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Make GPU delegate transformations compatible with -Wmissing-braces

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PiperOrigin-RevId: 261012887
This commit is contained in:
A. Unique TensorFlower 2019-07-31 15:40:18 -07:00 committed by TensorFlower Gardener
parent e2ac3e26c2
commit 1c46da48dc
2 changed files with 11 additions and 11 deletions
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6
tensorflow/lite/delegates/gpu/common/transformations/fuse_add_to_conv.cc
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@ -184,7 +184,7 @@ void FuseAddWithConvolution2D(const AddAttributes& add_attr,
const float add_value = add ? add->data[s] : *add_scalar;
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({d, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{d, k_y, k_x, s}});
attr->bias.data[d] += attr->weights.data[index] * add_value;
}
}
@ -206,7 +206,7 @@ void FuseAddWithDepthwiseConvolution2D(const AddAttributes& add_attr,
const int d = s * attr->weights.shape.o + g;
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({g, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{g, k_y, k_x, s}});
attr->bias.data[d] += attr->weights.data[index] * add_value;
}
}
@ -225,7 +225,7 @@ void FuseAddWithFullyConnected(const AddAttributes& add_attr,
for (int d = 0; d < attr->weights.shape.o; ++d) {
for (int s = 0; s < attr->weights.shape.i; ++s) {
const float add_value = add ? add->data[s] : *add_scalar;
const int index = attr->weights.shape.LinearIndex({d, 0, 0, s});
const int index = attr->weights.shape.LinearIndex({{d, 0, 0, s}});
attr->bias.data[d] += attr->weights.data[index] * add_value;
}
}

16
tensorflow/lite/delegates/gpu/common/transformations/fuse_mul_to_conv.cc
View File

@ -164,7 +164,7 @@ void FuseConvolution2DWithMultiply(const MultiplyScalarAttributes& mul_attr,
for (int s = 0; s < attr->weights.shape.i; ++s) {
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({d, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{d, k_y, k_x, s}});
attr->weights.data[index] *= multiplier;
}
}
@ -186,7 +186,7 @@ void FuseDepthwiseConvolution2DWithMultiply(
const float multiplier = mul ? mul->data[d] : *mul_scalar;
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({g, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{g, k_y, k_x, s}});
attr->weights.data[index] *= multiplier;
}
}
@ -207,7 +207,7 @@ void FuseConvolutionTransposedWithMultiply(
for (int s = 0; s < attr->weights.shape.i; ++s) {
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({d, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{d, k_y, k_x, s}});
attr->weights.data[index] *= multiplier;
}
}
@ -225,7 +225,7 @@ void FuseFullyConnectedWithMultiply(const MultiplyScalarAttributes& mul_attr,
for (int d = 0; d < attr->weights.shape.o; ++d) {
const float multiplier = mul ? mul->data[d] : *mul_scalar;
for (int s = 0; s < attr->weights.shape.i; ++s) {
const int index = attr->weights.shape.LinearIndex({d, 0, 0, s});
const int index = attr->weights.shape.LinearIndex({{d, 0, 0, s}});
attr->weights.data[index] *= multiplier;
}
if (!attr->bias.data.empty()) {
@ -243,7 +243,7 @@ void FuseMultiplyWithConvolution2D(const MultiplyScalarAttributes& mul_attr,
for (int d = 0; d < attr->weights.shape.o; ++d) {
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({d, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{d, k_y, k_x, s}});
attr->weights.data[index] *= multiplier;
}
}
@ -261,7 +261,7 @@ void FuseMultiplyWithDepthwiseConvolution2D(
for (int g = 0; g < attr->weights.shape.o; ++g) {
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({g, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{g, k_y, k_x, s}});
attr->weights.data[index] *= multiplier;
}
}
@ -279,7 +279,7 @@ void FuseMultiplyWithConvolutionTransposed(
for (int d = 0; d < attr->weights.shape.o; ++d) {
for (int k_y = 0; k_y < attr->weights.shape.h; ++k_y) {
for (int k_x = 0; k_x < attr->weights.shape.w; ++k_x) {
const int index = attr->weights.shape.LinearIndex({d, k_y, k_x, s});
const int index = attr->weights.shape.LinearIndex({{d, k_y, k_x, s}});
attr->weights.data[index] *= multiplier;
}
}
@ -294,7 +294,7 @@ void FuseMultiplyWithFullyConnected(const MultiplyScalarAttributes& mul_attr,
for (int s = 0; s < attr->weights.shape.i; ++s) {
const float multiplier = mul ? mul->data[s] : *mul_scalar;
for (int d = 0; d < attr->weights.shape.o; ++d) {
const int index = attr->weights.shape.LinearIndex({d, 0, 0, s});
const int index = attr->weights.shape.LinearIndex({{d, 0, 0, s}});
attr->weights.data[index] *= multiplier;
}
}