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