def main(_run, _config, _log):
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
os.makedirs(f'{_run.observers[0].dir}/val_logs', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(
f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = FewShotSeg(device=torch.device("cuda:2"),
n_grid=8,
overlap=True,
overlap_out='max')
model = nn.DataParallel(model.cuda(), device_ids=[
_config['gpu_id'],
])
model.train()
_log.info('###### Load data ######')
data_name = _config['dataset']
if data_name == 'Visceral':
make_data = visceral_fewshot
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][_config['label_sets']]
#labels=set([1,2])
val_labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
_config['label_sets']]
transforms = Compose([
Resize(size=_config['input_size']),
#RandomAffine(),
RandomBrightness(),
RandomContrast(),
RandomGamma()
])
dataset = make_data(base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'])
trainloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True)
support_volumes = ['10000132_1_CTce_ThAb.nii.gz']
query_volumes = [
'10000100_1_CTce_ThAb.nii.gz', '10000104_1_CTce_ThAb.nii.gz',
'10000105_1_CTce_ThAb.nii.gz', '10000106_1_CTce_ThAb.nii.gz',
'10000108_1_CTce_ThAb.nii.gz', '10000109_1_CTce_ThAb.nii.gz',
'10000110_1_CTce_ThAb.nii.gz', '10000111_1_CTce_ThAb.nii.gz',
'10000112_1_CTce_ThAb.nii.gz', '10000113_1_CTce_ThAb.nii.gz',
'10000127_1_CTce_ThAb.nii.gz', '10000128_1_CTce_ThAb.nii.gz',
'10000129_1_CTce_ThAb.nii.gz', '10000130_1_CTce_ThAb.nii.gz',
'10000131_1_CTce_ThAb.nii.gz', '10000133_1_CTce_ThAb.nii.gz',
'10000134_1_CTce_ThAb.nii.gz', '10000135_1_CTce_ThAb.nii.gz',
'10000136_1_CTce_ThAb.nii.gz'
]
volumes_path = "/home/qinji/PANet_Visceral/eval_dataset/volumes/"
segs_path = "/home/qinji/PANet_Visceral/eval_dataset/segmentations/"
support_vol_dict, support_mask_dict = load_vol_and_mask(
support_volumes, volumes_path, segs_path, labels=list(val_labels))
query_vol_dict, query_mask_dict = load_vol_and_mask(
query_volumes, volumes_path, segs_path, labels=list(val_labels))
print('Successfully Load eval data!')
_log.info('###### Set optimizer ######')
optimizer = torch.optim.SGD(model.parameters(), **_config['optim'])
scheduler = MultiStepLR(optimizer,
milestones=_config['lr_milestones'],
gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=_config['ignore_label'])
i_iter = 0
log_loss = {'loss': 0}
_log.info('###### Training ######')
best_val_dice = 0
model.eval()
eval(model, -1, support_vol_dict, support_mask_dict, query_vol_dict,
query_mask_dict, list(val_labels),
os.path.join(f'{_run.observers[0].dir}/val_logs', 'val_logs.txt'))
model.train()
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.long().cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
# Forward and Backward
optimizer.zero_grad()
query_pred = model(support_images, support_fg_mask, support_bg_mask,
query_images)
query_loss = criterion(query_pred, query_labels)
loss = query_loss
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
_run.log_scalar('loss', query_loss)
log_loss['loss'] += query_loss
# print loss and take snapshots
if (i_iter + 1) % _config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
print(f'step {i_iter+1}: loss: {loss}')
with open(
os.path.join(f'{_run.observers[0].dir}/val_logs',
'val_logs.txt'), 'a') as f:
f.write(f'step {i_iter+1}: loss: {loss}\n')
if (i_iter + 1) % _config['val_interval'] == 0:
_log.info('###### Validing ######')
model.eval()
average_labels_dice = eval(
model, i_iter, support_vol_dict, support_mask_dict,
query_vol_dict, query_mask_dict, list(val_labels),
os.path.join(f'{_run.observers[0].dir}/val_logs',
'val_logs.txt'))
model.train()
print(f'step {i_iter+1}: val_average_dice: {average_labels_dice}')
if average_labels_dice > best_val_dice:
_log.info('###### Taking snapshot ######')
best_val_dice = average_labels_dice
torch.save(
model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots',
'best_val.pth'))
_log.info('###### Saving final model ######')
torch.save(
model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots',
f'{i_iter + 1}.pth'))
def main(_run, _config, _log):
logdir = f'{_run.observers[0].dir}/'
print(logdir)
category = [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
]
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(
f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
data_name = _config['dataset']
max_label = 20 if data_name == 'VOC' else 80
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
print(_config['ckpt_dir'])
tbwriter = SummaryWriter(osp.join(_config['ckpt_dir']))
training_tags = {
'loss': "ATraining/total_loss",
"query_loss": "ATraining/query_loss",
'aligned_loss': "ATraining/aligned_loss",
'base_loss': "ATraining/base_loss",
}
infer_tags = {
'mean_iou': "MeanIoU/mean_iou",
"mean_iou_binary": "MeanIoU/mean_iou_binary",
}
_log.info('###### Create model ######')
if _config['model']['part']:
model = FewshotSegPartResnet(
pretrained_path=_config['path']['init_path'], cfg=_config)
_log.info('Model: FewshotSegPartResnet')
else:
model = FewshotSegResnet(pretrained_path=_config['path']['init_path'],
cfg=_config)
_log.info('Model: FewshotSegResnet')
model = nn.DataParallel(model.cuda(), device_ids=[
_config['gpu_id'],
])
model.train()
_log.info('###### Load data ######')
data_name = _config['dataset']
make_data = voc_fewshot
labels = CLASS_LABELS[data_name][_config['label_sets']]
transforms = Compose([Resize(size=_config['input_size']), RandomMirror()])
dataset = make_data(base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'],
n_unlabel=_config['task']['n_unlabels'],
cfg=_config)
trainloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=True,
num_workers=_config['num_workers'],
pin_memory=True,
drop_last=True)
_log.info('###### Set optimizer ######')
if _config['fix']:
print('Optimizer: fix')
optimizer = torch.optim.SGD(
params=[
{
"params": model.module.encoder.layer3.parameters(),
"lr": _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
},
{
"params": model.module.encoder.layer4.parameters(),
"lr": _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
},
],
momentum=_config['optim']['momentum'],
)
else:
print('Optimizer: Not fix')
optimizer = torch.optim.SGD(model.parameters(), **_config['optim'])
scheduler = MultiStepLR(optimizer,
milestones=_config['lr_milestones'],
gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=_config['ignore_label'])
log_loss = {'loss': 0, 'align_loss': 0, 'base_loss': 0}
_log.info('###### Training ######')
highest_iou = 0
metrics = {}
for i_iter, sample_batched in enumerate(trainloader):
if _config['fix']:
model.module.encoder.conv1.eval()
model.module.encoder.bn1.eval()
model.module.encoder.layer1.eval()
model.module.encoder.layer2.eval()
if _config['eval']:
if i_iter == 0:
break
# Prepare input
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.long().cuda()
for query_label in sample_batched['query_labels']
],
dim=0) #1*417*417
base_loss = torch.zeros(1).to(torch.device('cuda'))
# Forward and Backward
optimizer.zero_grad()
query_pred, _, align_loss = model(support_images, support_fg_mask,
support_bg_mask, query_images)
query_loss = criterion(query_pred,
query_labels) #1*3*417*417, 1*417*417
loss = query_loss + align_loss * _config[
'align_loss_scaler'] + base_loss * _config['base_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
align_loss = align_loss.detach().data.cpu().numpy()
base_loss = base_loss.detach().data.cpu().numpy()
log_loss['loss'] += query_loss
log_loss['align_loss'] += align_loss
log_loss['base_loss'] += base_loss
# print loss and take snapshots
if (i_iter + 1) % _config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
align_loss = log_loss['align_loss'] / (i_iter + 1)
base_loss = log_loss['base_loss'] / (i_iter + 1)
print(
f'step {i_iter+1}: loss: {loss}, align_loss: {align_loss}, base_loss: {base_loss}'
)
_log.info(
f'step {i_iter+1}: loss: {loss}, align_loss: {align_loss}, base_loss: {base_loss}'
)
metrics['loss'] = loss
metrics['query_loss'] = query_loss
metrics['aligned_loss'] = align_loss
metrics['base_loss'] = base_loss
# for k, v in metrics.items():
# tbwriter.add_scalar(training_tags[k], v, i_iter)
if (i_iter + 1) % _config['evaluate_interval'] == 0:
_log.info('###### Evaluation begins ######')
print(_config['ckpt_dir'])
model.eval()
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
transforms = Compose(transforms)
metric = Metric(max_label=max_label, n_runs=_config['n_runs'])
with torch.no_grad():
for run in range(1):
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['infer_max_iters'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'],
n_unlabel=_config['task']['n_unlabels'],
cfg=_config)
testloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=False,
num_workers=_config['num_workers'],
pin_memory=True,
drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
label_ids = list(sample_batched['class_ids'])
support_images = [[
shot.cuda() for shot in way
] for way in sample_batched['support_images']]
suffix = 'mask'
support_fg_mask = [[
shot[f'fg_{suffix}'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
support_bg_mask = [[
shot[f'bg_{suffix}'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
query_pred, _, _ = model(support_images,
support_fg_mask,
support_bg_mask, query_images)
curr_iou = metric.record(query_pred.argmax(dim=1)[0],
query_labels[0],
labels=label_ids,
n_run=run)
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels),
n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(
n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary',
classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
print(
f'meanIoU: {meanIoU}, meanIoU_binary: {meanIoU_binary}'
)
metrics = {}
metrics['mean_iou'] = meanIoU
metrics['mean_iou_binary'] = meanIoU_binary
for k, v in metrics.items():
tbwriter.add_scalar(infer_tags[k], v, i_iter)
if meanIoU > highest_iou:
print(
f'The highest iou is in iter: {i_iter} : {meanIoU}, save: {_config["ckpt_dir"]}/best.pth'
)
highest_iou = meanIoU
torch.save(
model.state_dict(),
os.path.join(f'{_config["ckpt_dir"]}/best.pth'))
else:
print(
f'The highest iou is in iter: {i_iter} : {meanIoU}'
)
torch.save(model.state_dict(),
os.path.join(f'{_config["ckpt_dir"]}/{i_iter + 1}.pth'))
model.train()
print(_config['ckpt_dir'])
_log.info(' --------- Testing begins ---------')
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
transforms = Compose(transforms)
ckpt = os.path.join(f'{_config["ckpt_dir"]}/best.pth')
print(f'{_config["ckpt_dir"]}/best.pth')
model.load_state_dict(torch.load(ckpt, map_location='cpu'))
model.eval()
metric = Metric(max_label=max_label, n_runs=5)
with torch.no_grad():
for run in range(5):
n_iter = 0
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['infer_max_iters'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'],
n_unlabel=_config['task']['n_unlabels'],
cfg=_config)
testloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=False,
num_workers=_config['num_workers'],
pin_memory=True,
drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
label_ids = list(sample_batched['class_ids'])
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
suffix = 'mask'
support_fg_mask = [[
shot[f'fg_{suffix}'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
support_bg_mask = [[
shot[f'bg_{suffix}'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
query_pred, _, _ = model(support_images, support_fg_mask,
support_bg_mask, query_images)
curr_iou = metric.record(query_pred.argmax(dim=1)[0],
query_labels[0],
labels=label_ids,
n_run=run)
n_iter += 1
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels),
n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(
labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary(
)
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_classIoU', classIoU.tolist())
_run.log_scalar('final_classIoU_std', classIoU_std.tolist())
_run.log_scalar('final_meanIoU', meanIoU.tolist())
_run.log_scalar('final_meanIoU_std', meanIoU_std.tolist())
_run.log_scalar('final_classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('final_classIoU_std_binary', classIoU_std_binary.tolist())
_run.log_scalar('final_meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
_log.info('----- Final Result -----')
_log.info(f'classIoU mean: {classIoU}')
_log.info(f'classIoU std: {classIoU_std}')
_log.info(f'meanIoU mean: {meanIoU}')
_log.info(f'meanIoU std: {meanIoU_std}')
_log.info(f'classIoU_binary mean: {classIoU_binary}')
_log.info(f'classIoU_binary std: {classIoU_std_binary}')
_log.info(f'meanIoU_binary mean: {meanIoU_binary}')
_log.info(f'meanIoU_binary std: {meanIoU_std_binary}')
_log.info("## ------------------------------------------ ##")
_log.info(f'###### Setting: {_run.observers[0].dir} ######')
_log.info(
"Running {num_run} runs, meanIoU:{miou:.4f}, meanIoU_binary:{mbiou:.4f} "
"meanIoU_std:{miou_std:.4f}, meanIoU_binary_std:{mbiou_std:.4f}".
format(num_run=5,
miou=meanIoU,
mbiou=meanIoU_binary,
miou_std=meanIoU_std,
mbiou_std=meanIoU_std_binary))
_log.info(f"Current setting is {_run.observers[0].dir}")
print(
"Running {num_run} runs, meanIoU:{miou:.4f}, meanIoU_binary:{mbiou:.4f} "
"meanIoU_std:{miou_std:.4f}, meanIoU_binary_std:{mbiou_std:.4f}".
format(num_run=5,
miou=meanIoU,
mbiou=meanIoU_binary,
miou_std=meanIoU_std,
mbiou_std=meanIoU_std_binary))
print(f"Current setting is {_run.observers[0].dir}")
print(_config['ckpt_dir'])
print(logdir)
def main(_run, _config, _log):
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(
os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = Encoder(pretrained_path=_config['path']['init_path'])
model = nn.DataParallel(model.cuda(), device_ids=[
_config['gpu_id'],
])
if not _config['notrain']:
model.load_state_dict(
torch.load(_config['snapshot'], map_location='cpu'))
model.eval()
_log.info('###### Prepare data ######')
data_name = _config['dataset']
if data_name == 'VOC':
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
make_data = coco_fewshot
max_label = 80
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
if _config['scribble_dilation'] > 0:
transforms.append(DilateScribble(size=_config['scribble_dilation']))
transforms = Compose(transforms)
_log.info('###### Testing begins ######')
metric = Metric(max_label=max_label, n_runs=_config['n_runs'])
with torch.no_grad():
for run in range(_config['n_runs']):
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
label_sets=_config['label_sets'],
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'])
if _config['dataset'] == 'COCO':
coco_cls_ids = dataset.datasets[0].dataset.coco.getCatIds()
testloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=False,
num_workers=1,
pin_memory=True,
drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
if _config['dataset'] == 'COCO':
label_ids = [
coco_cls_ids.index(x) + 1
for x in sample_batched['class_ids']
]
else:
label_ids = list(sample_batched['class_ids'])
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_images = torch.cat(
[torch.cat(way, dim=0) for way in support_images], dim=0)
suffix = 'scribble' if _config['scribble'] else 'mask'
if _config['bbox']:
support_fg_mask = []
support_bg_mask = []
for i, way in enumerate(sample_batched['support_mask']):
fg_masks = []
bg_masks = []
for j, shot in enumerate(way):
fg_mask, bg_mask = get_bbox(
shot['fg_mask'],
sample_batched['support_inst'][i][j])
fg_masks.append(fg_mask.float().cuda())
bg_masks.append(bg_mask.float().cuda())
support_fg_mask.append(fg_masks)
support_bg_mask.append(bg_masks)
else:
support_fg_mask = [[
shot[f'fg_mask'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
support_fg_mask = torch.cat(
[torch.cat(way, dim=0) for way in support_fg_mask],
dim=0)
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_images = torch.cat(query_images, dim=0)
query_labels = torch.cat([
query_label.long().cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
query_pred = model(support_images, query_images,
support_fg_mask)
query_pred = F.interpolate(query_pred,
size=query_images.shape[-2:],
mode='bilinear')
metric.record(np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(query_labels[0].cpu()),
labels=label_ids,
n_run=run)
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels),
n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(
labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary(
)
_log.info('----- Final Result -----')
_run.log_scalar('final_classIoU', classIoU.tolist())
_run.log_scalar('final_classIoU_std', classIoU_std.tolist())
_run.log_scalar('final_meanIoU', meanIoU.tolist())
_run.log_scalar('final_meanIoU_std', meanIoU_std.tolist())
_run.log_scalar('final_classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('final_classIoU_std_binary', classIoU_std_binary.tolist())
_run.log_scalar('final_meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
_log.info(f'classIoU mean: {classIoU}')
_log.info(f'classIoU std: {classIoU_std}')
_log.info(f'meanIoU mean: {meanIoU}')
_log.info(f'meanIoU std: {meanIoU_std}')
_log.info(f'classIoU_binary mean: {classIoU_binary}')
_log.info(f'classIoU_binary std: {classIoU_std_binary}')
_log.info(f'meanIoU_binary mean: {meanIoU_binary}')
_log.info(f'meanIoU_binary std: {meanIoU_std_binary}')
def main(config):
num = 100000
palette_path = config['palette_dir']
with open(palette_path) as f:
palette = f.readlines()
palette = list(
np.asarray([[int(p) for p in pal[0:-1].split(' ')]
for pal in palette]).reshape(768))
n_shots = config['task']['n_shots']
n_ways = config['task']['n_ways']
set_seed(config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=config['gpu_id'])
torch.set_num_threads(1)
model = FewShotSeg(cfg=config['model'])
model = nn.DataParallel(model.cuda(), device_ids=[
config['gpu_id'],
])
if config['train']:
model.load_state_dict(
torch.load(config['snapshots'], map_location='cpu'))
model.eval()
data_name = config['dataset']
if data_name == 'davis':
make_data = davis2017_test
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name]['val']
list_label = []
for i in labels:
list_label.append(i)
list_label = sorted(list_label)
transforms = [Resize(size=config['input_size'])]
transforms = Compose(transforms)
with torch.no_grad():
for run in range(config['n_runs']):
set_seed(config['seed'] + run)
dataset = make_data(base_dir=config['path'][data_name]['data_dir'],
split=config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels)
testloader = DataLoader(dataset,
batch_size=config['batch_size'],
shuffle=False,
num_workers=1,
pin_memory=True,
drop_last=False)
for iteration, batch in enumerate(testloader):
class_name = batch[2]
if not os.path.exists(f'./result/{num}/{class_name[0]}'):
os.makedirs(f'./result/{num}/{class_name[0]}')
num_frame = batch[1]
all_class_data = batch[0]
class_ids = all_class_data[0]['obj_ids']
support_images = [[
all_class_data[0]['image'].cuda() for _ in range(n_shots)
] for _ in range(n_ways)]
support_mask = all_class_data[0]['label'][
list_label[iteration]]
support_fg_mask = [[
get_fg_mask(support_mask, class_ids[way])
for shot in range(n_shots)
] for way in range(len(class_ids))]
support_bg_mask = [[
get_bg_mask(support_mask, class_ids)
for _ in range(n_shots)
] for _ in range(n_ways)]
s_fg_mask = [[shot['fg_mask'].float().cuda() for shot in way]
for way in support_fg_mask]
s_bg_mask = [[shot['bg_mask'].float().cuda() for shot in way]
for way in support_bg_mask]
# print(f'fg_mask {s_bg_mask[0][0].shape}')
# print(f'bg_mask {s_bg_mask[0][0].shape}')
# print(support_mask.shape)
for idx, data in enumerate(all_class_data):
query_images = [
all_class_data[idx]['image'].cuda()
for i in range(n_ways)
]
query_labels = torch.cat([
query_label.cuda() for query_label in [
all_class_data[idx]['label'][
list_label[iteration]],
]
])
# print(f'query_image{query_images[0].shape}')
if idx > 0:
pre_mask = [
pred_mask,
]
elif idx == 0:
pre_mask = [
support_mask.float().cuda(),
]
query_pred, _ = model(support_images, s_fg_mask, s_bg_mask,
query_images, pre_mask)
pred = query_pred.argmax(dim=1, keepdim=True)
pred = pred.data.cpu().numpy()
img = pred[0, 0]
img_e = Image.fromarray(img.astype('float32')).convert('P')
pred_mask = tr_F.resize(img_e,
config['input_size'],
interpolation=Image.NEAREST)
pred_mask = torch.Tensor(np.array(pred_mask))
pred_mask = torch.unsqueeze(pred_mask, dim=0)
pred_mask = pred_mask.float().cuda()
img_e.putpalette(palette)
# print(os.path.join(f'./result/{class_name[0]}/', '{:05d}.png'.format(idx)))
# print(batch[3][idx])
img_e.save(
os.path.join(f'./result/{num}/{class_name[0]}/',
'{:05d}.png'.format(idx)))
def main(_run, _config, _log):
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(
f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = FewShotSeg(pretrained_path=_config['path']['init_path'],
cfg=_config['model'])
model = nn.DataParallel(model.cuda(), device_ids=[
_config['gpu_id'],
])
model.train()
_log.info('###### Load data ######')
data_name = _config['dataset']
if data_name == 'ScanNet':
make_data = scannet_fewshot
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][_config['label_sets']]
transforms = Compose([Resize(size=_config['input_size']), RandomMirror()])
dataset = make_data(base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'])
trainloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=True,
num_workers=20,
pin_memory=True,
drop_last=True)
_log.info('###### Set optimizer ######')
optimizer = torch.optim.SGD(model.parameters(), **_config['optim'])
scheduler = MultiStepLR(optimizer,
milestones=_config['lr_milestones'],
gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=_config['ignore_label'])
i_iter = 0
log_loss = {'loss': 0, 'align_loss': 0}
_log.info('###### Training ######')
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
support_coords = [[shot.cuda() for shot in way]
for way in sample_batched['support_coords']]
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
query_coords = [
query_coord.cuda()
for query_coord in sample_batched['query_coords']
]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.long().cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
# Forward and Backward
optimizer.zero_grad()
query_pred, align_loss = model(support_coords, support_images,
support_fg_mask, support_bg_mask,
query_coords, query_images)
query_loss = criterion(query_pred, query_labels[None, ...])
loss = query_loss + align_loss * _config['align_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
align_loss = align_loss.detach().data.cpu().numpy(
) if align_loss != 0 else 0
_run.log_scalar('loss', query_loss)
_run.log_scalar('align_loss', align_loss)
log_loss['loss'] += query_loss
log_loss['align_loss'] += align_loss
# print loss and take snapshots
if (i_iter + 1) % _config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
align_loss = log_loss['align_loss'] / (i_iter + 1)
print(f'step {i_iter+1}: loss: {loss}, align_loss: {align_loss}')
if (i_iter + 1) % _config['save_pred_every'] == 0:
_log.info('###### Taking snapshot ######')
torch.save(
model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots',
f'{i_iter + 1}.pth'))
_log.info('###### Saving final model ######')
torch.save(
model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots',
f'{i_iter + 1}.pth'))
def main(_run, _config, _log):
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = FewShotSeg(pretrained_path=_config['path']['init_path'], cfg=_config['model'])
model = nn.DataParallel(model.cuda(), device_ids=[_config['gpu_id'],])
model.train()
# Using Saliency
# u2_model_dir = '/content/gdrive/My Drive/Research/U-2-Net/saved_models/'+ 'u2netp' + '/' + 'u2netp.pth'
# u2_net = U2NETP(3,1)
# u2_net.load_state_dict(torch.load(u2_model_dir))
# if torch.cuda.is_available():
# u2_net.cuda()
_log.info('###### Load data ######')
data_name = _config['dataset']
if data_name == 'VOC':
make_data = voc_fewshot
elif data_name == 'COCO':
make_data = coco_fewshot
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][_config['label_sets']]
transforms = Compose([Resize(size=_config['input_size']),
RandomMirror()])
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries']
)
trainloader = DataLoader(
dataset,
batch_size=_config['batch_size'],
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True
)
_log.info('###### Set optimizer ######')
print(_config['mode'])
optimizer = torch.optim.SGD(model.parameters(), **_config['optim'])
scheduler = MultiStepLR(optimizer, milestones=_config['lr_milestones'], gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=_config['ignore_label'])
i_iter = 0
log_loss = {'loss': 0, 'align_loss': 0, 'dist_loss': 0}
_log.info('###### Training ######')
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
query_images = [query_image.cuda()
for query_image in sample_batched['query_images']]
query_labels = torch.cat(
[query_label.long().cuda() for query_label in sample_batched['query_labels']], dim=0)
# Forward and Backward
optimizer.zero_grad()
# with torch.no_grad():
# # u2net
# inputs = query_images[0].type(torch.FloatTensor)
# labels = query_labels.type(torch.FloatTensor)
# if torch.cuda.is_available():
# inputs_v, labels_v = Variable(inputs.cuda(), requires_grad=False), Variable(labels.cuda(),
# requires_grad=False)
# else:
# inputs_v, labels_v = Variable(inputs, requires_grad=False), Variable(labels, requires_grad=False)
# d1,d2,d3,d4,d5,d6,d7= u2_net(inputs_v)
# # normalization
# pred = d1[:,0,:,:]
# pred = normPRED(pred)
pred = []
query_pred, align_loss, dist_loss = model(support_images, support_fg_mask, support_bg_mask,
query_images, pred)
query_loss = criterion(query_pred, query_labels)
loss = query_loss + dist_loss + align_loss * 0.2 #_config['align_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
dist_loss = dist_loss.detach().data.cpu().numpy() if dist_loss != 0 else 0
align_loss = align_loss.detach().data.cpu().numpy() if align_loss != 0 else 0
_run.log_scalar('loss', query_loss)
_run.log_scalar('align_loss', align_loss)
_run.log_scalar('dist_loss', dist_loss)
log_loss['loss'] += query_loss
log_loss['align_loss'] += align_loss
log_loss['dist_loss'] += dist_loss
# print loss and take snapshots
if (i_iter + 1) % _config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
align_loss = log_loss['align_loss'] / (i_iter + 1)
print(f'step {i_iter+1}: loss: {loss}, align_loss: {align_loss}, dist_loss: {dist_loss}')
if (i_iter + 1) % _config['save_pred_every'] == 0:
_log.info('###### Taking snapshot ######')
torch.save(model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots', f'{i_iter + 1}.pth'))
_log.info('###### Saving final model ######')
torch.save(model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots', f'{i_iter + 1}.pth'))
def main(config):
if not os.path.exists(config['snapshots']):
os.makedirs(config['snapshots'])
palette_path = config['palette_dir']
with open(palette_path) as f:
palette = f.readlines()
palette = list(np.asarray([[int(p) for p in pal[0:-1].split(' ')] for pal in palette]).reshape(768))
snap_shots_dir = config['snapshots']
# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
torch.cuda.set_device(2)
# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
set_seed(config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.set_num_threads(1)
model = FewShotSeg(cfg=config['model'])
model = nn.DataParallel(model.cuda(),device_ids=[2])
model.train()
data_name = config['dataset']
if data_name == 'davis':
make_data = davis2017_fewshot
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][config['label_sets']]
transforms = Compose([Resize(size=config['input_size']),
RandomMirror()])
dataset = make_data(
base_dir=config['path'][data_name]['data_dir'],
split=config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=config['n_steps'] * config['batch_size'],
n_ways=config['task']['n_ways'],
n_shots=config['task']['n_shots'],
n_queries=config['task']['n_queries']
)
trainloader = DataLoader(
dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True
)
optimizer = torch.optim.SGD(model.parameters(), **config['optim'])
scheduler = MultiStepLR(optimizer, milestones=config['lr_milestones'], gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=config['ignore_label'])
i_iter = 0
log_loss = {'loss': 0, 'align_loss': 0}
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_fg_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_bg_mask']]
img_size = sample_batched['img_size']
# support_label_t = [[shot.float().cuda() for shot in way]
# for way in sample_batched['support_bg_mask']]
query_images = [query_image.cuda()
for query_image in sample_batched['query_images']]
query_labels = torch.cat(
[query_label.long().cuda() for query_label in sample_batched['query_labels']], dim=0)
pre_masks = [query_label.float().cuda() for query_label in sample_batched['query_masks']]
# Forward and Backward
optimizer.zero_grad()
query_pred, align_loss = model(support_images, support_fg_mask, support_bg_mask,
query_images,pre_masks)
query_pred = F.interpolate(query_pred, size=img_size, mode= "bilinear")
query_loss = criterion(query_pred, query_labels)
loss = query_loss + align_loss * config['align_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
align_loss = align_loss.detach().data.cpu().numpy() if align_loss != 0 else 0
log_loss['loss'] += query_loss
log_loss['align_loss'] += align_loss
# print loss and take snapshots
if (i_iter + 1) % config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
align_loss = log_loss['align_loss'] / (i_iter + 1)
print(f'step {i_iter + 1}: loss: {loss}, align_loss: {align_loss}')
# if len(support_fg_mask)>1:
# pred = query_pred.argmax(dim=1, keepdim=True)
# pred = pred.data.cpu().numpy()
# img = pred[0, 0]
# for i in range(img.shape[0]):
# for j in range(img.shape[1]):
# if img[i][j] > 0:
# print(f'{img[i][j]} {len(support_fg_mask)}')
#
# img_e = Image.fromarray(img.astype('float32')).convert('P')
# img_e.putpalette(palette)
# img_e.save(os.path.join(config['path']['davis']['data_dir'], '{:05d}.png'.format(i_iter)))
if (i_iter + 1) % config['save_pred_every'] == 0:
torch.save(model.state_dict(),
os.path.join(f'{snap_shots_dir}', f'{i_iter + 1}.pth'))
torch.save(model.state_dict(),
os.path.join(f'{snap_shots_dir}', f'{i_iter + 1}.pth'))
def main(cfg, gpus):
# Network Builders
torch.cuda.set_device(gpus[0])
print('###### Create model ######')
net_objectness = ModelBuilder.build_objectness(
arch=cfg.MODEL.arch_objectness,
weights=cfg.MODEL.weights_enc_query,
fix_encoder=cfg.TRAIN.fix_encoder)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
input_dim=cfg.MODEL.decoder_dim,
fc_dim=cfg.MODEL.fc_dim,
ppm_dim=cfg.MODEL.ppm_dim,
num_class=2,
weights=cfg.MODEL.weights_decoder,
dropout_rate=cfg.MODEL.dropout_rate,
use_dropout=cfg.MODEL.use_dropout)
crit = nn.NLLLoss(ignore_index=255)
print('###### Load data ######')
data_name = cfg.DATASET.name
if data_name == 'VOC':
from dataloaders.customized_objectness import voc_fewshot
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
from dataloaders.customized_objectness import coco_fewshot
make_data = coco_fewshot
max_label = 80
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][cfg.TASK.fold_idx]
labels_val = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
cfg.TASK.fold_idx]
exclude_labels = labels_val
transforms = Compose([Resize(size=cfg.DATASET.input_size), RandomMirror()])
dataset = make_data(base_dir=cfg.DATASET.data_dir,
split=cfg.DATASET.data_split,
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=cfg.TRAIN.n_iters * cfg.TRAIN.n_batch,
n_ways=cfg.TASK.n_ways,
n_shots=cfg.TASK.n_shots,
n_queries=cfg.TASK.n_queries,
permute=cfg.TRAIN.permute_labels,
exclude_labels=exclude_labels)
trainloader = DataLoader(dataset,
batch_size=cfg.TRAIN.n_batch,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
#segmentation_module = nn.DataParallel(segmentation_module, device_ids=gpus)
net_objectness.cuda()
net_decoder.cuda()
# Set up optimizers
nets = (net_objectness, net_decoder, crit)
optimizers = create_optimizers(nets, cfg)
batch_time = AverageMeter()
data_time = AverageMeter()
ave_total_loss = AverageMeter()
ave_acc = AverageMeter()
history = {'train': {'iter': [], 'loss': [], 'acc': []}}
net_objectness.train(not cfg.TRAIN.fix_bn)
net_decoder.train(not cfg.TRAIN.fix_bn)
best_iou = 0
# main loop
tic = time.time()
print('###### Training ######')
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
feed_dict = data_preprocess(sample_batched, cfg)
data_time.update(time.time() - tic)
net_objectness.zero_grad()
net_decoder.zero_grad()
# adjust learning rate
adjust_learning_rate(optimizers, i_iter, cfg)
# forward pass
feat = net_objectness(feed_dict['img_data'], return_feature_maps=True)
pred = net_decoder(feat)
loss = crit(pred, feed_dict['seg_label'])
acc = pixel_acc(pred, feed_dict['seg_label'])
loss = loss.mean()
acc = acc.mean()
# Backward
loss.backward()
for optimizer in optimizers:
if optimizer:
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - tic)
tic = time.time()
# update average loss and acc
ave_total_loss.update(loss.data.item())
ave_acc.update(acc.data.item() * 100)
# calculate accuracy, and display
if i_iter % cfg.TRAIN.disp_iter == 0:
print('Iter: [{}][{}/{}], Time: {:.2f}, Data: {:.2f}, '
'lr_encoder: {:.6f}, lr_decoder: {:.6f}, '
'Accuracy: {:4.2f}, Loss: {:.6f}'.format(
i_iter, i_iter, cfg.TRAIN.n_iters, batch_time.average(),
data_time.average(), cfg.TRAIN.running_lr_encoder,
cfg.TRAIN.running_lr_decoder, ave_acc.average(),
ave_total_loss.average()))
history['train']['iter'].append(i_iter)
history['train']['loss'].append(loss.data.item())
history['train']['acc'].append(acc.data.item())
if (i_iter + 1) % cfg.TRAIN.save_freq == 0:
checkpoint(nets, history, cfg, i_iter + 1)
if (i_iter + 1) % cfg.TRAIN.eval_freq == 0:
metric = Metric(max_label=max_label, n_runs=cfg.VAL.n_runs)
with torch.no_grad():
print('----Evaluation----')
net_objectness.eval()
net_decoder.eval()
net_decoder.use_softmax = True
for run in range(cfg.VAL.n_runs):
print(f'### Run {run + 1} ###')
set_seed(cfg.VAL.seed + run)
print(f'### Load validation data ###')
dataset_val = make_data(base_dir=cfg.DATASET.data_dir,
split=cfg.DATASET.data_split,
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels_val,
max_iters=cfg.VAL.n_iters *
cfg.VAL.n_batch,
n_ways=cfg.TASK.n_ways,
n_shots=cfg.TASK.n_shots,
n_queries=cfg.TASK.n_queries,
permute=cfg.VAL.permute_labels,
exclude_labels=[])
if data_name == 'COCO':
coco_cls_ids = dataset_val.datasets[
0].dataset.coco.getCatIds()
testloader = DataLoader(dataset_val,
batch_size=cfg.VAL.n_batch,
shuffle=False,
num_workers=1,
pin_memory=True,
drop_last=False)
print(f"Total # of validation Data: {len(dataset)}")
#for sample_batched in tqdm.tqdm(testloader):
for sample_batched in testloader:
feed_dict = data_preprocess(sample_batched,
cfg,
is_val=True)
if data_name == 'COCO':
label_ids = [
coco_cls_ids.index(x) + 1
for x in sample_batched['class_ids']
]
else:
label_ids = list(sample_batched['class_ids'])
feat = net_objectness(feed_dict['img_data'],
return_feature_maps=True)
query_pred = net_decoder(
feat, segSize=cfg.DATASET.input_size)
metric.record(
np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(feed_dict['seg_label'][0].cpu()),
labels=label_ids,
n_run=run)
classIoU, meanIoU = metric.get_mIoU(
labels=sorted(labels_val), n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(
n_run=run)
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(
labels=sorted(labels_val))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary(
)
print('----- Evaluation Result -----')
print(f'best meanIoU_binary: {best_iou}')
print(f'meanIoU mean: {meanIoU}')
print(f'meanIoU std: {meanIoU_std}')
print(f'meanIoU_binary mean: {meanIoU_binary}')
print(f'meanIoU_binary std: {meanIoU_std_binary}')
if meanIoU_binary > best_iou:
best_iou = meanIoU_binary
checkpoint(nets, history, cfg, 'best')
net_objectness.train(not cfg.TRAIN.fix_bn)
net_decoder.train(not cfg.TRAIN.fix_bn)
net_decoder.use_softmax = False
print('Training Done!')
def main(cfg, gpus):
torch.cuda.set_device(gpus[0])
# Network Builders
net_enc_query = ModelBuilder.build_encoder(
arch=cfg.MODEL.arch_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_enc_query,
fix_encoder=cfg.TRAIN.fix_encoder)
net_enc_memory = ModelBuilder.build_encoder_memory_separate(
arch=cfg.MODEL.arch_memory_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_enc_memory,
num_class=cfg.TASK.n_ways+1,
RGB_mask_combine_val=cfg.DATASET.RGB_mask_combine_val,
segm_downsampling_rate=cfg.DATASET.segm_downsampling_rate)
net_att_query = ModelBuilder.build_attention(
arch=cfg.MODEL.arch_attention,
input_dim=cfg.MODEL.encoder_dim,
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_att_query)
net_att_memory = ModelBuilder.build_attention(
arch=cfg.MODEL.arch_attention,
input_dim=cfg.MODEL.fc_dim,
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_att_memory)
net_projection = ModelBuilder.build_projection(
arch=cfg.MODEL.arch_projection,
input_dim=cfg.MODEL.encoder_dim,
fc_dim=cfg.MODEL.projection_dim,
weights=cfg.MODEL.weights_projection)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
input_dim=cfg.MODEL.decoder_dim,
fc_dim=cfg.MODEL.decoder_fc_dim,
ppm_dim=cfg.MODEL.ppm_dim,
num_class=cfg.TASK.n_ways+1,
weights=cfg.MODEL.weights_decoder,
dropout_rate=cfg.MODEL.dropout_rate,
use_dropout=cfg.MODEL.use_dropout,
use_softmax=True)
if cfg.MODEL.weights_objectness and cfg.MODEL.weights_objectness_decoder:
'''net_objectness = ModelBuilder.build_objectness(
arch='resnet50_deeplab',
weights=cfg.MODEL.weights_objectness,
fix_encoder=True)
net_objectness_decoder = ModelBuilder.build_decoder(
arch='aspp_few_shot',
input_dim=2048,
fc_dim=256,
ppm_dim=256,
num_class=2,
weights=cfg.MODEL.weights_objectness_decoder,
dropout_rate=0.5,
use_dropout=True)'''
net_objectness = ModelBuilder.build_objectness(
arch='hrnetv2',
weights=cfg.MODEL.weights_objectness,
fix_encoder=True)
net_objectness_decoder = ModelBuilder.build_decoder(
arch='c1_nodropout',
input_dim=720,
fc_dim=720,
ppm_dim=256,
num_class=2,
weights=cfg.MODEL.weights_objectness_decoder,
use_dropout=False)
for param in net_objectness.parameters():
param.requires_grad = False
for param in net_objectness_decoder.parameters():
param.requires_grad = False
else:
net_objectness = None
net_objectness_decoder = None
crit = nn.NLLLoss(ignore_index=255)
segmentation_module = SegmentationAttentionSeparateModule(net_enc_query, net_enc_memory, net_att_query, net_att_memory, net_decoder, net_projection, net_objectness, net_objectness_decoder, crit, zero_memory=cfg.MODEL.zero_memory, zero_qval=cfg.MODEL.zero_qval, normalize_key=cfg.MODEL.normalize_key, p_scalar=cfg.MODEL.p_scalar, memory_feature_aggregation=cfg.MODEL.memory_feature_aggregation, memory_noLabel=cfg.MODEL.memory_noLabel, debug=cfg.is_debug or cfg.eval_att_voting, mask_feat_downsample_rate=cfg.MODEL.mask_feat_downsample_rate, att_mat_downsample_rate=cfg.MODEL.att_mat_downsample_rate, objectness_feat_downsample_rate=cfg.MODEL.objectness_feat_downsample_rate, segm_downsampling_rate=cfg.DATASET.segm_downsampling_rate, mask_foreground=cfg.MODEL.mask_foreground, global_pool_read=cfg.MODEL.global_pool_read, average_memory_voting=cfg.MODEL.average_memory_voting, average_memory_voting_nonorm=cfg.MODEL.average_memory_voting_nonorm, mask_memory_RGB=cfg.MODEL.mask_memory_RGB, linear_classifier_support=cfg.MODEL.linear_classifier_support, decay_lamb=cfg.MODEL.decay_lamb, linear_classifier_support_only=cfg.MODEL.linear_classifier_support_only, qread_only=cfg.MODEL.qread_only, feature_as_key=cfg.MODEL.feature_as_key, objectness_multiply=cfg.MODEL.objectness_multiply)
segmentation_module = nn.DataParallel(segmentation_module, device_ids=gpus)
segmentation_module.cuda()
segmentation_module.eval()
print('###### Prepare data ######')
data_name = cfg.DATASET.name
if data_name == 'VOC':
from dataloaders.customized import voc_fewshot
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
from dataloaders.customized import coco_fewshot
make_data = coco_fewshot
max_label = 80
split = cfg.DATASET.data_split + '2014'
annFile = f'{cfg.DATASET.data_dir}/annotations/instances_{split}.json'
cocoapi = COCO(annFile)
else:
raise ValueError('Wrong config for dataset!')
#labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][cfg.TASK.fold_idx]
labels = CLASS_LABELS[data_name][cfg.TASK.fold_idx]
transforms = [Resize_test(size=cfg.DATASET.input_size)]
transforms = Compose(transforms)
print('###### Testing begins ######')
metric = Metric(max_label=max_label, n_runs=cfg.VAL.n_runs)
with torch.no_grad():
for run in range(cfg.VAL.n_runs):
print(f'### Run {run + 1} ###')
set_seed(cfg.VAL.seed + run)
print(f'### Load data ###')
dataset = make_data(
base_dir=cfg.DATASET.data_dir,
split=cfg.DATASET.data_split,
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=cfg.VAL.n_iters * cfg.VAL.n_batch,
n_ways=cfg.TASK.n_ways,
n_shots=cfg.TASK.n_shots,
n_queries=cfg.TASK.n_queries,
permute=cfg.VAL.permute_labels,
exclude_labels=[]
)
if data_name == 'COCO':
coco_cls_ids = dataset.datasets[0].dataset.coco.getCatIds()
testloader = DataLoader(dataset, batch_size=cfg.VAL.n_batch, shuffle=False,
num_workers=1, pin_memory=True, drop_last=False)
print(f"Total # of Data: {len(dataset)}")
count = 0
if cfg.multi_scale_test:
scales = [224, 328, 424]
else:
scales = [328]
for sample_batched in tqdm.tqdm(testloader):
feed_dict = data_preprocess(sample_batched, cfg)
if data_name == 'COCO':
label_ids = [coco_cls_ids.index(x) + 1 for x in sample_batched['class_ids']]
else:
label_ids = list(sample_batched['class_ids'])
for q, scale in enumerate(scales):
if len(scales) > 1:
feed_dict['img_data'] = nn.functional.interpolate(feed_dict['img_data'].cuda(), size=(scale, scale), mode='bilinear')
if cfg.eval_att_voting or cfg.is_debug:
query_pred, qread, qval, qk_b, mk_b, mv_b, p, feature_enc, feature_memory = segmentation_module(feed_dict, segSize=(feed_dict['seg_label_noresize'].shape[1], feed_dict['seg_label_noresize'].shape[2]))
if cfg.eval_att_voting:
height, width = qread.shape[-2], qread.shape[-1]
assert p.shape[0] == height*width
img_refs_mask_resize = nn.functional.interpolate(feed_dict['img_refs_mask'][0].cuda(), size=(height, width), mode='nearest')
img_refs_mask_resize_flat = img_refs_mask_resize[:,0,:,:].view(img_refs_mask_resize.shape[0], -1)
mask_voting_flat = torch.mm(img_refs_mask_resize_flat, p)
mask_voting = mask_voting_flat.view(mask_voting_flat.shape[0], height, width)
mask_voting = torch.unsqueeze(mask_voting, 0)
query_pred = nn.functional.interpolate(mask_voting[:,0:-1], size=cfg.DATASET.input_size, mode='bilinear', align_corners=False)
if cfg.is_debug:
np.save('debug/img_refs_mask-%04d-%s-%s.npy'%(count, sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), img_refs_mask_resize.detach().cpu().float().numpy())
np.save('debug/query_pred-%04d-%s-%s.npy'%(count, sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), query_pred.detach().cpu().float().numpy())
if cfg.is_debug:
np.save('debug/qread-%04d-%s-%s.npy'%(count, sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), qread.detach().cpu().float().numpy())
np.save('debug/qval-%04d-%s-%s.npy'%(count, sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), qval.detach().cpu().float().numpy())
#np.save('debug/qk_b-%s-%s.npy'%(sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), qk_b.detach().cpu().float().numpy())
#np.save('debug/mk_b-%s-%s.npy'%(sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), mk_b.detach().cpu().float().numpy())
#np.save('debug/mv_b-%s-%s.npy'%(sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), mv_b.detach().cpu().float().numpy())
#np.save('debug/p-%04d-%s-%s.npy'%(count, sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), p.detach().cpu().float().numpy())
#np.save('debug/feature_enc-%s-%s.npy'%(sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), feature_enc[-1].detach().cpu().float().numpy())
#np.save('debug/feature_memory-%s-%s.npy'%(sample_batched['query_ids'][0][0], sample_batched['support_ids'][0][0][0]), feature_memory[-1].detach().cpu().float().numpy())
else:
#query_pred = segmentation_module(feed_dict, segSize=cfg.DATASET.input_size)
query_pred = segmentation_module(feed_dict, segSize=(feed_dict['seg_label_noresize'].shape[1], feed_dict['seg_label_noresize'].shape[2]))
if q == 0:
query_pred_final = query_pred/len(scales)
else:
query_pred_final += query_pred/len(scales)
query_pred = query_pred_final
metric.record(np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(feed_dict['seg_label_noresize'][0].cpu()),
labels=label_ids, n_run=run)
if cfg.VAL.visualize:
#print(as_numpy(feed_dict['seg_label'][0].cpu()).shape)
#print(as_numpy(np.array(query_pred.argmax(dim=1)[0].cpu())).shape)
#print(feed_dict['img_data'].cpu().shape)
query_name = sample_batched['query_ids'][0][0]
support_name = sample_batched['support_ids'][0][0][0]
if data_name == 'VOC':
img = imread(os.path.join(cfg.DATASET.data_dir, 'JPEGImages', query_name+'.jpg'))
else:
query_name = int(query_name)
img_meta = cocoapi.loadImgs(query_name)[0]
img = imread(os.path.join(cfg.DATASET.data_dir, split, img_meta['file_name']))
#img = imresize(img, cfg.DATASET.input_size)
visualize_result(
(img, as_numpy(feed_dict['seg_label_noresize'][0].cpu()), '%05d'%(count)),
as_numpy(np.array(query_pred.argmax(dim=1)[0].cpu())),
os.path.join(cfg.DIR, 'result')
)
count += 1
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels), n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
'''_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')'''
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary()
print('----- Final Result -----')
print('final_classIoU', classIoU.tolist())
print('final_classIoU_std', classIoU_std.tolist())
print('final_meanIoU', meanIoU.tolist())
print('final_meanIoU_std', meanIoU_std.tolist())
print('final_classIoU_binary', classIoU_binary.tolist())
print('final_classIoU_std_binary', classIoU_std_binary.tolist())
print('final_meanIoU_binary', meanIoU_binary.tolist())
print('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
print(f'classIoU mean: {classIoU}')
print(f'classIoU std: {classIoU_std}')
print(f'meanIoU mean: {meanIoU}')
print(f'meanIoU std: {meanIoU_std}')
print(f'classIoU_binary mean: {classIoU_binary}')
print(f'classIoU_binary std: {classIoU_std_binary}')
print(f'meanIoU_binary mean: {meanIoU_binary}')
print(f'meanIoU_binary std: {meanIoU_std_binary}')
def main(config):
if not os.path.exists(config['snapshots']):
os.makedirs(config['snapshots'])
snap_shots_dir = config['snapshots']
# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
torch.cuda.set_device(2)
# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
set_seed(config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.set_num_threads(1)
model = FewShotSeg(cfg=config['model'])
model = nn.DataParallel(model.cuda(), device_ids=[2])
model.train()
data_name = config['dataset']
if data_name == 'davis':
make_data = davis2017_fewshot
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][config['label_sets']]
transforms = Compose([Resize(size=config['input_size']), RandomMirror()])
dataset = make_data(base_dir=config['path'][data_name]['data_dir'],
split=config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=config['n_steps'] * config['batch_size'],
n_ways=config['task']['n_ways'],
n_shots=config['task']['n_shots'],
n_queries=config['task']['n_queries'])
trainloader = DataLoader(dataset,
batch_size=config['batch_size'],
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True)
optimizer = torch.optim.SGD(model.parameters(), **config['optim'])
scheduler = MultiStepLR(optimizer,
milestones=config['lr_milestones'],
gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=config['ignore_label'])
i_iter = 0
log_loss = {'loss': 0, 'align_loss': 0}
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_fg_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_bg_mask']]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.long().cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
# print(query_labels.shape)
pre_masks = [
query_label.float().cuda()
for query_label in sample_batched['query_masks']
]
# Forward and Backward
optimizer.zero_grad()
query_pred, align_loss = model(support_images, support_fg_mask,
support_bg_mask, query_images,
pre_masks)
query_loss = criterion(query_pred, query_labels)
loss = query_loss + align_loss * config['align_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
align_loss = align_loss.detach().data.cpu().numpy(
) if align_loss != 0 else 0
# _run.log_scalar('loss', query_loss)
# _run.log_scalar('align_loss', align_loss)
log_loss['loss'] += query_loss
log_loss['align_loss'] += align_loss
# print loss and take snapshots
if (i_iter + 1) % config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
align_loss = log_loss['align_loss'] / (i_iter + 1)
print(f'step {i_iter + 1}: loss: {loss}, align_loss: {align_loss}')
if (i_iter + 1) % config['save_pred_every'] == 0:
torch.save(model.state_dict(),
os.path.join(f'{snap_shots_dir}', f'{i_iter + 1}.pth'))
torch.save(model.state_dict(),
os.path.join(f'{snap_shots_dir}', f'{i_iter + 1}.pth'))
def main(cfg, gpus):
# Network Builders
torch.cuda.set_device(gpus[0])
print('###### Create model ######')
net_enc_query = ModelBuilder.build_encoder(
arch=cfg.MODEL.arch_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_enc_query,
fix_encoder=cfg.TRAIN.fix_encoder)
net_enc_memory = ModelBuilder.build_encoder_memory_separate(
arch=cfg.MODEL.arch_memory_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_enc_memory,
num_class=cfg.TASK.n_ways + 1,
RGB_mask_combine_val=cfg.DATASET.RGB_mask_combine_val,
segm_downsampling_rate=cfg.DATASET.segm_downsampling_rate)
net_att_query = ModelBuilder.build_attention(
arch=cfg.MODEL.arch_attention,
input_dim=cfg.MODEL.encoder_dim,
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_att_query)
net_att_memory = ModelBuilder.build_attention(
arch=cfg.MODEL.arch_attention,
input_dim=cfg.MODEL.fc_dim,
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_att_memory)
net_projection = ModelBuilder.build_projection(
arch=cfg.MODEL.arch_projection,
input_dim=cfg.MODEL.encoder_dim,
fc_dim=cfg.MODEL.projection_dim,
weights=cfg.MODEL.weights_projection)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
input_dim=cfg.MODEL.decoder_dim,
fc_dim=cfg.MODEL.decoder_fc_dim,
ppm_dim=cfg.MODEL.ppm_dim,
num_class=cfg.TASK.n_ways + 1,
weights=cfg.MODEL.weights_decoder,
dropout_rate=cfg.MODEL.dropout_rate,
use_dropout=cfg.MODEL.use_dropout)
if cfg.MODEL.weights_objectness and cfg.MODEL.weights_objectness_decoder:
'''net_objectness = ModelBuilder.build_objectness(
arch='resnet50_deeplab',
weights=cfg.MODEL.weights_objectness,
fix_encoder=True)
net_objectness_decoder = ModelBuilder.build_decoder(
arch='aspp_few_shot',
input_dim=2048,
fc_dim=256,
ppm_dim=256,
num_class=2,
weights=cfg.MODEL.weights_objectness_decoder,
dropout_rate=0.5,
use_dropout=True)'''
net_objectness = ModelBuilder.build_objectness(
arch='hrnetv2',
weights=cfg.MODEL.weights_objectness,
fix_encoder=True)
net_objectness_decoder = ModelBuilder.build_decoder(
arch='c1_nodropout',
input_dim=720,
fc_dim=720,
ppm_dim=256,
num_class=2,
weights=cfg.MODEL.weights_objectness_decoder,
use_dropout=False)
for param in net_objectness.parameters():
param.requires_grad = False
for param in net_objectness_decoder.parameters():
param.requires_grad = False
else:
net_objectness = None
net_objectness_decoder = None
crit = nn.NLLLoss(ignore_index=255)
segmentation_module = SegmentationAttentionSeparateModule(
net_enc_query,
net_enc_memory,
net_att_query,
net_att_memory,
net_decoder,
net_projection,
net_objectness,
net_objectness_decoder,
crit,
zero_memory=cfg.MODEL.zero_memory,
random_memory_bias=cfg.MODEL.random_memory_bias,
random_memory_nobias=cfg.MODEL.random_memory_nobias,
random_scale=cfg.MODEL.random_scale,
zero_qval=cfg.MODEL.zero_qval,
normalize_key=cfg.MODEL.normalize_key,
p_scalar=cfg.MODEL.p_scalar,
memory_feature_aggregation=cfg.MODEL.memory_feature_aggregation,
memory_noLabel=cfg.MODEL.memory_noLabel,
mask_feat_downsample_rate=cfg.MODEL.mask_feat_downsample_rate,
att_mat_downsample_rate=cfg.MODEL.att_mat_downsample_rate,
objectness_feat_downsample_rate=cfg.MODEL.
objectness_feat_downsample_rate,
segm_downsampling_rate=cfg.DATASET.segm_downsampling_rate,
mask_foreground=cfg.MODEL.mask_foreground,
global_pool_read=cfg.MODEL.global_pool_read,
average_memory_voting=cfg.MODEL.average_memory_voting,
average_memory_voting_nonorm=cfg.MODEL.average_memory_voting_nonorm,
mask_memory_RGB=cfg.MODEL.mask_memory_RGB,
linear_classifier_support=cfg.MODEL.linear_classifier_support,
decay_lamb=cfg.MODEL.decay_lamb,
linear_classifier_support_only=cfg.MODEL.
linear_classifier_support_only,
qread_only=cfg.MODEL.qread_only,
feature_as_key=cfg.MODEL.feature_as_key,
objectness_multiply=cfg.MODEL.objectness_multiply)
print('###### Load data ######')
data_name = cfg.DATASET.name
if data_name == 'VOC':
from dataloaders.customized_objectness_debug import voc_fewshot
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
from dataloaders.customized_objectness_debug import coco_fewshot
make_data = coco_fewshot
max_label = 80
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][cfg.TASK.fold_idx]
labels_val = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
cfg.TASK.fold_idx]
if cfg.DATASET.exclude_labels:
exclude_labels = labels_val
else:
exclude_labels = []
transforms = Compose([Resize(size=cfg.DATASET.input_size), RandomMirror()])
dataset = make_data(base_dir=cfg.DATASET.data_dir,
split=cfg.DATASET.data_split,
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=cfg.TRAIN.n_iters * cfg.TRAIN.n_batch,
n_ways=cfg.TASK.n_ways,
n_shots=cfg.TASK.n_shots,
n_queries=cfg.TASK.n_queries,
permute=cfg.TRAIN.permute_labels,
exclude_labels=exclude_labels,
use_ignore=cfg.use_ignore)
trainloader = DataLoader(dataset,
batch_size=cfg.TRAIN.n_batch,
shuffle=True,
num_workers=4,
pin_memory=True,
drop_last=True)
#segmentation_module = nn.DataParallel(segmentation_module, device_ids=gpus)
segmentation_module.cuda()
# Set up optimizers
nets = (net_enc_query, net_enc_memory, net_att_query, net_att_memory,
net_decoder, net_projection, crit)
optimizers = create_optimizers(nets, cfg)
batch_time = AverageMeter()
data_time = AverageMeter()
ave_total_loss = AverageMeter()
ave_acc = AverageMeter()
history = {'train': {'iter': [], 'loss': [], 'acc': []}}
segmentation_module.train(not cfg.TRAIN.fix_bn)
if net_objectness and net_objectness_decoder:
net_objectness.eval()
net_objectness_decoder.eval()
best_iou = 0
# main loop
tic = time.time()
print('###### Training ######')
for i_iter, sample_batched in enumerate(trainloader):
# Prepare input
feed_dict = data_preprocess(sample_batched, cfg)
data_time.update(time.time() - tic)
segmentation_module.zero_grad()
# adjust learning rate
adjust_learning_rate(optimizers, i_iter, cfg)
# forward pass
#print(batch_data)
loss, acc = segmentation_module(feed_dict)
loss = loss.mean()
acc = acc.mean()
# Backward
loss.backward()
for optimizer in optimizers:
if optimizer:
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - tic)
tic = time.time()
# update average loss and acc
ave_total_loss.update(loss.data.item())
ave_acc.update(acc.data.item() * 100)
# calculate accuracy, and display
if i_iter % cfg.TRAIN.disp_iter == 0:
print('Iter: [{}][{}/{}], Time: {:.2f}, Data: {:.2f}, '
'lr_encoder: {:.6f}, lr_decoder: {:.6f}, '
'Accuracy: {:4.2f}, Loss: {:.6f}'.format(
i_iter, i_iter, cfg.TRAIN.n_iters, batch_time.average(),
data_time.average(), cfg.TRAIN.running_lr_encoder,
cfg.TRAIN.running_lr_decoder, ave_acc.average(),
ave_total_loss.average()))
history['train']['iter'].append(i_iter)
history['train']['loss'].append(loss.data.item())
history['train']['acc'].append(acc.data.item())
if (i_iter + 1) % cfg.TRAIN.save_freq == 0:
checkpoint(nets, history, cfg, i_iter + 1)
if (i_iter + 1) % cfg.TRAIN.eval_freq == 0:
metric = Metric(max_label=max_label, n_runs=cfg.VAL.n_runs)
with torch.no_grad():
print('----Evaluation----')
segmentation_module.eval()
net_decoder.use_softmax = True
for run in range(cfg.VAL.n_runs):
print(f'### Run {run + 1} ###')
set_seed(cfg.VAL.seed + run)
print(f'### Load validation data ###')
dataset_val = make_data(base_dir=cfg.DATASET.data_dir,
split=cfg.DATASET.data_split,
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels_val,
max_iters=cfg.VAL.n_iters *
cfg.VAL.n_batch,
n_ways=cfg.TASK.n_ways,
n_shots=cfg.TASK.n_shots,
n_queries=cfg.TASK.n_queries,
permute=cfg.VAL.permute_labels,
exclude_labels=[])
if data_name == 'COCO':
coco_cls_ids = dataset_val.datasets[
0].dataset.coco.getCatIds()
testloader = DataLoader(dataset_val,
batch_size=cfg.VAL.n_batch,
shuffle=False,
num_workers=1,
pin_memory=True,
drop_last=False)
print(f"Total # of validation Data: {len(dataset)}")
#for sample_batched in tqdm.tqdm(testloader):
for sample_batched in testloader:
feed_dict = data_preprocess(sample_batched,
cfg,
is_val=True)
if data_name == 'COCO':
label_ids = [
coco_cls_ids.index(x) + 1
for x in sample_batched['class_ids']
]
else:
label_ids = list(sample_batched['class_ids'])
query_pred = segmentation_module(
feed_dict, segSize=cfg.DATASET.input_size)
metric.record(
np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(feed_dict['seg_label'][0].cpu()),
labels=label_ids,
n_run=run)
classIoU, meanIoU = metric.get_mIoU(
labels=sorted(labels_val), n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(
n_run=run)
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(
labels=sorted(labels_val))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary(
)
print('----- Evaluation Result -----')
print(f'best meanIoU mean: {best_iou}')
print(f'meanIoU mean: {meanIoU}')
print(f'meanIoU std: {meanIoU_std}')
print(f'meanIoU_binary mean: {meanIoU_binary}')
print(f'meanIoU_binary std: {meanIoU_std_binary}')
checkpoint(nets, history, cfg, 'latest')
if meanIoU > best_iou:
best_iou = meanIoU
checkpoint(nets, history, cfg, 'best')
segmentation_module.train(not cfg.TRAIN.fix_bn)
if net_objectness and net_objectness_decoder:
net_objectness.eval()
net_objectness_decoder.eval()
net_decoder.use_softmax = False
print('Training Done!')
def main(cfg, gpus):
torch.cuda.set_device(gpus[0])
# Network Builders
net_objectness = ModelBuilder.build_objectness(
arch=cfg.MODEL.arch_objectness,
weights=cfg.MODEL.weights_enc_query,
fix_encoder=cfg.TRAIN.fix_encoder)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
input_dim=cfg.MODEL.decoder_dim,
fc_dim=cfg.MODEL.fc_dim,
ppm_dim=cfg.MODEL.ppm_dim,
num_class=2,
weights=cfg.MODEL.weights_decoder,
dropout_rate=cfg.MODEL.dropout_rate,
use_dropout=cfg.MODEL.use_dropout,
use_softmax=True)
crit = nn.NLLLoss(ignore_index=255)
net_objectness.cuda()
net_objectness.eval()
net_decoder.cuda()
net_decoder.eval()
print('###### Prepare data ######')
data_name = cfg.DATASET.name
if data_name == 'VOC':
if cfg.VAL.test_with_classes:
from dataloaders.customized import voc_fewshot
else:
from dataloaders.customized_objectness import voc_fewshot
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
if cfg.VAL.test_with_classes:
from dataloaders.customized import coco_fewshot
else:
from dataloaders.customized_objectness import coco_fewshot
make_data = coco_fewshot
max_label = 80
split = cfg.DATASET.data_split + '2014'
annFile = f'{cfg.DATASET.data_dir}/annotations/instances_{split}.json'
cocoapi = COCO(annFile)
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
cfg.TASK.fold_idx]
#labels = CLASS_LABELS[data_name][cfg.TASK.fold_idx]
transforms = [Resize_test(size=cfg.DATASET.input_size)]
transforms = Compose(transforms)
print('###### Testing begins ######')
metric = Metric(max_label=max_label, n_runs=cfg.VAL.n_runs)
with torch.no_grad():
for run in range(cfg.VAL.n_runs):
print(f'### Run {run + 1} ###')
set_seed(cfg.VAL.seed + run)
print(f'### Load data ###')
dataset = make_data(
base_dir=cfg.DATASET.data_dir,
split=cfg.DATASET.data_split,
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=cfg.VAL.n_iters * cfg.VAL.n_batch,
n_ways=cfg.TASK.n_ways,
n_shots=cfg.TASK.n_shots,
n_queries=cfg.TASK.n_queries,
permute=cfg.VAL.permute_labels,
)
if data_name == 'COCO':
coco_cls_ids = dataset.datasets[0].dataset.coco.getCatIds()
testloader = DataLoader(dataset,
batch_size=cfg.VAL.n_batch,
shuffle=False,
num_workers=1,
pin_memory=True,
drop_last=False)
print(f"Total # of Data: {len(dataset)}")
count = 0
for sample_batched in tqdm.tqdm(testloader):
feed_dict = data_preprocess(sample_batched, cfg)
if data_name == 'COCO':
label_ids = [
coco_cls_ids.index(x) + 1
for x in sample_batched['class_ids']
]
else:
label_ids = list(sample_batched['class_ids'])
feat = net_objectness(feed_dict['img_data'],
return_feature_maps=True)
query_pred = net_decoder(
feat,
segSize=(feed_dict['seg_label_noresize'].shape[1],
feed_dict['seg_label_noresize'].shape[2]))
metric.record(np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(
feed_dict['seg_label_noresize'][0].cpu()),
labels=label_ids,
n_run=run)
if cfg.VAL.visualize:
#print(as_numpy(feed_dict['seg_label'][0].cpu()).shape)
#print(as_numpy(np.array(query_pred.argmax(dim=1)[0].cpu())).shape)
#print(feed_dict['img_data'].cpu().shape)
query_name = sample_batched['query_ids'][0][0]
support_name = sample_batched['support_ids'][0][0][0]
if data_name == 'VOC':
img = imread(
os.path.join(cfg.DATASET.data_dir, 'JPEGImages',
query_name + '.jpg'))
else:
query_name = int(query_name)
img_meta = cocoapi.loadImgs(query_name)[0]
img = imread(
os.path.join(cfg.DATASET.data_dir, split,
img_meta['file_name']))
#img = imresize(img, cfg.DATASET.input_size)
visualize_result(
(img, as_numpy(
feed_dict['seg_label_noresize'][0].cpu()), '%05d' %
(count)),
as_numpy(np.array(query_pred.argmax(dim=1)[0].cpu())),
os.path.join(cfg.DIR, 'result'))
count += 1
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels),
n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
'''_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')'''
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(
labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary(
)
print('----- Final Result -----')
print('final_classIoU', classIoU.tolist())
print('final_classIoU_std', classIoU_std.tolist())
print('final_meanIoU', meanIoU.tolist())
print('final_meanIoU_std', meanIoU_std.tolist())
print('final_classIoU_binary', classIoU_binary.tolist())
print('final_classIoU_std_binary', classIoU_std_binary.tolist())
print('final_meanIoU_binary', meanIoU_binary.tolist())
print('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
print(f'classIoU mean: {classIoU}')
print(f'classIoU std: {classIoU_std}')
print(f'meanIoU mean: {meanIoU}')
print(f'meanIoU std: {meanIoU_std}')
print(f'classIoU_binary mean: {classIoU_binary}')
print(f'classIoU_binary std: {classIoU_std_binary}')
print(f'meanIoU_binary mean: {meanIoU_binary}')
print(f'meanIoU_binary std: {meanIoU_std_binary}')
def main(_run, _config, _log):
os.makedirs(f'{_run.observers[0].dir}/features', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = FewShotSeg(pretrained_path=_config['path']['init_path'], cfg=_config['model'], task=_config['task'])
model = nn.DataParallel(model.cuda(), device_ids=[_config['gpu_id'],])
if not _config['notrain']:
model.load_state_dict(torch.load(_config['snapshot'], map_location='cpu'))
model.eval()
_log.info('###### Prepare data ######')
data_name = _config['dataset']
if data_name == 'VOC':
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
make_data = coco_fewshot
max_label = 80
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
transforms = Compose(transforms)
_log.info('###### Testing begins ######')
metric = Metric(max_label=max_label, n_runs=_config['n_runs'])
with torch.no_grad():
for run in range(_config['n_runs']):
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
features_dfs = []
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries']
)
if _config['dataset'] == 'COCO':
coco_cls_ids = dataset.datasets[0].dataset.coco.getCatIds()
testloader = DataLoader(dataset, batch_size=_config['batch_size'], shuffle=False,
num_workers=1, pin_memory=True, drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
if _config['dataset'] == 'COCO':
label_ids = [coco_cls_ids.index(x) + 1 for x in sample_batched['class_ids']]
else:
label_ids = list(sample_batched['class_ids'])
support_ids = [[sample_batched['support_ids'][way*_config['task']['n_shots'] + shot][0]
for shot in range(_config['task']['n_shots'])]
for way in range(_config['task']['n_ways'])]
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
query_images = [query_image.cuda()
for query_image in sample_batched['query_images']]
query_labels = torch.cat(
[query_label.cuda()for query_label in sample_batched['query_labels']], dim=0)
query_pred, _, supp_fts = model(support_images, support_fg_mask, support_bg_mask,
query_images)
metric.record(np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(query_labels[0].cpu()),
labels=label_ids, n_run=run)
# Save features row
for i, label_id in enumerate(label_ids):
lbl_df = pd.DataFrame(torch.cat(supp_fts[i]).cpu().numpy())
lbl_df['label'] = label_id.item()
lbl_df['id'] = pd.Series(support_ids[i])
features_dfs.append(lbl_df)
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels), n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
_log.info('### Exporting features CSV')
features_df = pd.concat(features_dfs)
features_df = features_df.drop_duplicates(subset=['id'])
cols = list(features_df)
cols = [cols[-1], cols[-2]] + cols[:-2]
features_df = features_df[cols]
features_df.to_csv(f'{_run.observers[0].dir}/features/features_run_{run+1}.csv', index=False)
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary()
_log.info('###### Saving features visualization ######')
all_fts = pd.concat([pd.read_csv(f'{_run.observers[0].dir}/features/features_run_{run+1}.csv') for run in range(_config['n_runs'])])
all_fts = all_fts.drop_duplicates(subset=['id'])
_log.info('### Obtaining Umap visualization ###')
plot_umap(all_fts, f'{_run.observers[0].dir}/features/Umap_fts.png')
_log.info('### Obtaining TSNE visualization ###')
plot_tsne(all_fts, f'{_run.observers[0].dir}/features/TSNE_fts.png')
_log.info('----- Final Result -----')
_run.log_scalar('final_classIoU', classIoU.tolist())
_run.log_scalar('final_classIoU_std', classIoU_std.tolist())
_run.log_scalar('final_meanIoU', meanIoU.tolist())
_run.log_scalar('final_meanIoU_std', meanIoU_std.tolist())
_run.log_scalar('final_classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('final_classIoU_std_binary', classIoU_std_binary.tolist())
_run.log_scalar('final_meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
_log.info(f'classIoU mean: {classIoU}')
_log.info(f'classIoU std: {classIoU_std}')
_log.info(f'meanIoU mean: {meanIoU}')
_log.info(f'meanIoU std: {meanIoU_std}')
_log.info(f'classIoU_binary mean: {classIoU_binary}')
_log.info(f'classIoU_binary std: {classIoU_std_binary}')
_log.info(f'meanIoU_binary mean: {meanIoU_binary}')
_log.info(f'meanIoU_binary std: {meanIoU_std_binary}')
def main(_run, _config, _log):
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(
f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = Encoder(pretrained_path=_config['path']['init_path'])
model = nn.DataParallel(model.cuda(), device_ids=[
_config['gpu_id'],
])
model.train()
_log.info('###### Load data ######')
data_name = _config['dataset']
if data_name == 'VOC':
make_data = voc_fewshot
elif data_name == 'COCO':
make_data = coco_fewshot
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name][_config['label_sets']]
transforms = Compose([Resize(size=_config['input_size']), RandomMirror()])
dataset = make_data(base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
label_sets=_config['label_sets'],
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'])
trainloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True)
_log.info('###### Set optimizer ######')
optimizer = torch.optim.SGD(model.parameters(), **_config['optim'])
scheduler = MultiStepLR(optimizer,
milestones=_config['lr_milestones'],
gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=_config['ignore_label'])
i_iter = 0
log_loss = {'loss': 0, 'mcl_loss': 0}
_log.info('###### Training ######')
for i_iter, sample_batched in enumerate(trainloader):
#support image,support mask label and support multi-class label
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
support_images = torch.cat(
[torch.cat(way, dim=0) for way in support_images], dim=0)
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_fg_mask = torch.cat(
[torch.cat(way, dim=0) for way in support_fg_mask], dim=0)
support_label_mc = [[shot[f'label_ori'].long().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_label_mc = torch.cat(
[torch.cat(way, dim=0) for way in support_label_mc], dim=0)
#query image,query mask label and query multi-class label
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_images = torch.cat(query_images, dim=0)
query_labels = torch.cat([
query_label.long().cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
query_label_mc = [
n_queries[f'label_ori'].long().cuda()
for n_queries in sample_batched['query_masks']
]
query_label_mc = torch.cat(query_label_mc, dim=0)
optimizer.zero_grad()
query_pred, support_pred_mc, query_pred_mc, support_pred = model(
support_images, query_images, support_fg_mask)
query_pred = F.interpolate(query_pred,
size=query_images.shape[-2:],
mode='bilinear')
support_pred = F.interpolate(support_pred,
size=support_images.shape[-2:],
mode='bilinear')
support_pred_mc = F.interpolate(support_pred_mc,
size=support_images.shape[-2:],
mode='bilinear')
query_pred_mc = F.interpolate(query_pred_mc,
size=query_images.shape[-2:],
mode='bilinear')
binary_loss = criterion(query_pred, query_labels) + criterion(
support_pred,
support_fg_mask.long().cuda())
mcl_loss = criterion(support_pred_mc, support_label_mc) + criterion(
query_pred_mc, query_label_mc)
loss = binary_loss + mcl_loss * _config['mcl_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step()
# Log loss
binary_loss = binary_loss.detach().data.cpu().numpy()
mcl_loss = mcl_loss.detach().data.cpu().numpy() if mcl_loss != 0 else 0
_run.log_scalar('loss', binary_loss)
_run.log_scalar('mcl_loss', mcl_loss)
log_loss['loss'] += binary_loss
log_loss['mcl_loss'] += mcl_loss
# print loss and take snapshots
if (i_iter + 1) % _config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
mcl_loss = log_loss['mcl_loss'] / (i_iter + 1)
print(f'step {i_iter+1}: loss: {loss}, mcl_loss: {mcl_loss}')
if (i_iter + 1) % _config['save_pred_every'] == 0:
_log.info('###### Taking snapshot ######')
torch.save(
model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots',
f'{i_iter + 1}.pth'))
_log.info('###### Saving final model ######')
torch.save(
model.state_dict(),
os.path.join(f'{_run.observers[0].dir}/snapshots',
f'{i_iter + 1}.pth'))
def main(_run, _config, _log):
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(
os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info('###### Create model ######')
model = FewShotSeg(pretrained_path=_config['path']['init_path'],
cfg=_config['model'])
model = nn.DataParallel(model.cuda(), device_ids=[
_config['gpu_id'],
])
if not _config['notrain']:
model.load_state_dict(
torch.load(_config['snapshot'], map_location='cpu'))
print("Snapshotttt")
print(_config['snapshot'])
model.eval()
# u2_model_dir = '/content/gdrive/My Drive/Research/U-2-Net/saved_models/'+ 'u2net' + '/' + 'u2net_bce_itr_3168_train_1.523160_tar_0.203136.pth'
# u2_net = U2NET(3,1)
# u2_net.load_state_dict(torch.load(u2_model_dir))
# if torch.cuda.is_available():
# u2_net.cuda()
_log.info('###### Prepare data ######')
data_name = _config['dataset']
if data_name == 'VOC':
make_data = voc_fewshot
max_label = 20
elif data_name == 'COCO':
make_data = coco_fewshot
max_label = 80
else:
raise ValueError('Wrong config for dataset!')
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][
_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
if _config['scribble_dilation'] > 0:
transforms.append(DilateScribble(size=_config['scribble_dilation']))
transforms = Compose(transforms)
_log.info('###### Testing begins ######')
metric = Metric(max_label=max_label, n_runs=_config['n_runs'])
with torch.no_grad():
for run in range(_config['n_runs']):
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'])
if _config['dataset'] == 'COCO':
coco_cls_ids = dataset.datasets[0].dataset.coco.getCatIds()
testloader = DataLoader(dataset,
batch_size=_config['batch_size'],
shuffle=False,
num_workers=1,
pin_memory=True,
drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
if _config['dataset'] == 'COCO':
label_ids = [
coco_cls_ids.index(x) + 1
for x in sample_batched['class_ids']
]
else:
label_ids = list(sample_batched['class_ids'])
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
suffix = 'scribble' if _config['scribble'] else 'mask'
if _config['bbox']:
support_fg_mask = []
support_bg_mask = []
for i, way in enumerate(sample_batched['support_mask']):
fg_masks = []
bg_masks = []
for j, shot in enumerate(way):
fg_mask, bg_mask = get_bbox(
shot['fg_mask'],
sample_batched['support_inst'][i][j])
fg_masks.append(fg_mask.float().cuda())
bg_masks.append(bg_mask.float().cuda())
support_fg_mask.append(fg_masks)
support_bg_mask.append(bg_masks)
else:
support_fg_mask = [[
shot[f'fg_{suffix}'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
support_bg_mask = [[
shot[f'bg_{suffix}'].float().cuda() for shot in way
] for way in sample_batched['support_mask']]
query_images = [
query_image.cuda()
for query_image in sample_batched['query_images']
]
query_labels = torch.cat([
query_label.cuda()
for query_label in sample_batched['query_labels']
],
dim=0)
# u2net
inputs = query_images[0].type(torch.FloatTensor)
labels_v = query_labels.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_v, labels_v = Variable(
inputs.cuda(),
requires_grad=False), Variable(labels_v.cuda(),
requires_grad=False)
else:
inputs_v, labels_v = Variable(
inputs,
requires_grad=False), Variable(labels_v,
requires_grad=False)
#d1,d2,d3,d4,d5,d6,d7= u2_net(inputs_v)
# normalization
# pred = d1[:,0,:,:]
# pred = normPRED(pred)
pred = []
query_pred, _, _ = model(support_images, support_fg_mask,
support_bg_mask, query_images, pred)
metric.record(np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(query_labels[0].cpu()),
labels=label_ids,
n_run=run)
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels),
n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(
labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary(
)
_log.info('----- Final Result -----')
_run.log_scalar('final_classIoU', classIoU.tolist())
_run.log_scalar('final_classIoU_std', classIoU_std.tolist())
_run.log_scalar('final_meanIoU', meanIoU.tolist())
_run.log_scalar('final_meanIoU_std', meanIoU_std.tolist())
_run.log_scalar('final_classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('final_classIoU_std_binary', classIoU_std_binary.tolist())
_run.log_scalar('final_meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
_log.info(f'classIoU mean: {classIoU}')
_log.info(f'classIoU std: {classIoU_std}')
_log.info(f'meanIoU mean: {meanIoU}')
_log.info(f'meanIoU std: {meanIoU_std}')
_log.info(f'classIoU_binary mean: {classIoU_binary}')
_log.info(f'classIoU_binary std: {classIoU_std_binary}')
_log.info(f'meanIoU_binary mean: {meanIoU_binary}')
_log.info(f'meanIoU_binary std: {meanIoU_std_binary}')
