def parse_args():
train_parser = train.get_args_parser()
parser = argparse.ArgumentParser("Submitit for detection", parents=[train_parser])
parser.add_argument("--ngpus", default=1, type=int,
help="Number of gpus to request on each node")
parser.add_argument("--vram", default="12GB", type=str)
parser.add_argument("--num_gpus", default=1, type=int)
parser.add_argument("--mem_per_gpu", default=20, type=int)
parser.add_argument("--nodes", default=1, type=int, help="Number of nodes to request")
parser.add_argument("--timeout", default=60, type=int, help="Duration of the job")
parser.add_argument("--job_dir", default="", type=str,
help="Job dir. Leave empty for automatic.")
parser.add_argument("--cluster", default=None, type=str,
help="Use to run jobs locally.")
parser.add_argument("--slurm_partition", default="NORMAL", type=str,
help="Partition. Leave empty for automatic.")
parser.add_argument("--slurm_constraint", default="", type=str,
help="Constraint. Leave empty for automatic.")
parser.add_argument("--slurm_comment", default="", type=str)
parser.add_argument("--slurm_gres", default="", type=str)
parser.add_argument("--slurm_exclude", default="", type=str)
parser.add_argument("--checkpoint_name", default="last.ckpt", type=str)
return parser.parse_args()
model.zero_grad()
output1 = model(image.to(device))
output = F.softmax(output1, dim=1)
prediction_score, pred_label_idx = torch.topk(output, 1)
pred_label_idx.squeeze_()
predicted_label = str(pred_label_idx.item() + 1)
print('Predicted:', predicted_label, '(',
prediction_score.squeeze().item(), ')')
make_grad(extractor, output1, image_orl, args.grad_min_level,
cam_extractors_names[idx])
extractor.clear_hooks()
if __name__ == '__main__':
parser = argparse.ArgumentParser('model training and evaluation script',
parents=[get_args_parser()])
args = parser.parse_args()
args_dict = vars(args)
args_for_evaluation = [
'num_classes', 'lambda_value', 'power', 'slots_per_class'
]
args_type = [int, float, int, int]
for arg_id, arg in enumerate(args_for_evaluation):
args_dict[arg] = args_type[arg_id](args_dict[arg])
for_vis(args)
label = cv2.resize(label, (1024, 512), interpolation=cv2.INTER_NEAREST)
images = torch.from_numpy(np.array(np.transpose(images, (0, 3, 1, 2)), dtype="float32")/255)
labels = torch.from_numpy(np.array([label], dtype="int64"))
show_single(args, ColorTransition().recover(labels[0]), "origin")
inputs = images.to(device, dtype=torch.float32)
labels = labels.to(device, dtype=torch.int64)
predict = for_test(args, model, inputs, labels, iou, lstm=args.lstm, need=True)
print(predict.size())
color_img = ColorTransition().recover(predict[0])
show_single(args, color_img, "ls_noise")
epoch_iou = iou.iou_demo()
print(epoch_iou)
if __name__ == '__main__':
parser = argparse.ArgumentParser('model training and evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
args.use_pre = False
device = args.device
model_name = "../saved_model/PSPNet_lstm_noise.pt"
init_model = load_model(args)
init_model = convert_model(init_model)
init_model = init_model.to(device)
if args.multi_gpu:
init_model = nn.DataParallel(init_model, device_ids=[0])
init_model.load_state_dict(torch.load(model_name), strict=True)
init_model.eval()
print("load param over")
# for lstm model inference
make_image_lstm(init_model, device)
def main():
parser = argparse.ArgumentParser('model training and evaluation script',
parents=[get_args_parser()])
args = parser.parse_args()
args_dict = vars(args)
args_for_evaluation = [
'num_classes', 'lambda_value', 'power', 'slots_per_class'
]
args_type = [int, float, int, int]
for arg_id, arg in enumerate(args_for_evaluation):
args_dict[arg] = args_type[arg_id](args_dict[arg])
model_name = f"{args.dataset}_" + f"{'use_slot_' if args.use_slot else 'no_slot_'}"\
+ f"{'negative_' if args.use_slot and args.loss_status != 1 else ''}"\
+ f"{'for_area_size_'+str(args.lambda_value) + '_'+ str(args.slots_per_class) + '_' if args.cal_area_size else ''}" + 'checkpoint.pth'
args.use_pre = False
device = torch.device(args.device)
transform = transforms.Compose([
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
])
# Con-text
if args.dataset == 'ConText':
train, val = MakeList(args).get_data()
dataset_val = ConText(val, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
args.batch_size,
shuffle=False,
num_workers=1,
pin_memory=True)
data = iter(data_loader_val).next()
image = data["image"][0]
label = data["label"][0].item()
image_orl = Image.fromarray(
(image.cpu().detach().numpy() * 255).astype(np.uint8).transpose(
(1, 2, 0)),
mode='RGB')
image = transform(image_orl)
transform = transforms.Compose([
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
elif args.dataset == 'ImageNet':
train, val = MakeListImage(args).get_data()
dataset_val = ConText(val, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
args.batch_size,
shuffle=False,
num_workers=1,
pin_memory=True)
iter_loader = iter(data_loader_val)
for i in range(0, 1):
data = iter_loader.next()
image = data["image"][0]
label = data["label"][0].item()
image_orl = Image.fromarray(
(image.cpu().detach().numpy() * 255).astype(np.uint8).transpose(
(1, 2, 0)),
mode='RGB')
image = transform(image_orl)
transform = transforms.Compose([
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# MNIST
elif args.dataset == 'MNIST':
dataset_val = datasets.MNIST('./data/mnist',
train=False,
transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
args.batch_size,
shuffle=False,
num_workers=1,
pin_memory=True)
image = iter(data_loader_val).next()[0][0]
label = ''
image_orl = Image.fromarray(
(image.cpu().detach().numpy() * 255).astype(np.uint8)[0], mode='L')
image = transform(image_orl)
transform = transforms.Compose(
[transforms.Normalize((0.1307, ), (0.3081, ))])
# CUB
elif args.dataset == 'CUB200':
dataset_val = CUB_200(args, train=False, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
args.batch_size,
shuffle=False,
num_workers=1,
pin_memory=True)
data = iter(data_loader_val).next()
image = data["image"][0]
label = data["label"][0].item()
image_orl = Image.fromarray(
(image.cpu().detach().numpy() * 255).astype(np.uint8).transpose(
(1, 2, 0)),
mode='RGB')
image = transform(image_orl)
transform = transforms.Compose([
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = transform(image)
print("label\t", label)
model = SlotModel(args)
# Map model to be loaded to specified single gpu.
checkpoint = torch.load(f"{args.output_dir}/" + model_name,
map_location=args.device)
for k, v in checkpoint.items():
print(k)
model.load_state_dict(checkpoint["model"])
test(args, model, device, image_orl, image, label, vis_id=args.vis_id)