diff options
author | 2018-05-23 09:16:52 -0700 | |
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committer | 2018-05-23 09:19:36 -0700 | |
commit | 7a82d0fd10901f4b59f38e838a24a04df8305f73 (patch) | |
tree | ffb2949fa729e90c29f0fe81be57224c00f1abb3 /tensorflow/contrib/lite | |
parent | d1f44e1c60d38cc36bc438b59338c3a4eecf0615 (diff) |
Support batch size > 1 in L2Normalization 8 bit quantized implementations.
PiperOrigin-RevId: 197736184
Diffstat (limited to 'tensorflow/contrib/lite')
3 files changed, 71 insertions, 39 deletions
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 6e5ceec85e..1b4660ef4f 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -2353,24 +2353,27 @@ inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - TFLITE_DCHECK_EQ(outer_size, 1); - int32 square_l2_norm = 0; - for (int i = 0; i < depth; i++) { - int32 diff = input_data[i] - input_zero_point; - square_l2_norm += diff * diff; - } - int32 inv_l2norm_multiplier; - int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplier(square_l2_norm, &inv_l2norm_multiplier, - &inv_l2norm_shift); - - for (int i = 0; i < depth; i++) { - int32 diff = input_data[i] - input_zero_point; - int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOne( - 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); - int32 unclamped_output_val = 128 + rescaled_diff; - int32 output_val = std::min(255, std::max(0, unclamped_output_val)); - output_data[i] = static_cast<uint8>(output_val); + for (int i = 0; i < outer_size; ++i) { + int32 square_l2_norm = 0; + for (int c = 0; c < depth; c++) { + int32 diff = input_data[c] - input_zero_point; + square_l2_norm += diff * diff; + } + int32 inv_l2norm_multiplier; + int inv_l2norm_shift; + GetInvSqrtQuantizedMultiplier(square_l2_norm, &inv_l2norm_multiplier, + &inv_l2norm_shift); + + for (int c = 0; c < depth; c++) { + int32 diff = *input_data - input_zero_point; + int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOne( + 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); + int32 unclamped_output_val = 128 + rescaled_diff; + int32 output_val = std::min(255, std::max(0, unclamped_output_val)); + *output_data = static_cast<uint8>(output_val); + ++input_data; + ++output_data; + } } } diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index a56fc0635b..cd4af48bee 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -895,25 +895,28 @@ inline void L2Normalization(const uint8* input_data, const Dims<4>& input_dims, const Dims<4>& output_dims) { const int depth = MatchingArraySize(input_dims, 0, output_dims, 0); const int outer_size = MatchingFlatSizeSkipDim(input_dims, 0, output_dims); - TFLITE_DCHECK_EQ(outer_size, 1); - int32 square_l2_norm = 0; - for (int i = 0; i < depth; i++) { - int32 diff = input_data[Offset(input_dims, i, 0, 0, 0)] - input_zero_point; - square_l2_norm += diff * diff; - } - int32 inv_l2norm_multiplier; - int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplier(square_l2_norm, &inv_l2norm_multiplier, - &inv_l2norm_shift); - - for (int i = 0; i < depth; i++) { - int32 diff = input_data[Offset(input_dims, i, 0, 0, 0)] - input_zero_point; - int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOne( - 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); - int32 unclamped_output_val = 128 + rescaled_diff; - int32 output_val = std::min(255, std::max(0, unclamped_output_val)); - output_data[Offset(output_dims, i, 0, 0, 0)] = - static_cast<uint8>(output_val); + for (int i = 0; i < outer_size; ++i) { + int32 square_l2_norm = 0; + for (int c = 0; c < depth; c++) { + int32 diff = + input_data[Offset(input_dims, c, i, 0, 0)] - input_zero_point; + square_l2_norm += diff * diff; + } + int32 inv_l2norm_multiplier; + int inv_l2norm_shift; + GetInvSqrtQuantizedMultiplier(square_l2_norm, &inv_l2norm_multiplier, + &inv_l2norm_shift); + + for (int c = 0; c < depth; c++) { + int32 diff = + input_data[Offset(input_dims, c, i, 0, 0)] - input_zero_point; + int32 rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOne( + 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); + int32 unclamped_output_val = 128 + rescaled_diff; + int32 output_val = std::min(255, std::max(0, unclamped_output_val)); + output_data[Offset(output_dims, c, i, 0, 0)] = + static_cast<uint8>(output_val); + } } } diff --git a/tensorflow/contrib/lite/kernels/l2norm_test.cc b/tensorflow/contrib/lite/kernels/l2norm_test.cc index 11cc666bad..070ed60040 100644 --- a/tensorflow/contrib/lite/kernels/l2norm_test.cc +++ b/tensorflow/contrib/lite/kernels/l2norm_test.cc @@ -67,7 +67,7 @@ class L2NormOpModel : public SingleOpModel { int output_; }; -TEST(L2NormOpTest, SimpleTest) { +TEST(L2NormOpTest, SimpleFloatTest) { L2NormOpModel m({1, 1, 1, 6}, TensorType_FLOAT32, ActivationFunctionType_NONE); m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); @@ -76,7 +76,7 @@ TEST(L2NormOpTest, SimpleTest) { ElementsAreArray({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05})); } -TEST(L2NormOpTest, MultipleBatchesTest) { +TEST(L2NormOpTest, MultipleBatchFloatTest) { L2NormOpModel m({3, 1, 1, 6}, TensorType_FLOAT32, ActivationFunctionType_NONE); m.SetInput({ @@ -105,6 +105,32 @@ TEST(L2NormOpTest, SimpleUint8Test) { ArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}, 0.1))); } +TEST(L2NormOpTest, MultipleBatchUint8Test) { + L2NormOpModel m({3, 1, 1, 6}, TensorType_UINT8, ActivationFunctionType_NONE); + + m.QuantizeAndPopulate<uint8_t>(m.input(), + { + -1.1, 0.6, 0.7, 1.2, -0.7, 0.1, // batch 1 + -1.1, 0.6, 0.7, 1.2, -0.7, 0.1, // batch 2 + -1.1, 0.6, 0.7, 1.2, -0.7, 0.1, // batch 3 + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput<uint8_t>(), + ElementsAreArray({ + 58, 166, 173, 205, 83, 134, // batch 1 + 58, 166, 173, 205, 83, 134, // batch 2 + 58, 166, 173, 205, 83, 134, // batch 3 + })); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + -0.55, 0.3, 0.35, 0.6, -0.35, 0.05, // batch 1 + -0.55, 0.3, 0.35, 0.6, -0.35, 0.05, // batch 2 + -0.55, 0.3, 0.35, 0.6, -0.35, 0.05, // batch 3 + }, + 0.1))); +} + } // namespace } // namespace tflite |