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!11618 [MS_LITE] control flow encoder

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From: @YeFeng_24
Reviewed-by: 
Signed-off-by:
pull/11618/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 6500655989 26725e81d1
commit 5280ab355e
7 changed files with 47 additions and 55 deletions
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14
mindspore/lite/src/ops/tensorlist_getitem.cc
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@ -120,8 +120,17 @@ int TensorListGetItem::InferShape(std::vector<lite::Tensor *> inputs_, std::vect
if (!infer_flag()) {
return RET_INFER_INVALID;
}
auto input0 = reinterpret_cast<TensorList *>(inputs_[0]);
auto get_index = inputs_[1];
MS_ASSERT(inputs_.size() >= 3);
MS_ASSERT(inputs_.at(0) != nullptr);
MS_ASSERT(inputs_.at(1) != nullptr);
MS_ASSERT(inputs_.at(2) != nullptr);
auto input0 = reinterpret_cast<TensorList *>(inputs_.at(0));
if (input0->tensors_data_type() != GetElementDType()) {
MS_LOG(ERROR) << "op dtype: " << GetElementDType()
<< " is not equal in_tensor[0] dtype: " << input0->tensors_data_type();
return RET_ERROR;
}
auto get_index = inputs_.at(1);
MS_ASSERT(get_index != nullptr);
if (get_index->ElementsNum() != 1) {
MS_LOG(ERROR) << "get_index->ElementsNum():" << get_index->ElementsNum() << " must be equal to 1!";
@ -178,6 +187,7 @@ int TensorListGetItem::InferShape(std::vector<lite::Tensor *> inputs_, std::vect
output->set_data_type(GetElementDType());
output->set_shape(element_shape_);
}
output->set_format(input0->GetTensor(index_)->format());
return RET_OK;
}
} // namespace lite

49
mindspore/lite/src/runtime/kernel/arm/fp32/tensorlist_getitem_fp32.cc
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@ -29,35 +29,38 @@ using mindspore::schema::PrimitiveType_TensorListGetItem;
namespace mindspore::kernel {
int TensorListGetItemCPUKernel::Init() {
auto input0 = reinterpret_cast<lite::TensorList *>(in_tensors_[0]);
MS_ASSERT(in_tensors_.size() >= 2);
MS_ASSERT(in_tensors_.at(0) != nullptr);
auto input0 = reinterpret_cast<lite::TensorList *>(in_tensors_.at(0));
if (dtype_ != input0->tensors_data_type()) {
MS_LOG(ERROR) << "op dtype:" << dtype_ << " is not equal in_tensors[0] dtype:" << input0->tensors_data_type();
MS_LOG(ERROR) << "op dtype: " << dtype_ << " is not equal in_tensor[0] dtype: " << input0->tensors_data_type();
return RET_ERROR;
}
if (in_tensors_[1]->ElementsNum() != 1) {
MS_LOG(ERROR) << "in_tensors_[1]->ElementsNum():" << in_tensors_[1]->ElementsNum() << " must be equal to 1!";
return RET_ERROR;
}
index_ = reinterpret_cast<int *>(in_tensors_[1]->data_c())[0];
return RET_OK;
}
int TensorListGetItemCPUKernel::Run() {
MS_ASSERT(in_tensors_.size() >= 2);
MS_ASSERT(in_tensors_.at(0) != nullptr);
MS_ASSERT(in_tensors_.at(1) != nullptr);
MS_ASSERT(out_tensors_.at(0) != nullptr);
auto input0 = reinterpret_cast<lite::TensorList *>(in_tensors_.at(0));
MS_ASSERT(in_tensors_.at(1)->data_c() != nullptr);
index_ = reinterpret_cast<int *>(in_tensors_.at(1)->data_c())[0];
int dim0 = input0->ElementsNum() - 1;
if (index_ < 0 || index_ > dim0) {
MS_LOG(ERROR) << "index tensor:[" << index_ << "] must be in [0, " << dim0 << "]!";
return RET_ERROR;
}
return RET_OK;
}
int TensorListGetItemCPUKernel::Run() {
auto input0 = reinterpret_cast<lite::TensorList *>(in_tensors_[0]);
auto src_ptr = input0->GetTensor(index_);
MS_ASSERT(src_ptr != nullptr);
if (src_ptr->data_type() != kTypeUnknown) {
if (src_ptr->ElementsNum() != out_tensors_[0]->ElementsNum()) {
if (src_ptr->ElementsNum() != out_tensors_.at(0)->ElementsNum()) {
MS_LOG(ERROR) << "src_ptr->ElementsNum():" << src_ptr->ElementsNum()
<< " must be equal to out_tensors_[0]->ElementsNum():" << out_tensors_[0]->ElementsNum();
<< " must be equal to out_tensors_[0]->ElementsNum():" << out_tensors_.at(0)->ElementsNum();
return RET_ERROR;
}
auto status = lite::Tensor::CopyTensorData(*src_ptr, out_tensors_[0]);
auto status = lite::Tensor::CopyTensorData(*src_ptr, out_tensors_.at(0));
if (status == RET_ERROR) {
MS_LOG(ERROR) << "copy tensor data failed!";
return RET_ERROR;
@ -65,19 +68,17 @@ int TensorListGetItemCPUKernel::Run() {
} else {
// reset 0 and dtype = dtype_
// TODO(DT_VARIANT): dtype = DT_VARIANT is not handle
memset(out_tensors_[0]->MutableData(), 0, out_tensors_[0]->Size());
auto out_data = out_tensors_[0]->MutableData();
if (out_data == nullptr) {
MS_LOG(ERROR) << "data of out_tensors_[0] is nullptr";
return RET_ERROR;
}
memset(out_data, 0, out_tensors_[0]->Size());
}
return RET_OK;
}
int TensorListGetItemCPUKernel::ReSize() {
auto ret = this->Init();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Init kernel failed!";
return ret;
}
return RET_OK;
}
int TensorListGetItemCPUKernel::ReSize() { return RET_OK; }
kernel::LiteKernel *CpuTensorListGetItemFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs,

31
mindspore/lite/src/runtime/kernel/arm/fp32/tensorlist_setitem_fp32.cc
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@ -30,20 +30,6 @@ namespace mindspore::kernel {
int TensorListSetItemCPUKernel::Init() { return RET_OK; }
int TensorListSetItemCPUKernel::IncrementOutputSize(int origin_size) {
output0_ = reinterpret_cast<lite::TensorList *>(out_tensors_[0]);
int new_tensors_size = origin_size + 1;
output0_->set_shape({new_tensors_size});
std::vector<std::vector<int>> out_shape;
out_shape.resize(new_tensors_size, in_tensors_[2]->shape());
auto ret = output0_->MallocTensorListData(in_tensors_[2]->data_type(), out_shape);
if (ret != RET_OK) {
MS_LOG(ERROR) << "increment output size malloc tensorlist data error";
return ret;
}
return RET_OK;
}
int TensorListSetItemCPUKernel::Run() {
input0_ = reinterpret_cast<lite::TensorList *>(in_tensors_[0]);
if (dtype_ != kTypeUnknown && dtype_ != input0_->tensors_data_type()) {
@ -61,10 +47,8 @@ int TensorListSetItemCPUKernel::Run() {
}
index_ = reinterpret_cast<int *>(in_tensors_[1]->data_c())[0];
if (index_ < 0 || index_ > dim0) {
if (IncrementOutputSize(output0_->shape()[0]) != RET_OK) {
MS_LOG(ERROR) << "Resizeoutput Error ,index tensor:[" << index_ << "] must be in [0, " << dim0 << "]!";
return RET_ERROR;
}
MS_LOG(ERROR) << "index tensor:[" << index_ << "] must be in [0, " << dim0 << "]!";
return RET_ERROR;
}
input2_ = in_tensors_[2];
MS_ASSERT(input2_ != nullptr);
@ -73,13 +57,6 @@ int TensorListSetItemCPUKernel::Run() {
}
output0_ = reinterpret_cast<lite::TensorList *>(out_tensors_[0]);
MS_ASSERT(output0_ != nullptr);
// new loop count
if (output0_->ElementsNum() != static_cast<int>(output0_->tensors().size()) && output0_->tensors().empty()) {
if (IncrementOutputSize(0) != RET_OK) {
MS_LOG(ERROR) << "Resizeoutput Error!";
return RET_ERROR;
}
}
// copy each tensor in tensors_
for (int i = 0; i < output0_->ElementsNum(); ++i) {
if (i == index_) {
@ -115,6 +92,10 @@ int TensorListSetItemCPUKernel::Run() {
}
if (src->data_type() != kTypeUnknown) {
if (src->Size() != dst->Size()) {
MS_LOG(ERROR) << "src->Size():" << src->Size() << " must be equal to dst->Size():" << dst->Size();
return RET_ERROR;
}
auto ret = lite::Tensor::CopyTensorData(*src, dst);
if (ret != RET_OK) {
MS_LOG(ERROR) << "CopyTensorData[" << i << "] is failed!";

1
mindspore/lite/src/runtime/kernel/arm/fp32/tensorlist_setitem_fp32.h
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@ -36,7 +36,6 @@ class TensorListSetItemCPUKernel : public LiteKernel {
int Init() override;
int ReSize() override;
int Run() override;
int IncrementOutputSize(int origin_size);
private:
lite::TensorList *input0_ = nullptr;

3
mindspore/lite/test/models_tf.cfg
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@ -3,4 +3,5 @@ decoder_step_201217_modified.pb 5
unet_model_reconstruct.pb 1;1,256,256,3
encoder_201228.pb 3;1:1,22:1
female_model_step2_int16_noiseout.pb 66
fasterrcnn_crop.pb 1;236,190,3
encoder_0111_control_flow.pb 4;1:1,44:1:1
encoder_0111.pb 4;1:1,44:1:1

2
mindspore/lite/test/models_tflite_posttraining.cfg
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@ -1,3 +1,3 @@
mobilenet.tflite 0.5
transformer_20200831_encoder_fp32.tflite 69
transformer_20200831_encoder_fp32.tflite 70
transformer_20200831_decoder_fp32.tflite 35

2
mindspore/lite/test/models_with_several_inputs_or_without_outputs.cfg
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@ -35,4 +35,4 @@ ml_video_edit_img_segment_adaptise.pb;2
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2
tiny-yolov3-11.onnx;2;1,416,416,3:1,2
# tiny-yolov3-11.onnx;2;1,416,416,3:1,2

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