Created by: pkuyym

Fixes #5070 (closed)

This operator can be used to compute various metrics including:

• macro average precision
• macro average recall
• macro f1 score
• micro average precision
• micro average recall
• micro f1 score

To compute the above metrics, we need to statistic counts for true positives, false positives and false negatives. Here count of true negatives is not necessary, but statisticing it may provide potential usage and the cost is trivial, so the operator also provides count of true negatives.

We define state as a 2-D tensor with shape [class number, 4]. Each row of a state contains statistic variables for corresponding class. Layout of each row is: TP(true positives), FP(false positives), TN(true negatives), FN(false negatives). If 'Input(Weights)' provided, TP, FP, TN, FN will be calculated by given weight instead of instance count.

This operator also supports metrics computing for cross-batch situation. To achieve this, 'Input(StatesInfo)' should be provided. State of current batch data will be accumulated to 'Input(StatesInfo)' and 'Output(AccumStatesInfo)' is the accumulation state.

'Output(BatchMetrics)' is metrics of current batch data while 'Output(AccumStatesInfo)' is metrics of accumulation data.