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8aa674c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import torch
from ..utils.post_processing import load_predictions, save_predictions
class BaseDetector(torch.nn.Module):
"""Base class for detectors."""
def __init__(self):
super(BaseDetector, self).__init__()
def forward(
self,
inputs,
masks,
metas,
gt_segments=None,
gt_labels=None,
return_loss=True,
infer_cfg=None,
post_cfg=None,
**kwargs
):
if return_loss:
return self.forward_train(inputs, masks, metas, gt_segments=gt_segments, gt_labels=gt_labels, **kwargs)
else:
return self.forward_detection(inputs, masks, metas, infer_cfg, post_cfg, **kwargs)
def forward_detection(self, inputs, masks, metas, infer_cfg, post_cfg, **kwargs):
# step1: inference the model
if infer_cfg.load_from_raw_predictions: # easier and faster to tune the hyper parameter in postprocessing
predictions = load_predictions(metas, infer_cfg)
else:
predictions = self.forward_test(inputs, masks, metas, infer_cfg)
if infer_cfg.save_raw_prediction: # save the predictions to disk
save_predictions(predictions, metas, infer_cfg.folder)
# step2: detection post processing
results = self.post_processing(predictions, metas, post_cfg, **kwargs)
return results
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