fasterRCNN = nn.DataParallel(fasterRCNN)
iters_per_epoch = int(train_size / args.batch_size)
if args.use_tfboard:
from tensorboardX import SummaryWriter
logger = SummaryWriter("logs")
for epoch in range(args.start_epoch, args.max_epochs + 1):
# setting to train mode
fasterRCNN.train()
loss_temp = 0
start = time.time()
if epoch % (args.lr_decay_step + 1) == 0:
adjust_learning_rate(optimizer, args.lr_decay_gamma)
lr *= args.lr_decay_gamma
data_iter = iter(dataloader)
for step in range(iters_per_epoch):
data = next(data_iter)
im_data.data.resize_(data[0].size()).copy_(data[0])
im_info.data.resize_(data[1].size()).copy_(data[1])
gt_boxes.data.resize_(data[2].size()).copy_(data[2])
num_boxes.data.resize_(data[3].size()).copy_(data[3])
fasterRCNN.zero_grad()
rois, cls_prob, bbox_pred, \
rpn_loss_cls, rpn_loss_box, \
RCNN_loss_cls, RCNN_loss_bbox, \
rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)
def train():
args = parse_args()
print('Called with args:')
print(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
device = torch.device('cuda')
else:
device = torch.device('cpu')
output_dir = args.save_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if args.target_only:
source_train_dataset = TDETDataset(['voc07_trainval'],
args.data_dir,
args.prop_method,
num_classes=20,
prop_min_scale=args.prop_min_scale,
prop_topk=args.num_prop)
else:
source_train_dataset = TDETDataset(
['coco60_train2014', 'coco60_val2014'],
args.data_dir,
args.prop_method,
num_classes=60,
prop_min_scale=args.prop_min_scale,
prop_topk=args.num_prop)
target_val_dataset = TDETDataset(['voc07_test'],
args.data_dir,
args.prop_method,
num_classes=20,
prop_min_scale=args.prop_min_scale,
prop_topk=args.num_prop)
lr = args.lr
if args.net == 'DC_VGG16_DET':
base_model = DC_VGG16_CLS(None, 20 if args.target_only else 80, 3, 4)
checkpoint = torch.load(args.pretrained_base_path)
base_model.load_state_dict(checkpoint['model'])
del checkpoint
model = DC_VGG16_DET(base_model, args.pooling_method)
optimizer = model.get_optimizer(args.lr)
log_file_name = os.path.join(
output_dir, 'log_{}_{}.txt'.format(args.net, args.session))
log_file = open(log_file_name, 'w')
log_file.write(str(args))
log_file.write('\n')
model.to(device)
model.train()
source_loss_sum = 0
source_pos_prop_sum = 0
source_neg_prop_sum = 0
start = time.time()
optimizer.zero_grad()
for step in range(args.start_iter, args.max_iter + 1):
if step % len(source_train_dataset) == 1:
source_rand_perm = np.random.permutation(len(source_train_dataset))
source_index = source_rand_perm[step % len(source_train_dataset)]
source_batch = source_train_dataset.get_data(
source_index,
h_flip=np.random.rand() > 0.5,
target_im_size=np.random.choice([480, 576, 688, 864, 1200]))
source_im_data = source_batch['im_data'].unsqueeze(0).to(device)
source_proposals = source_batch['proposals']
source_gt_boxes = source_batch['gt_boxes']
if args.target_only:
source_gt_labels = source_batch['gt_labels']
else:
source_gt_labels = source_batch['gt_labels'] + 20
source_pos_cls = [i for i in range(80) if i in source_gt_labels]
source_loss = 0
for cls in np.random.choice(source_pos_cls, 2):
indices = np.where(source_gt_labels.numpy() == cls)[0]
here_gt_boxes = source_gt_boxes[indices]
here_proposals, here_labels, _, pos_cnt, neg_cnt = sample_proposals(
here_gt_boxes, source_proposals, args.bs // 2, args.pos_ratio)
# plt.imshow(source_batch['raw_img'])
# draw_box(here_proposals[:pos_cnt] / source_batch['im_scale'], 'black')
# draw_box(here_proposals[pos_cnt:] / source_batch['im_scale'], 'yellow')
# plt.show()
here_proposals = here_proposals.to(device)
here_labels = here_labels.to(device)
here_loss = model(source_im_data, cls, here_proposals, here_labels)
source_loss = source_loss + here_loss
source_pos_prop_sum += pos_cnt
source_neg_prop_sum += neg_cnt
source_loss = source_loss / 2
source_loss_sum += source_loss.item()
source_loss.backward()
clip_gradient(model, 10.0)
optimizer.step()
optimizer.zero_grad()
if step % args.disp_interval == 0:
end = time.time()
source_loss_sum /= args.disp_interval
source_pos_prop_sum /= args.disp_interval
source_neg_prop_sum /= args.disp_interval
log_message = "[%s][session %d][iter %4d] loss: %.4f, pos_prop: %.1f, neg_prop: %.1f, lr: %.2e, time: %.1f" % \
(args.net, args.session, step, source_loss_sum, source_pos_prop_sum, source_neg_prop_sum, lr, end - start)
print(log_message)
log_file.write(log_message + '\n')
log_file.flush()
source_loss_sum = 0
source_pos_prop_sum = 0
source_neg_prop_sum = 0
start = time.time()
if step in (args.max_iter * 4 // 7, args.max_iter * 6 // 7):
adjust_learning_rate(optimizer, 0.1)
lr *= 0.1
if step % args.save_interval == 0 or step == args.max_iter:
validate(model, target_val_dataset, args, device)
save_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.session,
step))
checkpoint = dict()
checkpoint['net'] = args.net
checkpoint['session'] = args.session
checkpoint['pooling_method'] = args.pooling_method
checkpoint['iterations'] = step
checkpoint['model'] = model.state_dict()
save_checkpoint(checkpoint, save_name)
print('save model: {}'.format(save_name))
log_file.close()
def train():
args = parse_args()
print('Called with args:')
print(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
device = torch.device('cuda')
else:
device = torch.device('cpu')
output_dir = args.save_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
source_train_dataset = TDETDataset(['coco60_train2014', 'coco60_val2014'],
args.data_dir,
args.prop_method,
num_classes=60,
prop_min_scale=args.prop_min_scale,
prop_topk=args.num_prop)
target_train_dataset = TDETDataset(['voc07_trainval'],
args.data_dir,
args.prop_method,
num_classes=20,
prop_min_scale=args.prop_min_scale,
prop_topk=args.num_prop)
lr = args.lr
if args.net == 'NEW_TDET':
model = NEW_TDET(os.path.join(args.data_dir,
'pretrained_model/vgg16_caffe.pth'),
20,
pooling_method=args.pooling_method,
share_level=args.share_level,
mil_topk=args.mil_topk)
else:
raise Exception('network is not defined')
optimizer = model.get_optimizer(args.lr)
if args.resume:
load_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.checksession,
args.checkiter))
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name)
assert args.net == checkpoint['net']
args.start_iter = checkpoint['iterations'] + 1
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr = optimizer.param_groups[0]['lr']
print("loaded checkpoint %s" % (load_name))
log_file_name = os.path.join(
output_dir, 'log_{}_{}.txt'.format(args.net, args.session))
if args.resume:
log_file = open(log_file_name, 'a')
else:
log_file = open(log_file_name, 'w')
log_file.write(str(args))
log_file.write('\n')
model.to(device)
model.train()
source_loss_sum = 0
target_loss_sum = 0
source_pos_prop_sum = 0
source_neg_prop_sum = 0
target_prop_sum = 0
start = time.time()
for step in range(args.start_iter, args.max_iter + 1):
if step % len(source_train_dataset) == 1:
source_rand_perm = np.random.permutation(len(source_train_dataset))
if step % len(target_train_dataset) == 1:
target_rand_perm = np.random.permutation(len(target_train_dataset))
source_index = source_rand_perm[step % len(source_train_dataset)]
target_index = target_rand_perm[step % len(target_train_dataset)]
source_batch = source_train_dataset.get_data(
source_index,
h_flip=np.random.rand() > 0.5,
target_im_size=np.random.choice([480, 576, 688, 864, 1200]))
target_batch = target_train_dataset.get_data(
target_index,
h_flip=np.random.rand() > 0.5,
target_im_size=np.random.choice([480, 576, 688, 864, 1200]))
source_im_data = source_batch['im_data'].unsqueeze(0).to(device)
source_proposals = source_batch['proposals']
source_gt_boxes = source_batch['gt_boxes']
source_proposals, source_labels, _, pos_cnt, neg_cnt = sample_proposals(
source_gt_boxes, source_proposals, args.bs, args.pos_ratio)
source_proposals = source_proposals.to(device)
source_gt_boxes = source_gt_boxes.to(device)
source_labels = source_labels.to(device)
target_im_data = target_batch['im_data'].unsqueeze(0).to(device)
target_proposals = target_batch['proposals'].to(device)
target_image_level_label = target_batch['image_level_label'].to(device)
optimizer.zero_grad()
# source forward & backward
_, source_loss = model.forward_det(source_im_data, source_proposals,
source_labels)
source_loss_sum += source_loss.item()
source_loss = source_loss * (1 - args.alpha)
source_loss.backward()
# target forward & backward
if args.cam_like:
_, target_loss = model.forward_cls_camlike(
target_im_data, target_proposals, target_image_level_label)
else:
_, target_loss = model.forward_cls(target_im_data,
target_proposals,
target_image_level_label)
target_loss_sum += target_loss.item()
target_loss = target_loss * args.alpha
target_loss.backward()
clip_gradient(model, 10.0)
optimizer.step()
source_pos_prop_sum += pos_cnt
source_neg_prop_sum += neg_cnt
target_prop_sum += target_proposals.size(0)
if step % args.disp_interval == 0:
end = time.time()
loss_sum = source_loss_sum * (
1 - args.alpha) + target_loss_sum * args.alpha
loss_sum /= args.disp_interval
source_loss_sum /= args.disp_interval
target_loss_sum /= args.disp_interval
source_pos_prop_sum /= args.disp_interval
source_neg_prop_sum /= args.disp_interval
target_prop_sum /= args.disp_interval
log_message = "[%s][session %d][iter %4d] loss: %.4f, src_loss: %.4f, tar_loss: %.4f, pos_prop: %.1f, neg_prop: %.1f, tar_prop: %.1f, lr: %.2e, time: %.1f" % \
(args.net, args.session, step, loss_sum, source_loss_sum, target_loss_sum, source_pos_prop_sum, source_neg_prop_sum, target_prop_sum, lr, end - start)
print(log_message)
log_file.write(log_message + '\n')
log_file.flush()
source_loss_sum = 0
target_loss_sum = 0
source_pos_prop_sum = 0
source_neg_prop_sum = 0
target_prop_sum = 0
start = time.time()
if step in (args.max_iter * 4 // 7, args.max_iter * 6 // 7):
adjust_learning_rate(optimizer, 0.1)
lr *= 0.1
if step % args.save_interval == 0 or step == args.max_iter:
save_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.session,
step))
checkpoint = dict()
checkpoint['net'] = args.net
checkpoint['session'] = args.session
checkpoint['pooling_method'] = args.pooling_method
checkpoint['share_level'] = args.share_level
checkpoint['iterations'] = step
checkpoint['model'] = model.state_dict()
checkpoint['optimizer'] = optimizer.state_dict()
save_checkpoint(checkpoint, save_name)
print('save model: {}'.format(save_name))
log_file.close()
def train():
args = parse_args()
print('Called with args:')
print(args)
np.random.seed(3)
torch.manual_seed(4)
if torch.cuda.is_available():
torch.cuda.manual_seed(5)
device = torch.device('cuda')
else:
device = torch.device('cpu')
output_dir = args.save_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
train_dataset = WSDDNDataset(dataset_names=['voc07_trainval'],
data_dir=args.data_dir,
prop_method=args.prop_method,
num_classes=20,
min_prop_scale=args.min_prop)
lr = args.lr
if args.net == 'WSDDN_VGG16':
model = WSDDN_VGG16(
os.path.join(args.data_dir, 'pretrained_model/vgg16_caffe.pth'),
20)
else:
raise Exception('network is not defined')
params = []
for key, value in dict(model.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
params += [{
'params': [value],
'lr': lr * 2,
'weight_decay': 0
}]
else:
params += [{
'params': [value],
'lr': lr,
'weight_decay': 0.0005
}]
optimizer = torch.optim.SGD(params, momentum=0.9)
if args.resume:
load_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.checksession,
args.checkepoch))
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name)
assert args.net == checkpoint['net']
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr = optimizer.param_groups[0]['lr']
print("loaded checkpoint %s" % (load_name))
log_file_name = os.path.join(
output_dir, 'log_{}_{}.txt'.format(args.net, args.session))
if args.resume:
log_file = open(log_file_name, 'a')
else:
log_file = open(log_file_name, 'w')
log_file.write(str(args))
log_file.write('\n')
model.to(device)
for epoch in range(args.start_epoch, args.max_epochs + 1):
model.train()
loss_sum = 0
reg_sum = 0
iter_sum = 0
num_prop = 0
start = time.time()
optimizer.zero_grad()
rand_perm = np.random.permutation(len(train_dataset))
for step in range(1, len(train_dataset) + 1):
index = rand_perm[step - 1]
apply_h_flip = np.random.rand() > 0.5
target_im_size = np.random.choice([480, 576, 688, 864, 1200])
im_data, gt_boxes, box_labels, proposals, prop_scores, image_level_label, im_scale, raw_img, im_id = \
train_dataset.get_data(index, apply_h_flip, target_im_size)
# plt.imshow(raw_img)
# draw_box(proposals / im_scale)
# draw_box(gt_boxes / im_scale, 'black')
# plt.show()
im_data = im_data.unsqueeze(0).to(device)
rois = proposals.to(device)
image_level_label = image_level_label.to(device)
if args.use_prop_score:
prop_scores = prop_scores.to(device)
else:
prop_scores = None
scores, loss, reg = model(im_data, rois, prop_scores,
image_level_label)
reg = reg * args.alpha
num_prop += proposals.size(0)
loss_sum += loss.item()
reg_sum += reg.item()
loss = loss + reg
if args.bavg:
loss = loss / args.bs
loss.backward()
if step % args.bs == 0:
optimizer.step()
optimizer.zero_grad()
iter_sum += 1
if step % args.disp_interval == 0:
end = time.time()
print(
"[net %s][session %d][epoch %2d][iter %4d] loss: %.4f, reg: %.4f, num_prop: %.1f, lr: %.2e, time: %.1f"
%
(args.net, args.session, epoch, step, loss_sum / iter_sum,
reg_sum / iter_sum, num_prop / iter_sum, lr, end - start))
log_file.write(
"[net %s][session %d][epoch %2d][iter %4d] loss: %.4f, reg: %.4f, num_prop: %.1f, lr: %.2e, time: %.1f\n"
%
(args.net, args.session, epoch, step, loss_sum / iter_sum,
reg_sum / iter_sum, num_prop / iter_sum, lr, end - start))
loss_sum = 0
reg_sum = 0
num_prop = 0
iter_sum = 0
start = time.time()
log_file.flush()
if epoch == 10:
adjust_learning_rate(optimizer, 0.1)
lr *= 0.1
if epoch % args.save_interval == 0:
save_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.session,
epoch))
checkpoint = dict()
checkpoint['net'] = args.net
checkpoint['session'] = args.session
checkpoint['epoch'] = epoch + 1
checkpoint['model'] = model.state_dict()
checkpoint['optimizer'] = optimizer.state_dict()
save_checkpoint(checkpoint, save_name)
print('save model: {}'.format(save_name))
log_file.close()
def main():
## CALL ARGUMENTS
args = arguments()
print("Called Arguments")
## LOG PATH
date = datetime.datetime.now()
day = date.strftime('%m%d_%H%M')
log_path = "./logs/{}".format(day)
if not os.path.exists(log_path):
os.mkdir(log_path)
## INITIALIZE TENSORBOARD
if args.tfboard:
from utils.logger import Logger
logger = Logger(log_path)
## CONFIG SAVE AS TEXT
configs = "Dataset: {}\nLSTM Size: {}\nNumber of Proposals: {}\nStart Learning Rate: {}\nLearning Rate Decay: {}\nOptimizer: {}\nScore Threshold: {}\nEncoding: {}".format(args.dataset, args.lstm_size, args.num_prop_after, args.learning_rate, args.learning_rate_decay, args.optimizer, args.score_thresh, args.encoding)
txt_file = "{}/configs.txt".format(log_path)
f = open(txt_file, 'w')
f.write(configs)
f.close()
## DATASET CONFIGURATION
if args.dataset == 'cvpr19':
args.anchor_scale = [[400, 300, 200, 100]]
## CUDA CHECK
if not torch.cuda.is_available():
print("WARNING: Why don't you use CUDA?")
## DATALOADER (ITERATOR)
data_type = ['train', 'val', 'test']
#data_type = ['val']
loader = {}
for type in data_type:
EEGDetectionData = loaddata.EEGDetectionDataset(args, type) # Shape of data : (seq_len, num_ch) or (1, seq_len, num_ch)??
loader[type] = DataLoader(dataset=EEGDetectionData, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers) # Already shuffled
seq_len, num_ch = EEGDetectionData[0][0].shape[0], EEGDetectionData[0][0].shape[1]
args.seq_len, args.num_ch = seq_len, num_ch
## ENCODING OR NOT
if args.encoding:
args.encoding_size = int((args.num_ch) / (args.encoding_scale))
## CALL MODEL
model = rlstm(args)
model.create_architecture()
## OPTIMIZER
#params = []
#for key, value in dict(model.named_parameters()).items():
# if value.requires_grad:
# params += [{'params':[value],'lr':lr, 'weight_decay': args.weight_decay}]
lr = args.learning_rate
optimizer = getattr(torch.optim, args.optimizer)(model.parameters(), lr = args.learning_rate)
#if args.cuda:
model.cuda() # CUDA
## RESUME
if args.resume:
checkpoint = torch.load('./logs/0524_2245/save_model/thecho7_25.pth')
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("Resume the training")
## TRAINING
for epoch in range(args.start_epoch, args.epochs):
acc = {"train": 0, "val": 0, "test": 0}
counts = {"train": 0, "val": 0, "test": 0}
# TRAIN MODE
model.train()
loss_check = 0
start = time.time()
# Learning Rate Adjustment
if epoch % (args.checkpoint_interval) == 0:
adjust_learning_rate(optimizer, args.weight_decay)
lr *= args.learning_rate_decay
# Session mode - Train or Test
for split in data_type:
if split == 'train':
args.training = True
model.train()
else:
args.training = False
model.eval()
for i, data in enumerate(loader[split], 0):
print(split)
# READ BATCH DATA (IN OUR CASE, BATCH SIZE IS 1)
inputs, labels = data
inputs, proposals, labels = proposal_gen(inputs, labels, args)
inputs = inputs.cuda()
#labels = labels.cuda(async = True)
labels = labels.cuda()
inputs = Variable(inputs, volatile = (split != "train"))
labels = Variable(labels, volatile = (split != "train"))
# FORWARD
cls_loss, bbox_loss, acc = model(inputs, labels, proposals, split, acc)
'''
if split == 'train':
cls_loss = F.cross_entropy(cls_feat, labels.long()) # F.cross_entropy converts indices automatically
else:
cls_loss = Variable(torch.zeros(1).cuda()) # Garbage Value
# Penalized Loss (Division Method)
loss_div = 1
for j in range(args.num_prop_after):
if int(cls_feat.data.max(1)[1][j]) == int(labels[j]):
loss_div += 1
acc[split] += 1
cls_loss = cls_loss.div(loss_div)
# Result Print
_, cls_idx = cls_feat.data.max(1)
result_print = []
for j in range(args.num_prop_after):
result_print.append(int(cls_idx[j]))
print(" Result labels: {}".format(result_print))
'''
# BACKWARD
loss = cls_loss.mean() + bbox_loss.mean()
loss_check += loss.data[0]
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("CLASS LOSS: {} BBOX LOSS: {}".format(float(loss.data[0]), float(bbox_loss.data[0])))
counts[split] += args.num_prop_after
# LOSS DISPLAY
if i % args.checkpoint_interval == 1:
end = time.time()
if i > 1:
loss_check /= args.checkpoint_interval
print("[epoch: {} - loss_check: {}]".format(epoch, loss_check))
print("[Iter Accuracy: {}".format(acc["train"]/counts["train"]))
loss_cls = cls_loss.data[0]
if args.tfboard:
info = {'loss':loss_check, 'loss_cls':loss_cls}
for tag, value in info.items():
logger.scalar_summary(tag, value, i)
loss_check = 0
start = time.time()
# Print info at the end of the epoch
print("Epoch {}: TrA={:.4f}, VA={:.4f}, TeA={:.4f}".format(epoch, acc["train"]/counts["train"], acc["val"]/counts["val"], acc["test"]/counts["test"]))
## SAVE MODEL (TBI)
model_path = "{}/{}".format(log_path, "save_model")
if not os.path.exists(model_path):
os.mkdir(model_path)
save_name = os.path.join('{}'.format(model_path), 'thecho7_{}.pth'.format(epoch))
save_checkpoint({
'epoch': epoch,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, save_name)
print('Saving Model: {}......'.format(save_name))
def train():
args = parse_args()
print('Called with args:')
print(args)
assert args.bs % 2 == 0
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
device = torch.device('cuda')
else:
device = torch.device('cpu')
print(device)
output_dir = args.save_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
target_only = args.target_only
source_train_dataset = TDETDataset(['coco60_train2014', 'coco60_val2014'],
args.data_dir,
'eb',
num_classes=60)
target_train_dataset = TDETDataset(['voc07_trainval'],
args.data_dir,
'eb',
num_classes=20)
lr = args.lr
if args.net == 'CAM_DET':
model = CamDet(
os.path.join(args.data_dir, 'pretrained_model/vgg16_caffe.pth')
if not args.resume else None, 20 if target_only else 80,
args.hidden_dim)
else:
raise Exception('network is not defined')
optimizer = model.get_optimizer(args.lr)
if args.resume:
load_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.checksession,
args.checkiter))
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name)
assert args.net == checkpoint['net']
args.start_iter = checkpoint['iterations'] + 1
model.load_state_dict(checkpoint['model'])
print("loaded checkpoint %s" % (load_name))
del checkpoint
log_file_name = os.path.join(
output_dir, 'log_{}_{}.txt'.format(args.net, args.session))
if args.resume:
log_file = open(log_file_name, 'a')
else:
log_file = open(log_file_name, 'w')
log_file.write(str(args))
log_file.write('\n')
model.to(device)
model.train()
source_loss_sum = 0
target_loss_sum = 0
total_loss_sum = 0
start = time.time()
source_rand_perm = None
target_rand_perm = None
for step in range(args.start_iter, args.max_iter + 1):
if source_rand_perm is None or step % len(source_train_dataset) == 1:
source_rand_perm = np.random.permutation(len(source_train_dataset))
if target_rand_perm is None or step % len(target_train_dataset) == 1:
target_rand_perm = np.random.permutation(len(target_train_dataset))
source_index = source_rand_perm[step % len(source_train_dataset)]
target_index = target_rand_perm[step % len(target_train_dataset)]
optimizer.zero_grad()
if not target_only:
source_batch = source_train_dataset.get_data(
source_index,
h_flip=np.random.rand() > 0.5,
target_im_size=np.random.choice([480, 576, 688, 864, 1200]))
source_im_data = source_batch['im_data'].unsqueeze(0).to(device)
source_gt_labels = source_batch['gt_labels'] + 20
source_pos_cls = [i for i in range(80) if i in source_gt_labels]
source_pos_cls = torch.tensor(np.random.choice(
source_pos_cls,
min(args.bs, len(source_pos_cls)),
replace=False),
dtype=torch.long,
device=device)
source_loss, _, _ = model(source_im_data, source_pos_cls)
source_loss_sum += source_loss.item()
target_batch = target_train_dataset.get_data(
target_index,
h_flip=np.random.rand() > 0.5,
target_im_size=np.random.choice([480, 576, 688, 864, 1200]))
target_im_data = target_batch['im_data'].unsqueeze(0).to(device)
target_gt_labels = target_batch['gt_labels']
target_pos_cls = [i for i in range(80) if i in target_gt_labels]
target_pos_cls = torch.tensor(np.random.choice(
target_pos_cls, min(args.bs, len(target_pos_cls)), replace=False),
dtype=torch.long,
device=device)
target_loss, _, _, _ = model(target_im_data, target_pos_cls)
target_loss_sum += target_loss.item()
if args.target_only:
total_loss = target_loss
else:
total_loss = (source_loss + target_loss) * 0.5
total_loss.backward()
total_loss_sum += total_loss.item()
clip_gradient(model, 10.0)
optimizer.step()
if step % args.disp_interval == 0:
end = time.time()
total_loss_sum /= args.disp_interval
source_loss_sum /= args.disp_interval
target_loss_sum /= args.disp_interval
log_message = "[%s][session %d][iter %4d] loss: %.8f, src_loss: %.8f, tar_loss: %.8f, lr: %.2e, time: %.1f" % \
(args.net, args.session, step, total_loss_sum, source_loss_sum, target_loss_sum, lr, end - start)
print(log_message)
log_file.write(log_message + '\n')
log_file.flush()
total_loss_sum = 0
source_loss_sum = 0
target_loss_sum = 0
start = time.time()
if step in (args.max_iter * 4 // 7, args.max_iter * 6 // 7):
adjust_learning_rate(optimizer, 0.1)
lr *= 0.1
if step % args.save_interval == 0 or step == args.max_iter:
save_name = os.path.join(
output_dir, '{}_{}_{}.pth'.format(args.net, args.session,
step))
checkpoint = dict()
checkpoint['net'] = args.net
checkpoint['session'] = args.session
checkpoint['iterations'] = step
checkpoint['model'] = model.state_dict()
save_checkpoint(checkpoint, save_name)
print('save model: {}'.format(save_name))
log_file.close()
