def __call__(self, model: nn.Module, epoch_idx, output_dir, eval_rtn: dict,
test_rtn: dict, logger: logging.Logger,
writer: SummaryWriter):
# save model
acc = eval_rtn.get('err_spk', 0) - eval_rtn.get('err_sph', 1)
is_best = acc > self.best_accu
self.best_accu = acc if is_best else self.best_accu
model_filename = "epoch_{}.pth".format(epoch_idx)
save_checkpoint(model,
os.path.join(output_dir, model_filename),
meta={'epoch': epoch_idx})
os.system("ln -sf {} {}".format(
os.path.abspath(os.path.join(output_dir, model_filename)),
os.path.join(output_dir, "latest.pth")))
if is_best:
os.system("ln -sf {} {}".format(
os.path.abspath(os.path.join(output_dir, model_filename)),
os.path.join(output_dir, "best.pth")))
if logger is not None:
logger.info("EvalHook: best accu: {:.3f}, is_best: {}".format(
self.best_accu, is_best))
def main():
args = arguments()
num_templates = 25 # aka the number of clusters
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
img_transforms = transforms.Compose([transforms.ToTensor(), normalize])
train_loader, _ = get_dataloader(args.traindata,
args,
num_templates,
img_transforms=img_transforms)
model = DetectionModel(num_objects=1, num_templates=num_templates)
loss_fn = DetectionCriterion(num_templates)
# directory where we'll store model weights
weights_dir = "weights"
if not osp.exists(weights_dir):
os.mkdir(weights_dir)
# check for CUDA
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
optimizer = optim.SGD(model.learnable_parameters(args.lr),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
# Set the start epoch if it has not been
if not args.start_epoch:
args.start_epoch = checkpoint['epoch']
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=20,
last_epoch=args.start_epoch - 1)
# train and evalute for `epochs`
for epoch in range(args.start_epoch, args.epochs):
scheduler.step()
trainer.train(model,
loss_fn,
optimizer,
train_loader,
epoch,
device=device)
if (epoch + 1) % args.save_every == 0:
trainer.save_checkpoint(
{
'epoch': epoch + 1,
'batch_size': train_loader.batch_size,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
filename="checkpoint_{0}.pth".format(epoch + 1),
save_path=weights_dir)
def train(train_loader, model, optimizer, train_vars, control_vars, verbose=True):
curr_epoch_iter = 1
for batch_idx, (data, target) in enumerate(train_loader):
control_vars['batch_idx'] = batch_idx
if batch_idx < control_vars['iter_size']:
print_verbose("\rPerforming first iteration; current mini-batch: " +
str(batch_idx+1) + "/" + str(control_vars['iter_size']), verbose, n_tabs=0, erase_line=True)
# check if arrived at iter to start
if control_vars['curr_epoch_iter'] < control_vars['start_iter_mod']:
if batch_idx % control_vars['iter_size'] == 0:
print_verbose("\rGoing through iterations to arrive at last one saved... " +
str(int(control_vars['curr_epoch_iter']*100.0/control_vars['start_iter_mod'])) + "% of " +
str(control_vars['start_iter_mod']) + " iterations (" +
str(control_vars['curr_epoch_iter']) + "/" + str(control_vars['start_iter_mod']) + ")",
verbose, n_tabs=0, erase_line=True)
control_vars['curr_epoch_iter'] += 1
control_vars['curr_iter'] += 1
curr_epoch_iter += 1
continue
# save checkpoint after final iteration
if control_vars['curr_iter'] == control_vars['num_iter']:
print_verbose("\nReached final number of iterations: " + str(control_vars['num_iter']), verbose)
print_verbose("\tSaving final model checkpoint...", verbose)
final_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
trainer.save_checkpoint(final_model_dict,
filename=train_vars['checkpoint_filenamebase'] +
'final' + str(control_vars['num_iter']) + '.pth.tar')
control_vars['done_training'] = True
break
# start time counter
start = time.time()
# get data and targetas cuda variables
target_heatmaps, target_joints, _, target_prior = target
data, target_heatmaps, target_prior = Variable(data), Variable(target_heatmaps), Variable(target_prior)
if train_vars['use_cuda']:
data = data.cuda()
target_heatmaps = target_heatmaps.cuda()
target_prior = target_prior.cuda()
# visualize if debugging
# get model output
output = model(data)
# accumulate loss for sub-mini-batch
if train_vars['cross_entropy']:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
loss, loss_prior = my_losses.calculate_loss_HALNet_prior(loss_func,
output, target_heatmaps, target_prior, model.joint_ixs, model.WEIGHT_LOSS_INTERMED1,
model.WEIGHT_LOSS_INTERMED2, model.WEIGHT_LOSS_INTERMED3,
model.WEIGHT_LOSS_MAIN, control_vars['iter_size'])
loss.backward()
train_vars['total_loss'] += loss
train_vars['total_loss_prior'] += loss_prior
# accumulate pixel dist loss for sub-mini-batch
train_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
train_vars['total_pixel_loss'], output[3], target_heatmaps, control_vars['batch_size'])
if train_vars['cross_entropy']:
train_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
train_vars['total_pixel_loss_sample'], output[3], target_heatmaps, control_vars['batch_size'])
else:
train_vars['total_pixel_loss_sample'] = [-1] * len(model.joint_ixs)
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx+1) % control_vars['iter_size'] == 0
if minibatch_completed:
# optimise for mini-batch
optimizer.step()
# clear optimiser
optimizer.zero_grad()
# append total loss
train_vars['losses'].append(train_vars['total_loss'].data[0])
# erase total loss
total_loss = train_vars['total_loss'].data[0]
train_vars['total_loss'] = 0
# append total loss prior
train_vars['losses_prior'].append(train_vars['total_loss_prior'].data[0])
# erase total loss
total_loss_prior = train_vars['total_loss_prior'].data[0]
train_vars['total_loss_prior'] = 0
# append dist loss
train_vars['pixel_losses'].append(train_vars['total_pixel_loss'])
# erase pixel dist loss
train_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
train_vars['pixel_losses_sample'].append(train_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
train_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
if train_vars['losses'][-1] < train_vars['best_loss']:
train_vars['best_loss'] = train_vars['losses'][-1]
print_verbose(" This is a best loss found so far: " + str(train_vars['losses'][-1]), verbose)
train_vars['best_model_dict'] = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
if train_vars['losses_prior'][-1] < train_vars['best_loss_prior']:
train_vars['best_loss_prior'] = train_vars['losses_prior'][-1]
# log checkpoint
if control_vars['curr_iter'] % control_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, epoch, total_loss, train_vars, control_vars)
msg = ''
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
msg += print_verbose("Current loss (prior): " + str(total_loss_prior), verbose) + "\n"
msg += print_verbose("Best loss (prior): " + str(train_vars['best_loss_prior']), verbose) + "\n"
msg += print_verbose("Mean total loss (prior): " + str(np.mean(train_vars['losses_prior'])), verbose) + "\n"
msg += print_verbose("Mean loss (prior) for last " + str(control_vars['log_interval']) +
" iterations (average total loss): " + str(
np.mean(train_vars['losses_prior'][-control_vars['log_interval']:])), verbose) + "\n"
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
if not control_vars['output_filepath'] == '':
with open(control_vars['output_filepath'], 'a') as f:
f.write(msg + '\n')
if control_vars['curr_iter'] % control_vars['log_interval_valid'] == 0:
print_verbose("\nSaving model and checkpoint model for validation", verbose)
checkpoint_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
trainer.save_checkpoint(checkpoint_model_dict,
filename=train_vars['checkpoint_filenamebase'] + 'for_valid_' +
str(control_vars['curr_iter']) + '.pth.tar')
# print time lapse
prefix = 'Training (Epoch #' + str(epoch) + ' ' + str(control_vars['curr_epoch_iter']) + '/' +\
str(control_vars['tot_iter']) + ')' + ', (Batch ' + str(control_vars['batch_idx']+1) +\
'(' + str(control_vars['iter_size']) + ')' + '/' +\
str(control_vars['num_batches']) + ')' + ', (Iter #' + str(control_vars['curr_iter']) +\
'(' + str(control_vars['batch_size']) + ')' +\
' - log every ' + str(control_vars['log_interval']) + ' iter): '
control_vars['tot_toc'] = display_est_time_loop(control_vars['tot_toc'] + time.time() - start,
control_vars['curr_iter'], control_vars['num_iter'],
prefix=prefix)
control_vars['curr_iter'] += 1
control_vars['start_iter'] = control_vars['curr_iter'] + 1
control_vars['curr_epoch_iter'] += 1
return train_vars, control_vars
def main():
args = arguments()
segmentation = False
# check for CUDA
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
train_loader = get_dataloader(args.traindata, args, device=device)
model = CoattentionNet()
loss_fn = SiameseCriterion(device=device)
pretrained_dict = torch.load(
"../crowd-counting-revise/weight/checkpoint_104.pth")["model"]
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if "frontend" not in k
and "backend2" not in k and "main_classifier" not in k
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
# directory where we'll store model weights
weights_dir = "weight_all"
if not osp.exists(weights_dir):
os.mkdir(weights_dir)
optimizer = optim.Adam(model.learnable_parameters(args.lr),
lr=args.lr,
weight_decay=args.weight_decay)
#optimizer = optim.Adam(model.learnable_parameters(args.lr), lr=args.lr)
if args.resume:
checkpoint = torch.load(args.resume)
model = model.to(device)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
# Set the start epoch if it has not been
if not args.start_epoch:
args.start_epoch = checkpoint['epoch']
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=20,
last_epoch=args.start_epoch - 1)
print("Start training!")
# train and evalute for `epochs`
best_mae = sys.maxsize
for epoch in range(args.start_epoch, args.epochs):
if epoch % 4 == 0 and epoch != 0:
val_loader = val_dataloader(args, device=device)
with torch.no_grad():
if not segmentation:
mae, mse = evaluate_model(model,
val_loader,
device=device,
training=True,
debug=args.debug,
segmentation=segmentation)
if mae < best_mae:
best_mae = mae
best_mse = mse
best_model = "checkpoint_{0}.pth".format(epoch)
log_text = 'epoch: %4d, mae: %4.2f, mse: %4.2f' % (
epoch - 1, mae, mse)
log_print(log_text, color='green', attrs=['bold'])
log_text = 'BEST MAE: %0.1f, BEST MSE: %0.1f, BEST MODEL: %s' % (
best_mae, best_mse, best_model)
log_print(log_text, color='green', attrs=['bold'])
else:
_, _ = evaluate_model(model,
val_loader,
device=device,
training=True,
debug=args.debug,
segmentation=segmentation)
scheduler.step()
trainer.train(model,
loss_fn,
optimizer,
train_loader,
epoch,
device=device)
if (epoch + 1) % args.save_every == 0:
trainer.save_checkpoint(
{
'epoch': epoch + 1,
'batch_size': train_loader.batch_size,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
filename="checkpoint_{0}.pth".format(epoch + 1),
save_path=weights_dir)
def validate(valid_loader, model, optimizer, valid_vars, control_vars, verbose=True):
curr_epoch_iter = 1
for batch_idx, (data, target) in enumerate(valid_loader):
control_vars['batch_idx'] = batch_idx
if batch_idx < control_vars['iter_size']:
print_verbose("\rPerforming first iteration; current mini-batch: " +
str(batch_idx + 1) + "/" + str(control_vars['iter_size']), verbose, n_tabs=0, erase_line=True)
# start time counter
start = time.time()
# get data and targetas cuda variables
target_heatmaps, target_joints, target_joints_z = target
data, target_heatmaps = Variable(data), Variable(target_heatmaps)
if valid_vars['use_cuda']:
data = data.cuda()
target_heatmaps = target_heatmaps.cuda()
# visualize if debugging
# get model output
output = model(data)
# accumulate loss for sub-mini-batch
if valid_vars['cross_entropy']:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
loss = my_losses.calculate_loss_HALNet(loss_func,
output, target_heatmaps, model.joint_ixs, model.WEIGHT_LOSS_INTERMED1,
model.WEIGHT_LOSS_INTERMED2, model.WEIGHT_LOSS_INTERMED3,
model.WEIGHT_LOSS_MAIN, control_vars['iter_size'])
if DEBUG_VISUALLY:
for i in range(control_vars['max_mem_batch']):
filenamebase_idx = (batch_idx * control_vars['max_mem_batch']) + i
filenamebase = valid_loader.dataset.get_filenamebase(filenamebase_idx)
fig = visualize.create_fig()
#visualize.plot_joints_from_heatmaps(output[3][i].data.numpy(), fig=fig,
# title=filenamebase, data=data[i].data.numpy())
#visualize.plot_image_and_heatmap(output[3][i][8].data.numpy(),
# data=data[i].data.numpy(),
# title=filenamebase)
#visualize.savefig('/home/paulo/' + filenamebase.replace('/', '_') + '_heatmap')
labels_colorspace = conv.heatmaps_to_joints_colorspace(output[3][i].data.numpy())
data_crop, crop_coords, labels_heatmaps, labels_colorspace = \
converter.crop_image_get_labels(data[i].data.numpy(), labels_colorspace, range(21))
visualize.plot_image(data_crop, title=filenamebase, fig=fig)
visualize.plot_joints_from_colorspace(labels_colorspace, title=filenamebase, fig=fig, data=data_crop)
#visualize.savefig('/home/paulo/' + filenamebase.replace('/', '_') + '_crop')
visualize.show()
#loss.backward()
valid_vars['total_loss'] += loss
# accumulate pixel dist loss for sub-mini-batch
valid_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
valid_vars['total_pixel_loss'], output[3], target_heatmaps, control_vars['batch_size'])
if valid_vars['cross_entropy']:
valid_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
valid_vars['total_pixel_loss_sample'], output[3], target_heatmaps, control_vars['batch_size'])
else:
valid_vars['total_pixel_loss_sample'] = [-1] * len(model.joint_ixs)
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx+1) % control_vars['iter_size'] == 0
if minibatch_completed:
# append total loss
valid_vars['losses'].append(valid_vars['total_loss'].item())
# erase total loss
total_loss = valid_vars['total_loss'].item()
valid_vars['total_loss'] = 0
# append dist loss
valid_vars['pixel_losses'].append(valid_vars['total_pixel_loss'])
# erase pixel dist loss
valid_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
valid_vars['pixel_losses_sample'].append(valid_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
valid_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
if valid_vars['losses'][-1] < valid_vars['best_loss']:
valid_vars['best_loss'] = valid_vars['losses'][-1]
#print_verbose(" This is a best loss found so far: " + str(valid_vars['losses'][-1]), verbose)
# log checkpoint
if control_vars['curr_iter'] % control_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, 1, total_loss, valid_vars, control_vars)
model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': valid_vars,
}
trainer.save_checkpoint(model_dict,
filename=valid_vars['checkpoint_filenamebase'] +
str(control_vars['num_iter']) + '.pth.tar')
# print time lapse
prefix = 'Validating (Epoch #' + str(1) + ' ' + str(control_vars['curr_epoch_iter']) + '/' +\
str(control_vars['tot_iter']) + ')' + ', (Batch ' + str(control_vars['batch_idx']+1) +\
'(' + str(control_vars['iter_size']) + ')' + '/' +\
str(control_vars['num_batches']) + ')' + ', (Iter #' + str(control_vars['curr_iter']) +\
'(' + str(control_vars['batch_size']) + ')' +\
' - log every ' + str(control_vars['log_interval']) + ' iter): '
control_vars['tot_toc'] = display_est_time_loop(control_vars['tot_toc'] + time.time() - start,
control_vars['curr_iter'], control_vars['num_iter'],
prefix=prefix)
control_vars['curr_iter'] += 1
control_vars['start_iter'] = control_vars['curr_iter'] + 1
control_vars['curr_epoch_iter'] += 1
return valid_vars, control_vars
def train():
parser = argparse.ArgumentParser(
description='Parameters for training Model')
# configuration fiule
parser.add_argument('--opt', type=str, help='Path to option YAML file.')
args = parser.parse_args()
opt = option.parse(args.opt)
set_logger.setup_logger(opt['logger']['name'],
opt['logger']['path'],
screen=opt['logger']['screen'],
tofile=opt['logger']['tofile'])
logger = logging.getLogger(opt['logger']['name'])
day_time = datetime.date.today().strftime('%y%m%d')
# build model
model = opt['model']['MODEL']
logger.info("Building the model of {}".format(model))
# Extraction and Suppression model
if opt['model']['MODEL'] == 'DPRNN_Speaker_Extraction' or opt['model'][
'MODEL'] == 'DPRNN_Speaker_Suppression':
net = model_function.Extractin_Suppression_Model(
**opt['Dual_Path_Aux_Speaker'])
# Separation model
if opt['model']['MODEL'] == 'DPRNN_Speech_Separation':
net = model_function.Speech_Serapation_Model(
**opt['Dual_Path_Aux_Speaker'])
if opt['train']['gpuid']:
if len(opt['train']['gpuid']) > 1:
logger.info('We use GPUs : {}'.format(opt['train']['gpuid']))
else:
logger.info('We use GPUs : {}'.format(opt['train']['gpuid']))
device = torch.device('cuda:{}'.format(opt['train']['gpuid'][0]))
gpuids = opt['train']['gpuid']
if len(gpuids) > 1:
net = torch.nn.DataParallel(net, device_ids=gpuids)
net = net.to(device)
logger.info('Loading {} parameters: {:.3f} Mb'.format(
model, check_parameters(net)))
# build optimizer
logger.info("Building the optimizer of {}".format(model))
Optimizer = make_optimizer(net.parameters(), opt)
Scheduler = ReduceLROnPlateau(Optimizer,
mode='min',
factor=opt['scheduler']['factor'],
patience=opt['scheduler']['patience'],
verbose=True,
min_lr=opt['scheduler']['min_lr'])
# build dataloader
logger.info('Building the dataloader of {}'.format(model))
train_dataloader, val_dataloader = make_dataloader(opt)
logger.info('Train Datasets Length: {}, Val Datasets Length: {}'.format(
len(train_dataloader), len(val_dataloader)))
# build trainer
logger.info('............. Training ................')
total_epoch = opt['train']['epoch']
num_spks = opt['num_spks']
print_freq = opt['logger']['print_freq']
checkpoint_path = opt['train']['path']
early_stop = opt['train']['early_stop']
max_norm = opt['optim']['clip_norm']
best_loss = np.inf
no_improve = 0
ce_loss = torch.nn.CrossEntropyLoss()
weight = 0.1
epoch = 0
# Resume training settings
if opt['resume']['state']:
opt['resume']['path'] = opt['resume'][
'path'] + '/' + '200722_epoch{}.pth.tar'.format(
opt['resume']['epoch'])
ckp = torch.load(opt['resume']['path'], map_location='cpu')
epoch = ckp['epoch']
logger.info("Resume from checkpoint {}: epoch {:.3f}".format(
opt['resume']['path'], epoch))
net.load_state_dict(ckp['model_state_dict'])
net.to(device)
Optimizer.load_state_dict(ckp['optim_state_dict'])
while epoch < total_epoch:
epoch += 1
logger.info('Start training from epoch: {:d}, iter: {:d}'.format(
epoch, 0))
num_steps = len(train_dataloader)
# trainning process
total_SNRloss = 0.0
total_CEloss = 0.0
num_index = 1
start_time = time.time()
for inputs, targets in train_dataloader:
# Separation train
if opt['model']['MODEL'] == 'DPRNN_Speech_Separation':
mix = inputs
ref = targets
net.train()
mix = mix.to(device)
ref = [ref[i].to(device) for i in range(num_spks)]
net.zero_grad()
train_out = net(mix)
SNR_loss = Loss(train_out, ref)
loss = SNR_loss
# Extraction train
if opt['model']['MODEL'] == 'DPRNN_Speaker_Extraction':
mix, aux = inputs
ref, aux_len, sp_label = targets
net.train()
mix = mix.to(device)
aux = aux.to(device)
ref = ref.to(device)
aux_len = aux_len.to(device)
sp_label = sp_label.to(device)
net.zero_grad()
train_out = net([mix, aux, aux_len])
SNR_loss = Loss_SI_SDR(train_out[0], ref)
CE_loss = torch.mean(ce_loss(train_out[1], sp_label))
loss = SNR_loss + weight * CE_loss
total_CEloss += CE_loss.item()
# Suppression train
if opt['model']['MODEL'] == 'DPRNN_Speaker_Suppression':
mix, aux = inputs
ref, aux_len = targets
net.train()
mix = mix.to(device)
aux = aux.to(device)
ref = ref.to(device)
aux_len = aux_len.to(device)
net.zero_grad()
train_out = net([mix, aux, aux_len])
SNR_loss = Loss_SI_SDR(train_out[0], ref)
loss = SNR_loss
# BP processs
loss.backward()
torch.nn.utils.clip_grad_norm_(net.parameters(), max_norm)
Optimizer.step()
total_SNRloss += SNR_loss.item()
if num_index % print_freq == 0:
message = '<Training epoch:{:d} / {:d} , iter:{:d} / {:d}, lr:{:.3e}, SI-SNR_loss:{:.3f}, CE loss:{:.3f}>'.format(
epoch, total_epoch, num_index, num_steps,
Optimizer.param_groups[0]['lr'], total_SNRloss / num_index,
total_CEloss / num_index)
logger.info(message)
num_index += 1
end_time = time.time()
mean_SNRLoss = total_SNRloss / num_index
mean_CELoss = total_CEloss / num_index
message = 'Finished Training *** <epoch:{:d} / {:d}, iter:{:d}, lr:{:.3e}, ' \
'SNR loss:{:.3f}, CE loss:{:.3f}, Total time:{:.3f} min> '.format(
epoch, total_epoch, num_index, Optimizer.param_groups[0]['lr'], mean_SNRLoss, mean_CELoss, (end_time - start_time) / 60)
logger.info(message)
# development processs
val_num_index = 1
val_total_loss = 0.0
val_CE_loss = 0.0
val_acc_total = 0.0
val_acc = 0.0
val_start_time = time.time()
val_num_steps = len(val_dataloader)
for inputs, targets in val_dataloader:
net.eval()
with torch.no_grad():
# Separation development
if opt['model']['MODEL'] == 'DPRNN_Speech_Separation':
mix = inputs
ref = targets
mix = mix.to(device)
ref = [ref[i].to(device) for i in range(num_spks)]
Optimizer.zero_grad()
val_out = net(mix)
val_loss = Loss(val_out, ref)
val_total_loss += val_loss.item()
# Extraction development
if opt['model']['MODEL'] == 'DPRNN_Speaker_Extraction':
mix, aux = inputs
ref, aux_len, label = targets
mix = mix.to(device)
aux = aux.to(device)
ref = ref.to(device)
aux_len = aux_len.to(device)
label = label.to(device)
Optimizer.zero_grad()
val_out = net([mix, aux, aux_len])
val_loss = Loss_SI_SDR(val_out[0], ref)
val_ce = torch.mean(ce_loss(val_out[1], label))
val_acc = accuracy_speaker(val_out[1], label)
val_acc_total += val_acc
val_total_loss += val_loss.item()
val_CE_loss += val_ce.item()
# suppression development
if opt['model']['MODEL'] == 'DPRNN_Speaker_Suppression':
mix, aux = inputs
ref, aux_len = targets
mix = mix.to(device)
aux = aux.to(device)
ref = ref.to(device)
aux_len = aux_len.to(device)
Optimizer.zero_grad()
val_out = net([mix, aux, aux_len])
val_loss = Loss_SI_SDR(val_out[0], ref)
val_total_loss += val_loss.item()
if val_num_index % print_freq == 0:
message = '<Valid-Epoch:{:d} / {:d}, iter:{:d} / {:d}, lr:{:.3e}, ' \
'val_SISNR_loss:{:.3f}, val_CE_loss:{:.3f}, val_acc :{:.3f}>' .format(
epoch, total_epoch, val_num_index, val_num_steps, Optimizer.param_groups[0]['lr'],
val_total_loss / val_num_index,
val_CE_loss / val_num_index,
val_acc_total / val_num_index)
logger.info(message)
val_num_index += 1
val_end_time = time.time()
mean_val_total_loss = val_total_loss / val_num_index
mean_val_CE_loss = val_CE_loss / val_num_index
mean_acc = val_acc_total / val_num_index
message = 'Finished *** <epoch:{:d}, iter:{:d}, lr:{:.3e}, val SI-SNR loss:{:.3f}, val_CE_loss:{:.3f}, val_acc:{:.3f}' \
' Total time:{:.3f} min> '.format(epoch, val_num_index, Optimizer.param_groups[0]['lr'],
mean_val_total_loss, mean_val_CE_loss, mean_acc,
(val_end_time - val_start_time) / 60)
logger.info(message)
Scheduler.step(mean_val_total_loss)
if mean_val_total_loss >= best_loss:
no_improve += 1
logger.info(
'No improvement, Best SI-SNR Loss: {:.4f}'.format(best_loss))
if mean_val_total_loss < best_loss:
best_loss = mean_val_total_loss
no_improve = 0
save_checkpoint(epoch, checkpoint_path, net, Optimizer, day_time)
logger.info(
'Epoch: {:d}, Now Best SI-SNR Loss Change: {:.4f}'.format(
epoch, best_loss))
if no_improve == early_stop:
save_checkpoint(epoch, checkpoint_path, net, Optimizer, day_time)
logger.info("Stop training cause no impr for {:d} epochs".format(
no_improve))
break
# Hyperparams that have been found to work well
tconf = trainer.TrainerConfig(max_epochs=650,
batch_size=128,
learning_rate=6e-3,
lr_decay=True,
warmup_tokens=512 * 20,
final_tokens=200 * len(pretrain_dataset) *
block_size,
num_workers=4,
ckpt_path=args.writing_params_path)
# Initiate trainer, train, then save params of model
trainer = trainer.Trainer(model, pretrain_dataset, None, tconf)
trainer.train()
trainer.save_checkpoint()
elif args.function == 'finetune':
assert args.writing_params_path is not None
assert args.finetune_corpus_path is not None
# - Given:
# 1. A finetuning corpus specified in args.finetune_corpus_path
# 2. A path args.reading_params_path containing pretrained model
# parameters, or None if finetuning without a pretrained model
# 3. An output path args.writing_params_path for the model parameters
# - Goals:
# 1. If args.reading_params_path is specified, load these parameters
# into the model
# 2. Finetune the model on this corpus
# 3. Save the resulting model in args.writing_params_path
def train(train_loader, model, optimizer, train_vars):
verbose = train_vars['verbose']
for batch_idx, (data, target) in enumerate(train_loader):
train_vars['batch_idx'] = batch_idx
# print info about performing first iter
if batch_idx < train_vars['iter_size']:
print_verbose(
"\rPerforming first iteration; current mini-batch: " +
str(batch_idx + 1) + "/" + str(train_vars['iter_size']),
verbose,
n_tabs=0,
erase_line=True)
# check if arrived at iter to start
arrived_curr_iter, train_vars = run_until_curr_iter(
batch_idx, train_vars)
if not arrived_curr_iter:
continue
# save checkpoint after final iteration
if train_vars['curr_iter'] - 1 == train_vars['num_iter']:
train_vars = trainer.save_final_checkpoint(train_vars, model,
optimizer)
break
# start time counter
start = time.time()
# get data and target as torch Variables
_, target_joints, target_heatmaps, target_joints_z = target
# make target joints be relative
target_joints = target_joints[:, 3:]
data, target_heatmaps = Variable(data), Variable(target_heatmaps)
if train_vars['use_cuda']:
data = data.cuda()
target_heatmaps = target_heatmaps.cuda()
target_joints = target_joints.cuda()
target_joints_z = target_joints_z.cuda()
# get model output
output = model(data)
# accumulate loss for sub-mini-batch
if train_vars['cross_entropy']:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
weights_heatmaps_loss, weights_joints_loss = get_loss_weights(
train_vars['curr_iter'])
loss, loss_heatmaps, loss_joints = my_losses.calculate_loss_JORNet(
loss_func, output, target_heatmaps, target_joints,
train_vars['joint_ixs'], weights_heatmaps_loss,
weights_joints_loss, train_vars['iter_size'])
loss.backward()
train_vars['total_loss'] += loss.item()
train_vars['total_joints_loss'] += loss_joints.item()
train_vars['total_heatmaps_loss'] += loss_heatmaps.item()
# accumulate pixel dist loss for sub-mini-batch
train_vars[
'total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
train_vars['total_pixel_loss'], output[3], target_heatmaps,
train_vars['batch_size'])
if train_vars['cross_entropy']:
train_vars[
'total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
train_vars['total_pixel_loss_sample'], output[3],
target_heatmaps, train_vars['batch_size'])
else:
train_vars['total_pixel_loss_sample'] = [-1] * len(model.joint_ixs)
'''
For debugging training
for i in range(train_vars['max_mem_batch']):
filenamebase_idx = (batch_idx * train_vars['max_mem_batch']) + i
filenamebase = train_loader.dataset.get_filenamebase(filenamebase_idx)
visualize.plot_joints_from_heatmaps(target_heatmaps[i].data.cpu().numpy(),
title='GT joints: ' + filenamebase, data=data[i].data.cpu().numpy())
visualize.plot_joints_from_heatmaps(output[3][i].data.cpu().numpy(),
title='Pred joints: ' + filenamebase, data=data[i].data.cpu().numpy())
visualize.plot_image_and_heatmap(output[3][i][4].data.numpy(),
data=data[i].data.numpy(),
title='Thumb tib heatmap: ' + filenamebase)
visualize.show()
'''
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx + 1) % train_vars['iter_size'] == 0
if minibatch_completed:
# visualize
# ax, fig = visualize.plot_3D_joints(target_joints[0])
# visualize.plot_3D_joints(target_joints[1], ax=ax, fig=fig)
if train_vars['curr_iter'] % train_vars['log_interval'] == 0:
fig, ax = visualize.plot_3D_joints(target_joints[0])
visualize.savefig('joints_GT_' + str(train_vars['curr_iter']) +
'.png')
#visualize.plot_3D_joints(target_joints[1], fig=fig, ax=ax, color_root='C7')
#visualize.plot_3D_joints(output[7].data.cpu().numpy()[0], fig=fig, ax=ax, color_root='C7')
visualize.plot_3D_joints(output[7].data.cpu().numpy()[0])
visualize.savefig('joints_model_' +
str(train_vars['curr_iter']) + '.png')
#visualize.show()
#visualize.savefig('joints_' + str(train_vars['curr_iter']) + '.png')
# change learning rate to 0.01 after 45000 iterations
optimizer = change_learning_rate(optimizer, 0.01,
train_vars['curr_iter'])
# optimise for mini-batch
optimizer.step()
# clear optimiser
optimizer.zero_grad()
# append total loss
train_vars['losses'].append(train_vars['total_loss'])
# erase total loss
total_loss = train_vars['total_loss']
train_vars['total_loss'] = 0
# append total joints loss
train_vars['losses_joints'].append(train_vars['total_joints_loss'])
# erase total joints loss
train_vars['total_joints_loss'] = 0
# append total joints loss
train_vars['losses_heatmaps'].append(
train_vars['total_heatmaps_loss'])
# erase total joints loss
train_vars['total_heatmaps_loss'] = 0
# append dist loss
train_vars['pixel_losses'].append(train_vars['total_pixel_loss'])
# erase pixel dist loss
train_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
train_vars['pixel_losses_sample'].append(
train_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
train_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
if train_vars['losses'][-1] < train_vars['best_loss']:
train_vars['best_loss'] = train_vars['losses'][-1]
print_verbose(
" This is a best loss found so far: " +
str(train_vars['losses'][-1]), verbose)
train_vars['best_model_dict'] = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_vars': train_vars
}
# log checkpoint
if train_vars['curr_iter'] % train_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, epoch, total_loss,
train_vars, train_vars)
aa1 = target_joints[0].data.cpu().numpy()
aa2 = output[7][0].data.cpu().numpy()
output_joint_loss = np.sum(np.abs(aa1 - aa2)) / 63
msg = ''
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
msg += print_verbose(
'\tJoint Coord Avg Loss for first image of current mini-batch: '
+ str(output_joint_loss) + '\n', train_vars['verbose'])
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
if not train_vars['output_filepath'] == '':
with open(train_vars['output_filepath'], 'a') as f:
f.write(msg + '\n')
if train_vars['curr_iter'] % train_vars['log_interval_valid'] == 0:
print_verbose(
"\nSaving model and checkpoint model for validation",
verbose)
checkpoint_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_vars': train_vars,
}
trainer.save_checkpoint(
checkpoint_model_dict,
filename=train_vars['checkpoint_filenamebase'] +
'for_valid_' + str(train_vars['curr_iter']) + '.pth.tar')
# print time lapse
prefix = 'Training (Epoch #' + str(epoch) + ' ' + str(train_vars['curr_epoch_iter']) + '/' +\
str(train_vars['tot_iter']) + ')' + ', (Batch ' + str(train_vars['batch_idx']+1) +\
'(' + str(train_vars['iter_size']) + ')' + '/' +\
str(train_vars['num_batches']) + ')' + ', (Iter #' + str(train_vars['curr_iter']) +\
'(' + str(train_vars['batch_size']) + ')' +\
' - log every ' + str(train_vars['log_interval']) + ' iter): '
train_vars['tot_toc'] = display_est_time_loop(
train_vars['tot_toc'] + time.time() - start,
train_vars['curr_iter'],
train_vars['num_iter'],
prefix=prefix)
train_vars['curr_iter'] += 1
train_vars['start_iter'] = train_vars['curr_iter'] + 1
train_vars['curr_epoch_iter'] += 1
return train_vars
def validate(valid_loader,
model,
optimizer,
valid_vars,
control_vars,
verbose=True):
curr_epoch_iter = 1
for batch_idx, (data, target) in enumerate(valid_loader):
control_vars['batch_idx'] = batch_idx
if batch_idx < control_vars['iter_size']:
print_verbose(
"\rPerforming first iteration; current mini-batch: " +
str(batch_idx + 1) + "/" + str(control_vars['iter_size']),
verbose,
n_tabs=0,
erase_line=True)
# start time counter
start = time.time()
# get data and targetas cuda variables
target_heatmaps, target_joints, target_handroot = target
# make target joints be relative
target_joints = target_joints[:, 3:]
data, target_heatmaps = Variable(data), Variable(target_heatmaps)
if valid_vars['use_cuda']:
data = data.cuda()
target_joints = target_joints.cuda()
target_heatmaps = target_heatmaps.cuda()
target_handroot = target_handroot.cuda()
# visualize if debugging
# get model output
output = model(data)
# accumulate loss for sub-mini-batch
if model.cross_entropy:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
weights_heatmaps_loss, weights_joints_loss = get_loss_weights(
control_vars['curr_iter'])
loss, loss_heatmaps, loss_joints = my_losses.calculate_loss_JORNet(
loss_func, output, target_heatmaps, target_joints,
valid_vars['joint_ixs'], weights_heatmaps_loss,
weights_joints_loss, control_vars['iter_size'])
valid_vars['total_loss'] += loss
valid_vars['total_joints_loss'] += loss_joints
valid_vars['total_heatmaps_loss'] += loss_heatmaps
# accumulate pixel dist loss for sub-mini-batch
valid_vars[
'total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
valid_vars['total_pixel_loss'], output[3], target_heatmaps,
control_vars['batch_size'])
valid_vars[
'total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
valid_vars['total_pixel_loss_sample'], output[3],
target_heatmaps, control_vars['batch_size'])
# get boolean variable stating whether a mini-batch has been completed
for i in range(control_vars['max_mem_batch']):
filenamebase_idx = (batch_idx * control_vars['max_mem_batch']) + i
filenamebase = valid_loader.dataset.get_filenamebase(
filenamebase_idx)
print('')
print(filenamebase)
visualize.plot_image(data[i].data.numpy())
visualize.show()
output_batch_numpy = output[7][i].data.cpu().numpy()
print('\n-------------------------------')
reshaped_out = output_batch_numpy.reshape((20, 3))
for j in range(20):
print('[{}, {}, {}],'.format(reshaped_out[j, 0],
reshaped_out[j, 1],
reshaped_out[j, 2]))
print('-------------------------------')
fig, ax = visualize.plot_3D_joints(target_joints[i])
visualize.plot_3D_joints(output_batch_numpy,
fig=fig,
ax=ax,
color='C6')
visualize.title(filenamebase)
visualize.show()
temp = np.zeros((21, 3))
output_batch_numpy_abs = output_batch_numpy.reshape((20, 3))
temp[1:, :] = output_batch_numpy_abs
output_batch_numpy_abs = temp
output_joints_colorspace = camera.joints_depth2color(
output_batch_numpy_abs,
depth_intr_matrix=synthhands_handler.DEPTH_INTR_MTX,
handroot=target_handroot[i].data.cpu().numpy())
visualize.plot_3D_joints(output_joints_colorspace)
visualize.show()
aa1 = target_joints[i].data.cpu().numpy().reshape((20, 3))
aa2 = output[7][i].data.cpu().numpy().reshape((20, 3))
print('\n----------------------------------')
print(np.sum(np.abs(aa1 - aa2)) / 60)
print('----------------------------------')
#loss.backward()
valid_vars['total_loss'] += loss
valid_vars['total_joints_loss'] += loss_joints
valid_vars['total_heatmaps_loss'] += loss_heatmaps
# accumulate pixel dist loss for sub-mini-batch
valid_vars[
'total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
valid_vars['total_pixel_loss'], output[3], target_heatmaps,
control_vars['batch_size'])
valid_vars[
'total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
valid_vars['total_pixel_loss_sample'], output[3],
target_heatmaps, control_vars['batch_size'])
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx + 1) % control_vars['iter_size'] == 0
if minibatch_completed:
# append total loss
valid_vars['losses'].append(valid_vars['total_loss'].data[0])
# erase total loss
total_loss = valid_vars['total_loss'].data[0]
valid_vars['total_loss'] = 0
# append total joints loss
valid_vars['losses_joints'].append(
valid_vars['total_joints_loss'].data[0])
# erase total joints loss
valid_vars['total_joints_loss'] = 0
# append total joints loss
valid_vars['losses_heatmaps'].append(
valid_vars['total_heatmaps_loss'].data[0])
# erase total joints loss
valid_vars['total_heatmaps_loss'] = 0
# append dist loss
valid_vars['pixel_losses'].append(valid_vars['total_pixel_loss'])
# erase pixel dist loss
valid_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
valid_vars['pixel_losses_sample'].append(
valid_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
valid_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
#if valid_vars['losses'][-1] < valid_vars['best_loss']:
# valid_vars['best_loss'] = valid_vars['losses'][-1]
# print_verbose(" This is a best loss found so far: " + str(valid_vars['losses'][-1]), verbose)
# log checkpoint
if control_vars['curr_iter'] % control_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, 1, total_loss,
valid_vars, control_vars)
model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': valid_vars,
}
trainer.save_checkpoint(
model_dict,
filename=valid_vars['checkpoint_filenamebase'] +
str(control_vars['num_iter']) + '.pth.tar')
# print time lapse
prefix = 'Validating (Epoch #' + str(1) + ' ' + str(control_vars['curr_epoch_iter']) + '/' +\
str(control_vars['tot_iter']) + ')' + ', (Batch ' + str(control_vars['batch_idx']+1) +\
'(' + str(control_vars['iter_size']) + ')' + '/' +\
str(control_vars['num_batches']) + ')' + ', (Iter #' + str(control_vars['curr_iter']) +\
'(' + str(control_vars['batch_size']) + ')' +\
' - log every ' + str(control_vars['log_interval']) + ' iter): '
control_vars['tot_toc'] = display_est_time_loop(
control_vars['tot_toc'] + time.time() - start,
control_vars['curr_iter'],
control_vars['num_iter'],
prefix=prefix)
control_vars['curr_iter'] += 1
control_vars['start_iter'] = control_vars['curr_iter'] + 1
control_vars['curr_epoch_iter'] += 1
return valid_vars, control_vars
def train(train_loader, model, optimizer, train_vars):
verbose = train_vars['verbose']
for batch_idx, (data, target) in enumerate(train_loader):
train_vars['batch_idx'] = batch_idx
# print info about performing first iter
if batch_idx < train_vars['iter_size']:
print_verbose("\rPerforming first iteration; current mini-batch: " +
str(batch_idx+1) + "/" + str(train_vars['iter_size']), verbose, n_tabs=0, erase_line=True)
# check if arrived at iter to start
arrived_curr_iter, train_vars = run_until_curr_iter(batch_idx, train_vars)
if not arrived_curr_iter:
continue
# save checkpoint after final iteration
if train_vars['curr_iter'] - 1 == train_vars['num_iter']:
train_vars = save_final_checkpoint(train_vars, model, optimizer)
break
# start time counter
start = time.time()
# get data and target as torch Variables
_, target_joints, target_heatmaps, target_joints_z = target
data, target_heatmaps = Variable(data), Variable(target_heatmaps)
if train_vars['use_cuda']:
data = data.cuda()
target_heatmaps = target_heatmaps.cuda()
# get model output
output = model(data)
# accumulate loss for sub-mini-batch
if model.cross_entropy:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
loss = my_losses.calculate_loss_HALNet(loss_func,
output, target_heatmaps, model.joint_ixs, model.WEIGHT_LOSS_INTERMED1,
model.WEIGHT_LOSS_INTERMED2, model.WEIGHT_LOSS_INTERMED3,
model.WEIGHT_LOSS_MAIN, train_vars['iter_size'])
loss.backward()
train_vars['total_loss'] += loss
# accumulate pixel dist loss for sub-mini-batch
train_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
train_vars['total_pixel_loss'], output[3], target_heatmaps, train_vars['batch_size'])
if train_vars['cross_entropy']:
train_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
train_vars['total_pixel_loss_sample'], output[3], target_heatmaps, train_vars['batch_size'])
else:
train_vars['total_pixel_loss_sample'] = [-1] * len(model.joint_ixs)
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx+1) % train_vars['iter_size'] == 0
if minibatch_completed:
# optimise for mini-batch
optimizer.step()
# clear optimiser
optimizer.zero_grad()
# append total loss
train_vars['losses'].append(train_vars['total_loss'].item())
# erase total loss
total_loss = train_vars['total_loss'].item()
train_vars['total_loss'] = 0
# append dist loss
train_vars['pixel_losses'].append(train_vars['total_pixel_loss'])
# erase pixel dist loss
train_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
train_vars['pixel_losses_sample'].append(train_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
train_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
if train_vars['losses'][-1] < train_vars['best_loss']:
train_vars['best_loss'] = train_vars['losses'][-1]
print_verbose(" This is a best loss found so far: " + str(train_vars['losses'][-1]), verbose)
train_vars['best_model_dict'] = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_vars': train_vars
}
# log checkpoint
if train_vars['curr_iter'] % train_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, epoch, total_loss, train_vars, train_vars)
if train_vars['curr_iter'] % train_vars['log_interval_valid'] == 0:
print_verbose("\nSaving model and checkpoint model for validation", verbose)
checkpoint_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_vars': train_vars,
}
trainer.save_checkpoint(checkpoint_model_dict,
filename=train_vars['checkpoint_filenamebase'] + 'for_valid_' +
str(train_vars['curr_iter']) + '.pth.tar')
# print time lapse
prefix = 'Training (Epoch #' + str(epoch) + ' ' + str(train_vars['curr_epoch_iter']) + '/' +\
str(train_vars['tot_iter']) + ')' + ', (Batch ' + str(train_vars['batch_idx']+1) +\
'(' + str(train_vars['iter_size']) + ')' + '/' +\
str(train_vars['num_batches']) + ')' + ', (Iter #' + str(train_vars['curr_iter']) +\
'(' + str(train_vars['batch_size']) + ')' +\
' - log every ' + str(train_vars['log_interval']) + ' iter): '
train_vars['tot_toc'] = display_est_time_loop(train_vars['tot_toc'] + time.time() - start,
train_vars['curr_iter'], train_vars['num_iter'],
prefix=prefix)
train_vars['curr_iter'] += 1
train_vars['start_iter'] = train_vars['curr_iter'] + 1
train_vars['curr_epoch_iter'] += 1
return train_vars
def main_func(args):
cdf = mc.ConfidenceDepthFrameworkFactory()
val_loader, _ = df.create_data_loaders(args.data_path
, loader_type='val'
, data_type= args.data_type
, modality= args.data_modality
, num_samples= args.num_samples
, depth_divisor= args.divider
, max_depth= args.max_depth
, max_gt_depth= args.max_gt_depth
, workers= args.workers
, batch_size=1)
if not args.evaluate:
train_loader, _ = df.create_data_loaders(args.data_path
, loader_type='train'
, data_type=args.data_type
, modality=args.data_modality
, num_samples=args.num_samples
, depth_divisor=args.divider
, max_depth=args.max_depth
, max_gt_depth=args.max_gt_depth
, workers=args.workers
, batch_size=args.batch_size)
# evaluation mode
if args.evaluate:
cdfmodel,loss, epoch = trainer.resume(args.evaluate,cdf,True)
output_directory = create_eval_output_folder(args)
os.makedirs(output_directory)
print(output_directory)
save_arguments(args,output_directory)
trainer.validate(val_loader, cdfmodel, loss, epoch,print_frequency=args.print_freq,num_image_samples=args.val_images, output_folder=output_directory, conf_recall=args.pr,conf_threshold= args.thrs)
return
output_directory = create_output_folder(args)
os.makedirs(output_directory)
print(output_directory)
save_arguments(args, output_directory)
# optionally resume from a checkpoint
if args.resume:
cdfmodel, loss, loss_def, best_result_error, optimizer, scheduler = trainer.resume(args.resume,cdf,False)
# create new model
else:
cdfmodel = cdf.create_model(args.dcnet_modality, args.training_mode, args.dcnet_arch, args.dcnet_pretrained, args.confnet_arch, args.confnet_pretrained, args.lossnet_arch, args.lossnet_pretrained)
cdfmodel, opt_parameters = cdf.to_device(cdfmodel)
optimizer, scheduler = trainer.create_optimizer(args.optimizer, opt_parameters, args.momentum, args.weight_decay, args.lr, args.lrs, args.lrm)
loss, loss_definition = cdf.create_loss(args.criterion, ('ln' in args.training_mode), (0.5 if 'dc1' in args.training_mode else 1.0))
best_result_error = math.inf
for epoch in range(0, args.epochs):
trainer.train(train_loader, cdfmodel, loss, optimizer, output_directory, epoch)
epoch_result = trainer.validate(val_loader, cdfmodel, loss, epoch=epoch,print_frequency=args.print_freq,num_image_samples=args.val_images, output_folder=output_directory)
scheduler.step(epoch)
is_best = epoch_result.rmse < best_result_error
if is_best:
best_result_error = epoch_result.rmse
trainer.report_top_result(os.path.join(output_directory, 'best_result.txt'), epoch, epoch_result)
# if img_merge is not None:
# img_filename = output_directory + '/comparison_best.png'
# utils.save_image(img_merge, img_filename)
trainer.save_checkpoint(cdf, cdfmodel, loss_definition, optimizer, scheduler,best_result_error, is_best, epoch,
output_directory)
def train(train_loader, model, optimizer, train_vars, control_vars, verbose=True):
curr_epoch_iter = 1
for batch_idx, (data, target) in enumerate(train_loader):
control_vars['batch_idx'] = batch_idx
if batch_idx < control_vars['iter_size']:
print_verbose("\rPerforming first iteration; current mini-batch: " +
str(batch_idx+1) + "/" + str(control_vars['iter_size']), verbose, n_tabs=0, erase_line=True)
# check if arrived at iter to start
if control_vars['curr_epoch_iter'] < control_vars['start_iter_mod']:
control_vars['curr_epoch_iter'] = control_vars['start_iter_mod']
msg = ''
if batch_idx % control_vars['iter_size'] == 0:
msg += print_verbose("\rGoing through iterations to arrive at last one saved... " +
str(int(control_vars['curr_epoch_iter']*100.0/control_vars['start_iter_mod'])) + "% of " +
str(control_vars['start_iter_mod']) + " iterations (" +
str(control_vars['curr_epoch_iter']) + "/" + str(control_vars['start_iter_mod']) + ")",
verbose, n_tabs=0, erase_line=True)
control_vars['curr_epoch_iter'] += 1
control_vars['curr_iter'] += 1
curr_epoch_iter += 1
if not control_vars['output_filepath'] == '':
with open(control_vars['output_filepath'], 'a') as f:
f.write(msg + '\n')
continue
# save checkpoint after final iteration
if control_vars['curr_iter'] == control_vars['num_iter']:
print_verbose("\nReached final number of iterations: " + str(control_vars['num_iter']), verbose)
print_verbose("\tSaving final model checkpoint...", verbose)
final_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
trainer.save_checkpoint(final_model_dict,
filename=train_vars['checkpoint_filenamebase'] +
'final' + str(control_vars['num_iter']) + '.pth.tar')
control_vars['done_training'] = True
break
# start time counter
start = time.time()
# get data and targetas cuda variables
target_heatmaps, target_joints, target_roothand = target
data, target_heatmaps, target_joints, target_roothand = Variable(data), Variable(target_heatmaps),\
Variable(target_joints), Variable(target_roothand)
if train_vars['use_cuda']:
data = data.cuda()
target_heatmaps = target_heatmaps.cuda()
target_joints = target_joints.cuda()
# get model output
output = model(data)
'''
visualize.plot_joints_from_heatmaps(target_heatmaps[0, :, :, :].cpu().data.numpy(),
title='', data=data[0].cpu().data.numpy())
visualize.show()
visualize.plot_image_and_heatmap(target_heatmaps[0][4].cpu().data.numpy(),
data=data[0].cpu().data.numpy(),
title='')
visualize.show()
visualize.plot_image_and_heatmap(output[3][0][4].cpu().data.numpy(),
data=data[0].cpu().data.numpy(),
title='')
visualize.show()
'''
# accumulate loss for sub-mini-batch
if train_vars['cross_entropy']:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
weights_heatmaps_loss, weights_joints_loss = get_loss_weights(control_vars['curr_iter'])
loss, loss_heatmaps, loss_joints = my_losses.calculate_loss_JORNet(
loss_func, output, target_heatmaps, target_joints, train_vars['joint_ixs'],
weights_heatmaps_loss, weights_joints_loss, control_vars['iter_size'])
loss.backward()
train_vars['total_loss'] += loss.data[0]
train_vars['total_joints_loss'] += loss_joints.data[0]
train_vars['total_heatmaps_loss'] += loss_heatmaps.data[0]
# accumulate pixel dist loss for sub-mini-batch
train_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
train_vars['total_pixel_loss'], output[3], target_heatmaps, control_vars['batch_size'])
train_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
train_vars['total_pixel_loss_sample'], output[3], target_heatmaps, control_vars['batch_size'])
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx+1) % control_vars['iter_size'] == 0
if minibatch_completed:
# optimise for mini-batch
optimizer.step()
# clear optimiser
optimizer.zero_grad()
# append total loss
train_vars['losses'].append(train_vars['total_loss'])
# erase total loss
total_loss = train_vars['total_loss']
train_vars['total_loss'] = 0
# append total joints loss
train_vars['losses_joints'].append(train_vars['total_joints_loss'])
# erase total joints loss
train_vars['total_joints_loss'] = 0
# append total joints loss
train_vars['losses_heatmaps'].append(train_vars['total_heatmaps_loss'])
# erase total joints loss
train_vars['total_heatmaps_loss'] = 0
# append dist loss
train_vars['pixel_losses'].append(train_vars['total_pixel_loss'])
# erase pixel dist loss
train_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
train_vars['pixel_losses_sample'].append(train_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
train_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
if train_vars['losses'][-1] < train_vars['best_loss']:
train_vars['best_loss'] = train_vars['losses'][-1]
print_verbose(" This is a best loss found so far: " + str(train_vars['losses'][-1]), verbose)
train_vars['best_model_dict'] = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
if train_vars['losses_joints'][-1] < train_vars['best_loss_joints']:
train_vars['best_loss_joints'] = train_vars['losses_joints'][-1]
if train_vars['losses_heatmaps'][-1] < train_vars['best_loss_heatmaps']:
train_vars['best_loss_heatmaps'] = train_vars['losses_heatmaps'][-1]
# log checkpoint
if control_vars['curr_iter'] % control_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, epoch, total_loss, train_vars, control_vars)
aa1 = target_joints[0].data.cpu().numpy()
aa2 = output[7][0].data.cpu().numpy()
output_joint_loss = np.sum(np.abs(aa1 - aa2)) / 63
msg = ''
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
msg += print_verbose('\tJoint Coord Avg Loss for first image of current mini-batch: ' +
str(output_joint_loss) + '\n', control_vars['verbose'])
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
if not control_vars['output_filepath'] == '':
with open(control_vars['output_filepath'], 'a') as f:
f.write(msg + '\n')
if control_vars['curr_iter'] % control_vars['log_interval_valid'] == 0:
print_verbose("\nSaving model and checkpoint model for validation", verbose)
checkpoint_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
trainer.save_checkpoint(checkpoint_model_dict,
filename=train_vars['checkpoint_filenamebase'] + 'for_valid_' +
str(control_vars['curr_iter']) + '.pth.tar')
# print time lapse
prefix = 'Training (Epoch #' + str(epoch) + ' ' + str(control_vars['curr_epoch_iter']) + '/' +\
str(control_vars['tot_iter']) + ')' + ', (Batch ' + str(control_vars['batch_idx']+1) +\
'(' + str(control_vars['iter_size']) + ')' + '/' +\
str(control_vars['num_batches']) + ')' + ', (Iter #' + str(control_vars['curr_iter']) +\
'(' + str(control_vars['batch_size']) + ')' +\
' - log every ' + str(control_vars['log_interval']) + ' iter): '
control_vars['tot_toc'] = display_est_time_loop(control_vars['tot_toc'] + time.time() - start,
control_vars['curr_iter'], control_vars['num_iter'],
prefix=prefix)
control_vars['curr_iter'] += 1
control_vars['start_iter'] = control_vars['curr_iter'] + 1
control_vars['curr_epoch_iter'] += 1
return train_vars, control_vars
def main():
args = arguments.parse()
checkpoint = args.checkpoint if args.checkpoint else None
model, params = get_network(args.arch,
args.n_attrs,
checkpoint=checkpoint,
base_frozen=args.freeze_base)
criterion = get_criterion(loss_type=args.loss, args=args)
optimizer = get_optimizer(params,
fc_lr=float(args.lr),
opt_type=args.optimizer_type,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=10,
gamma=0.1,
last_epoch=args.start_epoch - 1)
if checkpoint:
state = torch.load(checkpoint)
model.load_state_dict(state["state_dict"])
scheduler.load_state_dict(state['scheduler'])
# Dataloader code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
val_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
normalize,
])
logger.info("Setting up training data")
train_loader = data.DataLoader(COCOAttributes(
args.attributes,
args.train_ann,
train=True,
split='train2014',
transforms=train_transforms,
dataset_root=args.dataset_root),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
logger.info("Setting up validation data")
val_loader = data.DataLoader(COCOAttributes(
args.attributes,
args.val_ann,
train=False,
split='val2014',
transforms=val_transforms,
dataset_root=args.dataset_root),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
best_prec1 = 0
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
logger.info("Beginning training...")
for epoch in range(args.start_epoch, args.epochs):
scheduler.step()
# train for one epoch
trainer.train(train_loader, model, criterion, optimizer, epoch,
args.print_freq)
# evaluate on validation set
# trainer.validate(val_loader, model, criterion, epoch, args.print_freq)
prec1 = 0
# remember best prec@1 and save checkpoint
best_prec1 = max(prec1, best_prec1)
trainer.save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'loss': args.loss,
'optimizer': args.optimizer_type,
'state_dict': model.state_dict(),
'scheduler': scheduler.state_dict(),
'batch_size': args.batch_size,
'best_prec1': best_prec1,
}, args.save_dir,
'{0}_{1}_checkpoint.pth.tar'.format(args.arch, args.loss).lower())
logger.info('Finished Training')
logger.info('Running evaluation')
evaluator = evaluation.Evaluator(model,
val_loader,
batch_size=args.batch_size,
name="{0}_{1}".format(
args.arch, args.loss))
with torch.no_grad():
evaluator.evaluate()