43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
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import torch
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from torch.autograd import Function
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# from extensions.StructuralLosses.StructuralLossesBackend import NNDistance, NNDistanceGrad
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from metrics.StructuralLosses.StructuralLossesBackend import NNDistance, NNDistanceGrad
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# Inherit from Function
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class NNDistanceFunction(Function):
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# Note that both forward and backward are @staticmethods
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@staticmethod
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# bias is an optional argument
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def forward(ctx, seta, setb):
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#print("Match Cost Forward")
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ctx.save_for_backward(seta, setb)
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'''
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input:
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set1 : batch_size * #dataset_points * 3
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set2 : batch_size * #query_points * 3
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returns:
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dist1, idx1, dist2, idx2
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'''
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dist1, idx1, dist2, idx2 = NNDistance(seta, setb)
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ctx.idx1 = idx1
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ctx.idx2 = idx2
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return dist1, dist2
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# This function has only a single output, so it gets only one gradient
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@staticmethod
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def backward(ctx, grad_dist1, grad_dist2):
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#print("Match Cost Backward")
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# This is a pattern that is very convenient - at the top of backward
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# unpack saved_tensors and initialize all gradients w.r.t. inputs to
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# None. Thanks to the fact that additional trailing Nones are
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# ignored, the return statement is simple even when the function has
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# optional inputs.
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seta, setb = ctx.saved_tensors
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idx1 = ctx.idx1
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idx2 = ctx.idx2
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grada, gradb = NNDistanceGrad(seta, setb, idx1, idx2, grad_dist1, grad_dist2)
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return grada, gradb
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nn_distance = NNDistanceFunction.apply
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