lints

Summary: lint issues (mostly flake) in implicitron

Reviewed By: patricklabatut

Differential Revision: D37920948

fbshipit-source-id: 8cb3c2a2838d111c80a211c98a404c210d4649ed

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -833,7 +833,7 @@ def _load_1bit_png_mask(file: str) -> np.ndarray:
     return mask
 
 
-def _load_depth_mask(path) -> np.ndarray:
+def _load_depth_mask(path: str) -> np.ndarray:
     if not path.lower().endswith(".png"):
         raise ValueError('unsupported depth mask file name "%s"' % path)
     m = _load_1bit_png_mask(path)

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -5,8 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import logging
-from dataclasses import field
-from typing import List, Optional
+from typing import Optional, Tuple
 
 import torch
 from pytorch3d.common.linear_with_repeat import LinearWithRepeat
@@ -206,7 +205,7 @@ class NeuralRadianceFieldImplicitFunction(NeuralRadianceFieldBase):
     transformer_dim_down_factor: float = 1.0
     n_hidden_neurons_xyz: int = 256
     n_layers_xyz: int = 8
-    append_xyz: List[int] = field(default_factory=lambda: [5])
+    append_xyz: Tuple[int, ...] = (5,)
 
     def _construct_xyz_encoder(self, input_dim: int):
         return MLPWithInputSkips(
@@ -224,7 +223,7 @@ class NeRFormerImplicitFunction(NeuralRadianceFieldBase):
     transformer_dim_down_factor: float = 2.0
     n_hidden_neurons_xyz: int = 80
     n_layers_xyz: int = 2
-    append_xyz: List[int] = field(default_factory=lambda: [1])
+    append_xyz: Tuple[int, ...] = (1,)
 
     def _construct_xyz_encoder(self, input_dim: int):
         return TransformerWithInputSkips(
@@ -286,7 +285,7 @@ class MLPWithInputSkips(torch.nn.Module):
         output_dim: int = 256,
         skip_dim: int = 39,
         hidden_dim: int = 256,
-        input_skips: List[int] = [5],
+        input_skips: Tuple[int, ...] = (5,),
         skip_affine_trans: bool = False,
         no_last_relu=False,
     ):
@@ -362,7 +361,7 @@ class TransformerWithInputSkips(torch.nn.Module):
         output_dim: int = 256,
         skip_dim: int = 39,
         hidden_dim: int = 64,
-        input_skips: List[int] = [5],
+        input_skips: Tuple[int, ...] = (5,),
         dim_down_factor: float = 1,
     ):
         """

pytorch3d/implicitron/models/renderer/multipass_ea.py

@@ -7,11 +7,10 @@
 from typing import List
 
 import torch
-from pytorch3d.implicitron.models.renderer.base import ImplicitFunctionWrapper
 from pytorch3d.implicitron.tools.config import registry, run_auto_creation
 from pytorch3d.renderer import RayBundle
 
-from .base import BaseRenderer, EvaluationMode, RendererOutput
+from .base import BaseRenderer, EvaluationMode, ImplicitFunctionWrapper, RendererOutput
 from .ray_point_refiner import RayPointRefiner
 from .raymarcher import RaymarcherBase
 
@@ -107,7 +106,7 @@ class MultiPassEmissionAbsorptionRenderer(  # pyre-ignore: 13
     def forward(
         self,
         ray_bundle: RayBundle,
-        implicit_functions: List[ImplicitFunctionWrapper] = [],
+        implicit_functions: List[ImplicitFunctionWrapper],
         evaluation_mode: EvaluationMode = EvaluationMode.EVALUATION,
         **kwargs,
     ) -> RendererOutput:

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from dataclasses import field
 from typing import Optional, Tuple
 
 import torch

pytorch3d/implicitron/tools/depth_cleanup.py

@@ -59,7 +59,7 @@ def cleanup_eval_depth(
     good_df_thr = std * sigma
     good_depth = (df <= good_df_thr).float() * pcl_mask
 
-    perc_kept = good_depth.sum(dim=1) / pcl_mask.sum(dim=1).clamp(1)
+    # perc_kept = good_depth.sum(dim=1) / pcl_mask.sum(dim=1).clamp(1)
     # print(f'Kept {100.0 * perc_kept.mean():1.3f} % points')
 
     good_depth_raster = torch.zeros_like(depth).view(ba, -1)

pytorch3d/implicitron/tools/eval_video_trajectory.py

@@ -200,9 +200,6 @@ def _visdom_plot_scene(
 
     viz = Visdom()
     viz.plotlyplot(p, env="cam_traj_dbg", win="cam_trajs")
-    import pdb
-
-    pdb.set_trace()
 
 
 def _figure_eight_knot(t: torch.Tensor, z_scale: float = 0.5):

pytorch3d/implicitron/tools/metric_utils.py

@@ -202,7 +202,7 @@ def neg_iou_loss(
     return 1.0 - iou(predict, target, mask=mask)
 
 
-def safe_sqrt(A: torch.Tensor, eps: float = float(1e-4)) -> torch.Tensor:
+def safe_sqrt(A: torch.Tensor, eps: float = 1e-4) -> torch.Tensor:
     """
     performs safe differentiable sqrt
     """

pytorch3d/implicitron/tools/model_io.py

@@ -20,12 +20,10 @@ logger = logging.getLogger(__name__)
 def load_stats(flstats):
     from pytorch3d.implicitron.tools.stats import Stats
 
-    try:
-        stats = Stats.load(flstats)
-    except:
-        logger.info("Cant load stats! %s" % flstats)
-        stats = None
-    return stats
+    if not os.path.isfile(flstats):
+        return None
+
+    return Stats.load(flstats)
 
 
 def get_model_path(fl) -> str:
@@ -40,7 +38,7 @@ def get_optimizer_path(fl) -> str:
     return flopt
 
 
-def get_stats_path(fl, eval_results: bool = False):
+def get_stats_path(fl, eval_results: bool = False) -> str:
     fl = os.path.splitext(fl)[0]
     if eval_results:
         for postfix in ("_2", ""):

pytorch3d/implicitron/tools/vis_utils.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import logging
-from typing import Any, Dict, List
+from typing import Any, Dict, Tuple
 
 import torch
 from visdom import Visdom
@@ -60,14 +60,14 @@ def visualize_basics(
     preds: Dict[str, Any],
     visdom_env_imgs: str,
     title: str = "",
-    visualize_preds_keys: List[str] = [
+    visualize_preds_keys: Tuple[str, ...] = (
         "image_rgb",
         "images_render",
         "fg_probability",
         "masks_render",
         "depths_render",
         "depth_map",
-    ],
+    ),
     store_history: bool = False,
 ) -> None:
     """