def frames_foler_demo():
args = parse_args()
config = build_config(args.config)
config.args = args
model = build_backbone(config.model).cuda()
ckpt = torch.load(args.ckpt, map_location="cuda:0")
model_ckpt = ckpt["model"]
model_ckpt = {k[7:]: v for k, v in model_ckpt.items()}
model.load_state_dict(model_ckpt)
data_config = edict({
"root_input":
"/home/cmd/projects/simdeblur/datasets/DVD/qualitative_datasets/alley/input",
"num_frames": 5,
"overlapping": True,
"sampling": "n_c"
})
frames_data = FramesFolder(data_config)
frames_dataloader = torch.utils.data.DataLoader(frames_data, 1)
model.eval()
with torch.no_grad():
for i, batch_data in enumerate(frames_dataloader):
out = model(batch_data["input_frames"].cuda())
print(batch_data["gt_names"], out.shape)
def build_model(cls, cfg):
"""
build a model
"""
# TODO change the build backbone to build model
model = build_backbone(cfg.model)
if cfg.args.gpus > 1:
rank = cfg.args.local_rank
model = nn.parallel.DistributedDataParallel(model.cuda(),
device_ids=[rank],
output_device=rank)
if cfg.args.local_rank == 0:
logger = logging.getLogger("simdeblur")
logger.info("Model:\n{}".format(model))
return model