def val(args, model=None, current_epoch=0):
top1 = AverageMeter()
top5 = AverageMeter()
top1.reset()
top5.reset()
if model is None:
model = get_model(args)
model.eval()
_, val_loader = data_loader(args, test_path=True)
save_atten = SAVE_ATTEN(save_dir=args.save_atten_dir)
global_counter = 0
prob = None
gt = None
for idx, dat in tqdm(enumerate(val_loader)):
img_path, img, label_in = dat
global_counter += 1
if args.tencrop == 'True':
bs, ncrops, c, h, w = img.size()
img = img.view(-1, c, h, w)
label_input = label_in.repeat(10, 1)
label = label_input.view(-1)
else:
label = label_in
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var, label_var)
logits0 = logits[0]
logits0 = F.softmax(logits0, dim=1)
if args.tencrop == 'True':
logits0 = logits0.view(bs, ncrops, -1).mean(1)
# Calculate classification results
prec1_1, prec5_1 = Metrics.accuracy(logits0.cpu().data,
label_in.long(),
topk=(1, 5))
# prec3_1, prec5_1 = Metrics.accuracy(logits[1].data, label.long(), topk=(1,5))
top1.update(prec1_1[0], img.size()[0])
top5.update(prec5_1[0], img.size()[0])
# save_atten.save_heatmap_segmentation(img_path, np_last_featmaps, label.cpu().numpy(),
# save_dir='./save_bins/heatmaps', size=(0,0), maskedimg=True)
#
np_last_featmaps = logits[2].cpu().data.numpy()
np_last_featmaps = logits[-1].cpu().data.numpy()
np_scores, pred_labels = torch.topk(logits0, k=args.num_classes, dim=1)
pred_np_labels = pred_labels.cpu().data.numpy()
save_atten.save_top_5_pred_labels(pred_np_labels[:, :5], img_path,
global_counter)
# pred_np_labels[:,0] = label.cpu().numpy() #replace the first label with gt label
# save_atten.save_top_5_atten_maps(np_last_featmaps, pred_np_labels, img_path)
print('Top1:', top1.avg, 'Top5:', top5.avg)
def val(args, model=None, current_epoch=0):
top1 = AverageMeter()
top5 = AverageMeter()
top1.reset()
top5.reset()
if model is None:
model, _ = get_model(args)
model.eval()
train_loader, val_loader = data_loader(args, test_path=True)
save_atten = SAVE_ATTEN(save_dir='../save_bins/')
global_counter = 0
prob = None
gt = None
for idx, dat in tqdm(enumerate(val_loader)):
img_path, img, label_in = dat
global_counter += 1
if args.tencrop == 'True':
bs, ncrops, c, h, w = img.size()
img = img.view(-1, c, h, w)
label_input = label_in.repeat(10, 1)
label = label_input.view(-1)
else:
label = label_in
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var, label_var)
logits0 = logits[0]
logits0 = F.softmax(logits0, dim=1)
if args.tencrop == 'True':
logits0 = logits0.view(bs, ncrops, -1).mean(1)
# Calculate classification results
if args.onehot == 'True':
val_mAP, prob, gt = cal_mAP(logits0, label_var, prob, gt)
# print val_mAP
else:
prec1_1, prec5_1 = Metrics.accuracy(logits0.cpu().data,
label_in.long(),
topk=(1, 5))
# prec3_1, prec5_1 = Metrics.accuracy(logits[1].data, label.long(), topk=(1,5))
top1.update(prec1_1[0], img.size()[0])
top5.update(prec5_1[0], img.size()[0])
# model.module.save_erased_img(img_path)
last_featmaps = model.module.get_localization_maps()
np_last_featmaps = last_featmaps.cpu().data.numpy()
# Save 100 sample masked images by heatmaps
# if idx < 100/args.batch_size:
save_atten.get_masked_img(img_path,
np_last_featmaps,
label_in.numpy(),
size=(0, 0),
maps_in_dir=True,
only_map=True)
# save_atten.save_heatmap_segmentation(img_path, np_last_featmaps, label.cpu().numpy(),
# save_dir='./save_bins/heatmaps', size=(0,0), maskedimg=True)
# save_atten.get_masked_img(img_path, np_last_featmaps, label_in.numpy(),size=(0,0),
# maps_in_dir=True, save_dir='../heatmaps',only_map=True )
# np_scores, pred_labels = torch.topk(logits0,k=args.num_classes,dim=1)
# # print pred_labels.size(), label.size()
# pred_np_labels = pred_labels.cpu().data.numpy()
# save_atten.save_top_5_pred_labels(pred_np_labels[:,:5], img_path, global_counter)
# # pred_np_labels[:,0] = label.cpu().numpy() #replace the first label with gt label
# # save_atten.save_top_5_atten_maps(np_last_featmaps, pred_np_labels, img_path)
if args.onehot == 'True':
print val_mAP
print 'AVG:', np.mean(val_mAP)
else:
print('Top1:', top1.avg, 'Top5:', top5.avg)
def train(args):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model, optimizer = get_model(args)
model.train()
train_loader, _ = data_loader(args)
with open(os.path.join(args.snapshot_dir, 'train_record.csv'), 'a') as fw:
config = json.dumps(vars(args), indent=4, separators=(',', ':'))
fw.write(config)
fw.write('#epoch,loss,pred@1,pred@5\n')
total_epoch = args.epoch
global_counter = args.global_counter
current_epoch = args.current_epoch
end = time.time()
max_iter = total_epoch * len(train_loader)
print('Max iter:', max_iter)
while current_epoch < total_epoch:
model.train()
losses.reset()
top1.reset()
top5.reset()
batch_time.reset()
res = my_optim.reduce_lr(args, optimizer, current_epoch)
if res:
for g in optimizer.param_groups:
out_str = 'Epoch:%d, %f\n' % (current_epoch, g['lr'])
fw.write(out_str)
steps_per_epoch = len(train_loader)
for idx, dat in enumerate(train_loader):
img_path, img, label = dat
global_counter += 1
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var, label_var)
loss_val, = model.module.get_loss(logits, label_var)
optimizer.zero_grad()
loss_val.backward()
optimizer.step()
if not args.onehot == 'True':
logits1 = torch.squeeze(logits[0])
prec1_1, prec5_1 = Metrics.accuracy(logits1.data,
label.long(),
topk=(1, 5))
top1.update(prec1_1[0], img.size()[0])
top5.update(prec5_1[0], img.size()[0])
losses.update(loss_val.data[0], img.size()[0])
batch_time.update(time.time() - end)
end = time.time()
if global_counter % 1000 == 0:
losses.reset()
top1.reset()
top5.reset()
if global_counter % args.disp_interval == 0:
# Calculate ETA
eta_seconds = (
(total_epoch - current_epoch) * steps_per_epoch +
(steps_per_epoch - idx)) * batch_time.avg
eta_str = "{:0>8}".format(
datetime.timedelta(seconds=int(eta_seconds)))
eta_seconds_epoch = steps_per_epoch * batch_time.avg
eta_str_epoch = "{:0>8}".format(
datetime.timedelta(seconds=int(eta_seconds_epoch)))
print(
'Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'ETA {eta_str}({eta_str_epoch})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
current_epoch,
global_counter % len(train_loader),
len(train_loader),
batch_time=batch_time,
eta_str=eta_str,
eta_str_epoch=eta_str_epoch,
loss=losses,
top1=top1,
top5=top5))
if current_epoch % 1 == 0:
save_checkpoint(args, {
'epoch': current_epoch,
'arch': 'resnet',
'global_counter': global_counter,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
},
is_best=False,
filename='%s_epoch_%d_glo_step_%d.pth.tar' %
(args.dataset, current_epoch, global_counter))
with open(os.path.join(args.snapshot_dir, 'train_record.csv'),
'a') as fw:
fw.write('%d,%.4f,%.3f,%.3f\n' %
(current_epoch, losses.avg, top1.avg, top5.avg))
current_epoch += 1
def train(args):
batch_time = AverageMeter()
losses = AverageMeter()
loss_cls = AverageMeter()
loss_dist = AverageMeter()
loss_aux = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model, optimizer = get_model(args)
model.train()
train_loader, _ = data_loader(args)
with open(os.path.join(args.snapshot_dir, 'train_record.csv'), 'a') as fw:
config = json.dumps(vars(args), indent=4, separators=(',', ':'))
fw.write(config)
fw.write('#epoch,loss,pred@1,pred@5\n')
total_epoch = args.epoch
global_counter = args.global_counter
current_epoch = args.current_epoch
end = time.time()
max_iter = total_epoch * len(train_loader)
print('Max iter:', max_iter)
while current_epoch < total_epoch:
model.train()
losses.reset()
loss_cls.reset()
loss_dist.reset()
loss_aux.reset()
top1.reset()
top5.reset()
batch_time.reset()
res = my_optim.reduce_lr(args, optimizer, current_epoch)
if res:
for g in optimizer.param_groups:
out_str = 'Epoch:%d, %f\n' % (current_epoch, g['lr'])
with open(os.path.join(args.snapshot_dir, 'train_record.csv'),
'a') as fw:
fw.write(out_str)
steps_per_epoch = len(train_loader)
for idx, dat in enumerate(train_loader):
img_path, img, label = dat
global_counter += 1
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var, label_var)
loss_list = model.module.get_loss(logits, label_var)
loss_val = loss_list[0]
optimizer.zero_grad()
loss_val.backward()
optimizer.step()
losses.update(loss_val.data.item(), img.size(0))
loss_cls.update(loss_list[1].data.item(), img.size(0))
loss_dist.update(loss_list[2].data.item(), img.size(0))
loss_aux.update(loss_list[3].data.item(), img.size(0))
batch_time.update(time.time() - end)
end = time.time()
if global_counter % 1000 == 0:
losses.reset()
top1.reset()
top5.reset()
loss_cls.reset()
loss_dist.reset()
loss_aux.reset()
batch_time.reset()
if global_counter % args.disp_interval == 0:
# Calculate ETA
# eta_seconds = ((total_epoch - current_epoch)*steps_per_epoch + (steps_per_epoch - idx))*batch_time.avg
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Loss_cls {loss_c.val:.4f} ({loss_c.avg:.4f})\t'
'Loss_dist {loss_d.val:.4f} ({loss_d.avg:.4f})\t'
'Loss_aux {loss_aux.val:.4f} ({loss_aux.avg:.4f})\t'.
format(current_epoch,
global_counter % len(train_loader),
len(train_loader),
batch_time=batch_time,
loss=losses,
loss_c=loss_cls,
loss_d=loss_dist,
loss_aux=loss_aux))
if current_epoch % args.save_interval == 0:
model_stat_dict = model.module.state_dict()
save_checkpoint(args, {
'epoch': current_epoch,
'arch': 'resnet',
'global_counter': global_counter,
'state_dict': model_stat_dict,
'optimizer': optimizer.state_dict(),
'center_feat_bank': model.module.center_feat_bank
},
is_best=False,
filename='%s_epoch_%d_glo_step_%d.pth' %
(args.dataset, current_epoch, global_counter))
with open(os.path.join(args.snapshot_dir, 'train_record.csv'),
'a') as fw:
fw.write('%d,%.4f,%.3f,%.3f\n' %
(current_epoch, losses.avg, top1.avg, top5.avg))
current_epoch += 1