add torchscript convert script

bin/to_jit.py

@@ -0,0 +1,75 @@
+import os
+from pathlib import Path
+
+import hydra
+import torch
+import yaml
+from omegaconf import OmegaConf
+from torch import nn
+
+from saicinpainting.training.trainers import load_checkpoint
+from saicinpainting.utils import register_debug_signal_handlers
+
+
+class JITWrapper(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, image, mask):
+        batch = {
+            "image": image,
+            "mask": mask
+        }
+        out = self.model(batch)
+        return out["inpainted"]
+
+
+@hydra.main(config_path="../configs/prediction", config_name="default.yaml")
+def main(predict_config: OmegaConf):
+    register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log
+
+    train_config_path = os.path.join(predict_config.model.path, "config.yaml")
+    with open(train_config_path, "r") as f:
+        train_config = OmegaConf.create(yaml.safe_load(f))
+
+    train_config.training_model.predict_only = True
+    train_config.visualizer.kind = "noop"
+
+    checkpoint_path = os.path.join(
+        predict_config.model.path, "models", predict_config.model.checkpoint
+    )
+    model = load_checkpoint(
+        train_config, checkpoint_path, strict=False, map_location="cpu"
+    )
+    model.eval()
+    jit_model_wrapper = JITWrapper(model)
+
+    image = torch.rand(1, 3, 120, 120)
+    mask = torch.rand(1, 1, 120, 120)
+    output = jit_model_wrapper(image, mask)
+
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+
+    image = image.to(device)
+    mask = mask.to(device)
+    traced_model = torch.jit.trace(jit_model_wrapper, (image, mask), strict=False).to(device)
+
+    save_path = Path(predict_config.save_path)
+    save_path.parent.mkdir(parents=True, exist_ok=True)
+
+    print(f"Saving big-lama.pt model to {save_path}")
+    traced_model.save(save_path)
+
+    print(f"Checking jit model output...")
+    jit_model = torch.jit.load(str(save_path))
+    jit_output = jit_model(image, mask)
+    diff = (output - jit_output).abs().sum()
+    print(f"diff: {diff}")
+
+
+if __name__ == "__main__":
+    main()