Test all CO3D model configs in test_forward_pass

Summary: Tests all possible model configs in test_forward_pass.py

Reviewed By: shapovalov

Differential Revision: D35851507

fbshipit-source-id: 4860ee1d37cf17a2faab5fc14d4b2ba0b96c4b8b

tests/implicitron/test_forward_pass.py

@@ -4,68 +4,102 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import os
 import unittest
 
 import torch
+from omegaconf import DictConfig, OmegaConf
 from pytorch3d.implicitron.models.generic_model import GenericModel
 from pytorch3d.implicitron.models.renderer.base import EvaluationMode
 from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
 from pytorch3d.renderer.cameras import look_at_view_transform, PerspectiveCameras
 
 
+if os.environ.get("FB_TEST", False):
+    from common_testing import get_pytorch3d_dir
+else:
+    from tests.common_testing import get_pytorch3d_dir
+
+IMPLICITRON_CONFIGS_DIR = (
+    get_pytorch3d_dir() / "projects" / "implicitron_trainer" / "configs"
+)
+
+
 class TestGenericModel(unittest.TestCase):
+    def setUp(self):
+        torch.manual_seed(42)
+
     def test_gm(self):
         # Simple test of a forward and backward pass of the default GenericModel.
         device = torch.device("cuda:1")
         expand_args_fields(GenericModel)
         model = GenericModel()
         model.to(device)
+        self._one_model_test(model, device)
+
+    def test_all_gm_configs(self):
+        # Tests all model settings in the implicitron_trainer config folder.
+        device = torch.device("cuda:0")
+        config_files = []
+
+        for pattern in ("repro_singleseq*.yaml", "repro_multiseq*.yaml"):
+            config_files.extend(
+                [
+                    f
+                    for f in IMPLICITRON_CONFIGS_DIR.glob(pattern)
+                    if not f.name.endswith("_base.yaml")
+                ]
+            )
+
+        for config_file in config_files:
+            with self.subTest(name=config_file.stem):
+                cfg = _load_model_config_from_yaml(str(config_file))
+                model = GenericModel(**cfg)
+                model.to(device)
+                self._one_model_test(model, device, eval_test=True)
+
+    def _one_model_test(
+        self,
+        model,
+        device,
+        n_train_cameras: int = 5,
+        eval_test: bool = True,
+    ):
 
-        n_train_cameras = 2
         R, T = look_at_view_transform(azim=torch.rand(n_train_cameras) * 360)
         cameras = PerspectiveCameras(R=R, T=T, device=device)
 
-        # TODO: make these default to None?
-        defaulted_args = {
-            "fg_probability": None,
-            "depth_map": None,
-            "mask_crop": None,
-            "sequence_name": None,
+        N, H, W = n_train_cameras, model.render_image_height, model.render_image_width
+
+        random_args = {
+            "camera": cameras,
+            "fg_probability": _random_input_tensor(N, 1, H, W, True, device),
+            "depth_map": _random_input_tensor(N, 1, H, W, False, device) + 0.1,
+            "mask_crop": _random_input_tensor(N, 1, H, W, True, device),
+            "sequence_name": ["sequence"] * N,
+            "image_rgb": _random_input_tensor(N, 3, H, W, False, device),
         }
 
-        with self.assertWarnsRegex(UserWarning, "No main objective found"):
-            model(
-                camera=cameras,
-                evaluation_mode=EvaluationMode.TRAINING,
-                **defaulted_args,
-                image_rgb=None,
-            )
-        target_image_rgb = torch.rand(
-            (n_train_cameras, 3, model.render_image_height, model.render_image_width),
-            device=device,
-        )
+        # training foward pass
+        model.train()
         train_preds = model(
-            camera=cameras,
+            **random_args,
             evaluation_mode=EvaluationMode.TRAINING,
-            image_rgb=target_image_rgb,
-            **defaulted_args,
         )
         self.assertGreater(train_preds["objective"].item(), 0)
         train_preds["objective"].backward()
 
-        model.eval()
-        with torch.no_grad():
-            # TODO: perhaps this warning should be skipped in eval mode?
-            with self.assertWarnsRegex(UserWarning, "No main objective found"):
+        if eval_test:
+            model.eval()
+            with torch.no_grad():
                 eval_preds = model(
-                    camera=cameras[0],
-                    **defaulted_args,
-                    image_rgb=None,
+                    **random_args,
+                    evaluation_mode=EvaluationMode.EVALUATION,
+                )
+                self.assertEqual(
+                    eval_preds["images_render"].shape,
+                    (1, 3, model.render_image_height, model.render_image_width),
                 )
-        self.assertEqual(
-            eval_preds["images_render"].shape,
-            (1, 3, model.render_image_height, model.render_image_width),
-        )
 
     def test_idr(self):
         # Forward pass of GenericModel with IDR.
@@ -104,3 +138,44 @@ class TestGenericModel(unittest.TestCase):
             **defaulted_args,
         )
         self.assertGreater(train_preds["objective"].item(), 0)
+
+
+def _random_input_tensor(
+    N: int,
+    C: int,
+    H: int,
+    W: int,
+    is_binary: bool,
+    device: torch.device,
+) -> torch.Tensor:
+    T = torch.rand(N, C, H, W, device=device)
+    if is_binary:
+        T = (T > 0.5).float()
+    return T
+
+
+def _load_model_config_from_yaml(config_path, strict=True) -> DictConfig:
+    default_cfg = get_default_args(GenericModel)
+    cfg = _load_model_config_from_yaml_rec(default_cfg, config_path)
+    return cfg
+
+
+def _load_model_config_from_yaml_rec(cfg: DictConfig, config_path: str) -> DictConfig:
+    cfg_loaded = OmegaConf.load(config_path)
+    if "generic_model_args" in cfg_loaded:
+        cfg_model_loaded = cfg_loaded.generic_model_args
+    else:
+        cfg_model_loaded = None
+    defaults = cfg_loaded.pop("defaults", None)
+    if defaults is not None:
+        for default_name in defaults:
+            if default_name in ("_self_", "default_config"):
+                continue
+            default_name = os.path.splitext(default_name)[0]
+            defpath = os.path.join(os.path.dirname(config_path), default_name + ".yaml")
+            cfg = _load_model_config_from_yaml_rec(cfg, defpath)
+            if cfg_model_loaded is not None:
+                cfg = OmegaConf.merge(cfg, cfg_model_loaded)
+    elif cfg_model_loaded is not None:
+        cfg = OmegaConf.merge(cfg, cfg_model_loaded)
+    return cfg