def _get_next_minibatch(self):
try:
dataBlob, labelBlob,weightBlob = self.iterator.next()
except StopIteration:
filenames = data.get_sentence(self.config.get('datafile'))
labels = data.get_labels(self.config.get('labelfile'))
weights = data.get_sample_weights(self.config.get('labelfile'))
self.iterator = iter(self.sampleIter(filenames,labels,weights))
dataBlob, labelBlob,weightBlob = self.iterator.next()
return {'data': dataBlob, 'labels': labelBlob, 'weights': weightBlob }
def setup(self, bottom, top):
"""Setup the ResamplerDataLayer."""
# parse the layer parameter string
layer_config = self.param_str
self.config = util.load_module(layer_config).config
filenames = data.get_sentence(self.config.get('datafile'))
labels = data.get_labels(self.config.get('labelfile'))
weights = data.get_sample_weights(self.config.get('labelfile'))
self.sampleIter = iterator.WeightedIterator(self.config, batch_size=self.config.get('batch_size'))
self.iterator = iter(self.sampleIter(filenames,labels,weights))
self._name_to_top_map = {
'data': 0,
'labels': 1, 'weights':2 }
top[0].reshape(self.config.get('batch_size'), 3, self.config.get('h'), self.config.get('w'))
top[1].reshape(self.config.get('batch_size'))
top[2].reshape(self.config.get('batch_size'))
