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Enable additional test-time source views for json dataset provider v2

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Summary: Adds additional source views to the eval batches for evaluating many-view models on CO3D Challenge

Reviewed By: bottler

Differential Revision: D38705904

fbshipit-source-id: cf7d00dc7db926fbd1656dd97a729674e9ff5adb
This commit is contained in:
David Novotny
2022-08-15 08:40:09 -07:00
committed by Facebook GitHub Bot
parent e8616cc8ba
commit 2ff2c7c836
4 changed files with 102 additions and 23 deletions
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1
tests/implicitron/data/data_source.yaml
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@@ -49,6 +49,7 @@ dataset_map_provider_JsonIndexDatasetMapProviderV2_args:
test_on_train: false
only_test_set: false
load_eval_batches: true
n_known_frames_for_test: 0
dataset_class_type: JsonIndexDataset
path_manager_factory_class_type: PathManagerFactory
dataset_JsonIndexDataset_args:

66
tests/implicitron/test_json_index_dataset_provider_v2.py
View File

@@ -37,29 +37,47 @@ class TestJsonIndexDatasetProviderV2(unittest.TestCase):
expand_args_fields(JsonIndexDatasetMapProviderV2)
categories = ["A", "B"]
subset_name = "test"
eval_batch_size = 5
with tempfile.TemporaryDirectory() as tmpd:
_make_random_json_dataset_map_provider_v2_data(tmpd, categories)
for category in categories:
dataset_provider = JsonIndexDatasetMapProviderV2(
category=category,
subset_name="test",
dataset_root=tmpd,
)
dataset_map = dataset_provider.get_dataset_map()
for set_ in ["train", "val", "test"]:
dataloader = torch.utils.data.DataLoader(
getattr(dataset_map, set_),
batch_size=3,
shuffle=True,
collate_fn=FrameData.collate,
_make_random_json_dataset_map_provider_v2_data(
tmpd,
categories,
eval_batch_size=eval_batch_size,
)
for n_known_frames_for_test in [0, 2]:
for category in categories:
dataset_provider = JsonIndexDatasetMapProviderV2(
category=category,
subset_name="test",
dataset_root=tmpd,
n_known_frames_for_test=n_known_frames_for_test,
)
for _ in dataloader:
pass
category_to_subset_list = (
dataset_provider.get_category_to_subset_name_list()
)
category_to_subset_list_ = {c: [subset_name] for c in categories}
self.assertTrue(category_to_subset_list == category_to_subset_list_)
dataset_map = dataset_provider.get_dataset_map()
for set_ in ["train", "val", "test"]:
if set_ in ["train", "val"]:
dataloader = torch.utils.data.DataLoader(
getattr(dataset_map, set_),
batch_size=3,
shuffle=True,
collate_fn=FrameData.collate,
)
else:
dataloader = torch.utils.data.DataLoader(
getattr(dataset_map, set_),
batch_sampler=dataset_map[set_].get_eval_batches(),
collate_fn=FrameData.collate,
)
for batch in dataloader:
if set_ == "test":
self.assertTrue(
batch.image_rgb.shape[0]
== n_known_frames_for_test + eval_batch_size
)
category_to_subset_list = (
dataset_provider.get_category_to_subset_name_list()
)
category_to_subset_list_ = {c: [subset_name] for c in categories}
self.assertTrue(category_to_subset_list == category_to_subset_list_)
def _make_random_json_dataset_map_provider_v2_data(
@@ -70,6 +88,7 @@ def _make_random_json_dataset_map_provider_v2_data(
H: int = 50,
W: int = 30,
subset_name: str = "test",
eval_batch_size: int = 5,
):
os.makedirs(root, exist_ok=True)
category_to_subset_list = {}
@@ -142,7 +161,10 @@ def _make_random_json_dataset_map_provider_v2_data(
with open(set_list_file, "w") as f:
json.dump(set_list, f)
eval_batches = [random.sample(test_frame_index, 5) for _ in range(10)]
eval_batches = [
random.sample(test_frame_index, eval_batch_size) for _ in range(10)
]
eval_b_dir = os.path.join(root, category, "eval_batches")
os.makedirs(eval_b_dir, exist_ok=True)
eval_b_file = os.path.join(eval_b_dir, f"eval_batches_{subset_name}.json")