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!5210 resize int8 precision improve 

Merge pull request !5210 from zhaozhenlong/lite/issue/resize_int8_weight_calc
This commit is contained in:
mindspore-ci-bot 2020-08-26 19:17:05 +08:00 committed by Gitee
parent 06b7b20658 54043d81c1
commit ab9641558f
4 changed files with 150 additions and 52 deletions
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72
mindspore/lite/nnacl/int8/resize.c
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@ -86,6 +86,62 @@ int ResizeBilinearInt8(const int8_t *input_data, int8_t *output_data, const int
return NNACL_OK;
}
int ResizeBilinearInt8WithFloatWeight(const int8_t *input_data, int8_t *output_data, const int *input_shape,
const int *output_shape, const bool align_corners, QuantArg *quant_in,
QuantArg *quant_out, const QuantMulArg *mul_arg, int tid, int thread_num) {
if (input_data == NULL || output_data == NULL || input_shape == NULL || output_shape == NULL) {
return NNACL_NULL_PTR;
}
int32_t in_n = input_shape[0];
int32_t in_h = input_shape[1];
int32_t in_w = input_shape[2];
int32_t in_c = input_shape[3];
int32_t new_height = output_shape[1];
int32_t new_width = output_shape[2];
float height_scale, width_scale;
ComputeScaleFloat(in_h, new_height, align_corners, &height_scale);
ComputeScaleFloat(in_w, new_width, align_corners, &width_scale);
int n, h, w, c;
for (n = 0; n < in_n; n++) {
for (h = tid; h < new_height; h += thread_num) {
float actual_y;
int bottom, top;
float bottom_weight, top_weight;
ComputeInterpolationArgsFloatWeight(h, height_scale, in_h, &actual_y, &bottom, &bottom_weight, &top, &top_weight);
for (w = 0; w < new_width; w++) {
float actual_x;
int left, right;
float left_weight, right_weight;
ComputeInterpolationArgsFloatWeight(w, width_scale, in_w, &actual_x, &left, &left_weight, &right,
&right_weight);
for (c = 0; c < in_c; c++) {
float bottom_left_value = ((int32_t)input_data[offset(input_shape, n, bottom, left, c)] - quant_in->zp_) *
bottom_weight * left_weight;
float bottom_right_value = ((int32_t)input_data[offset(input_shape, n, bottom, right, c)] - quant_in->zp_) *
bottom_weight * right_weight;
float top_left_value =
((int32_t)input_data[offset(input_shape, n, top, left, c)] - quant_in->zp_) * top_weight * left_weight;
float top_right_value =
((int32_t)input_data[offset(input_shape, n, top, right, c)] - quant_in->zp_) * top_weight * right_weight;
float interp_value = bottom_left_value + bottom_right_value + top_left_value + top_right_value;
const int out_interp_value = MultiplyByQuantizedMultiplier((int32_t)interp_value, mul_arg->multiplier_,
mul_arg->left_shift_, mul_arg->right_shift_) +
quant_out->zp_;
int8_t out_value;
out_value = out_interp_value > INT8_MAX ? INT8_MAX : out_interp_value;
out_value = out_value < INT8_MIN ? INT8_MIN : out_value;
output_data[offset(output_shape, n, h, w, c)] = out_value;
}
}
}
}
return NNACL_OK;
}
int ResizeNearestNeighborInt8Simple(const int8_t *input_data, int8_t *output_data, const int *input_shape,
const int *output_shape, const bool align_corners, int tid, int thread_num) {
int batch, y, x, c;
@ -133,6 +189,22 @@ void ComputeInterpolationArgs(const int32_t pos, const int32_t scale, const int3
*scaled_high_weight = *scaled_pos - (1 << 10) * (*low);
}
void ComputeScaleFloat(const int32_t in_value, const int32_t out_value, const bool align_corners, float *scale) {
*scale = (float)in_value / out_value;
if (align_corners && out_value > 1) {
*scale = (float)(in_value - 1) / (out_value - 1);
}
}
void ComputeInterpolationArgsFloatWeight(const int32_t pos, const float scale, const int32_t size, float *actual_pos,
int32_t *low, float *low_weight, int32_t *high, float *high_weight) {
*actual_pos = pos * scale;
*low = *actual_pos > 0 ? floor(*actual_pos) : 0;
*low_weight = 1.0 - (*actual_pos - *low);
*high = *low + 1 < size ? *low + 1 : size - 1;
*high_weight = *actual_pos - (*low);
}
void ComputeNearestNeighborInt(const int32_t pos, const int in_size, const int32_t new_size, const bool align_corners,
int32_t *nearest) {
if (new_size == 0) {

19
mindspore/lite/nnacl/int8/resize.h
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@ -31,6 +31,20 @@ int ResizeBilinearInt8(const int8_t *input_data, int8_t *output_data, const int
const bool align_corners, QuantArg *quant_in, QuantArg *quant_out, const QuantMulArg *mul_arg,
int tid, int thread_num);
int ResizeBilinearInt8WithFloatWeight(const int8_t *input_data, int8_t *output_data, const int *input_shape,
const int *output_shape, const bool align_corners, QuantArg *quant_in,
QuantArg *quant_out, const QuantMulArg *mul_arg, int tid, int thread_num);
void ComputeScale(const int32_t in_value, const int32_t out_value, const bool align_corners, int32_t *scale);
void ComputeInterpolationArgs(const int32_t pos, const int32_t scale, const int32_t size, int32_t *scaled_pos,
int32_t *low, int32_t *scaled_low_weight, int32_t *high, int32_t *scaled_high_weight);
void ComputeScaleFloat(const int32_t in_value, const int32_t out_value, const bool align_corners, float *scale);
void ComputeInterpolationArgsFloatWeight(const int32_t pos, const float scale, const int32_t size, float *actual_pos,
int32_t *low, float *low_weight, int32_t *high, float *high_weight);
int ResizeNearestNeighborInt8Simple(const int8_t *input_data, int8_t *output_data, const int *input_shape,
const int *output_shape, const bool align_corners, int tid, int thread_num);
@ -38,11 +52,6 @@ int ResizeNearestNeighborInt8(const int8_t *input_data, int8_t *output_data, con
const int *output_shape, const bool align_corners, const QuantMulArg *multiplier,
QuantArg *quant_in, QuantArg *quant_out, int tid, int thread_num);
void ComputeScale(const int32_t in_value, const int32_t out_value, const bool align_corners, int32_t *scale);
void ComputeInterpolationArgs(const int32_t pos, const int32_t scale, const int32_t size, int32_t *scaled_pos,
int32_t *low, int32_t *scaled_low_weight, int32_t *high, int32_t *scaled_high_weight);
void ComputeNearestNeighborInt(const int32_t pos, const int in_size, const int32_t new_size, const bool align_corners,
int32_t *nearest);
#ifdef __cplusplus

38
mindspore/lite/src/runtime/kernel/arm/int8/resize_int8.cc
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@ -35,9 +35,9 @@ int ResizeInt8CPUKernel::Init() {
if (ret != RET_OK) {
return ret;
}
quant_in_ = new(std::nothrow) QuantArg;
quant_in_ = new (std::nothrow) QuantArg;
MS_ASSERT(quant_in_);
quant_out_ = new(std::nothrow) QuantArg;
quant_out_ = new (std::nothrow) QuantArg;
MS_ASSERT(quant_out_);
auto input = in_tensors_.at(0);
quant_in_->zp_ = input->GetQuantParams().front().zeroPoint;
@ -46,7 +46,7 @@ int ResizeInt8CPUKernel::Init() {
quant_out_->zp_ = output->GetQuantParams().front().zeroPoint;
quant_out_->scale_ = output->GetQuantParams().front().scale;
multiplier_ = new(std::nothrow) QuantMulArg;
multiplier_ = new (std::nothrow) QuantMulArg;
MS_ASSERT(multiplier_);
QuantizeRoundParameter(quant_in_->scale_ / quant_out_->scale_, &multiplier_->multiplier_, &multiplier_->left_shift_,
&multiplier_->right_shift_);
@ -85,9 +85,14 @@ int ResizeInt8CPUKernel::RunImpl(int task_id) {
int ret = 0;
switch (method_) {
case static_cast<int>(schema::ResizeMethod_BILINEAR): {
ret = ResizeBilinearInt8(input_data, output_data, input_shape.data(), out_tensors_[0]->shape().data(),
align_corners_, quant_in_, quant_out_, multiplier_, task_id, context_->thread_num_);
if (quant_in_->zp_ == 0) {
ret = ResizeBilinearInt8(input_data, output_data, input_shape.data(), out_tensors_[0]->shape().data(),
align_corners_, quant_in_, quant_out_, multiplier_, task_id, context_->thread_num_);
} else {
ret = ResizeBilinearInt8WithFloatWeight(input_data, output_data, input_shape.data(),
out_tensors_[0]->shape().data(), align_corners_, quant_in_, quant_out_,
multiplier_, task_id, context_->thread_num_);
}
break;
}
case static_cast<int>(schema::ResizeMethod_NEAREST_NEIGHBOR): {
@ -95,25 +100,12 @@ int ResizeInt8CPUKernel::RunImpl(int task_id) {
bool same_scale = abs(quant_out_->scale_ - quant_in_->scale_) < 1e-6;
if (same_zp && same_scale) {
ret =
ResizeNearestNeighborInt8Simple(input_data,
output_data,
input_shape.data(),
out_tensors_[0]->shape().data(),
align_corners_,
task_id,
context_->thread_num_);
ResizeNearestNeighborInt8Simple(input_data, output_data, input_shape.data(), out_tensors_[0]->shape().data(),
align_corners_, task_id, context_->thread_num_);
} else {
ret =
ResizeNearestNeighborInt8(input_data,
output_data,
input_shape.data(),
out_tensors_[0]->shape().data(),
align_corners_,
multiplier_,
quant_in_,
quant_out_,
task_id,
context_->thread_num_);
ResizeNearestNeighborInt8(input_data, output_data, input_shape.data(), out_tensors_[0]->shape().data(),
align_corners_, multiplier_, quant_in_, quant_out_, task_id, context_->thread_num_);
}
break;
}

73
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_bilinear_int8_tests.cc
View File

@ -84,14 +84,13 @@ TEST_F(TestResizeBilinearInt8, Bilinear0) {
int8_t output_data[16] = {0};
std::vector<int> in_shape = {1, 2, 2, 1};
std::vector<int> out_shape = {1, 4, 4, 1};
const lite::tensor::QuantArg quant_in = {0.005f, 2};
const lite::tensor::QuantArg quant_out = {0.008f, 5};
const lite::tensor::QuantArg quant_in = {0.005f, 0};
const lite::tensor::QuantArg quant_out = {0.008f, 0};
bool align_corners = false;
int thread_num = 1;
int8_t expect[16] = {4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 5, 5, 6, 6};
int8_t expect[16] = {0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2};
Prepare(in_shape, out_shape, input_data, output_data, quant_in, quant_out, align_corners, thread_num);
kernel_->Init();
kernel_->Run();
CompareOutputInt8(output_data, expect, 16, err_percent_);
@ -104,20 +103,19 @@ TEST_F(TestResizeBilinearInt8, Bilinear1) {
int8_t input_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
int8_t output_data[160] = {0};
const lite::tensor::QuantArg quant_in = {0.005f, 2};
const lite::tensor::QuantArg quant_out = {0.008f, 5};
const lite::tensor::QuantArg quant_in = {0.005f, 0};
const lite::tensor::QuantArg quant_out = {0.008f, 0};
int thread_num = 1;
bool align_corners = false;
int8_t expect[160] = {4, 4, 5, 6, 6, 5, 6, 7, 7, 8, 7, 8, 8, 9, 9, 7, 8, 8, 9, 9, 7, 8, 8,
9, 9, 8, 9, 10, 10, 11, 10, 11, 11, 12, 13, 10, 11, 11, 12, 13, 10, 11, 11, 12, 13, 12,
12, 13, 13, 14, 13, 14, 14, 15, 16, 13, 14, 14, 15, 16, 10, 11, 11, 12, 13, 12, 12, 13, 13,
14, 13, 14, 14, 15, 16, 13, 14, 14, 15, 16, 16, 17, 18, 18, 19, 18, 18, 19, 20, 20, 19, 20,
21, 21, 22, 19, 20, 21, 21, 22, 19, 20, 21, 21, 22, 21, 22, 22, 23, 23, 23, 23, 24, 24, 25,
23, 23, 24, 24, 25, 23, 23, 24, 24, 25, 24, 25, 25, 26, 27, 26, 26, 27, 28, 28, 26, 26, 27,
28, 28, 23, 23, 24, 24, 25, 24, 25, 25, 26, 27, 26, 26, 27, 28, 28, 26, 26, 27, 28, 28};
int8_t expect[160] = {0, 1, 1, 2, 2, 2, 2, 3, 3, 4, 3, 4, 4, 5, 6, 3, 4, 4, 5, 6, 3, 4, 4,
5, 6, 5, 5, 6, 7, 7, 6, 7, 8, 8, 9, 6, 7, 8, 8, 9, 6, 7, 7, 8, 9, 8,
8, 9, 10, 10, 9, 10, 11, 11, 12, 9, 10, 11, 11, 12, 6, 7, 7, 8, 9, 8, 8, 9, 10,
10, 9, 10, 11, 11, 12, 9, 10, 11, 11, 12, 13, 13, 14, 14, 15, 14, 15, 15, 16, 17, 16, 16,
17, 18, 18, 16, 16, 17, 18, 18, 16, 16, 17, 18, 18, 17, 18, 18, 19, 20, 19, 19, 20, 21, 21,
19, 19, 20, 21, 21, 19, 19, 20, 21, 21, 20, 21, 22, 22, 23, 22, 23, 23, 24, 24, 22, 23, 23,
24, 24, 19, 19, 20, 21, 21, 20, 21, 22, 22, 23, 22, 23, 23, 24, 24, 22, 23, 23, 24, 24};
Prepare(in_shape, out_shape, input_data, output_data, quant_in, quant_out, align_corners, thread_num);
kernel_->Init();
kernel_->Run();
CompareOutputInt8(output_data, expect, 160, err_percent_);
@ -131,22 +129,49 @@ TEST_F(TestResizeBilinearInt8, Bilinear2) {
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
int8_t output_data[160] = {0};
const lite::tensor::QuantArg quant_in = {0.005f, 2};
const lite::tensor::QuantArg quant_out = {0.008f, 5};
const lite::tensor::QuantArg quant_in = {0.005f, 0};
const lite::tensor::QuantArg quant_out = {0.008f, 0};
int thread_num = 2;
bool align_corners = true;
int8_t expect[160] = {4, 4, 5, 6, 6, 5, 5, 6, 7, 7, 6, 6, 7, 8, 8, 7, 8, 8, 9, 9, 6, 6, 7,
8, 8, 7, 8, 8, 9, 9, 8, 9, 9, 10, 10, 9, 10, 10, 11, 11, 8, 9, 9, 10, 10, 9,
10, 10, 11, 11, 10, 11, 11, 12, 13, 11, 12, 12, 13, 14, 10, 11, 11, 12, 13, 11, 12, 12, 13,
14, 12, 13, 13, 14, 15, 13, 14, 14, 15, 16, 16, 17, 18, 18, 19, 17, 18, 19, 19, 20, 18, 19,
20, 20, 21, 19, 20, 21, 21, 22, 18, 19, 20, 20, 21, 19, 20, 21, 21, 22, 20, 21, 22, 22, 23,
21, 22, 23, 23, 24, 20, 21, 22, 22, 23, 21, 22, 23, 23, 24, 23, 23, 24, 24, 25, 24, 24, 25,
25, 26, 23, 23, 24, 24, 25, 24, 24, 25, 25, 26, 25, 25, 26, 26, 27, 26, 26, 27, 28, 28};
int8_t expect[160] = {0, 1, 1, 2, 2, 1, 2, 2, 3, 4, 2, 3, 3, 4, 5, 3, 4, 4, 5, 6, 2, 3, 3,
4, 5, 3, 4, 4, 5, 6, 4, 5, 5, 6, 7, 5, 6, 6, 7, 8, 4, 5, 5, 6, 7, 5,
6, 6, 7, 8, 6, 7, 8, 8, 9, 7, 8, 9, 9, 10, 6, 7, 7, 8, 9, 7, 8, 9, 9,
10, 8, 9, 10, 10, 11, 9, 10, 11, 11, 12, 13, 13, 14, 14, 15, 14, 14, 15, 15, 16, 15, 15,
16, 16, 17, 16, 16, 17, 18, 18, 15, 15, 16, 16, 17, 16, 16, 17, 18, 18, 17, 17, 18, 19, 19,
18, 18, 19, 20, 20, 17, 17, 18, 19, 19, 18, 18, 19, 20, 20, 19, 19, 20, 21, 21, 20, 20, 21,
22, 22, 19, 19, 20, 21, 21, 20, 20, 21, 22, 22, 21, 21, 22, 23, 23, 22, 23, 23, 24, 24};
Prepare(in_shape, out_shape, input_data, output_data, quant_in, quant_out, align_corners, thread_num);
kernel_->Init();
kernel_->Run();
CompareOutputInt8(output_data, expect, 160, err_percent_);
}
// 2*2*2*5 -> 2*4*4*5 thread num 2, align corners zp -128
TEST_F(TestResizeBilinearInt8, Bilinear3) {
std::vector<int> in_shape = {2, 2, 2, 5};
std::vector<int> out_shape = {2, 4, 4, 5};
int8_t input_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
int8_t output_data[160] = {0};
const lite::tensor::QuantArg quant_in = {0.005f, 2};
const lite::tensor::QuantArg quant_out = {0.005f, 2};
int thread_num = 2;
bool align_corners = true;
int8_t expect[160] = {0, 1, 2, 3, 4, 2, 3, 4, 5, 6, 3, 4, 5, 6, 7, 5, 6, 7, 8, 9, 3, 4, 5,
6, 7, 5, 6, 7, 8, 9, 7, 8, 9, 10, 11, 8, 9, 10, 11, 12, 7, 8, 9, 10, 11, 8,
9, 10, 11, 12, 10, 11, 12, 13, 14, 12, 13, 14, 15, 16, 10, 11, 12, 13, 14, 12, 13, 14, 15,
16, 13, 14, 15, 16, 17, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 22, 23, 24, 25, 26, 23, 24,
25, 26, 27, 25, 26, 27, 28, 29, 23, 24, 25, 26, 27, 25, 26, 27, 28, 29, 27, 28, 29, 30, 31,
28, 29, 30, 31, 32, 27, 28, 29, 30, 31, 28, 29, 30, 31, 32, 30, 31, 32, 33, 34, 32, 33, 34,
35, 36, 30, 31, 32, 33, 34, 32, 33, 34, 35, 36, 33, 34, 35, 36, 37, 35, 36, 37, 38, 39};
Prepare(in_shape, out_shape, input_data, output_data, quant_in, quant_out, align_corners, thread_num);
kernel_->Run();
err_percent_ = 0.325f;
CompareOutputInt8(output_data, expect, 160, err_percent_);
}
} // namespace mindspore