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Metrics

In terms of evaluating the performance of a specific model, we should have general metrics to measure the performance of different models. Different frameworks always have their own Metric module but with different features and APIs. Neural Compressor Metrics supports code-free configuration through a yaml file, with built-in metrics, so that Neural Compressor can achieve performance and accuracy without code changes from the user. In special cases, users can also register their own metric classes through below method.

How to use Metrics

Config built-in metric in a yaml file

Users can specify an Neural Compressor built-in metric such as shown below:

evaluation:
  accuracy:
    metric:
      topk: 1

Config custom metric in code

Users can also register their own metric as follows:

class Metric(object):
    def __init__(self):
        # init code here

    def update(self, preds, labels):
        # add preds and labels to storage

    def reset(self):
        # clear preds and labels storage

    def result(self):
        # calculate accuracy
        return accuracy

The result() function returns a higher-is-better scalar to reflect model accuracy on an evaluation dataset.

After defining the metric class, users need to register it with a user-defined metric name and the metric class:

from neural_compressor.quantization import Quantization, common
quantizer = Quantization(yaml_file)
quantizer.model = common.Model(graph)
quantizer.metric = common.Metric(NewMetric, 'metric_name')
quantizer.calib_dataloader = dataloader
q_model = quantizer()

Built-in metric support list

Neural Compressor supports some built-in metrics that are popularly used in industry.

Refer to this HelloWorld example on how to config a built-in metric.

TensorFlow

Metric Parameters Inputs Comments Usage(In yaml file)
topk(k) k (int, default=1): Number of top elements to look at for computing accuracy preds, labels Computes top k predictions accuracy. metric:
   topk:
     k: 1
Accuracy() None preds, labels Computes accuracy classification score. metric:
   Accuracy: {}
Loss() None preds, labels A dummy metric for directly printing loss, it calculates the average of predictions.
Please refer to MXNet docs for details.		 metric:
   Loss: {}
MAE(compare_label) compare_label (bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels preds, labels Computes Mean Absolute Error (MAE) loss. metric:
   MAE: {}
RMSE(compare_label) compare_label (bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels preds, labels Computes Root Mean Square Error (RMSE) loss. metric:
   RMSE: {}
MSE(compare_label) compare_label (bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels preds, labels Computes Mean Squared Error (MSE) loss. metric:
   MSE: {}
F1() None preds, labels Computes the F1 score of a binary classification problem. metric:
   F1: {}
mAP( anno_path, iou_thrs, map_points) anno_path (str): Annotation path. The annotation file should be a yaml file, please refer to label_map for its format.
iou_thrs (float or str, default=0.5): Minimal value for intersection over union that allows to make decision that prediction bounding box is true positive. You can specify one float value between 0 to 1 or string "05:0.05:0.95" for standard COCO thresholds.
map_points (int, default=0): The way to calculate mAP. 101 for 101-point interpolated AP, 11 for 11-point interpolated AP, 0 for area under PR curve.		 preds, labels preds is a tuple which supports 2 length: 3 and 4.
If its length is 3, it should contain boxes, scores, classes in turn.
If its length is 4, it should contain target_boxes_num, boxes, scores, classes in turn
labels is a tuple which contains bbox, str_label, int_label, image_id inturn
the length of one of str_label and int_label can be 0		 metric:
   mAP:
     anno_path: /path/to/annotation
     iou_thrs: 0.5
     map_points: 0

If anno_path is not set, metric will use official coco label id
COCOmAP( anno_path, iou_thrs, map_points) anno_path (str): Annotation path. The annotation file should be a yaml file, please refer to label_map for its format.
iou_thrs (float or str): Intersection over union threshold. Set to "0.5:0.05:0.95" for standard COCO thresholds.
map_points (int): The way to calculate mAP. Set to 101 for 101-point interpolated AP.		 preds, labels preds is a tuple which supports 2 length: 3 and 4.
If its length is 3, it should contain boxes, scores, classes in turn.
If its length is 4, it should contain target_boxes_num, boxes, scores, classes in turn
labels is a tuple which contains bbox, str_label, int_label, image_id inturn
the length of one of str_label and int_label can be 0		 metric:
   COCOmAP:
     anno_path: /path/to/annotation

If anno_path is not set, metric will use official coco label id
VOCmAP( anno_path, iou_thrs, map_points) anno_path(str): Annotation path. The annotation file should be a yaml file, please refer to label_map for its format.
iou_thrs(float or str): Intersection over union threshold. Set to 0.5.
map_points(int): The way to calculate mAP. The way to calculate mAP. Set to 0 for area under PR curve.		 preds, labels preds is a tuple which supports 2 length: 3 and 4.
If its length is 3, it should contain boxes, scores, classes in turn.
If its length is 4, it should contain target_boxes_num, boxes, scores, classes in turn
labels is a tuple which contains bbox, str_label, int_label, image_id inturn
the length of one of str_label and int_label can be 0		 metric:
   VOCmAP:
     anno_path: /path/to/annotation

If anno_path is not set, metric will use official coco label id
BLEU() None preds, labels BLEU score computation between labels and predictions. An approximate BLEU scoring method since we do not glue word pieces or decode the ids and tokenize the output. By default, we use ngram order of 4 and use brevity penalty. Also, this does not have beam search metric:
   BLEU: {}
SquadF1() None preds, labels Evaluate v1.1 of the SQuAD dataset metric:
   SquadF1: {}

PyTorch

Metric Parameters Inputs Comments Usage(In yaml file)
topk(k) k (int, default=1): Number of top elements to look at for computing accuracy preds, labels Calculates the top-k categorical accuracy. metric:
   topk:
     k: 1
Accuracy() None preds, labels Calculates the accuracy for binary, multiclass and multilabel data.
Please refer Pytorch docs for details.		 metric:
   Accuracy: {}
Loss() None preds, labels A dummy metric for directly printing loss, it calculates the average of predictions.
Please refer MXNet docs for details.		 metric:
   Loss: {}
MAE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Mean Absolute Error (MAE) loss. metric:
   MAE:
     compare_label： True
RMSE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Root Mean Squared Error (RMSE) loss. metric:
   RMSE:
     compare_label: True
MSE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Mean Squared Error (MSE) loss. metric:
   MSE:
     compare_label: True
F1() None preds, labels Computes the F1 score of a binary classification problem. metric:
   F1: {}

MXNet

Metric Parameters Inputs Comments Usage(In yaml file)
topk(k) k (int, default=1): Number of top elements to look at for computing accuracy preds, labels Computes top k predictions accuracy. metric:
   topk:
     k: 1
Accuracy() None preds, labels Computes accuracy classification score.
Please refer to MXNet docs for details.		 metric:
   Accuracy: {}
Loss() None preds, labels A dummy metric for directly printing loss, it calculates the average of predictions.
Please refer to MXNet docs for details.		 metric:
   Loss: {}
MAE() None preds, labels Computes Mean Absolute Error (MAE) loss.
Please refer to MXNet docs for details.		 metric:
   MAE: {}
RMSE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Root Mean Squared Error (RMSE) loss. metric:
   RMSE:
     compare_label: True
MSE() None preds, labels Computes Mean Squared Error (MSE) loss.
Please refer to MXNet docs for details.		 metric:
   MSE: {}
F1() None preds, labels Computes the F1 score of a binary classification problem.
Please refer to MXNet docs for details.		 metric:
   F1: {}

ONNXRT

Metric Parameters Inputs Comments Usage(In yaml file)
topk(k) k (int, default=1): Number of top elements to look at for computing accuracy preds, labels Computes top k predictions accuracy. metric:
   topk:
     k: 1
Accuracy() None preds, labels Computes accuracy classification score. metric:
   Accuracy: {}
Loss() None preds, labels A dummy metric for directly printing loss, it calculates the average of predictions.
Please refer to MXNet docs for details.		 metric:
   Loss: {}
MAE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Mean Absolute Error (MAE) loss. metric:
   MAE:
     compare_label： True
RMSE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Root Mean Squared Error (RMSE) loss. metric:
   RMSE:
     compare_label: True
MSE(compare_label) compare_label(bool, default=True): Whether to compare label. False if there are no labels and will use FP32 preds as labels. preds, labels Computes Mean Squared Error (MSE) loss. metric:
   MSE:
     compare_label: True
F1() None preds, labels Computes the F1 score of a binary classification problem. metric:
   F1: {}
mAP( anno_path, iou_thrs, map_points) anno_path(str): Annotation path. The annotation file should be a yaml file, please refer to label_map for its format. The annotation file should be a yaml file, please refer to label_map for its format.
iou_thrs(float or str, default=0.5): Minimal value for intersection over union that allows to make decision that prediction bounding box is true positive. You can specify one float value between 0 to 1 or string "05:0.05:0.95" for standard COCO thresholds.
map_points(int, default=0): The way to calculate mAP. 101 for 101-point interpolated AP, 11 for 11-point interpolated AP, 0 for area under PR curve.		 preds, labels preds is a tuple which supports 2 length: 3 and 4.
If its length is 3, it should contain boxes, scores, classes in turn.
If its length is 4, it should contain target_boxes_num, boxes, scores, classes in turn
labels is a tuple which contains bbox, str_label, int_label, image_id inturn
the length of one of str_label and int_label can be 0		 metric:
   mAP:
     anno_path: /path/to/annotation
     iou_thrs: 0.5
     map_points: 0

If anno_path is not set, metric will use official coco label id
COCOmAP( anno_path, iou_thrs, map_points) anno_path(str): Annotation path. The annotation file should be a yaml file, please refer to label_map for its format. The annotation file should be a yaml file, please refer to label_map for its format.
iou_thrs(float or str): Intersection over union threshold. Set to "0.5:0.05:0.95" for standard COCO thresholds.
map_points(int): The way to calculate mAP. Set to 101 for 101-point interpolated AP.		 preds, labels preds is a tuple which supports 2 length: 3 and 4.
If its length is 3, it should contain boxes, scores, classes in turn.
If its length is 4, it should contain target_boxes_num, boxes, scores, classes in turn
labels is a tuple which contains bbox, str_label, int_label, image_id inturn
the length of one of str_label and int_label can be 0		 metric:
   COCOmAP:
     anno_path: /path/to/annotation

If anno_path is not set, metric will use official coco label id
VOCmAP( anno_path, iou_thrs, map_points) anno_path(str): Annotation path. The annotation file should be a yaml file, please refer to label_map for its format. The annotation file should be a yaml file, please refer to label_map for its format .
iou_thrs(float or str): Intersection over union threshold. Set to 0.5.
map_points(int): The way to calculate mAP. The way to calculate mAP. Set to 0 for area under PR curve.		 preds, labels preds is a tuple which supports 2 length: 3 and 4.
If its length is 3, it should contain boxes, scores, classes in turn.
If its length is 4, it should contain target_boxes_num, boxes, scores, classes in turn
labels is a tuple which contains bbox, str_label, int_label, image_id inturn
the length of one of str_label and int_label can be 0		 metric:
   VOCmAP:
     anno_path: /path/to/annotation

If anno_path is not set, metric will use official coco label id
GLUE(task) task (str, default=mrpc): The name of the task. Choices include mrpc, qqp, qnli, rte, sts-b, cola, mnli, wnli. preds, labels Computes GLUE score for bert model. metric:
   GLUE:
     task: mrpc