def get_batch(self):
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
segdb = [self.segdb[self.index[i]] for i in range(cur_from, cur_to)]
data, label, im_info = get_segmentation_test_batch(segdb, self.config)
self.data = [[mx.nd.array(data[i][name]) for name in self.data_name] for i in xrange(len(data))]
self.im_info = im_info
def get_batch(self):
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
segdb = [self.segdb[self.index[i]] for i in range(cur_from, cur_to)]
data, label, im_info = get_segmentation_test_batch(segdb, self.config)
self.data = [[mx.nd.array(data[i][name]) for name in self.data_name] for i in range(len(data))]
self.im_info = im_info
def get_batch(self, scale=1.0, back=False):
if back:
cur_from = self.cur - self.batch_size
else:
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
segdb = [self.segdb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = [1] * len(self.ctx)
slices = _split_input_slice(self.batch_size, work_load_list)
rst = []
for idx, islice in enumerate(slices):
isegdb = [
segdb[i] for i in range(islice.start, islice.stop)
if i < len(segdb)
]
if not isegdb == []:
rst.append(
get_segmentation_test_batch(isegdb,
self.config,
is_train=False,
has_label=self.has_label,
scale=scale))
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]
if self.has_label:
self.label = [[mx.nd.array(label[key]) for key in self.label_name]
for label in all_label]
self.labels_gt = {
'label':
tensor_vstack([
label[key] for key in self.label_name
for label in all_label
])
}