def update(self, labels, preds, masks=None):
# pylint: disable=arguments-differ
"""Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data with class indices as values, one per sample.
preds : list of `NDArray`
Prediction values for samples. Each prediction value can either be the class index,
or a vector of likelihoods for all classes.
masks : list of `NDArray` or None, optional
Masks for samples, with the same shape as `labels`. value of its element must
be either 1 or 0. If None, all samples are considered valid.
"""
labels, preds = check_label_shapes(labels, preds, True)
masks = [None] * len(labels) if masks is None else masks
for label, pred_label, mask in zip(labels, preds, masks):
if pred_label.shape != label.shape:
# TODO(haibin) topk does not support fp16. Issue tracked at:
# https://github.com/apache/incubator-mxnet/issues/14125
# topk is used because argmax is slow:
# https://github.com/apache/incubator-mxnet/issues/11061
pred_label = ndarray.topk(pred_label.astype('float32',
copy=False),
k=1,
ret_typ='indices',
axis=self.axis)
# flatten before checking shapes to avoid shape miss match
pred_label = pred_label.astype('int32', copy=False).reshape((-1, ))
label = label.astype('int32', copy=False).reshape((-1, ))
check_label_shapes(label, pred_label)
if mask is not None:
mask = mask.astype('int32', copy=False).reshape((-1, ))
check_label_shapes(label, mask)
num_correct = ((pred_label == label) * mask).sum().asscalar()
num_inst = mask.sum().asscalar()
else:
num_correct = (pred_label == label).sum().asscalar()
num_inst = len(label)
self.sum_metric += num_correct
self.global_sum_metric += num_correct
self.num_inst += num_inst
self.global_num_inst += num_inst
def topk(input, k, dim, descending=True):
return nd.topk(input,
axis=dim,
k=k,
ret_typ='value',
is_ascend=not descending)
def update_masks(self, index, weight):
"""Updates the masks for sparse training.
Parameters
----------
index : int
The index for weight.
weight : NDArray
The weight matrix.
Returns
-------
boolean
If the masks were changed
"""
# determine number of updates without actually updating the count
if index not in self._index_update_count:
num_update = self.begin_num_update
else:
num_update = self._index_update_count[index]
num_update += 1
num_update = max(num_update, self.num_update)
# calculate epoch
epoch = int((num_update - 1) / self.batches_per_epoch) + 1
# determine if masks need to be updated, and get corresponding parameters
if index == 0:
self.masks_updated = True
if self.epoch != epoch:
self.epoch = epoch
if epoch == 1:
self.masks_updated = False
if self.weight_sparsity is not None:
logging.info(
log + 'bias-sparsity={}, weight-sparsity={}'.format(
self.bias_sparsity[0], self.weight_sparsity[0]))
else:
logging.info(
log + 'bias-threshold={}, weight-threshold={}'.format(
self.bias_threshold[0], self.weight_threshold[0]))
if self.pruning_switch_epoch[0] + 1 == epoch:
self.masks_updated = False
self.pruning_switch_epoch.pop(0)
if self.weight_sparsity is not None:
self.weight_sparsity.pop(0)
self.bias_sparsity.pop(0)
logging.info(
log + 'bias-sparsity={}, weight-sparsity={}'.format(
self.bias_sparsity[0], self.weight_sparsity[0]))
else:
self.weight_threshold.pop(0)
self.bias_threshold.pop(0)
logging.info(
log + 'bias-threshold={}, weight-threshold={}'.format(
self.bias_threshold[0], self.weight_threshold[0]))
# update masks if needed
if not self.masks_updated:
# initialize masks
if epoch == 1:
self.masks.append(None)
# if percentages are given
if self.weight_sparsity is not None:
if len(weight.shape) == 1:
sparsity = self.bias_sparsity[0]
else:
sparsity = self.weight_sparsity[0]
number_unpruned = int((100.0 - sparsity) * weight.size / 100.0)
self.masks[index] = topk(NDabs(weight),
axis=None,
ret_typ='mask',
k=number_unpruned)
# if thresholds are given
else:
if len(weight.shape) == 1:
threshold = self.bias_threshold[0]
else:
threshold = self.weight_threshold[0]
self.masks[index] = NDabs(weight) >= threshold
return not self.masks_updated
def update_masks(self, index, weight):
"""Updates the masks for sparse training.
Parameters
----------
index : int
The index for weight.
weight : NDArray
The weight matrix.
Returns
-------
boolean
If the masks were changed
"""
# determine number of updates without actually updating the count
if index not in self._index_update_count:
num_update = self.begin_num_update
else:
num_update = self._index_update_count[index]
num_update += 1
num_update = max(num_update, self.num_update)
# calculate epoch
epoch = int((num_update - 1) / self.batches_per_epoch) + 1
# determine if masks need to be updated, and get corresponding parameters
if index == 0:
self.masks_updated = True
if self.epoch != epoch:
self.epoch = epoch
if epoch == 1:
self.masks_updated = False
if self.weight_sparsity is not None:
logging.info(log + 'bias-sparsity={}, weight-sparsity={}'.format(self.bias_sparsity[0], self.weight_sparsity[0]))
else:
logging.info(log + 'bias-threshold={}, weight-threshold={}'.format(self.bias_threshold[0], self.weight_threshold[0]))
if self.pruning_switch_epoch[0] + 1 == epoch:
self.masks_updated = False
self.pruning_switch_epoch.pop(0)
if self.weight_sparsity is not None:
self.weight_sparsity.pop(0)
self.bias_sparsity.pop(0)
logging.info(log + 'bias-sparsity={}, weight-sparsity={}'.format(self.bias_sparsity[0], self.weight_sparsity[0]))
else:
self.weight_threshold.pop(0)
self.bias_threshold.pop(0)
logging.info(log + 'bias-threshold={}, weight-threshold={}'.format(self.bias_threshold[0], self.weight_threshold[0]))
# update masks if needed
if not self.masks_updated:
# initialize masks
if epoch == 1:
self.masks.append(None)
# if percentages are given
if self.weight_sparsity is not None:
if len(weight.shape) == 1:
sparsity = self.bias_sparsity[0]
else:
sparsity = self.weight_sparsity[0]
number_unpruned = int((100.0 - sparsity) * weight.size / 100.0)
self.masks[index] = topk(NDabs(weight), axis=None, ret_typ='mask',
k=number_unpruned)
# if thresholds are given
else:
if len(weight.shape) == 1:
threshold = self.bias_threshold[0]
else:
threshold = self.weight_threshold[0]
self.masks[index] = NDabs(weight) >= threshold
return not self.masks_updated
