dataloader for inference w/o gt

IS-Net/data_loader_cache.py

@@ -274,7 +274,7 @@ class GOSDatasetCache(Dataset):
 
 
             im_id = cached_dataset["im_name"][i]
-
+            print("im_path: ", im_path)
             im = im_reader(im_path)
             im, im_shp = im_preprocess(im,self.cache_size)
             im_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_im.pt")
@@ -285,12 +285,14 @@ class GOSDatasetCache(Dataset):
                 ims_pt_list.append(torch.unsqueeze(im,0))
             # ims_list.append(im.cpu().data.numpy().astype(np.uint8))
 
-
+            gt = np.zeros(im.shape[0:2])
+            if len(self.dataset["gt_path"])!=0:
                 gt = im_reader(self.dataset["gt_path"][i])
             gt, gt_shp = gt_preprocess(gt,self.cache_size)
             gt_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_gt.pt")
             torch.save(gt,gt_cache_file)
-            cached_dataset["gt_path"][i] = gt_cache_file
+            # cached_dataset["gt_path"][i] = gt_cache_file
+            cached_dataset["gt_path"].append(gt_cache_file)
             if(self.cache_boost):
                 gts_pt_list.append(torch.unsqueeze(gt,0))
             # gts_list.append(gt.cpu().data.numpy().astype(np.uint8))

IS-Net/train_valid_inference_main.py

@@ -474,7 +474,10 @@ def valid(net, valid_dataloaders, valid_datasets, hypar, epoch=0):
                 mi = torch.min(pred_val)
                 pred_val = (pred_val-mi)/(ma-mi) # max = 1
 
+                if len(valid_dataset.dataset["ori_gt_path"]) != 0:
                     gt = np.squeeze(io.imread(valid_dataset.dataset["ori_gt_path"][i_test])) # max = 255
+                else:
+                    gt = np.zeros((shapes_val[t][0],shapes_val[t][1]))
                 with torch.no_grad():
                     gt = torch.tensor(gt).cuda()
 
@@ -648,10 +651,17 @@ if __name__ == "__main__":
                  "im_ext": ".jpg",
                  "gt_ext": ".png",
                  "cache_dir":"../DIS5K-Cache/DIS-TE4"}
+    ### test your own dataset
+    dataset_demo = {"name": "your-dataset",
+                 "im_dir": "../your-dataset/im",
+                 "gt_dir": "",
+                 "im_ext": ".jpg",
+                 "gt_ext": "",
+                 "cache_dir":"../your-dataset/cache"}
 
     train_datasets = [dataset_tr] ## users can create mutiple dictionary for setting a list of datasets as training set
     # valid_datasets = [dataset_vd] ## users can create mutiple dictionary for setting a list of datasets as vaidation sets or inference sets
-    valid_datasets = [dataset_vd] #, dataset_te1, dataset_te2, dataset_te3, dataset_te4] # and hypar["mode"] = "valid" for inference,
+    valid_datasets = [dataset_vd] # dataset_vd, dataset_te1, dataset_te2, dataset_te3, dataset_te4] # and hypar["mode"] = "valid" for inference,
 
     ### --------------- STEP 2: Configuring the hyperparamters for Training, validation and inferencing ---------------
     hypar = {}
@@ -662,7 +672,7 @@ if __name__ == "__main__":
     ## "valid": for validation and inferening,
     ## in "valid" mode, it will calculate the accuracy as well as save the prediciton results into the "hypar["valid_out_dir"]", which shouldn't be ""
     ## otherwise only accuracy will be calculated and no predictions will be saved
-    hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
+    hypar["interm_sup"] = False ## in-dicate if activate intermediate feature supervision
 
     if hypar["mode"] == "train":
         hypar["valid_out_dir"] = "" ## for "train" model leave it as "", for "valid"("inference") mode: set it according to your local directory
@@ -671,9 +681,9 @@ if __name__ == "__main__":
         hypar["start_ite"] = 0 ## start iteration for the training, can be changed to match the restored training process
         hypar["gt_encoder_model"] = ""
     else: ## configure the segmentation output path and the to-be-used model weights path
-        hypar["valid_out_dir"] = "../DIS5K-Results-test" ## output inferenced segmentation maps into this fold
-        hypar["model_path"] ="../saved_models/IS-Net" ## load trained weights from this path
-        hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
+        hypar["valid_out_dir"] = "../your-results/"##"../DIS5K-Results-test" ## output inferenced segmentation maps into this fold
+        hypar["model_path"] = "../saved_models/IS-Net" ## load trained weights from this path
+        hypar["restore_model"] = "isnet.pth"##"isnet.pth" ## name of the to-be-loaded weights
 
     # if hypar["restore_model"]!="":
     #     hypar["start_ite"] = int(hypar["restore_model"].split("_")[2])