adding samples

app.py

@@ -91,7 +91,7 @@ def run_model(target_dir, model, anchor_size=100) -> dict:
                 for img_split in images.split(15, dim=0):
                     pbar.update(15)
                     predictions_s += to_cpu(
-                        model.reloc(img_split, ret_img=True, memory_save=False)
+                        model.reloc(img_split, ret_img=True, memory_save=True)
                     )
 
     predictions = {}
@@ -110,12 +110,12 @@ def run_model(target_dir, model, anchor_size=100) -> dict:
     predictions["images"] = torch.cat(
         [s["images"] for s in predictions_s], dim=0
     )  # (S, H, W, 3)
-    predictions["world_points"] = torch.cat(
-        [s["point_map"] for s in predictions_s], dim=0
-    )  # (S, H, W, 3)
-    predictions["world_points_conf"] = torch.cat(
-        [s["xyz_cnf"] for s in predictions_s], dim=0
-    )  # (S, H, W, 3)
+    # predictions["world_points"] = torch.cat(
+    #     [s["point_map"] for s in predictions_s], dim=0
+    # )  # (S, H, W, 3)
+    # predictions["world_points_conf"] = torch.cat(
+    #     [s["xyz_cnf"] for s in predictions_s], dim=0
+    # )  # (S, H, W, 3)
     predictions["pose_enc"] = extri_intri_to_pose_encoding(
         predictions["extrinsic"].unsqueeze(0),
         predictions["intrinsic"].unsqueeze(0),
@@ -378,8 +378,8 @@ def update_visualization(
         "pose_enc",
         "depth",
         "depth_conf",
-        "world_points",
-        "world_points_conf",
+        # "world_points",
+        # "world_points_conf",
         "images",
         "extrinsic",
         "intrinsic",
@@ -567,7 +567,7 @@ with gr.Blocks(
 
             with gr.Row():
                 prediction_mode = gr.Radio(
-                    ["Depthmap and Camera Branch", "Pointmap Branch"],
+                    ["Depthmap and Camera Branch"],
                     label="Select a Prediction Mode",
                     value="Depthmap and Camera Branch",
                     scale=1,

sailrecon/models/sail_recon.py

@@ -162,7 +162,6 @@ class SailRecon(nn.Module, PyTorchModelHubMixin):
                 predictions["point_map_by_unprojection"] = point_map_by_unprojection[
                     None
                 ]
-
                 predictions["point_map"] = xyz_map
                 predictions["rgbs"] = rgbs
                 predictions["xyz_cnf"] = xyz_cnf