def get_initial_model():
backbone = resnet18(pretrained=True,
pyramid_levels=num_levels,
k_upsample=3,
scale=scale,
mean=mean,
std=std,
k_bneck=1,
output_stride=ostride,
efficient=False)
model_tmp = SemsegModel(backbone, num_classes, k=1, bias=True)
model_tmp.load_state_dict(torch.load('weights/rn18_pyramid/model_best.pt'), strict=False)
model_tmp.criterion = BoundaryAwareFocalLoss(gamma=.5, num_classes=num_classes, ignore_id=ignore_id)
return model_tmp
dataset_train = Vistas(root, transforms=trans_train, subset='training', epoch=epoch)
dataset_val = Vistas(root, transforms=trans_val, subset='validation', epoch=epoch)
backbone = resnet18(pretrained=True,
k_up=3,
scale=scale,
mean=mean,
std=std,
output_stride=output_stride,
efficient=False)
model = SemsegModel(backbone, num_classes, k=1, bias=True)
if evaluating:
model.load_state_dict(torch.load(f'{dir_path}/stored/model_best.pt'), strict=False)
else:
model.criterion = BoundaryAwareFocalLoss(gamma=.5, num_classes=num_classes, ignore_id=ignore_id)
bn_count = 0
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
bn_count += 1
print(f'Num BN layers: {bn_count}')
if not evaluating:
lr = 8e-4
lr_min = 1e-6
fine_tune_factor = 4
weight_decay = 1e-4
optim_params = [
{'params': model.random_init_params(), 'lr': lr, 'weight_decay': weight_decay},
def get_initial_model():
tmp_resnet = resnet34(pretrained=True, k_up=3, scale=scale, mean=mean, std=std, output_stride=8, efficient=False)
tmp_model = SemsegModel(tmp_resnet, num_classes, k=1, bias=True)
tmp_model.load_state_dict(torch.load('weights/76-66_resnet34x8/stored/model_best.pt'), strict=False)
model.criterion = BoundaryAwareFocalLoss(gamma=.5, num_classes=num_classes, ignore_id=ignore_id)
return tmp_model