train_optim.py

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
import torchvision.transforms as transforms
from torch.utils.data.dataloader import DataLoader
torch.backends.cudnn.benchmark= True  # Provides a speedup

import util
import test
import parser
import commons
import datasets_ws
from model import network
from model.sync_batchnorm import convert_model
from model.functional import sare_ind, sare_joint

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

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

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

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

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

#### Initialize model
model = network.GeoLocalizationNet(args)
model = model.to(args.device)
if args.aggregation in ["netvlad", "crn"]:  # If using NetVLAD layer, initialize it
    if not args.resume:
        triplets_ds.is_inference = True
        model.aggregation.initialize_netvlad_layer(args, triplets_ds, model.backbone)
    args.features_dim *= args.netvlad_clusters

model = torch.nn.DataParallel(model)

#### Setup Optimizer and Loss
if args.aggregation == "crn":
    crn_params = list(model.module.aggregation.crn.parameters())
    net_params = list(model.module.backbone.parameters()) + \
                  list([m[1] for m in model.module.aggregation.named_parameters() if not m[0].startswith('crn')])
    if args.optim == "adam":
        optimizer = torch.optim.Adam([{'params': crn_params, 'lr': args.lr_crn_layer},
                                      {'params': net_params, 'lr': args.lr_crn_net}])
        logging.info("You're using CRN with Adam, it is advised to use SGD")
    elif args.optim == "sgd":
        optimizer = torch.optim.SGD([{'params': crn_params, 'lr': args.lr_crn_layer, 'momentum': 0.9, 'weight_decay': 0.001},
                                      {'params': net_params, 'lr': args.lr_crn_net, 'momentum': 0.9, 'weight_decay': 0.001}])
else:
    if args.optim == "adam":
        optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
    elif args.optim == "sgd":
        optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.001)
    elif args.optim == "adamw":                                        
        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wd)
    elif args.optim == "asgd":
        optimizer = torch.optim.ASGD(model.parameters(), lr=args.lr,weight_decay=args.wd)
        
if args.criterion == "triplet":
    criterion_triplet = nn.TripletMarginLoss(margin=args.margin, p=2, reduction="sum")
elif args.criterion == "sare_ind":
    criterion_triplet = sare_ind
elif args.criterion == "sare_joint":
    criterion_triplet = sare_joint

#### Resume model, optimizer, and other training parameters
if args.resume:
    if args.aggregation != 'crn':
        model, optimizer, best_r5, start_epoch_num, not_improved_num = util.resume_train(args, model, optimizer)
    else:
        # CRN uses pretrained NetVLAD, then requires loading with strict=False and
        # does not load the optimizer from the checkpoint file.
        model, _, best_r5, start_epoch_num, not_improved_num = util.resume_train(args, model, strict=False)
    logging.info(f"Resuming from epoch {start_epoch_num} with best recall@5 {best_r5:.1f}")
else:
    best_r5 = start_epoch_num = not_improved_num = 0

if args.backbone.startswith('vit'):
    logging.info(f"Output dimension of the model is {args.features_dim}")
else:
    logging.info(f"Output dimension of the model is {args.features_dim}, with {util.get_flops(model, args.resize)}")


if torch.cuda.device_count() >= 2:
    # When using more than 1GPU, use sync_batchnorm for torch.nn.DataParallel
    model = convert_model(model)
    model = model.cuda()

#### Training loop
for epoch_num in range(start_epoch_num, 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):
            
            # Flip all triplets or none
            if args.horizontal_flip:
                images = transforms.RandomHorizontalFlip()(images)
            
            # Compute features of all images (images contains queries, positives and negatives)
            features = model(images.to(args.device))
            loss_triplet = 0
            
            if args.criterion == "triplet":
                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])
            elif args.criterion == 'sare_joint':
                # sare_joint needs to receive all the negatives at once
                triplet_index_batch = triplets_local_indexes.view(args.train_batch_size, 10, 3)
                for batch_triplet_index in triplet_index_batch:
                    q = features[batch_triplet_index[0, 0]].unsqueeze(0)  # obtain query as tensor of shape 1xn_features
                    p = features[batch_triplet_index[0, 1]].unsqueeze(0)  # obtain positive as tensor of shape 1xn_features
                    n = features[batch_triplet_index[:, 2]]               # obtain negatives as tensor of shape 10xn_features
                    loss_triplet += criterion_triplet(q, p, n)
            elif args.criterion == "sare_ind":
                for triplet in triplets_local_indexes:
                    # triplet is a 1-D tensor with the 3 scalars indexes of the triplet
                    q_i, p_i, n_i = triplet
                    loss_triplet += criterion_triplet(features[q_i:q_i+1], features[p_i:p_i+1], features[n_i:n_i+1])
            
            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


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.save_dir, "best_model.pth"))["model_state_dict"]
model.load_state_dict(best_model_state_dict)

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