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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
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