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Test all CO3D model configs in test_forward_pass

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Summary: Tests all possible model configs in test_forward_pass.py

Reviewed By: shapovalov

Differential Revision: D35851507

fbshipit-source-id: 4860ee1d37cf17a2faab5fc14d4b2ba0b96c4b8b
This commit is contained in:
David Novotny 2022-05-11 05:40:05 -07:00 committed by Facebook GitHub Bot
parent 1f3953795c
commit 2374d19da5
1 changed files with 107 additions and 32 deletions
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139
tests/implicitron/test_forward_pass.py
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@ -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