Fix multiprocessing crash -> allow validation to be invoked standalone

val.py

@@ -34,66 +34,69 @@
 import function
 
 
-args = cfg.parse_args()
-if args.dataset == 'refuge' or args.dataset == 'refuge2':
-    args.data_path = '../dataset'
-
-GPUdevice = torch.device('cuda', args.gpu_device)
-
-net = get_network(args, args.net, use_gpu=args.gpu, gpu_device=GPUdevice, distribution = args.distributed)
-
-'''load pretrained model'''
-assert args.weights != 0
-print(f'=> resuming from {args.weights}')
-assert os.path.exists(args.weights)
-checkpoint_file = os.path.join(args.weights)
-assert os.path.exists(checkpoint_file)
-loc = 'cuda:{}'.format(args.gpu_device)
-checkpoint = torch.load(checkpoint_file, map_location=loc)
-start_epoch = checkpoint['epoch']
-best_tol = checkpoint['best_tol']
-
-state_dict = checkpoint['state_dict']
-if args.distributed != 'none':
-    from collections import OrderedDict
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        # name = k[7:] # remove `module.`
-        name = 'module.' + k
-        new_state_dict[name] = v
-    # load params
-else:
-    new_state_dict = state_dict
-
-net.load_state_dict(new_state_dict)
-
-# args.path_helper = checkpoint['path_helper']
-# logger = create_logger(args.path_helper['log_path'])
-# print(f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
-
-# args.path_helper = set_log_dir('logs', args.exp_name)
-# logger = create_logger(args.path_helper['log_path'])
-# logger.info(args)
-
-args.path_helper = set_log_dir('logs', args.exp_name)
-logger = create_logger(args.path_helper['log_path'])
-logger.info(args)
-
-'''segmentation data'''
-nice_train_loader, nice_test_loader = get_dataloader(args)
-
-'''begain valuation'''
-best_acc = 0.0
-best_tol = 1e4
-
-if args.mod == 'sam_adpt':
-    net.eval()
-
-    if args.dataset != 'REFUGE':
-        tol, (eiou, edice) = function.validation_sam(args, nice_test_loader, start_epoch, net)
-        logger.info(f'Total score: {tol}, IOU: {eiou}, DICE: {edice} || @ epoch {start_epoch}.')
+def main():
+    args = cfg.parse_args()
+    if args.dataset == 'refuge' or args.dataset == 'refuge2':
+        args.data_path = '../dataset'
+
+    GPUdevice = torch.device('cuda', args.gpu_device)
+
+    net = get_network(args, args.net, use_gpu=args.gpu, gpu_device=GPUdevice, distribution = args.distributed)
+
+    '''load pretrained model'''
+    assert args.weights != 0
+    print(f'=> resuming from {args.weights}')
+    assert os.path.exists(args.weights)
+    checkpoint_file = os.path.join(args.weights)
+    assert os.path.exists(checkpoint_file)
+    loc = 'cuda:{}'.format(args.gpu_device)
+    checkpoint = torch.load(checkpoint_file, map_location=loc)
+    start_epoch = checkpoint['epoch']
+    best_tol = checkpoint['best_tol']
+
+    state_dict = checkpoint['state_dict']
+    if args.distributed != 'none':
+        from collections import OrderedDict
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            # name = k[7:] # remove `module.`
+            name = 'module.' + k
+            new_state_dict[name] = v
+        # load params
     else:
-        tol, (eiou_cup, eiou_disc, edice_cup, edice_disc) = function.validation_sam(args, nice_test_loader, start_epoch, net)
-        logger.info(f'Total score: {tol}, IOU_CUP: {eiou_cup}, IOU_DISC: {eiou_disc}, DICE_CUP: {edice_cup}, DICE_DISC: {edice_disc} || @ epoch {start_epoch}.')
+        new_state_dict = state_dict
 
-
+    net.load_state_dict(new_state_dict)
+
+    # args.path_helper = checkpoint['path_helper']
+    # logger = create_logger(args.path_helper['log_path'])
+    # print(f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
+
+    # args.path_helper = set_log_dir('logs', args.exp_name)
+    # logger = create_logger(args.path_helper['log_path'])
+    # logger.info(args)
+
+    args.path_helper = set_log_dir('logs', args.exp_name)
+    logger = create_logger(args.path_helper['log_path'])
+    logger.info(args)
+
+    '''segmentation data'''
+    nice_train_loader, nice_test_loader = get_dataloader(args)
+
+    '''begain valuation'''
+    best_acc = 0.0
+    best_tol = 1e4
+
+    if args.mod == 'sam_adpt':
+        net.eval()
+
+        if args.dataset != 'REFUGE':
+            tol, (eiou, edice) = function.validation_sam(args, nice_test_loader, start_epoch, net)
+            logger.info(f'Total score: {tol}, IOU: {eiou}, DICE: {edice} || @ epoch {start_epoch}.')
+        else:
+            tol, (eiou_cup, eiou_disc, edice_cup, edice_disc) = function.validation_sam(args, nice_test_loader, start_epoch, net)
+            logger.info(f'Total score: {tol}, IOU_CUP: {eiou_cup}, IOU_DISC: {eiou_disc}, DICE_CUP: {edice_cup}, DICE_DISC: {edice_disc} || @ epoch {start_epoch}.')
+
+
+if __name__ == '__main__':
+    main()