typing for trainer

Summary: Enable pyre checking of the trainer code.

Reviewed By: shapovalov

Differential Revision: D36545438

fbshipit-source-id: db1ea8d1ade2da79a2956964eb0c7ba302fa40d1

projects/implicitron_trainer/experiment.py

@@ -220,7 +220,7 @@ def init_optimizer(
     lr: float = 0.0005,
     gamma: float = 0.1,
     momentum: float = 0.9,
-    betas: Tuple[float] = (0.9, 0.999),
+    betas: Tuple[float, ...] = (0.9, 0.999),
     milestones: tuple = (),
     max_epochs: int = 1000,
 ):
@@ -257,6 +257,7 @@ def init_optimizer(
 
     # Get the parameters to optimize
     if hasattr(model, "_get_param_groups"):  # use the model function
+        # pyre-ignore[29]
         p_groups = model._get_param_groups(lr, wd=weight_decay)
     else:
         allprm = [prm for prm in model.parameters() if prm.requires_grad]
@@ -297,9 +298,6 @@ def init_optimizer(
     for _ in range(last_epoch):
         scheduler.step()
 
-    # Add the max epochs here
-    scheduler.max_epochs = max_epochs
-
     optimizer.zero_grad()
     return optimizer, scheduler
 
@@ -421,7 +419,7 @@ def trainvalidate(
                 if total_norm > clip_grad:
                     logger.info(
                         f"Clipping gradient: {total_norm}"
-                        + f" with coef {clip_grad / total_norm}."
+                        + f" with coef {clip_grad / float(total_norm)}."
                     )
 
             optimizer.step()

projects/implicitron_trainer/visualize_reconstruction.py

@@ -24,8 +24,7 @@ import torch.nn.functional as Fu
 from experiment import init_model
 from omegaconf import OmegaConf
 from pytorch3d.implicitron.dataset.data_source import ImplicitronDataSource
-from pytorch3d.implicitron.dataset.dataset_base import FrameData
+from pytorch3d.implicitron.dataset.dataset_base import DatasetBase, FrameData
-from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
 from pytorch3d.implicitron.dataset.utils import is_train_frame
 from pytorch3d.implicitron.models.base_model import EvaluationMode
 from pytorch3d.implicitron.tools.configurable import get_default_args
@@ -41,7 +40,7 @@ from tqdm import tqdm
 
 
 def render_sequence(
-    dataset: JsonIndexDataset,
+    dataset: DatasetBase,
     sequence_name: str,
     model: torch.nn.Module,
     video_path,
@@ -64,6 +63,12 @@ def render_sequence(
 ):
     if seed is None:
         seed = hash(sequence_name)
+
+    if visdom_show_preds:
+        viz = get_visdom_connection(server=visdom_server, port=visdom_port)
+    else:
+        viz = None
+
     print(f"Loading all data of sequence '{sequence_name}'.")
     seq_idx = list(dataset.sequence_indices_in_order(sequence_name))
     train_data = _load_whole_dataset(dataset, seq_idx, num_workers=num_workers)
@@ -82,7 +87,7 @@ def render_sequence(
         up=up,
         focal_length=None,
         principal_point=torch.zeros(n_eval_cameras, 2),
-        traj_offset_canonical=[0.0, 0.0, traj_offset],
+        traj_offset_canonical=(0.0, 0.0, traj_offset),
     )
 
     # sample the source views reproducibly
@@ -118,7 +123,6 @@ def render_sequence(
             if visdom_show_preds and (
                 n % max(n_eval_cameras // 20, 1) == 0 or n == n_eval_cameras - 1
             ):
-                viz = get_visdom_connection(server=visdom_server, port=visdom_port)
                 show_predictions(
                     preds_total,
                     sequence_name=batch.sequence_name[0],

pytorch3d/implicitron/tools/eval_video_trajectory.py

@@ -21,9 +21,9 @@ def generate_eval_video_cameras(
     trajectory_scale: float = 0.2,
     scene_center: Tuple[float, float, float] = (0.0, 0.0, 0.0),
     up: Tuple[float, float, float] = (0.0, 0.0, 1.0),
-    focal_length: Optional[torch.FloatTensor] = None,
+    focal_length: Optional[torch.Tensor] = None,
-    principal_point: Optional[torch.FloatTensor] = None,
+    principal_point: Optional[torch.Tensor] = None,
-    time: Optional[torch.FloatTensor] = None,
+    time: Optional[torch.Tensor] = None,
     infer_up_as_plane_normal: bool = True,
     traj_offset: Optional[Tuple[float, float, float]] = None,
     traj_offset_canonical: Optional[Tuple[float, float, float]] = None,