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from ..builder import CLASSIFIERS |
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from ..heads import MultiLabelClsHead |
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from .image import ImageClassifier |
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@CLASSIFIERS.register_module() |
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class MetadataClassifier(ImageClassifier): |
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def forward_train(self, img, gt_label, img_metas, **kwargs): |
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"""Forward computation during training. |
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Args: |
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img (Tensor): of shape (N, C, H, W) encoding input images. |
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Typically these should be mean centered and std scaled. |
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gt_label (Tensor): It should be of shape (N, 1) encoding the |
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ground-truth label of input images for single label task. It |
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shoulf be of shape (N, C) encoding the ground-truth label |
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of input images for multi-labels task. |
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Returns: |
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dict[str, Tensor]: a dictionary of loss components |
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""" |
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if self.augments is not None: |
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img, gt_label = self.augments(img, gt_label) |
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x = self.extract_feat(img) |
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losses = dict() |
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loss = self.head.forward_train(x, gt_label, img_metas) |
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losses.update(loss) |
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return losses |
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def simple_test(self, img, img_metas=None, **kwargs): |
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"""Test without augmentation.""" |
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x = self.extract_feat(img) |
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if isinstance(self.head, MultiLabelClsHead): |
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assert 'softmax' not in kwargs, ( |
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'Please use `sigmoid` instead of `softmax` ' |
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'in multi-label tasks.') |
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res = self.head.simple_test(x, img_metas, **kwargs) |
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return res |
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