remove useless codes

config/__init__.py

@@ -87,7 +87,6 @@ from .model_config.yolov8_config import yolov8_cfg
 from .model_config.yolox_config import yolox_cfg, yolox_adamw_cfg
 ## My RTCDet series
 from .model_config.rtcdet_config import rtcdet_cfg, rtcdet_seg_cfg, rtcdet_pos_cfg, rtcdet_seg_pos_cfg
-from .model_config.ctrnet_config import ctrnet_cfg
 
 def build_model_config(args):
     print('==============================')
@@ -125,9 +124,6 @@ def build_model_config(args):
     # RTCDet
     elif args.model in ['rtcdet_n', 'rtcdet_t', 'rtcdet_s', 'rtcdet_m', 'rtcdet_l', 'rtcdet_x']:
         cfg = rtcdet_cfg[args.model]
-    # CenterNet
-    elif args.model in ['ctrnet_n', 'ctrnet_s', 'ctrnet_m', 'ctrnet_l', 'ctrnet_x']:
-        cfg = ctrnet_cfg[args.model]
 
     return cfg
 

config/model_config/ctrnet_config.py

@@ -1,59 +0,0 @@
-# Enhanced CenterNet
-
-
-ctrnet_cfg = {
-    'ctrnet_s':{
-        # ---------------- Model config ----------------
-        ## Backbone
-        'bk_pretrained': True,
-        'bk_pretrained_mae': False,
-        'bk_act': 'silu',
-        'bk_norm': 'BN',
-        'bk_depthwise': False,
-        'width': 0.50,
-        'depth': 0.34,
-        'ratio': 2.0,
-        'stride': 32,
-        'max_stride': 32,
-        'out_stride': 8,
-        ## Neck
-        'neck': 'sppf',
-        'neck_expand_ratio': 0.5,
-        'pooling_size': 5,
-        'neck_act': 'silu',
-        'neck_norm': 'BN',
-        'neck_depthwise': False,
-        ## Decoder
-        'dec_act': 'silu',
-        'dec_norm': 'BN',
-        'dec_depthwise': False,
-        ## Head
-        'head': 'decoupled_head',
-        'num_cls_head': 4,
-        'num_reg_head': 4,
-        'head_act': 'silu',
-        'head_norm': 'BN',
-        'head_depthwise': False,  
-        # ---------------- Train config ----------------
-        ## input
-        'multi_scale': [0.5, 1.25],   # 320 -> 800
-        'trans_type': 'yolov5_s',
-        # ---------------- Assignment config ----------------
-        ## Matcher
-        'matcher': "aligned_simota",
-        'matcher_hpy': {'main' : {'soft_center_radius': 3.0,
-                                  'topk_candidates': 1},   # one-to-one assignment
-                        'aux'  : {'soft_center_radius': 3.0,
-                                  'topk_candidates': 13},  # one-to-many assignment
-                                  },
-        # ---------------- Loss config ----------------
-        ## loss weight
-        'loss_cls_weight': 1.0,
-        'loss_box_weight': 2.0,
-        'aux_bbox_loss': False,
-        # ---------------- Train config ----------------
-        'trainer_type': 'rtcdet',
-    },
-
-}
-

models/detectors/__init__.py

@@ -13,7 +13,6 @@ from .yolov8.build import build_yolov8
 from .yolox.build import build_yolox
 # My RTCDet series
 from .rtcdet.build import build_rtcdet
-from .ctrnet.build import build_ctrnet
 
 
 # build object detector
@@ -67,10 +66,6 @@ def build_model(args,
     elif args.model in ['rtcdet_n', 'rtcdet_t', 'rtcdet_s', 'rtcdet_m', 'rtcdet_l', 'rtcdet_x']:
         model, criterion = build_rtcdet(
             args, model_cfg, device, num_classes, trainable, deploy)
-    # CenterNet
-    elif args.model in ['ctrnet_n', 'ctrnet_t', 'ctrnet_s', 'ctrnet_m', 'ctrnet_l', 'ctrnet_x']:
-        model, criterion = build_ctrnet(
-            args, model_cfg, device, num_classes, trainable, deploy)
 
     if trainable:
         # Load pretrained weight

models/detectors/ctrnet/README.md

@@ -1 +0,0 @@
-# Enchanced CenterNet

models/detectors/ctrnet/build.py

@@ -1,43 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import torch
-import torch.nn as nn
-
-from .loss import build_criterion
-from .ctrnet import CenterNet
-
-
-# build object detector
-def build_ctrnet(args, cfg, device, num_classes=80, trainable=False, deploy=False):
-    print('==============================')
-    print('Build {} ...'.format(args.model.upper()))
-    
-    print('==============================')
-    print('Model Configuration: \n', cfg)
-    
-    # -------------- Build CenterNet --------------
-    model = CenterNet(cfg                = cfg,
-                      device             = device, 
-                      num_classes        = num_classes,
-                      trainable          = trainable,
-                      conf_thresh        = args.conf_thresh,
-                      topk               = args.topk,
-                      deploy             = deploy,
-                      no_multi_labels    = args.no_multi_labels,
-                      nms_class_agnostic = args.nms_class_agnostic
-                      )
-
-    # -------------- Initialize CenterNet --------------
-    for m in model.modules():
-        if isinstance(m, nn.BatchNorm2d):
-            m.eps = 1e-3
-            m.momentum = 0.03    
-            
-    # -------------- Build criterion --------------
-    criterion = None
-    if trainable:
-        # build criterion for training
-        criterion = build_criterion(args, cfg, device, num_classes)
-        
-    return model, criterion

models/detectors/ctrnet/ctrnet.py

@@ -1,164 +0,0 @@
-# Objects as Points
-
-# --------------- Torch components ---------------
-import torch
-import torch.nn as nn
-
-# --------------- Model components ---------------
-from .ctrnet_encoder import build_encoder
-from .ctrnet_decoder import build_decoder
-from .ctrnet_neck    import build_neck
-from .ctrnet_head    import build_det_head
-from .ctrnet_pred    import build_det_pred
-
-
-# CenterNet
-class CenterNet(nn.Module):
-    def __init__(self,
-                 cfg,
-                 device,
-                 num_classes = 20,
-                 conf_thresh = 0.01,
-                 topk        = 1000,
-                 trainable   = False,
-                 deploy      = False,
-                 no_multi_labels = False,
-                 nms_class_agnostic = False,
-                 ):
-        super(CenterNet, self).__init__()
-        # ---------------- Basic Parameters ----------------
-        self.cfg = cfg
-        self.device = device
-        self.stride = cfg['out_stride']
-        self.num_classes = num_classes
-        self.trainable = trainable
-        self.conf_thresh = conf_thresh
-        self.num_classes = num_classes
-        self.topk_candidates = topk
-        self.deploy = deploy
-        self.no_multi_labels = no_multi_labels
-        self.nms_class_agnostic = nms_class_agnostic
-        self.head_dim = round(256 * cfg['width'])
-        
-        # ---------------- Network Parameters ----------------
-        ## Encoder
-        self.encoder, feat_dims = build_encoder(cfg)
-
-        ## Neck
-        self.neck = build_neck(cfg, feat_dims[-1], feat_dims[-1])
-        self.feat_dim = self.neck.out_dim
-        
-        ## Decoder
-        self.decoder = build_decoder(cfg, self.feat_dim, self.head_dim)
-
-        ## Head
-        self.det_head = nn.Sequential(
-            build_det_head(cfg, self.head_dim, self.head_dim),
-            build_det_pred(self.head_dim, self.head_dim, self.stride, num_classes, 4)
-        )
-        ## Aux Head
-        self.aux_det_head = nn.Sequential(
-            build_det_head(cfg, self.head_dim, self.head_dim),
-            build_det_pred(self.head_dim, self.head_dim, self.stride, num_classes, 4)
-        )
-
-    # Post process
-    def post_process(self, cls_pred, box_pred):
-        """
-        Input:
-            cls_pred: torch.Tensor -> [M, C]
-            box_pred: torch.Tensor -> [M, 4]
-        Output:
-            bboxes: np.array -> [N, 4]
-            scores: np.array -> [N,]
-            labels: np.array -> [N,]
-        """
-        cls_pred = cls_pred[0]
-        box_pred = box_pred[0]
-        if self.no_multi_labels:
-            # [M,]
-            scores, labels = torch.max(cls_pred.sigmoid(), dim=1)
-
-            # Keep top k top scoring indices only.
-            num_topk = min(self.topk_candidates, box_pred.size(0))
-
-            # topk candidates
-            predicted_prob, topk_idxs = scores.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]
-
-            labels = labels[topk_idxs]
-            bboxes = box_pred[topk_idxs]
-        else:
-            # [M, C] -> [MC,]
-            scores = cls_pred.sigmoid().flatten()
-
-            # Keep top k top scoring indices only.
-            num_topk = min(self.topk_candidates, box_pred.size(0))
-
-            # torch.sort is actually faster than .topk (at least on GPUs)
-            predicted_prob, topk_idxs = scores.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[anchor_idxs]
-
-        # to cpu & numpy
-        scores = scores.cpu().numpy()
-        labels = labels.cpu().numpy()
-        bboxes = bboxes.cpu().numpy()
-
-        return bboxes, scores, labels
-    
-    # Main process
-    def forward(self, x):
-        # ---------------- Backbone ----------------
-        pyramid_feats = self.encoder(x)
-
-        # ---------------- Neck ----------------
-        feat = self.neck(pyramid_feats[-1])
-
-        # ---------------- Encoder ----------------
-        feat = self.decoder(feat)
-
-        # ---------------- Head ----------------
-        outputs = self.det_head(feat)
-        if self.trainable:
-            outputs['aux_outputs'] = self.aux_det_head(feat)
-
-        # ---------------- Post-process ----------------
-        if not self.trainable:
-            cls_preds = outputs['pred_cls']
-            box_preds = outputs['pred_box']
-
-            if self.deploy:
-                scores = cls_preds[0].sigmoid()
-                bboxes = box_preds[0]
-                # [n_anchors_all, 4 + C]
-                outputs = torch.cat([bboxes, scores], dim=-1)
-
-            else:
-                # post process
-                bboxes, scores, labels = self.post_process(cls_preds, box_preds)
-
-                outputs = {
-                    "scores": scores,
-                    "labels": labels,
-                    "bboxes": bboxes
-                }
-            
-        return outputs
-    

models/detectors/ctrnet/ctrnet_basic.py

@@ -1,218 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torchvision.ops
-
-
-# --------------------- Basic modules ---------------------
-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()
-    else:
-        raise NotImplementedError
-        
-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()
-    else:
-        raise NotImplementedError
-
-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
-        if depthwise:
-            convs.append(get_conv2d(c1, c1, k=k, p=p, s=s, d=d, g=c1, bias=add_bias))
-            # depthwise conv
-            if norm_type:
-                convs.append(get_norm(norm_type, c1))
-            if act_type:
-                convs.append(get_activation(act_type))
-            # pointwise conv
-            convs.append(get_conv2d(c1, c2, k=1, p=0, s=1, 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))
-
-        else:
-            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)
-
-class DeConv(nn.Module):
-    def __init__(self,
-                 in_dim      :int,
-                 out_dim     :int,
-                 kernel_size :int  = 4,
-                 stride      :int  = 2,
-                 act_type    :str  = 'silu',
-                 norm_type   :str  = 'BN'
-                 ):
-        super(DeConv, self).__init__()
-        # ----------- Basic parameters -----------
-        if kernel_size == 4:
-            padding = 1
-            output_padding = 0
-        elif kernel_size == 3:
-            padding = 1
-            output_padding = 1
-        elif kernel_size == 2:
-            padding = 0
-            output_padding = 0
-
-        # ----------- Network parameters -----------
-        self.convs = nn.Sequential(
-            nn.ConvTranspose2d(in_dim, out_dim, kernel_size, stride=stride, padding=padding, output_padding=output_padding),
-            get_norm(norm_type, out_dim),
-            get_activation(act_type)
-        )
-
-    def forward(self, x):
-        return self.convs(x)
-    
-class DeformableConv(nn.Module):
-    def __init__(self,
-                 in_dim  :int,
-                 out_dim :int,
-                 kernel_size  :int = 3,
-                 stride       :int = 1,
-                 padding      :int = 1):
-        super(DeformableConv, self).__init__()
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        self.kernel_size = kernel_size
-        self.stride = stride if type(stride) == tuple else (stride, stride)
-        self.padding = padding
-        
-        # init weight and bias
-        self.weight = nn.Parameter(torch.Tensor(out_dim, in_dim, kernel_size, kernel_size))
-        self.bias = nn.Parameter(torch.Tensor(out_dim))
-
-        # offset conv
-        self.conv_offset_mask = nn.Conv2d(in_dim, 
-                                          3 * kernel_size * kernel_size,
-                                          kernel_size=kernel_size, 
-                                          stride=stride,
-                                          padding=self.padding, 
-                                          bias=True)
-        
-        # init        
-        self.reset_parameters()
-        self._init_weight()
-
-    def reset_parameters(self):
-        n = self.in_dim * (self.kernel_size**2)
-        stdv = 1. / math.sqrt(n)
-        self.weight.data.uniform_(-stdv, stdv)
-        self.bias.data.zero_()
-
-    def _init_weight(self):
-        # init offset_mask conv
-        nn.init.constant_(self.conv_offset_mask.weight, 0.)
-        nn.init.constant_(self.conv_offset_mask.bias, 0.)
-
-    def forward(self, x):
-        out = self.conv_offset_mask(x)
-        o1, o2, mask = torch.chunk(out, 3, dim=1)
-        offset = torch.cat((o1, o2), dim=1)
-        mask = torch.sigmoid(mask)
-
-        x = torchvision.ops.deform_conv2d(input=x, 
-                                          offset=offset, 
-                                          weight=self.weight, 
-                                          bias=self.bias, 
-                                          padding=self.padding,
-                                          mask=mask,
-                                          stride=self.stride)
-        return x
-    
-
-# --------------------- Yolov8 modules ---------------------
-## Yolov8-style BottleNeck
-class Bottleneck(nn.Module):
-    def __init__(self,
-                 in_dim,
-                 out_dim,
-                 expand_ratio = 0.5,
-                 kernel_sizes = [3, 3],
-                 shortcut     = True,
-                 act_type     = 'silu',
-                 norm_type    = 'BN',
-                 depthwise    = False,):
-        super(Bottleneck, self).__init__()
-        inter_dim = int(out_dim * expand_ratio)  # hidden channels            
-        self.cv1 = Conv(in_dim, inter_dim, k=kernel_sizes[0], p=kernel_sizes[0]//2, norm_type=norm_type, act_type=act_type, depthwise=depthwise)
-        self.cv2 = Conv(inter_dim, out_dim, k=kernel_sizes[1], p=kernel_sizes[1]//2, norm_type=norm_type, act_type=act_type, depthwise=depthwise)
-        self.shortcut = shortcut and in_dim == out_dim
-
-    def forward(self, x):
-        h = self.cv2(self.cv1(x))
-
-        return x + h if self.shortcut else h
-
-# Yolov8-style StageBlock
-class RTCBlock(nn.Module):
-    def __init__(self,
-                 in_dim,
-                 out_dim,
-                 num_blocks = 1,
-                 shortcut   = False,
-                 act_type   = 'silu',
-                 norm_type  = 'BN',
-                 depthwise  = False,):
-        super(RTCBlock, self).__init__()
-        self.inter_dim = out_dim // 2
-        self.input_proj = Conv(in_dim, out_dim, k=1, act_type=act_type, norm_type=norm_type)
-        self.m = nn.Sequential(*(
-            Bottleneck(self.inter_dim, self.inter_dim, 1.0, [3, 3], shortcut, act_type, norm_type, depthwise)
-            for _ in range(num_blocks)))
-        self.output_proj = Conv((2 + num_blocks) * self.inter_dim, out_dim, k=1, act_type=act_type, norm_type=norm_type)
-
-    def forward(self, x):
-        # Input proj
-        x1, x2 = torch.chunk(self.input_proj(x), 2, dim=1)
-        out = list([x1, x2])
-
-        # Bottlenecl
-        out.extend(m(out[-1]) for m in self.m)
-
-        # Output proj
-        out = self.output_proj(torch.cat(out, dim=1))
-
-        return out

models/detectors/ctrnet/ctrnet_decoder.py

@@ -1,47 +0,0 @@
-import math
-import torch.nn as nn
-
-from .ctrnet_basic import DeConv, RTCBlock
-
-
-def build_decoder(cfg, in_dim, out_dim):
-    return CTRDecoder(in_dim     = in_dim,
-                      out_dim    = out_dim,
-                      max_stride = cfg['max_stride'],
-                      out_stride = cfg['out_stride'],
-                      act_type   = cfg['dec_act'],
-                      norm_type  = cfg['dec_norm'],
-                      depthwise  = cfg['dec_depthwise']
-                      )
-
-
-class CTRDecoder(nn.Module):
-    def __init__(self,
-                 in_dim     :int,
-                 out_dim    :int,
-                 max_stride :int,
-                 out_stride :int,
-                 act_type   :str,
-                 norm_type  :str,
-                 depthwise  :bool
-                 ):
-        super().__init__()
-        # ---------- Basic parameters ----------
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-        self.out_stride = out_stride
-        self.num_layers = round(math.log2(max_stride // out_stride))
-
-        # ---------- Network parameters ----------
-        layers = []
-        for i in range(self.num_layers):
-            layer = nn.Sequential(
-                RTCBlock(in_dim, out_dim, 3, False, act_type, norm_type, depthwise),
-                DeConv(out_dim, out_dim, kernel_size=4, stride=2, act_type=act_type, norm_type=norm_type)
-            )
-            layers.append(layer)
-            in_dim = out_dim
-        self.layers = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.layers(x)

models/detectors/ctrnet/ctrnet_encoder.py

@@ -1,191 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .ctrnet_basic import Conv, RTCBlock
-except:
-    from ctrnet_basic import Conv, RTCBlock
-
-
-# Pretrained weights
-model_urls = {
-    # ImageNet-1K pretrained weight
-    "rtcnet_n": "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elan_cspnet_nano.pth",
-    "rtcnet_s": "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elan_cspnet_small.pth",
-    "rtcnet_m": None,
-    "rtcnet_l": None,
-    "rtcnet_x": None,
-    # MIM-pretrained weights
-    "mae_rtcnet_n": None,
-    "mae_rtcnet_s": None,
-    "mae_rtcnet_m": None,
-    "mae_rtcnet_l": None,
-    "mae_rtcnet_x": None,
-}
-
-
-# ---------------------------- Basic functions ----------------------------
-## Real-time Convolutional Backbone
-class RTCBackbone(nn.Module):
-    def __init__(self, width=1.0, depth=1.0, ratio=1.0, act_type='silu', norm_type='BN', depthwise=False):
-        super(RTCBackbone, self).__init__()
-        # ---------------- Basic parameters ----------------
-        self.width_factor = width
-        self.depth_factor = depth
-        self.last_stage_factor = ratio
-        self.feat_dims = [round(64 * width), round(128 * width), round(256 * width), round(512 * width), round(512 * width * ratio)]
-        # ---------------- Network parameters ----------------
-        ## P1/2
-        self.layer_1 = Conv(3, self.feat_dims[0], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type)
-        ## P2/4
-        self.layer_2 = nn.Sequential(
-            Conv(self.feat_dims[0], self.feat_dims[1], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
-            RTCBlock(in_dim     = self.feat_dims[1],
-                     out_dim    = self.feat_dims[1],
-                     num_blocks = round(3*depth),
-                     shortcut   = True,
-                     act_type   = act_type,
-                     norm_type  = norm_type,
-                     depthwise  = depthwise)
-        )
-        ## P3/8
-        self.layer_3 = nn.Sequential(
-            Conv(self.feat_dims[1], self.feat_dims[2], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
-            RTCBlock(in_dim     = self.feat_dims[2],
-                     out_dim    = self.feat_dims[2],
-                     num_blocks = round(6*depth),
-                     shortcut   = True,
-                     act_type   = act_type,
-                     norm_type  = norm_type,
-                     depthwise  = depthwise)
-        )
-        ## P4/16
-        self.layer_4 = nn.Sequential(
-            Conv(self.feat_dims[2], self.feat_dims[3], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
-            RTCBlock(in_dim     = self.feat_dims[3],
-                     out_dim    = self.feat_dims[3],
-                     num_blocks = round(6*depth),
-                     shortcut   = True,
-                     act_type   = act_type,
-                     norm_type  = norm_type,
-                     depthwise  = depthwise)
-        )
-        ## P5/32
-        self.layer_5 = nn.Sequential(
-            Conv(self.feat_dims[3], self.feat_dims[4], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
-            RTCBlock(in_dim     = self.feat_dims[4],
-                     out_dim    = self.feat_dims[4],
-                     num_blocks = round(3*depth),
-                     shortcut   = True,
-                     act_type   = act_type,
-                     norm_type  = norm_type,
-                     depthwise  = depthwise)
-        )
-
-    def forward(self, x):
-        c1 = self.layer_1(x)
-        c2 = self.layer_2(c1)
-        c3 = self.layer_3(c2)
-        c4 = self.layer_4(c3)
-        c5 = self.layer_5(c4)
-
-        outputs = [c3, c4, c5]
-
-        return outputs
-
-
-# ---------------------------- Functions ----------------------------
-## Build Backbone network
-def build_encoder(cfg, pretrained=False): 
-    # build backbone model
-    backbone = RTCBackbone(width=cfg['width'],
-                           depth=cfg['depth'],
-                           ratio=cfg['ratio'],
-                           act_type=cfg['bk_act'],
-                           norm_type=cfg['bk_norm'],
-                           depthwise=cfg['bk_depthwise']
-                           )
-    feat_dims = backbone.feat_dims[-3:]
-
-    # Model name
-    width, depth, ratio = cfg['width'], cfg['depth'], cfg['ratio']
-    model_name = "{}" if not cfg['bk_pretrained_mae'] else "mae_{}"
-    if  width == 0.25   and depth == 0.34 and ratio == 2.0:
-        model_name = model_name.format("rtcnet_n")
-    elif width == 0.375 and depth == 0.34 and ratio == 2.0:
-        model_name = model_name.format("rtcnet_t")
-    elif width == 0.50  and depth == 0.34 and ratio == 2.0:
-        model_name = model_name.format("rtcnet_s")
-    elif width == 0.75  and depth == 0.67 and ratio == 1.5:
-        model_name = model_name.format("rtcnet_m")
-    elif width == 1.0   and depth == 1.0  and ratio == 1.0:
-        model_name = model_name.format("rtcnet_l")
-    elif width == 1.25  and depth == 1.34  and ratio == 1.0:
-        model_name = model_name.format("rtcnet_x")
-    else:
-        raise NotImplementedError("No such model size : width={}, depth={}, ratio={}. ".format(width, depth, ratio))
-
-    # Load pretrained weight
-    if pretrained:
-        backbone = load_pretrained_weight(backbone, model_name)
-        
-    return backbone, feat_dims
-
-## Load pretrained weight
-def load_pretrained_weight(model, model_name):
-    # Load pretrained weight
-    url = model_urls[model_name]
-    if url is not None:
-        print('Loading pretrained weight ...')
-        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)
-        # load the weight
-        model.load_state_dict(checkpoint_state_dict)
-    else:
-        print('No backbone pretrained for {}.'.format(model_name))
-
-    return model
-
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    cfg = {
-        'bk_pretrained': True,
-        'bk_pretrained_mae': True,
-        'bk_act': 'silu',
-        'bk_norm': 'BN',
-        'bk_depthwise': False,
-        'width': 1.0,
-        'depth': 1.0,
-        'ratio': 1.0,
-    }
-    model, feats = build_encoder(cfg, pretrained=cfg['bk_pretrained'])
-    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)
-
-    x = torch.randn(1, 3, 640, 640)
-    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/ctrnet/ctrnet_head.py

@@ -1,139 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .ctrnet_basic import Conv
-except:
-    from ctrnet_basic import Conv
-
-
-def build_det_head(cfg, in_dim, out_dim):
-    head = SDetHead(in_dim       = in_dim,
-                    cls_head_dim = out_dim,
-                    reg_head_dim = out_dim,
-                    num_cls_head = cfg['num_cls_head'],
-                    num_reg_head = cfg['num_reg_head'],
-                    act_type     = cfg['head_act'],
-                    norm_type    = cfg['head_norm'],
-                    depthwise    = cfg['head_depthwise']
-                    )
-
-    return head
-
-
-# ---------------------------- Detection Head ----------------------------
-## Single-level Detection Head
-class SDetHead(nn.Module):
-    def __init__(self,
-                 in_dim       :int  = 256,
-                 cls_head_dim :int  = 256,
-                 reg_head_dim :int  = 256,
-                 num_cls_head :int  = 2,
-                 num_reg_head :int  = 2,
-                 act_type     :str  = "silu",
-                 norm_type    :str  = "BN",
-                 depthwise    :bool = False):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.in_dim = in_dim
-        self.num_cls_head = num_cls_head
-        self.num_reg_head = num_reg_head
-        self.act_type = act_type
-        self.norm_type = norm_type
-        self.depthwise = depthwise
-        
-        # --------- Network Parameters ----------
-        ## cls head
-        cls_feats = []
-        self.cls_head_dim = cls_head_dim
-        for i in range(num_cls_head):
-            if i == 0:
-                cls_feats.append(
-                    Conv(in_dim, self.cls_head_dim, k=3, p=1, s=1, 
-                         act_type=act_type,
-                         norm_type=norm_type,
-                         depthwise=depthwise)
-                        )
-            else:
-                cls_feats.append(
-                    Conv(self.cls_head_dim, self.cls_head_dim, k=3, p=1, s=1, 
-                        act_type=act_type,
-                        norm_type=norm_type,
-                        depthwise=depthwise)
-                        )      
-        ## reg head
-        reg_feats = []
-        self.reg_head_dim = reg_head_dim
-        for i in range(num_reg_head):
-            if i == 0:
-                reg_feats.append(
-                    Conv(in_dim, self.reg_head_dim, k=3, p=1, s=1, 
-                         act_type=act_type,
-                         norm_type=norm_type,
-                         depthwise=depthwise)
-                        )
-            else:
-                reg_feats.append(
-                    Conv(self.reg_head_dim, self.reg_head_dim, k=3, p=1, s=1, 
-                         act_type=act_type,
-                         norm_type=norm_type,
-                         depthwise=depthwise)
-                        )
-        self.cls_feats = nn.Sequential(*cls_feats)
-        self.reg_feats = nn.Sequential(*reg_feats)
-
-        self.init_weights()
-        
-    def init_weights(self):
-        """Initialize the parameters."""
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                # In order to be consistent with the source code,
-                # reset the Conv2d initialization parameters
-                m.reset_parameters()
-
-    def forward(self, x):
-        """
-            in_feats: (Tensor) [B, C, H, W]
-        """
-        cls_feats = self.cls_feats(x)
-        reg_feats = self.reg_feats(x)
-
-        outputs = {
-            "cls_feat": cls_feats,
-            "reg_feat": reg_feats
-        }
-
-        return outputs
-    
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    cfg = {
-        'head': 'decoupled_head',
-        'num_cls_head': 2,
-        'num_reg_head': 2,
-        'head_act': 'silu',
-        'head_norm': 'BN',
-        'head_depthwise': False,
-        'reg_max': 16,
-    }
-    fpn_dims = [256, 256, 256]
-    out_dim = 256
-    # Head-1
-    model = build_det_head(cfg, fpn_dims, out_dim, num_levels=3)
-    print(model)
-    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('Head-1: GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('Head-1: Params : {:.2f} M'.format(params / 1e6))

models/detectors/ctrnet/ctrnet_neck.py

@@ -1,108 +0,0 @@
-import torch
-import torch.nn as nn
-
-try:
-    from .ctrnet_basic import Conv
-except:
-    from ctrnet_basic import Conv
-
-
-# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
-class SPPF(nn.Module):
-    """
-        This code referenced to https://github.com/ultralytics/yolov5
-    """
-    def __init__(self, cfg, in_dim, out_dim, expand_ratio=0.5):
-        super().__init__()
-        # ---------------- Basic Parameters ----------------
-        inter_dim = int(in_dim * expand_ratio)
-        self.out_dim = out_dim
-        # ---------------- Network Parameters ----------------
-        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
-        self.cv2 = Conv(inter_dim * 4, out_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
-        self.m = nn.MaxPool2d(kernel_size=cfg['pooling_size'], stride=1, padding=cfg['pooling_size'] // 2)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        y1 = self.m(x)
-        y2 = self.m(y1)
-
-        return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
-
-
-# SPPF block with CSP module
-class SPPFBlockCSP(nn.Module):
-    """
-        CSP Spatial Pyramid Pooling Block
-    """
-    def __init__(self, cfg, in_dim, out_dim, expand_ratio):
-        super(SPPFBlockCSP, self).__init__()
-        # ---------------- Basic Parameters ----------------
-        inter_dim = int(in_dim * expand_ratio)
-        self.out_dim = out_dim
-        # ---------------- Network Parameters ----------------
-        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
-        self.cv2 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
-        self.m = nn.Sequential(
-            Conv(inter_dim, inter_dim, k=3, p=1, 
-                 act_type=cfg['neck_act'], norm_type=cfg['neck_norm'], 
-                 depthwise=cfg['neck_depthwise']),
-            SPPF(cfg, inter_dim, inter_dim, expand_ratio=1.0),
-            Conv(inter_dim, inter_dim, k=3, p=1, 
-                 act_type=cfg['neck_act'], norm_type=cfg['neck_norm'], 
-                 depthwise=cfg['neck_depthwise'])
-        )
-        self.cv3 = Conv(inter_dim * 2, self.out_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
-
-        
-    def forward(self, x):
-        x1 = self.cv1(x)
-        x2 = self.cv2(x)
-        x3 = self.m(x2)
-        y = self.cv3(torch.cat([x1, x3], dim=1))
-
-        return y
-
-
-def build_neck(cfg, in_dim, out_dim):
-    model = cfg['neck']
-    print('==============================')
-    print('Neck: {}'.format(model))
-    # build neck
-    if model == 'sppf':
-        neck = SPPF(cfg, in_dim, out_dim, cfg['neck_expand_ratio'])
-    elif model == 'csp_sppf':
-        neck = SPPFBlockCSP(cfg, in_dim, out_dim, cfg['neck_expand_ratio'])
-
-    return neck
-
-
-if __name__ == '__main__':
-    import time
-    from thop import profile
-    cfg = {
-        ## Neck: SPP
-        'neck': 'sppf',
-        'neck_expand_ratio': 0.5,
-        'pooling_size': 5,
-        'neck_act': 'silu',
-        'neck_norm': 'BN',
-        'neck_depthwise': False,
-    }
-    in_dim = 512
-    out_dim = 512
-    # Head-1
-    model = build_neck(cfg, in_dim, out_dim)
-    feat = torch.randn(1, in_dim, 20, 20)
-    t0 = time.time()
-    outputs = model(feat)
-    t1 = time.time()
-    print('Time: ', t1 - t0)
-    # for out in outputs:
-    #     print(out.shape)
-
-    print('==============================')
-    flops, params = profile(model, inputs=(feat, ), verbose=False)
-    print('==============================')
-    print('FPN: GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
-    print('FPN: Params : {:.2f} M'.format(params / 1e6))

models/detectors/ctrnet/ctrnet_pred.py

@@ -1,97 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-
-
-def build_det_pred(cls_dim, reg_dim, stride, num_classes, num_coords=4):
-    pred_layers = SDetPDLayer(cls_dim     = cls_dim,
-                              reg_dim     = reg_dim,
-                              stride      = stride,
-                              num_classes = num_classes,
-                              num_coords  = num_coords) 
-
-    return pred_layers
-
-
-# ---------------------------- Detection predictor ----------------------------
-## Single-level Detection Prediction Layer
-class SDetPDLayer(nn.Module):
-    def __init__(self,
-                 cls_dim     :int = 256,
-                 reg_dim     :int = 256,
-                 stride      :int = 32,
-                 num_classes :int = 80,
-                 num_coords  :int = 4):
-        super().__init__()
-        # --------- Basic Parameters ----------
-        self.stride = stride
-        self.cls_dim = cls_dim
-        self.reg_dim = reg_dim
-        self.num_classes = num_classes
-        self.num_coords = num_coords
-
-        # --------- Network Parameters ----------
-        self.cls_pred = nn.Conv2d(cls_dim, num_classes, kernel_size=1)
-        self.reg_pred = nn.Conv2d(reg_dim, num_coords, kernel_size=1)                
-
-        self.init_bias()
-        
-    def init_bias(self):
-        # cls pred bias
-        b = self.cls_pred.bias.view(1, -1)
-        b.data.fill_(math.log(5 / self.num_classes / (640. / self.stride) ** 2))
-        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        # reg pred bias
-        b = self.reg_pred.bias.view(-1, )
-        b.data.fill_(1.0)
-        self.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def generate_anchors(self, 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]
-        anchors = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
-        anchors += 0.5  # add center offset
-        anchors *= self.stride
-
-        return anchors
-        
-    def forward(self, inputs):
-        cls_feat, reg_feat = inputs['cls_feat'], inputs['reg_feat']
-        # pred
-        cls_pred = self.cls_pred(cls_feat)
-        reg_pred = self.reg_pred(reg_feat)
-
-        # generate anchor boxes: [M, 4]
-        B, _, H, W = cls_pred.size()
-        fmp_size = [H, W]
-        anchors = self.generate_anchors(fmp_size)
-        anchors = anchors.to(cls_pred.device)
-        # stride tensor: [M, 1]
-        stride_tensor = torch.ones_like(anchors[..., :1]) * self.stride
-        
-        # [B, C, H, W] -> [B, H, W, C] -> [B, M, C]
-        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 + anchors[..., :2]
-        wh_pred = torch.exp(reg_pred[..., 2:]) * self.stride
-        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)
-
-        # output dict
-        outputs = {"pred_cls": cls_pred,             # (Tensor) [B, M, C]
-                   "pred_reg": reg_pred,             # (Tensor) [B, M, 4]
-                   "pred_box": box_pred,             # (Tensor) [B, M, 4] 
-                   "anchors": anchors,               # (Tensor) [M, 2]
-                   "stride": self.stride,            # (Int)
-                   "stride_tensors": stride_tensor   # List(Tensor) [M, 1]
-                   }
-
-        return outputs

models/detectors/ctrnet/loss.py

@@ -1,231 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from utils.box_ops import get_ious
-from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized
-
-from .matcher import AlignedSimOTA
-
-
-class Criterion(object):
-    def __init__(self, args, cfg, device, num_classes=80):
-        self.args = args
-        self.cfg = cfg
-        self.device = device
-        self.num_classes = num_classes
-        self.max_epoch = args.max_epoch
-        self.no_aug_epoch = args.no_aug_epoch
-        self.aux_bbox_loss = cfg['aux_bbox_loss']
-        # --------------- Loss config ---------------
-        self.loss_cls_weight = cfg['loss_cls_weight']
-        self.loss_box_weight = cfg['loss_box_weight']
-        # --------------- Matcher config ---------------
-        self.matcher_hpy = cfg['matcher_hpy']['main']
-        self.matcher = AlignedSimOTA(soft_center_radius = self.matcher_hpy['soft_center_radius'],
-                                     topk_candidates    = self.matcher_hpy['topk_candidates'],
-                                     num_classes        = num_classes,
-                                     )
-        # --------------- Aux Matcher config ---------------
-        self.aux_matcher_hpy = cfg['matcher_hpy']['aux']
-        self.aux_matcher = AlignedSimOTA(soft_center_radius = self.aux_matcher_hpy['soft_center_radius'],
-                                         topk_candidates    = self.aux_matcher_hpy['topk_candidates'],
-                                         num_classes        = num_classes,
-                                         )
-
-    # -------------------- Basic 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):
-        ious = get_ious(pred_box, gt_box, box_mode="xyxy", iou_type='giou')
-        loss_box = 1.0 - ious
-
-        return loss_box
-    
-    def loss_bboxes_aux(self, pred_reg, gt_box, anchors, stride_tensors):
-        # xyxy -> cxcy&bwbh
-        gt_cxcy = (gt_box[..., :2] + gt_box[..., 2:]) * 0.5
-        gt_bwbh = gt_box[..., 2:] - gt_box[..., :2]
-        # encode gt box
-        gt_cxcy_encode = (gt_cxcy - anchors) / stride_tensors
-        gt_bwbh_encode = torch.log(gt_bwbh / stride_tensors)
-        gt_box_encode = torch.cat([gt_cxcy_encode, gt_bwbh_encode], dim=-1)
-        # l1 loss
-        loss_box_aux = F.l1_loss(pred_reg, gt_box_encode, reduction='none')
-
-        return loss_box_aux
-
-
-    # -------------------- Task loss functions --------------------
-    def compute_loss(self, outputs, targets, aux_loss=False, epoch=0):
-        """
-            Input:
-                outputs: (Dict) -> {
-                    'pred_cls': (List[torch.Tensor] -> [B, M, Nc]),
-                    'pred_reg': (List[torch.Tensor] -> [B, M, 4]),
-                    'pred_box': (List[torch.Tensor] -> [B, M, 4]),
-                    'strides':  (List[Int])
-                }
-                target: (List[Dict]) [
-                    {'boxes':  (torch.Tensor) -> [N, 4], 
-                     'labels': (torch.Tensor) -> [N,],
-                     ...}, ...
-                     ]
-            Output:
-                loss_dict: (Dict) -> {
-                    'loss_cls': (torch.Tensor) It is a scalar.),
-                    'loss_box': (torch.Tensor) It is a scalar.),
-                    'loss_box_aux': (torch.Tensor) It is a scalar.),
-                    'losses':  (torch.Tensor) It is a scalar.),
-                }
-        """
-        bs = outputs['pred_cls'].shape[0]
-        device = outputs['pred_cls'].device
-        stride = outputs['stride']
-        anchors = outputs['anchors']
-        # preds: [B, M, C]
-        cls_preds = outputs['pred_cls']
-        box_preds = outputs['pred_box']
-        
-        # --------------- label assignment ---------------
-        cls_targets = []
-        box_targets = []
-        assign_metrics = []
-        for batch_idx in range(bs):
-            tgt_labels = targets[batch_idx]["labels"].to(device)  # [N,]
-            tgt_bboxes = targets[batch_idx]["boxes"].to(device)   # [N, 4]
-            if not aux_loss:
-                assigned_result = self.matcher(stride=stride,
-                                               anchors=anchors,
-                                               pred_cls=cls_preds[batch_idx].detach(),
-                                               pred_box=box_preds[batch_idx].detach(),
-                                               gt_labels=tgt_labels,
-                                               gt_bboxes=tgt_bboxes
-                                               )
-            else:
-                assigned_result = self.aux_matcher(stride=stride,
-                                                   anchors=anchors,
-                                                   pred_cls=cls_preds[batch_idx].detach(),
-                                                   pred_box=box_preds[batch_idx].detach(),
-                                                   gt_labels=tgt_labels,
-                                                   gt_bboxes=tgt_bboxes
-                                                   )
-            cls_targets.append(assigned_result['assigned_labels'])
-            box_targets.append(assigned_result['assigned_bboxes'])
-            assign_metrics.append(assigned_result['assign_metrics'])
-
-        # List[B, M, C] -> Tensor[BM, C]
-        cls_targets = torch.cat(cls_targets, dim=0)
-        box_targets = torch.cat(box_targets, dim=0)
-        assign_metrics = torch.cat(assign_metrics, dim=0)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((cls_targets >= 0) & (cls_targets < bg_class_ind)).nonzero().squeeze(1)
-        num_fgs = assign_metrics.sum()
-
-        if is_dist_avail_and_initialized():
-            torch.distributed.all_reduce(num_fgs)
-        num_fgs = (num_fgs / get_world_size()).clamp(1.0).item()
-
-        # ------------------ Classification loss ------------------
-        cls_preds = cls_preds.view(-1, self.num_classes)
-        loss_cls = self.loss_classes(cls_preds, (cls_targets, assign_metrics))
-        loss_cls = loss_cls.sum() / num_fgs
-
-        # ------------------ Regression loss ------------------
-        box_preds_pos = box_preds.view(-1, 4)[pos_inds]
-        box_targets_pos = box_targets[pos_inds]
-        loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos)
-        loss_box = loss_box.sum() / num_fgs
-
-        # total loss
-        losses = self.loss_cls_weight * loss_cls + \
-                 self.loss_box_weight * loss_box
-
-        # ------------------ Aux regression loss ------------------
-        loss_box_aux = None
-        if epoch >= (self.max_epoch - self.no_aug_epoch - 1) and self.aux_bbox_loss:
-            ## reg_preds
-            reg_preds = outputs['pred_reg']
-            reg_preds_pos = reg_preds.view(-1, 4)[pos_inds]
-            ## anchor tensors
-            anchors_tensors = outputs['anchors'][None].repeat(bs, 1, 1)
-            anchors_tensors_pos = anchors_tensors.view(-1, 2)[pos_inds]
-            ## stride tensors
-            stride_tensors = outputs['stride_tensors'][None].repeat(bs, 1, 1)
-            stride_tensors_pos = stride_tensors.view(-1, 1)[pos_inds]
-            ## aux loss
-            loss_box_aux = self.loss_bboxes_aux(reg_preds_pos, box_targets_pos, anchors_tensors_pos, stride_tensors_pos)
-            loss_box_aux = loss_box_aux.sum() / num_fgs
-
-            losses += loss_box_aux
-
-        # Loss dict
-        if loss_box_aux is None:
-            loss_dict = dict(
-                    loss_cls = loss_cls,
-                    loss_box = loss_box,
-                    losses = losses
-            )
-        else:
-            loss_dict = dict(
-                    loss_cls = loss_cls,
-                    loss_box = loss_box,
-                    loss_box_aux = loss_box_aux,
-                    losses = losses
-                    )
-
-        return loss_dict
-
-    def __call__(self, outputs, targets, epoch=0):
-        # -------------- Main loss --------------
-        main_loss_dict = self.compute_loss(outputs, targets, False, epoch)
-        
-        # -------------- Aux loss --------------
-        aux_loss_dict = self.compute_loss(outputs['aux_outputs'], targets, True, epoch)
-
-        # Reformat loss dict
-        loss_dict = dict()
-        loss_dict['losses'] = main_loss_dict['losses'] + aux_loss_dict['losses']
-        for k in main_loss_dict:
-            if k != 'losses':
-                loss_dict[k] = main_loss_dict[k]
-        for k in aux_loss_dict:
-            if k != 'losses':
-                loss_dict[k+'_aux'] = aux_loss_dict[k]
-        
-        return loss_dict
-
-
-def build_criterion(args, cfg, device, num_classes):
-    criterion = Criterion(args, cfg, device, num_classes)
-
-    return criterion
-
-
-if __name__ == "__main__":
-    pass

models/detectors/ctrnet/matcher.py

@@ -1,159 +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 as nn
-import torch.nn.functional as F
-from utils.box_ops import box_iou
-
-
-# -------------------------- Aligned SimOTA assigner --------------------------
-class AlignedSimOTA(object):
-    def __init__(self, num_classes, soft_center_radius=3.0, topk_candidates=13):
-        self.num_classes = num_classes
-        self.soft_center_radius = soft_center_radius
-        self.topk_candidates = topk_candidates
-
-    @torch.no_grad()
-    def __call__(self, 
-                 stride, 
-                 anchors, 
-                 pred_cls, 
-                 pred_box, 
-                 gt_labels,
-                 gt_bboxes):
-        # [M,]
-        stride_tensor = torch.ones_like(anchors[:, 0]) * stride
-        num_gt = len(gt_labels)
-
-        # check gt
-        if num_gt == 0 or gt_bboxes.max().item() == 0.:
-            return {
-                'assigned_labels': gt_labels.new_full(pred_cls[..., 0].shape,
-                                                      self.num_classes,
-                                                      dtype=torch.long),
-                'assigned_bboxes': gt_bboxes.new_full(pred_box.shape, 0),
-                'assign_metrics': gt_bboxes.new_full(pred_cls[..., 0].shape, 0)
-            }
-        
-        # get inside points: [N, M]
-        is_in_gt = self.find_inside_points(gt_bboxes, anchors)
-        valid_mask = is_in_gt.sum(dim=0) > 0  # [M,]
-
-        # ----------------------------------- soft center prior -----------------------------------
-        gt_center = (gt_bboxes[..., :2] + gt_bboxes[..., 2:]) / 2.0
-        distance = (anchors.unsqueeze(0) - gt_center.unsqueeze(1)
-                    ).pow(2).sum(-1).sqrt() / stride_tensor.unsqueeze(0)  # [N, M]
-        distance = distance * valid_mask.unsqueeze(0)
-        soft_center_prior = torch.pow(10, distance - self.soft_center_radius)
-
-        # ----------------------------------- regression cost -----------------------------------
-        pair_wise_ious, _ = box_iou(gt_bboxes, pred_box)  # [N, M]
-        pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8) * 3.0
-
-        # ----------------------------------- classification cost -----------------------------------
-        ## select the predicted scores corresponded to the gt_labels
-        pairwise_pred_scores = pred_cls.permute(1, 0)  # [M, C] -> [C, M]
-        pairwise_pred_scores = pairwise_pred_scores[gt_labels.long(), :].float()   # [N, M]
-        ## scale factor
-        scale_factor = (pair_wise_ious - pairwise_pred_scores.sigmoid()).abs().pow(2.0)
-        ## cls cost
-        pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
-            pairwise_pred_scores, pair_wise_ious,
-            reduction="none") * scale_factor # [N, M]
-            
-        del pairwise_pred_scores
-
-        ## foreground cost matrix
-        cost_matrix = pair_wise_cls_loss + pair_wise_ious_loss + soft_center_prior
-        max_pad_value = torch.ones_like(cost_matrix) * 1e9
-        cost_matrix = torch.where(valid_mask[None].repeat(num_gt, 1),   # [N, M]
-                                  cost_matrix, max_pad_value)
-
-        # ----------------------------------- dynamic label assignment -----------------------------------
-        matched_pred_ious, matched_gt_inds, fg_mask_inboxes = self.dynamic_k_matching(
-            cost_matrix, pair_wise_ious, num_gt)
-        del pair_wise_cls_loss, cost_matrix, pair_wise_ious, pair_wise_ious_loss
-
-        # -----------------------------------process assigned labels -----------------------------------
-        assigned_labels = gt_labels.new_full(pred_cls[..., 0].shape,
-                                             self.num_classes)  # [M,]
-        assigned_labels[fg_mask_inboxes] = gt_labels[matched_gt_inds].squeeze(-1)
-        assigned_labels = assigned_labels.long()  # [M,]
-
-        assigned_bboxes = gt_bboxes.new_full(pred_box.shape, 0)        # [M, 4]
-        assigned_bboxes[fg_mask_inboxes] = gt_bboxes[matched_gt_inds]  # [M, 4]
-
-        assign_metrics = gt_bboxes.new_full(pred_cls[..., 0].shape, 0) # [M, 4]
-        assign_metrics[fg_mask_inboxes] = matched_pred_ious            # [M, 4]
-
-        assigned_dict = dict(
-            assigned_labels=assigned_labels,
-            assigned_bboxes=assigned_bboxes,
-            assign_metrics=assign_metrics
-            )
-        
-        return assigned_dict
-
-    def find_inside_points(self, gt_bboxes, anchors):
-        """
-            gt_bboxes: Tensor -> [N, 2]
-            anchors:   Tensor -> [M, 2]
-        """
-        num_anchors = anchors.shape[0]
-        num_gt = gt_bboxes.shape[0]
-
-        anchors_expand = anchors.unsqueeze(0).repeat(num_gt, 1, 1)           # [N, M, 2]
-        gt_bboxes_expand = gt_bboxes.unsqueeze(1).repeat(1, num_anchors, 1)  # [N, M, 4]
-
-        # offset
-        lt = anchors_expand - gt_bboxes_expand[..., :2]
-        rb = gt_bboxes_expand[..., 2:] - anchors_expand
-        bbox_deltas = torch.cat([lt, rb], dim=-1)
-
-        is_in_gts = bbox_deltas.min(dim=-1).values > 0
-
-        return is_in_gts
-    
-    def dynamic_k_matching(self, cost_matrix, pairwise_ious, num_gt):
-        """Use IoU and matching cost to calculate the dynamic top-k positive
-        targets.
-
-        Args:
-            cost_matrix (Tensor): Cost matrix.
-            pairwise_ious (Tensor): Pairwise iou matrix.
-            num_gt (int): Number of gt.
-            valid_mask (Tensor): Mask for valid bboxes.
-        Returns:
-            tuple: matched ious and gt indexes.
-        """
-        matching_matrix = torch.zeros_like(cost_matrix, dtype=torch.uint8)
-        # select candidate topk ious for dynamic-k calculation
-        candidate_topk = min(self.topk_candidates, pairwise_ious.size(1))
-        topk_ious, _ = torch.topk(pairwise_ious, candidate_topk, dim=1)
-        # calculate dynamic k for each gt
-        dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1)
-
-        # sorting the batch cost matirx is faster than topk
-        _, sorted_indices = torch.sort(cost_matrix, dim=1)
-        for gt_idx in range(num_gt):
-            topk_ids = sorted_indices[gt_idx, :dynamic_ks[gt_idx]]
-            matching_matrix[gt_idx, :][topk_ids] = 1
-
-        del topk_ious, dynamic_ks, topk_ids
-
-        prior_match_gt_mask = matching_matrix.sum(0) > 1
-        if prior_match_gt_mask.sum() > 0:
-            cost_min, cost_argmin = torch.min(
-                cost_matrix[:, prior_match_gt_mask], dim=0)
-            matching_matrix[:, prior_match_gt_mask] *= 0
-            matching_matrix[cost_argmin, prior_match_gt_mask] = 1
-
-        # get foreground mask inside box and center prior
-        fg_mask_inboxes = matching_matrix.sum(0) > 0
-        matched_pred_ious = (matching_matrix *
-                             pairwise_ious).sum(0)[fg_mask_inboxes]
-        matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-
-        return matched_pred_ious, matched_gt_inds, fg_mask_inboxes
-