Dubhe/model_measuring/kamal/core/metrics/accuracy.py

"""
 Copyright 2020 Tianshu AI Platform. All Rights Reserved.

 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.
 =============================================================
"""

import numpy as np
import torch
from kamal.core.metrics.stream_metrics import Metric
from typing import Callable

__all__=['Accuracy', 'TopkAccuracy']

class Accuracy(Metric):
    def __init__(self, attach_to=None):
        super(Accuracy, self).__init__(attach_to=attach_to)
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        outputs = outputs.max(1)[1]
        self._correct += ( outputs.view(-1)==targets.view(-1) ).sum()
        self._cnt += torch.numel( targets )

    def get_results(self):
        return (self._correct / self._cnt).detach().cpu()
    
    def reset(self):
        self._correct = self._cnt = 0.0


class TopkAccuracy(Metric):
    def __init__(self, topk=5, attach_to=None):
        super(TopkAccuracy, self).__init__(attach_to=attach_to)
        self._topk = topk
        self.reset()
    
    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        _, outputs = outputs.topk(self._topk, dim=1, largest=True, sorted=True)
        correct = outputs.eq( targets.view(-1, 1).expand_as(outputs) )
        self._correct += correct[:, :self._topk].view(-1).float().sum(0).item()
        self._cnt += len(targets)
    
    def get_results(self):
        return self._correct / self._cnt

    def reset(self):
        self._correct = 0.0
        self._cnt = 0.0


class StreamCEMAPMetrics():
    @property
    def PRIMARY_METRIC(self):
        return "eap"

    def __init__(self):
        self.reset()

    def update(self, logits, targets):
        preds = logits.max(1)[1]
        # targets: -1 negative, 0 difficult, 1 positive
        if isinstance(preds, torch.Tensor):
            preds = preds.cpu().numpy()
            targets = targets.cpu().numpy()

        self.preds = preds if self.preds is None else np.append(self.preds, preds, axis=0)
        self.targets = targets if self.targets is None else np.append(self.targets, targets, axis=0)

    def get_results(self):
        nTest = self.targets.shape[0]
        nLabel = self.targets.shape[1]
        eap = np.zeros(nTest)
        for i in range(0,nTest):
            R = np.sum(self.targets[i,:]==1)
            for j in range(0,nLabel):            
                if self.targets[i,j]==1:
                    r = np.sum(self.preds[i,np.nonzero(self.targets[i,:]!=0)]>=self.preds[i,j])
                    rb = np.sum(self.preds[i,np.nonzero(self.targets[i,:]==1)] >= self.preds[i,j])

                    eap[i] = eap[i] + rb/(r*1.0)
            eap[i] = eap[i]/R
        # emap = np.nanmean(ap)

        cap = np.zeros(nLabel)
        for i in range(0,nLabel):
            R = np.sum(self.targets[:,i]==1)
            for j in range(0,nTest):
                if self.targets[j,i]==1:
                    r = np.sum(self.preds[np.nonzero(self.targets[:,i]!=0),i] >= self.preds[j,i])
                    rb = np.sum(self.preds[np.nonzero(self.targets[:,i]==1),i] >= self.preds[j,i])
                    cap[i] = cap[i] + rb/(r*1.0)
            cap[i] = cap[i]/R
        # cmap = np.nanmean(ap)
        return {
            'eap': eap,
            'emap': np.nanmean(eap),
            'cap': cap,
            'cmap': np.nanmean(cap),
        }

    def reset(self):
        self.preds = None
        self.targets = None