forked from mindspore-Ecosystem/mindspore
modify lossmonitor and print convert type
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@ -45,8 +45,8 @@ static std::map<aclDataType, size_t> acl_data_type_size_map = {
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std::string GetParseType(const aclDataType &acl_data_type) {
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static const std::map<aclDataType, std::string> print_tensor_parse_map = {
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{ACL_INT8, "Int8"}, {ACL_UINT8, "Uint8"}, {ACL_INT16, "Int16"}, {ACL_UINT16, "Uint16"},
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{ACL_INT32, "Int32"}, {ACL_UINT32, "Uint32"}, {ACL_INT64, "Int64"}, {ACL_UINT64, "Uint64"},
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{ACL_INT8, "Int8"}, {ACL_UINT8, "UInt8"}, {ACL_INT16, "Int16"}, {ACL_UINT16, "UInt16"},
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{ACL_INT32, "Int32"}, {ACL_UINT32, "UInt32"}, {ACL_INT64, "Int64"}, {ACL_UINT64, "UInt64"},
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{ACL_FLOAT16, "Float16"}, {ACL_FLOAT, "Float32"}, {ACL_DOUBLE, "Float64"}, {ACL_BOOL, "Bool"}};
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auto type_iter = print_tensor_parse_map.find(acl_data_type);
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if (type_iter == print_tensor_parse_map.end()) {
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@ -58,7 +58,7 @@ class LossMonitor(Callback):
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loss = loss[0]
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if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
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loss = np.mean(loss.asnumpy())
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loss = float(np.mean(loss.asnumpy()))
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
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