Move load_stats to TrainingLoop

Summary:
Stats are logically connected to the training loop, not to the model. Hence, moving to the training loop.

Also removing resume_epoch from OptimizerFactory in favor of a single place - ModelFactory. This removes the need for config consistency checks etc.

Reviewed By: kjchalup

Differential Revision: D38313475

fbshipit-source-id: a1d188a63e28459df381ff98ad8acdcdb14887b7

tests/implicitron/data/overrides.yaml

@@ -1,3 +1,24 @@
+log_vars:
+- loss_rgb_psnr_fg
+- loss_rgb_psnr
+- loss_rgb_mse
+- loss_rgb_huber
+- loss_depth_abs
+- loss_depth_abs_fg
+- loss_mask_neg_iou
+- loss_mask_bce
+- loss_mask_beta_prior
+- loss_eikonal
+- loss_density_tv
+- loss_depth_neg_penalty
+- loss_autodecoder_norm
+- loss_prev_stage_rgb_mse
+- loss_prev_stage_rgb_psnr_fg
+- loss_prev_stage_rgb_psnr
+- loss_prev_stage_mask_bce
+- objective
+- epoch
+- sec/it
 mask_images: true
 mask_depths: true
 render_image_width: 400
@@ -28,27 +49,6 @@ loss_weights:
   loss_prev_stage_rgb_mse: 1.0
   loss_mask_bce: 0.0
   loss_prev_stage_mask_bce: 0.0
-log_vars:
-- loss_rgb_psnr_fg
-- loss_rgb_psnr
-- loss_rgb_mse
-- loss_rgb_huber
-- loss_depth_abs
-- loss_depth_abs_fg
-- loss_mask_neg_iou
-- loss_mask_bce
-- loss_mask_beta_prior
-- loss_eikonal
-- loss_density_tv
-- loss_depth_neg_penalty
-- loss_autodecoder_norm
-- loss_prev_stage_rgb_mse
-- loss_prev_stage_rgb_psnr_fg
-- loss_prev_stage_rgb_psnr
-- loss_prev_stage_mask_bce
-- objective
-- epoch
-- sec/it
 global_encoder_SequenceAutodecoder_args:
   autodecoder_args:
     encoding_dim: 0

tests/implicitron/test_config_use.py

@@ -90,5 +90,5 @@ class TestGenericModel(unittest.TestCase):
         remove_unused_components(instance_args)
         yaml = OmegaConf.to_yaml(instance_args, sort_keys=False)
         if DEBUG:
-            (DATA_DIR / "overrides.yaml_").write_text(yaml)
+            (DATA_DIR / "overrides_.yaml").write_text(yaml)
         self.assertEqual(yaml, (DATA_DIR / "overrides.yaml").read_text())