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[TRT] Fix batch norm converter for rank < 4 inputs. #161

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[TRT] Fix batch norm converter for rank < 4 inputs. #161

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14 changes: 14 additions & 0 deletions src/runtime/contrib/tensorrt/tensorrt_ops.h
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Original file line number Diff line number Diff line change
Expand Up @@ -528,6 +528,10 @@ class BatchNormOpConverter : public TrtOpConverter {
CHECK_EQ(gamma.count, var.count);
CHECK(bn_attr->axis == 1 || bn_attr->axis == 3);
const bool need_transpose = bn_attr->axis == 3;
const int required_rank = TRT_HAS_IMPLICIT_BATCH(params) ? 3 : 4;
auto input_dims = TrtDimsToVector(input->getDimensions());
CHECK(input_dims.size() > 0 && input_dims.size() <= required_rank);
const bool need_reshape = input_dims.size() != required_rank;

void* weight_scale_ptr = new float[gamma.count];
nvinfer1::Weights weight_scale{nvinfer1::DataType::kFLOAT, weight_scale_ptr, gamma.count};
Expand Down Expand Up @@ -557,10 +561,20 @@ class BatchNormOpConverter : public TrtOpConverter {
if (need_transpose) {
input = Transpose(params, input, {0, 3, 1, 2});
}
if (need_reshape) {
// Add dims of size 1 until rank is required_rank.
std::vector<int> new_shape(input_dims);
while (new_shape.size() < required_rank) new_shape.insert(new_shape.end(), 1);
input = Reshape(params, input, new_shape);
}
nvinfer1::IScaleLayer* scale_layer = params->network->addScale(
*input, nvinfer1::ScaleMode::kCHANNEL, weight_shift, weight_scale, power);
CHECK(scale_layer != nullptr);
auto output = scale_layer->getOutput(0);
if (need_reshape) {
// Remove added dims.
output = Reshape(params, output, input_dims);
}
if (need_transpose) {
output = Transpose(params, output, {0, 2, 3, 1});
}
Expand Down