import torch
import numpy as np
import torch_npu
from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor
class TestCross(TestCase):
def cpu_op_exec(self, input1, input2):
output = torch.cross(input1, input2)
output = output.numpy()
return output
def cpu_op_exec_dim(self, input1, input2, dim):
output = torch.cross(input1, input2, dim)
output = output.numpy()
return output
def npu_op_exec(self, input1, input2):
output = torch.cross(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_exec_dim(self, input1, input2, dim):
output = torch.cross(input1, input2, dim)
output = output.to("cpu")
output = output.numpy()
return output
def test_median_shape_format_dim(self):
shape_format_dim = [
[[np.float32, -1, (4, 3)], 1],
[[np.float32, -1, (4, 3, 3)], 1],
[[np.float32, -1, (3, 2)], 0],
]
for item in shape_format_dim:
cpu_input_left, npu_input_left = create_common_tensor(item[0], -2, 2)
cpu_input_right, npu_input_right = create_common_tensor(item[0], -2, 2)
cpu_output = self.cpu_op_exec_dim(cpu_input_left, cpu_input_right, item[1])
npu_output = self.npu_op_exec_dim(npu_input_left, npu_input_right, item[1])
self.assertRtolEqual(cpu_output, npu_output)
def test_median_shape_format(self):
shape_format = [
[[np.float32, -1, (5, 3)]],
[[np.float32, -1, (5, 3, 4)]],
]
for item in shape_format:
cpu_input_left, npu_input_left = create_common_tensor(item[0], -2, 2)
cpu_input_right, npu_input_right = create_common_tensor(item[0], -2, 2)
cpu_output = self.cpu_op_exec(cpu_input_left, cpu_input_right)
npu_output = self.npu_op_exec(npu_input_left, npu_input_right)
self.assertRtolEqual(cpu_output, npu_output)
if __name__ == "__main__":
run_tests()