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Merge "Move strided_slice_invalid_output_dims to spec directory."

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Xusong Wang 2019-07-22 19:47:38 +00:00 committed by Android (Google) Code Review
commit 0d0af2cbe3
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15
neuralnetworks/1.2/vts/functional/GeneratedTestsV1_2.cpp
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@ -44,21 +44,6 @@ std::vector<Request> createRequests(const std::vector<MixedTypedExample>& exampl
// in frameworks/ml/nn/runtime/tests/generated/ // in frameworks/ml/nn/runtime/tests/generated/
#include "vts/V1_2/all_generated_V1_2_vts_tests.cpp" #include "vts/V1_2/all_generated_V1_2_vts_tests.cpp"
// Generated from spec/strided_slice_invalid_output_dims.mod.py.
// TODO(b/132155416): Make this part of all_generated_V1_2_vts_tests.cpp.
namespace strided_slice_invalid_output_dims {
#include "generated/strided_slice_invalid_output_dims.example.cpp"
#include "generated/strided_slice_invalid_output_dims.model.cpp"
} // namespace strided_slice_invalid_output_dims
// TODO(b/132155416): Make this part of all_generated_V1_2_vts_tests.cpp.
TEST_F(ValidationTest, strided_slice_invalid_output_dims) {
const Model model = strided_slice_invalid_output_dims::createTestModel();
const std::vector<Request> requests =
createRequests(strided_slice_invalid_output_dims::get_examples());
validateFailure(model, requests);
}
} // namespace functional } // namespace functional
} // namespace vts } // namespace vts
} // namespace V1_2 } // namespace V1_2

116
neuralnetworks/1.2/vts/functional/generated/strided_slice_invalid_output_dims.example.cpp
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@ -1,116 +0,0 @@
// clang-format off
// Generated file (from: strided_slice_invalid_output_dims.mod.py). Do not edit
std::vector<MixedTypedExample>& get_examples() {
static std::vector<MixedTypedExample> examples = {
// Begin of an example
{
.operands = {
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> Dimensions map
.operandDimensions = {{0, {2, 3}}},
// int -> FLOAT32 map
.float32Operands = {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}}},
// int -> INT32 map
.int32Operands = {},
// int -> QUANT8_ASYMM map
.quant8AsymmOperands = {},
// int -> QUANT16_SYMM map
.quant16SymmOperands = {},
// int -> FLOAT16 map
.float16Operands = {},
// int -> BOOL8 map
.bool8Operands = {},
// int -> QUANT8_SYMM_PER_CHANNEL map
.quant8ChannelOperands = {},
// int -> QUANT16_ASYMM map
.quant16AsymmOperands = {},
// int -> QUANT8_SYMM map
.quant8SymmOperands = {},
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> Dimensions map
.operandDimensions = {{0, {3}}},
// int -> FLOAT32 map
.float32Operands = {{0, {1.0f, 2.0f, 3.0f}}},
// int -> INT32 map
.int32Operands = {},
// int -> QUANT8_ASYMM map
.quant8AsymmOperands = {},
// int -> QUANT16_SYMM map
.quant16SymmOperands = {},
// int -> FLOAT16 map
.float16Operands = {},
// int -> BOOL8 map
.bool8Operands = {},
// int -> QUANT8_SYMM_PER_CHANNEL map
.quant8ChannelOperands = {},
// int -> QUANT16_ASYMM map
.quant16AsymmOperands = {},
// int -> QUANT8_SYMM map
.quant8SymmOperands = {},
}
},
}, // End of an example
};
return examples;
};
std::vector<MixedTypedExample>& get_examples_dynamic_output_shape() {
static std::vector<MixedTypedExample> examples_dynamic_output_shape = {
// Begin of an example
{
.operands = {
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> Dimensions map
.operandDimensions = {{0, {2, 3}}},
// int -> FLOAT32 map
.float32Operands = {{0, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}}},
// int -> INT32 map
.int32Operands = {},
// int -> QUANT8_ASYMM map
.quant8AsymmOperands = {},
// int -> QUANT16_SYMM map
.quant16SymmOperands = {},
// int -> FLOAT16 map
.float16Operands = {},
// int -> BOOL8 map
.bool8Operands = {},
// int -> QUANT8_SYMM_PER_CHANNEL map
.quant8ChannelOperands = {},
// int -> QUANT16_ASYMM map
.quant16AsymmOperands = {},
// int -> QUANT8_SYMM map
.quant8SymmOperands = {},
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> Dimensions map
.operandDimensions = {{0, {3}}},
// int -> FLOAT32 map
.float32Operands = {{0, {1.0f, 2.0f, 3.0f}}},
// int -> INT32 map
.int32Operands = {},
// int -> QUANT8_ASYMM map
.quant8AsymmOperands = {},
// int -> QUANT16_SYMM map
.quant16SymmOperands = {},
// int -> FLOAT16 map
.float16Operands = {},
// int -> BOOL8 map
.bool8Operands = {},
// int -> QUANT8_SYMM_PER_CHANNEL map
.quant8ChannelOperands = {},
// int -> QUANT16_ASYMM map
.quant16AsymmOperands = {},
// int -> QUANT8_SYMM map
.quant8SymmOperands = {},
}
},
}, // End of an example
};
return examples_dynamic_output_shape;
};

216
neuralnetworks/1.2/vts/functional/generated/strided_slice_invalid_output_dims.model.cpp
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@ -1,216 +0,0 @@
// clang-format off
// Generated file (from: strided_slice_invalid_output_dims.mod.py). Do not edit
// Create the model
Model createTestModel() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {2, 3},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {2},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 8},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {2},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 8, .length = 8},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {2},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 16, .length = 8},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 24, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 28, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 32, .length = 4},
},
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {3},
.numberOfConsumers = 0,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::STRIDED_SLICE,
.inputs = {0, 1, 2, 3, 4, 5, 6},
.outputs = {7},
}
};
const std::vector<uint32_t> inputIndexes = {0};
const std::vector<uint32_t> outputIndexes = {7};
std::vector<uint8_t> operandValues = {
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0
};
const std::vector<hidl_memory> pools = {};
return {
.operands = operands,
.operations = operations,
.inputIndexes = inputIndexes,
.outputIndexes = outputIndexes,
.operandValues = operandValues,
.pools = pools,
};
}
inline bool is_ignored(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
// Create the model
Model createTestModel_dynamic_output_shape() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {2, 3},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {2},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 8},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {2},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 8, .length = 8},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {2},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 16, .length = 8},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 24, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 28, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 32, .length = 4},
},
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {0},
.numberOfConsumers = 0,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::STRIDED_SLICE,
.inputs = {0, 1, 2, 3, 4, 5, 6},
.outputs = {7},
}
};
const std::vector<uint32_t> inputIndexes = {0};
const std::vector<uint32_t> outputIndexes = {7};
std::vector<uint8_t> operandValues = {
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0
};
const std::vector<hidl_memory> pools = {};
return {
.operands = operands,
.operations = operations,
.inputIndexes = inputIndexes,
.outputIndexes = outputIndexes,
.operandValues = operandValues,
.pools = pools,
};
}
inline bool is_ignored_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}

43
neuralnetworks/1.2/vts/functional/spec/strided_slice_invalid_output_dims.mod.py
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@ -1,43 +0,0 @@
#
# Copyright (C) 2019 The Android Open Source Project
#
# 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.
#
# This test makes sure that executing STRIDED_SLICE results in a failure when
# the output dimensions do not match shrinkAxisMask.
#
# The test generator does not support generating tests resulting in execution
# failure, so the gTest part of this test has been written by hand.
# TODO(b/132155416): Move this under frameworks/ml/nn/runtime/test/specs/V1_2.
#
# Based on strided_slice_float_11.mod.py.
model = Model()
i1 = Input("input", "TENSOR_FLOAT32", "{2, 3}")
begins = Parameter("begins", "TENSOR_INT32", "{2}", [0, 0])
# The value "2" below makes the test invalid. See http://b/79856511#comment2.
ends = Parameter("ends", "TENSOR_INT32", "{2}", [2, 3])
strides = Parameter("strides", "TENSOR_INT32", "{2}", [1, 1])
beginMask = Int32Scalar("beginMask", 0)
endMask = Int32Scalar("endMask", 0)
shrinkAxisMask = Int32Scalar("shrinkAxisMask", 1)
output = Output("output", "TENSOR_FLOAT32", "{3}")
model = model.Operation("STRIDED_SLICE", i1, begins, ends, strides, beginMask, endMask, shrinkAxisMask).To(output)
Example({
i1: [1, 2, 3, 4, 5, 6],
output: [1, 2, 3],
})