Method testConcatNHWC

caffe2/python/gradient_check_test.py:113–135

(self)

        ]

    def testConcatNHWC(self):
        for input_size, d1, d2, d3, d4 in self.test_configs:
            op = core.CreateOperator("Concat",
                                     ["X1", "X2", "X3", "X4"],
                                     ["Y", "Y_dims"],
                                     order="NHWC"
                                     )
            Xs = [
                np.random.rand(2, input_size, input_size,
                               d1).astype(np.float32),
                np.random.rand(2, input_size, input_size,
                               d2).astype(np.float32),
                np.random.rand(2, input_size, input_size,
                               d3).astype(np.float32),
                np.random.rand(2, input_size, input_size, d4).astype(np.float32)
            ]
            for i in range(4):
                res = device_checker.CheckSimple(op, Xs, [0])
                self.assertTrue(res)
                for checker in gradient_checkers:
                    res, grad, grad_estimated = checker.CheckSimple(op, Xs, i,
                                                                    [0])
                    self.assertTrue(res)

    def testConcatNCHW(self):
        for input_size, d1, d2, d3, d4 in self.test_configs: