def main():
global args
args = parser.parse_args()
use_cuda = cuda_model.ifUseCuda(args.gpu_id, args.multiGpu)
model = PointerNet(args.input_size, args.hidden_size, args.nlayers,
args.dropout, args.bidir)
if args.resume is not None:
assert os.path.isfile(
args.resume), 'Error: no checkpoint directory found!'
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loading checkpoint '{:s}', epoch: {:d}".format(
args.resume, args.start_epoch))
model = cuda_model.convertModel2Cuda(model,
gpu_id=args.gpu_id,
multiGpu=args.multiGpu)
train_dataset = LocalDataLoader.Dataset(dataset_name='SumMe',
split='train',
clip_size=args.clip_size,
sample_rates=[1, 5, 10])
val_dataset = LocalDataLoader.Dataset(dataset_name='SumMe',
split='val',
clip_size=args.clip_size)
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=True,
num_workers=4)
NLL = torch.nn.NLLLoss()
model_optim = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr)
best_status = {
'train_accuracy': 0,
'val_accuracy': 0,
'train_loss': float('inf'),
'val_loss': float('inf')
}
isBest_status = {
'train_accuracy': 0,
'val_accuracy': 0,
'train_loss': 0,
'val_loss': 0
}
for epoch in range(args.start_epoch, args.nof_epoch):
total_losses = AverageMeter()
loc_losses = AverageMeter()
cls_losses = AverageMeter()
Accuracy_Top1 = AverageMeter()
Accuracy_Top3 = AverageMeter()
F1_Top1 = AverageMeter()
F1_Top3 = AverageMeter()
model.train()
pbar = progressbar.ProgressBar(max_value=len(train_dataloader))
for i_batch, sample_batched in enumerate(train_dataloader):
pbar.update(i_batch)
feature_batch = Variable(sample_batched[0])
gt_index_batch = Variable(sample_batched[1])
if use_cuda:
feature_batch = feature_batch.cuda()
gt_index_batch = gt_index_batch.cuda()
index_vector, segment_score = model(feature_batch)
index_vector = index_vector.contiguous().view(-1, args.clip_size)
gt_index_batch = gt_index_batch.view(-1)
accuracy = Metrics.accuracy_topN(index_vector.data,
gt_index_batch.data,
topk=[1, 3])
F1 = Metrics.accuracy_F1(index_vector.data,
gt_index_batch.data,
topk=[1, 3])
loc_loss = NLL(torch.log(index_vector), gt_index_batch)
total_loss = loc_loss
model_optim.zero_grad()
total_loss.backward()
model_optim.step()
total_losses.update(total_loss.data[0], feature_batch.size(0))
loc_losses.update(loc_loss.data[0], feature_batch.size(0))
Accuracy_Top1.update(accuracy[0][0], feature_batch.size(0))
Accuracy_Top3.update(accuracy[1][0], feature_batch.size(0))
F1_Top1.update(F1[0], feature_batch.size(0))
F1_Top3.update(F1[1], feature_batch.size(0))
print(
"Train -- Epoch :{:06d}, LR: {:.6f},\tloc-loss={:.4f}\ttop1={:.4f}\ttop3={:.4f}\tF1_1={:.4f}\tF1_3={:.4f}"
.format(epoch, model_optim.param_groups[0]['lr'], loc_losses.avg,
Accuracy_Top1.avg, Accuracy_Top3.avg, F1_Top1.avg,
F1_Top3.avg))
if best_status['train_loss'] > total_losses.avg:
best_status['train_loss'] = total_losses.avg
isBest_status['train_loss'] = 1
if best_status['train_accuracy'] < Accuracy_Top1.avg:
best_status['train_accuracy'] = Accuracy_Top1.avg
isBest_status['train_accuracy'] = 1
model.eval()
total_losses.reset()
loc_losses.reset()
cls_losses.reset()
Accuracy_Top1.reset()
Accuracy_Top3.reset()
F1_Top1.reset()
F1_Top3.reset()
pbar = progressbar.ProgressBar(max_value=len(val_dataloader))
for i_batch, sample_batched in enumerate(val_dataloader):
pbar.update(i_batch)
feature_batch = Variable(sample_batched[0])
gt_index_batch = Variable(sample_batched[1])
if use_cuda:
feature_batch = feature_batch.cuda()
gt_index_batch = gt_index_batch.cuda()
index_vector, segment_score = model(feature_batch)
index_vector = index_vector.contiguous().view(-1, args.clip_size)
gt_index_batch = gt_index_batch.view(-1)
accuracy = Metrics.accuracy_topN(index_vector.data,
gt_index_batch.data,
topk=[1, 3])
F1 = Metrics.accuracy_F1(index_vector.data,
gt_index_batch.data,
topk=[1, 3])
loc_loss = NLL(torch.log(index_vector), gt_index_batch)
total_loss = loc_loss
loc_losses.update(loc_loss.data[0], feature_batch.size(0))
total_losses.update(total_loss.data[0], feature_batch.size(0))
Accuracy_Top1.update(accuracy[0][0], feature_batch.size(0))
Accuracy_Top3.update(accuracy[1][0], feature_batch.size(0))
F1_Top1.update(F1[0], feature_batch.size(0))
F1_Top3.update(F1[1], feature_batch.size(0))
print(
"Test -- Epoch :{:06d}, LR: {:.6f},\tloc-loss={:.4f}\ttop1={:.4f}\ttop3={:.4f}\tF1_1={:.4f}\tF1_3={:.4f}"
.format(epoch, model_optim.param_groups[0]['lr'], loc_losses.avg,
Accuracy_Top1.avg, Accuracy_Top3.avg, F1_Top1.avg,
F1_Top3.avg))
# val_F1_score = val_evaluator.EvaluateTop1(model, use_cuda)
# print "Val F1 Score: {:f}".format(val_F1_score)
if best_status['val_loss'] > total_losses.avg:
best_status['val_loss'] = total_losses.avg
isBest_status['val_loss'] = 1
if best_status['val_accuracy'] < Accuracy_Top1.avg:
best_status['val_accuracy'] = Accuracy_Top1.avg
isBest_status['val_accuracy'] = 1
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'val_loss': best_status['val_loss'],
'val_accuracy': best_status['val_accuracy'],
'train_loss': best_status['train_loss'],
'train_accuracy': best_status['train_accuracy']
},
isBest_status,
file_direcotry='vsPtrDep_Localization')
for item in isBest_status.keys():
isBest_status[item] = 0
def main():
global args
args = parser.parse_args()
use_cuda = cuda_model.ifUseCuda(args.gpu_id, args.multiGpu)
model = PointerNet(args.input_size, args.hidden_size, args.nlayers,
args.dropout, args.bidir)
if args.resume is not None:
assert os.path.isfile(
args.resume), 'Error: no checkpoint directory found!'
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage)
args.start_epoch = checkpoint['epoch']
args.start_epoch = 0
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loading checkpoint '{:s}', epoch: {:d}".format(
args.resume, args.start_epoch))
model = cuda_model.convertModel2Cuda(model,
gpu_id=args.gpu_id,
multiGpu=args.multiGpu)
train_dataset = LocalDataLoader.Dataset(dataset_name="SumMe",
split='train',
clip_size=args.clip_size,
output_score=True,
output_rdIdx=True,
sample_rates=[1, 5, 10])
val_dataset = LocalDataLoader.Dataset(dataset_name="SumMe",
split='val',
clip_size=args.clip_size,
output_score=True,
output_rdIdx=True)
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=True,
num_workers=4)
model_optim = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.lr)
best_status = {
'train_accuracy': 0,
'val_accuracy': 0,
'train_loss': float('inf'),
'val_loss': float('inf')
}
isBest_status = {
'train_accuracy': 0,
'val_accuracy': 0,
'train_loss': 0,
'val_loss': 0
}
for epoch in range(args.start_epoch, args.nof_epoch):
total_losses = AverageMeter()
loc_losses = AverageMeter()
cls_losses = AverageMeter()
Accuracy_Top1 = AverageMeter()
Accuracy_Top3 = AverageMeter()
F1_Top1 = AverageMeter()
F1_Top3 = AverageMeter()
model.train()
pbar = progressbar.ProgressBar(max_value=len(train_dataloader))
for i_batch, sample_batched in enumerate(train_dataloader):
pbar.update(i_batch)
feature_batch = Variable(sample_batched[0])
gt_index_batch = Variable(sample_batched[1])
score_batch = Variable(sample_batched[2])
rd_index_batch = Variable(sample_batched[3])
if use_cuda:
feature_batch = feature_batch.cuda()
rd_index_batch = rd_index_batch.cuda()
gt_index_batch = gt_index_batch.cuda()
score_batch = score_batch.cuda()
rd_starting_index_batch = rd_index_batch[:, 0]
rd_ending_index_batch = rd_index_batch[:, 1]
_, segment_score = model(feature_batch,
starting_idx=rd_starting_index_batch,
ending_idx=rd_ending_index_batch)
overlap = loss_transforms.IoU_Overlaps(rd_index_batch.data,
gt_index_batch.data)
overlap = Variable(overlap, requires_grad=False)
if use_cuda:
overlap = overlap.cuda()
cls_loss = losses.ClsLocLoss_Regression(segment_score,
score_batch,
overlap,
thres=0.5)
total_loss = cls_loss
model_optim.zero_grad()
total_loss.backward()
model_optim.step()
total_losses.update(total_loss.data[0], feature_batch.size(0))
print("Train -- Epoch :{:06d}, LR: {:.6f},\tcls-loss={:.4f}".format(
epoch, model_optim.param_groups[0]['lr'], total_losses.avg))
if best_status['train_loss'] > total_losses.avg:
best_status['train_loss'] = total_losses.avg
isBest_status['train_loss'] = 1
if best_status['train_accuracy'] < Accuracy_Top1.avg:
best_status['train_accuracy'] = Accuracy_Top1.avg
isBest_status['train_accuracy'] = 1
model.eval()
total_losses.reset()
loc_losses.reset()
cls_losses.reset()
Accuracy_Top1.reset()
Accuracy_Top3.reset()
F1_Top1.reset()
F1_Top3.reset()
pbar = progressbar.ProgressBar(max_value=len(val_dataloader))
for i_batch, sample_batched in enumerate(val_dataloader):
pbar.update(i_batch)
feature_batch = Variable(sample_batched[0])
gt_index_batch = Variable(sample_batched[1])
score_batch = Variable(sample_batched[2])
rd_index_batch = Variable(sample_batched[3])
if use_cuda:
feature_batch = feature_batch.cuda()
rd_index_batch = rd_index_batch.cuda()
gt_index_batch = gt_index_batch.cuda()
score_batch = score_batch.cuda()
rd_starting_index_batch = rd_index_batch[:, 0]
rd_ending_index_batch = rd_index_batch[:, 1]
_, segment_score = model(feature_batch,
starting_idx=rd_starting_index_batch,
ending_idx=rd_ending_index_batch)
overlap = loss_transforms.IoU_Overlaps(rd_index_batch.data,
gt_index_batch.data)
overlap = Variable(overlap, requires_grad=False)
if use_cuda:
overlap = overlap.cuda()
cls_loss = losses.ClsLocLoss_Regression(segment_score,
score_batch,
overlap,
thres=0.5)
total_loss = cls_loss
# Here adjust the ratio based on loss values...
total_losses.update(total_loss.data[0], feature_batch.size(0))
print("Test -- Epoch :{:06d}, LR: {:.6f},\tcls-loss={:.4f}".format(
epoch,
model_optim.param_groups[0]['lr'],
total_losses.avg,
))
# val_F1_score = val_evaluator.EvaluateTop1(model, use_cuda)
# print "Val F1 Score: {:f}".format(val_F1_score)
if best_status['val_loss'] > total_losses.avg:
best_status['val_loss'] = total_losses.avg
isBest_status['val_loss'] = 1
if best_status['val_accuracy'] < Accuracy_Top1.avg:
best_status['val_accuracy'] = Accuracy_Top1.avg
isBest_status['val_accuracy'] = 1
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'val_loss': best_status['val_loss'],
'val_accuracy': best_status['val_accuracy'],
'train_loss': best_status['train_loss'],
'train_accuracy': best_status['train_accuracy']
},
isBest_status,
file_direcotry='vsPtrDep_Classification')
for item in isBest_status.keys():
isBest_status[item] = 0