Delete clipseg/metrics.py

clipseg/metrics.py

@@ -1,271 +0,0 @@
-from torch.functional import Tensor
-from general_utils import log
-from collections import defaultdict
-import numpy as np
-
-import torch
-from torch.nn import functional as nnf
-
-
-class BaseMetric(object):
-
-    def __init__(self, metric_names, pred_range=None, gt_index=0, pred_index=0, eval_intermediate=True,
-                 eval_validation=True):
-        self._names = tuple(metric_names)
-        self._eval_intermediate = eval_intermediate
-        self._eval_validation = eval_validation
-
-        self._pred_range = pred_range
-        self._pred_index = pred_index
-        self._gt_index = gt_index
-
-        self.predictions = []
-        self.ground_truths = []
-
-    def eval_intermediate(self):
-        return self._eval_intermediate
-
-    def eval_validation(self):
-        return self._eval_validation
-
-    def names(self):
-        return self._names
-
-    def add(self, predictions, ground_truth):
-        raise NotImplementedError
-
-    def value(self):
-        raise NotImplementedError
-
-    def scores(self):
-        # similar to value but returns dict
-        value = self.value()
-        if type(value) == dict:
-            return value
-        else:
-            assert type(value) in {list, tuple}
-            return list(zip(self.names(), self.value()))
-
-    def _get_pred_gt(self, predictions, ground_truth):
-        pred = predictions[self._pred_index]
-        gt = ground_truth[self._gt_index]
-
-        if self._pred_range is not None:
-            pred = pred[:, self._pred_range[0]: self._pred_range[1]]
-
-        return pred, gt
-
-
-class FixedIntervalMetrics(BaseMetric):
-
-    def __init__(self, sigmoid=False, ignore_mask=False, resize_to=None, 
-                 resize_pred=None, n_values=51, custom_threshold=None):
-
-
-        super().__init__(('ap', 'best_fgiou', 'best_miou', 'fgiou0.5', 'fgiou0.1', 'mean_iou_0p5', 'mean_iou_0p1', 'best_biniou', 'biniou_0.5', 'fgiou_thresh'))
-        self.intersections = []
-        self.unions = []
-        # self.threshold = threshold
-        self.sigmoid = sigmoid
-        self.resize_to = resize_to
-        self.resize_pred = resize_pred  # resize prediction to match ground truth
-        self.class_count = defaultdict(lambda: 0)
-        self.per_class = defaultdict(lambda : [0,0])
-        self.ignore_mask = ignore_mask
-        self.custom_threshold = custom_threshold
-
-        self.scores_ap = []
-        self.scores_iou = []
-        self.gts, self.preds = [], []
-        self.classes = []
-
-        # [1:-1] ignores 0 and 1
-        self.threshold_values = np.linspace(0, 1, n_values)[1:-1]
-
-        self.metrics = dict(tp=[], fp=[], fn=[], tn=[])
-
-    def add(self, pred, gt):
-        
-        pred_batch = pred[0].cpu()
-
-        if self.sigmoid:
-            pred_batch = torch.sigmoid(pred_batch)
-
-        gt_batch = gt[0].cpu()
-        mask_batch = gt[1] if len(gt) > 1 and not self.ignore_mask and gt[1].numel() > 0 else ([None] * len(pred_batch))
-        cls_batch = gt[2] if len(gt) > 2 else [None] * len(pred_batch)
-
-        if self.resize_to is not None:
-            gt_batch = nnf.interpolate(gt_batch, self.resize_to, mode='nearest')
-            pred_batch = nnf.interpolate(pred_batch, self.resize_to, mode='bilinear', align_corners=False)
-        
-        if isinstance(cls_batch, torch.Tensor):
-            cls_batch = cls_batch.cpu().numpy().tolist()
-
-        assert len(gt_batch) == len(pred_batch) == len(cls_batch), f'{len(gt_batch)} {len(pred_batch)} {len(cls_batch)}'
-
-        for predictions, ground_truth, mask, cls in zip(pred_batch, gt_batch, mask_batch, cls_batch):
-
-            if self.resize_pred:
-                predictions = nnf.interpolate(predictions.unsqueeze(0).float(), size=ground_truth.size()[-2:], mode='bilinear', align_corners=True)
-
-            p = predictions.flatten()
-            g = ground_truth.flatten()
-
-            assert len(p) == len(g)
-
-            if mask is not None:
-                m = mask.flatten().bool()
-                p = p[m]
-                g = g[m]
-
-            p_sorted = p.sort()
-            p = p_sorted.values
-            g = g[p_sorted.indices]
-
-            tps, fps, fns, tns = [], [], [], []
-            for thresh in self.threshold_values:
-
-                valid = torch.where(p > thresh)[0]
-                if len(valid) > 0:
-                    n = int(valid[0])
-                else:
-                    n = len(g)
-
-                fn = int(g[:n].sum())
-                tp = int(g[n:].sum())
-                fns += [fn]
-                tns += [n - fn]
-                tps += [tp]
-                fps += [len(g) - n - tp]
-
-            self.metrics['tp'] += [tps]
-            self.metrics['fp'] += [fps]
-            self.metrics['fn'] += [fns]
-            self.metrics['tn'] += [tns]
-
-            self.classes += [cls.item() if isinstance(cls, torch.Tensor) else cls]
-
-    def value(self):
-
-        import time
-        t_start = time.time()   
-
-        if set(self.classes) == set([None]):
-            all_classes = None
-            log.warning('classes were not provided, cannot compute mIoU')
-        else:
-            all_classes = set(int(c) for c in self.classes)
-            # log.info(f'compute metrics for {len(all_classes)} classes')
-
-        summed = {k: [sum([self.metrics[k][i][j] 
-                           for i in range(len(self.metrics[k]))])
-                      for j in range(len(self.threshold_values))]
-                  for k in self.metrics.keys()}
-
-        if all_classes is not None:
-
-            assert len(self.classes) == len(self.metrics['tp']) == len(self.metrics['fn'])
-            # group by class
-            metrics_by_class = {c: {k: [] for k in self.metrics.keys()} for c in all_classes}
-            for i in range(len(self.metrics['tp'])):
-                for k in self.metrics.keys():
-                    metrics_by_class[self.classes[i]][k] += [self.metrics[k][i]]
-            
-            # sum over all instances within the classes
-            summed_by_cls = {k: {c: np.array(metrics_by_class[c][k]).sum(0).tolist() for c in all_classes} for k in self.metrics.keys()}
-
-
-        # Compute average precision
-
-        assert (np.array(summed['fp']) + np.array(summed['tp']) ).sum(), 'no predictions is made'
-
-        # only consider values where a prediction is made
-        precisions = [summed['tp'][j] / (1 + summed['tp'][j] + summed['fp'][j]) for j in range(len(self.threshold_values))
-                      if summed['tp'][j] + summed['fp'][j] > 0]
-        recalls = [summed['tp'][j] / (1 + summed['tp'][j] + summed['fn'][j]) for j in range(len(self.threshold_values))
-                           if summed['tp'][j] + summed['fp'][j] > 0]
-
-        # remove duplicate recall-precision-pairs (and sort by recall value)
-        recalls, precisions = zip(*sorted(list(set(zip(recalls, precisions))), key=lambda x: x[0]))
-
-        from scipy.integrate import simps
-        ap = simps(precisions, recalls)
-
-        # Compute best IoU
-        fgiou_scores = [summed['tp'][j] / (1 + summed['tp'][j] + summed['fp'][j] + summed['fn'][j]) for j in range(len(self.threshold_values))]
-
-        biniou_scores = [
-            0.5*(summed['tp'][j] / (1 + summed['tp'][j] + summed['fp'][j] + summed['fn'][j])) + 
-            0.5*(summed['tn'][j] / (1 + summed['tn'][j] + summed['fn'][j] + summed['fp'][j])) 
-            for j in range(len(self.threshold_values))
-        ]
-        
-        index_0p5 = self.threshold_values.tolist().index(0.5)
-        index_0p1 = self.threshold_values.tolist().index(0.1)
-        index_0p2 = self.threshold_values.tolist().index(0.2)
-        index_0p3 = self.threshold_values.tolist().index(0.3)
-
-        if self.custom_threshold is not None:
-            index_ct = self.threshold_values.tolist().index(self.custom_threshold)
-
-        if all_classes is not None:
-            # mean IoU
-            mean_ious = [np.mean([summed_by_cls['tp'][c][j] / (1 + summed_by_cls['tp'][c][j] + summed_by_cls['fp'][c][j] + summed_by_cls['fn'][c][j]) 
-                            for c in all_classes])
-                        for j in range(len(self.threshold_values))]
-
-            mean_iou_dict = {
-                'miou_best': max(mean_ious) if all_classes is not None else None,
-                'miou_0.5': mean_ious[index_0p5] if all_classes is not None else None,
-                'miou_0.1': mean_ious[index_0p1] if all_classes is not None else None,
-                'miou_0.2': mean_ious[index_0p2] if all_classes is not None else None,
-                'miou_0.3': mean_ious[index_0p3] if all_classes is not None else None,
-                'miou_best_t': self.threshold_values[np.argmax(mean_ious)],
-                'mean_iou_ct': mean_ious[index_ct] if all_classes is not None and self.custom_threshold is not None else None,
-                'mean_iou_scores': mean_ious,
-            }
-
-        print(f'metric computation on {(len(all_classes) if all_classes is not None else "no")} classes took {time.time() - t_start:.1f}s')
-
-        return {
-            'ap': ap,
-
-            # fgiou
-            'fgiou_best': max(fgiou_scores),
-            'fgiou_0.5': fgiou_scores[index_0p5],
-            'fgiou_0.1': fgiou_scores[index_0p1],
-            'fgiou_0.2': fgiou_scores[index_0p2],
-            'fgiou_0.3': fgiou_scores[index_0p3],
-            'fgiou_best_t': self.threshold_values[np.argmax(fgiou_scores)],
-
-            # mean iou
-
-
-            # biniou
-            'biniou_best': max(biniou_scores),
-            'biniou_0.5': biniou_scores[index_0p5],
-            'biniou_0.1': biniou_scores[index_0p1],
-            'biniou_0.2': biniou_scores[index_0p2],
-            'biniou_0.3': biniou_scores[index_0p3],
-            'biniou_best_t': self.threshold_values[np.argmax(biniou_scores)],
-
-            # custom threshold
-            'fgiou_ct': fgiou_scores[index_ct] if self.custom_threshold is not None else None,
-            'biniou_ct': biniou_scores[index_ct] if self.custom_threshold is not None else None,
-            'ct': self.custom_threshold,
-
-            # statistics
-            'fgiou_scores': fgiou_scores,
-            'biniou_scores': biniou_scores,
-            'precision_recall_curve': sorted(list(set(zip(recalls, precisions)))),
-            'summed_statistics': summed,
-            'summed_by_cls_statistics': summed_by_cls,
-
-            **mean_iou_dict
-        }
-
-        # ('ap', 'best_fgiou', 'best_miou', 'fgiou0.5', 'fgiou0.1', 'mean_iou_0p5', 'mean_iou_0p1', 'best_biniou', 'biniou_0.5', 'fgiou_thresh'
-
-        # return ap, best_fgiou, best_mean_iou, iou_0p5, iou_0p1, mean_iou_0p5, mean_iou_0p1, best_biniou, biniou0p5, best_fgiou_thresh, {'summed': summed, 'summed_by_cls': summed_by_cls}
-