Bump version to 0.11.2 and update uv version to 0.11.3

- Updated version in pyproject.toml and blender_manifest.toml to 0.11.2.
- Enhanced uv executable path documentation in client.py.
- Added inference_mode parameter to reconstruct_request function in client.py.
- Implemented uv download functionality in install_env.py with improved error handling and checksum verification.
- Modified install_uv function to download and extract uv binary instead of using bundled version.
- Updated job progress handling in operators.py to track progress stages.
- Improved download progress reporting in worker.py and adjusted related tests.
- Refactored tests to accommodate changes in status reporting and model downloading.
- Removed vendor.lock.toml as part of the cleanup process.

tests/test_worker.py

@@ -369,7 +369,7 @@ class WorkerTest(unittest.TestCase):
             "_verify_model_complete",
             return_value=None,
         ):
-            args.model_dir = str(Path(directory) / "model")
+            args.model_dir = str(Path(directory) / "models" / "DA3")
             args.status = str(Path(directory) / "status.json")
             self.worker.download_model(args)
 
@@ -456,7 +456,7 @@ class WorkerTest(unittest.TestCase):
             "_verify_model_complete",
             return_value=None,
         ):
-            args.model_dir = str(Path(directory) / "model")
+            args.model_dir = str(Path(directory) / "models" / "DA3")
             args.status = str(Path(directory) / "status.json")
             self.worker.download_model(args)
 
@@ -937,15 +937,21 @@ class WorkerTest(unittest.TestCase):
         self.assertEqual(mesh_calls[0][2:], (35.0, True, 0.2))
 
     def test_reconstruct_copies_frames_for_multi_frame_input(self):
+        inference_inputs = []
         fake_da3 = ModuleType("da3_onnx")
         fake_da3.create_session = lambda _model_dir, _device: object()
-        fake_da3.infer = lambda *_args, **_kwargs: SimpleNamespace(
-            depth=np.full((2, 2, 2), 2.0, dtype=np.float32),
-            conf=np.ones((2, 2, 2), dtype=np.float32),
-            extrinsics=np.repeat(np.eye(4, dtype=np.float32)[None, :3], 2, axis=0),
-            intrinsics=np.repeat(np.eye(3, dtype=np.float32)[None], 2, axis=0),
-            processed_images=np.zeros((2, 2, 2, 3), dtype=np.uint8),
-        )
+
+        def fake_infer(_session, images, **_options):
+            inference_inputs.append(images)
+            return SimpleNamespace(
+                depth=np.full((1, 2, 2), 2.0, dtype=np.float32),
+                conf=np.ones((1, 2, 2), dtype=np.float32),
+                extrinsics=np.eye(4, dtype=np.float32)[None, :3],
+                intrinsics=np.eye(3, dtype=np.float32)[None],
+                processed_images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
+            )
+
+        fake_da3.infer = fake_infer
         fake_runtime = ModuleType("onnx_runtime")
         fake_runtime.provider_label = lambda _session: "CPU"
 
@@ -979,6 +985,7 @@ class WorkerTest(unittest.TestCase):
                     show_cameras=0,
                     gs_views_interval=1,
                     remove_background_model_dir="",
+                    inference_mode="per_frame",
                 )
                 self.worker.reconstruct(args)
 
@@ -989,6 +996,10 @@ class WorkerTest(unittest.TestCase):
             self.assertTrue(
                 (result_directory / "frame.0002.jpg").is_file()
             )
+            self.assertEqual(len(inference_inputs), 2)
+            self.assertTrue(all(len(paths) == 1 for paths in inference_inputs))
+            with np.load(result_directory / "results.npz") as result:
+                self.assertEqual(result["depth"].shape, (2, 2, 2))
             self.assertTrue(
                 (result_directory / "results.npz").is_file()
             )