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 TestNormalization(TestCase):
def op_exec(self, npu_flag, input1, dim):
m = torch.nn.BatchNorm2d(dim)
if npu_flag:
m = m.to("npu")
input_new = m(input1)
if npu_flag:
input_new = input_new.to("cpu")
input_new = input_new.detach().numpy()
input1.requires_grad_(True)
w = torch.ones_like(input1)
if npu_flag:
w = w.to("npu")
tmp = m(input1)
tmp.backward(w)
output = input1.grad
if npu_flag:
output = output.to("cpu")
output = output.detach().numpy()
return output, input_new
def test_batchnorm_shape_format_fp16(self, device='npu'):
format_list = [0]
shape_list = [[256, 672, 7, 7], [1024, 58, 28, 28]]
shape_format = [
[np.float16, i, j] for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output, cpu_input = self.op_exec(0, cpu_input1, item[2][1])
npu_output, npu_input = self.op_exec(1, npu_input1, item[2][1])
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
cpu_input = cpu_input.astype(npu_input.dtype)
self.assertRtolEqual(cpu_input, npu_input)
def test_batchnorm_shape_format_fp32(self, device='npu'):
format_list = [0]
shape_list = [(256, 32, 112, 112)]
shape_format = [
[np.float32, i, j] for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_output, cpu_input = self.op_exec(0, cpu_input1, item[2][1])
npu_output, npu_input = self.op_exec(1, npu_input1, item[2][1])
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
cpu_input = cpu_input.astype(npu_input.dtype)
self.assertRtolEqual(cpu_input, npu_input)
if __name__ == "__main__":
run_tests()