forked from mindspore-Ecosystem/mindspore
clean code for r1.8
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70d8d9dcd1
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c6ba5e3a72
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@ -986,7 +986,7 @@ void DfGraphConvertor::BuildWhileSubGraph() {
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graph_name += "_body_graph";
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graph_out = GetWhileBodyOutputs();
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}
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if (error_ == 0) {
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if (error_ == SUCCESS) {
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if (df_graph_->GetName() != graph_name) {
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MS_LOG(DEBUG) << "convert anf graph name : " << df_graph_->GetName() << " to df graph name: " << graph_name;
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}
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@ -206,7 +206,7 @@ GeTensorPtr ConvertStringTensor(const MeTensorPtr &tensor, const std::string &fo
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char *string_element = new char[string_max_length];
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size_t string_length = 0;
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for (size_t i = 0; i < elements_num; i++) {
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std::fill_n(string_element, string_max_length, '\0');
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(void)std::fill_n(string_element, string_max_length, '\0');
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for (size_t j = 0; j < string_max_length; j++) {
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char char_element = data_ptr[i * string_max_length * single_char_offset + single_char_offset * j];
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if (static_cast<int>(char_element) == 0) {
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@ -468,6 +468,10 @@ def ms_function(fn=None, input_signature=None, hash_args=None, jit_config=None):
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This allows the MindSpore runtime to apply optimizations based on graph.
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Note:
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If `input_signature` is specified, each input of `fn` must be a Tensor. And the input arguments for `fn`
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will not accept `**kwargs`.
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Args:
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fn (Function): The Python function that will be run as a graph. Default: None.
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input_signature (Tensor): The Tensor which describes the input arguments. The shape and dtype of the Tensor
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@ -713,7 +713,7 @@ class Model:
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dataset_helper.continue_send()
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self._eval_durning_train(valid_infos, cb_params, list_callback)
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self._eval_during_train(valid_infos, cb_params, list_callback)
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# In disaster recovery scenarios, need not to execute callbacks if this epoch executes failed.
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# Embedding cache server need not do epoch end callback, this process only run one step.
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@ -742,8 +742,8 @@ class Model:
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list_callback.on_train_end(run_context)
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def _eval_durning_train(self, valid_infos, cb_params, list_callback):
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"""Exec eval durnning train process."""
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def _eval_during_train(self, valid_infos, cb_params, list_callback):
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"""Exec eval during train process."""
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valid_dataset, valid_frequency, valid_dataset_sink_mode = valid_infos
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if valid_dataset and self._should_eval(cb_params.cur_epoch_num, valid_frequency):
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train_cur_step_num = cb_params.cur_step_num
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@ -921,7 +921,7 @@ class Model:
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if should_stop:
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break
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self._eval_durning_train(valid_infos, cb_params, list_callback)
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self._eval_during_train(valid_infos, cb_params, list_callback)
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train_dataset.reset()
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