Modify the code to be more in line with community requirements

modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/basemodel.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/citylostfound.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityscapes.py deleted: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/temp.txt modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/mypath.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_evaluate.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_predict.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/args.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/iouEval.py modified: examples/class_increment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/loss.py Singned-off-by: qxygxt <[email protected]>
kubeedge · Oct 31, 2023 · 2f14077 · 2f14077
1 parent f7e1f47
commit 2f14077
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diff --git a/...t_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/basemodel.py b/...t_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/basemodel.py
@@ -50,7 +50,6 @@ def __init__(self, **kwargs):
         self.train_args.epochs = kwargs.get("epochs", 2)
         self.train_args.eval_interval = kwargs.get("eval_interval", 2)
         self.train_args.no_val = kwargs.get("no_val", True)
-        # self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None)
         self.trainer = None
 
         label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results")
@@ -78,11 +77,6 @@ def train(self, train_data, valid_data=None, **kwargs):
                     'optimizer': self.trainer.optimizer.state_dict(),
                     'best_pred': self.trainer.best_pred,
                 }, is_best)
-
-            # if not self.trainer.args.no_val and \
-            #         epoch % self.train_args.eval_interval == (self.train_args.eval_interval - 1) \
-            #         and self.trainer.val_loader:
-            #     self.trainer.validation(epoch)
 
         self.trainer.writer.close()
 

diff --git a/...ion/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms.py b/...ion/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms.py
@@ -25,7 +25,7 @@ def __call__(self, sample):
         img -= self.mean
         img /= self.std
 
-        # mean and std for original depth images
+        # mean and std for original depth images, indicate the mean and standard deviation values for original depth images.
         mean_depth = 0.12176
         std_depth = 0.09752
 
@@ -69,6 +69,7 @@ def __call__(self, sample):
         depth = sample['depth']
         mask = sample['label']
         width, height = img.size
+        # coordinate of the left, right, top, bottom boundary of the cropping region.
         left = 140
         top = 30
         right = 2030
@@ -81,10 +82,6 @@ def __call__(self, sample):
         img = img.resize((width,height), Image.BILINEAR)
         depth = depth.resize((width,height), Image.BILINEAR)
         mask = mask.resize((width,height), Image.NEAREST)
-        # img = img.resize((512,1024), Image.BILINEAR)
-        # depth = depth.resize((512,1024), Image.BILINEAR)
-        # mask = mask.resize((512,1024), Image.NEAREST)
-
         return {'image': img,
                 'depth': depth,
                 'label': mask}

diff --git a/...ifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/citylostfound.py b/...ifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/citylostfound.py
@@ -83,7 +83,6 @@ def __getitem__(self, index):
     def relabel_lostandfound(self, input):
         input = tr.Relabel(0, self.ignore_index)(input)  # background->255 ignore
         input = tr.Relabel(1, 0)(input)  # road 1->0
-        # input = Relabel(255, 20)(input)  # unlabel 20
         input = tr.Relabel(2, 19)(input)  # obstacle  19
         return input
 
@@ -108,7 +107,7 @@ def transform_tr(self, sample):
             tr.CropBlackArea(),
             tr.RandomHorizontalFlip(),
             tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
-            # tr.RandomGaussianBlur(),
+            # help standardize the pixel values to have a mean of (0, 0, 0) and a standard deviation of (1, 1, 1).
             tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
             tr.ToTensor()])
 
@@ -126,7 +125,6 @@ def transform_val(self, sample):
     def transform_ts(self, sample):
 
         composed_transforms = transforms.Compose([
-            # tr.CropBlackArea(),
             tr.FixedResize(size=self.args.crop_size),
             tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
             tr.ToTensor()])
@@ -171,7 +169,7 @@ def __getitem__(self, index):
         _img = Image.open(img_path).convert('RGB')
         _tmp = np.array(Image.open(lbl_path), dtype=np.uint8)
         if self.split == 'train':
-            if index < 1036:  # lostandfound
+            if index < 1036:  # threshold for lostandfound
                 _tmp = self.relabel_lostandfound(_tmp)
             else:  # cityscapes
                 pass
@@ -212,7 +210,6 @@ def transform_tr(self, sample):
             tr_rgb.CropBlackArea(),
             tr_rgb.RandomHorizontalFlip(),
             tr_rgb.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
-            # tr.RandomGaussianBlur(),
             tr_rgb.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
             tr_rgb.ToTensor()])
 

diff --git a/...n/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityscapes.py b/...n/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityscapes.py
@@ -13,8 +13,7 @@ class CityscapesSegmentation(data.Dataset):
 
     def __init__(self, args, root=Path.db_root_dir('cityscapes'), data=None, split="train"):
 
-        # self.root = root
-        self.root = "/home/lsq/Dataset/"
+        self.root = root
         self.split = split
         self.args = args
         self.images = {}
@@ -95,7 +94,6 @@ def transform_tr(self, sample):
             tr.CropBlackArea(),
             tr.RandomHorizontalFlip(),
             tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
-            # tr.RandomGaussianBlur(),
             tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
             tr.ToTensor()])
 
@@ -113,8 +111,6 @@ def transform_val(self, sample):
     def transform_ts(self, sample):
 
         composed_transforms = transforms.Compose([
-            #tr.CropBlackArea(),
-            #tr.FixedResize(size=self.args.crop_size),
             tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
             tr.ToTensor()])
 

diff --git a/...tation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/temp.txt b/...tation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/dataloaders/datasets/temp.txt
diff --git a/...ment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/mypath.py b/...ment_semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/mypath.py
@@ -2,19 +2,19 @@ class Path(object):
     @staticmethod
     def db_root_dir(dataset):
         if dataset == 'cityscapes':
-            return '/home/robo/m0063/project/RFNet-master/Data/cityscapes/'      # folder that contains leftImg8bit/
+            return './ianvs/project/RFNet-master/Data/cityscapes/'      # folder that contains leftImg8bit/
         elif dataset == 'citylostfound':
-            return '/home/robo/m0063/project/RFNet-master/Data/cityscapesandlostandfound/'  # folder that mixes Cityscapes and Lost and Found
+            return './ianvs/project/RFNet-master/Data/cityscapesandlostandfound/'  # folder that mixes Cityscapes and Lost and Found
         elif dataset == 'cityrand':
-            return '/home/robo/m0063/project/RFNet-master/Data/cityrand/'
+            return './ianvs/project/RFNet-master/Data/cityrand/'
         elif dataset == 'target':
-            return '/home/robo/m0063/project/RFNet-master/Data/target/'
+            return './ianvs/project/RFNet-master/Data/target/'
         elif dataset == 'xrlab':
-            return '/home/robo/m0063/project/RFNet-master/Data/xrlab/'
+            return './ianvs/project/RFNet-master/Data/xrlab/'
         elif dataset == 'e1':
-            return '/home/robo/m0063/project/RFNet-master/Data/e1/'
+            return './ianvs/project/RFNet-master/Data/e1/'
         elif dataset == 'mapillary':
-            return '/home/robo/m0063/project/RFNet-master/Data/mapillary/'
+            return './ianvs/project/RFNet-master/Data/mapillary/'
         else:
             print('Dataset {} not available.'.format(dataset))
             raise NotImplementedError
diff --git a/...antic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_evaluate.py b/...antic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_evaluate.py
@@ -1,10 +1,5 @@
 import os
 os.environ['BACKEND_TYPE'] = 'PYTORCH'
-# os.environ["KB_SERVER"] = "http://0.0.0.0:9020"
-# os.environ["test_dataset_url"] = "./data_txt/sedna_data.txt"
-# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl"
-# os.environ["operator"] = "<"
-# os.environ["model_threshold"] = "0"
 
 from sedna.core.lifelong_learning import LifelongLearning
 from sedna.datasources import IndexDataParse

diff --git a/...mantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_predict.py b/...mantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/sedna_predict.py
@@ -1,9 +1,6 @@
 import os
 
 os.environ['BACKEND_TYPE'] = 'PYTORCH'
-# os.environ["UNSEEN_SAVE_URL"] = "s3://kubeedge/sedna-robo/unseen_samples/"
-# set at yaml
-# os.environ["PREDICT_RESULT_DIR"] = "./inference_results"
 os.environ["TEST_DATASET_URL"] = "./data_txt/door_test.txt"
 os.environ["EDGE_OUTPUT_URL"] = "./edge_kb"
 os.environ["ORIGINAL_DATASET_URL"] = "/tmp"

diff --git a/..._semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/args.py b/..._semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/args.py
@@ -32,7 +32,7 @@ def __init__(self, **kwargs):
         self.eval_interval = kwargs.get("eval_interval", 50)
         self.no_val = kwargs.get("no_val", True)
         self.cuda = True
-        self.savedir = '/home/QXY/dataset/save'
+        self.savedir = './dataset/mdil-ss/save'
 
 class ValArgs:
     def __init__(self, **kwargs):

diff --git a/...mantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/iouEval.py b/...mantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/iouEval.py
@@ -17,14 +17,9 @@ def reset(self):
         self.tp_obstacle = torch.zeros(1).double()
         self.idp_obstacle = torch.zeros(1).double()
         self.tp_nonobstacle = torch.zeros(1).double()
-        # self.cdi = torch.zeros(1).double()
 
     def addBatch(self, x, y):  # x=preds, y=targets
         # sizes should be "batch_size x nClasses x H x W"
-        # cdi = 0
-
-        # print ("X is cuda: ", x.is_cuda)
-        # print ("Y is cuda: ", y.is_cuda)
 
         if (x.is_cuda or y.is_cuda):
             x = x.cuda()
@@ -76,16 +71,10 @@ def addBatch(self, x, y):  # x=preds, y=targets
         idp_obstacle = (x_onehot[:, 19] - tpmult[:, 19]).sum()
         tp_nonobstacle = (-1*y_onehot+1).sum()
 
-        # for i in range(0, x.size(0)):
-        #     if tpmult[i].sum()/(y_onehot[i].sum() + 1e-15) >= 0.5:
-        #         cdi += 1
-
-
         self.cdp_obstacle += cdp_obstacle.double().cpu()
         self.tp_obstacle += tp_obstacle.double().cpu()
         self.idp_obstacle += idp_obstacle.double().cpu()
         self.tp_nonobstacle += tp_nonobstacle.double().cpu()
-        # self.cdi += cdi.double().cpu()
 
 
 
@@ -94,7 +83,6 @@ def getIoU(self):
         den = self.tp + self.fp + self.fn + 1e-15
         iou = num / den
         iou_not_zero = list(filter(lambda x: x != 0, iou))
-        # print(len(iou_not_zero))
         iou_mean = sum(iou_not_zero) / len(iou_not_zero)
         tfp = self.tp + self.fp + 1e-15
         acc = num / tfp

diff --git a/..._semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/loss.py b/..._semantic_segmentation/lifelong_learning_bench/testalgorithms/erfnet/ERFNet/utils/loss.py
@@ -48,12 +48,3 @@ def FocalLoss(self, logit, target, gamma=2, alpha=0.5):
 
 if __name__ == "__main__":
     loss = SegmentationLosses(cuda=True)
-    a = torch.rand(1, 3, 7, 7).cuda()
-    b = torch.rand(1, 7, 7).cuda()
-    print(loss.CrossEntropyLoss(a, b).item())
-    print(loss.FocalLoss(a, b, gamma=0, alpha=None).item())
-    print(loss.FocalLoss(a, b, gamma=2, alpha=0.5).item())
-
-
-
-