def eval(seq_loader,
faster_rcnn,
signal_type,
scale=1.,
test_num=10000,
stop=False):
carrada = download('Carrada')
pred_bboxes, pred_labels, pred_scores = list(), list(), list()
gt_bboxes, gt_labels, gt_difficults = list(), list(), list()
# print('*** Evaluation ***')
for n_seq, sequence_data in tqdm(enumerate(seq_loader)):
seq_name, seq = sequence_data
# Overfit an image
# seq = [seq[155]] # large
seq = [seq[115]] # medium
# seq = [seq[28]] # small
path_to_frames = os.path.join(carrada, seq_name[0])
frame_set = TestCarradaDataset(opt, seq, 'box', signal_type,
path_to_frames)
frame_loader = data_.DataLoader(frame_set,
batch_size=1,
shuffle=False,
num_workers=opt.num_workers)
for ii, (imgs, sizes, gt_bboxes_, gt_labels_,
gt_difficults_) in tqdm(enumerate(frame_loader)):
sizes = [sizes[0][0].item(), sizes[1][0].item()]
imgs = normalize(imgs)
pred_bboxes_, pred_labels_, pred_scores_ = faster_rcnn.predict(
imgs, signal_type, [sizes])
gt_bboxes += list(gt_bboxes_.numpy())
gt_labels += list(gt_labels_.numpy())
gt_difficults += list(gt_difficults_.numpy())
pred_bboxes += pred_bboxes_
pred_labels += pred_labels_
pred_scores += pred_scores_
# if ii == test_num: break
result = eval_detection_voc(pred_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
gt_difficults,
iou_thresh=0.5,
use_07_metric=True)
ious = bbox_iou(gt_bboxes[0], pred_bboxes[0])
try:
best_iou = ious.max()
except ValueError:
best_iou = 0
if stop:
import ipdb
ipdb.set_trace()
# print('Best IoU in validation: {}'.format(ious.max()))
return result, best_iou
def __init__(self):
config_path = os.path.join(RADAR_HOME, 'config.ini')
super().__init__(config_path)
self.warehouse = self.config['data']['warehouse']
self.carrada = download('Carrada',
fallback=os.path.join(self.warehouse,
'Carrada'))
self.annotations = self._load_selected_light_frame_oriented()
self.data_seq_ref = self._load_data_seq_ref()
self.train = dict()
self.validation = dict()
self.test = dict()
self._split()
self.label_names = CARRADA_BBOX_LABEL_NAMES
def eval(seq_loader, faster_rcnn, signal_type, test_num=10000):
carrada = download('Carrada')
pred_bboxes, pred_labels, pred_scores = list(), list(), list()
gt_bboxes, gt_labels, gt_difficults = list(), list(), list()
# print('*** Evaluation ***')
for n_seq, sequence_data in tqdm(enumerate(seq_loader)):
seq_name, seq = sequence_data
path_to_frames = os.path.join(carrada, seq_name[0])
frame_set = TestCarradaDataset(opt, seq, 'box', signal_type,
path_to_frames)
frame_loader = data_.DataLoader(frame_set,
batch_size=1,
shuffle=False,
num_workers=opt.num_workers)
for ii, (imgs, sizes, gt_bboxes_, gt_labels_, gt_difficults_) in tqdm(enumerate(frame_loader)):
sizes = [sizes[0][0].item(), sizes[1][0].item()]
imgs = normalize(imgs)
pred_bboxes_, pred_labels_, pred_scores_ = faster_rcnn.predict(imgs, [sizes])
gt_bboxes += list(gt_bboxes_.numpy())
gt_labels += list(gt_labels_.numpy())
gt_difficults += list(gt_difficults_.numpy())
pred_bboxes += pred_bboxes_
pred_labels += pred_labels_
pred_scores += pred_scores_
# if ii == test_num: break
result = eval_detection_voc(pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_difficults,
use_07_metric=True)
ious = bbox_iou(gt_bboxes[0], pred_bboxes[0])
try:
best_iou = ious.max()
except ValueError:
best_iou = 0
return result, best_iou
def train(**kwargs):
opt._parse(kwargs)
carrada = download('Carrada')
train_set = Carrada().get('Train')
val_set = Carrada().get('Validation')
test_set = Carrada().get('Test')
train_seqs = SequenceCarradaDataset(train_set)
val_seqs = SequenceCarradaDataset(val_set)
test_seqs = SequenceCarradaDataset(test_set)
train_seqs_loader = data_.DataLoader(train_seqs, \
batch_size=1, \
shuffle=True, \
# pin_memory=True,
num_workers=opt.num_workers)
val_seqs_loader = data_.DataLoader(val_seqs,
batch_size=1,
shuffle=False,
# pin_memory=True,
num_workers=opt.num_workers)
test_seqs_loader = data_.DataLoader(test_seqs,
batch_size=1,
shuffle=False,
# pin_memory=True,
num_workers=opt.num_workers)
# faster_rcnn = FasterRCNNVGG16(n_fg_class=3)
# faster_rcnn = FasterRCNNRESNET101(n_fg_class=3)
faster_rcnn = FasterRCNNRESNET18(n_fg_class=3)
print('model construct completed')
trainer = FasterRCNNTrainer(faster_rcnn).cuda()
scheduler = ExponentialLR(trainer.faster_rcnn.optimizer, gamma=0.9)
if opt.load_path:
trainer.load(opt.load_path)
print('load pretrained model from %s' % opt.load_path)
writer_path = os.path.join(opt.logs_path, opt.model_name)
os.makedirs(writer_path, exist_ok=True)
writer = SummaryWriter(writer_path)
iteration = 0
best_map = 0
lr_ = opt.lr
for epoch in range(opt.epoch):
print('Processing epoch: {}/{}'.format(epoch, opt.epoch))
trainer.reset_meters()
for n_seq, sequence_data in tqdm(enumerate(train_seqs_loader)):
seq_name, seq = sequence_data
path_to_frames = os.path.join(carrada, seq_name[0])
train_frame_set = CarradaDataset(opt, seq, 'box', opt.signal_type,
path_to_frames)
train_frame_loader = data_.DataLoader(train_frame_set,
batch_size=1,
shuffle=False,
num_workers=opt.num_workers)
for ii, (img, bbox_, label_, scale) in tqdm(enumerate(train_frame_loader)):
iteration += 1
scale = at.scalar(scale)
img, bbox, label = img.cuda().float(), bbox_.cuda(), label_.cuda()
img = normalize(img)
if opt.debug_step and (iteration+1) % opt.debug_step == 0:
trainer.train_step(img, bbox, label, scale, stop=True)
else:
trainer.train_step(img, bbox, label, scale)
if (iteration + 1) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
train_results = trainer.get_meter_data()
writer.add_scalar('Losses/rpn_loc', train_results['rpn_loc_loss'],
iteration)
writer.add_scalar('Losses/rpn_cls', train_results['rpn_cls_loss'],
iteration)
writer.add_scalar('Losses/roi_loc', train_results['roi_loc_loss'],
iteration)
writer.add_scalar('Losses/roi_cls', train_results['roi_cls_loss'],
iteration)
writer.add_scalar('Losses/total', train_results['total_loss'],
iteration)
if (iteration + 1) % opt.img_every == 0:
ori_img_ = at.tonumpy(img[0])
gt_img = visdom_bbox(ori_img_,
at.tonumpy(bbox_[0]),
at.tonumpy(label_[0]))
gt_img_grid = make_grid(torch.from_numpy(gt_img))
writer.add_image('Ground_truth_img', gt_img_grid, iteration)
# plot predicti bboxes
_bboxes, _labels, _scores = trainer.faster_rcnn.predict([ori_img_], opt.signal_type,
visualize=True)
# FLAG: vis
pred_img = visdom_bbox(ori_img_,
at.tonumpy(_bboxes[0]),
at.tonumpy(_labels[0]).reshape(-1),
at.tonumpy(_scores[0]))
pred_img_grid = make_grid(torch.from_numpy(pred_img))
writer.add_image('Predicted_img', pred_img_grid, iteration)
if opt.train_eval and (iteration + 1) % opt.train_eval == 0:
train_eval_result, train_best_iou = eval(train_seqs_loader, faster_rcnn,
opt.signal_type)
writer.add_scalar('Train/mAP', train_eval_result['map'],
iteration)
writer.add_scalar('Train/Best_IoU', train_best_iou,
iteration)
eval_result, best_val_iou = eval(val_seqs_loader, faster_rcnn, opt.signal_type,
test_num=opt.test_num)
writer.add_scalar('Validation/mAP', eval_result['map'],
iteration)
writer.add_scalar('Validation/Best_IoU', best_val_iou,
iteration)
lr_ = scheduler.get_lr()[0]
writer.add_scalar('learning_rate', lr_, iteration)
log_info = 'lr:{}, map:{},loss:{}'.format(str(lr_),
str(eval_result['map']),
str(trainer.get_meter_data()))
print(log_info)
if eval_result['map'] > best_map:
test_result, test_best_iou = eval(test_seqs_loader, faster_rcnn, opt.signal_type,
test_num=opt.test_num)
writer.add_scalar('Test/mAP', test_result['map'],
iteration)
writer.add_scalar('Test/Best_IoU', test_best_iou,
iteration)
best_map = eval_result['map']
best_test_map = test_result['map']
best_path = trainer.save(best_val_map=best_map, best_test_map=best_test_map)
# best_path = trainer.save(best_map=best_map)
if (epoch + 1) % opt.lr_step == 0:
scheduler.step()