addConstNode('concat/axis_flatten', [-1], graph_def)
addConstNode('PriorBox/concat/axis', [-2], graph_def)
- for label in ['ClassPredictor', 'BoxEncodingPredictor' if box_predictor is 'convolutional' else 'BoxPredictor']:
+ for label in ['ClassPredictor', 'BoxEncodingPredictor' if box_predictor == 'convolutional' else 'BoxPredictor']:
concatInputs = []
for i in range(num_layers):
# Flatten predictions
flatten = NodeDef()
- if box_predictor is 'convolutional':
+ if box_predictor == 'convolutional':
inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
else:
if i == 0:
priorBox = NodeDef()
priorBox.name = 'PriorBox_%d' % i
priorBox.op = 'PriorBox'
- if box_predictor is 'convolutional':
+ if box_predictor == 'convolutional':
priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
else:
if i == 0: