run_train.py

import datasets_ws
import network
import commons
import parser
import test
import util
import math
import torch
import logging
import numpy as np
from tqdm import tqdm
import torch.nn as nn
import multiprocessing
from os.path import join
from datetime import datetime
from torch.utils.data.dataloader import DataLoader
torch.backends.cudnn.benchmark = True  # Provides a speedup

if __name__ == "__main__":
    # Initial setup: parser, logging...
    args = parser.parse_arguments()
    start_time = datetime.now()
    args.output_folder = join("runs", args.exp_name,
                              start_time.strftime('%Y-%m-%d_%H-%M-%S'))
    commons.setup_logging(args.output_folder)
    commons.make_deterministic(args.seed)
    logging.info(f"Arguments: {args}")
    logging.info(f"The outputs are being saved in {args.output_folder}")
    logging.info(
        f"Using {torch.cuda.device_count()} GPUs and {multiprocessing.cpu_count()} CPUs")

    # Creation of Datasets
    logging.debug(
        f"Loading dataset Pitts30k from folder {args.datasets_folder}")

    triplets_ds = datasets_ws.TripletsDataset(
        args, args.datasets_folder, "pitts30k", "train", args.negs_num_per_query)
    logging.info(f"Train query set: {triplets_ds}")

    val_ds = datasets_ws.BaseDataset(
        args, args.datasets_folder, "pitts30k", "val")
    logging.info(f"Val set: {val_ds}")

    test_ds = datasets_ws.BaseDataset(
        args, args.datasets_folder, "pitts30k", "test")
    logging.info(f"Test set: {test_ds}")

    # Initialize model
    model = network.GeoLocalizationNet(args)
    model = model.to(args.device)

    # Setup Optimizer and Loss
    optimizer_params = {
        "backbone": {"params": model.backbone.parameters()},
        "aggregation": {"params": model.aggregation.parameters()},
    }

    if args.use_attention:
        optimizer_params["attention"] = {
            "params": model.attention.parameters(),
            "lr": args.attention_lr
        }

    if args.use_sgd:
        optimizer = torch.optim.SGD(
            optimizer_params.values(), lr=args.lr, momentum=args.momentum)
        print("Using SGD")
    elif args.use_adagrad:
        optimizer = torch.optim.Adagrad(optimizer_params.values(), lr=args.lr)
        print("Using Adagrad")
    else:
        optimizer = torch.optim.Adam(optimizer_params.values(), lr=args.lr)
        print("Using Adam")

    criterion_triplet = nn.TripletMarginLoss(
        margin=args.margin, p=2, reduction="sum")

    best_r5 = 0
    not_improved_num = 0

    logging.info(f"Output dimension of the model is {args.features_dim}")

    # Training loop
    for epoch_num in range(args.epochs_num):
        logging.info(f"Start training epoch: {epoch_num:02d}")

        epoch_start_time = datetime.now()
        epoch_losses = np.zeros((0, 1), dtype=np.float32)

        # How many loops should an epoch last (default is 5000/1000=5)
        loops_num = math.ceil(args.queries_per_epoch / args.cache_refresh_rate)
        for loop_num in range(loops_num):
            logging.debug(f"Cache: {loop_num} / {loops_num}")

            # Compute triplets to use in the triplet loss
            triplets_ds.is_inference = True
            triplets_ds.compute_triplets(args, model)
            triplets_ds.is_inference = False

            triplets_dl = DataLoader(dataset=triplets_ds, num_workers=args.num_workers,
                                     batch_size=args.train_batch_size,
                                     collate_fn=datasets_ws.collate_fn,
                                     pin_memory=(args.device == "cuda"),
                                     drop_last=True)

            model = model.train()

            # images shape: (train_batch_size*12)*3*H*W ; by default train_batch_size=4, H=480, W=640
            # triplets_local_indexes shape: (train_batch_size*10)*3 ; because 10 triplets per query
            for images, triplets_local_indexes, _ in tqdm(triplets_dl, ncols=100):

                # Compute features of all images (images contains queries, positives and negatives)
                features = model(images.to(args.device))
                loss_triplet = 0

                triplets_local_indexes = torch.transpose(
                    triplets_local_indexes.view(args.train_batch_size, args.negs_num_per_query, 3), 1, 0)
                for triplets in triplets_local_indexes:
                    queries_indexes, positives_indexes, negatives_indexes = triplets.T
                    loss_triplet += criterion_triplet(features[queries_indexes],
                                                      features[positives_indexes],
                                                      features[negatives_indexes])
                del features
                loss_triplet /= (args.train_batch_size *
                                 args.negs_num_per_query)

                optimizer.zero_grad()
                loss_triplet.backward()
                optimizer.step()

                # Keep track of all losses by appending them to epoch_losses
                batch_loss = loss_triplet.item()
                epoch_losses = np.append(epoch_losses, batch_loss)
                del loss_triplet

            logging.debug(f"Epoch[{epoch_num:02d}]({loop_num}/{loops_num}): " +
                          f"current batch triplet loss = {batch_loss:.4f}, " +
                          f"average epoch triplet loss = {epoch_losses.mean():.4f}")

        logging.info(f"Finished epoch {epoch_num:02d} in {str(datetime.now() - epoch_start_time)[:-7]}, "
                     f"average epoch triplet loss = {epoch_losses.mean():.4f}")

        # Compute recalls on validation set
        recalls, recalls_str = test.test(args, val_ds, model)
        logging.info(f"Recalls on val set {val_ds}: {recalls_str}")

        is_best = recalls[1] > best_r5

        # Save checkpoint, which contains all training parameters
        util.save_checkpoint(args, {"epoch_num": epoch_num, "model_state_dict": model.state_dict(),
                                    "optimizer_state_dict": optimizer.state_dict(), "recalls": recalls, "best_r5": best_r5,
                                    "not_improved_num": not_improved_num
                                    }, is_best, filename="last_model.pth")

        # If recall@5 did not improve for "many" epochs, stop training
        if is_best:
            logging.info(
                f"Improved: previous best R@5 = {best_r5:.1f}, current R@5 = {recalls[1]:.1f}")
            best_r5 = recalls[1]
            not_improved_num = 0
        else:
            not_improved_num += 1
            logging.info(
                f"Not improved: {not_improved_num} / {args.patience}: best R@5 = {best_r5:.1f}, current R@5 = {recalls[1]:.1f}")
            if not_improved_num >= args.patience:
                logging.info(
                    f"Performance did not improve for {not_improved_num} epochs. Stop training.")
                break
    torch.cuda.empty_cache()
    logging.info(f"Best R@5: {best_r5:.1f}")
    logging.info(
        f"Trained for {epoch_num+1:02d} epochs, in total in {str(datetime.now() - start_time)[:-7]}")

    # Test best model on test set
    best_model_state_dict = torch.load(join(args.output_folder, "best_model.pth"))[
        "model_state_dict"]
    model.load_state_dict(best_model_state_dict)

    recalls, recalls_str = test.test(args, test_ds, model)
    logging.info(f"Recalls on {test_ds}: {recalls_str}")