本文整理匯總了Python中mxnet.executor_manager._split_input_slice方法的典型用法代碼示例。如果您正苦於以下問題：Python executor_manager._split_input_slice方法的具體用法？Python executor_manager._split_input_slice怎麽用？Python executor_manager._split_input_slice使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mxnet.executor_manager的用法示例。
在下文中一共展示了executor_manager._split_input_slice方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: decide_slices
# 需要導入模塊: from mxnet import executor_manager [as 別名]
# 或者: from mxnet.executor_manager import _split_input_slice [as 別名]
def decide_slices(self, data_shapes):
"""Decide the slices for each context according to the workload.
Parameters
----------
data_shapes : list
list of (name, shape) specifying the shapes for the input data or label.
"""
assert len(data_shapes) > 0
major_axis = [DataDesc.get_batch_axis(x.layout) for x in data_shapes]
for (name, shape), axis in zip(data_shapes, major_axis):
if axis == -1:
continue
batch_size = shape[axis]
if self.batch_size is not None:
assert batch_size == self.batch_size, ("all data must have the same batch size: "
+ ("batch_size = %d, but " % self.batch_size)
+ ("%s has shape %s" % (name, shape)))
else:
self.batch_size = batch_size
self.slices = _split_input_slice(self.batch_size, self.workload)
return major_axis 示例2: get_batch_individual
# 需要導入模塊: from mxnet import executor_manager [as 別名]
# 或者: from mxnet.executor_manager import _split_input_slice [as 別名]
def get_batch_individual(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
rst = []
for idx, islice in enumerate(slices):
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
rst.append(self.parfetch(iroidb))
all_data = [_['data'] for _ in rst]
all_label = [_['label'] for _ in rst]
self.data = [[mx.nd.array(data[key]) for key in self.data_name] for data in all_data]
self.label = [[mx.nd.array(label[key]) for key in self.label_name] for label in all_label] 示例3: get_batch_parallel
# 需要導入模塊: from mxnet import executor_manager [as 別名]
# 或者: from mxnet.executor_manager import _split_input_slice [as 別名]
def get_batch_parallel(self):
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
rst = []
for idx, islice in enumerate(slices):
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
rst.append(par_assign_anchor_wrapper(self.cfg, iroidb, self.feat_sym, self.feat_strides, self.anchor_scales,
self.anchor_ratios, self.allowed_border))
all_data = [_['data'] for _ in rst]
all_label = [_['label'] for _ in rst]
self.data = [[mx.nd.array(data[key]) for key in self.data_name] for data in all_data]
self.label = [[mx.nd.array(label[key]) for key in self.label_name] for label in all_label] 示例4: get_batch_individual
# 需要導入模塊: from mxnet import executor_manager [as 別名]
# 或者: from mxnet.executor_manager import _split_input_slice [as 別名]
def get_batch_individual(self):
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
rst = []
for idx, islice in enumerate(slices):
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
rst.append(self.parfetch(iroidb))
all_data = [_['data'] for _ in rst]
all_label = [_['label'] for _ in rst]
self.data = [[mx.nd.array(data[key]) for key in self.data_name] for data in all_data]
self.label = [[mx.nd.array(label[key]) for key in self.label_name] for label in all_label]