def build_detection_val_loader(cfg, dataset_name: str, mapper=None):
dataset_dicts = get_detection_dataset_dicts(
[dataset_name],
filter_empty=False,
proposal_files=[
cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)]
]
if cfg.MODEL.LOAD_PROPOSALS
else None,
)
# dataset_dicts = get_detection_dataset_dicts(
# [dataset_name],
# filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
# min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
# if cfg.MODEL.KEYPOINT_ON
# else 0,
# proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
# )
dataset = DatasetFromList(dataset_dicts)
if mapper is None:
mapper = DatasetMapper(cfg, True)
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=cfg.DATALOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
return data_loader
def test_build_iterable_dataloader_inference(self):
# Test that build_detection_test_loader supports iterable dataset
N = 50
ds = DatasetFromList(list(range(N)))
ds = ToIterableDataset(ds, InferenceSampler(len(ds)))
dl = build_detection_test_loader(dataset=ds, mapper=lambda x: x, num_workers=3)
self._check_is_range(dl, N)
def test_build_batch_dataloader_inference(self):
# Test that build_batch_data_loader can be used for inference
N = 96
ds = DatasetFromList(list(range(N)))
sampler = InferenceSampler(len(ds))
dl = build_batch_data_loader(ds, sampler, 8, num_workers=3)
self._check_is_range(dl, N)
def build_detection_test_loader(cfg, dataset_name, mapper=None):
dataset_dicts = get_detection_dataset_dicts(
[dataset_name],
filter_empty=False,
proposal_files=[
cfg.DATASETS.PROPOSAL_FILES_TEST[list(
cfg.DATASETS.TEST).index(dataset_name)]
] if cfg.MODEL.LOAD_PROPOSALS else None,
)
dataset = DatasetFromList(dataset_dicts)
if mapper is None:
mapper = DatasetMapper(cfg, False)
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler,
1,
drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=cfg.DATALOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
return data_loader
def test_build_dataloader_inference(self):
N = 50
ds = DatasetFromList(list(range(N)))
sampler = InferenceSampler(len(ds))
dl = build_detection_test_loader(
dataset=ds, sampler=sampler, mapper=lambda x: x, num_workers=3
)
self._check_is_range(dl, N)
def do_test(cfg, model):
results = OrderedDict()
for dataset_name in cfg.DATASETS.TEST:
# data_loader = build_detection_test_loader(cfg, dataset_name)
if 'build_detection_test_loader':
if 'get_detection_dataset_dicts':
descs_valid: List[Dict] = DatasetCatalog.get(dataset_name)
# validation dataset is too large.
random.seed(2020)
descs_valid = random.sample(descs_valid, k=10)
dataset = DatasetFromList(descs_valid)
if 'DatasetMapper':
mapper = make_mapper(dataset_name,
is_train=False,
augmentations=None)
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(
sampler, 1, drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=cfg.DATALOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
evaluator = get_evaluator2(
cfg, dataset_name,
os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name))
global EVALUATOR
EVALUATOR = evaluator
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
# TODO: Multiprocessing?
if comm.is_main_process():
logger.info("Evaluation results for {} in csv format:".format(
dataset_name))
if 'print_csv_format(results_i)':
for tsk, res in results_i.items():
global RES
RES = res
res_df = pd.DataFrame(pd.Series(res, name='value'))
res_df = res_df[res_df['value'].notna()]
# res_df = res_df[res_df['value'] > 0]
res_df.index = res_df.index.map(
lambda x: '/'.join(x.split('/')[1:]))
pd.set_option('display.max_rows', None)
print(res_df)
pd.reset_option('display.max_rows')
def do_test(cfg, model):
for dataset_name in cfg.DATASETS.TEST:
# data_loader = build_detection_test_loader(cfg, dataset_name)
if 'build_detection_test_loader':
if dataset_name == 'coco_2017_val':
dicts_valid: List[Dict] = DatasetCatalog.get(dataset_name)
if "filter_empty and has_instances":
...
ds_valid = DatasetFromList(dicts_valid, copy=False)
mapper = DatasetMapper(cfg, is_train=False)
else: # Open-Image-Dataset
if 'get_detection_dataset_dicts':
descs_get: List[Dict] = DatasetCatalog.get(dataset_name)
# validation dataset is too large.
random.seed(2020)
descs_valid = random.choices(descs_get, k=N_IMAGES_PER_TEST)
# TODO: clear cache.
ds_valid = DatasetFromList(descs_valid)
if 'DatasetMapper':
mapper = make_mapper(dataset_name, is_train=False, augmentations=None)
ds_valid = MapDataset(ds_valid, mapper)
sampler = InferenceSampler(len(ds_valid))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False)
data_loader = torch.utils.data.DataLoader(
ds_valid,
num_workers=cfg.DATALOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
evaluator = get_evaluator2(
cfg, dataset_name, os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
)
results_i = inference_on_dataset(model, data_loader, evaluator)
if comm.is_main_process():
logger.info("Evaluation results for {} in csv format:".format(dataset_name))
# print_csv_format(results_i)
for tsk, res in results_i.items():
res_df = pd.DataFrame(pd.Series(res, name='value'))
res_df = res_df[res_df['value'].notna()]
res_df.index = res_df.index.map(lambda x: '/'.join(x.split('/')[1:]))
pd.set_option('display.max_rows', None)
print(res_df)
pd.reset_option('display.max_rows')
def test_build_dataloader_inference(self):
N = 50
ds = DatasetFromList(list(range(N)))
sampler = InferenceSampler(len(ds))
# test that parallel loader works correctly
dl = build_detection_test_loader(
dataset=ds, sampler=sampler, mapper=lambda x: x, num_workers=3
)
self._check_is_range(dl, N)
# test that batch_size works correctly
dl = build_detection_test_loader(
dataset=ds, sampler=sampler, mapper=lambda x: x, batch_size=4, num_workers=0
)
self._check_is_range(dl, N)
def build_detection_test_loader(cfg, dataset_name, mapper=None):
"""
Similar to `build_detection_train_loader`.
But this function uses the given `dataset_name` argument (instead of the names in cfg),
and uses batch size 1.
Args:
cfg: a detectron2 CfgNode
dataset_name (str): a name of the dataset that's available in the DatasetCatalog
mapper (callable): a callable which takes a sample (dict) from dataset
and returns the format to be consumed by the model.
By default it will be `DatasetMapper(cfg, False)`.
Returns:
DataLoader: a torch DataLoader, that loads the given detection
dataset, with test-time transformation and batching.
"""
_add_category_whitelists_to_metadata(cfg)
_add_category_maps_to_metadata(cfg)
dataset_dicts = combine_detection_dataset_dicts(
[dataset_name],
keep_instance_predicate=_get_test_keep_instance_predicate(cfg),
proposal_files=[
cfg.DATASETS.PROPOSAL_FILES_TEST[list(
cfg.DATASETS.TEST).index(dataset_name)]
] if cfg.MODEL.LOAD_PROPOSALS else None,
)
dataset = DatasetFromList(dataset_dicts)
if mapper is None:
mapper = DatasetMapper(cfg, False)
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler,
1,
drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=cfg.DATALOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
return data_loader
def build_hand_test_loader(cfg):
"""
"""
dataset_dicts, num_per_epoch = load_hand("test", cfg.HAND_PROJECT.DATA.ANNOT_SUBSET_TRAIN, cfg.HAND_PROJECT.DATA.BASE_PATH, selects=[1])
dataset = DatasetFromList(dataset_dicts)
mapper = DatasetMapper(cfg, False)
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False)
# data_loader = torch.utils.data.DataLoader(dataset, num_workers=cfg.DATALOADER.NUM_WORKERS, batch_sampler=batch_sampler, collate_fn=trivial_batch_collator)
data_loader = torch.utils.data.DataLoader(dataset, num_workers=0, batch_sampler=batch_sampler, collate_fn=trivial_batch_collator)
return data_loader
def build_batch_test_loader(dataset, *, mapper, sampler=None, num_workers=0):
"""
Similar to `build_detection_train_loader`, but uses a batch size of 1,
and :class:`InferenceSampler`. This sampler coordinates all workers to
produce the exact set of all samples.
This interface is experimental.
Args:
dataset (list or torch.utils.data.Dataset): a list of dataset dicts,
or a map-style pytorch dataset. They can be obtained by using
:func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
mapper (callable): a callable which takes a sample (dict) from dataset
and returns the format to be consumed by the model.
When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces
indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
which splits the dataset across all workers.
num_workers (int): number of parallel data loading workers
Returns:
DataLoader: a torch DataLoader, that loads the given detection
dataset, with test-time transformation and batching.
Examples:
::
data_loader = build_detection_test_loader(
DatasetRegistry.get("my_test"),
mapper=DatasetMapper(...))
# or, instantiate with a CfgNode:
data_loader = build_detection_test_loader(cfg, "my_test")
"""
if isinstance(dataset, list):
dataset = DatasetFromList(dataset, copy=False)
if mapper is not None:
dataset = MapDataset(dataset, mapper)
if sampler is None:
sampler = InferenceSampler(len(dataset))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler,
4,
drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=num_workers,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
return data_loader
def test_local_indices(self):
sizes = [0, 16, 2, 42]
world_sizes = [5, 2, 3, 4]
expected_results = [
[range(0) for _ in range(5)],
[range(8), range(8, 16)],
[range(1), range(1, 2), range(0)],
[range(11), range(11, 22),
range(22, 32),
range(32, 42)],
]
for size, world_size, expected_result in zip(sizes, world_sizes,
expected_results):
with self.subTest(f"size={size}, world_size={world_size}"):
local_indices = [
InferenceSampler._get_local_indices(size, world_size, r)
for r in range(world_size)
]
self.assertEqual(local_indices, expected_result)
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(
cfg, args
) # if you don't like any of the default setup, write your own setup code
if 'build dataloader':
file_paths = glob.glob(DS_DIR + 'test/*.jpg')
# import numpy as np
# file_paths = np.random.choice(file_paths, size=200, replace=False)
ds = DatasetFromList(file_paths)
ds = MapDataset(ds, map_func=kaggle_mapper)
sampler = InferenceSampler(len(ds))
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler,
BATCH_SIZE,
drop_last=False)
data_loader = torch.utils.data.DataLoader(
ds,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
if 'create model and load weights':
meta_arch = cfg.MODEL.META_ARCHITECTURE
model = META_ARCH_REGISTRY.get(meta_arch)(cfg)
model.eval()
model.to(torch.device(cfg.MODEL.DEVICE))
