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github-python-corpus

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# MaskFormer
from mask2former import SemanticSegmentorWithTTA, add_mask_former_config
from mask2former.data import (
COCOFullTaskNewBaselineDatasetMapper,
MaskFormerInstanceDatasetMapper,
MaskFormerPanopticDatasetMapper,
MaskFormerSemanticDatasetMapper,
MaskFormerBinarySemanticDatasetMapper,
MaskFormerBinaryFullDatasetMapper,
ProposalClasificationDatasetMapper,
)
from mask2former.data import (
build_detection_test_loader,
build_detection_train_loader,
dataset_sample_per_class,
dataset_sample_per_task_class,
)
from mask2former.evaluation import (
GeneralizedSemSegEvaluator,
GeneralizedPseudoSemSegEvaluator,
ClassificationEvaluator,
GeneralizedPanopticEvaluator,
InstanceSegEvaluator,
COCOEvaluator,
)
from mask2former.utils.events import WandbWriter, setup_wandb
from mask2former.utils.post_process_utils import dense_crf_post_process
import timm
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to DETR.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
if cfg.PSEUDO:
evaluator = partial(
GeneralizedPseudoSemSegEvaluator,
with_prior=cfg.PSEUDO_WITH_PRIOR,
reject_threshold=cfg.PSEUDO_REJECT_THRESHOLD,
)
else:
evaluator = GeneralizedSemSegEvaluator
evaluator_list.append(
evaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
post_process_func=dense_crf_post_process
if cfg.TEST.DENSE_CRF
else None,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type == "ade20k_panoptic_seg":
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
if evaluator_type in [
"coco_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(GeneralizedPanopticEvaluator(dataset_name, output_folder))
if evaluator_type == "classification":
evaluator_list.append(ClassificationEvaluator(dataset_name))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() >= comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))