test fixes and lints

Summary:
- followup recent pyre change D63415925
- make tests remove temporary files
- weights_only=True in torch.load
- lint fixes

3 test fixes from VRehnberg in #1914
- imageio channels fix
- frozen decorator in test_config
- load_blobs positional

Reviewed By: MichaelRamamonjisoa

Differential Revision: D66162167

fbshipit-source-id: 7737e174691b62f1708443a4fae07343cec5bfeb

Author: bottler

dev/linter.sh

@@ -36,5 +36,5 @@ then
 
   echo "Running pyre..."
   echo "To restart/kill pyre server, run 'pyre restart' or 'pyre kill' in fbcode/"
-  ( cd ~/fbsource/fbcode; pyre -l vision/fair/pytorch3d/ )
+  ( cd ~/fbsource/fbcode; arc pyre check //vision/fair/pytorch3d/... )
 fi

packaging/pytorch3d/meta.yaml

@@ -32,7 +32,6 @@ requirements:
 
 build:
   string: py{{py}}_{{ environ['CU_VERSION'] }}_pyt{{ environ['PYTORCH_VERSION_NODOT']}}
-  # script: LD_LIBRARY_PATH=$PREFIX/lib:$BUILD_PREFIX/lib:$LD_LIBRARY_PATH python setup.py install --single-version-externally-managed --record=record.txt # [not win]
   script: python setup.py install --single-version-externally-managed --record=record.txt # [not win]
   script_env:
     - CUDA_HOME
@@ -57,7 +56,6 @@ test:
     - pandas
     - sqlalchemy
   commands:
-    #pytest .
     python -m unittest discover -v -s tests -t .
 
 

projects/implicitron_trainer/impl/model_factory.py

@@ -116,7 +116,9 @@ def __call__(
                         "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                     }
                 model_state_dict = torch.load(
-                    model_io.get_model_path(model_path), map_location=map_location
+                    model_io.get_model_path(model_path),
+                    map_location=map_location,
+                    weights_only=True,
                 )
 
                 try:

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -241,7 +241,7 @@ def _get_optimizer_state(
                     map_location = {
                         "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                     }
-                optimizer_state = torch.load(opt_path, map_location)
+                optimizer_state = torch.load(opt_path, map_location, weights_only=True)
             else:
                 raise FileNotFoundError(f"Optimizer state {opt_path} does not exist.")
         return optimizer_state

projects/nerf/nerf/dataset.py

@@ -84,9 +84,9 @@ def get_nerf_datasets(
 
     if autodownload and any(not os.path.isfile(p) for p in (cameras_path, image_path)):
         # Automatically download the data files if missing.
-        download_data((dataset_name,), data_root=data_root)
+        download_data([dataset_name], data_root=data_root)
 
-    train_data = torch.load(cameras_path)
+    train_data = torch.load(cameras_path, weights_only=True)
     n_cameras = train_data["cameras"]["R"].shape[0]
 
     _image_max_image_pixels = Image.MAX_IMAGE_PIXELS

projects/nerf/test_nerf.py

@@ -63,7 +63,7 @@ def main(cfg: DictConfig):
         raise ValueError(f"Model checkpoint {checkpoint_path} does not exist!")
 
     print(f"Loading checkpoint {checkpoint_path}.")
-    loaded_data = torch.load(checkpoint_path)
+    loaded_data = torch.load(checkpoint_path, weights_only=True)
     # Do not load the cached xy grid.
     # - this allows setting an arbitrary evaluation image size.
     state_dict = {

projects/nerf/train_nerf.py

@@ -77,7 +77,7 @@ def main(cfg: DictConfig):
         # Resume training if requested.
         if cfg.resume and os.path.isfile(checkpoint_path):
             print(f"Resuming from checkpoint {checkpoint_path}.")
-            loaded_data = torch.load(checkpoint_path)
+            loaded_data = torch.load(checkpoint_path, weights_only=True)
             model.load_state_dict(loaded_data["model"])
             stats = pickle.loads(loaded_data["stats"])
             print(f"   => resuming from epoch {stats.epoch}.")

pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py

@@ -106,7 +106,7 @@ def __post_init__(self):
             self.layers = torch.nn.ModuleList()
             self.proj_layers = torch.nn.ModuleList()
             for stage in range(self.max_stage):
-                stage_name = f"layer{stage+1}"
+                stage_name = f"layer{stage + 1}"
                 feature_name = self._get_resnet_stage_feature_name(stage)
                 if (stage + 1) in self.stages:
                     if (
@@ -139,7 +139,7 @@ def __post_init__(self):
         self.stages = set(self.stages)  # convert to set for faster "in"
 
     def _get_resnet_stage_feature_name(self, stage) -> str:
-        return f"res_layer_{stage+1}"
+        return f"res_layer_{stage + 1}"
 
     def _resnet_normalize_image(self, img: torch.Tensor) -> torch.Tensor:
         return (img - self._resnet_mean) / self._resnet_std

pytorch3d/implicitron/tools/model_io.py

@@ -111,10 +111,10 @@ def load_model(fl, map_location: Optional[dict]):
     flstats = get_stats_path(fl)
     flmodel = get_model_path(fl)
     flopt = get_optimizer_path(fl)
-    model_state_dict = torch.load(flmodel, map_location=map_location)
+    model_state_dict = torch.load(flmodel, map_location=map_location, weights_only=True)
     stats = load_stats(flstats)
     if os.path.isfile(flopt):
-        optimizer = torch.load(flopt, map_location=map_location)
+        optimizer = torch.load(flopt, map_location=map_location, weights_only=True)
     else:
         optimizer = None
 

pytorch3d/io/experimental_gltf_io.py

@@ -163,9 +163,8 @@ def _read_chunks(
     if binary_data is not None:
         binary_data = np.frombuffer(binary_data, dtype=np.uint8)
 
-    # pyre-fixme[7]: Expected `Optional[Tuple[Dict[str, typing.Any],
-    #  ndarray[typing.Any, typing.Any]]]` but got `Tuple[typing.Any,
-    #  Optional[ndarray[typing.Any, dtype[typing.Any]]]]`.
+    assert binary_data is not None
+
     return json_data, binary_data