import torch import torch.nn as nn from .yolox_backbone import build_backbone from .yolox_fpn import build_fpn from .yolox_head import build_head from utils.misc import multiclass_nms # YOLOX class YOLOX(nn.Module): def __init__(self, cfg, device, num_classes=20, conf_thresh=0.01, nms_thresh=0.5, topk=100, trainable=False): super(YOLOX, self).__init__() # --------- Basic Parameters ---------- self.cfg = cfg self.device = device self.stride = [8, 16, 32] self.num_classes = num_classes self.trainable = trainable self.conf_thresh = conf_thresh self.nms_thresh = nms_thresh self.topk = topk # ------------------- Network Structure ------------------- ## 主干网络 self.backbone, feats_dim = build_backbone(cfg, trainable&cfg['pretrained']) ## 颈部网络: 特征金字塔 self.fpn = build_fpn(cfg=cfg, in_dims=feats_dim, out_dim=int(256*cfg['width'])) self.head_dim = self.fpn.out_dim ## 检测头 self.non_shared_heads = nn.ModuleList( [build_head(cfg, head_dim, head_dim, num_classes) for head_dim in self.head_dim ]) ## 预测层 self.obj_preds = nn.ModuleList( [nn.Conv2d(head.reg_out_dim, 1, kernel_size=1) for head in self.non_shared_heads ]) self.cls_preds = nn.ModuleList( [nn.Conv2d(head.cls_out_dim, self.num_classes, kernel_size=1) for head in self.non_shared_heads ]) self.reg_preds = nn.ModuleList( [nn.Conv2d(head.reg_out_dim, 4, kernel_size=1) for head in self.non_shared_heads ]) # ---------------------- Basic Functions ---------------------- ## generate anchor points def generate_anchors(self, level, fmp_size): """ fmp_size: (List) [H, W] """ # generate grid cells fmp_h, fmp_w = fmp_size anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)]) # [H, W, 2] -> [HW, 2] anchor_xy = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2) anchor_xy += 0.5 # add center offset anchor_xy *= self.stride[level] anchors = anchor_xy.to(self.device) return anchors ## post-process def post_process(self, obj_preds, cls_preds, box_preds): """ Input: obj_preds: List(Tensor) [[H x W, 1], ...] cls_preds: List(Tensor) [[H x W, C], ...] box_preds: List(Tensor) [[H x W, 4], ...] anchors: List(Tensor) [[H x W, 2], ...] """ all_scores = [] all_labels = [] all_bboxes = [] for obj_pred_i, cls_pred_i, box_pred_i in zip(obj_preds, cls_preds, box_preds): # (H x W x KA x C,) scores_i = (torch.sqrt(obj_pred_i.sigmoid() * cls_pred_i.sigmoid())).flatten() # Keep top k top scoring indices only. num_topk = min(self.topk, box_pred_i.size(0)) # torch.sort is actually faster than .topk (at least on GPUs) predicted_prob, topk_idxs = scores_i.sort(descending=True) topk_scores = predicted_prob[:num_topk] topk_idxs = topk_idxs[:num_topk] # filter out the proposals with low confidence score keep_idxs = topk_scores > self.conf_thresh scores = topk_scores[keep_idxs] topk_idxs = topk_idxs[keep_idxs] anchor_idxs = torch.div(topk_idxs, self.num_classes, rounding_mode='floor') labels = topk_idxs % self.num_classes bboxes = box_pred_i[anchor_idxs] all_scores.append(scores) all_labels.append(labels) all_bboxes.append(bboxes) scores = torch.cat(all_scores) labels = torch.cat(all_labels) bboxes = torch.cat(all_bboxes) # to cpu & numpy scores = scores.cpu().numpy() labels = labels.cpu().numpy() bboxes = bboxes.cpu().numpy() # nms scores, labels, bboxes = multiclass_nms( scores, labels, bboxes, self.nms_thresh, self.num_classes, False) return bboxes, scores, labels # ---------------------- Main Process for Inference ---------------------- @torch.no_grad() def inference_single_image(self, x): # backbone pyramid_feats = self.backbone(x) # fpn pyramid_feats = self.fpn(pyramid_feats) # non-shared heads all_obj_preds = [] all_cls_preds = [] all_box_preds = [] all_anchors = [] for level, (feat, head) in enumerate(zip(pyramid_feats, self.non_shared_heads)): cls_feat, reg_feat = head(feat) # [1, C, H, W] obj_pred = self.obj_preds[level](reg_feat) cls_pred = self.cls_preds[level](cls_feat) reg_pred = self.reg_preds[level](reg_feat) # anchors: [M, 2] fmp_size = cls_pred.shape[-2:] anchors = self.generate_anchors(level, fmp_size) # [1, C, H, W] -> [H, W, C] -> [M, C] obj_pred = obj_pred[0].permute(1, 2, 0).contiguous().view(-1, 1) cls_pred = cls_pred[0].permute(1, 2, 0).contiguous().view(-1, self.num_classes) reg_pred = reg_pred[0].permute(1, 2, 0).contiguous().view(-1, 4) # decode bbox ctr_pred = reg_pred[..., :2] * self.stride[level] + anchors[..., :2] wh_pred = torch.exp(reg_pred[..., 2:]) * self.stride[level] pred_x1y1 = ctr_pred - wh_pred * 0.5 pred_x2y2 = ctr_pred + wh_pred * 0.5 box_pred = torch.cat([pred_x1y1, pred_x2y2], dim=-1) all_obj_preds.append(obj_pred) all_cls_preds.append(cls_pred) all_box_preds.append(box_pred) all_anchors.append(anchors) # post process bboxes, scores, labels = self.post_process( all_obj_preds, all_cls_preds, all_box_preds) return bboxes, scores, labels def forward(self, x): if not self.trainable: return self.inference_single_image(x) else: # backbone pyramid_feats = self.backbone(x) # fpn pyramid_feats = self.fpn(pyramid_feats) # non-shared heads all_anchors = [] all_obj_preds = [] all_cls_preds = [] all_box_preds = [] for level, (feat, head) in enumerate(zip(pyramid_feats, self.non_shared_heads)): cls_feat, reg_feat = head(feat) # [B, C, H, W] obj_pred = self.obj_preds[level](reg_feat) cls_pred = self.cls_preds[level](cls_feat) reg_pred = self.reg_preds[level](reg_feat) B, _, H, W = cls_pred.size() fmp_size = [H, W] # generate anchor boxes: [M, 4] anchors = self.generate_anchors(level, fmp_size) # [B, C, H, W] -> [B, H, W, C] -> [B, M, C] obj_pred = obj_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 1) cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_classes) reg_pred = reg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 4) # decode bbox ctr_pred = reg_pred[..., :2] * self.stride[level] + anchors[..., :2] wh_pred = torch.exp(reg_pred[..., 2:]) * self.stride[level] pred_x1y1 = ctr_pred - wh_pred * 0.5 pred_x2y2 = ctr_pred + wh_pred * 0.5 box_pred = torch.cat([pred_x1y1, pred_x2y2], dim=-1) all_obj_preds.append(obj_pred) all_cls_preds.append(cls_pred) all_box_preds.append(box_pred) all_anchors.append(anchors) # output dict outputs = {"pred_obj": all_obj_preds, # List(Tensor) [B, M, 1] "pred_cls": all_cls_preds, # List(Tensor) [B, M, C] "pred_box": all_box_preds, # List(Tensor) [B, M, 4] "anchors": all_anchors, # List(Tensor) [B, M, 2] 'strides': self.stride} # List(Int) [8, 16, 32] return outputs