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from ..builder import DETECTORS |
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from .two_stage import TwoStageDetector |
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@DETECTORS.register_module() |
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class FastRCNN(TwoStageDetector): |
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"""Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_""" |
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def __init__(self, |
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backbone, |
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roi_head, |
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train_cfg, |
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test_cfg, |
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neck=None, |
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pretrained=None): |
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super(FastRCNN, self).__init__( |
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backbone=backbone, |
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neck=neck, |
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roi_head=roi_head, |
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train_cfg=train_cfg, |
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test_cfg=test_cfg, |
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pretrained=pretrained) |
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def forward_test(self, imgs, img_metas, proposals, **kwargs): |
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""" |
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Args: |
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imgs (List[Tensor]): the outer list indicates test-time |
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augmentations and inner Tensor should have a shape NxCxHxW, |
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which contains all images in the batch. |
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img_metas (List[List[dict]]): the outer list indicates test-time |
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augs (multiscale, flip, etc.) and the inner list indicates |
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images in a batch. |
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proposals (List[List[Tensor]]): the outer list indicates test-time |
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augs (multiscale, flip, etc.) and the inner list indicates |
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images in a batch. The Tensor should have a shape Px4, where |
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P is the number of proposals. |
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""" |
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for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: |
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if not isinstance(var, list): |
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raise TypeError(f'{name} must be a list, but got {type(var)}') |
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num_augs = len(imgs) |
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if num_augs != len(img_metas): |
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raise ValueError(f'num of augmentations ({len(imgs)}) ' |
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f'!= num of image meta ({len(img_metas)})') |
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if num_augs == 1: |
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return self.simple_test(imgs[0], img_metas[0], proposals[0], |
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**kwargs) |
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else: |
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assert NotImplementedError |
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