From c8e829eee7769fbc3120b9fe7687bb73402dfc87 Mon Sep 17 00:00:00 2001
From: ljwztc <296433517@qq.com>
Date: Wed, 20 Sep 2023 10:11:11 +0800
Subject: [PATCH] pseudo_label

---
 dataset/__pycache__/dataloader.cpython-37.pyc | Bin 12183 -> 13245 bytes
 dataset/dataloader.py                         |  47 +++++++++++
 pred_pseudo.py                                |  79 ++++++++++--------
 test.py                                       |   2 +-
 4 files changed, 90 insertions(+), 38 deletions(-)

diff --git a/dataset/__pycache__/dataloader.cpython-37.pyc b/dataset/__pycache__/dataloader.cpython-37.pyc
index 0f13e98190334d6f78751fb03ae58be51298cf01..fbf707d3e2575e1539dbaacb16ef613b720e2fbf 100644
GIT binary patch
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diff --git a/dataset/dataloader.py b/dataset/dataloader.py
index 01efe99..6f8a8e5 100644
--- a/dataset/dataloader.py
+++ b/dataset/dataloader.py
@@ -388,6 +388,53 @@ def get_loader(args):
         test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4, collate_fn=list_data_collate)
         return test_loader, val_transforms
 
+
+def get_loader_without_gt(args):
+    val_transforms = Compose(
+        [
+            LoadImaged(keys=["image"]),
+            AddChanneld(keys=["image"]),
+            Orientationd(keys=["image"], axcodes="RAS"),
+            # ToTemplatelabeld(keys=['label']),
+            # RL_Splitd(keys=['label']),
+            Spacingd(
+                keys=["image"],
+                pixdim=(args.space_x, args.space_y, args.space_z),
+                mode=("bilinear"),
+            ), # process h5 to here
+            ScaleIntensityRanged(
+                keys=["image"],
+                a_min=args.a_min,
+                a_max=args.a_max,
+                b_min=args.b_min,
+                b_max=args.b_max,
+                clip=True,
+            ),
+            CropForegroundd(keys=["image"], source_key="image"),
+            ToTensord(keys=["image"]),
+        ]
+    )
+
+    ## test dict part
+    test_img = []
+    test_name = []
+    for item in args.dataset_list:
+        for line in open(args.data_txt_path + item +'_test.txt'):
+            name = line.strip().split()[1].split('.')[0]
+            test_img.append(args.data_root_path + line.strip().split()[0])
+            test_name.append(name)
+    data_dicts_test = [{'image': image, 'name': name}
+                for image, name in zip(test_img, test_name)]
+    print('test len {}'.format(len(data_dicts_test)))
+    
+    if args.cache_dataset:
+        test_dataset = CacheDataset(data=data_dicts_test, transform=val_transforms, cache_rate=args.cache_rate)
+    else:
+        test_dataset = Dataset(data=data_dicts_test, transform=val_transforms)
+    test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4, collate_fn=list_data_collate)
+    return test_loader, val_transforms
+
+
 if __name__ == "__main__":
     train_loader, test_loader = partial_label_dataloader()
     for index, item in enumerate(test_loader):
diff --git a/pred_pseudo.py b/pred_pseudo.py
index 4f92791..33356c2 100644
--- a/pred_pseudo.py
+++ b/pred_pseudo.py
@@ -15,7 +15,7 @@
 from monai.inferers import sliding_window_inference
 
 from model.Universal_model import Universal_model
-from dataset.dataloader import get_loader
+from dataset.dataloader import get_loader_without_gt
 from utils import loss
 from utils.utils import dice_score, threshold_organ, visualize_label, merge_label, get_key
 from utils.utils import TEMPLATE, ORGAN_NAME, NUM_CLASS
@@ -25,7 +25,7 @@
 
 
 def validation(model, ValLoader, val_transforms, args):
-    save_dir = 'out/' + args.log_name + f'/pesudolbl_{args.epoch}'
+    save_dir = 'out/' + args.log_name #+ f'/pesudolbl_{args.epoch}'
     if not os.path.isdir(save_dir):
         os.mkdir(save_dir)
         os.mkdir(save_dir+'/predict')
@@ -53,17 +53,17 @@ def validation(model, ValLoader, val_transforms, args):
             content = 'case%s| '%(name[b])
             template_key = get_key(name[b])
             organ_list = TEMPLATE[template_key]
-            pred_hard_post = organ_post_process(pred_hard.numpy(), organ_list)
+            pred_hard_post = organ_post_process(pred_hard.numpy(), organ_list, args.log_name+'/'+name[0].split('/')[0]+'/'+name[0].split('/')[-1],args)
             pred_hard_post = torch.tensor(pred_hard_post)
 
-            for organ in organ_list:
-                if torch.sum(label[b,organ-1,:,:,:].cuda()) != 0:
-                    dice_organ, recall, precision = dice_score(pred_hard_post[b,organ-1,:,:,:].cuda(), label[b,organ-1,:,:,:].cuda())
-                    dice_list[template_key][0][organ-1] += dice_organ.item()
-                    dice_list[template_key][1][organ-1] += 1
-                    content += '%s: %.4f, '%(ORGAN_NAME[organ-1], dice_organ.item())
-                    print('%s: dice %.4f, recall %.4f, precision %.4f.'%(ORGAN_NAME[organ-1], dice_organ.item(), recall.item(), precision.item()))
-            print(content)
+            # for organ in organ_list:
+            #     if torch.sum(label[b,organ-1,:,:,:].cuda()) != 0:
+            #         dice_organ, recall, precision = dice_score(pred_hard_post[b,organ-1,:,:,:].cuda(), label[b,organ-1,:,:,:].cuda())
+            #         dice_list[template_key][0][organ-1] += dice_organ.item()
+            #         dice_list[template_key][1][organ-1] += 1
+            #         content += '%s: %.4f, '%(ORGAN_NAME[organ-1], dice_organ.item())
+            #         print('%s: dice %.4f, recall %.4f, precision %.4f.'%(ORGAN_NAME[organ-1], dice_organ.item(), recall.item(), precision.item()))
+            # print(content)
         
 
             ### testing phase for this function
@@ -76,27 +76,27 @@ def validation(model, ValLoader, val_transforms, args):
     
     ave_organ_dice = np.zeros((2, NUM_CLASS))
 
-    with open('out/'+args.log_name+f'/test_{args.epoch}.txt', 'w') as f:
-        for key in TEMPLATE.keys():
-            organ_list = TEMPLATE[key]
-            content = 'Task%s| '%(key)
-            for organ in organ_list:
-                dice = dice_list[key][0][organ-1] / dice_list[key][1][organ-1]
-                content += '%s: %.4f, '%(ORGAN_NAME[organ-1], dice)
-                ave_organ_dice[0][organ-1] += dice_list[key][0][organ-1]
-                ave_organ_dice[1][organ-1] += dice_list[key][1][organ-1]
-            print(content)
-            f.write(content)
-            f.write('\n')
-        content = 'Average | '
-        for i in range(NUM_CLASS):
-            content += '%s: %.4f, '%(ORGAN_NAME[i], ave_organ_dice[0][i] / ave_organ_dice[1][i])
-        print(content)
-        f.write(content)
-        f.write('\n')
-        print(np.mean(ave_organ_dice[0] / ave_organ_dice[1]))
-        f.write('%s: %.4f, '%('average', np.mean(ave_organ_dice[0] / ave_organ_dice[1])))
-        f.write('\n')
+    # with open('out/'+args.log_name+f'/test_{args.epoch}.txt', 'w') as f:
+    #     for key in TEMPLATE.keys():
+    #         organ_list = TEMPLATE[key]
+    #         content = 'Task%s| '%(key)
+    #         for organ in organ_list:
+    #             dice = dice_list[key][0][organ-1] / dice_list[key][1][organ-1]
+    #             content += '%s: %.4f, '%(ORGAN_NAME[organ-1], dice)
+    #             ave_organ_dice[0][organ-1] += dice_list[key][0][organ-1]
+    #             ave_organ_dice[1][organ-1] += dice_list[key][1][organ-1]
+    #         print(content)
+    #         f.write(content)
+    #         f.write('\n')
+    #     content = 'Average | '
+    #     for i in range(NUM_CLASS):
+    #         content += '%s: %.4f, '%(ORGAN_NAME[i], ave_organ_dice[0][i] / ave_organ_dice[1][i])
+    #     print(content)
+    #     f.write(content)
+    #     f.write('\n')
+    #     print(np.mean(ave_organ_dice[0] / ave_organ_dice[1]))
+    #     f.write('%s: %.4f, '%('average', np.mean(ave_organ_dice[0] / ave_organ_dice[1])))
+    #     f.write('\n')
         
     
     # np.save(save_dir + '/result.npy', dice_list)
@@ -115,11 +115,12 @@ def main():
     parser.add_argument("--device")
     parser.add_argument("--epoch", default=0)
     ## logging
-    parser.add_argument('--log_name', default='Nvidia/old_fold0', help='The path resume from checkpoint')
+    parser.add_argument('--log_name', default='Nvidia', help='The path resume from checkpoint')
     ## model load
-    parser.add_argument('--resume', default='./out/Nvidia/old_fold0/aepoch_500.pth', help='The path resume from checkpoint')
+    parser.add_argument('--resume', default='./pretrained_weights/swinunetr.pth', help='The path resume from checkpoint')
     parser.add_argument('--pretrain', default='./pretrained_weights/swin_unetr.base_5000ep_f48_lr2e-4_pretrained.pt', 
                         help='The path of pretrain model')
+    parser.add_argument('--backbone', default='swinunetr', help='backbone [swinunetr or unet]')
     ## hyperparameter
     parser.add_argument('--max_epoch', default=1000, type=int, help='Number of training epoches')
     parser.add_argument('--store_num', default=10, type=int, help='Store model how often')
@@ -133,7 +134,7 @@ def main():
     ### PAOT_123457891213: include 1 2 3 4 5 7 8 9 12 13
     ### PAOT_10_inner: same with NVIDIA for comparison
     ### PAOT_10: original division
-    parser.add_argument('--data_root_path', default='/home/jliu288/data/whole_organ/', help='data root path')
+    parser.add_argument('--data_root_path', default='/computenodes/node31/team1/jliu/data/ct_data/', help='data root path')
     parser.add_argument('--data_txt_path', default='./dataset/dataset_list/', help='data txt path')
     parser.add_argument('--batch_size', default=1, type=int, help='batch size')
     parser.add_argument('--num_workers', default=8, type=int, help='workers numebr for DataLoader')
@@ -174,7 +175,11 @@ def main():
     # args.epoch = checkpoint['epoch']
 
     for key, value in load_dict.items():
-        name = '.'.join(key.split('.')[1:])
+        if 'swinViT' in key or 'encoder' in key or 'decoder' in key:
+            name = '.'.join(key.split('.')[1:])
+            name = 'backbone.' + name
+        else:
+            name = '.'.join(key.split('.')[1:])
         store_dict[name] = value
 
     model.load_state_dict(store_dict)
@@ -184,7 +189,7 @@ def main():
 
     torch.backends.cudnn.benchmark = True
 
-    test_loader, val_transforms = get_loader(args)
+    test_loader, val_transforms = get_loader_without_gt(args)
 
     validation(model, test_loader, val_transforms, args)
 
diff --git a/test.py b/test.py
index e2f2956..3a6a7b7 100644
--- a/test.py
+++ b/test.py
@@ -145,7 +145,7 @@ def main():
     ### PAOT: include PAOT_123457891213 and PAOT_10
     ### PAOT_123457891213: include 1 2 3 4 5 7 8 9 12 13
     ### PAOT_10_inner
-    parser.add_argument('--data_root_path', default='DATA_ROOT', help='data root path')
+    parser.add_argument('--data_root_path', default='/computenodes/node31/team1/jliu/data/ct_data/', help='data root path')
     parser.add_argument('--data_txt_path', default='./dataset/dataset_list/', help='data txt path')
     parser.add_argument('--batch_size', default=1, type=int, help='batch size')
     parser.add_argument('--num_workers', default=8, type=int, help='workers numebr for DataLoader')