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train.py
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r""" training (validation) code """
import argparse
import torch.optim as optim
import torch.nn as nn
import torch
from model.sccnet import SCCNetwork
from common.logger import Logger, AverageMeter
from common.evaluation import Evaluator
from common import utils, my_optim
from data.dataset import FSSDataset
def train(epoch, model, dataloader, optimizer, training, loss_type='no', ld=1.0):
r""" Train HSNet """
train.count = getattr(train, 'count', 0)
# Force randomness during training / freeze randomness during testing
utils.fix_randseed(None) if training else utils.fix_randseed(0)
model.module.train_mode() if training else model.module.eval()
average_meter = AverageMeter(dataloader.dataset)
for idx, batch in enumerate(dataloader):
train.count += 1
if train.count > args.max_steps:
break
my_optim.adjust_learning_rate_poly(args, optimizer, train.count)
# 1. forward pass
batch = utils.to_cuda(batch)
logit_mask, logit_mask2 = model(batch['query_img'], batch['support_imgs'].squeeze(1),
batch['support_masks'].squeeze(1), batch['query_mask'])
pred_mask = logit_mask.argmax(dim=1)
# 2. Compute loss & update model parameters
loss = model.module.compute_objective(logit_mask, batch['query_mask'])
loss2 = model.module.compute_objective(logit_mask2, batch['query_mask'])
loss += ld * loss2
if loss_type == 'focal':
loss += 0.5*model.module.compute_focal_loss(pred_mask)
elif loss_type == 'area':
loss += 0.5 * model.module.compute_area_loss(pred_mask, batch['query_mask'])
if training:
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 3. Evaluate prediction
area_inter, area_union = Evaluator.classify_prediction(pred_mask, batch)
average_meter.update(area_inter, area_union, batch['class_id'], loss.detach().clone())
average_meter.write_process(idx, len(dataloader), epoch, write_batch_idx=50)
# Write evaluation results
average_meter.write_result('Training' if training else 'Validation', epoch)
avg_loss = utils.mean(average_meter.loss_buf)
miou, fb_iou = average_meter.compute_iou()
return avg_loss, miou, fb_iou
if __name__ == '__main__':
# Arguments parsing
parser = argparse.ArgumentParser(description='SCCNet Pytorch Implementation')
parser.add_argument('--datapath', type=str, default='../Datasets')
parser.add_argument('--benchmark', type=str, default='pascal', choices=['pascal', 'isaid', 'dlrsd'])
parser.add_argument('--logpath', type=str, default='')
parser.add_argument('--loss', type=str, default='no', choices=['no', 'focal', 'area'])
parser.add_argument('--bsz', type=int, default=20)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--power', type=float, default=0.9)
parser.add_argument('--ld', type=float, default=1.0)
parser.add_argument('--niter', type=int, default=2000)
parser.add_argument('--max_steps', type=int, default=50001)
parser.add_argument('--nworker', type=int, default=8)
parser.add_argument('--fold', type=int, default=0, choices=[0, 1, 2, 3])
parser.add_argument('--backbone', type=str, default='resnet101', choices=['vgg16', 'resnet50', 'resnet101'])
parser.add_argument('--img_size', type=int, default=400)
parser.add_argument('--use_original_imgsize', type=bool, default=False)
parser.add_argument('--aug', type=bool, default=False)
parser.add_argument('--freeze', type=bool, default=True)
args = parser.parse_args()
Logger.initialize(args, training=True)
# Model initialization
model = SCCNetwork(args.backbone, False, args.freeze)
Logger.log_params(model)
# Device setup
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Logger.info('# available GPUs: %d' % torch.cuda.device_count())
model = nn.DataParallel(model)
model.to(device)
# Helper classes (for training) initialization
optimizer = my_optim.get_finetune_optimizer(args, model)
Evaluator.initialize()
# Dataset initialization
FSSDataset.initialize(img_size=args.img_size,
datapath=args.datapath,
use_original_imgsize=args.use_original_imgsize)
dataloader_trn = FSSDataset.build_dataloader(args.benchmark, args.bsz,
args.nworker, args.fold,
'trn', aug=args.aug)
dataloader_val = FSSDataset.build_dataloader(args.benchmark, args.bsz,
args.nworker, args.fold,
'val')
# Train HSNet
best_val_miou = float('-inf')
best_val_loss = float('inf')
for epoch in range(args.niter):
trn_loss, trn_miou, trn_fb_iou = train(epoch,
model,
dataloader_trn,
optimizer,
training=True,
loss_type=args.loss,
ld=args.ld)
with torch.no_grad():
val_loss, val_miou, val_fb_iou = train(epoch,
model,
dataloader_val,
optimizer,
training=False)
# Save the best model
if val_miou > best_val_miou:
best_val_miou = val_miou
Logger.save_model_miou(model, epoch, val_miou)
Logger.tbd_writer.add_scalars('data/loss', {'trn_loss': trn_loss, 'val_loss': val_loss}, epoch)
Logger.tbd_writer.add_scalars('data/miou', {'trn_miou': trn_miou, 'val_miou': val_miou}, epoch)
Logger.tbd_writer.add_scalars('data/fb_iou', {'trn_fb_iou': trn_fb_iou, 'val_fb_iou': val_fb_iou}, epoch)
Logger.tbd_writer.flush()
if train.count > args.max_steps:
break
Logger.tbd_writer.close()
Logger.info('==================== Finished Training ====================')