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https://github.com/zebrajr/pytorch.git
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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
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
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import caffe2.contrib.playground.meter as Meter
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from caffe2.python import workspace
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import numpy as np
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class ComputeTopKAccuracy(Meter.Meter):
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# Python default arguments are evaluated once when the function is
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# defined, not each time the function is called
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# This means that if you use a mutable default argument and mutate it,
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# you will and have mutated that object for
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# all future calls to the function as well.
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# def __init__(self, blob_name=['softmax', 'label'], opts=None, topk=1):
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def __init__(self, blob_name=None, opts=None, topk=1):
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if blob_name is None:
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blob_name = ['softmax', 'label']
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self.blob_name = blob_name
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self.opts = opts
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self.topk = topk
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self.iter = 0
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self.value = 0
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def Reset(self):
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self.iter = 0
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self.value = 0
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def Add(self):
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for idx in range(self.opts['distributed']['first_xpu_id'],
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self.opts['distributed']['first_xpu_id'] +
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self.opts['distributed']['num_xpus']):
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prefix = '{}_{}/'.format(self.opts['distributed']['device'], idx)
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softmax = workspace.FetchBlob(prefix + self.blob_name[0])
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labels = workspace.FetchBlob(prefix + self.blob_name[1])
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output = np.squeeze(softmax)
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target = np.squeeze(labels)
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if len(output.shape) == 1:
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output = output.reshape((1, output.shape[0]))
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else:
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assert len(output.shape) == 2, \
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'wrong output size (1D or 2D expected)'
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assert len(target.shape) == 1, 'wrong target size (1D expected)'
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assert output.shape[0] == target.shape[0], \
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'target and output do not match'
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N = output.shape[0]
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pred = np.argsort(-output, axis=1)[:, :self.topk]
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correct = pred.astype(target.dtype) == np.repeat(
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target.reshape((N, 1)), [self.topk], axis=1)
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self.value += np.sum(correct[:, :self.topk])
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self.iter += N
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def Compute(self):
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result = self.value / self.iter
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self.Reset()
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return result
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