save_dir = './case_result'
config = {
'DEVICE': torch.device('cuda:0'),
'CAL_DEVICE': torch.device('cuda:2'),
'IN_LEN': 4,
'OUT_LEN': 1,
'BATCH_SIZE': 2,
'SCALE': 0.25,
'TASK': 'reg',
'DIM': 'HR',
}
data_loader = DataGenerator(data_path=global_config['DATA_PATH'],
config=config)
update_config(cfg, {'cfg': './params3.yaml'})
model = get_seg_model(cfg, 4, 1)
model = torch.nn.DataParallel(model, device_ids=[0, 3])
model = model.to(config['DEVICE'])
weight_path = '/home/warit/fcn/experiments/hrnet/model_logs/logs_4_1_03291123/model_28500.pth'
model.load_state_dict(torch.load(weight_path, map_location='cuda'))
files = sorted([file for file in glob.glob(global_config['DATA_PATH'])])
for i in tqdm(case):
file_name = i[0]
crop = i[1]
sp = save_dir + '/' + file_name[:-4]
if not os.path.exists(sp):
os.makedirs(sp)
test(model, data_loader, config, sp, files, file_name, crop=crop)
config=config),
]]
data_loader = DataGenerator(data_path=global_config['DATA_PATH'],
config=config)
encoder = Encoder(convlstm_encoder_params[0],
convlstm_encoder_params[1]).to(config['DEVICE'])
forecaster = Forecaster(convlstm_forecaster_params[0],
convlstm_forecaster_params[1],
config=config).to(config['DEVICE'])
encoder_forecaster = EF(encoder, forecaster).to(config['DEVICE'])
weight_path = '/home/warit/senior/experiments/conv_logs/logs_4_10_2_False_05032140/model_25500.pth'
encoder_forecaster.load_state_dict(torch.load(weight_path,
map_location='cuda'))
files = sorted([file for file in glob.glob(global_config['DATA_PATH'])])
for i in tqdm(case):
file_name = i[0]
crop = i[1]
sp = save_dir + '/' + file_name[:-4]
if not os.path.exists(sp):
os.makedirs(sp)
test(encoder_forecaster,
data_loader,
config,
sp,
files,
file_name,
crop=crop)