def run(args):
dataset = voc12.dataloader.VOC12ImageDataset(args.train_list, voc12_root=args.voc12_root, img_normal=None, to_torch=False)
dataset = torchutils.split_dataset(dataset, args.num_workers)
print('[ ', end='')
multiprocessing.spawn(_work, nprocs=args.num_workers, args=(dataset, args), join=True)
print(']')
def run(args):
n_gpus = torch.cuda.device_count()
if args.dataset == 'l8biome':
model = getattr(importlib.import_module(args.cam_network),
'CAM')(n_classes=2, in_channels=10, pretrained=False)
dataset = l8biome.dataloader.L8BiomeDatasetMSF(args.data_root,
'train',
scales=args.cam_scales)
# Only compute for cloudy images, clear should have empty mask
dataset.images = [img for img in dataset.images if 'cloudy' in img[2]]
else:
model = getattr(importlib.import_module(args.cam_network),
'CAM')(n_classes=20)
dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(
args.train_list, voc12_root=args.data_root, scales=args.cam_scales)
model.load_state_dict(torch.load(args.cam_weights_name + '.pth'),
strict=True)
model.eval()
dataset = torchutils.split_dataset(dataset, n_gpus)
print('[ ', end='')
multiprocessing.spawn(_work,
nprocs=n_gpus,
args=(model, dataset, args),
join=True)
print(']')
torch.cuda.empty_cache()
def run(args):
if args.dataset == 'voc12':
dataset = voc12.dataloader.VOC12ImageDataset(args.train_list,
dev_root=args.dev_root,
norm_mode=None,
to_torch=False)
elif args.dataset in ['adp_morph', 'adp_func']:
dataset = adp.dataloader.ADPImageDataset(
args.train_list,
dev_root=args.dev_root,
htt_type=args.dataset.split('_')[-1],
is_eval=args.split == 'evaluation',
norm_mode=None,
to_torch=False)
elif args.dataset in ['deepglobe', 'deepglobe_balanced']:
dataset = deepglobe.dataloader.DeepGlobeImageDataset(
args.train_list,
dev_root=args.dev_root,
is_balanced=args.dataset == 'deepglobe_balanced',
norm_mode=None,
to_torch=False)
else:
raise KeyError('Dataset %s not yet implemented' % args.dataset)
dataset = torchutils.split_dataset(dataset, args.num_workers)
# print('[ ', end='')
multiprocessing.spawn(_work,
nprocs=args.num_workers,
args=(dataset, args),
join=True)
def run(args):
model = getattr(importlib.import_module(args.irn_network), 'EdgeDisplacement')()
model.load_state_dict(torch.load(args.irn_weights_name), strict=False)
model.eval()
n_gpus = torch.cuda.device_count()
dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(args.infer_list,
voc12_root=args.voc12_root,
scales=(1.0,))
dataset = torchutils.split_dataset(dataset, n_gpus)
print("[ ", end='')
multiprocessing.spawn(_work, nprocs=n_gpus, args=(model, dataset, args), join=True)
print("]")
def run(args):
model = getattr(importlib.import_module(args.cam_network), 'CAM')()
print(args.cam_weights_name+'.pth')
model.load_state_dict(torch.load(args.cam_weights_name + '.pth'), strict=True)
model.eval()
n_gpus = torch.cuda.device_count()
dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(args.train_list,
voc12_root=args.voc12_root, scales=args.cam_scales)
dataset = torchutils.split_dataset(dataset, n_gpus)
print('[ ', end='')
multiprocessing.spawn(_work, nprocs=n_gpus, args=(model, dataset, args), join=True)
print(']')
torch.cuda.empty_cache()
def run(args):
model = getattr(importlib.import_module(args.cam_network),
'CAM')(args.model_dir, args.dataset, args.tag,
args.num_classes, args.use_cls)
if args.model_id == 'resnet50':
model.load_state_dict(torch.load(args.cam_weights_name + '.pth'),
strict=True)
model.eval()
n_gpus = torch.cuda.device_count()
if args.dataset == 'voc12':
dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(
args.val_list,
norm_mode=args.norm_mode,
outsize=args.outsize,
dev_root=args.dev_root,
scales=args.cam_scales)
elif args.dataset in ['adp_morph', 'adp_func']:
dataset = adp.dataloader.ADPClassificationDatasetMSF(
args.val_list,
norm_mode=args.norm_mode,
outsize=args.outsize,
dev_root=args.dev_root,
htt_type=args.dataset.split('_')[-1],
is_eval=args.split == 'evaluation',
scales=args.cam_scales)
elif args.dataset in ['deepglobe', 'deepglobe_balanced']:
dataset = deepglobe.dataloader.DeepGlobeClassificationDatasetMSF(
args.val_list,
norm_mode=args.norm_mode,
outsize=args.outsize,
dev_root=args.dev_root,
is_balanced=args.dataset == 'deepglobe_balanced',
scales=args.cam_scales)
else:
raise KeyError('Dataset %s not yet implemented' % args.dataset)
dataset = torchutils.split_dataset(dataset, n_gpus)
multiprocessing.spawn(_work,
nprocs=n_gpus,
args=(model, dataset, args),
join=True)
torch.cuda.empty_cache()
]
highres_cam = torch.sum(torch.stack(highres_cam, 0),
0)[:, 0, :size[0], :size[1]]
strided_cam /= F.adaptive_max_pool2d(strided_cam, (1, 1)) + 1e-5
highres_cam /= F.adaptive_max_pool2d(highres_cam, (1, 1)) + 1e-5
np.save(
os.path.join(args.cam_out_dir, img_name + '.npy'), {
"keys": valid_cat,
"cam": strided_cam.cpu(),
"high_res": highres_cam.cpu().numpy()
})
if __name__ == '__main__':
model = getattr(importlib.import_module(args.cam_network), 'CAM')()
model.load_state_dict(torch.load(args.cam_weights_name + '.pth'),
strict=True)
model.eval()
n_gpus = torch.cuda.device_count()
print(n_gpus)
dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(
args.train_list, voc12_root=args.voc12_root, scales=args.cam_scales)
dataset = torchutils.split_dataset(dataset, n_gpus)
# _work(0, model, dataset, args)
multiprocessing.spawn(_work,
nprocs=n_gpus,
args=(model, dataset, args),
join=True)
pyutils.make_directory(cam_out_dir)
# Setting Environment
if args.use_cuda and torch.cuda.is_available():
torch.cuda.set_device(args.gpu_id)
device = torch.device('cuda')
else:
device = torch.device('cpu')
n_gpus = torch.cuda.device_count()
# Dataset
train_dataset = voc12.dataloader.VOC12ClassificationDatasetMSF(args.train_list, voc12_root=args.voc12_root, \
scales=args.cam_scales)
train_datset = torchutils.split_dataset(train_dataset, n_gpus)
# Build model
model = getattr(importlib.import_module(args.cam_network), 'CAM')()
print(os.path.join(checkpoints_dir, args.cam_weights_name))
model.load_state_dict(torch.load(os.path.join(checkpoints_dir, args.cam_weights_name)), strict=True)
model.eval()
logger.info('[')
work(model, train_dataset, args, cam_out_dir)
logger.info(']')
torch.cuda.empty_cache()
pred = imutils.crf_inference_label(img,
bg_conf_cam,
n_labels=keys.shape[0])
bg_conf = keys[pred] ## mask
# 2. combine confident fg & bg
conf = fg_conf.copy()
conf[fg_conf == 0] = 255 # 这些背景crf后也被当作类别255
conf[bg_conf + fg_conf == 0] = 0 # bg和fg都认为是bg才是bg
imageio.imwrite(os.path.join(ir_label_out_dir, img_name + '.png'),
conf.astype(np.uint8))
if process_id == num_workers - 1 and iter % (len(databin) // 20) == 0:
print("%d " % ((5 * iter + 1) // (len(databin) // 20)), end='')
if __name__ == '__main__':
dataset = voc12.dataloader.VOC12ImageDataset(val_list,
voc12_root=voc12_root,
img_normal=None,
to_torch=False)
dataset = torchutils.split_dataset(dataset, num_workers)
print('[ ', end='')
multiprocessing.spawn(_work,
nprocs=num_workers,
args=(dataset, ),
join=True)
print(']')
