Update README.md

README.md

@@ -1,3 +1,58 @@
 ---
 license: apache-2.0
+pipeline_tag: image-classification
 ---
+
+Pytorch weights for Kornia ViT converted from the original google JAX vision-transformer repo.
+
+Original weights from https://github.com/google-research/vision_transformer: This weight is based on the
+[Original ViT_S/32 pretrained on imagenet21k](https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_none-wd_0.1-do_0.0-sd_0.0.npz)
+
+Weights converted to PyTorch for Kornia ViT implementation (by [@gau-nernst](https://github.com/gau-nernst) in [kornia/kornia#2786](https://github.com/kornia/kornia/pull/2786#discussion_r1482339811))
+<details>
+  
+  <summary>Convert jax checkpoint function</summary>
+
+  ```
+  def convert_jax_checkpoint(np_state_dict: dict[str, np.ndarray]):
+      
+      def get_weight(key: str) -> torch.Tensor:
+          return torch.from_numpy(np_state_dict[key])
+      
+      state_dict = dict()
+      state_dict["patch_embedding.cls_token"] = get_weight("cls")
+      state_dict["patch_embedding.backbone.weight"] = get_weight("embedding/kernel").permute(3, 2, 0, 1)  # conv »
+      state_dict["patch_embedding.backbone.bias"] = get_weight("embedding/bias")
+      state_dict["patch_embedding.positions"] = get_weight("Transformer/posembed_input/pos_embedding").squeeze(0)
+      
+      # for i, block in enumerate(self.encoder.blocks):
+      for i in range(100):
+          prefix1 = f"encoder.blocks.{i}"
+          prefix2 = f"Transformer/encoderblock_{i}"
+  
+          if f"{prefix2}/LayerNorm_0/scale" not in np_state_dict:
+              break
+  
+          state_dict[f"{prefix1}.0.fn.0.weight"] = get_weight(f"{prefix2}/LayerNorm_0/scale")
+          state_dict[f"{prefix1}.0.fn.0.bias"] = get_weight(f"{prefix2}/LayerNorm_0/bias")
+  
+          mha_prefix = f"{prefix2}/MultiHeadDotProductAttention_1"
+          qkv_weight = [get_weight(f"{mha_prefix}/{x}/kernel") for x in ["query", "key", "value"]]
+          qkv_bias = [get_weight(f"{mha_prefix}/{x}/bias") for x in ["query", "key", "value"]]
+          state_dict[f"{prefix1}.0.fn.1.qkv.weight"] = torch.cat(qkv_weight, 1).flatten(1).T
+          state_dict[f"{prefix1}.0.fn.1.qkv.bias"] = torch.cat(qkv_bias, 0).flatten()
+          state_dict[f"{prefix1}.0.fn.1.projection.weight"] = get_weight(f"{mha_prefix}/out/kernel").flatten(0, 1»
+          state_dict[f"{prefix1}.0.fn.1.projection.bias"] = get_weight(f"{mha_prefix}/out/bias")
+  
+          state_dict[f"{prefix1}.1.fn.0.weight"] = get_weight(f"{prefix2}/LayerNorm_2/scale")
+          state_dict[f"{prefix1}.1.fn.0.bias"] = get_weight(f"{prefix2}/LayerNorm_2/bias")
+          state_dict[f"{prefix1}.1.fn.1.0.weight"] = get_weight(f"{prefix2}/MlpBlock_3/Dense_0/kernel").T
+          state_dict[f"{prefix1}.1.fn.1.0.bias"] = get_weight(f"{prefix2}/MlpBlock_3/Dense_0/bias")
+          state_dict[f"{prefix1}.1.fn.1.3.weight"] = get_weight(f"{prefix2}/MlpBlock_3/Dense_1/kernel").T
+          state_dict[f"{prefix1}.1.fn.1.3.bias"] = get_weight(f"{prefix2}/MlpBlock_3/Dense_1/bias")
+  
+      state_dict["norm.weight"] = get_weight("Transformer/encoder_norm/scale")
+      state_dict["norm.bias"] = get_weight("Transformer/encoder_norm/bias")
+      return state_dict
+  ```
+</details>