def testFullyConvolutionalEndpointShapes(self):
num_classes = 10
inputs = create_test_input(2, 321, 321, 3)
with slim.arg_scope(nas_network.nas_arg_scope()):
_, end_points = self._pnasnet_small(inputs,
num_classes)
endpoint_to_shape = {
'Stem': [2, 81, 81, 128],
'Cell_0': [2, 41, 41, 100],
'Cell_1': [2, 21, 21, 200],
'Cell_2': [2, 21, 21, 200]}
for endpoint, shape in endpoint_to_shape.iteritems():
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
def testFullyConvolutionalEndpointShapes(self):
num_classes = 10
inputs = create_test_input(2, 321, 321, 3)
with slim.arg_scope(nas_network.nas_arg_scope()):
_, end_points = self._pnasnet_small(inputs, num_classes)
endpoint_to_shape = {
'Stem': [2, 81, 81, 128],
'Cell_0': [2, 41, 41, 100],
'Cell_1': [2, 21, 21, 200],
'Cell_2': [2, 21, 21, 200]
}
for endpoint, shape in endpoint_to_shape.iteritems():
self.assertListEqual(
end_points[endpoint].get_shape().as_list(), shape)
def testFullyConvolutionalEndpointShapes(self):
num_classes = 10
backbone = [0, 0, 0, 1, 2, 1, 2, 2, 3, 3, 2, 1]
inputs = create_test_input(None, 321, 321, 3)
with slim.arg_scope(nas_network.nas_arg_scope()):
_, end_points = self._pnasnet(inputs, backbone, num_classes)
endpoint_to_shape = {
'Stem': [None, 81, 81, 128],
'Cell_0': [None, 81, 81, 50],
'Cell_1': [None, 81, 81, 50],
'Cell_2': [None, 81, 81, 50],
'Cell_3': [None, 41, 41, 100],
'Cell_4': [None, 21, 21, 200],
'Cell_5': [None, 41, 41, 100],
'Cell_6': [None, 21, 21, 200],
'Cell_7': [None, 21, 21, 200],
'Cell_8': [None, 11, 11, 400],
'Cell_9': [None, 11, 11, 400],
'Cell_10': [None, 21, 21, 200],
'Cell_11': [None, 41, 41, 100]
}
for endpoint, shape in endpoint_to_shape.items():
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)