# Copyright (c) OpenMMLab. All rights reserved. import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import Conv2d, Linear, build_activation_layer from mmcv.cnn.bricks.transformer import FFN, build_positional_encoding from mmcv.runner import force_fp32 from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, build_assigner, build_sampler, multi_apply, reduce_mean) from mmdet.models.utils import build_transformer from ..builder import HEADS, build_loss from .anchor_free_head import AnchorFreeHead @HEADS.register_module() class DETRHead(AnchorFreeHead): """Implements the DETR transformer head. See `paper: End-to-End Object Detection with Transformers `_ for details. Args: num_classes (int): Number of categories excluding the background. in_channels (int): Number of channels in the input feature map. num_query (int): Number of query in Transformer. num_reg_fcs (int, optional): Number of fully-connected layers used in `FFN`, which is then used for the regression head. Default 2. transformer (obj:`mmcv.ConfigDict`|dict): Config for transformer. Default: None. sync_cls_avg_factor (bool): Whether to sync the avg_factor of all ranks. Default to False. positional_encoding (obj:`mmcv.ConfigDict`|dict): Config for position encoding. loss_cls (obj:`mmcv.ConfigDict`|dict): Config of the classification loss. Default `CrossEntropyLoss`. loss_bbox (obj:`mmcv.ConfigDict`|dict): Config of the regression loss. Default `L1Loss`. loss_iou (obj:`mmcv.ConfigDict`|dict): Config of the regression iou loss. Default `GIoULoss`. tran_cfg (obj:`mmcv.ConfigDict`|dict): Training config of transformer head. test_cfg (obj:`mmcv.ConfigDict`|dict): Testing config of transformer head. init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ _version = 2 def __init__(self, num_classes, in_channels, num_query=100, num_reg_fcs=2, transformer=None, sync_cls_avg_factor=False, positional_encoding=dict( type='SinePositionalEncoding', num_feats=128, normalize=True), loss_cls=dict( type='CrossEntropyLoss', bg_cls_weight=0.1, use_sigmoid=False, loss_weight=1.0, class_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=5.0), loss_iou=dict(type='GIoULoss', loss_weight=2.0), train_cfg=dict( assigner=dict( type='HungarianAssigner', cls_cost=dict(type='ClassificationCost', weight=1.), reg_cost=dict(type='BBoxL1Cost', weight=5.0), iou_cost=dict( type='IoUCost', iou_mode='giou', weight=2.0))), test_cfg=dict(max_per_img=100), init_cfg=None, **kwargs): # NOTE here use `AnchorFreeHead` instead of `TransformerHead`, # since it brings inconvenience when the initialization of # `AnchorFreeHead` is called. super(AnchorFreeHead, self).__init__(init_cfg) self.bg_cls_weight = 0 self.sync_cls_avg_factor = sync_cls_avg_factor class_weight = loss_cls.get('class_weight', None) if class_weight is not None and (self.__class__ is DETRHead): assert isinstance(class_weight, float), 'Expected ' \ 'class_weight to have type float. Found ' \ f'{type(class_weight)}.' # NOTE following the official DETR rep0, bg_cls_weight means # relative classification weight of the no-object class. bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight) assert isinstance(bg_cls_weight, float), 'Expected ' \ 'bg_cls_weight to have type float. Found ' \ f'{type(bg_cls_weight)}.' class_weight = torch.ones(num_classes + 1) * class_weight # set background class as the last indice class_weight[num_classes] = bg_cls_weight loss_cls.update({'class_weight': class_weight}) if 'bg_cls_weight' in loss_cls: loss_cls.pop('bg_cls_weight') self.bg_cls_weight = bg_cls_weight if train_cfg: assert 'assigner' in train_cfg, 'assigner should be provided '\ 'when train_cfg is set.' assigner = train_cfg['assigner'] assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \ 'The classification weight for loss and matcher should be' \ 'exactly the same.' assert loss_bbox['loss_weight'] == assigner['reg_cost'][ 'weight'], 'The regression L1 weight for loss and matcher ' \ 'should be exactly the same.' assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \ 'The regression iou weight for loss and matcher should be' \ 'exactly the same.' self.assigner = build_assigner(assigner) # DETR sampling=False, so use PseudoSampler sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) self.num_query = num_query self.num_classes = num_classes self.in_channels = in_channels self.num_reg_fcs = num_reg_fcs self.train_cfg = train_cfg self.test_cfg = test_cfg self.fp16_enabled = False self.loss_cls = build_loss(loss_cls) self.loss_bbox = build_loss(loss_bbox) self.loss_iou = build_loss(loss_iou) if self.loss_cls.use_sigmoid: self.cls_out_channels = num_classes else: self.cls_out_channels = num_classes + 1 self.act_cfg = transformer.get('act_cfg', dict(type='ReLU', inplace=True)) self.activate = build_activation_layer(self.act_cfg) self.positional_encoding = build_positional_encoding( positional_encoding) self.transformer = build_transformer(transformer) self.embed_dims = self.transformer.embed_dims assert 'num_feats' in positional_encoding num_feats = positional_encoding['num_feats'] assert num_feats * 2 == self.embed_dims, 'embed_dims should' \ f' be exactly 2 times of num_feats. Found {self.embed_dims}' \ f' and {num_feats}.' self._init_layers() def _init_layers(self): """Initialize layers of the transformer head.""" self.input_proj = Conv2d( self.in_channels, self.embed_dims, kernel_size=1) self.fc_cls = Linear(self.embed_dims, self.cls_out_channels) self.reg_ffn = FFN( self.embed_dims, self.embed_dims, self.num_reg_fcs, self.act_cfg, dropout=0.0, add_residual=False) self.fc_reg = Linear(self.embed_dims, 4) self.query_embedding = nn.Embedding(self.num_query, self.embed_dims) def init_weights(self): """Initialize weights of the transformer head.""" # The initialization for transformer is important self.transformer.init_weights() def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): """load checkpoints.""" # NOTE here use `AnchorFreeHead` instead of `TransformerHead`, # since `AnchorFreeHead._load_from_state_dict` should not be # called here. Invoking the default `Module._load_from_state_dict` # is enough. # Names of some parameters in has been changed. version = local_metadata.get('version', None) if (version is None or version < 2) and self.__class__ is DETRHead: convert_dict = { '.self_attn.': '.attentions.0.', '.ffn.': '.ffns.0.', '.multihead_attn.': '.attentions.1.', '.decoder.norm.': '.decoder.post_norm.' } state_dict_keys = list(state_dict.keys()) for k in state_dict_keys: for ori_key, convert_key in convert_dict.items(): if ori_key in k: convert_key = k.replace(ori_key, convert_key) state_dict[convert_key] = state_dict[k] del state_dict[k] super(AnchorFreeHead, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, feats, img_metas): """Forward function. Args: feats (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor. img_metas (list[dict]): List of image information. Returns: tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels. - all_cls_scores_list (list[Tensor]): Classification scores \ for each scale level. Each is a 4D-tensor with shape \ [nb_dec, bs, num_query, cls_out_channels]. Note \ `cls_out_channels` should includes background. - all_bbox_preds_list (list[Tensor]): Sigmoid regression \ outputs for each scale level. Each is a 4D-tensor with \ normalized coordinate format (cx, cy, w, h) and shape \ [nb_dec, bs, num_query, 4]. """ num_levels = len(feats) img_metas_list = [img_metas for _ in range(num_levels)] return multi_apply(self.forward_single, feats, img_metas_list) def forward_single(self, x, img_metas): """"Forward function for a single feature level. Args: x (Tensor): Input feature from backbone's single stage, shape [bs, c, h, w]. img_metas (list[dict]): List of image information. Returns: all_cls_scores (Tensor): Outputs from the classification head, shape [nb_dec, bs, num_query, cls_out_channels]. Note cls_out_channels should includes background. all_bbox_preds (Tensor): Sigmoid outputs from the regression head with normalized coordinate format (cx, cy, w, h). Shape [nb_dec, bs, num_query, 4]. """ # construct binary masks which used for the transformer. # NOTE following the official DETR repo, non-zero values representing # ignored positions, while zero values means valid positions. batch_size = x.size(0) input_img_h, input_img_w = img_metas[0]['batch_input_shape'] masks = x.new_ones((batch_size, input_img_h, input_img_w)) for img_id in range(batch_size): img_h, img_w, _ = img_metas[img_id]['img_shape'] masks[img_id, :img_h, :img_w] = 0 x = self.input_proj(x) # interpolate masks to have the same spatial shape with x masks = F.interpolate( masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1) # position encoding pos_embed = self.positional_encoding(masks) # [bs, embed_dim, h, w] # outs_dec: [nb_dec, bs, num_query, embed_dim] outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight, pos_embed) all_cls_scores = self.fc_cls(outs_dec) all_bbox_preds = self.fc_reg(self.activate( self.reg_ffn(outs_dec))).sigmoid() return all_cls_scores, all_bbox_preds @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) def loss(self, all_cls_scores_list, all_bbox_preds_list, gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore=None): """"Loss function. Only outputs from the last feature level are used for computing losses by default. Args: all_cls_scores_list (list[Tensor]): Classification outputs for each feature level. Each is a 4D-tensor with shape [nb_dec, bs, num_query, cls_out_channels]. all_bbox_preds_list (list[Tensor]): Sigmoid regression outputs for each feature level. Each is a 4D-tensor with normalized coordinate format (cx, cy, w, h) and shape [nb_dec, bs, num_query, 4]. gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (num_gts, ). img_metas (list[dict]): List of image meta information. gt_bboxes_ignore (list[Tensor], optional): Bounding boxes which can be ignored for each image. Default None. Returns: dict[str, Tensor]: A dictionary of loss components. """ # NOTE defaultly only the outputs from the last feature scale is used. all_cls_scores = all_cls_scores_list[-1] all_bbox_preds = all_bbox_preds_list[-1] assert gt_bboxes_ignore is None, \ 'Only supports for gt_bboxes_ignore setting to None.' num_dec_layers = len(all_cls_scores) all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)] all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] all_gt_bboxes_ignore_list = [ gt_bboxes_ignore for _ in range(num_dec_layers) ] img_metas_list = [img_metas for _ in range(num_dec_layers)] losses_cls, losses_bbox, losses_iou = multi_apply( self.loss_single, all_cls_scores, all_bbox_preds, all_gt_bboxes_list, all_gt_labels_list, img_metas_list, all_gt_bboxes_ignore_list) loss_dict = dict() # loss from the last decoder layer loss_dict['loss_cls'] = losses_cls[-1] loss_dict['loss_bbox'] = losses_bbox[-1] loss_dict['loss_iou'] = losses_iou[-1] # loss from other decoder layers num_dec_layer = 0 for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1], losses_bbox[:-1], losses_iou[:-1]): loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i num_dec_layer += 1 return loss_dict def loss_single(self, cls_scores, bbox_preds, gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list=None): """"Loss function for outputs from a single decoder layer of a single feature level. Args: cls_scores (Tensor): Box score logits from a single decoder layer for all images. Shape [bs, num_query, cls_out_channels]. bbox_preds (Tensor): Sigmoid outputs from a single decoder layer for all images, with normalized coordinate (cx, cy, w, h) and shape [bs, num_query, 4]. gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (num_gts, ). img_metas (list[dict]): List of image meta information. gt_bboxes_ignore_list (list[Tensor], optional): Bounding boxes which can be ignored for each image. Default None. Returns: dict[str, Tensor]: A dictionary of loss components for outputs from a single decoder layer. """ num_imgs = cls_scores.size(0) cls_scores_list = [cls_scores[i] for i in range(num_imgs)] bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)] cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list) (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets labels = torch.cat(labels_list, 0) label_weights = torch.cat(label_weights_list, 0) bbox_targets = torch.cat(bbox_targets_list, 0) bbox_weights = torch.cat(bbox_weights_list, 0) # classification loss cls_scores = cls_scores.reshape(-1, self.cls_out_channels) # construct weighted avg_factor to match with the official DETR repo cls_avg_factor = num_total_pos * 1.0 + \ num_total_neg * self.bg_cls_weight if self.sync_cls_avg_factor: cls_avg_factor = reduce_mean( cls_scores.new_tensor([cls_avg_factor])) cls_avg_factor = max(cls_avg_factor, 1) loss_cls = self.loss_cls( cls_scores, labels, label_weights, avg_factor=cls_avg_factor) # Compute the average number of gt boxes across all gpus, for # normalization purposes num_total_pos = loss_cls.new_tensor([num_total_pos]) num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item() # construct factors used for rescale bboxes factors = [] for img_meta, bbox_pred in zip(img_metas, bbox_preds): img_h, img_w, _ = img_meta['img_shape'] factor = bbox_pred.new_tensor([img_w, img_h, img_w, img_h]).unsqueeze(0).repeat( bbox_pred.size(0), 1) factors.append(factor) factors = torch.cat(factors, 0) # DETR regress the relative position of boxes (cxcywh) in the image, # thus the learning target is normalized by the image size. So here # we need to re-scale them for calculating IoU loss bbox_preds = bbox_preds.reshape(-1, 4) bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors # regression IoU loss, defaultly GIoU loss loss_iou = self.loss_iou( bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos) # regression L1 loss loss_bbox = self.loss_bbox( bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos) return loss_cls, loss_bbox, loss_iou def get_targets(self, cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list=None): """"Compute regression and classification targets for a batch image. Outputs from a single decoder layer of a single feature level are used. Args: cls_scores_list (list[Tensor]): Box score logits from a single decoder layer for each image with shape [num_query, cls_out_channels]. bbox_preds_list (list[Tensor]): Sigmoid outputs from a single decoder layer for each image, with normalized coordinate (cx, cy, w, h) and shape [num_query, 4]. gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (num_gts, ). img_metas (list[dict]): List of image meta information. gt_bboxes_ignore_list (list[Tensor], optional): Bounding boxes which can be ignored for each image. Default None. Returns: tuple: a tuple containing the following targets. - labels_list (list[Tensor]): Labels for all images. - label_weights_list (list[Tensor]): Label weights for all \ images. - bbox_targets_list (list[Tensor]): BBox targets for all \ images. - bbox_weights_list (list[Tensor]): BBox weights for all \ images. - num_total_pos (int): Number of positive samples in all \ images. - num_total_neg (int): Number of negative samples in all \ images. """ assert gt_bboxes_ignore_list is None, \ 'Only supports for gt_bboxes_ignore setting to None.' num_imgs = len(cls_scores_list) gt_bboxes_ignore_list = [ gt_bboxes_ignore_list for _ in range(num_imgs) ] (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply( self._get_target_single, cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list) num_total_pos = sum((inds.numel() for inds in pos_inds_list)) num_total_neg = sum((inds.numel() for inds in neg_inds_list)) return (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) def _get_target_single(self, cls_score, bbox_pred, gt_bboxes, gt_labels, img_meta, gt_bboxes_ignore=None): """"Compute regression and classification targets for one image. Outputs from a single decoder layer of a single feature level are used. Args: cls_score (Tensor): Box score logits from a single decoder layer for one image. Shape [num_query, cls_out_channels]. bbox_pred (Tensor): Sigmoid outputs from a single decoder layer for one image, with normalized coordinate (cx, cy, w, h) and shape [num_query, 4]. gt_bboxes (Tensor): Ground truth bboxes for one image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. gt_labels (Tensor): Ground truth class indices for one image with shape (num_gts, ). img_meta (dict): Meta information for one image. gt_bboxes_ignore (Tensor, optional): Bounding boxes which can be ignored. Default None. Returns: tuple[Tensor]: a tuple containing the following for one image. - labels (Tensor): Labels of each image. - label_weights (Tensor]): Label weights of each image. - bbox_targets (Tensor): BBox targets of each image. - bbox_weights (Tensor): BBox weights of each image. - pos_inds (Tensor): Sampled positive indices for each image. - neg_inds (Tensor): Sampled negative indices for each image. """ num_bboxes = bbox_pred.size(0) # assigner and sampler assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes, gt_labels, img_meta, gt_bboxes_ignore) sampling_result = self.sampler.sample(assign_result, bbox_pred, gt_bboxes) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds # label targets labels = gt_bboxes.new_full((num_bboxes, ), self.num_classes, dtype=torch.long) labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] label_weights = gt_bboxes.new_ones(num_bboxes) # bbox targets bbox_targets = torch.zeros_like(bbox_pred) bbox_weights = torch.zeros_like(bbox_pred) bbox_weights[pos_inds] = 1.0 img_h, img_w, _ = img_meta['img_shape'] # DETR regress the relative position of boxes (cxcywh) in the image. # Thus the learning target should be normalized by the image size, also # the box format should be converted from defaultly x1y1x2y2 to cxcywh. factor = bbox_pred.new_tensor([img_w, img_h, img_w, img_h]).unsqueeze(0) pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized) bbox_targets[pos_inds] = pos_gt_bboxes_targets return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, neg_inds) # over-write because img_metas are needed as inputs for bbox_head. def forward_train(self, x, img_metas, gt_bboxes, gt_labels=None, gt_bboxes_ignore=None, proposal_cfg=None, **kwargs): """Forward function for training mode. Args: x (list[Tensor]): Features from backbone. img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. gt_bboxes (Tensor): Ground truth bboxes of the image, shape (num_gts, 4). gt_labels (Tensor): Ground truth labels of each box, shape (num_gts,). gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). proposal_cfg (mmcv.Config): Test / postprocessing configuration, if None, test_cfg would be used. Returns: dict[str, Tensor]: A dictionary of loss components. """ assert proposal_cfg is None, '"proposal_cfg" must be None' outs = self(x, img_metas) if gt_labels is None: loss_inputs = outs + (gt_bboxes, img_metas) else: loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) return losses @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) def get_bboxes(self, all_cls_scores_list, all_bbox_preds_list, img_metas, rescale=False): """Transform network outputs for a batch into bbox predictions. Args: all_cls_scores_list (list[Tensor]): Classification outputs for each feature level. Each is a 4D-tensor with shape [nb_dec, bs, num_query, cls_out_channels]. all_bbox_preds_list (list[Tensor]): Sigmoid regression outputs for each feature level. Each is a 4D-tensor with normalized coordinate format (cx, cy, w, h) and shape [nb_dec, bs, num_query, 4]. img_metas (list[dict]): Meta information of each image. rescale (bool, optional): If True, return boxes in original image space. Default False. Returns: list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \ The first item is an (n, 5) tensor, where the first 4 columns \ are bounding box positions (tl_x, tl_y, br_x, br_y) and the \ 5-th column is a score between 0 and 1. The second item is a \ (n,) tensor where each item is the predicted class label of \ the corresponding box. """ # NOTE defaultly only using outputs from the last feature level, # and only the outputs from the last decoder layer is used. cls_scores = all_cls_scores_list[-1][-1] bbox_preds = all_bbox_preds_list[-1][-1] result_list = [] for img_id in range(len(img_metas)): cls_score = cls_scores[img_id] bbox_pred = bbox_preds[img_id] img_shape = img_metas[img_id]['img_shape'] scale_factor = img_metas[img_id]['scale_factor'] proposals = self._get_bboxes_single(cls_score, bbox_pred, img_shape, scale_factor, rescale) result_list.append(proposals) return result_list def _get_bboxes_single(self, cls_score, bbox_pred, img_shape, scale_factor, rescale=False): """Transform outputs from the last decoder layer into bbox predictions for each image. Args: cls_score (Tensor): Box score logits from the last decoder layer for each image. Shape [num_query, cls_out_channels]. bbox_pred (Tensor): Sigmoid outputs from the last decoder layer for each image, with coordinate format (cx, cy, w, h) and shape [num_query, 4]. img_shape (tuple[int]): Shape of input image, (height, width, 3). scale_factor (ndarray, optional): Scale factor of the image arange as (w_scale, h_scale, w_scale, h_scale). rescale (bool, optional): If True, return boxes in original image space. Default False. Returns: tuple[Tensor]: Results of detected bboxes and labels. - det_bboxes: Predicted bboxes with shape [num_query, 5], \ where the first 4 columns are bounding box positions \ (tl_x, tl_y, br_x, br_y) and the 5-th column are scores \ between 0 and 1. - det_labels: Predicted labels of the corresponding box with \ shape [num_query]. """ assert len(cls_score) == len(bbox_pred) max_per_img = self.test_cfg.get('max_per_img', self.num_query) # exclude background if self.loss_cls.use_sigmoid: cls_score = cls_score.sigmoid() scores, indexes = cls_score.view(-1).topk(max_per_img) det_labels = indexes % self.num_classes bbox_index = indexes // self.num_classes bbox_pred = bbox_pred[bbox_index] else: scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1) scores, bbox_index = scores.topk(max_per_img) bbox_pred = bbox_pred[bbox_index] det_labels = det_labels[bbox_index] det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred) det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1] det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0] det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1]) det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0]) if rescale: det_bboxes /= det_bboxes.new_tensor(scale_factor) det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1) return det_bboxes, det_labels def simple_test_bboxes(self, feats, img_metas, rescale=False): """Test det bboxes without test-time augmentation. Args: feats (tuple[torch.Tensor]): Multi-level features from the upstream network, each is a 4D-tensor. img_metas (list[dict]): List of image information. rescale (bool, optional): Whether to rescale the results. Defaults to False. Returns: list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. The first item is ``bboxes`` with shape (n, 5), where 5 represent (tl_x, tl_y, br_x, br_y, score). The shape of the second tensor in the tuple is ``labels`` with shape (n,) """ # forward of this head requires img_metas outs = self.forward(feats, img_metas) results_list = self.get_bboxes(*outs, img_metas, rescale=rescale) return results_list def forward_onnx(self, feats, img_metas): """Forward function for exporting to ONNX. Over-write `forward` because: `masks` is directly created with zero (valid position tag) and has the same spatial size as `x`. Thus the construction of `masks` is different from that in `forward`. Args: feats (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor. img_metas (list[dict]): List of image information. Returns: tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels. - all_cls_scores_list (list[Tensor]): Classification scores \ for each scale level. Each is a 4D-tensor with shape \ [nb_dec, bs, num_query, cls_out_channels]. Note \ `cls_out_channels` should includes background. - all_bbox_preds_list (list[Tensor]): Sigmoid regression \ outputs for each scale level. Each is a 4D-tensor with \ normalized coordinate format (cx, cy, w, h) and shape \ [nb_dec, bs, num_query, 4]. """ num_levels = len(feats) img_metas_list = [img_metas for _ in range(num_levels)] return multi_apply(self.forward_single_onnx, feats, img_metas_list) def forward_single_onnx(self, x, img_metas): """"Forward function for a single feature level with ONNX exportation. Args: x (Tensor): Input feature from backbone's single stage, shape [bs, c, h, w]. img_metas (list[dict]): List of image information. Returns: all_cls_scores (Tensor): Outputs from the classification head, shape [nb_dec, bs, num_query, cls_out_channels]. Note cls_out_channels should includes background. all_bbox_preds (Tensor): Sigmoid outputs from the regression head with normalized coordinate format (cx, cy, w, h). Shape [nb_dec, bs, num_query, 4]. """ # Note `img_shape` is not dynamically traceable to ONNX, # since the related augmentation was done with numpy under # CPU. Thus `masks` is directly created with zeros (valid tag) # and the same spatial shape as `x`. # The difference between torch and exported ONNX model may be # ignored, since the same performance is achieved (e.g. # 40.1 vs 40.1 for DETR) batch_size = x.size(0) h, w = x.size()[-2:] masks = x.new_zeros((batch_size, h, w)) # [B,h,w] x = self.input_proj(x) # interpolate masks to have the same spatial shape with x masks = F.interpolate( masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1) pos_embed = self.positional_encoding(masks) outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight, pos_embed) all_cls_scores = self.fc_cls(outs_dec) all_bbox_preds = self.fc_reg(self.activate( self.reg_ffn(outs_dec))).sigmoid() return all_cls_scores, all_bbox_preds def onnx_export(self, all_cls_scores_list, all_bbox_preds_list, img_metas): """Transform network outputs into bbox predictions, with ONNX exportation. Args: all_cls_scores_list (list[Tensor]): Classification outputs for each feature level. Each is a 4D-tensor with shape [nb_dec, bs, num_query, cls_out_channels]. all_bbox_preds_list (list[Tensor]): Sigmoid regression outputs for each feature level. Each is a 4D-tensor with normalized coordinate format (cx, cy, w, h) and shape [nb_dec, bs, num_query, 4]. img_metas (list[dict]): Meta information of each image. Returns: tuple[Tensor, Tensor]: dets of shape [N, num_det, 5] and class labels of shape [N, num_det]. """ assert len(img_metas) == 1, \ 'Only support one input image while in exporting to ONNX' cls_scores = all_cls_scores_list[-1][-1] bbox_preds = all_bbox_preds_list[-1][-1] # Note `img_shape` is not dynamically traceable to ONNX, # here `img_shape_for_onnx` (padded shape of image tensor) # is used. img_shape = img_metas[0]['img_shape_for_onnx'] max_per_img = self.test_cfg.get('max_per_img', self.num_query) batch_size = cls_scores.size(0) # `batch_index_offset` is used for the gather of concatenated tensor batch_index_offset = torch.arange(batch_size).to( cls_scores.device) * max_per_img batch_index_offset = batch_index_offset.unsqueeze(1).expand( batch_size, max_per_img) # supports dynamical batch inference if self.loss_cls.use_sigmoid: cls_scores = cls_scores.sigmoid() scores, indexes = cls_scores.view(batch_size, -1).topk( max_per_img, dim=1) det_labels = indexes % self.num_classes bbox_index = indexes // self.num_classes bbox_index = (bbox_index + batch_index_offset).view(-1) bbox_preds = bbox_preds.view(-1, 4)[bbox_index] bbox_preds = bbox_preds.view(batch_size, -1, 4) else: scores, det_labels = F.softmax( cls_scores, dim=-1)[..., :-1].max(-1) scores, bbox_index = scores.topk(max_per_img, dim=1) bbox_index = (bbox_index + batch_index_offset).view(-1) bbox_preds = bbox_preds.view(-1, 4)[bbox_index] det_labels = det_labels.view(-1)[bbox_index] bbox_preds = bbox_preds.view(batch_size, -1, 4) det_labels = det_labels.view(batch_size, -1) det_bboxes = bbox_cxcywh_to_xyxy(bbox_preds) # use `img_shape_tensor` for dynamically exporting to ONNX img_shape_tensor = img_shape.flip(0).repeat(2) # [w,h,w,h] img_shape_tensor = img_shape_tensor.unsqueeze(0).unsqueeze(0).expand( batch_size, det_bboxes.size(1), 4) det_bboxes = det_bboxes * img_shape_tensor # dynamically clip bboxes x1, y1, x2, y2 = det_bboxes.split((1, 1, 1, 1), dim=-1) from mmdet.core.export import dynamic_clip_for_onnx x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, img_shape) det_bboxes = torch.cat([x1, y1, x2, y2], dim=-1) det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(-1)), -1) return det_bboxes, det_labels