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- import torch
- import numpy as np
- class YoloMatcher(object):
- def __init__(self, num_classes):
- self.num_classes = num_classes
- @torch.no_grad()
- def __call__(self, fmp_size, stride, targets):
- """
- img_size: (Int) input image size
- stride: (Int) -> stride of YOLOv1 output.
- targets: (Dict) dict{'boxes': [...],
- 'labels': [...],
- 'orig_size': ...}
- """
- # prepare
- bs = len(targets)
- fmp_h, fmp_w = fmp_size
- gt_objectness = np.zeros([bs, fmp_h, fmp_w, 1])
- gt_classes = np.zeros([bs, fmp_h, fmp_w, self.num_classes])
- gt_bboxes = np.zeros([bs, fmp_h, fmp_w, 4])
- 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):
- x1, y1, x2, y2 = gt_box
- # xyxy -> cxcywh
- xc, yc = (x2 + x1) * 0.5, (y2 + y1) * 0.5
- bw, bh = x2 - x1, y2 - y1
- # check
- if bw < 1. or bh < 1.:
- return False
- # grid
- xs_c = xc / stride
- ys_c = yc / stride
- grid_x = int(xs_c)
- grid_y = int(ys_c)
- if grid_x < fmp_w and grid_y < fmp_h:
- gt_objectness[batch_index, grid_y, grid_x] = 1.0
- gt_classes[batch_index, grid_y, grid_x, int(gt_label)] = 1.0
- gt_bboxes[batch_index, grid_y, grid_x] = np.array([x1, y1, x2, y2])
- # [B, M, C]
- gt_objectness = gt_objectness.reshape(bs, -1, 1)
- gt_classes = gt_classes.reshape(bs, -1, self.num_classes)
- gt_bboxes = gt_bboxes.reshape(bs, -1, 4)
- # to tensor
- gt_objectness = torch.from_numpy(gt_objectness).float()
- gt_classes = torch.from_numpy(gt_classes).float()
- gt_bboxes = torch.from_numpy(gt_bboxes).float()
- return gt_objectness, gt_classes, gt_bboxes
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