metrics.py

# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from cmath import nan
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from sklearn.metrics import hamming_loss
from sklearn.metrics import accuracy_score as accuracy_metric
from sklearn.metrics import multilabel_confusion_matrix
from sklearn.preprocessing import binarize

from easydict import EasyDict

from ppcls.metric.avg_metrics import AvgMetrics
from ppcls.utils.misc import AverageMeter, AttrMeter
from ppcls.utils import logger


class TopkAcc(AvgMetrics):
    def __init__(self, topk=(1, 5)):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk
        self.reset()

    def reset(self):
        self.avg_meters = {
            f"top{k}": AverageMeter(f"top{k}")
            for k in self.topk
        }

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x["logits"]

        output_dims = x.shape[-1]

        metric_dict = dict()
        for idx, k in enumerate(self.topk):
            if output_dims < k:
                msg = f"The output dims({output_dims}) is less than k({k}), and the argument {k} of Topk has been removed."
                logger.warning(msg)
                self.avg_meters.pop(f"top{k}")
                continue
            metric_dict[f"top{k}"] = paddle.metric.accuracy(x, label, k=k)
            self.avg_meters[f"top{k}"].update(metric_dict[f"top{k}"],
                                              x.shape[0])

        self.topk = list(filter(lambda k: k <= output_dims, self.topk))

        return metric_dict


class mAP(nn.Layer):
    def __init__(self, descending=True):
        super().__init__()
        self.descending = descending

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=self.descending)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')

        num_rel = paddle.sum(equal_flag, axis=1)
        num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
        num_rel_index = paddle.nonzero(num_rel.astype("int"))
        num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])

        if paddle.numel(num_rel_index).item() == 0:
            metric_dict["mAP"] = np.nan
            return metric_dict

        equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)

        acc_sum = paddle.cumsum(equal_flag, axis=1)
        div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
        precision = paddle.divide(acc_sum, div)

        #calc map
        precision_mask = paddle.multiply(equal_flag, precision)
        ap = paddle.sum(precision_mask, axis=1) / paddle.sum(equal_flag,
                                                             axis=1)
        metric_dict["mAP"] = float(paddle.mean(ap))
        return metric_dict


class mINP(nn.Layer):
    def __init__(self, descending=True):
        super().__init__()
        self.descending = descending

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=self.descending)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.indechmx_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')

        num_rel = paddle.sum(equal_flag, axis=1)
        num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
        num_rel_index = paddle.nonzero(num_rel.astype("int"))
        num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
        equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)

        #do accumulative sum
        div = paddle.arange(equal_flag.shape[1]).astype("float32") + 2
        minus = paddle.divide(equal_flag, div)
        auxilary = paddle.subtract(equal_flag, minus)
        hard_index = paddle.argmax(auxilary, axis=1).astype("float32")
        all_INP = paddle.divide(paddle.sum(equal_flag, axis=1), hard_index)
        mINP = paddle.mean(all_INP)
        metric_dict["mINP"] = float(mINP)
        return metric_dict


class TprAtFpr(nn.Layer):
    def __init__(self, max_fpr=1 / 1000.):
        super().__init__()
        self.gt_pos_score_list = []
        self.gt_neg_score_list = []
        self.softmax = nn.Softmax(axis=-1)
        self.max_fpr = max_fpr
        self.max_tpr = 0.

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x["logits"]
        x = self.softmax(x)
        for i, label_i in enumerate(label):
            if label_i[0] == 0:
                self.gt_neg_score_list.append(x[i][1].numpy())
            else:
                self.gt_pos_score_list.append(x[i][1].numpy())
        return {}

    def reset(self):
        self.gt_pos_score_list = []
        self.gt_neg_score_list = []
        self.max_tpr = 0.

    @property
    def avg(self):
        return self.max_tpr

    @property
    def avg_info(self):
        max_tpr = 0.
        result = ""
        gt_pos_score_list = np.array(self.gt_pos_score_list)
        gt_neg_score_list = np.array(self.gt_neg_score_list)
        for i in range(0, 10000):
            threshold = i / 10000.
            if len(gt_pos_score_list) == 0:
                continue
            tpr = np.sum(
                gt_pos_score_list > threshold) / len(gt_pos_score_list)
            if len(gt_neg_score_list) == 0 and tpr > max_tpr:
                max_tpr = tpr
                result = "threshold: {}, fpr: 0.0, tpr: {:.5f}".format(
                    threshold, tpr)
                msg = f"The number of negative samples is 0, please add negative samples."
                logger.warning(msg)
            fpr = np.sum(
                gt_neg_score_list > threshold) / len(gt_neg_score_list)
            if fpr <= self.max_fpr and tpr > max_tpr:
                max_tpr = tpr
                result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(
                    threshold, fpr, tpr)
        self.max_tpr = max_tpr
        return result


class MultilabelMeanAccuracy(nn.Layer):
    def __init__(self, class_num=40):
        super().__init__()
        self.gt_all_score_list = []
        self.gt_label_score_list = []
        self.max_acc = 0.
        self.class_num = class_num

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x["logits"]
        x = F.sigmoid(x)
        label = label[:, 0, :]
        for i in range(len(x)):
            self.gt_all_score_list.append(x[i].numpy())
            self.gt_label_score_list.append(label[i].numpy())
        return {}

    def reset(self):
        self.gt_all_score_list = []
        self.gt_label_score_list = []
        self.max_acc = 0.

    @property
    def avg(self):
        return self.max_acc

    @property
    def avg_info(self):
        max_acc = 0.
        result = ""
        gt_all_score_list = np.array(self.gt_all_score_list)
        gt_label_score_list = np.array(self.gt_label_score_list)
        for i in range(10):
            threshold = 0.4 + i * 0.05
            pred_label = (gt_all_score_list > threshold).astype(int)
            TP = np.sum(
                (gt_label_score_list == 1) * (pred_label == 1)).astype(float)
            TN = np.sum(
                (gt_label_score_list == 0) * (pred_label == 0)).astype(float)
            acc = (TP + TN) / len(gt_all_score_list)
            if max_acc <= acc:
                max_acc = acc
                result = "threshold: {}, mean_acc: {}".format(
                    threshold, max_acc / self.class_num)
        self.max_acc = max_acc / self.class_num
        return result


class Recallk(nn.Layer):
    def __init__(self, topk=(1, 5), descending=True):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk
        self.descending = descending

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        #get cmc
        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=self.descending)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')
        real_query_num = paddle.sum(equal_flag, axis=1)
        real_query_num = paddle.sum(
            paddle.greater_than(real_query_num, paddle.to_tensor(0.)).astype(
                "float32"))

        acc_sum = paddle.cumsum(equal_flag, axis=1)
        mask = paddle.greater_than(acc_sum,
                                   paddle.to_tensor(0.)).astype("float32")
        all_cmc = (paddle.sum(mask, axis=0) / real_query_num).numpy()

        for k in self.topk:
            metric_dict["recall{}".format(k)] = all_cmc[k - 1]
        return metric_dict


class Precisionk(nn.Layer):
    def __init__(self, topk=(1, 5), descending=True):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk
        self.descending = descending

    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
                keep_mask):
        metric_dict = dict()

        #get cmc
        choosen_indices = paddle.argsort(
            similarities_matrix, axis=1, descending=self.descending)
        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
        gallery_labels_transpose = paddle.broadcast_to(
            gallery_labels_transpose,
            shape=[
                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
            ])
        choosen_label = paddle.index_sample(gallery_labels_transpose,
                                            choosen_indices)
        equal_flag = paddle.equal(choosen_label, query_img_id)
        if keep_mask is not None:
            keep_mask = paddle.index_sample(
                keep_mask.astype('float32'), choosen_indices)
            equal_flag = paddle.logical_and(equal_flag,
                                            keep_mask.astype('bool'))
        equal_flag = paddle.cast(equal_flag, 'float32')

        Ns = paddle.arange(gallery_img_id.shape[0]) + 1
        equal_flag_cumsum = paddle.cumsum(equal_flag, axis=1)
        Precision_at_k = (paddle.mean(equal_flag_cumsum, axis=0) / Ns).numpy()

        for k in self.topk:
            metric_dict["precision@{}".format(k)] = Precision_at_k[k - 1]

        return metric_dict


class DistillationTopkAcc(TopkAcc):
    def __init__(self, model_key, feature_key=None, topk=(1, 5)):
        super().__init__(topk=topk)
        self.model_key = model_key
        self.feature_key = feature_key

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x[self.model_key]
        if self.feature_key is not None:
            x = x[self.feature_key]
        return super().forward(x, label)


class GoogLeNetTopkAcc(TopkAcc):
    def __init__(self, topk=(1, 5)):
        super().__init__()
        assert isinstance(topk, (int, list, tuple))
        if isinstance(topk, int):
            topk = [topk]
        self.topk = topk

    def forward(self, x, label):
        return super().forward(x[0], label)


class MultiLabelMetric(AvgMetrics):
    def __init__(self, bi_threshold=0.5):
        super().__init__()
        self.bi_threshold = bi_threshold

    def _multi_hot_encode(self, output):
        logits = F.sigmoid(output).numpy()
        return binarize(logits, threshold=self.bi_threshold)


class HammingDistance(MultiLabelMetric):
    """
    Soft metric based label for multilabel classification
    Returns:
        The smaller the return value is, the better model is.
    """

    def __init__(self):
        super().__init__()
        self.reset()

    def reset(self):
        self.avg_meters = {"HammingDistance": AverageMeter("HammingDistance")}

    def forward(self, output, target):
        preds = super()._multi_hot_encode(output)
        metric_dict = dict()
        metric_dict["HammingDistance"] = paddle.to_tensor(
            hamming_loss(target, preds))
        self.avg_meters["HammingDistance"].update(
            float(metric_dict["HammingDistance"]), output.shape[0])
        return metric_dict


class AccuracyScore(MultiLabelMetric):
    """
    Hard metric for multilabel classification
    Args:
        base: ["sample", "label"], default="sample"
            if "sample", return metric score based sample,
            if "label", return metric score based label.
    Returns:
        accuracy:
    """

    def __init__(self, base="label"):
        super().__init__()
        assert base in ["sample", "label"
                        ], 'must be one of ["sample", "label"]'
        self.base = base
        self.reset()

    def reset(self):
        self.avg_meters = {"AccuracyScore": AverageMeter("AccuracyScore")}

    def forward(self, output, target):
        preds = super()._multi_hot_encode(output)
        metric_dict = dict()
        if self.base == "sample":
            accuracy = accuracy_metric(target, preds)
        elif self.base == "label":
            mcm = multilabel_confusion_matrix(target, preds)
            tns = mcm[:, 0, 0]
            fns = mcm[:, 1, 0]
            tps = mcm[:, 1, 1]
            fps = mcm[:, 0, 1]
            accuracy = (sum(tps) + sum(tns)) / (
                sum(tps) + sum(tns) + sum(fns) + sum(fps))
        metric_dict["AccuracyScore"] = paddle.to_tensor(accuracy)
        self.avg_meters["AccuracyScore"].update(
            float(metric_dict["AccuracyScore"]), output.shape[0])
        return metric_dict


def get_attr_metrics(gt_label, preds_probs, threshold):
    """
    index: evaluated label index
    adapted from "https://github.com/valencebond/Rethinking_of_PAR/blob/master/metrics/pedestrian_metrics.py"
    """
    pred_label = (preds_probs > threshold).astype(int)

    eps = 1e-20
    result = EasyDict()

    has_fuyi = gt_label == -1
    pred_label[has_fuyi] = -1

    ###############################
    # label metrics
    # TP + FN
    result.gt_pos = np.sum((gt_label == 1), axis=0).astype(float)
    # TN + FP
    result.gt_neg = np.sum((gt_label == 0), axis=0).astype(float)
    # TP
    result.true_pos = np.sum((gt_label == 1) * (pred_label == 1),
                             axis=0).astype(float)
    # TN
    result.true_neg = np.sum((gt_label == 0) * (pred_label == 0),
                             axis=0).astype(float)
    # FP
    result.false_pos = np.sum(((gt_label == 0) * (pred_label == 1)),
                              axis=0).astype(float)
    # FN
    result.false_neg = np.sum(((gt_label == 1) * (pred_label == 0)),
                              axis=0).astype(float)

    ################
    # instance metrics
    result.gt_pos_ins = np.sum((gt_label == 1), axis=1).astype(float)
    result.true_pos_ins = np.sum((pred_label == 1), axis=1).astype(float)
    # true positive
    result.intersect_pos = np.sum((gt_label == 1) * (pred_label == 1),
                                  axis=1).astype(float)
    # IOU
    result.union_pos = np.sum(((gt_label == 1) + (pred_label == 1)),
                              axis=1).astype(float)

    return result


class ATTRMetric(nn.Layer):
    def __init__(self, threshold=0.5):
        super().__init__()
        self.threshold = threshold

    def reset(self):
        self.attrmeter = AttrMeter(threshold=0.5)

    def forward(self, output, target):
        metric_dict = get_attr_metrics(target[:, 0, :].numpy(),
                                       output.numpy(), self.threshold)
        self.attrmeter.update(metric_dict)
        return metric_dict