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)