def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if config['nclips_train'] > 1:
input_var = list(input.split(config['clip_size'], 2))
for idx, inp in enumerate(input_var):
input_var[idx] = inp.to(device)
else:
input_var = [input.to(device)]
target1 = target // 8
target2 = target % 8
target1 = target1.to(device)
target2 = target2.to(device)
model.zero_grad()
# compute output and loss
output = model(input_var)
loss_list = []
for i in range(50):
loss_list.append(criterion(output[i], target1))
for i in range(50):
loss_list.append(criterion(output[50 + i], target2))
loss = torch.stack(loss_list).sum()
# measure accuracy and record loss
prec1_1, prec5_1 = accuracy(output[49].detach().cpu(), target1.detach().cpu(), topk=(1, 5))
prec1_2, prec5_2 = accuracy(output[-1].detach().cpu(), target2.detach().cpu(), topk=(1, 5))
prec1, prec5 = ((prec1_1 + prec1_2) / 2.0, (prec5_1 + prec5_2) / 2.0)
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
return losses.avg, top1.avg, top5.avg
def validate(val_loader, model, criterion, class_to_idx=None):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
logits_matrix = []
features_matrix = []
targets_list = []
item_id_list = []
end = time.time()
with torch.no_grad():
for i, (input, target, item_id) in enumerate(val_loader):
if config['nclips_val'] > 1:
input_var = list(input.split(config['clip_size'], 2))
for idx, inp in enumerate(input_var):
input_var[idx] = inp.to(device)
else:
input_var = [input.to(device)]
target1 = target // 8
target2 = target % 8
target1 = target1.to(device)
target2 = target2.to(device)
'''
if input_var[-1].shape != (8, 3, 72, 84, 84):
print(f'len(input_var) : {len(input_var)}')
print(f'input_var[-1].shape : {input_var[-1].shape}')
print(f'target.shape : {target.shape}')
'''
# compute output and loss
output = model(input_var)
'''
if input_var[-1].shape != (8, 3, 72, 84, 84):
print(f'output.shape : {output.shape}')
'''
loss_list = []
for i in range(50):
loss_list.append(criterion(output[i], target1))
for i in range(50):
loss_list.append(criterion(output[50 + i], target2))
loss = torch.stack(loss_list).sum()
# measure accuracy and record loss
prec1_1, prec5_1 = accuracy(output[49].detach().cpu(), target1.detach().cpu(), topk=(1, 5))
prec1_2, prec5_2 = accuracy(output[-1].detach().cpu(), target2.detach().cpu(), topk=(1, 5))
prec1, prec5 = ((prec1_1 + prec1_2) / 2.0, (prec5_1 + prec5_2) / 2.0)
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return losses.avg, top1.avg, top5.avg
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
pbar = tqdm(train_loader)
for i, (input, target) in enumerate(pbar):
# measure data loading time
data_time.update(time.time() - end)
if config['nclips_train'] > 1:
input_var = list(input.split(config['clip_size'], 2))
for idx, inp in enumerate(input_var):
input_var[idx] = inp.to(device)
else:
input_var = [input.to(device)]
target = target.to(device)
model.zero_grad()
# compute output and loss
output = model(input_var)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.detach().cpu(),
target.detach().cpu(),
topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print_str = ('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5))
pbar.set_description(print_str)
# print(print_str)
return losses.avg, top1.avg, top5.avg
def validate(val_loader, model, criterion, class_to_idx=None):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
logits_matrix = []
features_matrix = []
targets_list = []
item_id_list = []
end = time.time()
with torch.no_grad():
pbar = tqdm(val_loader)
for i, (input, target, item_id) in enumerate(pbar):
if config['nclips_val'] > 1:
input_var = list(input.split(config['clip_size'], 2))
for idx, inp in enumerate(input_var):
input_var[idx] = inp.to(device)
else:
input_var = [input.to(device)]
target = target.to(device)
# compute output and loss
output, features = model(input_var, config['save_features'])
loss = criterion(output, target)
if args.eval_only:
logits_matrix.append(output.cpu().data.numpy())
features_matrix.append(features.cpu().data.numpy())
targets_list.append(target.cpu().numpy())
item_id_list.append(item_id)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.detach().cpu(),
target.detach().cpu(),
topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print_str = (
'Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i,
len(val_loader),
batch_time=batch_time,
loss=losses,
top1=top1,
top5=top5))
pbar.set_description(print_str)
# print(print_str)
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(top1=top1,
top5=top5))
sys.stdout.flush()
if args.eval_only:
logits_matrix = np.concatenate(logits_matrix)
features_matrix = np.concatenate(features_matrix)
targets_list = np.concatenate(targets_list)
item_id_list = np.concatenate(item_id_list)
print(logits_matrix.shape, targets_list.shape, item_id_list.shape)
save_results(logits_matrix, features_matrix, targets_list,
item_id_list, class_to_idx, config)
get_submission(logits_matrix, item_id_list, class_to_idx, config)
return losses.avg, top1.avg, top5.avg
def validate(val_loader, model, criterion, class_to_idx=None):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
logits_matrix = []
targets_list = []
with torch.no_grad():
for i, (input, target) in enumerate(val_loader):
input, target = input.to(device), target.to(device)
# compute output and loss
output = model(input)
loss = criterion(output, target)
if args.eval_only:
logits_matrix.append(output.detach().cpu().numpy())
targets_list.append(target.detach().cpu().numpy())
# measure accuracy and record loss
prec1, prec5 = accuracy(output.detach(),
target.detach().cpu(),
topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
if i % config["print_freq"] == 0:
print('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i,
len(val_loader),
loss=losses,
top1=top1,
top5=top5))
file = open(config["output_dir"] + "results.txt", "a")
file.write('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i,
len(val_loader),
loss=losses,
top1=top1,
top5=top5))
file.close()
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(
top1=top1, top5=top5))
file = open(config["output_dir"] + "results.txt", "a")
file.write(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(
top1=top1, top5=top5))
file.close()
if args.eval_only:
logits_matrix = np.concatenate(logits_matrix)
targets_list = np.concatenate(targets_list)
print(logits_matrix.shape, targets_list.shape)
save_results(logits_matrix, targets_list, class_to_idx, config)
return losses.avg, top1.avg, top5.avg
for j in range(50):
loss_list.append(criterion(output[j], target1))
for j in range(50):
loss_list.append(criterion(output[50 + j], target2))
loss = torch.stack(loss_list).sum()
# measure accuracy and record loss
prec1_1, prec5_1 = accuracy(output[49].detach().cpu(),
target1.detach().cpu(),
topk=(1, 5))
prec1_2, prec5_2 = accuracy(output[-1].detach().cpu(),
target2.detach().cpu(),
topk=(1, 5))
prec1, prec5 = ((prec1_1 + prec1_2) / 2.0,
(prec5_1 + prec5_2) / 2.0)
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i,
len(val_loader),
def train(train_loader, model, optimizer, epoch, criterion, tb_logger=None):
global args
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc_top1 = AverageMeter()
acc_top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (global_img_tensors, box_tensors, box_categories,
video_label) in enumerate(train_loader):
model.zero_grad()
# measure data loading time
data_time.update(time.time() - end)
# local_img_tensor is (b, nr_frames, nr_boxes, 3, h, w)
# global_img_tensor is (b, nr_frames, 3, h, w)
# compute output
output = model(global_img_tensors, box_categories, box_tensors,
video_label)
output = output.view((-1, len(train_loader.dataset.classes)))
loss = criterion(output, video_label.long().cuda())
acc1, acc5 = accuracy(output.cpu(), video_label, topk=(1, 5))
# measure accuracy and record loss
losses.update(loss.item(), global_img_tensors.size(0))
acc_top1.update(acc1.item(), global_img_tensors.size(0))
acc_top5.update(acc5.item(), global_img_tensors.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if args.clip_gradient is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.clip_gradient)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc1 {acc_top1.val:.1f} ({acc_top1.avg:.1f})\t'
'Acc5 {acc_top5.val:.1f} ({acc_top5.avg:.1f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
acc_top1=acc_top1,
acc_top5=acc_top5))
# log training data into tensorboard
if tb_logger is not None and i % args.log_freq == 0:
logs = OrderedDict()
logs['Train_IterLoss'] = losses.val
logs['Train_Acc@1'] = acc_top1.val
logs['Train_Acc@5'] = acc_top5.val
# how many iterations we have trained
iter_count = epoch * len(train_loader) + i
for key, value in logs.items():
tb_logger.log_scalar(value, key, iter_count)
tb_logger.flush()
def validate(val_loader,
model,
criterion,
epoch=None,
tb_logger=None,
class_to_idx=None):
batch_time = AverageMeter()
losses = AverageMeter()
acc_top1 = AverageMeter()
acc_top5 = AverageMeter()
logits_matrix = []
targets_list = []
# switch to evaluate mode
model.eval()
end = time.time()
for i, (global_img_tensors, box_tensors, box_categories,
video_label) in enumerate(val_loader):
# compute output
with torch.no_grad():
output = model(global_img_tensors,
box_categories,
box_tensors,
video_label,
is_inference=True)
output = output.view((-1, len(val_loader.dataset.classes)))
loss = criterion(output, video_label.long().cuda())
acc1, acc5 = accuracy(output.cpu(), video_label, topk=(1, 5))
if args.evaluate:
logits_matrix.append(output.cpu().data.numpy())
targets_list.append(video_label.cpu().numpy())
# measure accuracy and record loss
losses.update(loss.item(), global_img_tensors.size(0))
acc_top1.update(acc1.item(), global_img_tensors.size(0))
acc_top5.update(acc5.item(), global_img_tensors.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0 or i + 1 == len(val_loader):
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc1 {acc_top1.val:.1f} ({acc_top1.avg:.1f})\t'
'Acc5 {acc_top5.val:.1f} ({acc_top5.avg:.1f})'.format(
i,
len(val_loader),
batch_time=batch_time,
loss=losses,
acc_top1=acc_top1,
acc_top5=acc_top5))
if args.evaluate:
logits_matrix = np.concatenate(logits_matrix)
targets_list = np.concatenate(targets_list)
save_results(logits_matrix, targets_list, class_to_idx, args)
if epoch is not None and tb_logger is not None:
logs = OrderedDict()
logs['Val_EpochLoss'] = losses.avg
logs['Val_EpochAcc@1'] = acc_top1.avg
logs['Val_EpochAcc@5'] = acc_top5.avg
# how many iterations we have trained
for key, value in logs.items():
tb_logger.log_scalar(value, key, epoch + 1)
tb_logger.flush()
return losses.avg
def validate_ensemble(val_loader,
classifier,
model0,
model1,
model2,
model3,
model4,
list_id_files,
criterion,
class_to_idx=None):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model0.eval()
model1.eval()
model2.eval()
model3.eval()
model4.eval()
classifier.eval()
logits_matrix = []
targets_list = []
label_list = []
correct = 0
total = 0
with torch.no_grad():
for i, (input, target) in enumerate(val_loader):
input, target = input.to(device), target.to(device)
# compute output and loss
output0, feature0 = model0(input)
output1, feature1 = model1(input)
output2, feature2 = model2(input)
output3, feature3 = model3(input)
output4, feature4 = model4(input)
#sav=torch.cat((feature0,feature1,feature2,feature3,feature4),1)
sav = torch.cat((output0, output1, output2, output3, output4), 1)
class_video = classifier(sav)
loss = criterion(class_video, target)
if args.eval_only:
logits_matrix.append(class_video.detach().cpu().numpy())
targets_list.append(target.detach().cpu().numpy())
_, predicted = torch.max(class_video.data, 1)
label_list.append(predicted.detach().cpu().numpy())
total += target.size(0)
correct += (predicted == target).sum()
# measure accuracy and record loss
prec1, prec5 = accuracy(class_video.detach(),
target.detach().cpu(),
topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
if i % config["print_freq"] == 0:
print('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i,
len(val_loader),
loss=losses,
top1=top1,
top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(
top1=top1, top5=top5))
if args.eval_only:
logits_matrix = np.concatenate(logits_matrix)
targets_list = np.concatenate(targets_list)
label_list = np.concatenate(label_list)
print('Accuracy of the model: %d %%' % (100 * correct / total))
print('Accuracy2 of the model: %d %%' %
(100 * ((label_list == targets_list).sum()) / total))
path_to_save2 = os.path.join(config['output_dir'],
config['model_name'],
"test_results.csv")
with open(path_to_save2, mode='w') as csv_file:
my_csv_writer = csv.writer(csv_file,
delimiter=';',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
for i in range(len(list_id_files)):
my_csv_writer.writerow(
[list_id_files[i], class_to_idx[label_list[i]]])
print(logits_matrix.shape, targets_list.shape)
print(class_to_idx)
save_results(logits_matrix, targets_list, class_to_idx, config)
return losses.avg, top1.avg, top5.avg
def trainEnsemble():
global args, best_prec1
# set run output folder
model_name = "classifier"
output_dir = config["output_dir"]
save_dir = os.path.join(output_dir, model_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
os.makedirs(os.path.join(save_dir, 'plots'))
# adds a handler for Ctrl+C
def signal_handler(signal, frame):
"""
Remove the output dir, if you exit with Ctrl+C and
if there are less then 3 files.
It prevents the noise of experimental runs.
"""
num_files = len(glob.glob(save_dir + "/*"))
if num_files < 1:
shutil.rmtree(save_dir)
print('You pressed Ctrl+C!')
sys.exit(0)
# assign Ctrl+C signal handler
signal.signal(signal.SIGINT, signal_handler)
# create model
#model = ConvColumn(config['num_classes'])
model0 = ConvColumn6(config['num_classes'])
model0 = torch.nn.DataParallel(model0, device_ids=gpus).to(device)
if os.path.isfile("trainings/jpeg_model/jester_conv6/checkpoint.pth.tar"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
"trainings/jpeg_model/jester_conv6/checkpoint.pth.tar")
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model0.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
"trainings/jpeg_model/jester_conv6/checkpoint.pth.tar",
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config['checkpoint']))
model1 = ConvColumn7(config['num_classes'])
model1 = torch.nn.DataParallel(model1, device_ids=gpus).to(device)
if os.path.isfile("trainings/jpeg_model/jester_conv7/model_best.pth.tar"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
"trainings/jpeg_model/jester_conv7/model_best.pth.tar")
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model1.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
"trainings/jpeg_model/jester_conv7/model_best.pth.tar",
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config['checkpoint']))
classifier = Classifier(config['num_classes'])
classifier = torch.nn.DataParallel(classifier, device_ids=gpus).to(device)
if os.path.isfile("trainings/jpeg_model/classifier/model_best.pth.tar"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
"trainings/jpeg_model/classifier/model_best.pth.tar")
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
classifier.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
"trainings/jpeg_model/classifier/model_best.pth.tar",
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config['checkpoint']))
model3 = ConvColumn9(config['num_classes'])
model3 = torch.nn.DataParallel(model3, device_ids=gpus).to(device)
if os.path.isfile("trainings/jpeg_model/jester_conv9/model_best.pth.tar"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
"trainings/jpeg_model/jester_conv9/model_best.pth.tar")
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model3.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
"trainings/jpeg_model/jester_conv9/model_best.pth.tar",
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config['checkpoint']))
model2 = ConvColumn8(config['num_classes'])
model2 = torch.nn.DataParallel(model2, device_ids=gpus).to(device)
if os.path.isfile("trainings/jpeg_model/jester_conv8/model_best.pth.tar"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
"trainings/jpeg_model/jester_conv8/model_best.pth.tar")
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model2.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
"trainings/jpeg_model/jester_conv8/model_best.pth.tar",
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config['checkpoint']))
model4 = ConvColumn5(config['num_classes'])
model4 = torch.nn.DataParallel(model4, device_ids=gpus).to(device)
if os.path.isfile("trainings/jpeg_model/ConvColumn5/model_best.pth.tar"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
"trainings/jpeg_model/ConvColumn5/model_best.pth.tar")
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model4.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
"trainings/jpeg_model/ConvColumn5/model_best.pth.tar",
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config['checkpoint']))
transform_train = Compose([
RandomAffine(degrees=[-10, 10],
translate=[0.15, 0.15],
scale=[0.9, 1.1],
shear=[-5, 5]),
CenterCrop(84),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
transform_valid = Compose([
CenterCrop(84),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_data = VideoFolder(
root=config['train_data_folder'],
csv_file_input=config['train_data_csv'],
csv_file_labels=config['labels_csv'],
clip_size=config['clip_size'],
nclips=1,
step_size=config['step_size'],
is_val=False,
transform=transform_train,
)
print(" > Using {} processes for data loader.".format(
config["num_workers"]))
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config['batch_size'],
shuffle=True,
num_workers=config['num_workers'],
pin_memory=True,
drop_last=True)
val_data = VideoFolder(
root=config['val_data_folder'],
csv_file_input=config['val_data_csv'],
csv_file_labels=config['labels_csv'],
clip_size=config['clip_size'],
nclips=1,
step_size=config['step_size'],
is_val=True,
transform=transform_valid,
)
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
pin_memory=True,
drop_last=False)
list_id_files = []
for i in val_data.csv_data:
list_id_files.append(i.path[16:])
print(len(list_id_files))
###########
assert len(train_data.classes) == config["num_classes"]
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss().to(device)
# define optimizer
lr = config["lr"]
last_lr = config["last_lr"]
momentum = config['momentum']
weight_decay = config['weight_decay']
optimizer = torch.optim.Adam(classifier.parameters(), lr=lr, amsgrad=True)
#torch.optim.SGD(classifier.parameters(), lr,
#momentum=momentum,
#weight_decay=weight_decay)
# set callbacks
plotter = PlotLearning(os.path.join(save_dir, "plots"),
config["num_classes"])
lr_decayer = MonitorLRDecay(0.6, 3)
val_loss = 9999999
# set end condition by num epochs
num_epochs = int(config["num_epochs"])
if num_epochs == -1:
num_epochs = 999999
if args.test_only:
print("test")
test_data = VideoFolder_test(
root=config['val_data_folder'],
csv_file_input=config['test_data_csv'],
clip_size=config['clip_size'],
nclips=1,
step_size=config['step_size'],
is_val=True,
transform=transform_valid,
)
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
pin_memory=True,
drop_last=False)
list_id_files_test = []
for i in test_data.csv_data:
list_id_files_test.append(i.path[16:])
print(len(list_id_files_test))
test_ensemble(test_loader, classifier, model1, model2, model3,
list_id_files_test, criterion, train_data.classes_dict)
return
if args.eval_only:
val_loss, val_top1, val_top5 = validate_ensemble(
val_loader, classifier, model1, model2, model3, list_id_files,
criterion, train_data.classes_dict)
return
# switch to evaluate mode
model0.eval()
model1.eval()
model2.eval()
model3.eval()
model4.eval()
classifier.train()
logits_matrix = []
targets_list = []
new_input = np.array([])
train_writer = tensorboardX.SummaryWriter("logs")
for epoch in range(0, num_epochs):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
lr = lr_decayer(val_loss, lr)
print(" > Current LR : {}".format(lr))
if lr < last_lr and last_lr > 0:
print(" > Training is done by reaching the last learning rate {}".
format(last_lr))
sys.exit(1)
for i, (input, target) in enumerate(train_loader):
input, target = input.to(device), target.to(device)
with torch.no_grad():
# compute output and loss
output0, feature0 = model0(input)
output1, feature1 = model1(input)
output2, feature2 = model2(input)
output3, feature3 = model3(input)
output4, feature4 = model4(input)
#sav=torch.cat((feature0,feature1,feature2,feature3,feature4),1)
sav = torch.cat((output0, output1, output2, output3, output4),
1)
classifier.zero_grad()
class_video = classifier(sav)
loss = criterion(class_video, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(class_video.detach(),
target.detach().cpu(),
topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % config["print_freq"] == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
0,
i,
len(train_loader),
loss=losses,
top1=top1,
top5=top5))
val_loss, val_top1, val_top5 = validate_ensemble(
val_loader, classifier, model0, model1, model2, model3, model4,
list_id_files, criterion)
train_writer.add_scalar('loss', loss, losses.avg)
train_writer.add_scalar('top1', top1.avg, epoch + 1)
train_writer.add_scalar('top5', top5.avg, epoch + 1)
train_writer.add_scalar('val_loss', val_loss, epoch + 1)
train_writer.add_scalar('val_top1', val_top1, epoch + 1)
train_writer.add_scalar('val_top5', val_top5, epoch + 1)
# remember best prec@1 and save checkpoint
is_best = val_top1 > best_prec1
best_prec1 = max(val_top1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': "Classifier",
'state_dict': classifier.state_dict(),
'best_prec1': best_prec1,
}, is_best, config)
def predict(model, result_file_path=args.result_path):
global args
acc_top1 = AverageMeter()
acc_top5 = AverageMeter()
# Pickle path for intermediate batch result:
temp_pickle = os.path.splitext(result_file_path)[0] + '.p'
if os.path.exists(temp_pickle):
os.remove(temp_pickle)
#model = torch.nn.DataParallel(model)
# Read Model in
checkpoint = torch.load(args.model_state_dict_path)
model.load_state_dict(checkpoint['state_dict'])
epoch = checkpoint['epoch']
best_loss = checkpoint['best_loss']
# Set eval mode
model.eval()
# Data Loader
dataset = VideoFolder(
root=args.root_frames,
num_boxes=args.num_boxes,
file_input=args.json_data_list,
file_labels=args.json_file_labels,
word2vec_weights=args.word2vec_weights_path,
frames_duration=args.num_frames,
video_root=args.video_root,
args=args,
is_val=True,
if_augment=True,
model=args.model
)
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, drop_last=True,
pin_memory=True
)
for i, (vid_names, frame_list, global_img_tensors, box_tensors, box_categories, word2vec_features, video_label) in enumerate(data_loader):
# Move data to GPU
global_img_tensors = global_img_tensors.to(cuda_device)
box_categories = box_categories.to(cuda_device)
box_tensors = box_tensors.to(cuda_device)
video_label = video_label.to(cuda_device)
if not isinstance(word2vec_features, list): # word2vec has dummpy [] holder if no word2vec provided
word2vec_features = word2vec_features.to(cuda_device)
with torch.no_grad():
output = model(global_img_tensors, box_categories, box_tensors, word2vec_features, video_label)
attention = model.batch_attention_weight
print("Atten shape", attention.size())
output = output.view((-1, len(data_loader.dataset.classes)))
[acc1, acc5], pred = accuracy(output, video_label, topk=(1, 5), return_predict=True)
acc_top1.update(acc1.item(), global_img_tensors.size(0))
acc_top5.update(acc5.item(), global_img_tensors.size(0))
with open(temp_pickle, 'ab+') as fp:
result_dict = {
'vid_names' : vid_names,
'frame_list' : frame_list.int().tolist(), # Convert tensor to int type and then to list.
'video_label' : video_label.int().cpu().numpy(),
'prediction' : pred.int().cpu().numpy(),
'attention' : attention.cpu().numpy()
}
pickle.dump(result_dict, fp)
batch_result = 'Epoch: [{0}][{1}/{2}]\t' \
'Acc1 {acc_top1.val:.1f} ({acc_top1.avg:.1f})\t' \
'Acc5 {acc_top5.val:.1f} ({acc_top5.avg:.1f})'.format(
epoch, i, len(data_loader), acc_top1=acc_top1, acc_top5=acc_top5)
print(batch_result)
aggre_batch_result(temp_pickle, result_file_path=result_file_path)
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target, item_id) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if config['nclips_train_val'] > 1:
input_var = list(input.split(config['clip_size'], 2))
for idx, inp in enumerate(input_var):
input_var[idx] = inp.to(device)
else:
input_var = [input.to(device)]
target = target.to(device)
model.zero_grad()
# compute output and loss
output = model(input_var)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = utils.accuracy(output.detach().cpu(),
target.detach().cpu(),
topk=(1, 5))
# for frame_ind in range(input_var[0].shape[2]):
# wandb.log({f"input_var_{frame_ind}": wandb.Image(input_var[0][0, :, frame_ind, :])})
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
wandb.log({'train_loss': loss.item()})
wandb.log({'train_prec1': prec1.item()})
wandb.log({'train_prec5': prec5.item()})
wandb.log({'train_top1_avg': top1.avg})
wandb.log({'train_top5_avg': top5.avg})
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config["print_freq"] == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5))
return losses.avg, top1.avg, top5.avg
def train(train_loader, model, optimizer, epoch, criterion, tb_logger=None):
global args
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc_top1 = AverageMeter()
acc_top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
# check_gpu('Start')
#for i, (vid_names, frame_tensors, global_img_tensors, box_tensors, box_categories, word2vec_features, video_label) in enumerate(train_loader):
for i, (vid_names, frame_list, global_img_tensors, box_tensors,
box_categories, word2vec_features,
video_label) in enumerate(train_loader):
################################################################
# output to tensorboard
# print("print frame shape:", frame_tensors.shape) #[72,8,3,224,224]
# print("len vid_names:", len(vid_names))
#for v, video in enumerate(frame_tensors):
#print("video shape:", video.shape) #[8, 3, H, W]
#frames = [frame for frame in video] #[8, 3, H, W]
#print("frames shape:", frames[0].shape) #[3, H, W]
#img_grid = torchvision.utils.make_grid(video, nrow=8)
#print("image grid shape:", img_grid.shape) #[3, H, 8*W]
#transposed_frame = np.transpose(img_grid.numpy(), (1, 2, 0))
#print("transposed shape:", transposed_frame)
#plt.imshow(transposed_frame)
#writer.add_images(vid_names[v], video)
################################################################
model.zero_grad()
# measure data loading time
data_time.update(time.time() - end)
# local_img_tensor is (b, nr_frames, nr_boxes, 3, h, w)
# global_img_tensor is (b, nr_frames, 3, h, w)
# compute output
global_img_tensors = global_img_tensors.to(cuda_device)
box_categories = box_categories.to(cuda_device)
box_tensors = box_tensors.to(cuda_device)
video_label = video_label.to(cuda_device)
#print("type w2v:", word2vec_features)
if not isinstance(
word2vec_features,
list): # word2vec has dummpy [] holder if no word2vec provided
word2vec_features = word2vec_features.to(cuda_device)
output = model(global_img_tensors, box_categories, box_tensors,
word2vec_features, video_label)
output = output.view((-1, len(train_loader.dataset.classes)))
# check_gpu('after train')
#loss = criterion(output, video_label.long().cuda())
loss = criterion(output, video_label)
# check_gpu('after loss')
acc1, acc5 = accuracy(output.cpu(), video_label.cpu(), topk=(1, 5))
# measure accuracy and record loss
losses.update(loss.item(), global_img_tensors.size(0))
acc_top1.update(acc1.item(), global_img_tensors.size(0))
acc_top5.update(acc5.item(), global_img_tensors.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if args.clip_gradient is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.clip_gradient)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
batch_result = 'Epoch: [{0}][{1}/{2}]\t' \
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' \
'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \
'Acc1 {acc_top1.val:.1f} ({acc_top1.avg:.1f})\t' \
'Acc5 {acc_top5.val:.1f} ({acc_top5.avg:.1f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, acc_top1=acc_top1, acc_top5=acc_top5)
print(batch_result)
# log training data into tensorboard
if tb_logger is not None and i % args.log_freq == 0:
logs = OrderedDict()
logs['Train_IterLoss'] = losses.val
logs['Train_Acc@1'] = acc_top1.val
logs['Train_Acc@5'] = acc_top5.val
# how many iterations we have trained
iter_count = epoch * len(train_loader) + i
for key, value in logs.items():
tb_logger.log_scalar(value, key, iter_count)
tb_logger.flush()
# Finish training for one epoch
return losses.avg, acc_top1.avg, acc_top5.avg