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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
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import numpy as np
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from caffe2.python.crf import CRFWithLoss
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def crf_update_predictions(model, crf_with_loss, classes):
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return apply_crf(
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model.param_init_net,
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model.net,
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crf_with_loss.transitions,
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classes,
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crf_with_loss.num_classes,
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)
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def apply_crf(init_net, net, transitions, predictions, num_classes):
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padded_classes = CRFWithLoss.pad_predictions(
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predictions, init_net, net, num_classes
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)
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bestPath = net.ViterbiPath([padded_classes, transitions])
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new_padded_classes = net.SwapBestPath([padded_classes, bestPath])
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# Revert the effect of pad_predictions by removing the last two rows and
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# the last two columns
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new_classes = net.RemovePadding(
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[new_padded_classes], padding_width=1, end_padding_width=1
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)
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slice_starts = np.array([0, 0]).astype(np.int32)
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slice_ends = np.array([-1, -3]).astype(np.int32)
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slice_starts = net.GivenTensorIntFill([], shape=[2], values=slice_starts)
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slice_ends = net.GivenTensorIntFill([], shape=[2], values=slice_ends)
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new_classes = net.Slice([new_classes, slice_starts, slice_ends])
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return new_classes
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