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!14517 fix codedex warning 

From: @yuchaojie
Reviewed-by: @kisnwang,@zhoufeng54
Signed-off-by: @zhoufeng54
This commit is contained in:
mindspore-ci-bot 2021-04-01 21:21:13 +08:00 committed by Gitee
parent ad039207e1 dc9a8eb1cc
commit 6db503ec9d
2 changed files with 20 additions and 14 deletions
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3
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc
View File

@ -216,8 +216,9 @@ AnfNodePtr CreateAllReduceAndMul(const FuncGraphPtr &graph, const AnfNodePtr &al
// use SyncBatchNorm's opid as AllReduce's fusion attr
auto sync_bn_opname = sync_bn_cnode->fullname_with_scope();
auto opid_pos = sync_bn_opname.rfind("-op");
if (opid_pos == std::string::npos) {
if (opid_pos == std::string::npos || opid_pos + 3 >= sync_bn_opname.size()) {
MS_LOG(EXCEPTION) << "op[" << sync_bn_cnode->DebugString() << "] has no opid.";
return nullptr;
}
int64_t opid = std::stol(sync_bn_opname.substr(opid_pos + 3));
// user defined fusion should be greater than 1

31
mindspore/ccsrc/backend/optimizer/ascend/mindir/avg_pool_grad_unify_mindir.cc
View File

@ -67,19 +67,8 @@ int64_t windowed_output_size(int64_t input_size, int64_t ksize, int64_t stride,
return output;
}
ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std::vector<int64_t> &x_shape,
const std::vector<int64_t> &k_size, const std::vector<int64_t> &stride,
const PadMode pad_mode, const TypeId x_dtype) {
MS_EXCEPTION_IF_NULL(func_graph);
auto kernel_graph = func_graph->cast<KernelGraphPtr>();
MS_EXCEPTION_IF_NULL(kernel_graph);
if (x_shape.size() != kShapeDimNum || k_size.size() != kShapeDimNum || stride.size() != kShapeDimNum) {
MS_LOG(EXCEPTION) << "The dim of x_shape or kernel_size or strides of AvgPoolGrad should be 4.";
}
int64_t pad_top, pad_bottom, pad_left, pad_right;
int64_t h_output = windowed_output_size(x_shape[2], k_size[2], stride[2], pad_mode, &pad_top, &pad_bottom);
int64_t w_output = windowed_output_size(x_shape[3], k_size[3], stride[3], pad_mode, &pad_left, &pad_right);
std::vector<std::vector<float>> GetAssistInputMatrix(const std::vector<int64_t> &x_shape, int64_t pad_top,
int64_t pad_bottom, int64_t pad_left, int64_t pad_right) {
// `assist_input_matrix` is a 2d matrix with input_shape after padding,
// the value of element which is padded is 0, else are 1.
// For each element of output, it is mapped for slide window: `[h*h_stride : h*h_stride + h_ksize,
@ -102,6 +91,22 @@ ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std
assist_input_matrix.emplace_back(tmp_one_vector);
}
}
return assist_input_matrix;
}
ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const std::vector<int64_t> &x_shape,
const std::vector<int64_t> &k_size, const std::vector<int64_t> &stride,
const PadMode pad_mode, const TypeId x_dtype) {
MS_EXCEPTION_IF_NULL(func_graph);
auto kernel_graph = func_graph->cast<KernelGraphPtr>();
MS_EXCEPTION_IF_NULL(kernel_graph);
if (x_shape.size() != kShapeDimNum || k_size.size() != kShapeDimNum || stride.size() != kShapeDimNum) {
MS_LOG(EXCEPTION) << "The dim of x_shape or kernel_size or strides of AvgPoolGrad should be 4.";
}
int64_t pad_top, pad_bottom, pad_left, pad_right;
int64_t h_output = windowed_output_size(x_shape[2], k_size[2], stride[2], pad_mode, &pad_top, &pad_bottom);
int64_t w_output = windowed_output_size(x_shape[3], k_size[3], stride[3], pad_mode, &pad_left, &pad_right);
auto assist_input_matrix = GetAssistInputMatrix(x_shape, pad_top, pad_bottom, pad_left, pad_right);
// calculate output
std::vector<float> hw_output(h_output * w_output, 0.0);