Module `3_mxrcnn.lib.mx-rcnn.symnet.metric`

Expand source code

import mxnet as mx
import numpy as np


def get_names():
    pred = ['rpn_cls_prob', 'rpn_bbox_loss', 'rcnn_cls_prob', 'rcnn_bbox_loss', 'rcnn_label']
    label = ['rpn_label', 'rpn_bbox_target', 'rpn_bbox_weight']
    return pred, label


class RPNAccMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RPNAccMetric, self).__init__('RPNAcc')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rpn_cls_prob')]
        label = labels[self.label.index('rpn_label')]

        # pred (b, c, p) or (b, c, h, w)
        pred_label = mx.ndarray.argmax_channel(pred).asnumpy().astype('int32')
        pred_label = pred_label.reshape((pred_label.shape[0], -1))
        # label (b, p)
        label = label.asnumpy().astype('int32')

        # filter with keep_inds
        keep_inds = np.where(label != -1)
        pred_label = pred_label[keep_inds]
        label = label[keep_inds]

        self.sum_metric += np.sum(pred_label.flat == label.flat)
        self.num_inst += len(pred_label.flat)


class RCNNAccMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RCNNAccMetric, self).__init__('RCNNAcc')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rcnn_cls_prob')]
        label = preds[self.pred.index('rcnn_label')]

        last_dim = pred.shape[-1]
        pred_label = pred.asnumpy().reshape(-1, last_dim).argmax(axis=1).astype('int32')
        label = label.asnumpy().reshape(-1,).astype('int32')

        self.sum_metric += np.sum(pred_label.flat == label.flat)
        self.num_inst += len(pred_label.flat)


class RPNLogLossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RPNLogLossMetric, self).__init__('RPNLogLoss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rpn_cls_prob')]
        label = labels[self.label.index('rpn_label')]

        # label (b, p)
        label = label.asnumpy().astype('int32').reshape((-1))
        # pred (b, c, p) or (b, c, h, w) --> (b, p, c) --> (b*p, c)
        pred = pred.asnumpy().reshape((pred.shape[0], pred.shape[1], -1)).transpose((0, 2, 1))
        pred = pred.reshape((label.shape[0], -1))

        # filter with keep_inds
        keep_inds = np.where(label != -1)[0]
        label = label[keep_inds]
        cls = pred[keep_inds, label]

        cls += 1e-14
        cls_loss = -1 * np.log(cls)
        cls_loss = np.sum(cls_loss)
        self.sum_metric += cls_loss
        self.num_inst += label.shape[0]


class RCNNLogLossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RCNNLogLossMetric, self).__init__('RCNNLogLoss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rcnn_cls_prob')]
        label = preds[self.pred.index('rcnn_label')]

        last_dim = pred.shape[-1]
        pred = pred.asnumpy().reshape(-1, last_dim)
        label = label.asnumpy().reshape(-1,).astype('int32')
        cls = pred[np.arange(label.shape[0]), label]

        cls += 1e-14
        cls_loss = -1 * np.log(cls)
        cls_loss = np.sum(cls_loss)
        self.sum_metric += cls_loss
        self.num_inst += label.shape[0]


class RPNL1LossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RPNL1LossMetric, self).__init__('RPNL1Loss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        bbox_loss = preds[self.pred.index('rpn_bbox_loss')].asnumpy()
        bbox_weight = labels[self.label.index('rpn_bbox_weight')].asnumpy()

        # calculate num_inst (average on those fg anchors)
        num_inst = np.sum(bbox_weight > 0) / 4

        self.sum_metric += np.sum(bbox_loss)
        self.num_inst += num_inst


class RCNNL1LossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RCNNL1LossMetric, self).__init__('RCNNL1Loss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        bbox_loss = preds[self.pred.index('rcnn_bbox_loss')].asnumpy()
        label = preds[self.pred.index('rcnn_label')].asnumpy()

        # calculate num_inst
        keep_inds = np.where(label != 0)[0]
        num_inst = len(keep_inds)

        self.sum_metric += np.sum(bbox_loss)
        self.num_inst += num_inst

Functions

def get_names()

Expand source code

def get_names():
    pred = ['rpn_cls_prob', 'rpn_bbox_loss', 'rcnn_cls_prob', 'rcnn_bbox_loss', 'rcnn_label']
    label = ['rpn_label', 'rpn_bbox_target', 'rpn_bbox_weight']
    return pred, label

Classes

class RCNNAccMetric

Base class for all evaluation metrics.

Note

This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.

Parameters

name : str: Name of this metric instance for display.
output_names : list of str, or None: Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names : list of str, or None: Name of labels that should be used when updating with update_dict. By default include all labels.

Expand source code

class RCNNAccMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RCNNAccMetric, self).__init__('RCNNAcc')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rcnn_cls_prob')]
        label = preds[self.pred.index('rcnn_label')]

        last_dim = pred.shape[-1]
        pred_label = pred.asnumpy().reshape(-1, last_dim).argmax(axis=1).astype('int32')
        label = label.asnumpy().reshape(-1,).astype('int32')

        self.sum_metric += np.sum(pred_label.flat == label.flat)
        self.num_inst += len(pred_label.flat)

Ancestors

mxnet.metric.EvalMetric

Methods

def update(self, labels, preds)

Updates the internal evaluation result.

Parameters

labels : list of NDArray: The labels of the data.
preds : list of NDArray: Predicted values.

Expand source code

def update(self, labels, preds):
    pred = preds[self.pred.index('rcnn_cls_prob')]
    label = preds[self.pred.index('rcnn_label')]

    last_dim = pred.shape[-1]
    pred_label = pred.asnumpy().reshape(-1, last_dim).argmax(axis=1).astype('int32')
    label = label.asnumpy().reshape(-1,).astype('int32')

    self.sum_metric += np.sum(pred_label.flat == label.flat)
    self.num_inst += len(pred_label.flat)

class RCNNL1LossMetric

Base class for all evaluation metrics.

Note

This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.

Parameters

name : str: Name of this metric instance for display.
output_names : list of str, or None: Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names : list of str, or None: Name of labels that should be used when updating with update_dict. By default include all labels.

Expand source code

class RCNNL1LossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RCNNL1LossMetric, self).__init__('RCNNL1Loss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        bbox_loss = preds[self.pred.index('rcnn_bbox_loss')].asnumpy()
        label = preds[self.pred.index('rcnn_label')].asnumpy()

        # calculate num_inst
        keep_inds = np.where(label != 0)[0]
        num_inst = len(keep_inds)

        self.sum_metric += np.sum(bbox_loss)
        self.num_inst += num_inst

Ancestors

mxnet.metric.EvalMetric

Methods

def update(self, labels, preds)

Updates the internal evaluation result.

Parameters

labels : list of NDArray: The labels of the data.
preds : list of NDArray: Predicted values.

Expand source code

def update(self, labels, preds):
    bbox_loss = preds[self.pred.index('rcnn_bbox_loss')].asnumpy()
    label = preds[self.pred.index('rcnn_label')].asnumpy()

    # calculate num_inst
    keep_inds = np.where(label != 0)[0]
    num_inst = len(keep_inds)

    self.sum_metric += np.sum(bbox_loss)
    self.num_inst += num_inst

class RCNNLogLossMetric

Base class for all evaluation metrics.

Note

This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.

Parameters

name : str: Name of this metric instance for display.
output_names : list of str, or None: Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names : list of str, or None: Name of labels that should be used when updating with update_dict. By default include all labels.

Expand source code

class RCNNLogLossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RCNNLogLossMetric, self).__init__('RCNNLogLoss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rcnn_cls_prob')]
        label = preds[self.pred.index('rcnn_label')]

        last_dim = pred.shape[-1]
        pred = pred.asnumpy().reshape(-1, last_dim)
        label = label.asnumpy().reshape(-1,).astype('int32')
        cls = pred[np.arange(label.shape[0]), label]

        cls += 1e-14
        cls_loss = -1 * np.log(cls)
        cls_loss = np.sum(cls_loss)
        self.sum_metric += cls_loss
        self.num_inst += label.shape[0]

Ancestors

mxnet.metric.EvalMetric

Methods

def update(self, labels, preds)

Updates the internal evaluation result.

Parameters

labels : list of NDArray: The labels of the data.
preds : list of NDArray: Predicted values.

Expand source code

def update(self, labels, preds):
    pred = preds[self.pred.index('rcnn_cls_prob')]
    label = preds[self.pred.index('rcnn_label')]

    last_dim = pred.shape[-1]
    pred = pred.asnumpy().reshape(-1, last_dim)
    label = label.asnumpy().reshape(-1,).astype('int32')
    cls = pred[np.arange(label.shape[0]), label]

    cls += 1e-14
    cls_loss = -1 * np.log(cls)
    cls_loss = np.sum(cls_loss)
    self.sum_metric += cls_loss
    self.num_inst += label.shape[0]

class RPNAccMetric

Base class for all evaluation metrics.

Note

This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.

Parameters

name : str: Name of this metric instance for display.
output_names : list of str, or None: Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names : list of str, or None: Name of labels that should be used when updating with update_dict. By default include all labels.

Expand source code

class RPNAccMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RPNAccMetric, self).__init__('RPNAcc')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rpn_cls_prob')]
        label = labels[self.label.index('rpn_label')]

        # pred (b, c, p) or (b, c, h, w)
        pred_label = mx.ndarray.argmax_channel(pred).asnumpy().astype('int32')
        pred_label = pred_label.reshape((pred_label.shape[0], -1))
        # label (b, p)
        label = label.asnumpy().astype('int32')

        # filter with keep_inds
        keep_inds = np.where(label != -1)
        pred_label = pred_label[keep_inds]
        label = label[keep_inds]

        self.sum_metric += np.sum(pred_label.flat == label.flat)
        self.num_inst += len(pred_label.flat)

Ancestors

mxnet.metric.EvalMetric

Methods

def update(self, labels, preds)

Updates the internal evaluation result.

Parameters

labels : list of NDArray: The labels of the data.
preds : list of NDArray: Predicted values.

Expand source code

def update(self, labels, preds):
    pred = preds[self.pred.index('rpn_cls_prob')]
    label = labels[self.label.index('rpn_label')]

    # pred (b, c, p) or (b, c, h, w)
    pred_label = mx.ndarray.argmax_channel(pred).asnumpy().astype('int32')
    pred_label = pred_label.reshape((pred_label.shape[0], -1))
    # label (b, p)
    label = label.asnumpy().astype('int32')

    # filter with keep_inds
    keep_inds = np.where(label != -1)
    pred_label = pred_label[keep_inds]
    label = label[keep_inds]

    self.sum_metric += np.sum(pred_label.flat == label.flat)
    self.num_inst += len(pred_label.flat)

class RPNL1LossMetric

Base class for all evaluation metrics.

Note

This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.

Parameters

name : str: Name of this metric instance for display.
output_names : list of str, or None: Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names : list of str, or None: Name of labels that should be used when updating with update_dict. By default include all labels.

Expand source code

class RPNL1LossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RPNL1LossMetric, self).__init__('RPNL1Loss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        bbox_loss = preds[self.pred.index('rpn_bbox_loss')].asnumpy()
        bbox_weight = labels[self.label.index('rpn_bbox_weight')].asnumpy()

        # calculate num_inst (average on those fg anchors)
        num_inst = np.sum(bbox_weight > 0) / 4

        self.sum_metric += np.sum(bbox_loss)
        self.num_inst += num_inst

Ancestors

mxnet.metric.EvalMetric

Methods

def update(self, labels, preds)

Updates the internal evaluation result.

Parameters

labels : list of NDArray: The labels of the data.
preds : list of NDArray: Predicted values.

Expand source code

def update(self, labels, preds):
    bbox_loss = preds[self.pred.index('rpn_bbox_loss')].asnumpy()
    bbox_weight = labels[self.label.index('rpn_bbox_weight')].asnumpy()

    # calculate num_inst (average on those fg anchors)
    num_inst = np.sum(bbox_weight > 0) / 4

    self.sum_metric += np.sum(bbox_loss)
    self.num_inst += num_inst

class RPNLogLossMetric

Base class for all evaluation metrics.

Note

This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.

Parameters

name : str: Name of this metric instance for display.
output_names : list of str, or None: Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names : list of str, or None: Name of labels that should be used when updating with update_dict. By default include all labels.

Expand source code

class RPNLogLossMetric(mx.metric.EvalMetric):
    def __init__(self):
        super(RPNLogLossMetric, self).__init__('RPNLogLoss')
        self.pred, self.label = get_names()

    def update(self, labels, preds):
        pred = preds[self.pred.index('rpn_cls_prob')]
        label = labels[self.label.index('rpn_label')]

        # label (b, p)
        label = label.asnumpy().astype('int32').reshape((-1))
        # pred (b, c, p) or (b, c, h, w) --> (b, p, c) --> (b*p, c)
        pred = pred.asnumpy().reshape((pred.shape[0], pred.shape[1], -1)).transpose((0, 2, 1))
        pred = pred.reshape((label.shape[0], -1))

        # filter with keep_inds
        keep_inds = np.where(label != -1)[0]
        label = label[keep_inds]
        cls = pred[keep_inds, label]

        cls += 1e-14
        cls_loss = -1 * np.log(cls)
        cls_loss = np.sum(cls_loss)
        self.sum_metric += cls_loss
        self.num_inst += label.shape[0]

Ancestors

mxnet.metric.EvalMetric

Methods

def update(self, labels, preds)

Updates the internal evaluation result.

Parameters

labels : list of NDArray: The labels of the data.
preds : list of NDArray: Predicted values.

Expand source code

def update(self, labels, preds):
    pred = preds[self.pred.index('rpn_cls_prob')]
    label = labels[self.label.index('rpn_label')]

    # label (b, p)
    label = label.asnumpy().astype('int32').reshape((-1))
    # pred (b, c, p) or (b, c, h, w) --> (b, p, c) --> (b*p, c)
    pred = pred.asnumpy().reshape((pred.shape[0], pred.shape[1], -1)).transpose((0, 2, 1))
    pred = pred.reshape((label.shape[0], -1))

    # filter with keep_inds
    keep_inds = np.where(label != -1)[0]
    label = label[keep_inds]
    cls = pred[keep_inds, label]

    cls += 1e-14
    cls_loss = -1 * np.log(cls)
    cls_loss = np.sum(cls_loss)
    self.sum_metric += cls_loss
    self.num_inst += label.shape[0]