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* add gfl_trt * add gfl_head.py * add batch_integral * lint code * add gfl unit test * fix unit test * add gfl benchmark * fix unit test bug * Update gfl_head.py * Update __init__.py remove '**_forward_single' * fix lint error and ut error * fix docs and benchmark Co-authored-by: VVsssssk <shenkun@pjlab.org.cn>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,185 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import torch | ||
| import torch.nn.functional as F | ||
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|
||
| from mmdeploy.codebase.mmdet import (get_post_processing_params, | ||
| multiclass_nms, pad_with_value) | ||
| from mmdeploy.core import FUNCTION_REWRITER | ||
| from mmdeploy.utils import Backend, get_backend, is_dynamic_shape | ||
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| @FUNCTION_REWRITER.register_rewriter( | ||
| func_name='mmdet.models.dense_heads.gfl_head.' | ||
| 'GFLHead.get_bboxes') | ||
| def gfl_head__get_bbox(ctx, | ||
| self, | ||
| cls_scores, | ||
| bbox_preds, | ||
| score_factors=None, | ||
| img_metas=None, | ||
| cfg=None, | ||
| rescale=False, | ||
| with_nms=True, | ||
| **kwargs): | ||
| """Rewrite `get_bboxes` of `GFLHead` for default backend. | ||
| Rewrite this function to deploy model, transform network output for a | ||
| batch into bbox predictions. | ||
| Args: | ||
| ctx (ContextCaller): The context with additional information. | ||
| self: The instance of the original class. | ||
| cls_scores (list[Tensor]): Classification scores for all | ||
| scale levels, each is a 4D-tensor, has shape | ||
| (batch_size, num_priors * num_classes, H, W). | ||
| bbox_preds (list[Tensor]): Box energies / deltas for all | ||
| scale levels, each is a 4D-tensor, has shape | ||
| (batch_size, num_priors * 4, H, W). | ||
| score_factors (list[Tensor], Optional): Score factor for | ||
| all scale level, each is a 4D-tensor, has shape | ||
| (batch_size, num_priors * 1, H, W). Default None. | ||
| img_metas (list[dict], Optional): Image meta info. Default None. | ||
| cfg (mmcv.Config, Optional): Test / postprocessing configuration, | ||
| if None, test_cfg would be used. Default None. | ||
| rescale (bool): If True, return boxes in original image space. | ||
| Default False. | ||
| with_nms (bool): If True, do nms before return boxes. | ||
| Default True. | ||
| Returns: | ||
| If with_nms == True: | ||
| tuple[Tensor, Tensor]: tuple[Tensor, Tensor]: (dets, labels), | ||
| `dets` of shape [N, num_det, 5] and `labels` of shape | ||
| [N, num_det]. | ||
| Else: | ||
| tuple[Tensor, Tensor, Tensor]: batch_mlvl_bboxes, | ||
| batch_mlvl_scores, batch_mlvl_centerness | ||
| """ | ||
| deploy_cfg = ctx.cfg | ||
| is_dynamic_flag = is_dynamic_shape(deploy_cfg) | ||
| backend = get_backend(deploy_cfg) | ||
| num_levels = len(cls_scores) | ||
|
|
||
| featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] | ||
| mlvl_priors = self.prior_generator.grid_priors( | ||
| featmap_sizes, dtype=bbox_preds[0].dtype, device=bbox_preds[0].device) | ||
|
|
||
| mlvl_cls_scores = [cls_scores[i].detach() for i in range(num_levels)] | ||
| mlvl_bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)] | ||
| if score_factors is None: | ||
| with_score_factors = False | ||
| mlvl_score_factor = [None for _ in range(num_levels)] | ||
| else: | ||
| with_score_factors = True | ||
| mlvl_score_factor = [ | ||
| score_factors[i].detach() for i in range(num_levels) | ||
| ] | ||
| mlvl_score_factors = [] | ||
| assert img_metas is not None | ||
| img_shape = img_metas[0]['img_shape'] | ||
|
|
||
| assert len(cls_scores) == len(bbox_preds) == len(mlvl_priors) | ||
| batch_size = cls_scores[0].shape[0] | ||
| cfg = self.test_cfg | ||
| pre_topk = cfg.get('nms_pre', -1) | ||
|
|
||
| mlvl_valid_bboxes = [] | ||
| mlvl_valid_scores = [] | ||
| mlvl_valid_priors = [] | ||
|
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| for cls_score, bbox_pred, score_factors, priors, stride in zip( | ||
| mlvl_cls_scores, mlvl_bbox_preds, mlvl_score_factor, mlvl_priors, | ||
| self.prior_generator.strides): | ||
| assert cls_score.size()[-2:] == bbox_pred.size()[-2:] | ||
| assert stride[0] == stride[1] | ||
|
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||
| scores = cls_score.permute(0, 2, 3, 1).reshape(batch_size, -1, | ||
| self.cls_out_channels) | ||
| if self.use_sigmoid_cls: | ||
| scores = scores.sigmoid() | ||
| nms_pre_score = scores | ||
| else: | ||
| scores = scores.softmax(-1) | ||
| nms_pre_score = scores | ||
| if with_score_factors: | ||
| score_factors = score_factors.permute(0, 2, 3, | ||
| 1).reshape(batch_size, | ||
| -1).sigmoid() | ||
| score_factors = score_factors.unsqueeze(2) | ||
| bbox_pred = batched_integral(self.integral, | ||
| bbox_pred.permute(0, 2, 3, 1)) * stride[0] | ||
| if not is_dynamic_flag: | ||
| priors = priors.data | ||
| priors = priors.expand(batch_size, -1, priors.size(-1)) | ||
| if pre_topk > 0: | ||
| if with_score_factors: | ||
| nms_pre_score = nms_pre_score * score_factors | ||
| if backend == Backend.TENSORRT: | ||
| priors = pad_with_value(priors, 1, pre_topk) | ||
| bbox_pred = pad_with_value(bbox_pred, 1, pre_topk) | ||
| scores = pad_with_value(scores, 1, pre_topk, 0.) | ||
| nms_pre_score = pad_with_value(nms_pre_score, 1, pre_topk, 0.) | ||
| if with_score_factors: | ||
| score_factors = pad_with_value(score_factors, 1, pre_topk, | ||
| 0.) | ||
|
|
||
| # Get maximum scores for foreground classes. | ||
| if self.use_sigmoid_cls: | ||
| max_scores, _ = nms_pre_score.max(-1) | ||
| else: | ||
| max_scores, _ = nms_pre_score[..., :-1].max(-1) | ||
| _, topk_inds = max_scores.topk(pre_topk) | ||
| batch_inds = torch.arange( | ||
| batch_size, | ||
| device=bbox_pred.device).view(-1, 1).expand_as(topk_inds) | ||
| priors = priors[batch_inds, topk_inds, :] | ||
| bbox_pred = bbox_pred[batch_inds, topk_inds, :] | ||
| scores = scores[batch_inds, topk_inds, :] | ||
| if with_score_factors: | ||
| score_factors = score_factors[batch_inds, topk_inds, :] | ||
|
|
||
| mlvl_valid_bboxes.append(bbox_pred) | ||
| mlvl_valid_scores.append(scores) | ||
| priors = self.anchor_center(priors) | ||
| mlvl_valid_priors.append(priors) | ||
| if with_score_factors: | ||
| mlvl_score_factors.append(score_factors) | ||
|
|
||
| batch_mlvl_bboxes_pred = torch.cat(mlvl_valid_bboxes, dim=1) | ||
| batch_scores = torch.cat(mlvl_valid_scores, dim=1) | ||
| batch_priors = torch.cat(mlvl_valid_priors, dim=1) | ||
| batch_bboxes = self.bbox_coder.decode( | ||
| batch_priors, batch_mlvl_bboxes_pred, max_shape=img_shape) | ||
| if with_score_factors: | ||
| batch_score_factors = torch.cat(mlvl_score_factors, dim=1) | ||
|
|
||
| if not self.use_sigmoid_cls: | ||
| batch_scores = batch_scores[..., :self.num_classes] | ||
|
|
||
| if with_score_factors: | ||
| batch_scores = batch_scores * batch_score_factors | ||
| if not with_nms: | ||
| return batch_bboxes, batch_scores | ||
| post_params = get_post_processing_params(deploy_cfg) | ||
| max_output_boxes_per_class = post_params.max_output_boxes_per_class | ||
| iou_threshold = cfg.nms.get('iou_threshold', post_params.iou_threshold) | ||
| score_threshold = cfg.get('score_thr', post_params.score_threshold) | ||
| pre_top_k = post_params.pre_top_k | ||
| keep_top_k = cfg.get('max_per_img', post_params.keep_top_k) | ||
| return multiclass_nms( | ||
| batch_bboxes, | ||
| batch_scores, | ||
| max_output_boxes_per_class, | ||
| iou_threshold=iou_threshold, | ||
| score_threshold=score_threshold, | ||
| pre_top_k=pre_top_k, | ||
| keep_top_k=keep_top_k) | ||
|
|
||
|
|
||
| def batched_integral(intergral, x): | ||
| batch_size = x.size(0) | ||
| x = F.softmax(x.reshape(batch_size, -1, intergral.reg_max + 1), dim=2) | ||
| x = F.linear(x, | ||
| intergral.project.type_as(x).unsqueeze(0)).reshape( | ||
| batch_size, -1, 4) | ||
| return x |
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