Add RTDETR Trainer (#2745)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

docs/models/rtdetr.md

@@ -35,6 +35,7 @@ from ultralytics import RTDETR
 
 model = RTDETR("rtdetr-l.pt")
 model.info()  # display model information
+model.train(data="coco8.yaml")   # train
 model.predict("path/to/image.jpg")  # predict
 ```
 
@@ -51,7 +52,7 @@ model.predict("path/to/image.jpg")  # predict
 |------------|--------------------|
 | Inference  | :heavy_check_mark: |
 | Validation | :heavy_check_mark: |
-| Training   | :x: (Coming soon)  |
+| Training   | :heavy_check_mark: |
 
 # Citations and Acknowledgements
 
@@ -70,4 +71,4 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
 
 We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
 
-*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*
+*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*

docs/reference/yolo/utils/loss.md

@@ -8,6 +8,11 @@ keywords: Ultralytics, YOLO, loss functions, object detection, keypoint detectio
 :::ultralytics.yolo.utils.loss.VarifocalLoss
 <br><br>
 
+# FocalLoss
+---
+:::ultralytics.yolo.utils.loss.FocalLoss
+<br><br>
+
 # BboxLoss
 ---
 :::ultralytics.yolo.utils.loss.BboxLoss

docs/reference/yolo/utils/metrics.md

@@ -3,11 +3,6 @@ description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, Clas
 keywords: YOLOv5, metrics, losses, confusion matrix, detection metrics, pose metrics, classification metrics, intersection over area, intersection over union, keypoint intersection over union, average precision, per class average precision, Ultralytics Docs
 ---
 
-# FocalLoss
----
-:::ultralytics.yolo.utils.metrics.FocalLoss
-<br><br>
-
 # ConfusionMatrix
 ---
 :::ultralytics.yolo.utils.metrics.ConfusionMatrix

tests/test_python.py

@@ -7,7 +7,7 @@ import numpy as np
 import torch
 from PIL import Image
 
-from ultralytics import YOLO
+from ultralytics import RTDETR, YOLO
 from ultralytics.yolo.data.build import load_inference_source
 from ultralytics.yolo.utils import LINUX, ONLINE, ROOT, SETTINGS
 
@@ -174,7 +174,10 @@ def test_export_paddle(enabled=False):
 
 def test_all_model_yamls():
     for m in list((ROOT / 'models').rglob('yolo*.yaml')):
-        YOLO(m.name)
+        if m.name == 'yolov8-rtdetr.yaml':  # except the rtdetr model
+            RTDETR(m.name)
+        else:
+            YOLO(m.name)
 
 
 def test_workflow():

ultralytics/models/v8/yolov8-rtdetr.yaml

@@ -0,0 +1,46 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80  # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
+  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
+  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
+  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]]  # 9
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
+  - [-1, 3, C2f, [512]]  # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
+  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
+  - [-1, 3, C2f, [256]]  # 15 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]]  # cat head P4
+  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]]  # cat head P5
+  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)
+
+  - [[15, 18, 21], 1, RTDETRDecoder, [nc]]  # Detect(P3, P4, P5)

ultralytics/nn/modules/head.py

@@ -163,111 +163,178 @@ class RTDETRDecoder(nn.Module):
             self,
             nc=80,
             ch=(512, 1024, 2048),
-            hidden_dim=256,
-            num_queries=300,
-            strides=(8, 16, 32),  # TODO
-            nl=3,
-            num_decoder_points=4,
-            nhead=8,
-            num_decoder_layers=6,
-            dim_feedforward=1024,
+            hd=256,  # hidden dim
+            nq=300,  # num queries
+            ndp=4,  # num decoder points
+            nh=8,  # num head
+            ndl=6,  # num decoder layers
+            d_ffn=1024,  # dim of feedforward
             dropout=0.,
             act=nn.ReLU(),
             eval_idx=-1,
             # training args
-            num_denoising=100,
+            nd=100,  # num denoising
             label_noise_ratio=0.5,
             box_noise_scale=1.0,
             learnt_init_query=False):
         super().__init__()
-        assert len(ch) <= nl
-        assert len(strides) == len(ch)
-        for _ in range(nl - len(strides)):
-            strides.append(strides[-1] * 2)
-
-        self.hidden_dim = hidden_dim
-        self.nhead = nhead
-        self.feat_strides = strides
-        self.nl = nl
+        self.hidden_dim = hd
+        self.nhead = nh
+        self.nl = len(ch)  # num level
         self.nc = nc
-        self.num_queries = num_queries
-        self.num_decoder_layers = num_decoder_layers
+        self.num_queries = nq
+        self.num_decoder_layers = ndl
 
         # backbone feature projection
-        self._build_input_proj_layer(ch)
+        self.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)
+        # NOTE: simplified version but it's not consistent with .pt weights.
+        # self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)
 
         # Transformer module
-        decoder_layer = DeformableTransformerDecoderLayer(hidden_dim, nhead, dim_feedforward, dropout, act, nl,
-                                                          num_decoder_points)
-        self.decoder = DeformableTransformerDecoder(hidden_dim, decoder_layer, num_decoder_layers, eval_idx)
+        decoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)
+        self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)
 
         # denoising part
-        self.denoising_class_embed = nn.Embedding(nc, hidden_dim)
-        self.num_denoising = num_denoising
+        self.denoising_class_embed = nn.Embedding(nc, hd)
+        self.num_denoising = nd
         self.label_noise_ratio = label_noise_ratio
         self.box_noise_scale = box_noise_scale
 
         # decoder embedding
         self.learnt_init_query = learnt_init_query
         if learnt_init_query:
-            self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
-        self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, num_layers=2)
+            self.tgt_embed = nn.Embedding(nq, hd)
+        self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)
 
         # encoder head
-        self.enc_output = nn.Sequential(nn.Linear(hidden_dim, hidden_dim), nn.LayerNorm(hidden_dim))
-        self.enc_score_head = nn.Linear(hidden_dim, nc)
-        self.enc_bbox_head = MLP(hidden_dim, hidden_dim, 4, num_layers=3)
+        self.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))
+        self.enc_score_head = nn.Linear(hd, nc)
+        self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)
 
         # decoder head
-        self.dec_score_head = nn.ModuleList([nn.Linear(hidden_dim, nc) for _ in range(num_decoder_layers)])
-        self.dec_bbox_head = nn.ModuleList([
-            MLP(hidden_dim, hidden_dim, 4, num_layers=3) for _ in range(num_decoder_layers)])
+        self.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])
+        self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])
 
         self._reset_parameters()
 
-    def forward(self, feats, gt_meta=None):
+    def forward(self, x, batch=None):
+        from ultralytics.vit.utils.ops import get_cdn_group
+
         # input projection and embedding
-        memory, spatial_shapes, _ = self._get_encoder_input(feats)
+        feats, shapes = self._get_encoder_input(x)
 
         # prepare denoising training
-        if self.training:
-            raise NotImplementedError
-            # denoising_class, denoising_bbox_unact, attn_mask, dn_meta = \
-            #     get_contrastive_denoising_training_group(gt_meta,
-            #                                 self.num_classes,
-            #                                 self.num_queries,
-            #                                 self.denoising_class_embed.weight,
-            #                                 self.num_denoising,
-            #                                 self.label_noise_ratio,
-            #                                 self.box_noise_scale)
-        else:
-            denoising_class, denoising_bbox_unact, attn_mask = None, None, None
+        dn_embed, dn_bbox, attn_mask, dn_meta = \
+            get_cdn_group(batch,
+                          self.nc,
+                          self.num_queries,
+                          self.denoising_class_embed.weight,
+                          self.num_denoising,
+                          self.label_noise_ratio,
+                          self.box_noise_scale,
+                          self.training)
 
-        target, init_ref_points_unact, enc_topk_bboxes, enc_topk_logits = \
-            self._get_decoder_input(memory, spatial_shapes, denoising_class, denoising_bbox_unact)
+        embed, refer_bbox, enc_bboxes, enc_scores = \
+            self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)
 
         # decoder
-        out_bboxes, out_logits = self.decoder(target,
-                                              init_ref_points_unact,
-                                              memory,
-                                              spatial_shapes,
+        dec_bboxes, dec_scores = self.decoder(embed,
+                                              refer_bbox,
+                                              feats,
+                                              shapes,
                                               self.dec_bbox_head,
                                               self.dec_score_head,
                                               self.query_pos_head,
                                               attn_mask=attn_mask)
         if not self.training:
-            out_logits = out_logits.sigmoid_()
-        return out_bboxes, out_logits  # enc_topk_bboxes, enc_topk_logits, dn_meta
+            dec_scores = dec_scores.sigmoid_()
+        return dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta
 
+    def _generate_anchors(self, shapes, grid_size=0.05, dtype=torch.float32, device='cpu', eps=1e-2):
+        anchors = []
+        for i, (h, w) in enumerate(shapes):
+            grid_y, grid_x = torch.meshgrid(torch.arange(end=h, dtype=dtype, device=device),
+                                            torch.arange(end=w, dtype=dtype, device=device),
+                                            indexing='ij')
+            grid_xy = torch.stack([grid_x, grid_y], -1)  # (h, w, 2)
+
+            valid_WH = torch.tensor([h, w], dtype=dtype, device=device)
+            grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH  # (1, h, w, 2)
+            wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0 ** i)
+            anchors.append(torch.cat([grid_xy, wh], -1).view(-1, h * w, 4))  # (1, h*w, 4)
+
+        anchors = torch.cat(anchors, 1)  # (1, h*w*nl, 4)
+        valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True)  # 1, h*w*nl, 1
+        anchors = torch.log(anchors / (1 - anchors))
+        anchors = torch.where(valid_mask, anchors, torch.inf)
+        return anchors, valid_mask
+
+    def _get_encoder_input(self, x):
+        # get projection features
+        x = [self.input_proj[i](feat) for i, feat in enumerate(x)]
+        # get encoder inputs
+        feats = []
+        shapes = []
+        for feat in x:
+            h, w = feat.shape[2:]
+            # [b, c, h, w] -> [b, h*w, c]
+            feats.append(feat.flatten(2).permute(0, 2, 1))
+            # [nl, 2]
+            shapes.append([h, w])
+
+        # [b, h*w, c]
+        feats = torch.cat(feats, 1)
+        return feats, shapes
+
+    def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
+        bs = len(feats)
+        # prepare input for decoder
+        anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
+        features = self.enc_output(torch.where(valid_mask, feats, 0))  # bs, h*w, 256
+
+        enc_outputs_scores = self.enc_score_head(features)  # (bs, h*w, nc)
+        # dynamic anchors + static content
+        enc_outputs_bboxes = self.enc_bbox_head(features) + anchors  # (bs, h*w, 4)
+
+        # query selection
+        # (bs, num_queries)
+        topk_ind = torch.topk(enc_outputs_scores.max(-1).values, self.num_queries, dim=1).indices.view(-1)
+        # (bs, num_queries)
+        batch_ind = torch.arange(end=bs, dtype=topk_ind.dtype).unsqueeze(-1).repeat(1, self.num_queries).view(-1)
+
+        # Unsigmoided
+        refer_bbox = enc_outputs_bboxes[batch_ind, topk_ind].view(bs, self.num_queries, -1)
+        # refer_bbox = torch.gather(enc_outputs_bboxes, 1, topk_ind.reshape(bs, self.num_queries).unsqueeze(-1).repeat(1, 1, 4))
+
+        enc_bboxes = refer_bbox.sigmoid()
+        if dn_bbox is not None:
+            refer_bbox = torch.cat([dn_bbox, refer_bbox], 1)
+        if self.training:
+            refer_bbox = refer_bbox.detach()
+        enc_scores = enc_outputs_scores[batch_ind, topk_ind].view(bs, self.num_queries, -1)
+
+        if self.learnt_init_query:
+            embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
+        else:
+            embeddings = features[batch_ind, topk_ind].view(bs, self.num_queries, -1)
+            if self.training:
+                embeddings = embeddings.detach()
+        if dn_embed is not None:
+            embeddings = torch.cat([dn_embed, embeddings], 1)
+
+        return embeddings, refer_bbox, enc_bboxes, enc_scores
+
+    # TODO
     def _reset_parameters(self):
         # class and bbox head init
-        bias_cls = bias_init_with_prob(0.01)
-        linear_init_(self.enc_score_head)
+        bias_cls = bias_init_with_prob(0.01) / 80 * self.nc
+        # NOTE: the weight initialization in `linear_init_` would cause NaN when training with custom datasets.
+        # linear_init_(self.enc_score_head)
         constant_(self.enc_score_head.bias, bias_cls)
         constant_(self.enc_bbox_head.layers[-1].weight, 0.)
         constant_(self.enc_bbox_head.layers[-1].bias, 0.)
         for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
-            linear_init_(cls_)
+            # linear_init_(cls_)
             constant_(cls_.bias, bias_cls)
             constant_(reg_.layers[-1].weight, 0.)
             constant_(reg_.layers[-1].bias, 0.)
@@ -280,103 +347,3 @@ class RTDETRDecoder(nn.Module):
         xavier_uniform_(self.query_pos_head.layers[1].weight)
         for layer in self.input_proj:
             xavier_uniform_(layer[0].weight)
-
-    def _build_input_proj_layer(self, ch):
-        self.input_proj = nn.ModuleList()
-        for in_channels in ch:
-            self.input_proj.append(
-                nn.Sequential(nn.Conv2d(in_channels, self.hidden_dim, kernel_size=1, bias=False),
-                              nn.BatchNorm2d(self.hidden_dim)))
-        in_channels = ch[-1]
-        for _ in range(self.nl - len(ch)):
-            self.input_proj.append(
-                nn.Sequential(nn.Conv2D(in_channels, self.hidden_dim, kernel_size=3, stride=2, padding=1, bias=False),
-                              nn.BatchNorm2d(self.hidden_dim)))
-            in_channels = self.hidden_dim
-
-    def _generate_anchors(self, spatial_shapes, grid_size=0.05, dtype=torch.float32, device='cpu', eps=1e-2):
-        anchors = []
-        for lvl, (h, w) in enumerate(spatial_shapes):
-            grid_y, grid_x = torch.meshgrid(torch.arange(end=h, dtype=torch.float32),
-                                            torch.arange(end=w, dtype=torch.float32),
-                                            indexing='ij')
-            grid_xy = torch.stack([grid_x, grid_y], -1)
-
-            valid_WH = torch.tensor([h, w]).to(torch.float32)
-            grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH
-            wh = torch.ones_like(grid_xy) * grid_size * (2.0 ** lvl)
-            anchors.append(torch.concat([grid_xy, wh], -1).reshape([-1, h * w, 4]))
-
-        anchors = torch.concat(anchors, 1)
-        valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True)
-        anchors = torch.log(anchors / (1 - anchors))
-        anchors = torch.where(valid_mask, anchors, torch.inf)
-        return anchors.to(device=device, dtype=dtype), valid_mask.to(device=device)
-
-    def _get_encoder_input(self, feats):
-        # get projection features
-        proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)]
-        if self.nl > len(proj_feats):
-            len_srcs = len(proj_feats)
-            for i in range(len_srcs, self.nl):
-                if i == len_srcs:
-                    proj_feats.append(self.input_proj[i](feats[-1]))
-                else:
-                    proj_feats.append(self.input_proj[i](proj_feats[-1]))
-
-        # get encoder inputs
-        feat_flatten = []
-        spatial_shapes = []
-        level_start_index = [0]
-        for feat in proj_feats:
-            _, _, h, w = feat.shape
-            # [b, c, h, w] -> [b, h*w, c]
-            feat_flatten.append(feat.flatten(2).permute(0, 2, 1))
-            # [nl, 2]
-            spatial_shapes.append([h, w])
-            # [l], start index of each level
-            level_start_index.append(h * w + level_start_index[-1])
-
-        # [b, l, c]
-        feat_flatten = torch.concat(feat_flatten, 1)
-        level_start_index.pop()
-        return feat_flatten, spatial_shapes, level_start_index
-
-    def _get_decoder_input(self, memory, spatial_shapes, denoising_class=None, denoising_bbox_unact=None):
-        bs, _, _ = memory.shape
-        # prepare input for decoder
-        anchors, valid_mask = self._generate_anchors(spatial_shapes, dtype=memory.dtype, device=memory.device)
-        memory = torch.where(valid_mask, memory, 0)
-        output_memory = self.enc_output(memory)
-
-        enc_outputs_class = self.enc_score_head(output_memory)  # (bs, h*w, nc)
-        enc_outputs_coord_unact = self.enc_bbox_head(output_memory) + anchors  # (bs, h*w, 4)
-
-        # (bs, topk)
-        _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.num_queries, dim=1)
-        # extract region proposal boxes
-        # (bs, topk_ind)
-        batch_ind = torch.arange(end=bs, dtype=topk_ind.dtype).unsqueeze(-1).repeat(1, self.num_queries).view(-1)
-        topk_ind = topk_ind.view(-1)
-
-        # Unsigmoided
-        reference_points_unact = enc_outputs_coord_unact[batch_ind, topk_ind].view(bs, self.num_queries, -1)
-
-        enc_topk_bboxes = torch.sigmoid(reference_points_unact)
-        if denoising_bbox_unact is not None:
-            reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1)
-        if self.training:
-            reference_points_unact = reference_points_unact.detach()
-        enc_topk_logits = enc_outputs_class[batch_ind, topk_ind].view(bs, self.num_queries, -1)
-
-        # extract region features
-        if self.learnt_init_query:
-            target = self.tgt_embed.weight.unsqueeze(0).repeat(bs, 1, 1)
-        else:
-            target = output_memory[batch_ind, topk_ind].view(bs, self.num_queries, -1)
-            if self.training:
-                target = target.detach()
-        if denoising_class is not None:
-            target = torch.concat([denoising_class, target], 1)
-
-        return target, reference_points_unact, enc_topk_bboxes, enc_topk_logits

ultralytics/nn/modules/transformer.py

@@ -229,23 +229,23 @@ class MSDeformAttn(nn.Module):
         xavier_uniform_(self.output_proj.weight.data)
         constant_(self.output_proj.bias.data, 0.)
 
-    def forward(self, query, reference_points, value, value_spatial_shapes, value_mask=None):
+    def forward(self, query, refer_bbox, value, value_shapes, value_mask=None):
         """
         https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/transformers/deformable_transformer.py
         Args:
-            query (Tensor): [bs, query_length, C]
-            reference_points (Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),
+            query (torch.Tensor): [bs, query_length, C]
+            refer_bbox (torch.Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),
                 bottom-right (1, 1), including padding area
-            value (Tensor): [bs, value_length, C]
-            value_spatial_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
+            value (torch.Tensor): [bs, value_length, C]
+            value_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
             value_mask (Tensor): [bs, value_length], True for non-padding elements, False for padding elements
 
         Returns:
             output (Tensor): [bs, Length_{query}, C]
         """
         bs, len_q = query.shape[:2]
-        _, len_v = value.shape[:2]
-        assert sum(s[0] * s[1] for s in value_spatial_shapes) == len_v
+        len_v = value.shape[1]
+        assert sum(s[0] * s[1] for s in value_shapes) == len_v
 
         value = self.value_proj(value)
         if value_mask is not None:
@@ -255,18 +255,17 @@ class MSDeformAttn(nn.Module):
         attention_weights = self.attention_weights(query).view(bs, len_q, self.n_heads, self.n_levels * self.n_points)
         attention_weights = F.softmax(attention_weights, -1).view(bs, len_q, self.n_heads, self.n_levels, self.n_points)
         # N, Len_q, n_heads, n_levels, n_points, 2
-        n = reference_points.shape[-1]
-        if n == 2:
-            offset_normalizer = torch.as_tensor(value_spatial_shapes, dtype=query.dtype, device=query.device).flip(-1)
+        num_points = refer_bbox.shape[-1]
+        if num_points == 2:
+            offset_normalizer = torch.as_tensor(value_shapes, dtype=query.dtype, device=query.device).flip(-1)
             add = sampling_offsets / offset_normalizer[None, None, None, :, None, :]
-            sampling_locations = reference_points[:, :, None, :, None, :] + add
-
-        elif n == 4:
-            add = sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
-            sampling_locations = reference_points[:, :, None, :, None, :2] + add
+            sampling_locations = refer_bbox[:, :, None, :, None, :] + add
+        elif num_points == 4:
+            add = sampling_offsets / self.n_points * refer_bbox[:, :, None, :, None, 2:] * 0.5
+            sampling_locations = refer_bbox[:, :, None, :, None, :2] + add
         else:
-            raise ValueError(f'Last dim of reference_points must be 2 or 4, but got {n}.')
-        output = multi_scale_deformable_attn_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights)
+            raise ValueError(f'Last dim of reference_points must be 2 or 4, but got {num_points}.')
+        output = multi_scale_deformable_attn_pytorch(value, value_shapes, sampling_locations, attention_weights)
         output = self.output_proj(output)
         return output
 
@@ -308,33 +307,24 @@ class DeformableTransformerDecoderLayer(nn.Module):
         tgt = self.norm3(tgt)
         return tgt
 
-    def forward(self,
-                tgt,
-                reference_points,
-                src,
-                src_spatial_shapes,
-                src_padding_mask=None,
-                attn_mask=None,
-                query_pos=None):
+    def forward(self, embed, refer_bbox, feats, shapes, padding_mask=None, attn_mask=None, query_pos=None):
         # self attention
-        q = k = self.with_pos_embed(tgt, query_pos)
-        if attn_mask is not None:
-            attn_mask = torch.where(attn_mask.astype('bool'), torch.zeros(attn_mask.shape, tgt.dtype),
-                                    torch.full(attn_mask.shape, float('-inf'), tgt.dtype))
-        tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1))[0].transpose(0, 1)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
+        q = k = self.with_pos_embed(embed, query_pos)
+        tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1),
+                             attn_mask=attn_mask)[0].transpose(0, 1)
+        embed = embed + self.dropout1(tgt)
+        embed = self.norm1(embed)
 
         # cross attention
-        tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos), reference_points, src, src_spatial_shapes,
-                               src_padding_mask)
-        tgt = tgt + self.dropout2(tgt2)
-        tgt = self.norm2(tgt)
+        tgt = self.cross_attn(self.with_pos_embed(embed, query_pos), refer_bbox.unsqueeze(2), feats, shapes,
+                              padding_mask)
+        embed = embed + self.dropout2(tgt)
+        embed = self.norm2(embed)
 
         # ffn
-        tgt = self.forward_ffn(tgt)
+        embed = self.forward_ffn(embed)
 
-        return tgt
+        return embed
 
 
 class DeformableTransformerDecoder(nn.Module):
@@ -349,41 +339,40 @@ class DeformableTransformerDecoder(nn.Module):
         self.hidden_dim = hidden_dim
         self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx
 
-    def forward(self,
-                tgt,
-                reference_points,
-                src,
-                src_spatial_shapes,
-                bbox_head,
-                score_head,
-                query_pos_head,
-                attn_mask=None,
-                src_padding_mask=None):
-        output = tgt
-        dec_out_bboxes = []
-        dec_out_logits = []
-        ref_points = None
-        ref_points_detach = torch.sigmoid(reference_points)
+    def forward(
+            self,
+            embed,  # decoder embeddings
+            refer_bbox,  # anchor
+            feats,  # image features
+            shapes,  # feature shapes
+            bbox_head,
+            score_head,
+            pos_mlp,
+            attn_mask=None,
+            padding_mask=None):
+        output = embed
+        dec_bboxes = []
+        dec_cls = []
+        last_refined_bbox = None
+        refer_bbox = refer_bbox.sigmoid()
         for i, layer in enumerate(self.layers):
-            ref_points_input = ref_points_detach.unsqueeze(2)
-            query_pos_embed = query_pos_head(ref_points_detach)
-            output = layer(output, ref_points_input, src, src_spatial_shapes, src_padding_mask, attn_mask,
-                           query_pos_embed)
+            output = layer(output, refer_bbox, feats, shapes, padding_mask, attn_mask, pos_mlp(refer_bbox))
 
-            inter_ref_bbox = torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(ref_points_detach))
+            # refine bboxes, (bs, num_queries+num_denoising, 4)
+            refined_bbox = torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(refer_bbox))
 
             if self.training:
-                dec_out_logits.append(score_head[i](output))
+                dec_cls.append(score_head[i](output))
                 if i == 0:
-                    dec_out_bboxes.append(inter_ref_bbox)
+                    dec_bboxes.append(refined_bbox)
                 else:
-                    dec_out_bboxes.append(torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(ref_points)))
+                    dec_bboxes.append(torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(last_refined_bbox)))
             elif i == self.eval_idx:
-                dec_out_logits.append(score_head[i](output))
-                dec_out_bboxes.append(inter_ref_bbox)
+                dec_cls.append(score_head[i](output))
+                dec_bboxes.append(refined_bbox)
                 break
 
-            ref_points = inter_ref_bbox
-            ref_points_detach = inter_ref_bbox.detach() if self.training else inter_ref_bbox
+            last_refined_bbox = refined_bbox
+            refer_bbox = refined_bbox.detach() if self.training else refined_bbox
 
-        return torch.stack(dec_out_bboxes), torch.stack(dec_out_logits)
+        return torch.stack(dec_bboxes), torch.stack(dec_cls)

ultralytics/nn/tasks.py

@@ -210,7 +210,9 @@ class BaseModel(nn.Module):
         """
         if not hasattr(self, 'criterion'):
             self.criterion = self.init_criterion()
-        return self.criterion(self.predict(batch['img']) if preds is None else preds, batch)
+
+        preds = self.forward(batch['img']) if preds is None else preds
+        return self.criterion(preds, batch)
 
     def init_criterion(self):
         raise NotImplementedError('compute_loss() needs to be implemented by task heads')
@@ -410,7 +412,7 @@ class RTDETRDetectionModel(DetectionModel):
         """Compute the classification loss between predictions and true labels."""
         from ultralytics.vit.utils.loss import RTDETRDetectionLoss
 
-        return RTDETRDetectionLoss(num_classes=self.nc, use_vfl=True)
+        return RTDETRDetectionLoss(nc=self.nc, use_vfl=True)
 
     def loss(self, batch, preds=None):
         if not hasattr(self, 'criterion'):
@@ -420,31 +422,36 @@ class RTDETRDetectionModel(DetectionModel):
         # NOTE: preprocess gt_bbox and gt_labels to list.
         bs = len(img)
         batch_idx = batch['batch_idx']
-        gt_bbox, gt_class = [], []
+        gt_groups = []
         for i in range(bs):
-            gt_bbox.append(batch['bboxes'][batch_idx == i].to(img.device))
-            gt_class.append(batch['cls'][batch_idx == i].to(device=img.device, dtype=torch.long))
-        targets = {'cls': gt_class, 'bboxes': gt_bbox}
+            gt_groups.append((batch_idx == i).sum().item())
+        targets = {
+            'cls': batch['cls'].to(img.device, dtype=torch.long).view(-1),
+            'bboxes': batch['bboxes'].to(device=img.device),
+            'batch_idx': batch_idx.to(img.device, dtype=torch.long).view(-1),
+            'gt_groups': gt_groups}
 
         preds = self.predict(img, batch=targets) if preds is None else preds
-        dec_out_bboxes, dec_out_logits, enc_topk_bboxes, enc_topk_logits, dn_meta = preds
-        # NOTE: `dn_meta` means it's eval mode, loss calculation for eval mode is not supported.
+        dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta = preds
         if dn_meta is None:
-            return 0, torch.zeros(3, device=dec_out_bboxes.device)
-        dn_out_bboxes, dec_out_bboxes = torch.split(dec_out_bboxes, dn_meta['dn_num_split'], dim=2)
-        dn_out_logits, dec_out_logits = torch.split(dec_out_logits, dn_meta['dn_num_split'], dim=2)
+            dn_bboxes, dn_scores = None, None
+        else:
+            dn_bboxes, dec_bboxes = torch.split(dec_bboxes, dn_meta['dn_num_split'], dim=2)
+            dn_scores, dec_scores = torch.split(dec_scores, dn_meta['dn_num_split'], dim=2)
 
-        out_bboxes = torch.cat([enc_topk_bboxes.unsqueeze(0), dec_out_bboxes])
-        out_logits = torch.cat([enc_topk_logits.unsqueeze(0), dec_out_logits])
+        dec_bboxes = torch.cat([enc_bboxes.unsqueeze(0), dec_bboxes])  # (7, bs, 300, 4)
+        dec_scores = torch.cat([enc_scores.unsqueeze(0), dec_scores])
 
-        loss = self.criterion((out_bboxes, out_logits),
+        loss = self.criterion((dec_bboxes, dec_scores),
                               targets,
-                              dn_out_bboxes=dn_out_bboxes,
-                              dn_out_logits=dn_out_logits,
+                              dn_bboxes=dn_bboxes,
+                              dn_scores=dn_scores,
                               dn_meta=dn_meta)
-        return sum(loss.values()), torch.as_tensor([loss[k].detach() for k in ['loss_giou', 'loss_class', 'loss_bbox']])
+        # NOTE: There are like 12 losses in RTDETR, backward with all losses but only show the main three losses.
+        return sum(loss.values()), torch.as_tensor([loss[k].detach() for k in ['loss_giou', 'loss_class', 'loss_bbox']],
+                                                   device=img.device)
 
-    def predict(self, x, profile=False, visualize=False, batch=None):
+    def predict(self, x, profile=False, visualize=False, batch=None, augment=False):
         """
         Perform a forward pass through the network.
 

ultralytics/vit/__init__.py

@@ -3,4 +3,4 @@
 from .rtdetr import RTDETR
 from .sam import SAM
 
-__all__ = 'RTDETR', 'SAM', 'SAM'  # allow simpler import
+__all__ = 'RTDETR', 'SAM'  # allow simpler import

ultralytics/vit/rtdetr/model.py

@@ -5,15 +5,15 @@
 
 from pathlib import Path
 
-from ultralytics.nn.tasks import DetectionModel, attempt_load_one_weight, yaml_model_load
+from ultralytics.nn.tasks import RTDETRDetectionModel, attempt_load_one_weight, yaml_model_load
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir
+from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, ROOT, is_git_dir
 from ultralytics.yolo.utils.checks import check_imgsz
-from ultralytics.yolo.utils.torch_utils import model_info
+from ultralytics.yolo.utils.torch_utils import model_info, smart_inference_mode
 
-from ...yolo.utils.torch_utils import smart_inference_mode
 from .predict import RTDETRPredictor
+from .train import RTDETRTrainer
 from .val import RTDETRValidator
 
 
@@ -24,6 +24,7 @@ class RTDETR:
             raise NotImplementedError('RT-DETR only supports creating from pt file or yaml file.')
         # Load or create new YOLO model
         self.predictor = None
+        self.ckpt = None
         suffix = Path(model).suffix
         if suffix == '.yaml':
             self._new(model)
@@ -34,7 +35,7 @@ class RTDETR:
         cfg_dict = yaml_model_load(cfg)
         self.cfg = cfg
         self.task = 'detect'
-        self.model = DetectionModel(cfg_dict, verbose=verbose)  # build model
+        self.model = RTDETRDetectionModel(cfg_dict, verbose=verbose)  # build model
 
         # Below added to allow export from yamls
         self.model.args = DEFAULT_CFG_DICT  # attach args to model
@@ -42,10 +43,20 @@ class RTDETR:
 
     @smart_inference_mode()
     def _load(self, weights: str):
-        self.model, _ = attempt_load_one_weight(weights)
+        self.model, self.ckpt = attempt_load_one_weight(weights)
         self.model.args = DEFAULT_CFG_DICT  # attach args to model
         self.task = self.model.args['task']
 
+    @smart_inference_mode()
+    def load(self, weights='yolov8n.pt'):
+        """
+        Transfers parameters with matching names and shapes from 'weights' to model.
+        """
+        if isinstance(weights, (str, Path)):
+            weights, self.ckpt = attempt_load_one_weight(weights)
+        self.model.load(weights)
+        return self
+
     @smart_inference_mode()
     def predict(self, source=None, stream=False, **kwargs):
         """
@@ -74,8 +85,30 @@ class RTDETR:
         return self.predictor(source, stream=stream)
 
     def train(self, **kwargs):
-        """Function trains models but raises an error as RTDETR models do not support training."""
-        raise NotImplementedError("RTDETR models don't support training")
+        """
+        Trains the model on a given dataset.
+
+        Args:
+            **kwargs (Any): Any number of arguments representing the training configuration.
+        """
+        overrides = dict(task='detect', mode='train')
+        overrides.update(kwargs)
+        overrides['deterministic'] = False
+        if not overrides.get('data'):
+            raise AttributeError("Dataset required but missing, i.e. pass 'data=coco128.yaml'")
+        if overrides.get('resume'):
+            overrides['resume'] = self.ckpt_path
+        self.task = overrides.get('task') or self.task
+        self.trainer = RTDETRTrainer(overrides=overrides)
+        if not overrides.get('resume'):  # manually set model only if not resuming
+            self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
+            self.model = self.trainer.model
+        self.trainer.train()
+        # Update model and cfg after training
+        if RANK in (-1, 0):
+            self.model, _ = attempt_load_one_weight(str(self.trainer.best))
+            self.overrides = self.model.args
+            self.metrics = getattr(self.trainer.validator, 'metrics', None)  # TODO: no metrics returned by DDP
 
     def val(self, **kwargs):
         """Run validation given dataset."""

ultralytics/vit/rtdetr/train.py

@@ -0,0 +1,78 @@
+from copy import copy
+
+import torch
+
+from ultralytics.nn.tasks import RTDETRDetectionModel
+from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr
+from ultralytics.yolo.v8.detect import DetectionTrainer
+
+from .val import RTDETRDataset, RTDETRValidator
+
+
+class RTDETRTrainer(DetectionTrainer):
+
+    def get_model(self, cfg=None, weights=None, verbose=True):
+        """Return a YOLO detection model."""
+        model = RTDETRDetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+        return model
+
+    def build_dataset(self, img_path, mode='val', batch=None):
+        """Build RTDETR Dataset
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
+        """
+        return RTDETRDataset(
+            img_path=img_path,
+            imgsz=self.args.imgsz,
+            batch_size=batch,
+            augment=mode == 'train',  # no augmentation
+            hyp=self.args,
+            rect=False,  # no rect
+            cache=self.args.cache or None,
+            prefix=colorstr(f'{mode}: '),
+            data=self.data)
+
+    def get_validator(self):
+        """Returns a DetectionValidator for RTDETR model validation."""
+        self.loss_names = 'giou_loss', 'cls_loss', 'l1_loss'
+        return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
+
+    def preprocess_batch(self, batch):
+        """Preprocesses a batch of images by scaling and converting to float."""
+        batch = super().preprocess_batch(batch)
+        bs = len(batch['img'])
+        batch_idx = batch['batch_idx']
+        gt_bbox, gt_class = [], []
+        for i in range(bs):
+            gt_bbox.append(batch['bboxes'][batch_idx == i].to(batch_idx.device))
+            gt_class.append(batch['cls'][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
+        return batch
+
+
+def train(cfg=DEFAULT_CFG, use_python=False):
+    """Train and optimize RTDETR model given training data and device."""
+    model = 'rtdetr-l.yaml'
+    data = cfg.data or 'coco128.yaml'  # or yolo.ClassificationDataset("mnist")
+    device = cfg.device if cfg.device is not None else ''
+
+    # NOTE: F.grid_sample which is in rt-detr does not support deterministic=True
+    # NOTE: amp training causes nan outputs and end with error while doing bipartite graph matching
+    args = dict(model=model,
+                data=data,
+                device=device,
+                imgsz=640,
+                exist_ok=True,
+                batch=4,
+                deterministic=False,
+                amp=False)
+    trainer = RTDETRTrainer(overrides=args)
+    trainer.train()
+
+
+if __name__ == '__main__':
+    train()

ultralytics/vit/rtdetr/val.py

@@ -2,10 +2,12 @@
 
 from pathlib import Path
 
+import cv2
+import numpy as np
 import torch
 
 from ultralytics.yolo.data import YOLODataset
-from ultralytics.yolo.data.augment import Compose, Format, LetterBox
+from ultralytics.yolo.data.augment import Compose, Format, v8_transforms
 from ultralytics.yolo.utils import colorstr, ops
 from ultralytics.yolo.v8.detect import DetectionValidator
 
@@ -18,9 +20,41 @@ class RTDETRDataset(YOLODataset):
     def __init__(self, *args, data=None, **kwargs):
         super().__init__(*args, data=data, use_segments=False, use_keypoints=False, **kwargs)
 
+    # NOTE: add stretch version load_image for rtdetr mosaic
+    def load_image(self, i):
+        """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
+        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
+        if im is None:  # not cached in RAM
+            if fn.exists():  # load npy
+                im = np.load(fn)
+            else:  # read image
+                im = cv2.imread(f)  # BGR
+                if im is None:
+                    raise FileNotFoundError(f'Image Not Found {f}')
+            h0, w0 = im.shape[:2]  # orig hw
+            im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)
+
+            # Add to buffer if training with augmentations
+            if self.augment:
+                self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
+                self.buffer.append(i)
+                if len(self.buffer) >= self.max_buffer_length:
+                    j = self.buffer.pop(0)
+                    self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None
+
+            return im, (h0, w0), im.shape[:2]
+
+        return self.ims[i], self.im_hw0[i], self.im_hw[i]
+
     def build_transforms(self, hyp=None):
         """Temporarily, only for evaluation."""
-        transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scaleFill=True)])
+        if self.augment:
+            hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
+            hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
+            transforms = v8_transforms(self, self.imgsz, hyp, stretch=True)
+        else:
+            # transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scaleFill=True)])
+            transforms = Compose([])
         transforms.append(
             Format(bbox_format='xywh',
                    normalize=True,
@@ -65,6 +99,8 @@ class RTDETRValidator(DetectionValidator):
             # Do not need threshold for evaluation as only got 300 boxes here.
             # idx = score > self.args.conf
             pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1)  # filter
+            # sort by confidence to correctly get internal metrics.
+            pred = pred[score.argsort(descending=True)]
             outputs[i] = pred  # [idx]
 
         return outputs
@@ -100,7 +136,8 @@ class RTDETRValidator(DetectionValidator):
                 tbox[..., [0, 2]] *= shape[1]  # native-space pred
                 tbox[..., [1, 3]] *= shape[0]  # native-space pred
                 labelsn = torch.cat((cls, tbox), 1)  # native-space labels
-                correct_bboxes = self._process_batch(predn, labelsn)
+                # NOTE: To get correct metrics, the inputs of `_process_batch` should always be float32 type.
+                correct_bboxes = self._process_batch(predn.float(), labelsn)
                 # TODO: maybe remove these `self.` arguments as they already are member variable
                 if self.args.plots:
                     self.confusion_matrix.process_batch(predn, labelsn)

ultralytics/vit/sam/amg.py

@@ -256,10 +256,8 @@ def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tup
         return mask, False
     fill_labels = [0] + small_regions
     if not correct_holes:
-        fill_labels = [i for i in range(n_labels) if i not in fill_labels]
         # If every region is below threshold, keep largest
-        if not fill_labels:
-            fill_labels = [int(np.argmax(sizes)) + 1]
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels] or [int(np.argmax(sizes)) + 1]
     mask = np.isin(regions, fill_labels)
     return mask, True
 

ultralytics/vit/sam/modules/sam.py

@@ -18,14 +18,12 @@ class Sam(nn.Module):
     mask_threshold: float = 0.0
     image_format: str = 'RGB'
 
-    def __init__(
-        self,
-        image_encoder: ImageEncoderViT,
-        prompt_encoder: PromptEncoder,
-        mask_decoder: MaskDecoder,
-        pixel_mean: List[float] = [123.675, 116.28, 103.53],
-        pixel_std: List[float] = [58.395, 57.12, 57.375],
-    ) -> None:
+    def __init__(self,
+                 image_encoder: ImageEncoderViT,
+                 prompt_encoder: PromptEncoder,
+                 mask_decoder: MaskDecoder,
+                 pixel_mean: List[float] = None,
+                 pixel_std: List[float] = None) -> None:
         """
         SAM predicts object masks from an image and input prompts.
 
@@ -38,6 +36,10 @@ class Sam(nn.Module):
           pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
           pixel_std (list(float)): Std values for normalizing pixels in the input image.
         """
+        if pixel_mean is None:
+            pixel_mean = [123.675, 116.28, 103.53]
+        if pixel_std is None:
+            pixel_std = [58.395, 57.12, 57.375]
         super().__init__()
         self.image_encoder = image_encoder
         self.prompt_encoder = prompt_encoder

ultralytics/vit/utils/loss.py

@@ -0,0 +1,291 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.vit.utils.ops import HungarianMatcher
+from ultralytics.yolo.utils.loss import FocalLoss, VarifocalLoss
+from ultralytics.yolo.utils.metrics import bbox_iou
+
+
+class DETRLoss(nn.Module):
+
+    def __init__(self,
+                 nc=80,
+                 loss_gain=None,
+                 aux_loss=True,
+                 use_fl=True,
+                 use_vfl=False,
+                 use_uni_match=False,
+                 uni_match_ind=0):
+        """
+        Args:
+            nc (int): The number of classes.
+            loss_gain (dict): The coefficient of loss.
+            aux_loss (bool): If 'aux_loss = True', loss at each decoder layer are to be used.
+            use_focal_loss (bool): Use focal loss or not.
+            use_vfl (bool): Use VarifocalLoss or not.
+            use_uni_match (bool): Whether to use a fixed layer to assign labels for auxiliary branch.
+            uni_match_ind (int): The fixed indices of a layer.
+        """
+        super().__init__()
+
+        if loss_gain is None:
+            loss_gain = {'class': 1, 'bbox': 5, 'giou': 2, 'no_object': 0.1, 'mask': 1, 'dice': 1}
+        self.nc = nc
+        self.matcher = HungarianMatcher(cost_gain={'class': 2, 'bbox': 5, 'giou': 2})
+        self.loss_gain = loss_gain
+        self.aux_loss = aux_loss
+        self.fl = FocalLoss() if use_fl else None
+        self.vfl = VarifocalLoss() if use_vfl else None
+
+        self.use_uni_match = use_uni_match
+        self.uni_match_ind = uni_match_ind
+        self.device = None
+
+    def _get_loss_class(self, pred_scores, targets, gt_scores, num_gts, postfix=''):
+        # logits: [b, query, num_classes], gt_class: list[[n, 1]]
+        name_class = f'loss_class{postfix}'
+        bs, nq = pred_scores.shape[:2]
+        # one_hot = F.one_hot(targets, self.nc + 1)[..., :-1]  # (bs, num_queries, num_classes)
+        one_hot = torch.zeros((bs, nq, self.nc + 1), dtype=torch.int64, device=targets.device)
+        one_hot.scatter_(2, targets.unsqueeze(-1), 1)
+        one_hot = one_hot[..., :-1]
+        gt_scores = gt_scores.view(bs, nq, 1) * one_hot
+
+        if self.fl:
+            if num_gts and self.vfl:
+                loss_cls = self.vfl(pred_scores, gt_scores, one_hot)
+            else:
+                loss_cls = self.fl(pred_scores, one_hot.float())
+            loss_cls /= max(num_gts, 1) / nq
+        else:
+            loss_cls = nn.BCEWithLogitsLoss(reduction='none')(pred_scores, gt_scores).mean(1).sum()  # YOLO CLS loss
+
+        return {name_class: loss_cls.squeeze() * self.loss_gain['class']}
+
+    def _get_loss_bbox(self, pred_bboxes, gt_bboxes, postfix=''):
+        # boxes: [b, query, 4], gt_bbox: list[[n, 4]]
+        name_bbox = f'loss_bbox{postfix}'
+        name_giou = f'loss_giou{postfix}'
+
+        loss = {}
+        if len(gt_bboxes) == 0:
+            loss[name_bbox] = torch.tensor(0., device=self.device)
+            loss[name_giou] = torch.tensor(0., device=self.device)
+            return loss
+
+        loss[name_bbox] = self.loss_gain['bbox'] * F.l1_loss(pred_bboxes, gt_bboxes, reduction='sum') / len(gt_bboxes)
+        loss[name_giou] = 1.0 - bbox_iou(pred_bboxes, gt_bboxes, xywh=True, GIoU=True)
+        loss[name_giou] = loss[name_giou].sum() / len(gt_bboxes)
+        loss[name_giou] = self.loss_gain['giou'] * loss[name_giou]
+        loss = {k: v.squeeze() for k, v in loss.items()}
+        return loss
+
+    def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
+        # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
+        name_mask = f'loss_mask{postfix}'
+        name_dice = f'loss_dice{postfix}'
+
+        loss = {}
+        if sum(len(a) for a in gt_mask) == 0:
+            loss[name_mask] = torch.tensor(0., device=self.device)
+            loss[name_dice] = torch.tensor(0., device=self.device)
+            return loss
+
+        num_gts = len(gt_mask)
+        src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
+        src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
+        # TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
+        loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
+                                                                        torch.tensor([num_gts], dtype=torch.float32))
+        loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
+        return loss
+
+    def _dice_loss(self, inputs, targets, num_gts):
+        inputs = F.sigmoid(inputs)
+        inputs = inputs.flatten(1)
+        targets = targets.flatten(1)
+        numerator = 2 * (inputs * targets).sum(1)
+        denominator = inputs.sum(-1) + targets.sum(-1)
+        loss = 1 - (numerator + 1) / (denominator + 1)
+        return loss.sum() / num_gts
+
+    def _get_loss_aux(self,
+                      pred_bboxes,
+                      pred_scores,
+                      gt_bboxes,
+                      gt_cls,
+                      gt_groups,
+                      match_indices=None,
+                      postfix='',
+                      masks=None,
+                      gt_mask=None):
+        """Get auxiliary losses"""
+        # NOTE: loss class, bbox, giou, mask, dice
+        loss = torch.zeros(5 if masks is not None else 3, device=pred_bboxes.device)
+        if match_indices is None and self.use_uni_match:
+            match_indices = self.matcher(pred_bboxes[self.uni_match_ind],
+                                         pred_scores[self.uni_match_ind],
+                                         gt_bboxes,
+                                         gt_cls,
+                                         gt_groups,
+                                         masks=masks[self.uni_match_ind] if masks is not None else None,
+                                         gt_mask=gt_mask)
+        for i, (aux_bboxes, aux_scores) in enumerate(zip(pred_bboxes, pred_scores)):
+            aux_masks = masks[i] if masks is not None else None
+            loss_ = self._get_loss(aux_bboxes,
+                                   aux_scores,
+                                   gt_bboxes,
+                                   gt_cls,
+                                   gt_groups,
+                                   masks=aux_masks,
+                                   gt_mask=gt_mask,
+                                   postfix=postfix,
+                                   match_indices=match_indices)
+            loss[0] += loss_[f'loss_class{postfix}']
+            loss[1] += loss_[f'loss_bbox{postfix}']
+            loss[2] += loss_[f'loss_giou{postfix}']
+            # if masks is not None and gt_mask is not None:
+            #     loss_ = self._get_loss_mask(aux_masks, gt_mask, match_indices, postfix)
+            #     loss[3] += loss_[f'loss_mask{postfix}']
+            #     loss[4] += loss_[f'loss_dice{postfix}']
+
+        loss = {
+            f'loss_class_aux{postfix}': loss[0],
+            f'loss_bbox_aux{postfix}': loss[1],
+            f'loss_giou_aux{postfix}': loss[2]}
+        # if masks is not None and gt_mask is not None:
+        #     loss[f'loss_mask_aux{postfix}'] = loss[3]
+        #     loss[f'loss_dice_aux{postfix}'] = loss[4]
+        return loss
+
+    def _get_index(self, match_indices):
+        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(match_indices)])
+        src_idx = torch.cat([src for (src, _) in match_indices])
+        dst_idx = torch.cat([dst for (_, dst) in match_indices])
+        return (batch_idx, src_idx), dst_idx
+
+    def _get_assigned_bboxes(self, pred_bboxes, gt_bboxes, match_indices):
+        pred_assigned = torch.cat([
+            t[I] if len(I) > 0 else torch.zeros(0, t.shape[-1], device=self.device)
+            for t, (I, _) in zip(pred_bboxes, match_indices)])
+        gt_assigned = torch.cat([
+            t[J] if len(J) > 0 else torch.zeros(0, t.shape[-1], device=self.device)
+            for t, (_, J) in zip(gt_bboxes, match_indices)])
+        return pred_assigned, gt_assigned
+
+    def _get_loss(self,
+                  pred_bboxes,
+                  pred_scores,
+                  gt_bboxes,
+                  gt_cls,
+                  gt_groups,
+                  masks=None,
+                  gt_mask=None,
+                  postfix='',
+                  match_indices=None):
+        """Get losses"""
+        if match_indices is None:
+            match_indices = self.matcher(pred_bboxes,
+                                         pred_scores,
+                                         gt_bboxes,
+                                         gt_cls,
+                                         gt_groups,
+                                         masks=masks,
+                                         gt_mask=gt_mask)
+
+        idx, gt_idx = self._get_index(match_indices)
+        pred_bboxes, gt_bboxes = pred_bboxes[idx], gt_bboxes[gt_idx]
+
+        bs, nq = pred_scores.shape[:2]
+        targets = torch.full((bs, nq), self.nc, device=pred_scores.device, dtype=gt_cls.dtype)
+        targets[idx] = gt_cls[gt_idx]
+
+        gt_scores = torch.zeros([bs, nq], device=pred_scores.device)
+        if len(gt_bboxes):
+            gt_scores[idx] = bbox_iou(pred_bboxes.detach(), gt_bboxes, xywh=True).squeeze(-1)
+
+        loss = {}
+        loss.update(self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix))
+        loss.update(self._get_loss_bbox(pred_bboxes, gt_bboxes, postfix))
+        # if masks is not None and gt_mask is not None:
+        #     loss.update(self._get_loss_mask(masks, gt_mask, match_indices, postfix))
+        return loss
+
+    def forward(self, pred_bboxes, pred_scores, batch, postfix='', **kwargs):
+        """
+        Args:
+            pred_bboxes (torch.Tensor): [l, b, query, 4]
+            pred_scores (torch.Tensor): [l, b, query, num_classes]
+            batch (dict): A dict includes:
+                gt_cls (torch.Tensor) with shape [num_gts, ],
+                gt_bboxes (torch.Tensor): [num_gts, 4],
+                gt_groups (List(int)): a list of batch size length includes the number of gts of each image.
+            postfix (str): postfix of loss name.
+        """
+        self.device = pred_bboxes.device
+        match_indices = kwargs.get('match_indices', None)
+        gt_cls, gt_bboxes, gt_groups = batch['cls'], batch['bboxes'], batch['gt_groups']
+
+        total_loss = self._get_loss(pred_bboxes[-1],
+                                    pred_scores[-1],
+                                    gt_bboxes,
+                                    gt_cls,
+                                    gt_groups,
+                                    postfix=postfix,
+                                    match_indices=match_indices)
+
+        if self.aux_loss:
+            total_loss.update(
+                self._get_loss_aux(pred_bboxes[:-1], pred_scores[:-1], gt_bboxes, gt_cls, gt_groups, match_indices,
+                                   postfix))
+
+        return total_loss
+
+
+class RTDETRDetectionLoss(DETRLoss):
+
+    def forward(self, preds, batch, dn_bboxes=None, dn_scores=None, dn_meta=None):
+        pred_bboxes, pred_scores = preds
+        total_loss = super().forward(pred_bboxes, pred_scores, batch)
+
+        if dn_meta is not None:
+            dn_pos_idx, dn_num_group = dn_meta['dn_pos_idx'], dn_meta['dn_num_group']
+            assert len(batch['gt_groups']) == len(dn_pos_idx)
+
+            # denoising match indices
+            match_indices = self.get_dn_match_indices(dn_pos_idx, dn_num_group, batch['gt_groups'])
+
+            # compute denoising training loss
+            dn_loss = super().forward(dn_bboxes, dn_scores, batch, postfix='_dn', match_indices=match_indices)
+            total_loss.update(dn_loss)
+        else:
+            total_loss.update({f'{k}_dn': torch.tensor(0., device=self.device) for k in total_loss.keys()})
+
+        return total_loss
+
+    @staticmethod
+    def get_dn_match_indices(dn_pos_idx, dn_num_group, gt_groups):
+        """Get the match indices for denoising.
+
+        Args:
+            dn_pos_idx (List[torch.Tensor]): A list includes positive indices of denoising.
+            dn_num_group (int): The number of groups of denoising.
+            gt_groups (List(int)): a list of batch size length includes the number of gts of each image.
+
+        Returns:
+            dn_match_indices (List(tuple)): Matched indices.
+
+        """
+        dn_match_indices = []
+        idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
+        for i, num_gt in enumerate(gt_groups):
+            if num_gt > 0:
+                gt_idx = torch.arange(end=num_gt, dtype=torch.int32) + idx_groups[i]
+                gt_idx = gt_idx.repeat(dn_num_group)
+                assert len(dn_pos_idx[i]) == len(gt_idx), 'Expected the same length, '
+                f'but got {len(dn_pos_idx[i])} and {len(gt_idx)} respectively.'
+                dn_match_indices.append((dn_pos_idx[i], gt_idx))
+            else:
+                dn_match_indices.append((torch.zeros([0], dtype=torch.int32), torch.zeros([0], dtype=torch.int32)))
+        return dn_match_indices

ultralytics/vit/utils/ops.py

@@ -0,0 +1,230 @@
+# TODO: license
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.optimize import linear_sum_assignment
+
+from ultralytics.yolo.utils.metrics import bbox_iou
+from ultralytics.yolo.utils.ops import xywh2xyxy, xyxy2xywh
+
+
+class HungarianMatcher(nn.Module):
+
+    def __init__(self, cost_gain=None, use_fl=True, with_mask=False, num_sample_points=12544, alpha=0.25, gamma=2.0):
+        """
+        Args:
+            matcher_coeff (dict): The coefficient of hungarian matcher cost.
+        """
+        super().__init__()
+        if cost_gain is None:
+            cost_gain = {'class': 1, 'bbox': 5, 'giou': 2, 'mask': 1, 'dice': 1}
+        self.cost_gain = cost_gain
+        self.use_fl = use_fl
+        self.with_mask = with_mask
+        self.num_sample_points = num_sample_points
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None):
+        """
+        Args:
+            pred_bboxes (Tensor): [b, query, 4]
+            pred_scores (Tensor): [b, query, num_classes]
+            gt_cls (torch.Tensor) with shape [num_gts, ]
+            gt_bboxes (torch.Tensor): [num_gts, 4]
+            gt_groups (List(int)): a list of batch size length includes the number of gts of each image.
+            masks (Tensor|None): [b, query, h, w]
+            gt_mask (List(Tensor)): list[[n, H, W]]
+
+        Returns:
+            A list of size batch_size, containing tuples of (index_i, index_j) where:
+                - index_i is the indices of the selected predictions (in order)
+                - index_j is the indices of the corresponding selected targets (in order)
+            For each batch element, it holds:
+                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
+        """
+        bs, nq, nc = pred_scores.shape
+
+        if sum(gt_groups) == 0:
+            return [(torch.tensor([], dtype=torch.int32), torch.tensor([], dtype=torch.int32)) for _ in range(bs)]
+
+        # We flatten to compute the cost matrices in a batch
+        # [batch_size * num_queries, num_classes]
+        pred_scores = pred_scores.detach().view(-1, nc)
+        pred_scores = F.sigmoid(pred_scores) if self.use_fl else F.softmax(pred_scores, dim=-1)
+        # [batch_size * num_queries, 4]
+        pred_bboxes = pred_bboxes.detach().view(-1, 4)
+
+        # Compute the classification cost
+        pred_scores = pred_scores[:, gt_cls]
+        if self.use_fl:
+            neg_cost_class = (1 - self.alpha) * (pred_scores ** self.gamma) * (-(1 - pred_scores + 1e-8).log())
+            pos_cost_class = self.alpha * ((1 - pred_scores) ** self.gamma) * (-(pred_scores + 1e-8).log())
+            cost_class = pos_cost_class - neg_cost_class
+        else:
+            cost_class = -pred_scores
+
+        # Compute the L1 cost between boxes
+        cost_bbox = (pred_bboxes.unsqueeze(1) - gt_bboxes.unsqueeze(0)).abs().sum(-1)  # (bs*num_queries, num_gt)
+
+        # Compute the GIoU cost between boxes, (bs*num_queries, num_gt)
+        cost_giou = 1.0 - bbox_iou(pred_bboxes.unsqueeze(1), gt_bboxes.unsqueeze(0), xywh=True, GIoU=True).squeeze(-1)
+
+        # Final cost matrix
+        C = self.cost_gain['class'] * cost_class + \
+            self.cost_gain['bbox'] * cost_bbox + \
+            self.cost_gain['giou'] * cost_giou
+        # Compute the mask cost and dice cost
+        if self.with_mask:
+            C += self._cost_mask(bs, gt_groups, masks, gt_mask)
+
+        C = C.view(bs, nq, -1).cpu()
+        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))]
+        gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
+        # (idx for queries, idx for gt)
+        return [(torch.tensor(i, dtype=torch.int32), torch.tensor(j, dtype=torch.int32) + gt_groups[k])
+                for k, (i, j) in enumerate(indices)]
+
+    def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
+        assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
+        # all masks share the same set of points for efficient matching
+        sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
+        sample_points = 2.0 * sample_points - 1.0
+
+        out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
+        out_mask = out_mask.flatten(0, 1)
+
+        tgt_mask = torch.cat(gt_mask).unsqueeze(1)
+        sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
+        tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
+
+        with torch.cuda.amp.autocast(False):
+            # binary cross entropy cost
+            pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
+            neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
+            cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
+            cost_mask /= self.num_sample_points
+
+            # dice cost
+            out_mask = F.sigmoid(out_mask)
+            numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
+            denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
+            cost_dice = 1 - (numerator + 1) / (denominator + 1)
+
+            C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
+        return C
+
+
+def get_cdn_group(batch,
+                  num_classes,
+                  num_queries,
+                  class_embed,
+                  num_dn=100,
+                  cls_noise_ratio=0.5,
+                  box_noise_scale=1.0,
+                  training=False):
+    """Get contrastive denoising training group
+
+    Args:
+        batch (dict): A dict includes:
+            gt_cls (torch.Tensor) with shape [num_gts, ],
+            gt_bboxes (torch.Tensor): [num_gts, 4],
+            gt_groups (List(int)): a list of batch size length includes the number of gts of each image.
+        num_classes (int): Number of classes.
+        num_queries (int): Number of queries.
+        class_embed (torch.Tensor): Embedding weights to map cls to embedding space.
+        num_dn (int): Number of denoising.
+        cls_noise_ratio (float): Noise ratio for class.
+        box_noise_scale (float): Noise scale for bbox.
+        training (bool): If it's training or not.
+
+    Returns:
+
+    """
+    if (not training) or num_dn <= 0:
+        return None, None, None, None
+    gt_groups = batch['gt_groups']
+    total_num = sum(gt_groups)
+    max_nums = max(gt_groups)
+    if max_nums == 0:
+        return None, None, None, None
+
+    num_group = num_dn // max_nums
+    num_group = 1 if num_group == 0 else num_group
+    # pad gt to max_num of a batch
+    bs = len(gt_groups)
+    gt_cls = batch['cls']  # (bs*num, )
+    gt_bbox = batch['bboxes']  # bs*num, 4
+    b_idx = batch['batch_idx']
+
+    # each group has positive and negative queries.
+    dn_cls = gt_cls.repeat(2 * num_group)  # (2*num_group*bs*num, )
+    dn_bbox = gt_bbox.repeat(2 * num_group, 1)  # 2*num_group*bs*num, 4
+    dn_b_idx = b_idx.repeat(2 * num_group).view(-1)  # (2*num_group*bs*num, )
+
+    # positive and negative mask
+    # (bs*num*num_group, ), the second total_num*num_group part as negative samples
+    neg_idx = torch.arange(total_num * num_group, dtype=torch.long, device=gt_bbox.device) + num_group * total_num
+
+    if cls_noise_ratio > 0:
+        # half of bbox prob
+        mask = torch.rand(dn_cls.shape) < (cls_noise_ratio * 0.5)
+        idx = torch.nonzero(mask).squeeze(-1)
+        # randomly put a new one here
+        new_label = torch.randint_like(idx, 0, num_classes, dtype=dn_cls.dtype, device=dn_cls.device)
+        dn_cls[idx] = new_label
+
+    if box_noise_scale > 0:
+        known_bbox = xywh2xyxy(dn_bbox)
+
+        diff = (dn_bbox[..., 2:] * 0.5).repeat(1, 2) * box_noise_scale  # 2*num_group*bs*num, 4
+
+        rand_sign = torch.randint_like(dn_bbox, 0, 2) * 2.0 - 1.0
+        rand_part = torch.rand_like(dn_bbox)
+        rand_part[neg_idx] += 1.0
+        rand_part *= rand_sign
+        known_bbox += rand_part * diff
+        known_bbox.clip_(min=0.0, max=1.0)
+        dn_bbox = xyxy2xywh(known_bbox)
+        dn_bbox = inverse_sigmoid(dn_bbox)
+
+    # total denoising queries
+    num_dn = int(max_nums * 2 * num_group)
+    # class_embed = torch.cat([class_embed, torch.zeros([1, class_embed.shape[-1]], device=class_embed.device)])
+    dn_cls_embed = class_embed[dn_cls]  # bs*num * 2 * num_group, 256
+    padding_cls = torch.zeros(bs, num_dn, dn_cls_embed.shape[-1], device=gt_cls.device)
+    padding_bbox = torch.zeros(bs, num_dn, 4, device=gt_bbox.device)
+
+    map_indices = torch.cat([torch.tensor(range(num), dtype=torch.long) for num in gt_groups])
+    pos_idx = torch.stack([map_indices + max_nums * i for i in range(num_group)], dim=0)
+
+    map_indices = torch.cat([map_indices + max_nums * i for i in range(2 * num_group)])
+    padding_cls[(dn_b_idx, map_indices)] = dn_cls_embed
+    padding_bbox[(dn_b_idx, map_indices)] = dn_bbox
+
+    tgt_size = num_dn + num_queries
+    attn_mask = torch.zeros([tgt_size, tgt_size], dtype=torch.bool)
+    # match query cannot see the reconstruct
+    attn_mask[num_dn:, :num_dn] = True
+    # reconstruct cannot see each other
+    for i in range(num_group):
+        if i == 0:
+            attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True
+        if i == num_group - 1:
+            attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), :max_nums * i * 2] = True
+        else:
+            attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True
+            attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), :max_nums * 2 * i] = True
+    dn_meta = {
+        'dn_pos_idx': [p.reshape(-1) for p in pos_idx.cpu().split([n for n in gt_groups], dim=1)],
+        'dn_num_group': num_group,
+        'dn_num_split': [num_dn, num_queries]}
+
+    return padding_cls.to(class_embed.device), padding_bbox.to(class_embed.device), attn_mask.to(
+        class_embed.device), dn_meta
+
+
+def inverse_sigmoid(x, eps=1e-6):
+    x = x.clip(min=0., max=1.)
+    return torch.log(x / (1 - x + eps) + eps)

ultralytics/yolo/data/augment.py

@@ -759,7 +759,7 @@ class Format:
         return masks, instances, cls
 
 
-def v8_transforms(dataset, imgsz, hyp):
+def v8_transforms(dataset, imgsz, hyp, stretch=False):
     """Convert images to a size suitable for YOLOv8 training."""
     pre_transform = Compose([
         Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic),
@@ -770,7 +770,7 @@ def v8_transforms(dataset, imgsz, hyp):
             scale=hyp.scale,
             shear=hyp.shear,
             perspective=hyp.perspective,
-            pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
+            pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
         )])
     flip_idx = dataset.data.get('flip_idx', None)  # for keypoints augmentation
     if dataset.use_keypoints:

ultralytics/yolo/engine/trainer.py

@@ -278,7 +278,8 @@ class BaseTrainer:
         self.epoch_time_start = time.time()
         self.train_time_start = time.time()
         nb = len(self.train_loader)  # number of batches
-        nw = max(round(self.args.warmup_epochs * nb), 100)  # number of warmup iterations
+        nw = max(round(self.args.warmup_epochs *
+                       nb), 100) if self.args.warmup_epochs > 0 else -1  # number of warmup iterations
         last_opt_step = -1
         self.run_callbacks('on_train_start')
         LOGGER.info(f'Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n'

ultralytics/yolo/utils/loss.py

@@ -24,10 +24,34 @@ class VarifocalLoss(nn.Module):
         weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
         with torch.cuda.amp.autocast(enabled=False):
             loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction='none') *
-                    weight).sum()
+                    weight).mean(1).sum()
         return loss
 
 
+# Losses
+class FocalLoss(nn.Module):
+    """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
+
+    def __init__(self, ):
+        super().__init__()
+
+    def forward(self, pred, label, gamma=1.5, alpha=0.25):
+        """Calculates and updates confusion matrix for object detection/classification tasks."""
+        loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
+        # p_t = torch.exp(-loss)
+        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
+
+        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
+        pred_prob = pred.sigmoid()  # prob from logits
+        p_t = label * pred_prob + (1 - label) * (1 - pred_prob)
+        modulating_factor = (1.0 - p_t) ** gamma
+        loss *= modulating_factor
+        if alpha > 0:
+            alpha_factor = label * alpha + (1 - label) * (1 - alpha)
+            loss *= alpha_factor
+        return loss.mean(1).sum()
+
+
 class BboxLoss(nn.Module):
 
     def __init__(self, reg_max, use_dfl=False):

ultralytics/yolo/utils/metrics.py

@@ -9,7 +9,6 @@ from pathlib import Path
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import torch.nn as nn
 
 from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept, plt_settings
 
@@ -175,40 +174,6 @@ def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#iss
     return 1.0 - 0.5 * eps, 0.5 * eps
 
 
-# Losses
-class FocalLoss(nn.Module):
-    """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
-
-    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        """Initialize FocalLoss object with given loss function and hyperparameters."""
-        super().__init__()
-        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
-
-    def forward(self, pred, true):
-        """Calculates and updates confusion matrix for object detection/classification tasks."""
-        loss = self.loss_fcn(pred, true)
-        # p_t = torch.exp(-loss)
-        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
-
-        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = (1.0 - p_t) ** self.gamma
-        loss *= alpha_factor * modulating_factor
-
-        if self.reduction == 'mean':
-            return loss.mean()
-        elif self.reduction == 'sum':
-            return loss.sum()
-        else:  # 'None'
-            return loss
-
-
 class ConfusionMatrix:
     """
     A class for calculating and updating a confusion matrix for object detection and classification tasks.

ultralytics/yolo/utils/torch_utils.py

@@ -327,6 +327,9 @@ def init_seeds(seed=0, deterministic=False):
             os.environ['PYTHONHASHSEED'] = str(seed)
         else:
             LOGGER.warning('WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.')
+    else:
+        torch.use_deterministic_algorithms(False)
+        torch.backends.cudnn.deterministic = False
 
 
 class ModelEMA: