forked from mindspore-Ecosystem/mindspore
!17838 code check for master
Merge pull request !17838 from liuyang/code_check_master
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commit
6587b5e5a4
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@ -35,7 +35,7 @@ void Broadcast::set_group(const std::string &group) {
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}
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int64_t Broadcast::get_root_rank() const {
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auto value_ptr = this->GetAttr(kRootRank);
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return GetValue<float>(value_ptr);
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return GetValue<int64_t>(value_ptr);
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}
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std::string Broadcast::get_group() const {
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@ -57,7 +57,7 @@ AbstractBasePtr FakeQuantWithMinMaxVarsPerChannelInfer(const abstract::AnalysisE
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auto max_type = input_args[2]->BuildType();
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std::vector<std::string> type_name = {"x", "min", "max"};
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std::vector<TypePtr> type = {x_type, min_type, max_type};
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for (int64_t i = 0; i < 3; i++) {
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for (size_t i = 0; i < 3; i++) {
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(void)CheckAndConvertUtils::CheckTensorTypeValid(type_name[i], type[i], {kFloat16, kFloat32}, op_name);
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}
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auto tensor_type = x_type->cast<TensorTypePtr>();
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@ -74,11 +74,11 @@ abstract::ShapePtr InferShape(const PrimitivePtr &primitive, const std::vector<A
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int64_t out_h = -1;
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int64_t out_w = -1;
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if (pad_mode == VALID) {
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out_h = ceil((in_h - (kernel_h - 1)) / stride_h);
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out_w = ceil((in_w - (kernel_w - 1)) / stride_w);
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out_h = static_cast<int64_t>(ceil((in_h - (kernel_h - 1)) / static_cast<float>(stride_h)));
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out_w = static_cast<int64_t>(ceil((in_w - (kernel_w - 1)) / static_cast<float>(stride_w)));
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} else if (pad_mode == SAME) {
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out_h = ceil(in_h / stride_h);
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out_w = ceil(in_w / stride_w);
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out_h = static_cast<int64_t>(ceil(in_h / static_cast<float>(stride_h)));
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out_w = static_cast<int64_t>(ceil(in_w / static_cast<float>(stride_w)));
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}
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std::vector<int64_t> out_shape = {batch, channel, out_h, out_w};
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if (format == NHWC) {
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@ -19,8 +19,8 @@
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namespace mindspore {
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namespace ops {
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std::vector<int64_t> PoolGrad::_grad_check_vector(std::string arg_name, std::vector<int64_t> arg_val,
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std::string op_name) {
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std::vector<int64_t> PoolGrad::_grad_check_vector(const std::string &arg_name, std::vector<int64_t> arg_val,
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const std::string &op_name) {
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std::vector<int64_t> ret;
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std::string error_msg = "For '" + op_name + "'" + " the '" + arg_name +
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"' should be a vector of one or two or four "
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@ -46,8 +46,8 @@ class PoolGrad : public PrimitiveC {
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std::vector<int64_t> get_strides() const;
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PadMode get_pad_mode() const;
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Format get_format() const;
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std::vector<int64_t> _grad_check_vector(const std::string arg_name, const std::vector<int64_t> arg_val,
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const std::string op_name);
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std::vector<int64_t> _grad_check_vector(const std::string &arg_name, const std::vector<int64_t> arg_val,
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const std::string &op_name);
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};
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} // namespace ops
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} // namespace mindspore
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@ -30,7 +30,7 @@ void SmoothL1LossGrad::set_beta(const float beta) { this->AddAttr(kBeta, MakeVal
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float SmoothL1LossGrad::get_beta() const {
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auto value_ptr = this->GetAttr(kBeta);
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return GetValue<int64_t>(value_ptr);
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return GetValue<int32_t>(value_ptr);
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}
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AbstractBasePtr SmoothL1LossGradInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
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@ -28,6 +28,7 @@ namespace mindspore {
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namespace ops {
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namespace {
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abstract::ShapePtr InferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
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MS_EXCEPTION_IF_NULL(primitive);
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for (const auto &item : input_args) {
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MS_EXCEPTION_IF_NULL(item);
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}
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@ -35,7 +35,7 @@ abstract::ShapePtr InferShape(const PrimitivePtr &primitive, const std::vector<A
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auto block_shape_vector = GetValue<std::vector<int64_t>>(primitive->GetAttr(kBlockSize));
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auto paddings = GetValue<std::vector<std::vector<int64_t>>>(primitive->GetAttr(kPaddings));
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for (size_t i = 0; i < 2; i++) {
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auto padded = output_shape[i + 2] + paddings[i][0] + paddings[i][1];
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auto padded = LongToSize(output_shape[i + 2] + paddings[i][0] + paddings[i][1]);
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CheckAndConvertUtils::CheckInteger("padded shape", padded % block_shape_vector.size(), kEqual, 0, prim_name);
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output_shape[i + 2] = padded / block_shape_vector.size();
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}
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@ -58,8 +58,8 @@ void SpaceToBatch::set_paddings(const std::vector<std::vector<int64_t>> &padding
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int64_t w = paddings[0].size();
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std::vector<int64_t> temp_w = {2, 2};
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CheckAndConvertUtils::Check(kPaddings, {h, w}, kEqual, "paddings_shape(2,2)", temp_w, this->name());
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for (int64_t i = 0; i < h; i++) {
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for (int64_t j = 0; j < w; j++) {
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for (size_t i = 0; i < LongToSize(h); i++) {
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for (size_t j = 0; j < LongToSize(w); j++) {
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CheckAndConvertUtils::CheckInteger(kPadding, paddings[i][j], kGreaterEqual, 0, this->name());
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}
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}
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@ -33,9 +33,9 @@ AbstractBasePtr UnstackInfer(const abstract::AnalysisEnginePtr &, const Primitiv
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if (axis < 0) {
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axis = axis + dim;
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}
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auto output_num = x_shape[axis];
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auto output_num = x_shape[LongToSize(axis)];
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CheckAndConvertUtils::CheckInteger("output_num", output_num, kGreaterThan, 0, prim_name);
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auto output_valid_check = x_shape[axis] - output_num;
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auto output_valid_check = x_shape[LongToSize(axis)] - output_num;
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CheckAndConvertUtils::CheckInteger("The dimension which to unstack divides output_num", output_valid_check, kEqual, 0,
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prim_name);
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std::vector<int64_t> infer_shape(x_shape.begin(), x_shape.begin() + axis);
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@ -39,10 +39,10 @@ AbstractBasePtr WhereInfer(const abstract::AnalysisEnginePtr &, const PrimitiveP
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auto num1 = input_args[1]->BuildValue()->cast<tensor::TensorPtr>()->ElementsNum();
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auto input2_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[2]->BuildShape())[kShape];
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auto num2 = input_args[2]->BuildValue()->cast<tensor::TensorPtr>()->ElementsNum();
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int64_t nummax = num > num1 ? num : (num1 > num2 ? num1 : num2);
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int64_t axisout = 0;
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int64_t temp = 0;
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for (int64_t j = 0; j < (int64_t)input0_shape.size(); j++) {
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size_t nummax = num > num1 ? num : (num1 > num2 ? num1 : num2);
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size_t axisout = 0;
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size_t temp = 0;
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for (size_t j = 0; j < input0_shape.size(); j++) {
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if (input0_shape[j] == input1_shape[j] && input0_shape[j] != input2_shape[j]) {
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axisout = j;
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break;
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@ -56,7 +56,7 @@ AbstractBasePtr WhereInfer(const abstract::AnalysisEnginePtr &, const PrimitiveP
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break;
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}
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temp += 1;
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if (temp == (int64_t)input0_shape.size()) {
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if (temp == input0_shape.size()) {
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return std::make_shared<abstract::AbstractTensor>(input0_type, input0_shape);
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}
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}
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