from utils.print_easydict import print_easydict
args = parse_args('Deep learning of graph matching evaluation code.')
import importlib
mod = importlib.import_module(cfg.MODULE)
Net = mod.Net
torch.manual_seed(cfg.RANDOM_SEED)
image_dataset = ScannetDataset(cfg.DATASET_FULL_NAME,
sets='test',
length=cfg.EVAL.SAMPLES,
obj_resize=cfg.PAIR.RESCALE,
expand_region=cfg.EXPAND_REGION)
dataloader = get_dataloader(image_dataset, shuffle=False)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net(cfg.OUTPUT_SIZE, cfg.SCALES)
model = model.to(device)
#model = DataParallel(model, device_ids=cfg.GPUS)
if not Path(cfg.OUTPUT_PATH).exists():
Path(cfg.OUTPUT_PATH).mkdir(parents=True)
now_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
with DupStdoutFileManager(
str(Path(cfg.OUTPUT_PATH) /
('eval_log_' + now_time + '.log'))) as _:
print_easydict(cfg)
classes = dataloader.dataset.classes
dataset_len = {
'train': cfg.TRAIN.EPOCH_ITERS * cfg.BATCH_SIZE,
'test': cfg.EVAL.SAMPLES
}
image_dataset = {
x: ScannetDataset(cfg.DATASET_FULL_NAME,
sets=x,
length=dataset_len[x],
cls=cfg.TRAIN.CLASS if x == 'train' else None,
obj_resize=cfg.PAIR.RESCALE,
expand_region=cfg.EXPAND_REGION)
for x in ('train', 'test')
}
dataloader = {
x: get_dataloader(image_dataset[x], fix_seed=(x == 'test'))
for x in ('train', 'test')
}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net(
cfg.OUTPUT_SIZE,
cfg.SCALES,
)
model = model.cuda()
criterion = CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=cfg.TRAIN.LR,
from utils.print_easydict import print_easydict
args = parse_args('Deep learning of graph matching evaluation code.')
import importlib
mod = importlib.import_module(cfg.MODULE)
Net = mod.Net
torch.manual_seed(cfg.RANDOM_SEED)
image_dataset = ScannetDataset(cfg.DATASET_FULL_NAME,
sets='test',
length=cfg.EVAL.SAMPLES,
obj_resize=cfg.PAIR.RESCALE,
expand_region=cfg.EXPAND_REGION)
dataloader = get_dataloader(image_dataset)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net(cfg.OUTPUT_SIZE, cfg.SCALES)
model = model.to(device)
if not Path(cfg.OUTPUT_PATH).exists():
Path(cfg.OUTPUT_PATH).mkdir(parents=True)
now_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
with DupStdoutFileManager(
str(Path(cfg.OUTPUT_PATH) /
('eval_log_' + now_time + '.log'))) as _:
print_easydict(cfg)
classes = dataloader.dataset.classes
pcks = eval_model(