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predict_validation_test.cpp
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//*****************************************************************************
// Copyright 2020-2021 Intel Corporation
//
// 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 <string>
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "../kfs_frontend/kfs_grpc_inference_service.hpp"
#include "../tfs_frontend/tfs_utils.hpp"
#include "../modelconfig.hpp"
#include "../modelinstance.hpp"
#include "../predict_request_validation_utils.hpp"
#include "test_utils.hpp"
using ::testing::NiceMock;
using ::testing::Return;
using ::testing::ReturnRef;
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-variable"
class TfsPredictValidation : public ::testing::Test {
protected:
std::unique_ptr<ov::Core> ieCore;
std::unique_ptr<NiceMock<MockedMetadataModelIns>> instance;
tensorflow::serving::PredictRequest request;
ovms::ModelConfig modelConfig{"model_name", "model_path"};
ovms::tensor_map_t servableInputs;
ovms::tensor_map_t servableOutputs = ovms::tensor_map_t({{"SOME_OUTPUT", std::make_shared<ovms::TensorInfo>("OUTPUT", ovms::Precision::FP32, ovms::shape_t{1, 1, 1, 1}, ovms::Layout{"NHWC"})}}); // not used for TFS
void SetUp() override {
ieCore = std::make_unique<ov::Core>();
instance = std::make_unique<NiceMock<MockedMetadataModelIns>>(*ieCore);
servableInputs = ovms::tensor_map_t({
{"Input_FP32_1_224_224_3_NHWC",
std::make_shared<ovms::TensorInfo>("Input_FP32_1_3_224_224_NHWC", ovms::Precision::FP32, ovms::shape_t{1, 224, 224, 3}, ovms::Layout{"NHWC"})},
{"Input_U8_1_3_62_62_NCHW",
std::make_shared<ovms::TensorInfo>("Input_U8_1_3_62_62_NCHW", ovms::Precision::U8, ovms::shape_t{1, 3, 62, 62}, ovms::Layout{"NCHW"})},
{"Input_I64_1_6_128_128_16_NCDHW",
std::make_shared<ovms::TensorInfo>("Input_I64_1_6_128_128_16_NCDHW", ovms::Precision::I64, ovms::shape_t{1, 6, 128, 128, 16}, ovms::Layout{"NCDHW"})},
{"Input_U16_1_2_8_4_NCHW",
std::make_shared<ovms::TensorInfo>("Input_U16_1_2_8_4_NCHW", ovms::Precision::U16, ovms::shape_t{1, 2, 8, 4}, ovms::Layout{"NCHW"})},
});
ON_CALL(*instance, getInputsInfo()).WillByDefault(ReturnRef(servableInputs));
ON_CALL(*instance, getOutputsInfo()).WillByDefault(ReturnRef(servableOutputs));
ON_CALL(*instance, getBatchSize()).WillByDefault(Return(1));
ON_CALL(*instance, getModelConfig()).WillByDefault(ReturnRef(modelConfig));
preparePredictRequest(request,
{
{"Input_FP32_1_224_224_3_NHWC",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 224, 224, 3}, ovms::Precision::FP32}},
{"Input_U8_1_3_62_62_NCHW",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 3, 62, 62}, ovms::Precision::U8}},
{"Input_I64_1_6_128_128_16_NCDHW",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 6, 128, 128, 16}, ovms::Precision::I64}},
});
// U16 uses int_val instead of tensor_content so it needs separate test
auto& inputD = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
inputD.set_dtype(tensorflow::DataType::DT_UINT16);
inputD.mutable_int_val()->Resize(1 * 2 * 8 * 4, 1);
inputD.mutable_tensor_shape()->add_dim()->set_size(1);
inputD.mutable_tensor_shape()->add_dim()->set_size(2);
inputD.mutable_tensor_shape()->add_dim()->set_size(8);
inputD.mutable_tensor_shape()->add_dim()->set_size(4);
}
static void prepareTensorContent(tensorflow::TensorProto& proto) {
if (proto.tensor_shape().dim_size() == 0) {
*proto.mutable_tensor_content() = "";
return;
}
size_t numberOfElements = 1;
for (int i = 0; i < proto.tensor_shape().dim_size(); i++) {
numberOfElements *= proto.tensor_shape().dim(i).size();
}
*proto.mutable_tensor_content() = std::string(numberOfElements * tensorflow::DataTypeSize(proto.dtype()), '1');
}
};
TEST_F(TfsPredictValidation, ValidRequest) {
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
TEST_F(TfsPredictValidation, RequestWithScalar) {
servableInputs = ovms::tensor_map_t({{"Input_FP32_Scalar",
std::make_shared<ovms::TensorInfo>("Input_FP32_Scalar", ovms::Precision::FP32, ovms::shape_t{}, ovms::Layout{"..."})}});
preparePredictRequest(request,
{{"Input_FP32_Scalar",
std::tuple<ovms::signed_shape_t, ovms::Precision>{std::vector<int64_t>{}, ovms::Precision::FP32}}});
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
// Requesting 0 batch via TFS API
// Mocked endpoints tested: dynamic batch (-1), range (0-100) and static 0.
TEST_F(TfsPredictValidation, RequestWithZeroBatch) {
std::vector<ovms::Shape> shapes{
ovms::Shape{ovms::Dimension::any(), 400, 99}, // dynamic
ovms::Shape{ovms::Dimension{0, 100}, 400, 99}, // range
ovms::Shape{0, 400, 99} // static
};
ovms::signed_shape_t actualRequestShape{0, 400, 99};
for (const auto& shape : shapes) {
servableInputs = ovms::tensor_map_t({{"Input",
std::make_shared<ovms::TensorInfo>("Input", ovms::Precision::FP32, shape, ovms::Layout{"N..."})}});
preparePredictRequest(request,
{{"Input",
std::tuple<ovms::signed_shape_t, ovms::Precision>{actualRequestShape, ovms::Precision::FP32}}});
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
}
// Requesting 0 dimension in position other than batch via TFS API
// Mocked endpoints tested: dynamic shape (-1), range (0-100) and static 0.
TEST_F(TfsPredictValidation, RequestWithZeroDim) {
std::vector<ovms::Shape> shapes{
ovms::Shape{200, ovms::Dimension::any(), 99}, // dynamic
ovms::Shape{200, ovms::Dimension{0, 100}, 99}, // range
ovms::Shape{200, 0, 99} // static
};
ovms::signed_shape_t actualRequestShape{200, 0, 99};
for (const auto& shape : shapes) {
servableInputs = ovms::tensor_map_t({{"Input",
std::make_shared<ovms::TensorInfo>("Input", ovms::Precision::FP32, shape, ovms::Layout{"N..."})}});
preparePredictRequest(request,
{{"Input",
std::tuple<ovms::signed_shape_t, ovms::Precision>{actualRequestShape, ovms::Precision::FP32}}});
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
}
TEST_F(TfsPredictValidation, RequestNotEnoughInputs) {
request.mutable_inputs()->erase("Input_U8_1_3_62_62_NCHW");
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_INPUTS);
}
TEST_F(TfsPredictValidation, RequestTooManyInputs) {
auto& inputD = (*request.mutable_inputs())["input_d"];
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_INPUTS);
}
TEST_F(TfsPredictValidation, RequestWrongInputName) {
auto input = (*request.mutable_inputs())["Input_I64_1_6_128_128_16_NCDHW"];
request.mutable_inputs()->erase("Input_I64_1_6_128_128_16_NCDHW");
(*request.mutable_inputs())["Some_Input"] = input;
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_MISSING_INPUT);
}
TEST_F(TfsPredictValidation, RequestTooManyShapeDimensions) {
auto& input = (*request.mutable_inputs())["Input_FP32_1_224_224_3_NHWC"];
input.mutable_tensor_shape()->add_dim()->set_size(16);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_SHAPE_DIMENSIONS);
}
TEST_F(TfsPredictValidation, RequestNotEnoughShapeDimensions) {
auto& input = (*request.mutable_inputs())["Input_FP32_1_224_224_3_NHWC"];
input.mutable_tensor_shape()->clear_dim();
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_SHAPE_DIMENSIONS);
}
TEST_F(TfsPredictValidation, RequestWrongBatchSize) {
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(10); // dim(0) is batch size
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidation, RequestWrongBatchSizeAuto) {
modelConfig.setBatchingParams("auto");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(10); // dim(0) is batch size
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED);
}
TEST_F(TfsPredictValidation, ValidRequestBinaryInputs) {
modelConfig.setBatchingParams("auto");
std::string inputName = "Binary_Input";
tensorflow::serving::PredictRequest binaryInputRequest;
auto& input = (*binaryInputRequest.mutable_inputs())[inputName];
input.set_dtype(tensorflow::DataType::DT_STRING);
const int requestBatchSize = 1;
for (int i = 0; i < requestBatchSize; i++) {
input.add_string_val("val");
}
input.mutable_tensor_shape()->add_dim()->set_size(requestBatchSize);
servableInputs.clear();
ovms::shape_t shape = {1, 3, 224, 224};
servableInputs[inputName] = std::make_shared<ovms::TensorInfo>(
inputName,
ovms::Precision::FP32,
shape,
ovms::Layout{"NHWC"});
auto status = instance->mockValidate(&binaryInputRequest);
EXPECT_TRUE(status.ok());
}
TEST_F(TfsPredictValidation, Batch0RequestBinaryInputs) {
modelConfig.setBatchingParams("0");
std::string inputName = "Binary_Input";
tensorflow::serving::PredictRequest binaryInputRequest;
auto& input = (*binaryInputRequest.mutable_inputs())[inputName];
input.set_dtype(tensorflow::DataType::DT_STRING);
const int requestBatchSize = 0;
input.mutable_tensor_shape()->add_dim()->set_size(requestBatchSize);
servableInputs.clear();
ovms::shape_t shape = {0, 3, 224, 224};
servableInputs[inputName] = std::make_shared<ovms::TensorInfo>(
inputName,
ovms::Precision::FP32,
shape,
ovms::Layout{"NHWC"});
auto status = instance->mockValidate(&binaryInputRequest);
EXPECT_TRUE(status.ok());
}
TEST_F(TfsPredictValidation, RequestWrongBatchSizeBinaryInputs) {
std::string inputName = "Binary_Input";
tensorflow::serving::PredictRequest binaryInputRequest;
auto& input = (*binaryInputRequest.mutable_inputs())[inputName];
input.set_dtype(tensorflow::DataType::DT_STRING);
const int requestBatchSize = 2;
for (int i = 0; i < requestBatchSize; i++) {
input.add_string_val("val");
}
input.mutable_tensor_shape()->add_dim()->set_size(requestBatchSize);
servableInputs.clear();
ovms::shape_t shape = {1, 3, 224, 224};
servableInputs[inputName] = std::make_shared<ovms::TensorInfo>(
inputName,
ovms::Precision::FP32,
shape,
ovms::Layout{"NHWC"});
auto status = instance->mockValidate(&binaryInputRequest);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidation, BinaryRequestEndpointScalar) {
std::string inputName = "Binary_Input";
tensorflow::serving::PredictRequest binaryInputRequest;
auto& input = (*binaryInputRequest.mutable_inputs())[inputName];
input.set_dtype(tensorflow::DataType::DT_STRING);
const int requestBatchSize = 2;
for (int i = 0; i < requestBatchSize; i++) {
input.add_string_val("val");
}
input.mutable_tensor_shape()->add_dim()->set_size(requestBatchSize);
servableInputs.clear();
ovms::shape_t shape = {};
servableInputs[inputName] = std::make_shared<ovms::TensorInfo>(
inputName,
ovms::Precision::FP32,
shape,
ovms::Layout{"..."});
auto status = instance->mockValidate(&binaryInputRequest);
EXPECT_EQ(status, ovms::StatusCode::NOT_IMPLEMENTED);
}
TEST_F(TfsPredictValidation, RequestWrongBatchSizeAutoBinaryInputs) {
modelConfig.setBatchingParams("auto");
std::string inputName = "Binary_Input";
tensorflow::serving::PredictRequest binaryInputRequest;
auto& input = (*binaryInputRequest.mutable_inputs())[inputName];
input.set_dtype(tensorflow::DataType::DT_STRING);
const int requestBatchSize = 2;
for (int i = 0; i < requestBatchSize; i++) {
input.add_string_val("val");
}
input.mutable_tensor_shape()->add_dim()->set_size(requestBatchSize);
servableInputs.clear();
ovms::shape_t shape = {1, 3, 224, 224};
servableInputs[inputName] = std::make_shared<ovms::TensorInfo>(
inputName,
ovms::Precision::FP32,
shape,
ovms::Layout{"NHWC"});
auto status = instance->mockValidate(&binaryInputRequest);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestWithScalarBatchSizeAuto) {
modelConfig.setBatchingParams("auto");
// First is incorrect, second is correct, but endpoint is abnormal anyway (scalar with batch size auto)
preparePredictRequest(request, {{"im_data", {{3, 3, 800, 1344}, ovms::Precision::FP32}},
{"im_info", {std::vector<int64_t>{}, ovms::Precision::FP32}}});
servableInputs.clear();
servableInputs = ovms::tensor_map_t{
{"im_data", std::make_shared<ovms::TensorInfo>("im_data", ovms::Precision::FP32, ovms::shape_t{1, 3, 800, 1344}, ovms::Layout{"NCHW"})},
{"im_info", std::make_shared<ovms::TensorInfo>("im_info", ovms::Precision::FP32, ovms::shape_t{}, ovms::Layout{"..."})},
};
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INTERNAL_ERROR);
// First and second is correct, but endpoint is abnormal (scalar with batch size auto)
preparePredictRequest(request, {{"im_data", {{1, 3, 800, 1344}, ovms::Precision::FP32}},
{"im_info", {std::vector<int64_t>{}, ovms::Precision::FP32}}});
status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INTERNAL_ERROR);
}
TEST_F(TfsPredictValidation, RequestWrongAndCorrectBatchSizeAuto) {
modelConfig.setBatchingParams("auto");
// First is incorrect, second is correct
preparePredictRequest(request, {{"im_data", {{3, 3, 800, 1344}, ovms::Precision::FP32}},
{"im_info", {{1, 3}, ovms::Precision::FP32}}});
servableInputs.clear();
servableInputs = ovms::tensor_map_t{
{"im_data", std::make_shared<ovms::TensorInfo>("im_data", ovms::Precision::FP32, ovms::shape_t{1, 3, 800, 1344}, ovms::Layout{"NCHW"})},
{"im_info", std::make_shared<ovms::TensorInfo>("im_info", ovms::Precision::FP32, ovms::shape_t{1, 3}, ovms::Layout{"NC"})},
};
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED);
preparePredictRequest(request, {{"im_data", {{1, 3, 800, 1344}, ovms::Precision::FP32}},
{"im_info", {{3, 3}, ovms::Precision::FP32}}});
status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED);
}
// Requesting 0 batch via TFS API
// Mocked endpoints tested: static shape with option batch=auto
// Expect force model batch change status.
TEST_F(TfsPredictValidation, RequestZeroDimBatchAuto) {
modelConfig.setBatchingParams("auto");
preparePredictRequest(request, {{"im_data", {{0, 3, 800, 1344}, ovms::Precision::FP32}}});
servableInputs.clear();
servableInputs = ovms::tensor_map_t{
{"im_data", std::make_shared<ovms::TensorInfo>("im_data", ovms::Precision::FP32, ovms::shape_t{1, 3, 800, 1344}, ovms::Layout{"NCHW"})},
};
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestWrongAndCorrectShapeAuto) {
modelConfig.parseShapeParameter("auto");
preparePredictRequest(request, {{"im_data", {{1, 3, 900, 1344}, ovms::Precision::FP32}},
{"im_info", {{1, 3}, ovms::Precision::FP32}}});
// First is incorrect, second is correct
servableInputs.clear();
servableInputs = ovms::tensor_map_t{
{"im_data", std::make_shared<ovms::TensorInfo>("im_data", ovms::Precision::FP32, ovms::shape_t{1, 3, 800, 1344}, ovms::Layout{"NCHW"})},
{"im_info", std::make_shared<ovms::TensorInfo>("im_info", ovms::Precision::FP32, ovms::shape_t{1, 3}, ovms::Layout{"NC"})},
};
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
// First is correct, second is incorrect
preparePredictRequest(request, {{"im_data", {{1, 3, 800, 1344}, ovms::Precision::FP32}},
{"im_info", {{1, 6}, ovms::Precision::FP32}}});
status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
// Requesting with shape containing 0-value dim via TFS API
// Mocked endpoints tested: static shape with option shape=auto
// Expect force model reshape status.
TEST_F(TfsPredictValidation, RequestZeroDimShapeAuto) {
modelConfig.parseShapeParameter("auto");
preparePredictRequest(request, {{"im_data", {{1, 3, 0, 1344}, ovms::Precision::FP32}}});
// First is incorrect, second is correct
servableInputs.clear();
servableInputs = ovms::tensor_map_t{
{"im_data", std::make_shared<ovms::TensorInfo>("im_data", ovms::Precision::FP32, ovms::shape_t{1, 3, 800, 1344}, ovms::Layout{"NCHW"})},
};
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestWithScalarShapeAuto) {
modelConfig.parseShapeParameter("auto");
// First is incorrect, second is correct, expect reshape request due to shape=auto
preparePredictRequest(request, {{"im_data", {{1, 3, 801, 1344}, ovms::Precision::FP32}},
{"im_info", {std::vector<int64_t>{}, ovms::Precision::FP32}}});
servableInputs.clear();
servableInputs = ovms::tensor_map_t{
{"im_data", std::make_shared<ovms::TensorInfo>("im_data", ovms::Precision::FP32, ovms::shape_t{1, 3, 800, 1344}, ovms::Layout{"NCHW"})},
{"im_info", std::make_shared<ovms::TensorInfo>("im_info", ovms::Precision::FP32, ovms::shape_t{}, ovms::Layout{"..."})},
};
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED) << status.string();
// First and second is correct, expect no further reshaping due to shape=auto
preparePredictRequest(request, {{"im_data", {{1, 3, 800, 1344}, ovms::Precision::FP32}},
{"im_info", {std::vector<int64_t>{}, ovms::Precision::FP32}}});
status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::OK) << status.string();
}
TEST_F(TfsPredictValidation, RequestValidBatchSizeAuto) {
modelConfig.setBatchingParams("auto");
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::OK);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValues) {
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(4);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(63);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(63);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_SHAPE);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesTwoInputsOneWrong) { // one input fails validation, request denied
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"auto\"}");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(4);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(63);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(63);
auto& input2 = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
input2.mutable_tensor_shape()->mutable_dim(0)->set_size(2);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesAuto) {
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"auto\"}");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(4);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(63);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(63);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesAutoTwoInputs) {
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"auto\", \"Input_U16_1_2_8_4_NCHW\": \"auto\"}");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(4);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(63);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(63);
prepareTensorContent(input);
auto& input2 = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(2);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(16);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(8);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesAutoNoNamedInput) {
modelConfig.parseShapeParameter("auto");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(4);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(63);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(63);
prepareTensorContent(input);
auto& input2 = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(2);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(16);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(8);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesAutoFirstDim) {
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"auto\"}");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(2);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(3);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(62);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(62);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
TEST_F(TfsPredictValidation, RequestValidShapeValuesTwoInputsFixed) {
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"(1,3,62,62)\", \"Input_U16_1_2_8_4_NCHW\": \"(1,2,8,4)\"}");
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::OK);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesFixed) {
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"(1,3,62,62)\"}");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(1);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(4);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(63);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(63);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_SHAPE);
}
TEST_F(TfsPredictValidation, RequestWrongShapeValuesFixedFirstDim) {
modelConfig.parseShapeParameter("{\"Input_U8_1_3_62_62_NCHW\": \"(1,3,62,62)\"}");
auto& input = (*request.mutable_inputs())["Input_U8_1_3_62_62_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(2);
input.mutable_tensor_shape()->mutable_dim(1)->set_size(3);
input.mutable_tensor_shape()->mutable_dim(2)->set_size(62);
input.mutable_tensor_shape()->mutable_dim(3)->set_size(62);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidation, RequestIncorrectContentSize) {
auto& input = (*request.mutable_inputs())["Input_I64_1_6_128_128_16_NCDHW"];
*input.mutable_tensor_content() = std::string(1 * 6, '1');
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_CONTENT_SIZE);
}
TEST_F(TfsPredictValidation, RequestIncorrectContentSizeForScalarEndpoint) {
servableInputs = ovms::tensor_map_t({{"Input_FP32_Scalar",
std::make_shared<ovms::TensorInfo>("Input_FP32_Scalar", ovms::Precision::FP32, ovms::shape_t{}, ovms::Layout{"..."})}});
preparePredictRequest(request,
{{"Input_FP32_Scalar",
std::tuple<ovms::signed_shape_t, ovms::Precision>{std::vector<int64_t>{}, ovms::Precision::FP32}}});
auto& input = (*request.mutable_inputs())["Input_FP32_Scalar"];
*input.mutable_tensor_content() = std::string(sizeof(float) + 1, '1');
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_CONTENT_SIZE);
}
TEST_F(TfsPredictValidation, RequestIncorrectContentSizeBatchAuto) {
modelConfig.setBatchingParams("auto");
auto& input = (*request.mutable_inputs())["Input_I64_1_6_128_128_16_NCDHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(3);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_CONTENT_SIZE);
}
TEST_F(TfsPredictValidation, RequestIncorrectContentSizeShapeAuto) {
modelConfig.parseShapeParameter("auto");
auto& input = (*request.mutable_inputs())["Input_I64_1_6_128_128_16_NCDHW"];
input.mutable_tensor_shape()->mutable_dim(1)->set_size(8);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_CONTENT_SIZE);
}
TEST_F(TfsPredictValidation, RequestIncorrectValueCount) {
auto& input = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
input.mutable_int_val()->Clear();
input.mutable_int_val()->Resize(2, 1);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_VALUE_COUNT);
}
TEST_F(TfsPredictValidation, RequestIncorrectValueCountBatchAuto) {
modelConfig.setBatchingParams("auto");
auto& input = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(3);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_VALUE_COUNT);
}
TEST_F(TfsPredictValidation, RequestIncorrectValueCountShapeAuto) {
modelConfig.parseShapeParameter("auto");
auto& input = (*request.mutable_inputs())["Input_U16_1_2_8_4_NCHW"];
input.mutable_tensor_shape()->mutable_dim(2)->set_size(10);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_VALUE_COUNT);
}
TEST_F(TfsPredictValidation, RequestWrongPrecision) {
auto& input = (*request.mutable_inputs())["Input_FP32_1_224_224_3_NHWC"];
input.set_dtype(tensorflow::DataType::DT_UINT8);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_PRECISION);
}
TEST_F(TfsPredictValidation, RequestNegativeValueInShape) {
auto& input = (*request.mutable_inputs())["Input_FP32_1_224_224_3_NHWC"];
input.mutable_tensor_shape()->mutable_dim(1)->set_size(-4);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_SHAPE);
}
class TfsPredictValidationArbitraryBatchPosition : public TfsPredictValidation {
protected:
void SetUp() override {
TfsPredictValidation::SetUp();
servableInputs = ovms::tensor_map_t({
{"Input_FP32_224_224_3_1_HWCN",
std::make_shared<ovms::TensorInfo>("Input_FP32_224_224_3_1_HWCN", ovms::Precision::FP32, ovms::shape_t{224, 224, 3, 1}, ovms::Layout{"HWCN"})},
{"Input_U8_3_1_128_CNH",
std::make_shared<ovms::TensorInfo>("Input_U8_3_1_128_CNH", ovms::Precision::U8, ovms::shape_t{3, 1, 128}, ovms::Layout{"CNH"})},
});
preparePredictRequest(request,
{
{"Input_FP32_224_224_3_1_HWCN",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{224, 224, 3, 1}, ovms::Precision::FP32}},
{"Input_U8_3_1_128_CNH",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{3, 1, 128}, ovms::Precision::U8}},
});
}
};
TEST_F(TfsPredictValidationArbitraryBatchPosition, Valid) {
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
TEST_F(TfsPredictValidationArbitraryBatchPosition, RequestWrongBatchSize) {
auto& input = (*request.mutable_inputs())["Input_FP32_224_224_3_1_HWCN"];
// Edit fourth dimension (N), expect validator to report wrong batch size instead of wrong shape.
input.mutable_tensor_shape()->mutable_dim(3)->set_size(10);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidationArbitraryBatchPosition, RequestWrongBatchSizeAuto) {
modelConfig.setBatchingParams("auto");
auto& input = (*request.mutable_inputs())["Input_FP32_224_224_3_1_HWCN"];
// Edit fourth dimension (N), expect validator to report batch size change request instead of reshape request.
input.mutable_tensor_shape()->mutable_dim(3)->set_size(10);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED);
}
TEST_F(TfsPredictValidationArbitraryBatchPosition, RequestWrongShapeValues) {
auto& input = (*request.mutable_inputs())["Input_FP32_224_224_3_1_HWCN"];
// Edit first dimension (H), expect validator to report wrong shape instead of wrong batch size.
input.mutable_tensor_shape()->mutable_dim(0)->set_size(10);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_SHAPE);
}
TEST_F(TfsPredictValidationArbitraryBatchPosition, RequestWrongShapeValuesAuto) {
modelConfig.parseShapeParameter("auto");
auto& input = (*request.mutable_inputs())["Input_FP32_224_224_3_1_HWCN"];
// Edit first dimension (H), expect validator to report reshape request instead of requesting batch size change.
input.mutable_tensor_shape()->mutable_dim(0)->set_size(10);
prepareTensorContent(input);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::RESHAPE_REQUIRED);
}
class TfsPredictValidationDynamicModel : public TfsPredictValidation {
protected:
void SetUp() override {
TfsPredictValidation::SetUp();
servableInputs = ovms::tensor_map_t({{"Input_FP32_any_224:512_224:512_3_NHWC",
std::make_shared<ovms::TensorInfo>("Input_FP32_any_224:512_224:512_3_NHWC", ovms::Precision::FP32, ovms::Shape{ovms::Dimension::any(), {224, 512}, {224, 512}, 3}, ovms::Layout{"NHWC"})},
{"Input_U8_100:200_any_CN",
std::make_shared<ovms::TensorInfo>("Input_U8_100:200_any_CN", ovms::Precision::U8, ovms::Shape{{100, 200}, ovms::Dimension::any()}, ovms::Layout{"CN"})}});
ON_CALL(*instance, getBatchSize()).WillByDefault(Return(ovms::Dimension::any()));
const ovms::dimension_value_t requestBatchSize = 16;
preparePredictRequest(request,
{
{"Input_FP32_any_224:512_224:512_3_NHWC",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{requestBatchSize, 300, 320, 3}, ovms::Precision::FP32}},
{"Input_U8_100:200_any_CN",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{101, requestBatchSize}, ovms::Precision::U8}},
});
}
};
TEST_F(TfsPredictValidationDynamicModel, ValidRequest) {
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
TEST_F(TfsPredictValidationDynamicModel, RequestBatchNotInRangeFirstPosition) {
auto& input = (*request.mutable_inputs())["Input_FP32_any_224:512_224:512_3_NHWC"];
input.mutable_tensor_shape()->mutable_dim(1)->set_size(98); // Should be in 1-5 range
servableInputs["Input_FP32_any_224:512_224:512_3_NHWC"] = std::make_shared<ovms::TensorInfo>("Input_FP32_any_224:512_224:512_3_NHWC", ovms::Precision::FP32, ovms::Shape{{1, 5}, {224, 512}, {224, 512}, 3}, ovms::Layout{"NHWC"});
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidationDynamicModel, RequestDimensionNotInRangeFirstPosition) {
auto& input = (*request.mutable_inputs())["Input_U8_100:200_any_CN"];
input.mutable_tensor_shape()->mutable_dim(0)->set_size(98); // Should be in 100-200 range
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_SHAPE);
}
TEST_F(TfsPredictValidationDynamicModel, RequestBatchNotInRangeSecondPosition) {
auto& input = (*request.mutable_inputs())["Input_U8_100:200_any_CN"];
input.mutable_tensor_shape()->mutable_dim(1)->set_size(98); // Should be in 1-5 range
servableInputs["Input_U8_100:200_any_CN"] = std::make_shared<ovms::TensorInfo>("Input_U8_100:200_any_CN", ovms::Precision::U8, ovms::Shape{{100, 200}, {1, 5}}, ovms::Layout{"CN"});
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE);
}
TEST_F(TfsPredictValidationDynamicModel, RequestDimensionNotInRangeSecondPosition) {
auto& input = (*request.mutable_inputs())["Input_FP32_any_224:512_224:512_3_NHWC"];
input.mutable_tensor_shape()->mutable_dim(1)->set_size(223); // Should be in 224-512 range
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_SHAPE);
}
TEST_F(TfsPredictValidationDynamicModel, RequestDimensionInRangeWrongTensorContent) {
auto& input = (*request.mutable_inputs())["Input_U8_100:200_any_CN"];
size_t numberOfElements = 1;
for (int i = 0; i < input.tensor_shape().dim_size(); i++) {
numberOfElements *= input.tensor_shape().dim(i).size();
}
numberOfElements -= 1;
*input.mutable_tensor_content() = std::string(numberOfElements * tensorflow::DataTypeSize(input.dtype()), '1');
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_CONTENT_SIZE);
}
class TfsPredictValidationPrecision : public ::testing::TestWithParam<ovms::Precision> {
protected:
void SetUp() override {
auto precision = ovms::Precision::FP32;
mockedInputsInfo[tensorName] = std::make_shared<ovms::TensorInfo>(tensorName, precision, ovms::shape_t{1, DUMMY_MODEL_INPUT_SIZE}, ovms::Layout{"NC"});
}
tensorflow::serving::PredictRequest request;
const char* tensorName = DUMMY_MODEL_INPUT_NAME;
ovms::tensor_map_t mockedInputsInfo;
ovms::tensor_map_t mockedOutputsInfo;
};
TEST_P(TfsPredictValidationPrecision, ValidPrecisions) {
ovms::Precision testedPrecision = GetParam();
mockedInputsInfo[tensorName] = createTensorInfoCopyWithPrecision(mockedInputsInfo[tensorName], testedPrecision);
preparePredictRequest(request,
{
{tensorName,
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, DUMMY_MODEL_INPUT_SIZE}, testedPrecision}},
});
auto status = ovms::request_validation_utils::validate(request, mockedInputsInfo, this->mockedOutputsInfo, "dummy", ovms::model_version_t{1});
EXPECT_EQ(status, ovms::StatusCode::OK) << "Precision validation failed:"
<< toString(testedPrecision)
<< " should pass validation";
}
INSTANTIATE_TEST_SUITE_P(
Test,
TfsPredictValidationPrecision,
::testing::ValuesIn(SUPPORTED_INPUT_PRECISIONS),
[](const ::testing::TestParamInfo<TfsPredictValidationPrecision::ParamType>& info) {
return toString(info.param);
});
class KFSPredictValidation : public ::testing::Test {
protected:
std::unique_ptr<ov::Core> ieCore;
std::unique_ptr<NiceMock<MockedMetadataModelIns>> instance;
::KFSRequest request;
ovms::ModelConfig modelConfig{"model_name", "model_path"};
ovms::tensor_map_t servableInputs;
ovms::tensor_map_t servableOutputs = ovms::tensor_map_t({{"SOME_OUTPUT", std::make_shared<ovms::TensorInfo>("OUTPUT", ovms::Precision::FP32, ovms::shape_t{1, 1, 1, 1}, ovms::Layout{"NHWC"})}}); // not used for KFS
void SetUp() override {
ieCore = std::make_unique<ov::Core>();
instance = std::make_unique<NiceMock<MockedMetadataModelIns>>(*ieCore);
servableInputs = ovms::tensor_map_t({
{"Input_FP32_1_224_224_3_NHWC",
std::make_shared<ovms::TensorInfo>("Input_FP32_1_3_224_224_NHWC", ovms::Precision::FP32, ovms::shape_t{1, 224, 224, 3}, ovms::Layout{"NHWC"})},
{"Input_U8_1_3_62_62_NCHW",
std::make_shared<ovms::TensorInfo>("Input_U8_1_3_62_62_NCHW", ovms::Precision::U8, ovms::shape_t{1, 3, 62, 62}, ovms::Layout{"NCHW"})},
{"Input_I64_1_6_128_128_16_NCDHW",
std::make_shared<ovms::TensorInfo>("Input_I64_1_6_128_128_16_NCDHW", ovms::Precision::I64, ovms::shape_t{1, 6, 128, 128, 16}, ovms::Layout{"NCDHW"})},
{"Input_U16_1_2_8_4_NCHW",
std::make_shared<ovms::TensorInfo>("Input_U16_1_2_8_4_NCHW", ovms::Precision::U16, ovms::shape_t{1, 2, 8, 4}, ovms::Layout{"NCHW"})},
});
ON_CALL(*instance, getInputsInfo()).WillByDefault(ReturnRef(servableInputs));
ON_CALL(*instance, getOutputsInfo()).WillByDefault(ReturnRef(servableOutputs));
ON_CALL(*instance, getBatchSize()).WillByDefault(Return(1));
ON_CALL(*instance, getModelConfig()).WillByDefault(ReturnRef(modelConfig));
preparePredictRequest(request,
{{"Input_FP32_1_224_224_3_NHWC",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 224, 224, 3}, ovms::Precision::FP32}},
{"Input_U8_1_3_62_62_NCHW",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 3, 62, 62}, ovms::Precision::U8}},
{"Input_I64_1_6_128_128_16_NCDHW",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 6, 128, 128, 16}, ovms::Precision::I64}},
{"Input_U16_1_2_8_4_NCHW",
std::tuple<ovms::signed_shape_t, ovms::Precision>{{1, 2, 8, 4}, ovms::Precision::U16}}});
}
};
TEST_F(KFSPredictValidation, ValidRequest) {
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok()) << status.string();
}
TEST_F(KFSPredictValidation, RequestWithScalar) {
servableInputs = ovms::tensor_map_t({{"Input_FP32_Scalar",
std::make_shared<ovms::TensorInfo>("Input_FP32_Scalar", ovms::Precision::FP32, ovms::shape_t{}, ovms::Layout{"..."})}});
preparePredictRequest(request,
{{"Input_FP32_Scalar",
std::tuple<ovms::signed_shape_t, ovms::Precision>{std::vector<int64_t>{}, ovms::Precision::FP32}}});
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
// Requesting 0 batch via KServe API
// Mocked endpoints tested: dynamic batch (-1), range (0-100) and static 0.
TEST_F(KFSPredictValidation, RequestWithZeroBatch) {
std::vector<ovms::Shape> shapes{
ovms::Shape{ovms::Dimension::any(), 400, 99}, // dynamic
ovms::Shape{ovms::Dimension{0, 100}, 400, 99}, // range
ovms::Shape{0, 400, 99} // static
};
ovms::signed_shape_t actualRequestShape{0, 400, 99};
for (const auto& shape : shapes) {
servableInputs = ovms::tensor_map_t({{"Input",
std::make_shared<ovms::TensorInfo>("Input", ovms::Precision::FP32, shape, ovms::Layout{"N..."})}});
preparePredictRequest(request,
{{"Input",
std::tuple<ovms::signed_shape_t, ovms::Precision>{actualRequestShape, ovms::Precision::FP32}}});
auto status = instance->mockValidate(&request);
EXPECT_TRUE(status.ok());
}
}
TEST_F(KFSPredictValidation, RequestNotEnoughInputs) {
request.mutable_inputs()->RemoveLast();
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_INPUTS) << status.string();
}
TEST_F(KFSPredictValidation, RequestTooManyInputs) {
auto inputWrongName = request.add_inputs();
inputWrongName->set_name("Some_Input");
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_INPUTS) << status.string();
}
TEST_F(KFSPredictValidation, RequestWrongInputName) {
request.mutable_inputs()->RemoveLast(); // remove redundant input
auto inputWrongName = request.add_inputs();
inputWrongName->set_name("Some_Input");
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_MISSING_INPUT) << status.string();
}
TEST_F(KFSPredictValidation, RequestTooManyShapeDimensions) {
auto someInput = request.mutable_inputs()->Mutable(request.mutable_inputs()->size() - 1); // modify last
someInput->mutable_shape()->Add(16);
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_SHAPE_DIMENSIONS) << status.string();
}
TEST_F(KFSPredictValidation, RequestNotEnoughShapeDimensions) {
auto someInput = request.mutable_inputs()->Mutable(request.mutable_inputs()->size() - 1); // modify last
someInput->mutable_shape()->Clear();
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_NO_OF_SHAPE_DIMENSIONS) << status.string();
}
TEST_F(KFSPredictValidation, RequestWrongBatchSize) {
auto someInput = request.mutable_inputs()->Mutable(request.mutable_inputs()->size() - 1); // modify last
someInput->mutable_shape()->Set(0, 10); // dim(0) is batch size
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::INVALID_BATCH_SIZE) << status.string();
}
TEST_F(KFSPredictValidation, RequestWrongBatchSizeAuto) {
modelConfig.setBatchingParams("auto");
auto someInput = request.mutable_inputs()->Mutable(request.mutable_inputs()->size() - 1); // modify last
someInput->mutable_shape()->Set(0, 10); // dim(0) is batch size. Change from 1
auto bufferId = request.mutable_inputs()->size() - 1;
auto previousSize = request.raw_input_contents()[bufferId].size();
request.mutable_raw_input_contents(bufferId)->assign(size_t(previousSize * 10), '1');
auto status = instance->mockValidate(&request);
EXPECT_EQ(status, ovms::StatusCode::BATCHSIZE_CHANGE_REQUIRED) << status.string();
}
TEST_F(KFSPredictValidation, ValidRequestBinaryInputs) {
std::string inputName = "Binary_Input";
::KFSRequest binaryInputRequest;
auto input = binaryInputRequest.add_inputs();
input->set_name(inputName);