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