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-rw-r--r--tests/nnfw_api/src/one_op_tests/Slice.test.cc187
1 files changed, 187 insertions, 0 deletions
diff --git a/tests/nnfw_api/src/one_op_tests/Slice.test.cc b/tests/nnfw_api/src/one_op_tests/Slice.test.cc
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index 000000000..8cd9d7037
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+++ b/tests/nnfw_api/src/one_op_tests/Slice.test.cc
@@ -0,0 +1,187 @@
+/*
+ * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "GenModelTest.h"
+
+struct SliceVariationParam
+{
+ std::vector<int32_t> input_shape;
+ std::vector<int32_t> begins;
+ std::vector<int32_t> sizes;
+ TestCaseData tcd;
+
+ circle::TensorType input_type = circle::TensorType::TensorType_FLOAT32;
+ float scale = 0.0f;
+ int64_t zero_point = 0;
+ circle::TensorType begins_type = circle::TensorType::TensorType_INT32;
+};
+
+class SliceVariation : public GenModelTest,
+ public ::testing::WithParamInterface<SliceVariationParam>
+{
+};
+
+INSTANTIATE_TEST_SUITE_P(
+ GenModelTest, SliceVariation,
+ ::testing::Values(
+ SliceVariationParam{
+ {2, 2, 3, 1},
+ {0, 1, 1, 0},
+ {1, 1, 2, 1},
+ uniformTCD<float>({{1, 2, 3, 11, 12, 13, 21, 22, 23, 31, 32, 33}}, {{12, 13}})},
+ SliceVariationParam{
+ {2, 2, 3, 1},
+ {0, 1, 1, 0},
+ {1, 1, 2, 1},
+ uniformTCD<uint8_t>({{1, 2, 3, 11, 12, 13, 21, 22, 23, 31, 32, 33}}, {{12, 13}}),
+ circle::TensorType::TensorType_UINT8,
+ 1,
+ 0},
+ SliceVariationParam{
+ {2, 2, 3, 1},
+ {0, 1, 1, 0},
+ {1, 1, 2, 1},
+ uniformTCD<float>({{1, 2, 3, 11, 12, 13, 21, 22, 23, 31, 32, 33}}, {{12, 13}}),
+ circle::TensorType::TensorType_FLOAT32,
+ 0,
+ 0,
+ circle::TensorType::TensorType_INT64}));
+
+TEST_P(SliceVariation, Test)
+{
+ auto &param = GetParam();
+
+ CircleGen cgen;
+
+ int in = cgen.addTensor({param.input_shape, param.input_type}, param.scale, param.zero_point);
+ int out = cgen.addTensor({param.sizes, param.input_type}, param.scale, param.zero_point);
+ if (param.begins_type == circle::TensorType::TensorType_INT32)
+ {
+ uint32_t begins_buf = cgen.addBuffer(param.begins);
+ int rank = param.begins.size();
+ int begins = cgen.addTensor({{rank}, param.begins_type, begins_buf});
+
+ uint32_t sizes_buf = cgen.addBuffer(param.sizes);
+ int sizes = cgen.addTensor({{rank}, param.begins_type, sizes_buf});
+
+ cgen.addOperatorSlice({{in, begins, sizes}, {out}});
+ }
+ else if (param.begins_type == circle::TensorType::TensorType_INT64)
+ {
+ std::vector<int64_t> begins_64(param.begins.size());
+ std::vector<int64_t> sizes_64(param.sizes.size());
+ for (int i = 0; i < param.begins.size(); i++)
+ {
+ begins_64[i] = param.begins[i];
+ sizes_64[i] = param.sizes[i];
+ }
+
+ uint32_t begins_buf = cgen.addBuffer(begins_64);
+ int rank = param.begins.size();
+ int begins = cgen.addTensor({{rank}, param.begins_type, begins_buf});
+
+ uint32_t sizes_buf = cgen.addBuffer(sizes_64);
+ int sizes = cgen.addTensor({{rank}, param.begins_type, sizes_buf});
+
+ cgen.addOperatorSlice({{in, begins, sizes}, {out}});
+ }
+ cgen.setInputsAndOutputs({in}, {out});
+
+ _context = std::make_unique<GenModelTestContext>(cgen.finish());
+ _context->addTestCase(param.tcd);
+
+ // acl don't support int64 yet
+ if (param.begins_type == circle::TensorType::TensorType_INT64)
+ {
+ _context->setBackends({"cpu"});
+ }
+ else
+ {
+ _context->setBackends({"cpu", "acl_cl", "acl_neon"});
+ }
+
+ SUCCEED();
+}
+
+TEST_F(GenModelTest, neg_OneOp_Slice_Type)
+{
+ CircleGen cgen;
+ int in = cgen.addTensor({{1, 3, 3, 2}, circle::TensorType::TensorType_FLOAT32});
+ std::vector<float> begins_data = {0, 0, 1, 0};
+ uint32_t begins_buf = cgen.addBuffer(begins_data);
+ int begins = cgen.addTensor({{4}, circle::TensorType::TensorType_FLOAT32, begins_buf});
+ std::vector<float> sizes_data = {1, 2, 1, 1};
+ uint32_t sizes_buf = cgen.addBuffer(sizes_data);
+ int sizes = cgen.addTensor({{4}, circle::TensorType::TensorType_FLOAT32, sizes_buf});
+ int out = cgen.addTensor({{1, 2, 1, 1}, circle::TensorType::TensorType_FLOAT32});
+ cgen.addOperatorSlice({{in, begins, sizes}, {out}});
+ cgen.setInputsAndOutputs({in}, {out});
+
+ _context = std::make_unique<GenModelTestContext>(cgen.finish());
+ _context->expectFailModelLoad();
+
+ SUCCEED();
+}
+
+TEST_P(SliceVariation, neg_DiffType)
+{
+ auto &param = GetParam();
+
+ CircleGen cgen;
+
+ int in = cgen.addTensor({param.input_shape, param.input_type}, param.scale, param.zero_point);
+ int out = cgen.addTensor({param.sizes, param.input_type}, param.scale, param.zero_point);
+ if (param.begins_type == circle::TensorType::TensorType_INT32)
+ {
+ uint32_t begins_buf = cgen.addBuffer(param.begins);
+ std::vector<int64_t> sizes_64(param.sizes.size());
+ for (int i = 0; i < param.begins.size(); i++)
+ {
+ sizes_64[i] = param.sizes[i];
+ }
+
+ int rank = param.begins.size();
+ int begins = cgen.addTensor({{rank}, param.begins_type, begins_buf});
+
+ uint32_t sizes_buf = cgen.addBuffer(sizes_64);
+ int sizes = cgen.addTensor({{rank}, circle::TensorType::TensorType_INT64, sizes_buf});
+
+ cgen.addOperatorSlice({{in, begins, sizes}, {out}});
+ }
+ else if (param.begins_type == circle::TensorType::TensorType_INT64)
+ {
+ std::vector<int64_t> begins_64(param.begins.size());
+ for (int i = 0; i < param.begins.size(); i++)
+ {
+ begins_64[i] = param.begins[i];
+ }
+
+ uint32_t begins_buf = cgen.addBuffer(begins_64);
+ int rank = param.begins.size();
+ int begins = cgen.addTensor({{rank}, param.begins_type, begins_buf});
+
+ uint32_t sizes_buf = cgen.addBuffer(param.sizes);
+ int sizes = cgen.addTensor({{rank}, circle::TensorType::TensorType_INT32, sizes_buf});
+
+ cgen.addOperatorSlice({{in, begins, sizes}, {out}});
+ }
+ cgen.setInputsAndOutputs({in}, {out});
+
+ _context = std::make_unique<GenModelTestContext>(cgen.finish());
+ _context->expectFailModelLoad();
+
+ SUCCEED();
+}
generated by cgit v1.2.3 (git 2.25.1) at 2024-08-16 23:56:31 +0000