update json dump

run_inference.py

@@ -148,7 +148,7 @@ if __name__ == "__main__":
     pirads_model.eval()
     cspca_risk_list = []
     cspca_model.eval()
-    top5_patches = []
+    patches_top_5_list = []
     with torch.no_grad():
         for idx, batch_data in enumerate(loader):
             data = batch_data["image"].as_subclass(torch.Tensor).to(args.device)
@@ -176,18 +176,22 @@ if __name__ == "__main__":
             for i in range(5):
                 patch_temp = data[0, top5_indices.cpu().numpy()[i]][0].cpu().numpy()
                 patches_top_5.append(patch_temp)
+            patches_top_5_list.append(patches_top_5)
+    coords_list = []
+    for i in args.data_list:
+        parent_image = get_parent_image([i], args)
 
-    parent_image = get_parent_image(args)
-
-    coords = get_patch_coordinate(patches_top_5, parent_image, args)
+        coords = get_patch_coordinate(patches_top_5, parent_image, args)
+        coords_list.append(coords)
+    output_dict = {}
 
     for i,j in enumerate(files):
         logging.info(f"File: {j}, PIRADS score: {pirads_list[i]}, csPCa risk score: {cspca_risk_list[i]:.4f}")
 
-    output_dict = {
-        'Predicted PIRAD Score': pirads_list[i] + 2.0,
-        'csPCa risk': cspca_risk_list[i],
-        'Top left coordinate of top 5 patches(x,y,z)': coords,
-    }
+        output_dict[j] = {
+            'Predicted PIRAD Score': pirads_list[i] + 2.0,
+            'csPCa risk': cspca_risk_list[i],
+            'Top left coordinate of top 5 patches(x,y,z)': coords_list[i],
+        }
     with open(os.path.join(args.output_dir, "results.json"), 'w') as f:
         json.dump(output_dict, f, indent=4)

src/utils.py

@@ -162,7 +162,7 @@ def get_patch_coordinate(patches_top_5, parent_image, args):
     return coords
 
 
-def get_parent_image(args):
+def get_parent_image(temp_data_list, args):
     transform_image = Compose(
         [
             LoadImaged(keys=["image", "mask"], reader=ITKReader(), ensure_channel_first=True, dtype=np.float32),
@@ -172,7 +172,7 @@ def get_parent_image(args):
             ToTensord(keys=["image", "label"]),
         ]
     )
-    dataset_image = Dataset(data=args.data_list, transform=transform_image)
+    dataset_image = Dataset(data=temp_data_list, transform=transform_image)
     return dataset_image[0]['image'][0].numpy()
 
 '''

temp.ipynb

@@ -577,9 +577,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "56072a2b",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'image': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/t2_histmatched/1009449_11049598.nrrd',\n",
+       "  'dwi': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/DWI_histmatched/1009449_11049598.nrrd',\n",
+       "  'adc': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/ADC_histmatched/1009449_11049598.nrrd',\n",
+       "  'heatmap': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/heatmaps/1009449_11049598.nrrd',\n",
+       "  'mask': '/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/datatemp/processed/prostate_mask/1009449_11049598.nrrd',\n",
+       "  'label': 0}]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db1163d2",
+   "metadata": {},
    "outputs": [],
    "source": []
   }