def main():
logger.info(args)
model = Net(numAngle=CONFIGS["MODEL"]["NUMANGLE"],
numRho=CONFIGS["MODEL"]["NUMRHO"],
backbone=CONFIGS["MODEL"]["BACKBONE"])
model = model.cuda(device=CONFIGS["TRAIN"]["GPU_ID"])
if args.model:
if isfile(args.model):
logger.info("=> loading pretrained model '{}'".format(args.model))
checkpoint = torch.load(args.model)
model.load_state_dict(checkpoint['state_dict'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.model, checkpoint['epoch']))
else:
logger.info("=> no pretrained model found at '{}'".format(
args.model))
# dataloader
test_loader = get_loader(CONFIGS["DATA"]["TEST_DIR"],
CONFIGS["DATA"]["TEST_LABEL_FILE"],
batch_size=1,
num_thread=CONFIGS["DATA"]["WORKERS"],
test=True)
logger.info("Data loading done.")
logger.info("Start testing.")
total_time = test(test_loader, model, args)
logger.info(
"Test done! Total %d imgs at %.4f secs without image io, fps: %.3f" %
(len(test_loader), total_time, len(test_loader) / total_time))
def main():
logger.info(args)
model = Net(numAngle=CONFIGS["MODEL"]["NUMANGLE"], numRho=CONFIGS["MODEL"]["NUMRHO"], backbone=CONFIGS["MODEL"]["BACKBONE"])
model = model.cuda(device=CONFIGS["TRAIN"]["GPU_ID"])
# load the pretrained model (you are free to load your own models)
state_dict = torch.hub.load_state_dict_from_url("http://data.kaizhao.net/projects/deep-hough-transform/dht_r50_fpn_sel-c9a29d40.pth", check_hash=True)
model.load_state_dict(state_dict)
if args.model:
if isfile(args.model):
logger.info("=> loading pretrained model '{}'".format(args.model))
checkpoint = torch.load(args.model)
model.load_state_dict(checkpoint)
logger.info("=> loaded checkpoint '{}'"
.format(args.model))
else:
logger.info("=> no pretrained model found at '{}'".format(args.model))
exit()
# dataloader
test_loader = get_loader(CONFIGS["DATA"]["TEST_DIR"], CONFIGS["DATA"]["TEST_LABEL_FILE"],
batch_size=1, num_thread=CONFIGS["DATA"]["WORKERS"], test=True)
logger.info("Data loading done.")
logger.info("Start testing.")
total_time = test(test_loader, model, args)
logger.info("Test done! Total %d imgs at %.4f secs without image io, fps: %.3f" % (len(test_loader), total_time, len(test_loader) / total_time))
logger = Logger('./logs')
batch_size = 1
load_checkpoint= True
print( '%s: calling main function ... ' % os.path.basename(__file__))
csv_path = '../data/detection_test.csv'
img_path = '../data/detection_test_t'
dataset = Rand_num(csv_path, img_path, 112*4, None)
sampler = SequentialSampler(dataset)
loader = DataLoader(dataset, batch_size = batch_size, sampler = sampler, shuffle = False, num_workers=2)
net = Net(14)
if load_checkpoint:
net.load_state_dict(torch.load(SAVE_PATH))
net.cuda()
accu_tp=[]
accu_fp=[]
accu_iou=[]
for epoch in range(1):
for num, data in enumerate(loader, 0):
# get the inputs
images, inputs, labels = data
inputs, labels = inputs.float()[0]/256, labels.float()
#
# # wrap them in Variable
#
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
outputs = net.forward(inputs, False)
img = (inputs*256).data.cpu().int().numpy()[0,0]
def main():
logger.info(args)
assert os.path.isdir(CONFIGS["DATA"]["DIR"])
if CONFIGS['TRAIN']['SEED'] is not None:
random.seed(CONFIGS['TRAIN']['SEED'])
torch.manual_seed(CONFIGS['TRAIN']['SEED'])
cudnn.deterministic = True
model = Net(numAngle=CONFIGS["MODEL"]["NUMANGLE"],
numRho=CONFIGS["MODEL"]["NUMRHO"],
backbone=CONFIGS["MODEL"]["BACKBONE"])
if CONFIGS["TRAIN"]["DATA_PARALLEL"]:
logger.info("Model Data Parallel")
model = nn.DataParallel(model).cuda()
else:
model = model.cuda(device=CONFIGS["TRAIN"]["GPU_ID"])
# optimizer
optimizer = torch.optim.Adam(
model.parameters(),
lr=CONFIGS["OPTIMIZER"]["LR"],
weight_decay=CONFIGS["OPTIMIZER"]["WEIGHT_DECAY"])
# learning rate scheduler
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=CONFIGS["OPTIMIZER"]["STEPS"],
gamma=CONFIGS["OPTIMIZER"]["GAMMA"])
best_acc1 = 0
if args.resume:
if isfile(args.resume):
logger.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
logger.info("=> no checkpoint found at '{}'".format(args.resume))
# dataloader
train_loader = get_loader(CONFIGS["DATA"]["DIR"],
CONFIGS["DATA"]["LABEL_FILE"],
batch_size=CONFIGS["DATA"]["BATCH_SIZE"],
num_thread=CONFIGS["DATA"]["WORKERS"],
split='train')
val_loader = get_loader(CONFIGS["DATA"]["VAL_DIR"],
CONFIGS["DATA"]["VAL_LABEL_FILE"],
batch_size=1,
num_thread=CONFIGS["DATA"]["WORKERS"],
split='val')
logger.info("Data loading done.")
# Tensorboard summary
writer = SummaryWriter(log_dir=os.path.join(CONFIGS["MISC"]["TMP"]))
start_epoch = 0
best_acc = best_acc1
is_best = False
start_time = time.time()
if CONFIGS["TRAIN"]["RESUME"] is not None:
raise (NotImplementedError)
if CONFIGS["TRAIN"]["TEST"]:
validate(val_loader, model, 0, writer, args)
return
logger.info("Start training.")
for epoch in range(start_epoch, CONFIGS["TRAIN"]["EPOCHS"]):
train(train_loader, model, optimizer, epoch, writer, args)
acc = validate(val_loader, model, epoch, writer, args)
#return
scheduler.step()
if best_acc < acc:
is_best = True
best_acc = acc
else:
is_best = False
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc,
'optimizer': optimizer.state_dict()
},
is_best,
path=CONFIGS["MISC"]["TMP"])
t = time.time() - start_time
elapsed = DayHourMinute(t)
t /= (epoch + 1) - start_epoch # seconds per epoch
t = (CONFIGS["TRAIN"]["EPOCHS"] - epoch - 1) * t
remaining = DayHourMinute(t)
logger.info(
"Epoch {0}/{1} finishied, auxiliaries saved to {2} .\t"
"Elapsed {elapsed.days:d} days {elapsed.hours:d} hours {elapsed.minutes:d} minutes.\t"
"Remaining {remaining.days:d} days {remaining.hours:d} hours {remaining.minutes:d} minutes."
.format(epoch,
CONFIGS["TRAIN"]["EPOCHS"],
CONFIGS["MISC"]["TMP"],
elapsed=elapsed,
remaining=remaining))
logger.info("Optimization done, ALL results saved to %s." %
CONFIGS["MISC"]["TMP"])