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import numpy as np
from caffe2.python.crf import CRFWithLoss
def crf_update_predictions(model, crf_with_loss, classes):
return apply_crf(
model.param_init_net,
model.net,
crf_with_loss.transitions,
classes,
crf_with_loss.num_classes,
)
def apply_crf(init_net, net, transitions, predictions, num_classes):
padded_classes = CRFWithLoss.pad_predictions(
predictions, init_net, net, num_classes
)
bestPath = net.ViterbiPath([padded_classes, transitions])
new_padded_classes = net.SwapBestPath([padded_classes, bestPath])
# Revert the effect of pad_predictions by removing the last two rows and
# the last two columns
new_classes = net.RemovePadding(
[new_padded_classes], padding_width=1, end_padding_width=1
)
slice_starts = np.array([0, 0]).astype(np.int32)
slice_ends = np.array([-1, -3]).astype(np.int32)
slice_starts = net.GivenTensorIntFill([], shape=[2], values=slice_starts)
slice_ends = net.GivenTensorIntFill([], shape=[2], values=slice_ends)
new_classes = net.Slice([new_classes, slice_starts, slice_ends])
return new_classes
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