mirror of
https://github.com/open-mmlab/mmcv.git
synced 2025-06-03 21:54:52 +08:00
de6b0021af
* init npu * add npu extension and focal loss adapter * clean code * clean code * clean code * clean code * fix autocast bugs on npu (#2273) fix autocast bugs on npu (#2273) * code format * code format * code format * bug fix * pytorch_npu_helper.hpp clean code * Npu dev (#2306) * fix autocast bugs on npu * using scatter_kwargs in mmcv.device.scatter_gather * raise ImportError when compile with npu * add npu test case (#2307) * add npu test case * Update focal_loss.py * add comment * clean lint * update dtype assert * update DDP forward and comment * fix bug Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * sigmoidfocalloss npu adapter bug fix * BugFix: modify softmaxFocalLoss adapter * BugFix: remove equal sign in the code * add npu install information in README * add modulatedDeformConv npu adapter * init npu * add npu extension and focal loss adapter * clean code * clean code * clean code * add modulatedDeformConv npu adapter * merge master branch 20221103 * Add masked_ Conv2d operator in NPU * add nms_npu * fix bug * fix code check * fix code check * fix code check * Masked_conv2d NPU * Masked_conv2d NPU * Masked_conv2d NPU * remove npu-install-info in README.md * annotate the clang-format in pre-commit-config-zh-ch.yaml * Clean code: fix the clean code problem in masked_conv2d and modulated_deform_conv Co-authored-by: wangjiangben <wangjiangben_hw@126.com> Co-authored-by: wangjiangben-hw <111729245+wangjiangben-hw@users.noreply.github.com> Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> Co-authored-by: zcc-zjut <zcczxy2019@163.com> Co-authored-by: wangxiaoxin_sherie <wangxiaoxin7@huawei.com> Co-authored-by: momo609 <963372609@qq.com>
46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
|
|
|
|
|
|
class TestMaskedConv2d:
|
|
|
|
@pytest.mark.parametrize('device', [
|
|
pytest.param(
|
|
'cuda',
|
|
marks=pytest.mark.skipif(
|
|
not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
|
|
pytest.param(
|
|
'mlu',
|
|
marks=pytest.mark.skipif(
|
|
not IS_MLU_AVAILABLE, reason='requires MLU support')),
|
|
pytest.param(
|
|
'npu',
|
|
marks=pytest.mark.skipif(
|
|
not IS_NPU_AVAILABLE, reason='requires NPU support'))
|
|
])
|
|
def test_masked_conv2d_all_close(self, device):
|
|
from mmcv.ops import MaskedConv2d
|
|
np_input = np.load(
|
|
'tests/data/for_masked_conv2d/masked_conv2d_for_input.npy')
|
|
np_mask = np.load(
|
|
'tests/data/for_masked_conv2d/masked_conv2d_for_mask.npy')
|
|
np_weight = np.load(
|
|
'tests/data/for_masked_conv2d/masked_conv2d_for_weight.npy')
|
|
np_bias = np.load(
|
|
'tests/data/for_masked_conv2d/masked_conv2d_for_bias.npy')
|
|
np_output = np.load(
|
|
'tests/data/for_masked_conv2d/masked_conv2d_for_output.npy')
|
|
input = torch.tensor(np_input, dtype=torch.float, device=device)
|
|
mask = torch.tensor(np_mask, dtype=torch.float, device=device)
|
|
weight = torch.tensor(np_weight, dtype=torch.float, device=device)
|
|
bias = torch.tensor(np_bias, dtype=torch.float, device=device)
|
|
conv = MaskedConv2d(3, 3, 3, 1, 1).to(device)
|
|
conv.weight = torch.nn.Parameter(weight)
|
|
conv.bias = torch.nn.Parameter(bias)
|
|
output = conv(input, mask)
|
|
assert np.allclose(output.data.cpu().numpy(), np_output, 1e-3)
|