def __init__(self, image_size=224, num_classes=200):
self.image_size = image_size
self.net = ClassNet(num_classes=num_classes).cuda()
self.transform_train, self.transform_test = MyTransform.transform_train_cam(
image_size=self.image_size)
self.transform_un_normalize = MyTransform.transform_un_normalize()
pass
def __init__(self, image_size=224, num_classes=200):
self.image_size = image_size
self.net = CAMNet(num_classes=num_classes).cuda()
self.name_to_label_id, self.label_info_dict = DataUtil.get_class_name()
self.transform_train, self.transform_test = MyTransform.transform_train_cam(
image_size=self.image_size)
self.transform_un_normalize = MyTransform.transform_un_normalize()
pass
def eval_ss(self, data_loader, epoch=0, model_file_name=None, save_path=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name), txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
un_norm = MyTransform.transform_un_normalize()
self.net.eval()
metrics = StreamSegMetrics(self.config.ss_num_classes)
with torch.no_grad():
for i, (inputs, labels, _) in tqdm(enumerate(data_loader), total=len(data_loader)):
inputs = inputs.float().cuda()
labels = labels.long().cuda()
outputs = self.net(inputs)
preds = outputs.detach().max(dim=1)[1].cpu().numpy()
targets = labels.cpu().numpy()
metrics.update(targets, preds)
if save_path:
for j, (input_one, label_one, pred_one) in enumerate(zip(inputs, targets, preds)):
un_norm(input_one.cpu()).save(os.path.join(save_path, "{}_{}.JPEG".format(i, j)))
DataUtil.gray_to_color(np.asarray(label_one, dtype=np.uint8)).save(
os.path.join(save_path, "{}_{}_l.png".format(i, j)))
DataUtil.gray_to_color(np.asarray(pred_one, dtype=np.uint8)).save(
os.path.join(save_path, "{}_{}_p.png".format(i, j)))
pass
pass
pass
pass
score = metrics.get_results()
Tools.print("{} {}".format(epoch, metrics.to_str(score)), txt_path=self.config.ss_save_result_txt)
return score
def __init__(self, image_size=256, num_classes=200):
self.image_size = image_size
self.num_classes = num_classes
self.net = ClassNet(num_classes=self.num_classes).cuda()
self.name_to_label_id, self.label_info_dict = DataUtil.get_class_name(data_root=self.get_data_root_path())
self.transform_train, self.transform_test = MyTransform.transform_train_cam(image_size=self.image_size)
pass
def __init__(self, config):
self.config = config
self.net = CAMNet(num_classes=self.config.mlc_num_classes).cuda()
self.dataset_vis_cam = DatasetUtil.get_dataset_by_type(
DatasetUtil.dataset_type_vis_cam,
image_size=self.config.mlc_size,
data_root=self.config.data_root_path,
return_image_info=True,
sampling=self.config.sampling)
self.data_loader_vis_cam = DataLoader(self.dataset_vis_cam,
self.config.mlc_batch_size,
shuffle=False,
num_workers=16)
self.transform_un_normalize = MyTransform.transform_un_normalize()
pass
def __init__(self, image_size=256, num_classes=2):
self.net = ClassNet(num_classes=num_classes).cuda()
self.transform_train, self.transform_test = MyTransform.transform_train_cam(image_size=image_size)
# self.transform_test = MyTransform.transform_vis_cam(image_size=image_size)
pass