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[Unittest] Add NCNN tensorslice unittest and fix tensorslice.cpp bugs. (#115)

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* add tensorslice unittest

* reply code review

* fix lint

* fix typo
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hanrui1sensetime 2021-10-09 11:42:32 +08:00 committed by GitHub
parent f56a30025a
commit cba43e4c22
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2 changed files with 48 additions and 2 deletions
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15
backend_ops/ncnn/ops/tensorslice/tensorslice.cpp
View File

@ -53,12 +53,17 @@ int TensorSlice::forward(const Mat& bottom_blob, Mat& top_blob,
size_t elemsize = bottom_blob.elemsize;
const int* start_ptr = starts;
const int* end_ptr = ends;
const float* axes_ptr = axes;
const int* axes_ptr = axes;
const int* step_ptr = steps;
if (starts.w > dims || ends.w > dims) {
fprintf(stderr, "start/end attributes shape error!\n");
return -100;
}
if (axes.w != 1) {
fprintf(stderr,
"axes.w must be 1 because any of multiaxes slice is regarded as "
"multi-staged onnx slice in pytorch2onnx.");
}
if (dims == 1) {
for (int i = 0; i < axes.w; i++) {
int positive_axis = axes_ptr[i] < 0 ? dims + axes_ptr[i] : axes_ptr[i];
@ -106,6 +111,8 @@ int TensorSlice::forward(const Mat& bottom_blob, Mat& top_blob,
int start = start_ptr[i];
int end = end_ptr[i];
int dim_shape = get_shape_by_axes(bottom_blob, positive_axis, dims);
int dim_shape_test =
get_shape_by_axes(bottom_blob, positive_axis, dims - 1);
if (dim_shape < 0) {
return -1;
}
@ -127,6 +134,7 @@ int TensorSlice::forward(const Mat& bottom_blob, Mat& top_blob,
return -100;
}
active_indice[positive_axis - 1] = temp_indice;
active_indice[positive_axis - 1].resize(temp_indice.size());
}
top_blob.create((int)active_indice[1].size(), (int)active_indice[0].size(),
elemsize, opt.blob_allocator);
@ -138,6 +146,7 @@ int TensorSlice::forward(const Mat& bottom_blob, Mat& top_blob,
}
return 0;
}
if (dims == 3) {
std::vector<std::vector<int> > active_indice;
std::vector<int> indices;
@ -177,7 +186,8 @@ int TensorSlice::forward(const Mat& bottom_blob, Mat& top_blob,
fprintf(stderr, "step should not be 0!\n");
return -100;
}
active_indice[positive_axis] = temp_indice;
active_indice[positive_axis - 1] = temp_indice;
active_indice[positive_axis - 1].resize(temp_indice.size());
}
top_blob.create((int)active_indice[2].size(), (int)active_indice[1].size(),
(int)active_indice[0].size(), elemsize, opt.blob_allocator);
@ -192,6 +202,7 @@ int TensorSlice::forward(const Mat& bottom_blob, Mat& top_blob,
}
return 0;
}
return 0;
}

35
tests/test_ops/test_ops.py
View File

@ -545,3 +545,38 @@ def test_constantofshape(backend,
ncnn_outputs = ncnn_model(dict(zip(input_names, [input.float()])))
ncnn_outputs = [ncnn_outputs[name] for name in output_names]
assert_allclose(model_outputs, ncnn_outputs, tolerate_small_mismatch)
@pytest.mark.parametrize('backend', [TEST_NCNN])
@pytest.mark.parametrize('dim', [1, 2, 3])
def test_tensorslice(backend, dim, input_list=None, save_dir=None):
backend.check_env()
if input_list is None:
input = torch.rand((8, 12, 17)[-dim:]).unsqueeze(0)
else:
input = input_list[0]
assert input.dim() == dim + 1, f'input.dim() must equal to \
dim + 1, expected: {dim + 1}, got: {input.dim()}'
assert input.shape[0] == 1, (f'ncnn batch must be 1, \
but got {input.shape[0]}')
cfg = dict()
register_extra_symbolics(cfg=cfg, backend=backend.backend_name, opset=11)
def tensorslice_function(inputs):
if dim == 1:
return inputs[:, 2:17:7]
if dim == 2:
return inputs[:, 3:12:4, 2:15:3]
if dim == 3:
return inputs[:, 0:8:2, 2:12:4, 2:17:7]
wrapped_model = WrapFunction(tensorslice_function)
backend.run_and_validate(
wrapped_model, [input.float()],
'tensorslice',
input_names=['inputs'],
output_names=['outputs'],
save_dir=save_dir)