def main():
global args
args = (parser.parse_args())
use_cuda = cuda_model.ifUseCuda(args.gpu_id, args.multiGpu)
script_name_stem = dir_utils.get_stem(__file__)
if args.resume is None:
save_directory = dir_utils.get_dir(os.path.join(project_root, 'ckpts', '{:s}'.format(args.dataset), '{:s}-{:s}-assgin{:.2f}-alpha{:.4f}-dim{:d}-dropout{:.4f}-seqlen{:d}-{:s}-{:s}'.
format(script_name_stem, args.sufix, args.hassign_thres, args.alpha, args.hidden_dim, args.dropout, args.seq_len, 'L2', match_type[args.hmatch])))
else:
save_directory = args.resume
log_file = os.path.join(save_directory, 'log-{:s}.txt'.format(dir_utils.get_date_str()))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
model = PointerNetwork(input_dim=args.input_dim, embedding_dim=args.embedding_dim,
hidden_dim=args.hidden_dim, max_decoding_len=args.net_outputs, dropout=args.dropout, n_enc_layers=2, output_classes=2)
logger.info("Number of Params\t{:d}".format(sum([p.data.nelement() for p in model.parameters()])))
logger.info('Saving logs to {:s}'.format(log_file))
if args.resume is not None:
ckpt_idx = args.fileid
ckpt_filename = os.path.join(args.resume, 'checkpoint_{:04d}.pth.tar'.format(ckpt_idx))
assert os.path.isfile(ckpt_filename), 'Error: no checkpoint directory found!'
checkpoint = torch.load(ckpt_filename, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'], strict=False)
train_iou = checkpoint['IoU']
args.start_epoch = checkpoint['epoch']
logger.info("=> loading checkpoint '{}', current iou: {:.04f}".format(ckpt_filename, train_iou))
model = cuda_model.convertModel2Cuda(model, gpu_id=args.gpu_id, multiGpu=args.multiGpu)
train_dataset = cDataset(dataset_split='train', seq_length=args.seq_len, sample_rate=[4], rdOffset=True, rdDrop=True)
val_dataset =cDataset(dataset_split='val', seq_length=args.seq_len, sample_rate=[4], rdDrop=False, rdOffset=False)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
val_dataloader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
model_optim = optim.Adam(filter(lambda p:p.requires_grad, model.parameters()), lr=float(args.lr))
optim_scheduler = optim.lr_scheduler.ReduceLROnPlateau(model_optim, 'min', patience=10)
cls_weights = torch.FloatTensor([0.05, 1.0]).cuda()
# cls_weights = None
widgets = ['Test: ', ' -- [ ', progressbar.Counter(), '|', str(len(train_dataloader)), ' ] ',
progressbar.Bar(), ' cls loss: ', progressbar.FormatLabel(''),
' loc loss: ', progressbar.FormatLabel(''),
' IoU : ', progressbar.FormatLabel(''),
' (', progressbar.ETA(), ' ) ']
# bar = progressbar.ProgressBar(max_value=step_per_epoch, widgets=widgets)
# bar.start()
for epoch in range(args.start_epoch, args.nof_epoch+args.start_epoch):
total_losses = AverageMeter()
loc_losses = AverageMeter()
cls_losses = AverageMeter()
matched_IOU = AverageMeter()
true_IOU = AverageMeter()
model.train()
pbar = progressbar.ProgressBar(max_value=len(train_dataloader), widgets=widgets)
pbar.start()
for i_batch, sample_batch in enumerate(train_dataloader):
# pbar.update(i_batch)
feature_batch = Variable(sample_batch[0])
start_indices = Variable(sample_batch[1])
end_indices = Variable(sample_batch[2])
gt_valids = Variable(sample_batch[3])
# gt_overlaps = Variable(sample_batch[4])
# seq_labels = Variable(sample_batch[3])
if use_cuda:
feature_batch = feature_batch.cuda()
start_indices = start_indices.cuda()
end_indices = end_indices.cuda()
gt_positions = torch.stack([start_indices, end_indices], dim=-1)
head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, _ = model(feature_batch)
pred_positions = torch.stack([head_positions, tail_positions], dim=-1)
# pred_scores = F.sigmoid(cls_scores)
if args.hmatch:
assigned_scores, assigned_locations, total_valid, total_iou = h_match.Assign_Batch_v2(gt_positions, pred_positions, gt_valids, thres=args.hassign_thres)
else:
#FIXME: do it later!
assigned_scores, assigned_locations, total_valid, total_iou = f_match.Assign_Batch_v2(gt_positions, pred_positions, gt_valids, thres=args.hassign_thres)
# _, _, total_valid, total_iou = h_match.Assign_Batch_v2(gt_positions, pred_positions, gt_valids, thres=args.hassign_thres)
true_valid, true_iou = h_match.totalMatch_Batch(gt_positions, pred_positions, gt_valids)
assert true_valid == total_valid, 'WRONG'
if total_valid>0:
matched_IOU.update(total_iou / total_valid, total_valid)
true_IOU.update(true_iou/total_valid, total_valid)
assigned_scores = Variable(torch.LongTensor(assigned_scores),requires_grad=False)
# assigned_overlaps = Variable(torch.FloatTensor(assigned_overlaps), requires_grad=False)
assigned_locations = Variable(torch.LongTensor(assigned_locations), requires_grad=False)
if use_cuda:
assigned_scores = assigned_scores.cuda()
assigned_locations = assigned_locations.cuda()
# assigned_overlaps = assigned_overlaps.cuda()
# pred_scores = pred_scores.contiguous().view(-1)
# assigned_scores = assigned_scores.contiguous().view(-1)
# assigned_overlaps = assigned_overlaps.contiguous().view(-1)
# cls_loss = ClsLocLoss2_OneClsRegression(pred_scores, assigned_scores, assigned_overlaps)
cls_scores = cls_scores.contiguous().view(-1, cls_scores.size()[-1])
assigned_scores = assigned_scores.contiguous().view(-1)
cls_loss = F.cross_entropy(cls_scores, assigned_scores, weight=cls_weights)
if total_valid>0:
assigned_head_positions = assigned_locations[:,:,0]
assigned_head_positions = assigned_head_positions.contiguous().view(-1)
#
assigned_tail_positions = assigned_locations[:,:,1]
assigned_tail_positions = assigned_tail_positions.contiguous().view(-1)
head_pointer_probs = head_pointer_probs.contiguous().view(-1, head_pointer_probs.size()[-1])
tail_pointer_probs = tail_pointer_probs.contiguous().view(-1, tail_pointer_probs.size()[-1])
assigned_head_positions = torch.masked_select(assigned_head_positions, assigned_scores.byte())
assigned_tail_positions = torch.masked_select(assigned_tail_positions, assigned_scores.byte())
head_pointer_probs = torch.index_select(head_pointer_probs, dim=0, index=assigned_scores.nonzero().squeeze(1))
tail_pointer_probs = torch.index_select(tail_pointer_probs, dim=0, index=assigned_scores.nonzero().squeeze(1))
# if args.EMD:
assigned_head_positions = to_one_hot(assigned_head_positions, args.seq_len)
assigned_tail_positions = to_one_hot(assigned_tail_positions, args.seq_len)
prediction_head_loss = Simple_L2(head_pointer_probs, assigned_head_positions, needSoftMax=True)
prediction_tail_loss = Simple_L2(tail_pointer_probs, assigned_tail_positions, needSoftMax=True)
# else:
# prediction_head_loss = F.cross_entropy(head_pointer_probs, assigned_head_positions)
# prediction_tail_loss = F.cross_entropy(tail_pointer_probs, assigned_tail_positions)
loc_losses.update(prediction_head_loss.data.item() + prediction_tail_loss.data.item(),
total_valid)#FIXME
total_loss = args.alpha*(prediction_head_loss + prediction_tail_loss) + cls_loss
else:
total_loss = cls_loss
model_optim.zero_grad()
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
model_optim.step()
cls_losses.update(cls_loss.data.item(), feature_batch.size(0))
total_losses.update(total_loss.item(), feature_batch.size(0))
widgets[-8] = progressbar.FormatLabel('{:04.4f}'.format(cls_losses.avg))
widgets[-6] = progressbar.FormatLabel('{:04.4f}'.format(loc_losses.avg))
widgets[-4] = progressbar.FormatLabel('{:01.4f}'.format(matched_IOU.avg))
pbar.update(i_batch)
logger.info(
"Train -- Epoch :{:06d}, LR: {:.6f},\tloss={:.4f}, \t c-loss:{:.4f}, \tloc-loss:{:.4f}\tAvg-matched_IOU:{:.4f}\t Avg-true-IOU:{:.4f}".format(
epoch,
model_optim.param_groups[0]['lr'], total_losses.avg, cls_losses.avg, loc_losses.avg, matched_IOU.avg, true_IOU.avg))
train_iou = matched_IOU.avg
optim_scheduler.step(total_losses.avg)
model.eval()
matched_IOU = AverageMeter()
pbar = progressbar.ProgressBar(max_value=len(val_dataloader))
for i_batch, sample_batch in enumerate(val_dataloader):
pbar.update(i_batch)
feature_batch = Variable(sample_batch[0])
start_indices = Variable(sample_batch[1])
end_indices = Variable(sample_batch[2])
gt_valids = Variable(sample_batch[3])
# valid_indices = Variable(sample_batch[3])
if use_cuda:
feature_batch = feature_batch.cuda()
start_indices = start_indices.cuda()
end_indices = end_indices.cuda()
gt_positions = torch.stack([start_indices, end_indices], dim=-1)
head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, _ = model(
feature_batch)
pred_positions = torch.stack([head_positions, tail_positions], dim=-1)
# assigned_scores, assigned_locations, total_valid, total_iou = h_match.Assign_Batch_eval(gt_positions, pred_positions, gt_valids, thres=args.hassign_thres) #FIXME
matched_valid, matched_iou = h_match.totalMatch_Batch(gt_positions, pred_positions, gt_valids)
if matched_valid>0:
matched_IOU.update(matched_iou / matched_valid, matched_valid)
logger.info(
"Val -- Epoch :{:06d}, LR: {:.6f},\tloc-Avg-matched_IOU:{:.4f}".format(
epoch,model_optim.param_groups[0]['lr'], matched_IOU.avg, ))
if epoch % 1 == 0 :
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'loss':total_losses.avg,
'cls_loss': cls_losses.avg,
'loc_loss': loc_losses.avg,
'train-IOU':train_iou,
'IoU': matched_IOU.avg}, (epoch+1), file_direcotry=save_directory)
def main():
global args
args = (parser.parse_args())
use_cuda = cuda_model.ifUseCuda(args.gpu_id, args.multiGpu)
script_name_stem = dir_utils.get_stem(__file__)
save_directory = dir_utils.get_dir(
os.path.join(
project_root, 'ckpts',
'{:s}-{:s}-{:s}-split-{:d}-claweight-{:s}-{:.1f}-assgin{:.2f}-alpha{:.4f}-dim{:d}-dropout{:.4f}-seqlen{:d}-samplerate-{:d}-{:s}-{:s}'
.format(script_name_stem, args.dataset, args.eval_metrics,
args.split, str(args.set_cls_weight), args.cls_pos_weight,
args.hassign_thres, args.alpha, args.hidden_dim,
args.dropout, args.seq_len, args.sample_rate,
loss_type[args.EMD], match_type[args.hmatch])))
log_file = os.path.join(save_directory,
'log-{:s}.txt'.format(dir_utils.get_date_str()))
logger = log_utils.get_logger(log_file)
log_utils.print_config(vars(args), logger)
model = PointerNetwork(input_dim=args.input_dim,
embedding_dim=args.embedding_dim,
hidden_dim=args.hidden_dim,
max_decoding_len=args.net_outputs,
dropout=args.dropout,
n_enc_layers=2,
output_classes=2)
hassign_thres = args.hassign_thres
logger.info("Number of Params\t{:d}".format(
sum([p.data.nelement() for p in model.parameters()])))
logger.info('Saving logs to {:s}'.format(log_file))
if args.resume is not None:
ckpt_idx = 48
ckpt_filename = args.resume.format(ckpt_idx)
assert os.path.isfile(
ckpt_filename), 'Error: no checkpoint directory found!'
checkpoint = torch.load(ckpt_filename,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'], strict=False)
train_iou = checkpoint['IoU']
args.start_epoch = checkpoint['epoch']
logger.info("=> loading checkpoint '{}', current iou: {:.04f}".format(
ckpt_filename, train_iou))
model = cuda_model.convertModel2Cuda(model,
gpu_id=args.gpu_id,
multiGpu=args.multiGpu)
# get train/val split
if args.dataset == 'SumMe':
train_val_test_perms = np.arange(25)
elif args.dataset == 'TVSum':
train_val_test_perms = np.arange(50)
# fixed permutation
random.Random(0).shuffle(train_val_test_perms)
train_val_test_perms = train_val_test_perms.reshape([5, -1])
train_val_perms = np.delete(train_val_test_perms, args.split,
0).reshape([-1])
train_perms = train_val_perms[:17]
val_perms = train_val_perms[17:]
test_perms = train_val_test_perms[args.split]
logger.info(" training split: " + str(train_perms))
logger.info(" val split: " + str(val_perms))
logger.info(" test split: " + str(test_perms))
if args.location == 'home':
data_path = os.path.join(os.path.expanduser('~'), 'datasets')
else:
data_path = os.path.join('/nfs/%s/boyu/SDN' % (args.location),
'datasets')
train_dataset = vsSumLoader3_c3dd.cDataset(dataset_name=args.dataset,
split='train',
seq_length=args.seq_len,
overlap=0.9,
sample_rate=[args.sample_rate],
train_val_perms=train_perms,
data_path=data_path)
val_evaluator = Evaluator.Evaluator(dataset_name=args.dataset,
split='val',
seq_length=args.seq_len,
overlap=0.9,
sample_rate=[args.sample_rate],
sum_budget=0.15,
train_val_perms=val_perms,
eval_metrics=args.eval_metrics,
data_path=data_path)
test_evaluator = Evaluator.Evaluator(dataset_name=args.dataset,
split='test',
seq_length=args.seq_len,
overlap=0.9,
sample_rate=[args.sample_rate],
sum_budget=0.15,
train_val_perms=test_perms,
eval_metrics=args.eval_metrics,
data_path=data_path)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# val_dataloader = DataLoader(val_dataset,
# batch_size=args.batch_size,
# shuffle=False,
# num_workers=4)
model_optim = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=float(args.lr))
optim_scheduler = optim.lr_scheduler.ReduceLROnPlateau(model_optim,
'min',
patience=10)
alpha = args.alpha
# cls_weights = torch.FloatTensor([0.2, 1.0]).cuda()
if args.set_cls_weight:
cls_weights = torch.FloatTensor([
1. * train_dataset.n_positive_train_samples /
train_dataset.n_total_train_samples, args.cls_pos_weight
]).cuda()
else:
cls_weights = torch.FloatTensor([0.5, 0.5]).cuda()
logger.info(" total: {:d}, total pos: {:d}".format(
train_dataset.n_total_train_samples,
train_dataset.n_positive_train_samples))
logger.info(" classify weight: " + str(cls_weights[0]) +
str(cls_weights[1]))
for epoch in range(args.start_epoch, args.nof_epoch + args.start_epoch):
total_losses = AverageMeter()
loc_losses = AverageMeter()
cls_losses = AverageMeter()
Accuracy = AverageMeter()
IOU = AverageMeter()
ordered_IOU = AverageMeter()
model.train()
pbar = progressbar.ProgressBar(max_value=len(train_dataloader))
for i_batch, sample_batch in enumerate(train_dataloader):
pbar.update(i_batch)
feature_batch = Variable(sample_batch[0])
start_indices = Variable(sample_batch[1])
end_indices = Variable(sample_batch[2])
gt_valids = Variable(sample_batch[3])
# seq_labels = Variable(sample_batch[3])
if use_cuda:
feature_batch = feature_batch.cuda()
start_indices = start_indices.cuda()
end_indices = end_indices.cuda()
gt_positions = torch.stack([start_indices, end_indices], dim=-1)
head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, _ = model(
feature_batch)
pred_positions = torch.stack([head_positions, tail_positions],
dim=-1)
if args.hmatch:
assigned_scores, assigned_locations, total_valid, total_iou = h_match.Assign_Batch_v2(
gt_positions,
pred_positions,
gt_valids,
thres=hassign_thres)
else:
assigned_scores, assigned_locations = f_match.Assign_Batch(
gt_positions,
pred_positions,
gt_valids,
thres=hassign_thres)
_, _, total_valid, total_iou = h_match.Assign_Batch_v2(
gt_positions,
pred_positions,
gt_valids,
thres=hassign_thres)
if total_valid > 0:
IOU.update(total_iou / total_valid, total_valid)
assigned_scores = Variable(torch.LongTensor(assigned_scores),
requires_grad=False)
assigned_locations = Variable(torch.LongTensor(assigned_locations),
requires_grad=False)
if use_cuda:
assigned_scores = assigned_scores.cuda()
assigned_locations = assigned_locations.cuda()
cls_scores = cls_scores.contiguous().view(-1,
cls_scores.size()[-1])
assigned_scores = assigned_scores.contiguous().view(-1)
cls_loss = F.cross_entropy(cls_scores,
assigned_scores,
weight=cls_weights)
if total_valid > 0:
assigned_head_positions = assigned_locations[:, :, 0]
assigned_head_positions = assigned_head_positions.contiguous(
).view(-1)
#
assigned_tail_positions = assigned_locations[:, :, 1]
assigned_tail_positions = assigned_tail_positions.contiguous(
).view(-1)
head_pointer_probs = head_pointer_probs.contiguous().view(
-1,
head_pointer_probs.size()[-1])
tail_pointer_probs = tail_pointer_probs.contiguous().view(
-1,
tail_pointer_probs.size()[-1])
assigned_head_positions = torch.masked_select(
assigned_head_positions, assigned_scores.byte())
assigned_tail_positions = torch.masked_select(
assigned_tail_positions, assigned_scores.byte())
head_pointer_probs = torch.index_select(
head_pointer_probs,
dim=0,
index=assigned_scores.nonzero().squeeze(1))
tail_pointer_probs = torch.index_select(
tail_pointer_probs,
dim=0,
index=assigned_scores.nonzero().squeeze(1))
if args.EMD:
assigned_head_positions = to_one_hot(
assigned_head_positions, args.seq_len)
assigned_tail_positions = to_one_hot(
assigned_tail_positions, args.seq_len)
prediction_head_loss = EMD_L2(head_pointer_probs,
assigned_head_positions,
needSoftMax=True)
prediction_tail_loss = EMD_L2(tail_pointer_probs,
assigned_tail_positions,
needSoftMax=True)
else:
prediction_head_loss = F.cross_entropy(
head_pointer_probs, assigned_head_positions)
prediction_tail_loss = F.cross_entropy(
tail_pointer_probs, assigned_tail_positions)
loc_losses.update(
prediction_head_loss.data.item() +
prediction_tail_loss.data.item(), feature_batch.size(0))
total_loss = alpha * (prediction_head_loss +
prediction_tail_loss) + cls_loss
else:
total_loss = cls_loss
model_optim.zero_grad()
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
model_optim.step()
cls_losses.update(cls_loss.data.item(), feature_batch.size(0))
total_losses.update(total_loss.item(), feature_batch.size(0))
logger.info(
"Train -- Epoch :{:06d}, LR: {:.6f},\tloss={:.4f}, \t c-loss:{:.4f}, \tloc-loss:{:.4f}\tcls-Accuracy:{:.4f}\tloc-Avg-IOU:{:.4f}\t topIOU:{:.4f}"
.format(epoch, model_optim.param_groups[0]['lr'], total_losses.avg,
cls_losses.avg, loc_losses.avg, Accuracy.avg, IOU.avg,
ordered_IOU.avg))
optim_scheduler.step(total_losses.avg)
model.eval()
# IOU = AverageMeter()
# pbar = progressbar.ProgressBar(max_value=len(val_evaluator))
# for i_batch, sample_batch in enumerate(val_dataloader):
# pbar.update(i_batch)
# feature_batch = Variable(sample_batch[0])
# start_indices = Variable(sample_batch[1])
# end_indices = Variable(sample_batch[2])
# gt_valids = Variable(sample_batch[3])
# # valid_indices = Variable(sample_batch[3])
# if use_cuda:
# feature_batch = feature_batch.cuda()
# start_indices = start_indices.cuda()
# end_indices = end_indices.cuda()
# gt_positions = torch.stack([start_indices, end_indices], dim=-1)
# head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, _ = model(
# feature_batch)#Update: compared to the previous version, we now update the matching rules
# pred_positions = torch.stack([head_positions, tail_positions], dim=-1)
# pred_scores = cls_scores[:, :, -1]
# #TODO: should NOT change here for evaluation!
# assigned_scores, assigned_locations, total_valid, total_iou = h_match.Assign_Batch_v2(gt_positions, pred_positions, gt_valids, thres=hassign_thres)
# if total_valid>0:
# IOU.update(total_iou / total_valid, total_valid)
val_F1s = val_evaluator.Evaluate(model)
test_F1s = test_evaluator.Evaluate(model)
logger.info("Val -- Epoch :{:06d}, LR: {:.6f},\tF1s:{:.4f}".format(
epoch, model_optim.param_groups[0]['lr'], val_F1s))
logger.info("Test -- Epoch :{:06d},\tF1s:{:.4f}".format(
epoch, test_F1s))
if epoch % 1 == 0:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'loss': total_losses.avg,
'cls_loss': cls_losses.avg,
'loc_loss': loc_losses.avg,
'IoU': IOU.avg,
'val_F1s': val_F1s,
'test_F1s': test_F1s
}, (epoch + 1),
file_direcotry=save_directory)
def main():
global args
args = (parser.parse_args())
ckpt_idx = args.fileid
savefile_stem = os.path.basename(args.eval)
proposal_save_file = 'Dev/S2N-release/Devs_ActionProp/PropEval/baselines_results/{:s}-{:04d}-check-02_thumos14_test.csv'.format(
savefile_stem, ckpt_idx)
feature_directory = os.path.join(
user_home_directory, 'datasets/THUMOS14/features/c3dd-fc7-red500')
ground_truth_file = os.path.join(
user_home_directory,
'Dev/NetModules/Devs_ActionProp/action_det_prep/thumos14_tag_test_proposal_list_c3dd.csv'
)
ground_truth = pd.read_csv(ground_truth_file, sep=' ')
target_video_frms = ground_truth[['video-name',
'video-frames']].drop_duplicates().values
frm_nums = {}
for s_target_videofrms in target_video_frms:
frm_nums[s_target_videofrms[0]] = s_target_videofrms[1]
target_file_names = ground_truth['video-name'].unique()
feature_file_ext = 'npy'
use_cuda = cuda_model.ifUseCuda(args.gpu_id, args.multiGpu)
# Pretty print the run args
pp.pprint(vars(args))
model = PointerNetwork(input_dim=args.input_dim,
embedding_dim=args.embedding_dim,
hidden_dim=args.hidden_dim,
max_decoding_len=args.net_outputs,
output_classes=2)
print("Number of Params\t{:d}".format(
sum([p.data.nelement() for p in model.parameters()])))
model = cuda_model.convertModel2Cuda(model,
gpu_id=args.gpu_id,
multiGpu=args.multiGpu)
model.eval()
if args.eval is not None:
# if os.path.isfile(args.resume):
ckpt_filename = os.path.join(
args.eval, 'checkpoint_{:04d}.pth.tar'.format(ckpt_idx))
assert os.path.isfile(
ckpt_filename), 'Error: no checkpoint directory found!'
checkpoint = torch.load(ckpt_filename,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'], strict=True)
train_iou = checkpoint['IoU']
print("=> loading checkpoint '{}', current iou: {:.04f}".format(
ckpt_filename, train_iou))
predict_results = {}
overlap = 0.9
seq_length = 360
sample_rate = 4
for video_idx, s_target_filename in enumerate(target_file_names):
if not os.path.exists(
os.path.join(
feature_directory, '{:s}.{:s}'.format(
s_target_filename, feature_file_ext))):
print('{:s} Not found'.format(s_target_filename))
continue
s_feature_path = os.path.join(
feature_directory, '{:s}.{:s}'.format(s_target_filename,
feature_file_ext))
singlevideo_data = SingleVideoLoader(feature_path=s_feature_path,
seq_length=seq_length,
overlap=overlap,
sample_rate=sample_rate)
n_video_len = singlevideo_data.n_features
n_video_clips = len(singlevideo_data.video_clips)
singlevideo_dataset = DataLoader(singlevideo_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
predict_proposals = []
for batch_idx, data in enumerate(singlevideo_dataset):
clip_feature = Variable(data[0], requires_grad=False)
clip_start_positions = Variable(data[1], requires_grad=False)
clip_end_positions = Variable(data[2], requires_grad=False)
if use_cuda:
clip_feature = clip_feature.cuda()
clip_start_positions = clip_start_positions.cuda()
clip_end_positions = clip_end_positions.cuda()
clip_start_positions = clip_start_positions.repeat(
1, args.net_outputs)
clip_end_positions = clip_end_positions.repeat(1, args.net_outputs)
head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, _ = model(
clip_feature)
# cls_scores = F.sigmoid(cls_scores)
cls_scores = F.softmax(cls_scores, dim=2)
head_positions, tail_positions = SDN.helper.switch_positions(
head_positions, tail_positions)
head_positions = (head_positions * sample_rate +
clip_start_positions)
tail_positions = (tail_positions * sample_rate +
clip_start_positions)
# cls_scores = cls_scores.contiguous().view(-1)
cls_scores = cls_scores[:, :, 1].contiguous().view(-1)
head_positions = head_positions.contiguous().view(-1)
tail_positions = tail_positions.contiguous().view(-1)
outputs = torch.stack(
[head_positions.float(),
tail_positions.float(), cls_scores],
dim=-1)
outputs = outputs.data.cpu().numpy()
for output_idx, s_output in enumerate(outputs):
if s_output[0] == s_output[1]:
s_output[0] -= sample_rate / 2
s_output[1] += sample_rate / 2
s_output[0] = max(0, s_output[0])
s_output[1] = min(n_video_len, s_output[1])
outputs[output_idx] = s_output
predict_proposals.append(outputs)
predict_proposals = np.concatenate(predict_proposals, axis=0)
sorted_idx = np.argsort(predict_proposals[:, -1])[::-1]
predict_proposals = predict_proposals[sorted_idx]
n_proposals = len(predict_proposals)
pred_positions = predict_proposals[:, :2]
pred_scores = predict_proposals[:, -1]
nms_positions, nms_scores = non_maxima_supression(pred_positions,
pred_scores,
overlap=0.99)
nms_predictions = np.concatenate(
(nms_positions, np.expand_dims(nms_scores, -1)), axis=-1)
predict_results[s_target_filename] = predict_proposals
print(
"[{:d} | {:d}]{:s}\t {:d} Frames\t {:d} Clips\t{:d} Proposals, Non-repeat:{:d}"
.format(video_idx, len(target_file_names), s_target_filename,
n_video_len, n_video_clips, n_proposals,
nms_predictions.shape[0]))
data_frame = pkl_frame2dataframe(predict_results, frm_nums)
results = pd.DataFrame(
data_frame,
columns=['f-end', 'f-init', 'score', 'video-frames', 'video-name'])
results.to_csv(os.path.join(user_home_directory, proposal_save_file),
sep=' ',
index=False)