Delete app.py

app.py

@@ -1,104 +0,0 @@
-import datetime
-import random
-from argparse import ArgumentParser
-from pathlib import Path
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from datasets.video_dataset import AtlasDataset
-from models.video_model import VideoModel
-from util.atlas_loss import AtlasLoss
-from util.util import get_optimizer
-from util.video_logger import DataLogger
-
-
-def train_model(config):
-    # set seed
-    seed = config["seed"]
-    if seed == -1:
-        seed = np.random.randint(2 ** 32)
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    print(f"running with seed: {seed}.")
-
-    dataset = AtlasDataset(config)
-    model = VideoModel(config)
-    criterion = AtlasLoss(config)
-    optimizer = get_optimizer(config, model.parameters())
-
-    logger = DataLogger(config, dataset)
-    with tqdm(range(1, config["n_epochs"] + 1)) as tepoch:
-        for epoch in tepoch:
-            inputs = dataset[0]
-            optimizer.zero_grad()
-            outputs = model(inputs)
-            losses = criterion(outputs, inputs)
-
-            loss = 0.
-            if config["finetune_foreground"]:
-                loss += losses["foreground"]["loss"]
-            elif config["finetune_background"]:
-                loss += losses["background"]["loss"]
-
-            lr = optimizer.param_groups[0]["lr"]
-            log_data = logger.log_data(epoch, lr, losses, model, dataset)
-
-            loss.backward()
-            optimizer.step()
-            optimizer.param_groups[0]["lr"] = max(config["min_lr"], config["gamma"] * optimizer.param_groups[0]["lr"])
-
-            if config["use_wandb"]:
-                wandb.log(log_data)
-            else:
-                if epoch % config["log_images_freq"] == 0:
-                    logger.save_locally(log_data)
-
-            tepoch.set_description(f"Epoch {epoch}")
-            tepoch.set_postfix(loss=loss.item())
-
-
-if __name__ == "__main__":
-    parser = ArgumentParser()
-    parser.add_argument(
-        "--config",
-        default="./configs/video_config.yaml",
-        help="Config path",
-    )
-    parser.add_argument(
-        "--example_config",
-        default="car-turn_winter.yaml",
-        help="Example config name",
-    )
-    args = parser.parse_args()
-    config_path = args.config
-
-    with open(config_path, "r") as f:
-        config = yaml.safe_load(f)
-    with open(f"./configs/video_example_configs/{args.example_config}", "r") as f:
-        example_config = yaml.safe_load(f)
-    config["example_config"] = args.example_config
-    config.update(example_config)
-
-    run_name = f"-{config['checkpoint_path'].split('/')[-2]}"
-    if config["use_wandb"]:
-        import wandb
-
-        wandb.init(project=config["wandb_project"], entity=config["wandb_entity"], config=config, name=run_name)
-        wandb.run.name = str(wandb.run.id) + wandb.run.name
-        config = dict(wandb.config)
-    else:
-        now = datetime.datetime.now()
-        run_name = f"{now.strftime('%Y-%m-%d_%H-%M-%S')}{run_name}"
-        path = Path(f"{config['results_folder']}/{run_name}")
-        path.mkdir(parents=True, exist_ok=True)
-        with open(path / "config.yaml", "w") as f:
-            yaml.dump(config, f)
-        config["results_folder"] = str(path)
-
-    train_model(config)
-    if config["use_wandb"]:
-        wandb.finish()