modify loss_qfl

models/detectors/rtcdetv2/loss.py

@@ -1,236 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from utils.box_ops import bbox2dist, get_ious
-from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized
-
-from .matcher import AlignedSimOTA
-
-
-# ----------------------- Criterion for training -----------------------
-class Criterion(object):
-    def __init__(self, args, cfg, device, num_classes=80):
-        self.cfg = cfg
-        self.args = args
-        self.device = device
-        self.num_classes = num_classes
-        self.max_epoch = args.max_epoch
-        self.no_aug_epoch = args.no_aug_epoch
-        # ---------------- Loss weight ----------------
-        self.loss_box_aux    = cfg['loss_box_aux']
-        self.loss_cls_weight = cfg['loss_cls_weight']
-        self.loss_box_weight = cfg['loss_box_weight']
-        self.loss_dfl_weight = cfg['loss_dfl_weight']
-        # ---------------- Matcher ----------------
-        self.matcher_hpy = cfg["matcher_hpy"]
-        self.matcher = AlignedSimOTA(
-            num_classes            = num_classes,
-            center_sampling_radius = self.matcher_hpy['center_sampling_radius'],
-            topk_candidates        = self.matcher_hpy['topk_candidates']
-            )
-
-    # ----------------- Loss functions -----------------
-    def loss_classes(self, pred_cls, target, beta=2.0):
-        # Quality FocalLoss
-        """
-            pred_cls: (torch.Tensor): [N, C]。
-            target:   (tuple([torch.Tensor], [torch.Tensor])): label -> (N,), score -> (N,)
-        """
-        label, score = target
-        pred_sigmoid = pred_cls.sigmoid()
-        scale_factor = pred_sigmoid
-        zerolabel = scale_factor.new_zeros(pred_cls.shape)
-
-        ce_loss = F.binary_cross_entropy_with_logits(
-            pred_cls, zerolabel, reduction='none') * scale_factor.pow(beta)
-        
-        bg_class_ind = pred_cls.shape[-1]
-        pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
-        pos_label = label[pos].long()
-
-        scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
-
-        ce_loss[pos, pos_label] = F.binary_cross_entropy_with_logits(
-            pred_cls[pos, pos_label], score[pos],
-            reduction='none') * scale_factor.abs().pow(beta)
-
-        return ce_loss
-    
-    def loss_bboxes(self, pred_box, gt_box):
-        # regression loss
-        ious = get_ious(pred_box, gt_box, 'xyxy', 'giou')
-        loss_box = 1.0 - ious
-
-        return loss_box
-    
-    def loss_dfl(self, pred_reg, gt_box, anchor, stride, bbox_weight=None):
-        # rescale coords by stride
-        gt_box_s = gt_box / stride
-        anchor_s = anchor / stride
-
-        # compute deltas
-        gt_ltrb_s = bbox2dist(anchor_s, gt_box_s, self.cfg['reg_max'] - 1)
-
-        gt_left = gt_ltrb_s.to(torch.long)
-        gt_right = gt_left + 1
-
-        weight_left = gt_right.to(torch.float) - gt_ltrb_s
-        weight_right = 1 - weight_left
-
-        # loss left
-        loss_left = F.cross_entropy(
-            pred_reg.view(-1, self.cfg['reg_max']),
-            gt_left.view(-1),
-            reduction='none').view(gt_left.shape) * weight_left
-        # loss right
-        loss_right = F.cross_entropy(
-            pred_reg.view(-1, self.cfg['reg_max']),
-            gt_right.view(-1),
-            reduction='none').view(gt_left.shape) * weight_right
-
-        loss_dfl = (loss_left + loss_right).mean(-1)
-        
-        if bbox_weight is not None:
-            loss_dfl *= bbox_weight
-
-        return loss_dfl
-
-    def loss_bboxes_aux(self, pred_delta, gt_box, anchors, stride_tensors):
-        gt_delta_tl = (anchors - gt_box[..., :2]) / stride_tensors
-        gt_delta_rb = (gt_box[..., 2:] - anchors) / stride_tensors
-        gt_delta = torch.cat([gt_delta_tl, gt_delta_rb], dim=1)
-        loss_box_aux = F.l1_loss(pred_delta, gt_delta, reduction='none')
-
-        return loss_box_aux
-    
-    # ----------------- Main process -----------------
-    def __call__(self, outputs, targets, epoch=0):
-        bs = outputs['pred_cls'][0].shape[0]
-        device = outputs['pred_cls'][0].device
-        fpn_strides = outputs['strides']
-        anchors = outputs['anchors']
-        num_anchors = sum([ab.shape[0] for ab in anchors])
-        # preds: [B, M, C]
-        cls_preds = torch.cat(outputs['pred_cls'], dim=1)
-        reg_preds = torch.cat(outputs['pred_reg'], dim=1)
-        box_preds = torch.cat(outputs['pred_box'], dim=1)
-
-        # --------------- label assignment ---------------
-        cls_targets = []
-        box_targets = []
-        iou_targets = []
-        fg_masks = []
-        for batch_idx in range(bs):
-            tgt_labels = targets[batch_idx]["labels"].to(device)
-            tgt_bboxes = targets[batch_idx]["boxes"].to(device)
-
-            # check target
-            if len(tgt_labels) == 0 or tgt_bboxes.max().item() == 0.:
-                # There is no valid gt
-                cls_target = cls_preds.new_full((num_anchors, self.num_classes))
-                iou_target = cls_preds.new_zeros((num_anchors,))
-                box_target = cls_preds.new_zeros((0, 4))
-                fg_mask = cls_preds.new_zeros(num_anchors).bool()
-            else:
-                (
-                    fg_mask,
-                    assigned_labels,
-                    assigned_ious,
-                    assigned_indexs
-                ) = self.matcher(
-                    fpn_strides = fpn_strides,
-                    anchors = anchors,
-                    pred_cls = cls_preds[batch_idx], 
-                    pred_box = box_preds[batch_idx],
-                    tgt_labels = tgt_labels,
-                    tgt_bboxes = tgt_bboxes
-                    )
-                # prepare cls targets
-                cls_target = assigned_labels.new_full((num_anchors, self.num_classes))
-                cls_target[fg_mask] = assigned_labels
-                iou_target = assigned_labels.new_zero((num_anchors))
-                iou_target[fg_mask] = assigned_ious
-                # prepare box targets
-                box_target = tgt_bboxes[assigned_indexs]
-
-            cls_targets.append(cls_target)
-            box_targets.append(box_target)
-            iou_targets.append(iou_target)
-            fg_masks.append(fg_mask)
-
-        cls_targets = torch.cat(cls_targets, 0)   # [M,]
-        box_targets = torch.cat(box_targets, 0)   # [M, 4]
-        iou_targets = torch.cat(iou_targets, 0)   # [M,]
-        fg_masks = torch.cat(fg_masks, 0)
-        num_fgs = fg_masks.sum()
-
-        # average loss normalizer across all the GPUs
-        if is_dist_avail_and_initialized():
-            torch.distributed.all_reduce(num_fgs)
-        num_fgs = (num_fgs / get_world_size()).clamp(1.0)
-        
-        # ------------------ Classification loss ------------------
-        cls_preds = cls_preds.view(-1, self.num_classes)
-        loss_cls = self.loss_classes(cls_preds, (cls_targets, iou_targets))
-        loss_cls = loss_cls.sum() / num_fgs
-
-        # ------------------ Regression loss ------------------
-        loss_box = self.loss_bboxes(box_preds.view(-1, 4)[fg_masks], box_targets)
-        loss_box = loss_box.sum() / num_fgs
-
-        # ------------------ Distribution focal loss  ------------------
-        ## process anchors
-        anchors = torch.cat(anchors, dim=0)
-        anchors = anchors[None].repeat(bs, 1, 1).view(-1, 2)
-        ## process stride tensors
-        strides = torch.cat(outputs['stride_tensor'], dim=0)
-        strides = strides.unsqueeze(0).repeat(bs, 1, 1).view(-1, 1)
-        ## fg preds
-        reg_preds_pos = reg_preds.view(-1, 4*self.cfg['reg_max'])[fg_masks]
-        anchors_pos = anchors[fg_masks]
-        strides_pos = strides[fg_masks]
-        ## compute dfl
-        loss_dfl = self.loss_dfl(reg_preds_pos, box_targets, anchors_pos, strides_pos)
-        loss_dfl = loss_dfl.sum() / num_fgs
-
-        # total loss
-        losses = self.loss_cls_weight * loss_cls + \
-                 self.loss_box_weight * loss_box + \
-                 self.loss_dfl_weight * loss_dfl
-
-        loss_dict = dict(
-                loss_cls = loss_cls,
-                loss_box = loss_box,
-                loss_dfl = loss_dfl,
-                losses = losses
-        )
-
-        # ------------------ Aux regression loss ------------------
-        if epoch >= (self.max_epoch - self.no_aug_epoch - 1) and self.loss_box_aux:
-            ## delta_preds
-            delta_preds = torch.cat(outputs['pred_delta'], dim=1)
-            delta_preds_pos = delta_preds.view(-1, 4)[fg_masks]
-            ## aux loss
-            loss_box_aux = self.loss_bboxes_aux(delta_preds_pos, box_targets, anchors_pos, strides_pos)
-            loss_box_aux = loss_box_aux.sum() / num_fgs
-
-            losses += loss_box_aux
-            loss_dict['loss_box_aux'] = loss_box_aux
-
-
-        return loss_dict
-
-
-def build_criterion(args, cfg, device, num_classes):
-    criterion = Criterion(
-        args=args,
-        cfg=cfg,
-        device=device,
-        num_classes=num_classes
-        )
-
-    return criterion
-
-
-if __name__ == "__main__":
-    pass

models/detectors/rtcdetv2/matcher.py

@@ -1,181 +0,0 @@
-# ----------------------------------------------------------------------------------------------------------------------
-    # This code referenced to https://github.com/open-mmlab/mmyolo/models/task_modules/assigners/batch_dsl_assigner.py
-# ----------------------------------------------------------------------------------------------------------------------
-import torch
-import torch.nn.functional as F
-from utils.box_ops import *
-
-
-# -------------------------- RTMDet's Aligned SimOTA Assigner --------------------------
-## Aligned SimOTA
-class AlignedSimOTA(object):
-    def __init__(self, num_classes, center_sampling_radius=2.5, topk_candidates=10):
-        self.num_classes = num_classes
-        self.center_sampling_radius = center_sampling_radius
-        self.topk_candidates = topk_candidates
-
-
-    @torch.no_grad()
-    def __call__(self, 
-                 fpn_strides, 
-                 anchors, 
-                 pred_cls, 
-                 pred_box, 
-                 tgt_labels,
-                 tgt_bboxes):
-        # [M,]
-        strides_tensor = torch.cat([torch.ones_like(anchor_i[:, 0]) * stride_i
-                                for stride_i, anchor_i in zip(fpn_strides, anchors)], dim=-1)
-        # List[F, M, 2] -> [M, 2]
-        anchors = torch.cat(anchors, dim=0)
-        num_anchor = anchors.shape[0]        
-        num_gt = len(tgt_labels)
-
-        # ----------------------- Find inside points -----------------------
-        fg_mask, is_in_boxes_and_center = self.get_in_boxes_info(
-            tgt_bboxes, anchors, strides_tensor, num_anchor, num_gt)
-        cls_preds = pred_cls[fg_mask].float()   # [Mp, C]
-        box_preds = pred_box[fg_mask].float()   # [Mp, 4]
-
-        # ----------------------- Reg cost -----------------------
-        pair_wise_ious, _ = box_iou(tgt_bboxes, box_preds)      # [N, Mp]
-        reg_cost = -torch.log(pair_wise_ious + 1e-8)            # [N, Mp]
-
-        # ----------------------- Cls cost -----------------------
-        with torch.cuda.amp.autocast(enabled=False):
-            # [Mp, C] -> [N, Mp, C]
-            cls_preds_expand = cls_preds.unsqueeze(0).repeat(num_gt, 1, 1)
-            # prepare cls_target
-            cls_targets = F.one_hot(tgt_labels.long(), self.num_classes).float()
-            cls_targets = cls_targets.unsqueeze(1).repeat(1, cls_preds_expand.size(1), 1)
-            cls_targets *= pair_wise_ious.unsqueeze(-1)  # iou-aware
-            # [N, Mp]
-            cls_cost = F.binary_cross_entropy_with_logits(cls_preds_expand, cls_targets, reduction="none").sum(-1)
-        del cls_preds_expand
-
-        #----------------------- Dynamic K-Matching -----------------------
-        cost_matrix = (
-            cls_cost
-            + 3.0 * reg_cost
-            + 100000.0 * (~is_in_boxes_and_center)
-        ) # [N, Mp]
-
-        (
-            assigned_labels,         # [num_fg,]
-            assigned_ious,           # [num_fg,]
-            assigned_indexs,         # [num_fg,]
-        ) = self.dynamic_k_matching(
-            cost_matrix,
-            pair_wise_ious,
-            tgt_labels,
-            num_gt,
-            fg_mask
-            )
-        del cls_cost, cost_matrix, pair_wise_ious, reg_cost
-
-        return fg_mask, assigned_labels, assigned_ious, assigned_indexs
-
-
-    def get_in_boxes_info(
-        self,
-        gt_bboxes,   # [N, 4]
-        anchors,     # [M, 2]
-        strides,     # [M,]
-        num_anchors, # M
-        num_gt,      # N
-        ):
-        # anchor center
-        x_centers = anchors[:, 0]
-        y_centers = anchors[:, 1]
-
-        # [M,] -> [1, M] -> [N, M]
-        x_centers = x_centers.unsqueeze(0).repeat(num_gt, 1)
-        y_centers = y_centers.unsqueeze(0).repeat(num_gt, 1)
-
-        # [N,] -> [N, 1] -> [N, M]
-        gt_bboxes_l = gt_bboxes[:, 0].unsqueeze(1).repeat(1, num_anchors) # x1
-        gt_bboxes_t = gt_bboxes[:, 1].unsqueeze(1).repeat(1, num_anchors) # y1
-        gt_bboxes_r = gt_bboxes[:, 2].unsqueeze(1).repeat(1, num_anchors) # x2
-        gt_bboxes_b = gt_bboxes[:, 3].unsqueeze(1).repeat(1, num_anchors) # y2
-
-        b_l = x_centers - gt_bboxes_l
-        b_r = gt_bboxes_r - x_centers
-        b_t = y_centers - gt_bboxes_t
-        b_b = gt_bboxes_b - y_centers
-        bbox_deltas = torch.stack([b_l, b_t, b_r, b_b], 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
-        center_radius = self.center_sampling_radius
-
-        # [N, 2]
-        gt_centers = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) * 0.5
-        
-        # [1, M]
-        center_radius_ = center_radius * strides.unsqueeze(0)
-
-        gt_bboxes_l = gt_centers[:, 0].unsqueeze(1).repeat(1, num_anchors) - center_radius_ # x1
-        gt_bboxes_t = gt_centers[:, 1].unsqueeze(1).repeat(1, num_anchors) - center_radius_ # y1
-        gt_bboxes_r = gt_centers[:, 0].unsqueeze(1).repeat(1, num_anchors) + center_radius_ # x2
-        gt_bboxes_b = gt_centers[:, 1].unsqueeze(1).repeat(1, num_anchors) + center_radius_ # y2
-
-        c_l = x_centers - gt_bboxes_l
-        c_r = gt_bboxes_r - x_centers
-        c_t = y_centers - gt_bboxes_t
-        c_b = gt_bboxes_b - y_centers
-        center_deltas = torch.stack([c_l, c_t, c_r, c_b], 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
-        ):
-        # Dynamic K
-        # ---------------------------------------------------------------
-        matching_matrix = torch.zeros_like(cost, dtype=torch.uint8)
-
-        ious_in_boxes_matrix = pair_wise_ious
-        n_candidate_k = min(self.topk_candidates, 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
-
-        fg_mask[fg_mask.clone()] = fg_mask_inboxes
-
-        assigned_indexs = matching_matrix[:, fg_mask_inboxes].argmax(0)
-        assigned_labels = gt_classes[assigned_indexs]
-
-        assigned_ious = (matching_matrix * pair_wise_ious).sum(0)[
-            fg_mask_inboxes
-        ]
-        return assigned_labels, assigned_ious, assigned_indexs
-    

models/detectors/rtcdetv2/rtcdetv2_backbone.py

@@ -1,164 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .rtcdetv2_basic import Conv, ResXStage
-except:
-    from rtcdetv2_basic import Conv, ResXStage
-    
-model_urls = {
-    'resxnet_pico':   None,
-    'resxnet_nano':   None,
-    'resxnet_tiny':   None,
-    'resxnet_small':  None,
-    'resxnet_medium': None,
-    'resxnet_large':  None,
-    'resxnet_huge':   None,
-}
-
-# --------------------- ResXNet -----------------------
-class ResXNet(nn.Module):
-    def __init__(self,
-                 embed_dim    = 96,
-                 expand_ratio = 0.25,
-                 ffn_ratio    = 4.0,
-                 num_branches = 4,
-                 num_stages   = [3, 3, 9, 3],
-                 act_type     = 'silu',
-                 norm_type    = 'BN',
-                 depthwise    = False):
-        super(ResXNet, self).__init__()
-        # ------------------ Basic parameters ------------------
-        self.embed_dim = embed_dim
-        self.expand_ratio = expand_ratio
-        self.ffn_ratio = ffn_ratio
-        self.num_branches = num_branches
-        self.num_stages = num_stages
-        self.feat_dims = [embed_dim * 2, embed_dim * 4, embed_dim * 8]
-        
-        # ------------------ Network parameters ------------------
-        ## P2/4
-        self.layer_1 = nn.Sequential(
-            Conv(3, embed_dim, k=7, p=3, s=2, act_type=act_type, norm_type=norm_type),
-            nn.MaxPool2d((3, 3), stride=2, padding=1)
-        )
-        self.layer_2 = ResXStage(embed_dim, embed_dim, self.expand_ratio, self.ffn_ratio, self.num_branches, self.num_stages[0], True, act_type, norm_type, depthwise)
-        ## P3/8
-        self.layer_3 = nn.Sequential(
-            Conv(embed_dim, embed_dim*2, k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise),             
-            ResXStage(embed_dim*2, embed_dim*2, self.expand_ratio, self.ffn_ratio, self.num_branches, self.num_stages[1], True, act_type, norm_type, depthwise)
-        )
-        ## P4/16
-        self.layer_4 = nn.Sequential(
-            Conv(embed_dim*2, embed_dim*4, k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise),             
-            ResXStage(embed_dim*4, embed_dim*4, self.expand_ratio, self.ffn_ratio, self.num_branches, self.num_stages[2], True, act_type, norm_type, depthwise)
-        )
-        ## P5/32
-        self.layer_5 = nn.Sequential(
-            Conv(embed_dim*4, embed_dim*8, k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise),             
-            ResXStage(embed_dim*8, embed_dim*8, self.expand_ratio, self.ffn_ratio, self.num_branches, self.num_stages[3], True, act_type, norm_type, depthwise)
-        )
-
-    def forward(self, x):
-        c2 = self.layer_1(x)
-        c2 = self.layer_2(c2)
-        c3 = self.layer_3(c2)
-        c4 = self.layer_4(c3)
-        c5 = self.layer_5(c4)
-
-        outputs = [c3, c4, c5]
-
-        return outputs
-
-
-# ---------------------------- Functions ----------------------------
-## load pretrained weight
-def load_weight(model, model_name):
-    # load weight
-    print('Loading pretrained weight ...')
-    url = model_urls[model_name]
-    if url is not None:
-        checkpoint = torch.hub.load_state_dict_from_url(
-            url=url, map_location="cpu", check_hash=True)
-        # checkpoint state dict
-        checkpoint_state_dict = checkpoint.pop("model")
-        # model state dict
-        model_state_dict = model.state_dict()
-        # check
-        for k in list(checkpoint_state_dict.keys()):
-            if k in model_state_dict:
-                shape_model = tuple(model_state_dict[k].shape)
-                shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
-                if shape_model != shape_checkpoint:
-                    checkpoint_state_dict.pop(k)
-            else:
-                checkpoint_state_dict.pop(k)
-                print(k)
-
-        model.load_state_dict(checkpoint_state_dict)
-    else:
-        print('No pretrained for {}'.format(model_name))
-
-    return model
-
-## build ELAN-Net
-def build_backbone(cfg, pretrained=False): 
-    # model
-    backbone = ResXNet(
-        embed_dim=cfg['embed_dim'],
-        expand_ratio=cfg['expand_ratio'],
-        ffn_ratio=cfg['ffn_ratio'],
-        num_branches=cfg['num_branches'],
-        num_stages=cfg['num_stages'],
-        act_type=cfg['bk_act'],
-        norm_type=cfg['bk_norm'],
-        depthwise=cfg['bk_depthwise']
-        )
-    # check whether to load imagenet pretrained weight
-    if pretrained:
-        if cfg['width'] == 0.25 and cfg['depth'] == 0.34 and cfg['bk_depthwise']:
-            backbone = load_weight(backbone, model_name='resxnet_pico')
-        elif cfg['width'] == 0.25 and cfg['depth'] == 0.34:
-            backbone = load_weight(backbone, model_name='resxnet_nano')
-        elif cfg['width'] == 0.375 and cfg['depth'] == 0.34:
-            backbone = load_weight(backbone, model_name='resxnet_tiny')
-        elif cfg['width'] == 0.5 and cfg['depth'] == 0.34:
-            backbone = load_weight(backbone, model_name='resxnet_small')
-        elif cfg['width'] == 0.75 and cfg['depth'] == 0.67:
-            backbone = load_weight(backbone, model_name='resxnet_medium')
-        elif cfg['width'] == 1.0 and cfg['depth'] == 1.0:
-            backbone = load_weight(backbone, model_name='resxnet_large')
-        elif cfg['width'] == 1.25 and cfg['depth'] == 1.34:
-            backbone = load_weight(backbone, model_name='resxnet_huge')
-
-    return backbone, backbone.feat_dims
-
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    cfg = {
-        'pretrained': True,
-        'bk_act': 'silu',
-        'bk_norm': 'BN',
-        'bk_depthwise': False,
-        'embed_dim': 96,
-        'expand_ratio': 0.25,
-        'ffn_ratio': 4.0,
-        'num_branches': 4,
-        'num_stages'  : [3, 3, 9, 3],
-    }
-    model, feats = build_backbone(cfg)
-    x = torch.randn(1, 3, 640, 640)
-    t0 = time.time()
-    outputs = model(x)
-    t1 = time.time()
-    print('Time: ', t1 - t0)
-    for out in outputs:
-        print(out.shape)
-
-    print('==============================')
-    flops, params = profile(model, inputs=(x, ), verbose=False)
-    print('==============================')
-    print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('Params : {:.2f} M'.format(params / 1e6))

models/detectors/rtcdetv2/rtcdetv2_basic.py

@@ -1,185 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-
-# ---------------------------- 2D CNN ----------------------------
-class SiLU(nn.Module):
-    """export-friendly version of nn.SiLU()"""
-
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-def get_conv2d(c1, c2, k, p, s, d, g, bias=False):
-    conv = nn.Conv2d(c1, c2, k, stride=s, padding=p, dilation=d, groups=g, bias=bias)
-
-    return conv
-
-def get_activation(act_type=None):
-    if act_type == 'relu':
-        return nn.ReLU(inplace=True)
-    elif act_type == 'lrelu':
-        return nn.LeakyReLU(0.1, inplace=True)
-    elif act_type == 'mish':
-        return nn.Mish(inplace=True)
-    elif act_type == 'silu':
-        return nn.SiLU(inplace=True)
-    elif act_type is None:
-        return nn.Identity()
-
-def get_norm(norm_type, dim):
-    if norm_type == 'BN':
-        return nn.BatchNorm2d(dim)
-    elif norm_type == 'GN':
-        return nn.GroupNorm(num_groups=32, num_channels=dim)
-    elif norm_type is None:
-        return nn.Identity()
-        
-class Conv(nn.Module):
-    def __init__(self, 
-                 c1,                   # in channels
-                 c2,                   # out channels 
-                 k=1,                  # kernel size 
-                 p=0,                  # padding
-                 s=1,                  # padding
-                 d=1,                  # dilation
-                 act_type='lrelu',     # activation
-                 norm_type='BN',       # normalization
-                 depthwise=False):
-        super(Conv, self).__init__()
-        convs = []
-        add_bias = False if norm_type else True
-        p = p if d == 1 else d
-
-        if depthwise:
-            # Depthwise Conv
-            assert c1 == c2
-            convs.append(get_conv2d(c1, c2, k=k, p=p, s=s, d=d, g=c1, bias=add_bias))
-            # depthwise conv
-            if norm_type:
-                convs.append(get_norm(norm_type, c2))
-            if act_type:
-                convs.append(get_activation(act_type))
-        else:
-            # Naive Conv
-            convs.append(get_conv2d(c1, c2, k=k, p=p, s=s, d=d, g=1, bias=add_bias))
-            if norm_type:
-                convs.append(get_norm(norm_type, c2))
-            if act_type:
-                convs.append(get_activation(act_type))
-            
-        self.convs = nn.Sequential(*convs)
-
-
-    def forward(self, x):
-        return self.convs(x)
-
-
-# ----------------------------  Modules ----------------------------
-## Mixed ConvModule
-class MixedConvModule(nn.Module):
-    def __init__(self,
-                 in_dim       :int,
-                 out_dim      :int,
-                 expand_ratio :float = 0.25,
-                 num_branches :int   = 4,
-                 shortcut     :bool  = True,
-                 act_type     :str   = 'relu',
-                 norm_type    :str   = 'BN',
-                 depthwise    :bool  = False):
-        super(MixedConvModule, self).__init__()
-        # ----------- Basic Parameters -----------
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        self.expand_ratio = expand_ratio
-        self.num_branches = num_branches
-        self.shortcut = shortcut
-        self.inter_dim = round(in_dim * expand_ratio)
-        # ----------- Network Parameters -----------
-        self.input_proj = Conv(in_dim, self.inter_dim, k=1, act_type=None, norm_type=norm_type)
-        self.branches = nn.ModuleList([
-            Conv(self.inter_dim, self.inter_dim, k=3, p=1, s=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-            for _ in range(num_branches)])
-        self.output_proj = Conv(self.inter_dim * self.num_branches, out_dim, k=1, act_type=act_type, norm_type=norm_type)
-
-    def forward(self, x):
-        y = self.input_proj(x)
-        outs = []
-        for layer in self.branches:
-            y = layer(y)
-            outs.append(y)
-        outs = torch.cat(outs, dim=1)
-
-        return x + self.output_proj(outs) if self.shortcut else self.output_proj(outs)
-
-## Conv-style FFN
-class ConvFFN(nn.Module):
-    def __init__(self,
-                 in_dim       :int,
-                 out_dim      :int,
-                 expand_ratio :float = 2.0,
-                 shortcut     :bool  = True,
-                 act_type     :str   = 'silu',
-                 norm_type    :str   = 'BN',
-                 depthwise    :bool  = False):
-        super(ConvFFN, self).__init__()
-        # ----------- Basic Parameters -----------
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        self.shortcut = shortcut
-        self.expand_dim = round(in_dim * expand_ratio)
-        # ----------- Network Parameters -----------
-        self.conv_ffn = nn.Sequential(
-            Conv(in_dim, self.expand_dim, k=1, act_type=act_type, norm_type=norm_type),
-            Conv(self.expand_dim, in_dim, k=1, act_type=None, norm_type=norm_type)
-        )
-
-    def forward(self, x):
-        return x + self.conv_ffn(x) if self.shortcut else self.conv_ffn(x)
-
-## ResBlock
-class ResXBlock(nn.Module):
-    def __init__(self,
-                 in_dim       :int,
-                 out_dim      :int,
-                 expand_ratio :float = 0.25,
-                 ffn_ratio    :float = 2.0,
-                 num_branches :int   = 4,
-                 shortcut     :bool  = True,
-                 act_type     :str   ='silu',
-                 norm_type    :str   ='BN',
-                 depthwise    :bool  = False):
-        super(ResXBlock, self).__init__()
-        self.layer1 = MixedConvModule(in_dim, out_dim, expand_ratio, num_branches, shortcut, act_type, norm_type, depthwise)
-        self.layer2 = ConvFFN(out_dim, out_dim, ffn_ratio, shortcut, act_type, norm_type, depthwise)
-
-    def forward(self, x):
-        x = self.layer1(x)
-        x = self.layer2(x)
-        return x
-
-## ResXStage
-class ResXStage(nn.Module):
-    def __init__(self,
-                 in_dim       :int,
-                 out_dim      :int,
-                 expand_ratio :float = 0.25,
-                 ffn_ratio    :float = 2.0,
-                 num_branches :int   = 4,
-                 num_blocks   :int   = 1,
-                 shortcut     :bool  = True,
-                 act_type     :str   ='silu',
-                 norm_type    :str   ='BN',
-                 depthwise    :bool  = False):
-        super(ResXStage, self).__init__()
-        stages = []
-        for i in range(num_blocks):
-            if i == 0:
-                stages.append(ResXBlock(in_dim, out_dim, expand_ratio, ffn_ratio, num_branches, shortcut, act_type, norm_type, depthwise))
-            else:
-                stages.append(ResXBlock(out_dim, out_dim, expand_ratio, ffn_ratio, num_branches, shortcut, act_type, norm_type, depthwise))
-        self.stages = nn.Sequential(*stages)
-
-    def forward(self, x):
-        return self.stages(x)

models/detectors/rtcdetv2/rtcdetv2_pafpn.py

@@ -1,181 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-try:
-    from .rtcdetv2_basic import Conv, ResXStage
-except:
-    from rtcdetv2_basic import Conv, ResXStage
-
-
-# PaFPN-CSP
-class RTCDetv2PaFPN(nn.Module):
-    def __init__(self, 
-                 in_dims=[256, 512, 1024],
-                 out_dim=256,
-                 width=1.0,
-                 depth=1.0,
-                 act_type='silu',
-                 norm_type='BN',
-                 depthwise=False):
-        super(RTCDetv2PaFPN, self).__init__()
-        # ------------- Basic parameters -------------
-        self.in_dims = in_dims
-        self.out_dim = out_dim
-        self.expand_ratios = [0.25, 0.25, 0.25, 0.25]
-        self.ffn_ratios = [4.0, 4.0, 4.0, 4.0]
-        self.num_branches = [4, 4, 4, 4]
-        self.num_blocks = [round(2 * depth), round(2 * depth), round(2 * depth), round(2 * depth)]
-        c3, c4, c5 = in_dims
-
-        # top down
-        ## P5 -> P4
-        self.reduce_layer_1 = Conv(c5, round(384*width), k=1, act_type=act_type, norm_type=norm_type)
-        self.top_down_layer_1 = ResXStage(in_dim       = c4 + round(384*width),
-                                          out_dim      = int(384*width),
-                                          expand_ratio = self.expand_ratios[0],
-                                          ffn_ratio    = self.ffn_ratios[0],
-                                          num_branches = self.num_branches[0],
-                                          num_blocks   = self.num_blocks[0],
-                                          shortcut     = False,
-                                          act_type     = act_type,
-                                          norm_type    = norm_type,
-                                          depthwise    = depthwise
-                                          )
-
-        ## P4 -> P3
-        self.reduce_layer_2 = Conv(c4, round(192*width), k=1, norm_type=norm_type, act_type=act_type)
-        self.top_down_layer_2 = ResXStage(in_dim       = c3 + round(192*width), 
-                                          out_dim      = round(192*width),
-                                          expand_ratio = self.expand_ratios[1],
-                                          ffn_ratio    = self.ffn_ratios[1],
-                                          num_branches = self.num_branches[1],
-                                          num_blocks   = self.num_blocks[1],
-                                          shortcut     = False,
-                                          act_type     = act_type,
-                                          norm_type    = norm_type,
-                                          depthwise    = depthwise
-                                          )
-
-        # bottom up
-        ## P3 -> P4
-        self.downsample_layer_1 = Conv(round(192*width), round(192*width), k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.bottom_up_layer_1 = ResXStage(in_dim       = round(192*width) + round(192*width),
-                                           out_dim      = round(384*width),
-                                           expand_ratio = self.expand_ratios[2],
-                                           ffn_ratio    = self.ffn_ratios[2],
-                                           num_branches = self.num_branches[2],
-                                           num_blocks   = self.num_blocks[2],
-                                           shortcut     = False,
-                                           act_type     = act_type,
-                                           norm_type    = norm_type,
-                                           depthwise    = depthwise
-                                           )
-
-        ## P4 -> P5
-        self.downsample_layer_2 = Conv(round(384*width), round(384*width), k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
-        self.bottom_up_layer_2 = ResXStage(in_dim       = round(384*width) + round(384*width),
-                                           out_dim      = round(768*width),
-                                           expand_ratio = self.expand_ratios[3],
-                                           ffn_ratio    = self.ffn_ratios[3],
-                                           num_branches = self.num_branches[3],
-                                           num_blocks   = self.num_blocks[3],
-                                           shortcut     = False,
-                                           act_type     = act_type,
-                                           norm_type    = norm_type,
-                                           depthwise    = depthwise
-                                           )
-
-        # output proj layers
-        if out_dim is not None:
-            # output proj layers
-            self.out_layers = nn.ModuleList([
-                Conv(in_dim, out_dim, k=1,
-                        norm_type=norm_type, act_type=act_type)
-                        for in_dim in [round(192 * width), round(384 * width), round(768 * width)]
-                        ])
-            self.out_dim = [out_dim] * 3
-
-        else:
-            self.out_layers = None
-            self.out_dim = [round(192 * width), round(384 * width), round(768 * width)]
-
-
-    def forward(self, features):
-        c3, c4, c5 = features
-
-        c6 = self.reduce_layer_1(c5)
-        c7 = F.interpolate(c6, scale_factor=2.0)   # s32->s16
-        c8 = torch.cat([c7, c4], dim=1)
-        c9 = self.top_down_layer_1(c8)
-        # P3/8
-        c10 = self.reduce_layer_2(c9)
-        c11 = F.interpolate(c10, scale_factor=2.0)   # s16->s8
-        c12 = torch.cat([c11, c3], dim=1)
-        c13 = self.top_down_layer_2(c12)  # to det
-        # p4/16
-        c14 = self.downsample_layer_1(c13)
-        c15 = torch.cat([c14, c10], dim=1)
-        c16 = self.bottom_up_layer_1(c15)  # to det
-        # p5/32
-        c17 = self.downsample_layer_2(c16)
-        c18 = torch.cat([c17, c6], dim=1)
-        c19 = self.bottom_up_layer_2(c18)  # to det
-
-        out_feats = [c13, c16, c19] # [P3, P4, P5]
-
-        # output proj layers
-        if self.out_layers is not None:
-            # output proj layers
-            out_feats_proj = []
-            for feat, layer in zip(out_feats, self.out_layers):
-                out_feats_proj.append(layer(feat))
-            return out_feats_proj
-
-        return out_feats
-
-
-def build_fpn(cfg, in_dims, out_dim=None):
-    model = cfg['fpn']
-    # build neck
-    if model == 'rtcdetv2_pafpn':
-        fpn_net = RTCDetv2PaFPN(in_dims   = in_dims,
-                                out_dim   = out_dim,
-                                width     = cfg['width'],
-                                depth     = cfg['depth'],
-                                act_type  = cfg['fpn_act'],
-                                norm_type = cfg['fpn_norm'],
-                                depthwise = cfg['fpn_depthwise']
-                                )
-
-
-    return fpn_net
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    cfg = {
-        'width': 1.0,
-        'depth': 1.0,
-        'fpn': 'rtcdetv2_pafpn',
-        'fpn_act': 'silu',
-        'fpn_norm': 'BN',
-        'fpn_depthwise': False,
-    }
-    fpn_dims = [192, 384, 768]
-    out_dim = 192
-    # Head-1
-    model = build_fpn(cfg, fpn_dims, out_dim)
-    fpn_feats = [torch.randn(1, fpn_dims[0], 80, 80), torch.randn(1, fpn_dims[1], 40, 40), torch.randn(1, fpn_dims[2], 20, 20)]
-    t0 = time.time()
-    outputs = model(fpn_feats)
-    t1 = time.time()
-    print('Time: ', t1 - t0)
-    # for out in outputs:
-    #     print(out.shape)
-
-    print('==============================')
-    flops, params = profile(model, inputs=(fpn_feats, ), verbose=False)
-    print('==============================')
-    print('FPN: GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('FPN: Params : {:.2f} M'.format(params / 1e6))