix + "channel_mixing/Dense_1/bias"))
-
-        self.norm.weight.copy_(get_w("pre_head_layer_norm/scale"))
-        self.norm.bias.copy_(get_w("pre_head_layer_norm/bias"))
-        return self
+            load_flax_ln(layer.norm1, jax_weights, f"MixerBlock_{i}/LayerNorm_0")
+            load_flax_linear(layer.token_mixing.linear1, jax_weights, f"MixerBlock_{i}/token_mixing/Dense_0")
+            load_flax_linear(layer.token_mixing.linear2, jax_weights, f"MixerBlock_{i}/token_mixing/Dense_1")
+
+            load_flax_ln(layer.norm2, jax_weights, f"MixerBlock_{i}/LayerNorm_1")
+            load_flax_linear(layer.channel_mixing.linear1, jax_weights, f"MixerBlock_{i}/channel_mixing/Dense_0")
+            load_flax_linear(layer.channel_mixing.linear2, jax_weights, f"MixerBlock_{i}/channel_mixing/Dense_1")

vision_toolbox/backbones/swin.py

@@ -78,7 +78,7 @@ def forward(self, x: Tensor) -> Tensor:
             attn_bias = attn_bias + self.attn_mask.unsqueeze(1)  # add n_heads dim
 
         x, nH, nW = window_partition(x, self.window_size)  # (B * nH * nW, win_size * win_size, C)
-        x = super().forward(x, attn_bias)
+        x = super().forward(x, attn_bias=attn_bias)
         x = window_unpartition(x, self.window_size, nH, nW)  # (B, H, W, C)
 
         if self.shift > 0:

vision_toolbox/backbones/vit.py

@@ -5,7 +5,6 @@
 from __future__ import annotations
 
 from functools import partial
-from typing import Mapping
 
 import numpy as np
 import torch
@@ -27,10 +26,14 @@ def __init__(self, d_model: int, n_heads: int, bias: bool = True, dropout: float
         self.dropout = dropout
         self.scale = (d_model // n_heads) ** (-0.5)
 
-    def forward(self, x: Tensor, attn_bias: Tensor | None = None) -> Tensor:
-        q = self.q_proj(x).unflatten(-1, (self.n_heads, -1)).transpose(-2, -3)  # (B, n_heads, L, head_dim)
-        k = self.k_proj(x).unflatten(-1, (self.n_heads, -1)).transpose(-2, -3)
-        v = self.v_proj(x).unflatten(-1, (self.n_heads, -1)).transpose(-2, -3)
+    def forward(
+        self, q: Tensor, k: Tensor | None = None, v: Tensor | None = None, *, attn_bias: Tensor | None = None
+    ) -> Tensor:
+        k = q if k is None else k
+        v = k if v is None else v
+        q = self.q_proj(q).unflatten(-1, (self.n_heads, -1)).transpose(-2, -3)  # (B, n_heads, L, head_dim)
+        k = self.k_proj(k).unflatten(-1, (self.n_heads, -1)).transpose(-2, -3)
+        v = self.v_proj(v).unflatten(-1, (self.n_heads, -1)).transpose(-2, -3)
 
         if hasattr(F, "scaled_dot_product_attention"):
             out = F.scaled_dot_product_attention(q, k, v, attn_bias, self.dropout if self.training else 0.0)
@@ -89,6 +92,22 @@ def forward(self, x: Tensor) -> Tensor:
         return x
 
 
+class MHAPooling(nn.Module):
+    def __init__(
+        self, d_model: int, n_heads: int, bias: bool = True, mlp_ratio: float = 4.0, norm_eps: float = 1e-6
+    ) -> None:
+        super().__init__()
+        self.probe = nn.Parameter(torch.zeros(1, 1, d_model))
+        self.mha = MHA(d_model, n_heads, bias)
+        self.norm = nn.LayerNorm(d_model, norm_eps)
+        self.mlp = MLP(d_model, int(d_model * mlp_ratio))
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.mha(self.probe, x).squeeze(1)
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
 class ViT(nn.Module):
     def __init__(
         self,
@@ -98,6 +117,7 @@ def __init__(
         patch_size: int,
         img_size: int,
         cls_token: bool = True,
+        pool_type: str = "cls_token",
         bias: bool = True,
         mlp_ratio: float = 4.0,
         dropout: float = 0.0,
@@ -118,13 +138,23 @@ def __init__(
             self.layers.append(block)
 
         self.norm = nn.LayerNorm(d_model, norm_eps)
+        self.pool_type = pool_type
+        self.pooler = MHAPooling(d_model, n_heads, bias, mlp_ratio, norm_eps) if pool_type == "mha" else None
 
     def forward(self, imgs: Tensor) -> Tensor:
         out = self.patch_embed(imgs).flatten(2).transpose(1, 2) + self.pe  # (N, C, H, W) -> (N, H*W, C)
         if self.cls_token is not None:
             out = torch.cat([self.cls_token, out], 1)
         out = self.layers(out)
-        return self.norm(out[:, 0]) if self.cls_token is not None else self.norm(out).mean(1)
+
+        if self.pool_type == "cls_token":
+            return self.norm(out[:, 0])
+        elif self.pool_type == "gap":
+            return self.norm(out).mean(1)
+        elif self.pool_type == "mha":
+            return self.pooler(self.norm(out))
+        else:
+            raise RuntimeError
 
     @torch.no_grad()
     def resize_pe(self, size: int, interpolation_mode: str = "bicubic") -> None:
@@ -136,7 +166,7 @@ def resize_pe(self, size: int, interpolation_mode: str = "bicubic") -> None:
         self.pe = nn.Parameter(pe)
 
     @staticmethod
-    def from_config(variant: str, img_size: int, pretrained: bool = False) -> ViT:
+    def from_config(variant: str, img_size: int, *, weights: str | None = None) -> ViT:
         variant, patch_size = variant.split("_")
 
         d_model, depth, n_heads = dict(
@@ -148,9 +178,13 @@ def from_config(variant: str, img_size: int, pretrained: bool = False) -> ViT:
             H=(1280, 32, 16),
         )[variant]
         patch_size = int(patch_size)
-        m = ViT(d_model, depth, n_heads, patch_size, img_size)
+        kwargs = dict()
+        if weights == "siglip":
+            kwargs.update(cls_token=False, pool_type="mha")
+
+        m = ViT(d_model, depth, n_heads, patch_size, img_size, **kwargs)
 
-        if pretrained:
+        if weights == "augreg":
             assert img_size == 224
             ckpt = {
                 ("Ti", 16): "Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz",
@@ -160,49 +194,96 @@ def from_config(variant: str, img_size: int, pretrained: bool = False) -> ViT:
                 ("B", 16): "B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz",
                 ("L", 16): "L_16-i21k-300ep-lr_0.001-aug_strong1-wd_0.1-do_0.0-sd_0.0.npz",
             }[(variant, patch_size)]
-            base_url = "https://storage.googleapis.com/vit_models/augreg/"
-            m.load_jax_weights(torch_hub_download(base_url + ckpt))
+            m.load_flax_ckpt(f"augreg/{ckpt}")
+
+        elif weights == "siglip":
+            ckpt = {
+                ("B", 16, 224): "webli_en_b16_224_63724782.npz",
+                ("B", 16, 256): "webli_en_b16_256_60500360.npz",
+                ("B", 16, 384): "webli_en_b16_384_68578854.npz",
+                ("B", 16, 512): "webli_en_b16_512_68580893.npz",
+                ("L", 16, 256): "webli_en_l16_256_60552751.npz",
+                ("L", 16, 384): "webli_en_l16_384_63634585.npz",
+            }[(variant, patch_size, img_size)]
+            m.load_flax_ckpt(f"siglip/{ckpt}", big_vision=True, prefix="params/img/")
+
+        elif not weights is None:
+            raise ValueError(f"Unsupported weights={weights}")
 
         return m
 
-    # weights from https://github.com/google-research/vision_transformer
     @torch.no_grad()
-    def load_jax_weights(self, path: str) -> ViT:
-        jax_weights: Mapping[str, np.ndarray] = np.load(path)
-
-        def get_w(key: str) -> Tensor:
-            return torch.from_numpy(jax_weights[key])
-
-        self.cls_token.copy_(get_w("cls"))
-        pe = get_w("Transformer/posembed_input/pos_embedding")
-        self.cls_token.add_(pe[:, 0])
-        self.pe.copy_(pe[:, 1:])
-        self.patch_embed.weight.copy_(get_w("embedding/kernel").permute(3, 2, 0, 1))
-        self.patch_embed.bias.copy_(get_w("embedding/bias"))
-
-        for idx, layer in enumerate(self.layers):
-            layer: ViTBlock
-            prefix = f"Transformer/encoderblock_{idx}/"
-            mha_prefix = prefix + "MultiHeadDotProductAttention_1/"
-
-            layer.mha[0].weight.copy_(get_w(prefix + "LayerNorm_0/scale"))
-            layer.mha[0].bias.copy_(get_w(prefix + "LayerNorm_0/bias"))
-            layer.mha[1].q_proj.weight.copy_(get_w(mha_prefix + "query/kernel").flatten(1).T)
-            layer.mha[1].k_proj.weight.copy_(get_w(mha_prefix + "key/kernel").flatten(1).T)
-            layer.mha[1].v_proj.weight.copy_(get_w(mha_prefix + "value/kernel").flatten(1).T)
-            layer.mha[1].q_proj.bias.copy_(get_w(mha_prefix + "query/bias").flatten())
-            layer.mha[1].k_proj.bias.copy_(get_w(mha_prefix + "key/bias").flatten())
-            layer.mha[1].v_proj.bias.copy_(get_w(mha_prefix + "value/bias").flatten())
-            layer.mha[1].out_proj.weight.copy_(get_w(mha_prefix + "out/kernel").flatten(0, 1).T)
-            layer.mha[1].out_proj.bias.copy_(get_w(mha_prefix + "out/bias"))
-
-            layer.mlp[0].weight.copy_(get_w(prefix + "LayerNorm_2/scale"))
-            layer.mlp[0].bias.copy_(get_w(prefix + "LayerNorm_2/bias"))
-            layer.mlp[1].linear1.weight.copy_(get_w(prefix + "MlpBlock_3/Dense_0/kernel").T)
-            layer.mlp[1].linear1.bias.copy_(get_w(prefix + "MlpBlock_3/Dense_0/bias"))
-            layer.mlp[1].linear2.weight.copy_(get_w(prefix + "MlpBlock_3/Dense_1/kernel").T)
-            layer.mlp[1].linear2.bias.copy_(get_w(prefix + "MlpBlock_3/Dense_1/bias"))
-
-        self.norm.weight.copy_(get_w("Transformer/encoder_norm/scale"))
-        self.norm.bias.copy_(get_w("Transformer/encoder_norm/bias"))
-        return self
+    def load_flax_ckpt(self, ckpt: str, *, big_vision: bool = False, prefix: str = "") -> None:
+        if big_vision:
+            # https://github.com/google-research/big_vision
+            gcs_bucket = "big_vision"
+            mha_norm = "LayerNorm_0"
+            mha = "MultiHeadDotProductAttention_0"
+            mlp_norm = "LayerNorm_1"
+            mlp = "MlpBlock_0"
+
+        else:
+            # https://github.com/google-research/vision_transformer
+            gcs_bucket = "vit_models"
+            mha_norm = "LayerNorm_0"
+            mha = "MultiHeadDotProductAttention_1"
+            mlp_norm = "LayerNorm_2"
+            mlp = "MlpBlock_3"
+
+        path = torch_hub_download(f"https://storage.googleapis.com/{gcs_bucket}/{ckpt}")
+        jax_weights = {k[len(prefix) :]: torch.from_numpy(v) for k, v in np.load(path).items() if k.startswith(prefix)}
+
+        if self.cls_token is not None:
+            self.cls_token.copy_(jax_weights.pop("cls"))
+        if big_vision:
+            self.pe.copy_(jax_weights.pop("pos_embedding"))
+        else:
+            pe = jax_weights.pop("Transformer/posembed_input/pos_embedding")
+            self.cls_token.add_(pe[:, 0])
+            self.pe.copy_(pe[:, 1:])
+        load_flax_conv2d(self.patch_embed, jax_weights, "embedding")
+        load_flax_ln(self.norm, jax_weights, "Transformer/encoder_norm")
+
+        for i, layer in enumerate(self.layers):
+            load_flax_ln(layer.mha[0], jax_weights, f"Transformer/encoderblock_{i}/{mha_norm}")
+            load_flax_mha(layer.mha[1], jax_weights, f"Transformer/encoderblock_{i}/{mha}")
+            load_flax_ln(layer.mlp[0], jax_weights, f"Transformer/encoderblock_{i}/{mlp_norm}")
+            load_flax_linear(layer.mlp[1].linear1, jax_weights, f"Transformer/encoderblock_{i}/{mlp}/Dense_0")
+            load_flax_linear(layer.mlp[1].linear2, jax_weights, f"Transformer/encoderblock_{i}/{mlp}/Dense_1")
+
+        # big_vision only
+        if self.pooler is not None:
+            self.pooler.probe.copy_(jax_weights.pop("MAPHead_0/probe"))
+            load_flax_mha(self.pooler.mha, jax_weights, "MAPHead_0/MultiHeadDotProductAttention_0")
+            load_flax_ln(self.pooler.norm, jax_weights, "MAPHead_0/LayerNorm_0")
+            load_flax_linear(self.pooler.mlp.linear1, jax_weights, "MAPHead_0/MlpBlock_0/Dense_0")
+            load_flax_linear(self.pooler.mlp.linear2, jax_weights, "MAPHead_0/MlpBlock_0/Dense_1")
+
+        if len(jax_weights) > 0:
+            print(jax_weights.keys())
+
+
+def load_flax_ln(norm: nn.LayerNorm, weights: dict[str, Tensor], prefix: str) -> None:
+    norm.weight.copy_(weights.pop(f"{prefix}/scale"))
+    norm.bias.copy_(weights.pop(f"{prefix}/bias"))
+
+
+def load_flax_linear(linear: nn.Linear, weights: dict[str, Tensor], prefix: str) -> None:
+    linear.weight.copy_(weights.pop(f"{prefix}/kernel").T)
+    linear.bias.copy_(weights.pop(f"{prefix}/bias"))
+
+
+def load_flax_conv2d(conv2d: nn.Conv2d, weights: dict[str, Tensor], prefix: str) -> None:
+    conv2d.weight.copy_(weights.pop(f"{prefix}/kernel").permute(3, 2, 0, 1))
+    conv2d.bias.copy_(weights.pop(f"{prefix}/bias"))
+
+
+def load_flax_mha(mha: MHA, weights: dict[str, Tensor], prefix: str) -> None:
+    mha.q_proj.weight.copy_(weights.pop(f"{prefix}/query/kernel").flatten(1).T)
+    mha.q_proj.bias.copy_(weights.pop(f"{prefix}/query/bias").flatten())
+    mha.k_proj.weight.copy_(weights.pop(f"{prefix}/key/kernel").flatten(1).T)
+    mha.k_proj.bias.copy_(weights.pop(f"{prefix}/key/bias").flatten())
+    mha.v_proj.weight.copy_(weights.pop(f"{prefix}/value/kernel").flatten(1).T)
+    mha.v_proj.bias.copy_(weights.pop(f"{prefix}/value/bias").flatten())
+    mha.out_proj.weight.copy_(weights.pop(f"{prefix}/out/kernel").flatten(0, 1).T)
+    mha.out_proj.bias.copy_(weights.pop(f"{prefix}/out/bias").flatten())