Move strided_slice_invalid_output_dims to spec directory. am: ee7fe25e99 (f70d6185) · Commits · e / os / android_hardware_interfaces

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/
		#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 vts
		} // namespace V1_2

neuralnetworks/1.2/vts/functional/generated/strided_slice_invalid_output_dims.example.cpp

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		// 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;
		};

neuralnetworks/1.2/vts/functional/generated/strided_slice_invalid_output_dims.model.cpp

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		// 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();
		}

neuralnetworks/1.2/vts/functional/spec/strided_slice_invalid_output_dims.mod.py

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		#
		# 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],
		})