backbone_params = list(model.node_layers.parameters()) + list(
model.edge_layers.parameters())
backbone_params += list(model.final_layers.parameters())
backbone_ids = [id(item) for item in backbone_params]
new_params = [
param for param in model.parameters() if id(param) not in backbone_ids
]
opt_params = [
dict(params=backbone_params, lr=cfg.TRAIN.LR * 0.01),
dict(params=new_params, lr=cfg.TRAIN.LR),
]
optimizer = optim.Adam(opt_params)
if not Path(cfg.model_dir).exists():
Path(cfg.model_dir).mkdir(parents=True)
num_epochs, _, __ = lr_schedules[cfg.TRAIN.lr_schedule]
with DupStdoutFileManager(str(Path(cfg.model_dir) /
("train_log.log"))) as _:
model, accs = train_eval_model(
model,
criterion,
optimizer,
dataloader,
num_epochs=num_epochs,
resume=cfg.warmstart_path is not None,
start_epoch=0,
)
mod = importlib.import_module(cfg.MODULE)
Net = mod.Net
torch.manual_seed(cfg.RANDOM_SEED)
image_dataset = GMDataset(cfg.DATASET_FULL_NAME,
sets='test',
length=cfg.EVAL.SAMPLES,
obj_resize=cfg.PAIR.RESCALE)
dataloader = get_dataloader(image_dataset)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net()
model = model.cuda()
model = DataParallel(model, device_ids=range(torch.cuda.device_count()))
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(
model,
dataloader,
eval_epoch=cfg.EVAL.EPOCH if cfg.EVAL.EPOCH != 0 else None,
verbose=True)
xlsx_data['t_mae'] = []
xlsx_data['err_r_deg_mean'] = []
xlsx_data['err_t_mean'] = []
xlsx_data['CCD'] = []
xlsx_data['acc'] = []
xlsx_data['acc'] = []
return xlsx_data
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
output_dir = os.path.join(ROOT_DIR, 'output')
allalgfile = np.sort(os.listdir(output_dir))
xlsx_data = xlsx_init({})
for file in allalgfile:
with DupStdoutFileManager(output_dir + '/' + file +
'/acc_record.log') as _:
npyfile = np.sort([
txt for txt in os.listdir(output_dir + '/' + file)
if txt.endswith('metric.npy')
])
for npyfile_i in npyfile:
metric_all = np.load(output_dir + '/' + file + '/' + npyfile_i,
allow_pickle=True).item()
print(file + '/' + npyfile_i)
summary_metrics = summarize_metrics(metric_all)
print_metrics(summary_metrics)
xlsx_data = output2dict(file + '/' + npyfile_i, summary_metrics,
xlsx_data)
for i in [0.1, 0.5, 1, 5, 10, 20]:
cur_acc = sum(
(metric_all['r_mae'] <= i) *