YOLO-Tutorial-v2/yolo/models/yolov3/matcher.py

import numpy as np
import torch


class Yolov3Matcher(object):
    def __init__(self, num_classes, num_anchors, anchor_size, iou_thresh):
        self.num_classes = num_classes
        self.num_anchors = num_anchors
        self.iou_thresh = iou_thresh
        self.anchor_boxes = np.array(
            [[0., 0., anchor[0], anchor[1]]
            for anchor in anchor_size]
            )  # [KA, 4]

    def compute_iou(self, anchor_boxes, gt_box):
        """
            anchor_boxes : ndarray -> [KA, 4] (cx, cy, bw, bh).
            gt_box       : ndarray -> [1, 4] (cx, cy, bw, bh).
        """
        # anchors: [KA, 4]
        anchors_xyxy = np.zeros_like(anchor_boxes)
        anchors_area = anchor_boxes[..., 2] * anchor_boxes[..., 3]
        # convert [cx, cy, bw, bh] -> [x1, y1, x2, y2]
        anchors_xyxy[..., :2] = anchor_boxes[..., :2] - anchor_boxes[..., 2:] * 0.5  # x1y1
        anchors_xyxy[..., 2:] = anchor_boxes[..., :2] + anchor_boxes[..., 2:] * 0.5  # x2y2
        
        # expand gt_box: [1, 4] -> [KA, 4]
        gt_box = np.array(gt_box).reshape(-1, 4)
        gt_box = np.repeat(gt_box, anchors_xyxy.shape[0], axis=0)
        gt_box_area = gt_box[..., 2] * gt_box[..., 3]
        # convert [cx, cy, bw, bh] -> [x1, y1, x2, y2]
        gt_box_xyxy = np.zeros_like(gt_box)
        gt_box_xyxy[..., :2] = gt_box[..., :2] - gt_box[..., 2:] * 0.5  # x1y1
        gt_box_xyxy[..., 2:] = gt_box[..., :2] + gt_box[..., 2:] * 0.5  # x2y2

        # intersection
        inter_w = np.minimum(anchors_xyxy[:, 2], gt_box_xyxy[:, 2]) - \
                  np.maximum(anchors_xyxy[:, 0], gt_box_xyxy[:, 0])
        inter_h = np.minimum(anchors_xyxy[:, 3], gt_box_xyxy[:, 3]) - \
                  np.maximum(anchors_xyxy[:, 1], gt_box_xyxy[:, 1])
        inter_area = inter_w * inter_h
        
        # union
        union_area = anchors_area + gt_box_area - inter_area

        # iou
        iou = inter_area / union_area
        iou = np.clip(iou, a_min=1e-10, a_max=1.0)
        
        return iou

    @torch.no_grad()
    def __call__(self, fmp_sizes, fpn_strides, targets):
        """
            fmp_size: (List) [fmp_h, fmp_w]
            fpn_strides: (List) -> [8, 16, 32, ...] stride of network output.
            targets: (Dict) dict{'boxes': [...], 
                                 'labels': [...], 
                                 'orig_size': ...}
        """
        assert len(fmp_sizes) == len(fpn_strides)
        # prepare
        bs = len(targets)
        gt_objectness = [
            torch.zeros([bs, fmp_h, fmp_w, self.num_anchors, 1]) 
            for (fmp_h, fmp_w) in fmp_sizes
            ]
        gt_classes = [
            torch.zeros([bs, fmp_h, fmp_w, self.num_anchors, self.num_classes]) 
            for (fmp_h, fmp_w) in fmp_sizes
            ]
        gt_bboxes = [
            torch.zeros([bs, fmp_h, fmp_w, self.num_anchors, 4]) 
            for (fmp_h, fmp_w) in fmp_sizes
            ]

        for batch_index in range(bs):
            targets_per_image = targets[batch_index]
            # [N,]
            tgt_cls = targets_per_image["labels"].numpy()
            # [N, 4]
            tgt_box = targets_per_image['boxes'].numpy()

            for gt_box, gt_label in zip(tgt_box, tgt_cls):
                # get a bbox coords
                x1, y1, x2, y2 = gt_box.tolist()
                # xyxy -> cxcywh
                xc, yc = (x2 + x1) * 0.5, (y2 + y1) * 0.5
                bw, bh = x2 - x1, y2 - y1
                gt_box = [0, 0, bw, bh]

                # check target
                if bw < 1. or bh < 1.:
                    # invalid target
                    continue

                # compute IoU
                iou = self.compute_iou(self.anchor_boxes, gt_box)
                iou_mask = (iou > self.iou_thresh)

                label_assignment_results = []
                if iou_mask.sum() == 0:
                    # We assign the anchor box with highest IoU score.
                    iou_ind = np.argmax(iou)

                    level = iou_ind // self.num_anchors              # pyramid level
                    anchor_idx = iou_ind - level * self.num_anchors  # anchor index

                    # get the corresponding stride
                    stride = fpn_strides[level]

                    # compute the grid cell
                    xc_s = xc / stride
                    yc_s = yc / stride
                    grid_x = int(xc_s)
                    grid_y = int(yc_s)

                    label_assignment_results.append([grid_x, grid_y, level, anchor_idx])
                else:            
                    for iou_ind, iou_m in enumerate(iou_mask):
                        if iou_m:
                            level = iou_ind // self.num_anchors              # pyramid level
                            anchor_idx = iou_ind - level * self.num_anchors  # anchor index

                            # get the corresponding stride
                            stride = fpn_strides[level]

                            # compute the gride cell
                            xc_s = xc / stride
                            yc_s = yc / stride
                            grid_x = int(xc_s)
                            grid_y = int(yc_s)

                            label_assignment_results.append([grid_x, grid_y, level, anchor_idx])

                # label assignment
                for result in label_assignment_results:
                    grid_x, grid_y, level, anchor_idx = result
                    fmp_h, fmp_w = fmp_sizes[level]

                    if grid_x < fmp_w and grid_y < fmp_h:
                        # obj
                        gt_objectness[level][batch_index, grid_y, grid_x, anchor_idx] = 1.0
                        # cls
                        cls_ont_hot = torch.zeros(self.num_classes)
                        cls_ont_hot[int(gt_label)] = 1.0
                        gt_classes[level][batch_index, grid_y, grid_x, anchor_idx] = cls_ont_hot
                        # box
                        gt_bboxes[level][batch_index, grid_y, grid_x, anchor_idx] = torch.as_tensor([x1, y1, x2, y2])

        # [B, M, C]
        gt_objectness = torch.cat([gt.view(bs, -1, 1) for gt in gt_objectness], dim=1).float()
        gt_classes = torch.cat([gt.view(bs, -1, self.num_classes) for gt in gt_classes], dim=1).float()
        gt_bboxes = torch.cat([gt.view(bs, -1, 4) for gt in gt_bboxes], dim=1).float()

        return gt_objectness, gt_classes, gt_bboxes