-// Copyright (C) 2018 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
+//
+// Copyright 2016-2018 Intel Corporation.
+//
+// This software and the related documents are Intel copyrighted materials,
+// and your use of them is governed by the express license under which they
+// were provided to you (End User License Agreement for the Intel(R) Software
+// Development Products (Version May 2017)). Unless the License provides
+// otherwise, you may not use, modify, copy, publish, distribute, disclose or
+// transmit this software or the related documents without Intel's prior
+// written permission.
+//
+// This software and the related documents are provided as is, with no
+// express or implied warranties, other than those that are expressly
+// stated in the License.
//
#include <vector>
#include <gtest/gtest.h>
#include <inference_engine/layer_transform.hpp>
+#include <gna-api-types-xnn.h>
#include "gna_plugin/quantization/model_quantizer.hpp"
#include "gna_plugin/quantization/layer_quantizer.hpp"
#include "gna_matcher.hpp"
auto weights = make_shared_blob<uint8_t >(Precision::U8, C, {440});
weights->allocate();
- fillWeights(weights, 100);
+ fillWeights(weights, {100});
net_reader.SetWeights(weights);
auto newNet = q.quantize(net_reader.getNetwork(), 1000);
.inNotCompactMode().gna().propagate_forward().called_with().diagonal_inserted_into_nnet();
}
-TEST_F(I16QuantisationTest, SplitFollowedByFCAndEltwiseOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
- std::vector<float> expected_result = {12.0, 12.0, 12.0, 12.0, 12.0,
- 12.0, 12.0, 12.0, 12.0, 12.0};
- assert_that().onInferModel(FCWithPaddingAfterSplitModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
-
-TEST_F(I16QuantisationTest, SliceFollowedByFCAndEltwiseOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
- std::vector<float> expected_result = {14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0};
- assert_that().onInferModel(FCWithPaddingAfterSliceModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
-
-TEST_F(I16QuantisationTest, SliceFollowedByAlignedFCAndEltwiseOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
- std::vector<float> expected_result = {18.0, 18.0, 18.0, 18.0};
- assert_that().onInferModel(SliceModelWithAlignedOutputs())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
+TEST_F(I16QuantisationTest, DISABLED_SliceFollowedBy2FCsAnd2Eltwises_AlignedFilterInsertion) {
+ assert_that().onInferModel(twoFCWithPaddingAfterSliceModel())
+ .inNotCompactMode().gna().propagate_forward().called_with().diagonal_inserted_into_nnet();
}
-TEST_F(I16QuantisationTest, SliceFollowedBy2FCsAnd2EltwisesOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
- std::vector<float> expected_result = {27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0};
- assert_that().onInferModel(twoFCWithPaddingAfterSliceModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
+// ToDo requires implementation of aligning filter for concat inputs and improvement of
+// qunatization/scaling algorithm for concat
+TEST_F(I16QuantisationTest, DISABLED_DoubleConcatPropageteForwardWithSuccess_AlignedFilterInsertion) {
+ assert_that().onInferModel(doubleConcatModel())
+ .inNotCompactMode().gna().propagate_forward().called_with().diagonal_inserted_into_nnet();
}
TEST_F(I16QuantisationTest, EltwiseSumm_onlyOneIdentityInsertion) {
.inNotCompactMode().gna().propagate_forward().called_with().pwl_inserted_into_nnet().twice();
}
-TEST_F(I16QuantisationTest, ConcatPropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
- std::vector<float> expected_result = {121.0, 121.0, 121.0, 121.0, 121.0,
- 121.0, 121.0, 121.0, 121.0, 121.0,
- 121.0, 121.0, 121.0, 121.0, 121.0,
- 121.0, 121.0, 121.0, 121.0, 121.0};
-
- assert_that().onInferModel(concatModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
+TEST_F(I16QuantisationTest, multiple_inputs_supported) {
+ assert_that().onInferModel(two_inputs_to_affine())
+ .inNotCompactMode().gna().propagate_forward().called_with().pwl_inserted_into_nnet().once();
}
+TEST_F(I16QuantisationTest, multiple_inputs_can_handle_individual_scale_factors) {
+ std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
+ std::vector<float> input2_data = {2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0};
+ std::vector<float> result = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
-TEST_F(I16QuantisationTest, DoubleConcatPropageteForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
- std::vector<float> expected_result = {141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0,
- 141.0, 141.0, 141.0, 141.0, 141.0};
+ assert_that().onInferModel(two_inputs_to_affine())
+ .inNotCompactMode().gna().propagate_forward()
+ .called_with().inputScale("input_1", 2).And()
+ .inputScale("input_2", 2).returns().result().filledWith(16384).that().equal_to(result);
+}
- assert_that().onInferModel(doubleConcatModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
+TEST_F(I16QuantisationTest, DISABLED_multiple_inputs_into_concat_supported) {
+ assert_that().onInferModel(two_inputs_to_concat())
+ .inNotCompactMode().gna().propagate_forward().called_with().pwl_inserted_into_nnet().once();
}
TEST_F(I16QuantisationTest, ScaleShift_Affine_WillResultInIdentityInsertion) {
.inNotCompactMode().gna().propagate_forward().called_with().pwl_inserted_into_nnet().twice();
}
+TEST_F(I16QuantisationTest, ScaleShiftWithBroadcast_ResultInDiagonalInsertion) {
+
+ auto & affineWeights = storage<std::vector<uint16_t>>();
+
+ affineWeights = {
+ 2048, 4096, 6144, 8192, 10240, 12288, 14336, 16384,
+ 2048, 4096, 6144, 8192, 10240, 12288, 14336, 16384,
+ 2048, 4096, 6144, 8192, 10240, 12288, 14336, 16384,
+ 2048, 4096, 6144, 8192, 10240, 12288, 14336, 16384,
+ 2048, 4096, 6144, 8192, 10240, 12288, 14336, 16384,
+ };
+
+ assert_that().onInferModel(ScaleShift3DModel()).withWeigthsPattern({1.0f,2.0f,3.0f,4.0f,5.0f,6.0f,7.0f,8.0f})
+ .inNotCompactMode().gna().propagate_forward().called_with().called_with().affine_weights_eq(affineWeights);
+}
+
// TODO: this mode not required in rel life scenarios so far
TEST_F(I16QuantisationTest, DISABLED_AffineWithOutputToMemoryAndToAnotherNode_ResultInCopyInsertion) {
assert_that().onInferModel(affineToMemoryModel()).inNotCompactMode().gna().propagate_forward().
called_with().copy_inserted_into_nnet();
}
-TEST_F(I16QuantisationTest, CropWithoutOffsetPropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
- std::vector<float> expected_result = {11.0, 11.0, 11.0, 11.0, 11.0,
- 11.0, 11.0, 11.0, 11.0, 11.0};
+TEST_F(I16QuantisationTest, DISABLED_permutationOfWeightsBetweenConvAndAffine) {
+ auto & affineWeights = storage<std::vector<uint16_t>>();
- assert_that().onInferModel(cropWithoutOffsetModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
+ // least likely that width and height both are multiple of 7
+ auto weigthsPattern = {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f};
-TEST_F(I16QuantisationTest, CropWithAlignedOffsetPropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
- std::vector<float> expected_result = {3.0, 3.0, 3.0, 3.0, 3.0,
- 3.0, 3.0, 3.0, 3.0, 3.0};
+ // here weights are transpozed
+ save().onInferModel(affineAfterConvNoPermute()).withWeigthsPattern(weigthsPattern)
+ .inNotCompactMode().from().propagate_forward().affine_weights_transpozed({128, 61}).to(affineWeights);
- assert_that().onInferModel(cropWithAlignedOffsetModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
+ // here weights shouldn't be transposed
+ assert_that().onInferModel(affineAfterConvWithPermute()).withWeigthsPattern(weigthsPattern)
+ .inNotCompactMode().gna().propagate_forward().called_with().affine_weights_eq(affineWeights);
}
-TEST_F(I16QuantisationTest, CropWithOffsetPropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0};
- std::vector<float> expected_result = {7.0, 7.0, 7.0, 7.0, 7.0,
- 7.0, 7.0, 7.0, 7.0, 7.0};
+TEST_F(I16QuantisationTest, DISABLED_noPermutationOfWeightsBetweenConvAndAffineIfPermuteLayerWithCorrectArgs) {
+ auto & affineWeights = storage<std::vector<uint16_t>>();
- assert_that().onInferModel(cropWithOffsetModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
-
-TEST_F(I16QuantisationTest, CropWithMaxOffsetPropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
- std::vector<float> expected_result = {1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0};
-
- assert_that().onInferModel(cropWithMaxOffsetModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
+ // least likely that width and height both are multiple of 7
+ auto weigthsPattern = {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f};
-TEST_F(I16QuantisationTest, CropWithOffsetAfterFCPropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
- std::vector<float> expected_result = {111.0, 111.0, 111.0, 111.0, 111.0,
- 111.0, 111.0, 111.0, 111.0, 111.0};
+ save().onInferModel(affineAfterConvWithPermute()).withWeigthsPattern(weigthsPattern)
+ .inNotCompactMode().from().propagate_forward().affine_weights().to(affineWeights);
- assert_that().onInferModel(cropWithOffsetExtendedModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
-
-TEST_F(I16QuantisationTest, CopySimpleCasePropagateForwardWithSuccessOnCPU) {
- std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
- 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
- std::vector<float> expected_result = {12.0, 12.0, 12.0, 12.0, 12.0,
- 12.0, 12.0, 12.0, 12.0, 12.0,
- 11.0, 11.0, 11.0, 11.0, 11.0,
- 11.0, 11.0, 11.0, 11.0, 11.0,};
-
- assert_that().onInferModel(copyModel())
- .inNotCompactMode().gna().propagate_forward().onCPU()
- .called_with_input_and_expected_output(input_data, expected_result);
-}
+ assert_that().onInferModel(affineAfterConvNoPermute()).withWeigthsPattern(weigthsPattern)
+ .inNotCompactMode().gna().propagate_forward().called_with().affine_weights_transposed(affineWeights, {128, 61});
+}
\ No newline at end of file
projects / platform / upstream / dldt.git / blobdiff