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| #!/usr/bin/env python3 | |
| # -*- coding:utf-8 -*- | |
| # The code is based on | |
| # https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/models/yolo_head.py | |
| # Copyright (c) Megvii, Inc. and its affiliates. | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from yolov6.utils.figure_iou import IOUloss, pairwise_bbox_iou | |
| class ComputeLoss: | |
| '''Loss computation func. | |
| This func contains SimOTA and siou loss. | |
| ''' | |
| def __init__(self, | |
| reg_weight=5.0, | |
| iou_weight=3.0, | |
| cls_weight=1.0, | |
| center_radius=2.5, | |
| eps=1e-7, | |
| in_channels=[256, 512, 1024], | |
| strides=[8, 16, 32], | |
| n_anchors=1, | |
| iou_type='ciou' | |
| ): | |
| self.reg_weight = reg_weight | |
| self.iou_weight = iou_weight | |
| self.cls_weight = cls_weight | |
| self.center_radius = center_radius | |
| self.eps = eps | |
| self.n_anchors = n_anchors | |
| self.strides = strides | |
| self.grids = [torch.zeros(1)] * len(in_channels) | |
| # Define criteria | |
| self.l1_loss = nn.L1Loss(reduction="none") | |
| self.bcewithlog_loss = nn.BCEWithLogitsLoss(reduction="none") | |
| self.iou_loss = IOUloss(iou_type=iou_type, reduction="none") | |
| def __call__( | |
| self, | |
| outputs, | |
| targets | |
| ): | |
| dtype = outputs[0].type() | |
| device = targets.device | |
| loss_cls, loss_obj, loss_iou, loss_l1 = torch.zeros(1, device=device), torch.zeros(1, device=device), \ | |
| torch.zeros(1, device=device), torch.zeros(1, device=device) | |
| num_classes = outputs[0].shape[-1] - 5 | |
| outputs, outputs_origin, gt_bboxes_scale, xy_shifts, expanded_strides = self.get_outputs_and_grids( | |
| outputs, self.strides, dtype, device) | |
| total_num_anchors = outputs.shape[1] | |
| bbox_preds = outputs[:, :, :4] # [batch, n_anchors_all, 4] | |
| bbox_preds_org = outputs_origin[:, :, :4] # [batch, n_anchors_all, 4] | |
| obj_preds = outputs[:, :, 4].unsqueeze(-1) # [batch, n_anchors_all, 1] | |
| cls_preds = outputs[:, :, 5:] # [batch, n_anchors_all, n_cls] | |
| # targets | |
| batch_size = bbox_preds.shape[0] | |
| targets_list = np.zeros((batch_size, 1, 5)).tolist() | |
| for i, item in enumerate(targets.cpu().numpy().tolist()): | |
| targets_list[int(item[0])].append(item[1:]) | |
| max_len = max((len(l) for l in targets_list)) | |
| targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device) | |
| num_targets_list = (targets.sum(dim=2) > 0).sum(dim=1) # number of objects | |
| num_fg, num_gts = 0, 0 | |
| cls_targets, reg_targets, l1_targets, obj_targets, fg_masks = [], [], [], [], [] | |
| for batch_idx in range(batch_size): | |
| num_gt = int(num_targets_list[batch_idx]) | |
| num_gts += num_gt | |
| if num_gt == 0: | |
| cls_target = outputs.new_zeros((0, num_classes)) | |
| reg_target = outputs.new_zeros((0, 4)) | |
| l1_target = outputs.new_zeros((0, 4)) | |
| obj_target = outputs.new_zeros((total_num_anchors, 1)) | |
| fg_mask = outputs.new_zeros(total_num_anchors).bool() | |
| else: | |
| gt_bboxes_per_image = targets[batch_idx, :num_gt, 1:5].mul_(gt_bboxes_scale) | |
| gt_classes = targets[batch_idx, :num_gt, 0] | |
| bboxes_preds_per_image = bbox_preds[batch_idx] | |
| cls_preds_per_image = cls_preds[batch_idx] | |
| obj_preds_per_image = obj_preds[batch_idx] | |
| try: | |
| ( | |
| gt_matched_classes, | |
| fg_mask, | |
| pred_ious_this_matching, | |
| matched_gt_inds, | |
| num_fg_img, | |
| ) = self.get_assignments( | |
| batch_idx, | |
| num_gt, | |
| total_num_anchors, | |
| gt_bboxes_per_image, | |
| gt_classes, | |
| bboxes_preds_per_image, | |
| cls_preds_per_image, | |
| obj_preds_per_image, | |
| expanded_strides, | |
| xy_shifts, | |
| num_classes | |
| ) | |
| except RuntimeError: | |
| print( | |
| "OOM RuntimeError is raised due to the huge memory cost during label assignment. \ | |
| CPU mode is applied in this batch. If you want to avoid this issue, \ | |
| try to reduce the batch size or image size." | |
| ) | |
| torch.cuda.empty_cache() | |
| print("------------CPU Mode for This Batch-------------") | |
| _gt_bboxes_per_image = gt_bboxes_per_image.cpu().float() | |
| _gt_classes = gt_classes.cpu().float() | |
| _bboxes_preds_per_image = bboxes_preds_per_image.cpu().float() | |
| _cls_preds_per_image = cls_preds_per_image.cpu().float() | |
| _obj_preds_per_image = obj_preds_per_image.cpu().float() | |
| _expanded_strides = expanded_strides.cpu().float() | |
| _xy_shifts = xy_shifts.cpu() | |
| ( | |
| gt_matched_classes, | |
| fg_mask, | |
| pred_ious_this_matching, | |
| matched_gt_inds, | |
| num_fg_img, | |
| ) = self.get_assignments( | |
| batch_idx, | |
| num_gt, | |
| total_num_anchors, | |
| _gt_bboxes_per_image, | |
| _gt_classes, | |
| _bboxes_preds_per_image, | |
| _cls_preds_per_image, | |
| _obj_preds_per_image, | |
| _expanded_strides, | |
| _xy_shifts, | |
| num_classes | |
| ) | |
| gt_matched_classes = gt_matched_classes.cuda() | |
| fg_mask = fg_mask.cuda() | |
| pred_ious_this_matching = pred_ious_this_matching.cuda() | |
| matched_gt_inds = matched_gt_inds.cuda() | |
| torch.cuda.empty_cache() | |
| num_fg += num_fg_img | |
| if num_fg_img > 0: | |
| cls_target = F.one_hot( | |
| gt_matched_classes.to(torch.int64), num_classes | |
| ) * pred_ious_this_matching.unsqueeze(-1) | |
| obj_target = fg_mask.unsqueeze(-1) | |
| reg_target = gt_bboxes_per_image[matched_gt_inds] | |
| l1_target = self.get_l1_target( | |
| outputs.new_zeros((num_fg_img, 4)), | |
| gt_bboxes_per_image[matched_gt_inds], | |
| expanded_strides[0][fg_mask], | |
| xy_shifts=xy_shifts[0][fg_mask], | |
| ) | |
| cls_targets.append(cls_target) | |
| reg_targets.append(reg_target) | |
| obj_targets.append(obj_target) | |
| l1_targets.append(l1_target) | |
| fg_masks.append(fg_mask) | |
| cls_targets = torch.cat(cls_targets, 0) | |
| reg_targets = torch.cat(reg_targets, 0) | |
| obj_targets = torch.cat(obj_targets, 0) | |
| l1_targets = torch.cat(l1_targets, 0) | |
| fg_masks = torch.cat(fg_masks, 0) | |
| num_fg = max(num_fg, 1) | |
| # loss | |
| loss_iou += (self.iou_loss(bbox_preds.view(-1, 4)[fg_masks].T, reg_targets)).sum() / num_fg | |
| loss_l1 += (self.l1_loss(bbox_preds_org.view(-1, 4)[fg_masks], l1_targets)).sum() / num_fg | |
| loss_obj += (self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets*1.0)).sum() / num_fg | |
| loss_cls += (self.bcewithlog_loss(cls_preds.view(-1, num_classes)[fg_masks], cls_targets)).sum() / num_fg | |
| total_losses = self.reg_weight * loss_iou + loss_l1 + loss_obj + loss_cls | |
| return total_losses, torch.cat((self.reg_weight * loss_iou, loss_l1, loss_obj, loss_cls)).detach() | |
| def decode_output(self, output, k, stride, dtype, device): | |
| grid = self.grids[k].to(device) | |
| batch_size = output.shape[0] | |
| hsize, wsize = output.shape[2:4] | |
| if grid.shape[2:4] != output.shape[2:4]: | |
| yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)]) | |
| grid = torch.stack((xv, yv), 2).view(1, 1, hsize, wsize, 2).type(dtype).to(device) | |
| self.grids[k] = grid | |
| output = output.reshape(batch_size, self.n_anchors * hsize * wsize, -1) | |
| output_origin = output.clone() | |
| grid = grid.view(1, -1, 2) | |
| output[..., :2] = (output[..., :2] + grid) * stride | |
| output[..., 2:4] = torch.exp(output[..., 2:4]) * stride | |
| return output, output_origin, grid, hsize, wsize | |
| def get_outputs_and_grids(self, outputs, strides, dtype, device): | |
| xy_shifts = [] | |
| expanded_strides = [] | |
| outputs_new = [] | |
| outputs_origin = [] | |
| for k, output in enumerate(outputs): | |
| output, output_origin, grid, feat_h, feat_w = self.decode_output( | |
| output, k, strides[k], dtype, device) | |
| xy_shift = grid | |
| expanded_stride = torch.full((1, grid.shape[1], 1), strides[k], dtype=grid.dtype, device=grid.device) | |
| xy_shifts.append(xy_shift) | |
| expanded_strides.append(expanded_stride) | |
| outputs_new.append(output) | |
| outputs_origin.append(output_origin) | |
| xy_shifts = torch.cat(xy_shifts, 1) # [1, n_anchors_all, 2] | |
| expanded_strides = torch.cat(expanded_strides, 1) # [1, n_anchors_all, 1] | |
| outputs_origin = torch.cat(outputs_origin, 1) | |
| outputs = torch.cat(outputs_new, 1) | |
| feat_h *= strides[-1] | |
| feat_w *= strides[-1] | |
| gt_bboxes_scale = torch.Tensor([[feat_w, feat_h, feat_w, feat_h]]).type_as(outputs) | |
| return outputs, outputs_origin, gt_bboxes_scale, xy_shifts, expanded_strides | |
| def get_l1_target(self, l1_target, gt, stride, xy_shifts, eps=1e-8): | |
| l1_target[:, 0:2] = gt[:, 0:2] / stride - xy_shifts | |
| l1_target[:, 2:4] = torch.log(gt[:, 2:4] / stride + eps) | |
| return l1_target | |
| def get_assignments( | |
| self, | |
| batch_idx, | |
| num_gt, | |
| total_num_anchors, | |
| gt_bboxes_per_image, | |
| gt_classes, | |
| bboxes_preds_per_image, | |
| cls_preds_per_image, | |
| obj_preds_per_image, | |
| expanded_strides, | |
| xy_shifts, | |
| num_classes | |
| ): | |
| fg_mask, is_in_boxes_and_center = self.get_in_boxes_info( | |
| gt_bboxes_per_image, | |
| expanded_strides, | |
| xy_shifts, | |
| total_num_anchors, | |
| num_gt, | |
| ) | |
| bboxes_preds_per_image = bboxes_preds_per_image[fg_mask] | |
| cls_preds_ = cls_preds_per_image[fg_mask] | |
| obj_preds_ = obj_preds_per_image[fg_mask] | |
| num_in_boxes_anchor = bboxes_preds_per_image.shape[0] | |
| # cost | |
| pair_wise_ious = pairwise_bbox_iou(gt_bboxes_per_image, bboxes_preds_per_image, box_format='xywh') | |
| pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8) | |
| gt_cls_per_image = ( | |
| F.one_hot(gt_classes.to(torch.int64), num_classes) | |
| .float() | |
| .unsqueeze(1) | |
| .repeat(1, num_in_boxes_anchor, 1) | |
| ) | |
| with torch.cuda.amp.autocast(enabled=False): | |
| cls_preds_ = ( | |
| cls_preds_.float().sigmoid_().unsqueeze(0).repeat(num_gt, 1, 1) | |
| * obj_preds_.float().sigmoid_().unsqueeze(0).repeat(num_gt, 1, 1) | |
| ) | |
| pair_wise_cls_loss = F.binary_cross_entropy( | |
| cls_preds_.sqrt_(), gt_cls_per_image, reduction="none" | |
| ).sum(-1) | |
| del cls_preds_, obj_preds_ | |
| cost = ( | |
| self.cls_weight * pair_wise_cls_loss | |
| + self.iou_weight * pair_wise_ious_loss | |
| + 100000.0 * (~is_in_boxes_and_center) | |
| ) | |
| ( | |
| num_fg, | |
| gt_matched_classes, | |
| pred_ious_this_matching, | |
| matched_gt_inds, | |
| ) = self.dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask) | |
| del pair_wise_cls_loss, cost, pair_wise_ious, pair_wise_ious_loss | |
| return ( | |
| gt_matched_classes, | |
| fg_mask, | |
| pred_ious_this_matching, | |
| matched_gt_inds, | |
| num_fg, | |
| ) | |
| def get_in_boxes_info( | |
| self, | |
| gt_bboxes_per_image, | |
| expanded_strides, | |
| xy_shifts, | |
| total_num_anchors, | |
| num_gt, | |
| ): | |
| expanded_strides_per_image = expanded_strides[0] | |
| xy_shifts_per_image = xy_shifts[0] * expanded_strides_per_image | |
| xy_centers_per_image = ( | |
| (xy_shifts_per_image + 0.5 * expanded_strides_per_image) | |
| .unsqueeze(0) | |
| .repeat(num_gt, 1, 1) | |
| ) # [n_anchor, 2] -> [n_gt, n_anchor, 2] | |
| gt_bboxes_per_image_lt = ( | |
| (gt_bboxes_per_image[:, 0:2] - 0.5 * gt_bboxes_per_image[:, 2:4]) | |
| .unsqueeze(1) | |
| .repeat(1, total_num_anchors, 1) | |
| ) | |
| gt_bboxes_per_image_rb = ( | |
| (gt_bboxes_per_image[:, 0:2] + 0.5 * gt_bboxes_per_image[:, 2:4]) | |
| .unsqueeze(1) | |
| .repeat(1, total_num_anchors, 1) | |
| ) # [n_gt, 2] -> [n_gt, n_anchor, 2] | |
| b_lt = xy_centers_per_image - gt_bboxes_per_image_lt | |
| b_rb = gt_bboxes_per_image_rb - xy_centers_per_image | |
| bbox_deltas = torch.cat([b_lt, b_rb], 2) | |
| is_in_boxes = bbox_deltas.min(dim=-1).values > 0.0 | |
| is_in_boxes_all = is_in_boxes.sum(dim=0) > 0 | |
| # in fixed center | |
| gt_bboxes_per_image_lt = (gt_bboxes_per_image[:, 0:2]).unsqueeze(1).repeat( | |
| 1, total_num_anchors, 1 | |
| ) - self.center_radius * expanded_strides_per_image.unsqueeze(0) | |
| gt_bboxes_per_image_rb = (gt_bboxes_per_image[:, 0:2]).unsqueeze(1).repeat( | |
| 1, total_num_anchors, 1 | |
| ) + self.center_radius * expanded_strides_per_image.unsqueeze(0) | |
| c_lt = xy_centers_per_image - gt_bboxes_per_image_lt | |
| c_rb = gt_bboxes_per_image_rb - xy_centers_per_image | |
| center_deltas = torch.cat([c_lt, c_rb], 2) | |
| is_in_centers = center_deltas.min(dim=-1).values > 0.0 | |
| is_in_centers_all = is_in_centers.sum(dim=0) > 0 | |
| # in boxes and in centers | |
| is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all | |
| is_in_boxes_and_center = ( | |
| is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor] | |
| ) | |
| return is_in_boxes_anchor, is_in_boxes_and_center | |
| def dynamic_k_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask): | |
| matching_matrix = torch.zeros_like(cost, dtype=torch.uint8) | |
| ious_in_boxes_matrix = pair_wise_ious | |
| n_candidate_k = min(10, ious_in_boxes_matrix.size(1)) | |
| topk_ious, _ = torch.topk(ious_in_boxes_matrix, n_candidate_k, dim=1) | |
| dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1) | |
| dynamic_ks = dynamic_ks.tolist() | |
| for gt_idx in range(num_gt): | |
| _, pos_idx = torch.topk( | |
| cost[gt_idx], k=dynamic_ks[gt_idx], largest=False | |
| ) | |
| matching_matrix[gt_idx][pos_idx] = 1 | |
| del topk_ious, dynamic_ks, pos_idx | |
| anchor_matching_gt = matching_matrix.sum(0) | |
| if (anchor_matching_gt > 1).sum() > 0: | |
| _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) | |
| matching_matrix[:, anchor_matching_gt > 1] *= 0 | |
| matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1 | |
| fg_mask_inboxes = matching_matrix.sum(0) > 0 | |
| num_fg = fg_mask_inboxes.sum().item() | |
| fg_mask[fg_mask.clone()] = fg_mask_inboxes | |
| matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) | |
| gt_matched_classes = gt_classes[matched_gt_inds] | |
| pred_ious_this_matching = (matching_matrix * pair_wise_ious).sum(0)[ | |
| fg_mask_inboxes | |
| ] | |
| return num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds | |