Add scripts to export the encoder of Clay

environment.yml

@@ -7,33 +7,36 @@ dependencies:
   - einops~=0.7.0
   - fiona~=1.9.5
   - geopandas-base~=0.14.1
-  - h5netcdf~=1.3.0
-  - jupyter-book~=1.0.0
-  - jupyterlab~=4.0.7
   - jsonargparse~=4.27.0
-  - lancedb~=0.10.2
   - lightning~=2.1.0
   - matplotlib-base~=3.8.2
   - planetary-computer~=1.0.0
   - python-box~=7.1.0
-  - pytorch~=2.1.0  # [osx]
-  - pytorch~=2.1.0 *cuda12*  # [linux]
+  - pytorch~=2.3.1  # [osx]
+  - pytorch~=2.3.1 *cuda12*  # [linux]
   - python~=3.11.0
   - pyarrow~=16.1.0
-  - rioxarray~=0.15.0
   - rasterio~=1.3.10
   - s3fs~=2024.3.1
   - scikit-image~=0.22.0
   - scikit-learn~=1.4.0
   - stackstac~=0.5.0
   - timm~=0.9.16
-  - torchdata~=0.7.1
-  - torchgeo~=0.5.2
-  - torchvision~=0.16.1
+  - torchvision~=0.18.1
   - transformers~=4.35.2
   - typeshed-client~=2.4.0
   - vit-pytorch~=1.6.4
-  - wandb~=0.15.12
   - zarr~=2.16.1
+  - pip:
+    - geoarrow-pyarrow==0.1.2
+    - jupyter-book==1.0.2
+    - jupyterlab==4.2.4
+    - onnx==1.16.1
+    - onnxscript
+    - onnxruntime
+    - torchdata==0.7.1
+    - torchgeo==0.5.2
+    - stacchip==0.1.35
+    - wandb==0.17.5
 platforms:
   - linux-64

finetune/embedder/factory.py

@@ -0,0 +1,303 @@
+"""Export the Clay model to ONNX and pytorch ExportedProgram format.
+
+This script exports the Clay model to ONNX and pytorch ExportedProgram format
+for deployment. The model is exported with dynamic shapes for inference.
+
+How to use:
+
+```bash
+python -m finetune.embedder.factory \
+    --img_size 256 \
+    --ckpt_path checkpoints/clay-v1-base.ckpt \
+    --device cuda \
+    --name clay-v1-encoder.onnx \
+    --onnx
+# exports Clay encoder to ONNX format that can handle chips of size 256x256
+# for different sensors like Sentinel-2, Landsat-8, NAIP, LINZ & Sentinel 1.
+```
+
+```bash
+python -m finetune.embedder.factory \
+    --img_size 224 \
+    --ckpt_path checkpoints/clay-v1-base.ckpt \
+    --device cuda \
+    --name clay-v1-encoder.pt2 \
+    --ep
+# exports Clay encoder to pytorch ExportedProgram format that can handle chips
+# of size 224x224 for different sensors like Sentinel-2, Landsat-8, NAIP, LINZ
+# & Sentinel 1.
+```
+
+"""
+
+import argparse
+import re
+import warnings
+from pathlib import Path
+
+import torch
+from einops import repeat
+from torch import nn
+from torch.export import Dim
+
+from src.model import Encoder
+from src.utils import posemb_sincos_2d_with_gsd
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+class EmbeddingEncoder(Encoder):
+    """Clay Encoder without mask and shuffle."""
+
+    def __init__(  # noqa: PLR0913
+        self,
+        img_size,
+        patch_size,
+        dim,
+        depth,
+        heads,
+        dim_head,
+        mlp_ratio,
+    ):
+        super().__init__(
+            mask_ratio=0.0,
+            shuffle=False,
+            patch_size=patch_size,
+            dim=dim,
+            depth=depth,
+            heads=heads,
+            dim_head=dim_head,
+            mlp_ratio=mlp_ratio,
+        )
+        self.img_size = img_size
+
+        # Using fixed grid size for inference
+        self.grid_size = img_size // patch_size
+        self.num_patches = self.grid_size**2
+
+    def add_encodings(self, patches, time, latlon, gsd):
+        """Add position encoding to the patches"""
+        B, L, D = patches.shape
+
+        grid_size = self.grid_size
+
+        pos_encoding = (
+            posemb_sincos_2d_with_gsd(
+                h=grid_size,
+                w=grid_size,
+                dim=(self.dim - 8),
+                gsd=gsd,
+            )
+            .to(patches.device)
+            .detach()
+        )  # [L (D - 8)]
+
+        time_latlon = torch.hstack((time, latlon)).to(patches.device).detach()  # [B 8]
+
+        pos_encoding = repeat(pos_encoding, "L D -> B L D", B=B)  # [B L (D - 8)]
+        time_latlon = repeat(time_latlon, "B D -> B L D", L=L)  # [B L 8]
+        pos_metadata_encoding = torch.cat(
+            (pos_encoding, time_latlon), dim=-1
+        )  # [B L D]
+
+        patches = patches + pos_metadata_encoding  # [B L D] + [B L D] -> [B L D]
+        return patches  # [B L D]
+
+    # def forward(self, cube, time, latlon, waves, gsd):
+    def forward(self, datacube):
+        cube, time, latlon, gsd, waves = (
+            datacube["pixels"],  # [B C H W]
+            datacube["time"],  # [B 2]
+            datacube["latlon"],  # [B 2]
+            datacube["gsd"],  # 1
+            datacube["waves"],  # [N]
+        )  # [B C H W]
+        B, C, H, W = cube.shape
+
+        patches, _ = self.to_patch_embed(
+            cube, waves
+        )  # [B L D] - patchify & create embeddings per patch
+
+        # Add time & latlon as encoding to patches
+        patches = self.add_encodings(
+            patches,
+            time,
+            latlon,
+            gsd,
+        )  # [B L D] - add position encoding to the embeddings
+
+        # Add class tokens
+        cls_tokens = repeat(self.cls_token, "1 1 D -> B 1 D", B=B)  # [B 1 D]
+        patches = torch.cat((cls_tokens, patches), dim=1)  # [B (1 + L) D]
+
+        # pass the patches through the transformer
+        patches = self.transformer(patches)  # [B (1 + L) D]
+
+        # get the cls token
+        embeddings = patches[:, 0, :]  # [B D]
+
+        return embeddings
+
+
+class Embedder(nn.Module):
+    def __init__(self, img_size=256, ckpt_path=None, device="cpu"):
+        super().__init__()
+        self.clay_encoder = (
+            EmbeddingEncoder(  # Default parameters for the Clay base model
+                img_size=img_size,
+                patch_size=8,
+                dim=768,
+                depth=12,
+                heads=12,
+                dim_head=64,
+                mlp_ratio=4.0,
+            ).to(device)
+        )
+        self.img_size = img_size
+        self.device = torch.device(device)
+        self.load_clay_weights(ckpt_path)
+
+    def load_clay_weights(self, ckpt_path):
+        "Load the weights from the Clay model encoder."
+        ckpt = torch.load(ckpt_path, map_location=self.device)
+        state_dict = ckpt.get("state_dict")
+        state_dict = {
+            re.sub(r"^model\.encoder\.", "", name): param
+            for name, param in state_dict.items()
+            if name.startswith("model.encoder")
+        }
+
+        with torch.no_grad():
+            for name, param in self.clay_encoder.named_parameters():
+                if name in state_dict and param.size() == state_dict[name].size():
+                    param.data.copy_(state_dict[name])  # Copy the weights
+                else:
+                    print(f"No matching parameter for {name} with size {param.size()}")
+
+        for param in self.clay_encoder.parameters():
+            param.requires_grad = False
+
+        self.clay_encoder.eval()
+
+    def forward(self, datacube):
+        embeddings = self.clay_encoder(datacube)
+
+        return embeddings
+
+    def fake_datacube(self):
+        "Generate a fake datacube for model export."
+        dummy_datacube = {
+            "pixels": torch.randn(2, 3, self.img_size, self.img_size),
+            "time": torch.randn(2, 4),
+            "latlon": torch.randn(2, 4),
+            "waves": torch.randn(3),
+            "gsd": torch.randn(1),
+        }
+        dummy_datacube = {k: v.to(self.device) for k, v in dummy_datacube.items()}
+        return dummy_datacube
+
+    def export_to_onnx(self, name):
+        "Save the model to ONNX format."
+
+        datacube = self.fake_datacube()
+        export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
+
+        # Export the model to ONNX format
+        onnx_program = torch.onnx.dynamo_export(
+            self.eval(), datacube, export_options=export_options
+        )
+
+        # Save the exported model
+        onnx_program.save(f"checkpoints/compiled/{name}")
+        print(f"Model exported to ONNX format: checkpoints/compiled/{name}")
+
+        return onnx_program
+
+    def export_to_torchep(self, name):
+        "Save the model to pytorch ExportedProgram format."
+
+        datacube = self.fake_datacube()
+
+        # dynamic shapes for model export
+        batch_size = Dim("batch_size", min=2, max=1000)
+        channel_bands = Dim("channel_bands", min=1, max=10)
+        dynamic_shapes = {
+            "datacube": {
+                "pixels": {0: batch_size, 1: channel_bands},
+                "time": {0: batch_size},
+                "latlon": {0: batch_size},
+                "waves": {0: channel_bands},
+                "gsd": {0: None},
+            }
+        }
+
+        # Export the model to pytorch ExportedProgram format
+        ep = torch.export.export(
+            self.eval(),
+            (datacube,),
+            dynamic_shapes=dynamic_shapes,
+            strict=True,
+        )
+
+        # Save the exported model
+        torch.export.save(ep, f"checkpoints/compiled/{name}")
+        print(
+            f"Model exported to pytorch ExportedProgram format: checkpoints/compiled/{name}"  # noqa: E501
+        )
+
+        return ep
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Export the Clay model.")
+    parser.add_argument(
+        "--img_size",
+        type=int,
+        default=256,
+        help="Image size for the model",
+    )
+    parser.add_argument(
+        "--ckpt_path",
+        type=str,
+        default="checkpoints/clay-v1-base.ckpt",
+        help="Path to the Clay model checkpoint",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+        help="Device to use for the model",
+    )
+    parser.add_argument(
+        "--name",
+        type=str,
+        default="clay-base.pt",
+        help="Name of the exported model",
+    )
+    parser.add_argument(
+        "--onnx",
+        action="store_true",
+        help="Export the model to ONNX format",
+    )
+    parser.add_argument(
+        "--ep",
+        action="store_true",
+        help="Export the model to pytorch ExportedProgram format",
+    )
+
+    args = parser.parse_args()
+
+    Path("checkpoints/compiled").mkdir(parents=True, exist_ok=True)
+    embedder = Embedder(
+        img_size=args.img_size,
+        ckpt_path=args.ckpt_path,
+        device=args.device,
+    )
+
+    if args.onnx:
+        embedder.export_to_onnx(args.name)
+    elif args.ep:
+        embedder.export_to_torchep(args.name)
+    else:
+        print("Please specify the format to export the model.")
+        parser.print_help()