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Update create_and_fill_tensor to work with InputGenerateData (openvin…
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…otoolkit#20008)

* Update create_and_fill_tensor to work with InputGenerateData
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@olpipi
olpipi authored Dec 13, 2023
1 parent 20ad8b4 commit d18d8a4
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Showing 98 changed files with 862 additions and 545 deletions.
10 changes: 5 additions & 5 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/adaptive_pooling.cpp
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Expand Up @@ -159,11 +159,11 @@ class AdaPoolLayerCPUTest : public testing::WithParamInterface<AdaPoolLayerCPUTe
dataPtr[i] = pooledVector[i];
}
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
2560,
0,
256);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 2560;
in_data.resolution = 256;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
}
inputs.insert({funcInput.get_node_shared_ptr(), tensor});
}
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6 changes: 5 additions & 1 deletion src/plugins/intel_cpu/tests/functional/single_layer_tests/batch_to_space.cpp
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Expand Up @@ -66,7 +66,11 @@ class BatchToSpaceCPULayerTest : public testing::WithParamInterface<BatchToSpace
const auto& static_shape = targetInputStaticShapes[i];
switch (i) {
case 0: {
tensor = ov::test::utils::create_and_fill_tensor(param_type, static_shape, 2560, 0, 256);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 2560;
in_data.resolution = 256;
tensor = ov::test::utils::create_and_fill_tensor(param_type, static_shape, in_data);
break;
}
case 1: {
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10 changes: 5 additions & 5 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/broadcast.cpp
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Expand Up @@ -171,11 +171,11 @@ class BroadcastLayerCPUTest : public testing::WithParamInterface<BroadcastLayerC
}
} else {
if (funcInput.get_element_type().is_real()) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
10,
0,
1000);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 10;
in_data.resolution = 1000;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i]);
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13 changes: 6 additions & 7 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/bucketize.cpp
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Expand Up @@ -58,13 +58,12 @@ class BucketizeLayerCPUTest : public testing::WithParamInterface<BucketizeCPUPar
const auto& funcInputs = function->inputs();

auto data_size = shape_size(targetInputStaticShapes[0]);
ov::Tensor tensorData = ov::test::utils::create_and_fill_tensor(funcInputs[0].get_element_type(),
targetInputStaticShapes[0],
data_size * 5,
0,
10,
7235346);

ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = data_size * 5;
in_data.resolution = 10;
in_data.seed = 7235346;
ov::Tensor tensorData = ov::test::utils::create_and_fill_tensor(funcInputs[0].get_element_type(), targetInputStaticShapes[0], in_data);
ov::Tensor tensorBucket =
ov::test::utils::create_and_fill_tensor_unique_sequence(funcInputs[1].get_element_type(),
targetInputStaticShapes[1],
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7 changes: 5 additions & 2 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/activation.cpp
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Expand Up @@ -78,8 +78,11 @@ void ActivationLayerCPUTest::generate_inputs(const std::vector<ov::Shape>& targe
const auto& funcInput = funcInputs[i];
ov::Tensor tensor;
if (funcInput.get_element_type().is_real()) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i],
range, startFrom, resolution);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = startFrom;
in_data.range = range;
in_data.resolution = resolution;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
}
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6 changes: 5 additions & 1 deletion src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/conversion.cpp
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Expand Up @@ -98,7 +98,11 @@ void ConvertCPULayerTest::generate_inputs(const std::vector<ov::Shape>& targetIn

auto shape = targetInputStaticShapes.front();
size_t size = shape_size(shape);
ov::Tensor tensor = ov::test::utils::create_and_fill_tensor(funcInputs[0].get_element_type(), shape, 2 * size);

ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 2 * size;
ov::Tensor tensor = ov::test::utils::create_and_fill_tensor(funcInputs[0].get_element_type(), shape, in_data);

if (inPrc == ov::element::f32) {
auto* rawBlobDataPtr = static_cast<float*>(tensor.data());
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6 changes: 5 additions & 1 deletion src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/eltwise.cpp
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Expand Up @@ -85,7 +85,11 @@ ov::Tensor EltwiseLayerCPUTest::generate_eltwise_input(const ov::element::Type&
break;
}
}
return ov::test::utils::create_and_fill_tensor(type, shape, params.range, params.start_from, params.resolution);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = params.start_from;
in_data.range = params.range;
in_data.resolution = params.resolution;
return ov::test::utils::create_and_fill_tensor(type, shape, in_data);
}

void EltwiseLayerCPUTest::generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) {
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10 changes: 5 additions & 5 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/reduce.cpp
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Expand Up @@ -145,11 +145,11 @@ void ReduceCPULayerTest::generate_inputs(const std::vector<ov::Shape>& targetInp
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::Tensor tensor;
if (reductionType == utils::ReductionType::Prod) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
10,
5);
if (reductionType == ngraph::helpers::ReductionType::Prod) {
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 5;
in_data.range = 10;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
if (netPrecision == ElementType::f32) {
auto* rawBlobDataPtr = static_cast<float*>(tensor.data());
for (size_t i = 0; i < tensor.get_size(); ++i) {
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10 changes: 5 additions & 5 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/convolution_backprop_data.cpp
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Expand Up @@ -109,11 +109,11 @@ class DeconvolutionLayerCPUTest : public testing::WithParamInterface<DeconvLayer
targetInputStaticShapes[i],
outShapeData[inferRequestNum].data());
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
2560,
0,
256);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 2560;
in_data.resolution = 256;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
}

inputs.insert({funcInput.get_node_shared_ptr(), tensor});
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13 changes: 6 additions & 7 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/ctc_greedy_decoder.cpp
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Expand Up @@ -92,14 +92,13 @@ class CTCGreedyDecoderLayerCPUTest : public testing::WithParamInterface<CTCGreed
ov::Tensor tensor;
if (i == 0) {
if (funcInput.get_element_type().is_real()) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
10,
0,
1000);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 10;
in_data.resolution = 1000;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i]);
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
}
} else {
auto T = targetInputStaticShapes[i][0];
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21 changes: 10 additions & 11 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/ctc_greedy_decoder_seq_len.cpp
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Expand Up @@ -116,14 +116,13 @@ class CTCGreedyDecoderSeqLenLayerCPUTest : public testing::WithParamInterface<CT
ov::Tensor tensor;
if (i == 0) {
if (funcInput.get_element_type().is_real()) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
10,
0,
1000);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 10;
in_data.resolution = 1000;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i]);
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
}
} else if (i == 1) {
const auto seqLen = dataShape[1];
Expand All @@ -147,10 +146,10 @@ class CTCGreedyDecoderSeqLenLayerCPUTest : public testing::WithParamInterface<CT

} else if (i == 2) {
// blank should be valid class type
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
dataShape[2],
0);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = dataShape[2];
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
}
inputs.insert({funcInput.get_node_shared_ptr(), tensor});
}
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6 changes: 5 additions & 1 deletion src/plugins/intel_cpu/tests/functional/single_layer_tests/ctc_loss.cpp
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Expand Up @@ -134,7 +134,11 @@ class CTCLossLayerCPUTest : public testing::WithParamInterface<CTCLossLayerCPUTe
const auto& funcInput = funcInputs[i];
ov::Tensor tensor;
if (i == 0) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), dataShape, 10, 0, 10);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = 0;
in_data.range = 10;
in_data.resolution = 10;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), dataShape, in_data);
} else if (i == 1) {
tensor = ov::Tensor{funcInput.get_element_type(), {shapeN}};
if (funcInput.get_element_type() == ElementType::i32) {
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39 changes: 29 additions & 10 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/deformable_convolution.cpp
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Expand Up @@ -101,29 +101,48 @@ class DefConvLayerCPUTest : public testing::WithParamInterface<DefConvLayerCPUTe
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::Tensor tensor;
ov::test::utils::InputGenerateData in_data;
if (i == 0) { // "a_data"
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), inShape, 2, -1, 100);
in_data.start_from = -1;
in_data.range = 2;
in_data.resolution = 100;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), inShape, in_data);
} else if (i == 1) { // "b_offset_vals"
if (offsetType == OffsetType::NATURAL) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), offShape, 10, 0, 1);
in_data.start_from = 0;
in_data.range = 10;
in_data.resolution = 1;
} else if (offsetType == OffsetType::ZERO) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), offShape, 1, 0, 1);
in_data.start_from = 0;
in_data.range = 1;
in_data.resolution = 1;
} else if (offsetType == OffsetType::REAL_POSITIVE) {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), offShape, 2, 0, 100);
in_data.start_from = 0;
in_data.range = 2;
in_data.resolution = 100;
} else if (offsetType == OffsetType::REAL_NEGATIVE) {
tensor =
ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), offShape, 2, -2, 100);
in_data.start_from = -2;
in_data.range = 2;
in_data.resolution = 100;
} else if (offsetType == OffsetType::REAL_MISC) {
tensor =
ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), offShape, 4, -2, 100);
in_data.start_from = -2;
in_data.range = 4;
in_data.resolution = 100;
} else {
OPENVINO_THROW("Unexpected offset type");
}
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), offShape, in_data);
} else if (i == 2) { // "c_filter_vals"
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), filtShape, 2, -1, 100);
in_data.start_from = -1;
in_data.range = 2;
in_data.resolution = 100;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), filtShape, in_data);
} else if (i == 3) { // "c_modulation_scalars"
auto modShape = targetInputStaticShapes[3];
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), modShape, 1, 0, 100);
in_data.start_from = -1;
in_data.range = 2;
in_data.resolution = 100;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), modShape, in_data);
} else {
OPENVINO_THROW("Unknown input of DeformableConvolution");
}
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9 changes: 5 additions & 4 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/eye.cpp
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Expand Up @@ -120,10 +120,11 @@ class EyeLayerCPUTest : public testing::WithParamInterface<EyeLayerCPUTestParams
batchShapePtr[j] = outBatchShape[j];
}
} else {
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[i],
1,
(i == 0 ? rowNum : (i == 1 ? colNum : shift)));
ov::test::utils::InputGenerateData in_data;
in_data.start_from = i == 0 ? rowNum : (i == 1 ? colNum : 6);
in_data.range = 1;

tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], in_data);
}
inputs.insert({funcInput.get_node_shared_ptr(), tensor});
}
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8 changes: 4 additions & 4 deletions src/plugins/intel_cpu/tests/functional/single_layer_tests/fake_quantize.cpp
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Expand Up @@ -131,10 +131,10 @@ class FakeQuantizeLayerCPUTest : public testing::WithParamInterface<fqLayerTestP
ASSERT_EQ(funcInputs.size(), 1);
const auto& funcInput = funcInputs[0];
ov::Tensor tensor;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
targetInputStaticShapes[0],
inDataHighBounds - inDataLowBounds,
inDataLowBounds);
ov::test::utils::InputGenerateData in_data;
in_data.start_from = inDataLowBounds;
in_data.range = inDataHighBounds - inDataLowBounds;
tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[0], in_data);
inputs.insert({funcInput.get_node_shared_ptr(), tensor});
}

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