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train.py
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import torchvision.transforms as trsfrm
from dataloader.cub_dataset import CUB
from dataloader.trsfrms import must_transform
from dataloader import sampler
from evaluation.recall import give_recall
from loss.mvrloss import MVR_Proxy, MVR_Triplet, MVR_MS, MVR_MS_reg
from model.bn_inception import bn_inception
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data.sampler import BatchSampler
import torch
import numpy as np
import random
import os
from tqdm import tqdm
import argparse
import logging
## Triplet margin : 0.3478912374083307 - exp1
## Triplet reg : 0.5061600574032541 - exp1
## Triplet margin : 0.2781877469005122 - exp2
## Triplet reg : 0.4919607680052035 - exp2
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='MVR'
)
parser.add_argument('--gpu_id', default=0, type=int,
help='ID of GPU that is used for training.'
)
parser.add_argument("--tnsrbrd_dir", default="./runs", type=str)
parser.add_argument("--model_save_dir", default="./MVR_MS/exp", type=str)
parser.add_argument("--batch_size", default=80, type=int)
parser.add_argument("--lr", default=3e-5, type=float)
parser.add_argument("--wdecay", default=5e-3, type=float)
parser.add_argument("--mvr_reg", default=0.3, type=float)
parser.add_argument("--bn_freeze", default=False, type=bool)
parser.add_argument("--emb_dim", default=64, type=int)
parser.add_argument("--exp_name", default="exp", type=str)
parser.add_argument("--patience", default=20, type=int)
parser.add_argument("--balanced_sampler_train", default=True, type=bool)
parser.add_argument("--balanced_sampler_validation", default=False, type=bool)
parser.add_argument("--loss", default="ms_reg",type=str)
parser.add_argument("--margin", default=0.28, type=float)
parser.add_argument("--images_per_class", default=5, type=int)
parser.add_argument("--ms_thresh", default=0.6, type=float)
parser.add_argument("--seed", default=1, type=int)
args = parser.parse_args()
# seeds
seed = args.seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Tensorboard
tnsrbrd_dir = args.tnsrbrd_dir
writer = SummaryWriter(tnsrbrd_dir)
# model save
model_save_dir = args.model_save_dir
if os.path.exists(model_save_dir) != True:
os.makedirs(model_save_dir)
# log
log_dir = os.path.join("./log", args.exp_name)
if os.path.exists(log_dir) != True:
os.makedirs(log_dir)
logging.basicConfig(
format="%(asctime)s %(message)s",
level=logging.INFO,
handlers=[
logging.FileHandler(log_dir + "/records.log"),
logging.StreamHandler()
]
)
logging.info(
f"Learning Rate {args.lr}, Weight_decay {args.wdecay}, batch_size {args.batch_size}, emb_dim {args.emb_dim}, patience {args.patience}, mvr_reg = {args.mvr_reg}")
# Transforms
transforms_tr = trsfrm.Compose([must_transform(), trsfrm.RandomResizedCrop(224), trsfrm.RandomHorizontalFlip()])
transforms_test = trsfrm.Compose([must_transform(), trsfrm.Resize(256), trsfrm.CenterCrop(224)])
# Dataset
root_dir = "data/CUB_200_2011/images"
cub_train = CUB(root_dir, 'trainval', transforms_tr)
cub_val = CUB(root_dir, 'test', transforms_test)
cuda = torch.device('cuda:{}'.format(args.gpu_id))
# Model definition
net = bn_inception(64, pretrained=True, is_norm=True, bn_freeze=args.bn_freeze)
net.to(cuda)
# DataLoader
numWorkers = 4
batch_size = args.batch_size
# SAMPLER IMPLEMENTATION
if args.balanced_sampler_train:
tr_balanced_sampler = sampler.BalancedSampler(cub_train, batch_size=batch_size, images_per_class=args.images_per_class)
tr_batch_sampler = BatchSampler(tr_balanced_sampler, batch_size=batch_size, drop_last=True)
tr_dataloader = torch.utils.data.DataLoader(
cub_train,
num_workers=numWorkers,
pin_memory=True,
batch_sampler=tr_batch_sampler
)
else:
tr_dataloader = DataLoader(cub_train, batch_size=batch_size, shuffle=True, num_workers=numWorkers, pin_memory=True)
if args.balanced_sampler_validation:
val_balanced_sampler = sampler.BalancedSampler(cub_val, batch_size=batch_size, images_per_class = 8)
val_batch_sampler = BatchSampler(val_balanced_sampler, batch_size = batch_size, drop_last = True)
val_dataloader = torch.utils.data.DataLoader(
cub_val,
num_workers = numWorkers,
pin_memory = True,
batch_sampler = val_batch_sampler
)
else:
val_dataloader = DataLoader(cub_val, batch_size=batch_size, shuffle=False, num_workers=numWorkers,
pin_memory=True)
# Loss
no_tr_class = max(cub_train.target) + 1
emb_dim = args.emb_dim
if args.loss == "triplet":
loss_func = MVR_Triplet(margin=args.margin, reg=args.mvr_reg)
elif args.loss == "proxy":
loss_func = MVR_Proxy(reg=args.mvr_reg, no_class=no_tr_class, embedding_dimension=emb_dim)
elif args.loss == "ms":
loss_func = MVR_MS(2.0, 50.0, 0.33121341100189616, 0.1)
elif args.loss == "ms_reg":
loss_func = MVR_MS_reg(2.0, 50.0, 0.6, 0.1, args.margin) # 0.5872421417546484)
loss_func.to(cuda)
# Optimizer
if args.wdecay <= 0.0:
optimizer = torch.optim.Adam([{"params": net.parameters()},
{"params": loss_func.parameters()}], lr=args.lr)
else:
optimizer = torch.optim.Adam([{"params": net.parameters(), "weight_decay": args.wdecay},
{"params": loss_func.parameters()}], lr=args.lr)
# Initial
best_recall = 0
patience = 0
patience_level = args.patience
epoch_counter = 1
total_iter_train = int(len(cub_train) / batch_size)
while patience < patience_level:
avg_loss = 0
net.train()
for img, lbl in tqdm(tr_dataloader):
optimizer.zero_grad()
img = img.to(cuda)
lbl = lbl.to(cuda)
embeddings = net(img)
loss = loss_func(embeddings, lbl)
avg_loss = avg_loss + loss.item()
loss.backward()
optimizer.step()
avg_loss = avg_loss / total_iter_train
writer.add_scalar("Training/avg_loss", avg_loss, epoch_counter)
net.eval()
with torch.no_grad():
Recalls = give_recall(net, val_dataloader, cuda=cuda)
writer.add_scalar("Accuracy/Recall 1", Recalls[0], epoch_counter)
writer.add_scalar("Accuracy/Recall 2", Recalls[1], epoch_counter)
writer.add_scalar("Accuracy/Recall 4", Recalls[2], epoch_counter)
writer.add_scalar("Accuracy/Recall 8", Recalls[3], epoch_counter)
if Recalls[0] > best_recall:
best_recall = Recalls[0]
torch.save(net.state_dict(), os.path.join(model_save_dir, "best.pth"))
patience = 0
else:
patience += 1
epoch_counter += 1
logging.info("Best Recall : {}".format(best_recall))