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Configuration error
Configuration error
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c9cbb49
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cf5e410
Delete app.py
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app.py
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import datetime
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import random
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from argparse import ArgumentParser
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from pathlib import Path
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import numpy as np
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import torch
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import yaml
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from tqdm import tqdm
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from datasets.video_dataset import AtlasDataset
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from models.video_model import VideoModel
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from util.atlas_loss import AtlasLoss
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from util.util import get_optimizer
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from util.video_logger import DataLogger
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def train_model(config):
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# set seed
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seed = config["seed"]
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if seed == -1:
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seed = np.random.randint(2 ** 32)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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print(f"running with seed: {seed}.")
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dataset = AtlasDataset(config)
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model = VideoModel(config)
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criterion = AtlasLoss(config)
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optimizer = get_optimizer(config, model.parameters())
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logger = DataLogger(config, dataset)
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with tqdm(range(1, config["n_epochs"] + 1)) as tepoch:
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for epoch in tepoch:
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inputs = dataset[0]
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optimizer.zero_grad()
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outputs = model(inputs)
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losses = criterion(outputs, inputs)
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loss = 0.
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if config["finetune_foreground"]:
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loss += losses["foreground"]["loss"]
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elif config["finetune_background"]:
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loss += losses["background"]["loss"]
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lr = optimizer.param_groups[0]["lr"]
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log_data = logger.log_data(epoch, lr, losses, model, dataset)
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loss.backward()
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optimizer.step()
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optimizer.param_groups[0]["lr"] = max(config["min_lr"], config["gamma"] * optimizer.param_groups[0]["lr"])
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if config["use_wandb"]:
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wandb.log(log_data)
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else:
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if epoch % config["log_images_freq"] == 0:
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logger.save_locally(log_data)
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tepoch.set_description(f"Epoch {epoch}")
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tepoch.set_postfix(loss=loss.item())
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument(
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"--config",
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default="./configs/video_config.yaml",
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help="Config path",
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)
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parser.add_argument(
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"--example_config",
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default="car-turn_winter.yaml",
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help="Example config name",
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)
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args = parser.parse_args()
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config_path = args.config
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with open(config_path, "r") as f:
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config = yaml.safe_load(f)
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with open(f"./configs/video_example_configs/{args.example_config}", "r") as f:
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example_config = yaml.safe_load(f)
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config["example_config"] = args.example_config
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config.update(example_config)
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run_name = f"-{config['checkpoint_path'].split('/')[-2]}"
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if config["use_wandb"]:
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import wandb
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wandb.init(project=config["wandb_project"], entity=config["wandb_entity"], config=config, name=run_name)
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wandb.run.name = str(wandb.run.id) + wandb.run.name
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config = dict(wandb.config)
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else:
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now = datetime.datetime.now()
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run_name = f"{now.strftime('%Y-%m-%d_%H-%M-%S')}{run_name}"
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path = Path(f"{config['results_folder']}/{run_name}")
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path.mkdir(parents=True, exist_ok=True)
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with open(path / "config.yaml", "w") as f:
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yaml.dump(config, f)
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config["results_folder"] = str(path)
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train_model(config)
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if config["use_wandb"]:
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wandb.finish()
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