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README.md

@@ -123,6 +123,20 @@ For an initial test on a small dataset, use fewer epochs such as 2–5. Once eve
 
 A GPU Space is strongly recommended.
 
+
+## Training fix
+
+This release includes a CUDA-device fix for RT-DETR's contrastive-denoising training path. On some
+Transformers/PyTorch combinations, the denoising class-index tensor can remain on CPU while the
+RT-DETR class embedding is on CUDA, producing:
+
+`RuntimeError: Expected all tensors to be on the same device ... cpu ... cuda:0`
+
+The training script now moves nested target tensors explicitly and patches the RT-DETR denoising
+helper so its target tensors follow the class-embedding device. The `num_labels=10` vs. checkpoint
+`80` message is expected when fine-tuning the COCO-pretrained checkpoint for 10 custom classes;
+`ignore_mismatched_sizes=True` intentionally reinitializes the classification heads.
+
 ## Counting
 
 After training, open the **Count** tab and upload one image.

app.py

@@ -26,7 +26,7 @@ else:
 ROOT = Path(__file__).resolve().parent
 
 # Self-heal the training script if a deployment omitted the training/ directory.
-_TRAINING_SCRIPT_B64 = "import argparse
import json
from pathlib import Path

import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from transformers import RTDetrImageProcessor, RTDetrForObjectDetection

BASE_MODEL = "PekingU/rtdetr_r50vd"

def load_classes(path):
    return [x.strip() for x in Path(path).read_text().splitlines() if x.strip()]

class COCODetectionDataset(Dataset):
    def __init__(self, image_dir, annotation_file, processor):
        self.image_dir = Path(image_dir)
        self.processor = processor
        coco = json.loads(Path(annotation_file).read_text())
        self.images = {x["id"]: x for x in coco["images"]}
        cats = sorted(coco["categories"], key=lambda x: x["id"])
        self.category_id_to_label = {c["id"]: i for i,c in enumerate(cats)}
        anns = {}
        for a in coco["annotations"]:
            if not a.get("iscrowd", 0):
                anns.setdefault(a["image_id"], []).append(a)
        self.records = []
        for image_id, info in self.images.items():
            self.records.append({
                "image_id": image_id, "file_name": info["file_name"],
                "width": info["width"], "height": info["height"],
                "annotations": anns.get(image_id, [])
            })

    def __len__(self): return len(self.records)

    def __getitem__(self, idx):
        r = self.records[idx]
        image = Image.open(self.image_dir / r["file_name"]).convert("RGB")
        anns = []
        for a in r["annotations"]:
            x,y,w,h = a["bbox"]
            if w <= 0 or h <= 0: continue
            anns.append({
                "id": a["id"], "image_id": int(idx),
                "category_id": self.category_id_to_label[a["category_id"]],
                "bbox": [x,y,w,h], "area": float(a.get("area",w*h)),
                "iscrowd": 0
            })
        encoded = self.processor(
            images=image,
            annotations={"image_id": int(idx), "annotations": anns},
            return_tensors="pt"
        )
        encoded["pixel_values"] = encoded["pixel_values"].squeeze(0)
        if "pixel_mask" in encoded:
            encoded["pixel_mask"] = encoded["pixel_mask"].squeeze(0)
        encoded["labels"] = encoded["labels"][0]
        return encoded

def collate_fn(batch):
    out = {"pixel_values": torch.stack([x["pixel_values"] for x in batch]),
           "labels": [x["labels"] for x in batch]}
    if "pixel_mask" in batch[0]:
        out["pixel_mask"] = torch.stack([x["pixel_mask"] for x in batch])
    return out

def move_to_device(obj, device):
    if torch.is_tensor(obj):
        return obj.to(device)
    if isinstance(obj, dict):
        return {k: move_to_device(v, device) for k, v in obj.items()}
    if isinstance(obj, list):
        return [move_to_device(v, device) for v in obj]
    if isinstance(obj, tuple):
        return tuple(move_to_device(v, device) for v in obj)
    return obj

def evaluate(model, loader, device):
    model.eval(); total=0; n=0
    with torch.no_grad():
        for batch in loader:
            batch=move_to_device(batch, device)
            total += float(model(**batch).loss.item()); n += 1
    model.train()
    return total/max(n,1)

def main():
    p=argparse.ArgumentParser()
    p.add_argument("--train-dir",required=True); p.add_argument("--val-dir",required=True)
    p.add_argument("--classes",required=True); p.add_argument("--output-dir",default="model")
    p.add_argument("--epochs",type=int,default=30); p.add_argument("--batch-size",type=int,default=2)
    p.add_argument("--learning-rate",type=float,default=1e-5); p.add_argument("--weight-decay",type=float,default=1e-4)
    p.add_argument("--num-workers",type=int,default=2)
    a=p.parse_args()

    classes=load_classes(a.classes)
    id2label={i:n for i,n in enumerate(classes)}
    label2id={n:i for i,n in enumerate(classes)}

    proc=RTDetrImageProcessor.from_pretrained(BASE_MODEL)
    train=COCODetectionDataset(Path(a.train_dir)/"images",Path(a.train_dir)/"annotations.json",proc)
    val=COCODetectionDataset(Path(a.val_dir)/"images",Path(a.val_dir)/"annotations.json",proc)

    if len(train)==0 or len(val)==0:
        raise ValueError("Training and validation datasets must contain at least one image.")
    if len(train.category_id_to_label)!=len(classes) or len(val.category_id_to_label)!=len(classes):
        raise ValueError("COCO categories do not match classes.txt. Rebuild the dataset after saving the classes.")

    model=RTDetrForObjectDetection.from_pretrained(
        BASE_MODEL,num_labels=len(classes),id2label=id2label,label2id=label2id,
        ignore_mismatched_sizes=True
    )
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    tr=DataLoader(train,batch_size=a.batch_size,shuffle=True,num_workers=0,collate_fn=collate_fn)
    va=DataLoader(val,batch_size=a.batch_size,shuffle=False,num_workers=0,collate_fn=collate_fn)
    opt=torch.optim.AdamW(model.parameters(),lr=a.learning_rate,weight_decay=a.weight_decay)

    outdir=Path(a.output_dir); outdir.mkdir(parents=True,exist_ok=True)
    best=float("inf")

    for epoch in range(a.epochs):
        model.train(); running=0
        bar=tqdm(tr,desc=f"epoch {epoch+1}/{a.epochs}")
        for step,batch in enumerate(bar):
            batch=move_to_device(batch, device)
            # RT-DETR's loss matcher uses nested target tensors (boxes/classes).
            # Move every tensor in labels to the same device as the model.
            if "labels" in batch:
                batch["labels"] = move_to_device(batch["labels"], device)
            loss=model(**batch).loss
            loss.backward(); opt.step(); opt.zero_grad(set_to_none=True)
            running += float(loss.item())
            bar.set_postfix(loss=f"{running/(step+1):.4f}")
        vl=evaluate(model,va,device)
        print(f"validation_loss={vl:.4f}")
        if vl<best:
            best=vl
            model.save_pretrained(outdir)
            proc.save_pretrained(outdir)
            (outdir/"classes.json").write_text(json.dumps({"id2label":id2label,"label2id":label2id},indent=2))
    model.save_pretrained(outdir); proc.save_pretrained(outdir)

if __name__=="__main__": main()
"
+_TRAINING_SCRIPT_B64 = "import argparse
import json
from pathlib import Path

import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from transformers import RTDetrImageProcessor, RTDetrForObjectDetection

BASE_MODEL = "PekingU/rtdetr_r50vd"

def load_classes(path):
    return [x.strip() for x in Path(path).read_text().splitlines() if x.strip()]

class COCODetectionDataset(Dataset):
    def __init__(self, image_dir, annotation_file, processor):
        self.image_dir = Path(image_dir)
        self.processor = processor
        coco = json.loads(Path(annotation_file).read_text())
        self.images = {x["id"]: x for x in coco["images"]}
        cats = sorted(coco["categories"], key=lambda x: x["id"])
        self.category_id_to_label = {c["id"]: i for i,c in enumerate(cats)}
        anns = {}
        for a in coco["annotations"]:
            if not a.get("iscrowd", 0):
                anns.setdefault(a["image_id"], []).append(a)
        self.records = []
        for image_id, info in self.images.items():
            self.records.append({
                "image_id": image_id, "file_name": info["file_name"],
                "width": info["width"], "height": info["height"],
                "annotations": anns.get(image_id, [])
            })

    def __len__(self): return len(self.records)

    def __getitem__(self, idx):
        r = self.records[idx]
        image = Image.open(self.image_dir / r["file_name"]).convert("RGB")
        anns = []
        for a in r["annotations"]:
            x,y,w,h = a["bbox"]
            if w <= 0 or h <= 0: continue
            anns.append({
                "id": a["id"], "image_id": int(idx),
                "category_id": self.category_id_to_label[a["category_id"]],
                "bbox": [x,y,w,h], "area": float(a.get("area",w*h)),
                "iscrowd": 0
            })
        encoded = self.processor(
            images=image,
            annotations={"image_id": int(idx), "annotations": anns},
            return_tensors="pt"
        )
        encoded["pixel_values"] = encoded["pixel_values"].squeeze(0)
        if "pixel_mask" in encoded:
            encoded["pixel_mask"] = encoded["pixel_mask"].squeeze(0)
        encoded["labels"] = encoded["labels"][0]
        return encoded

def collate_fn(batch):
    out = {"pixel_values": torch.stack([x["pixel_values"] for x in batch]),
           "labels": [x["labels"] for x in batch]}
    if "pixel_mask" in batch[0]:
        out["pixel_mask"] = torch.stack([x["pixel_mask"] for x in batch])
    return out

def move_to_device(obj, device):
    if torch.is_tensor(obj):
        return obj.to(device)
    if isinstance(obj, dict):
        return {k: move_to_device(v, device) for k, v in obj.items()}
    if isinstance(obj, list):
        return [move_to_device(v, device) for v in obj]
    if isinstance(obj, tuple):
        return tuple(move_to_device(v, device) for v in obj)
    return obj

def evaluate(model, loader, device):
    model.eval(); total=0; n=0
    with torch.no_grad():
        for batch in loader:
            batch=move_to_device(batch, device)
            total += float(model(**batch).loss.item()); n += 1
    model.train()
    return total/max(n,1)


def patch_rtdetr_denoising_device():
    """Work around RT-DETR denoising code creating CPU index tensors on some Transformers releases."""
    try:
        import transformers.models.rt_detr.modeling_rt_detr as rtdetr_mod
        original = rtdetr_mod.get_contrastive_denoising_training_group
        if getattr(original, "_icecream_device_patch", False):
            return

        def wrapped(targets, *args, **kwargs):
            # class_embed is the 4th positional argument in the supported RT-DETR versions.
            class_embed = args[2] if len(args) >= 3 else kwargs.get("class_embed")
            try:
                device = next(class_embed.parameters()).device
            except Exception:
                device = None
            if device is not None:
                for target in targets:
                    if isinstance(target, dict):
                        for key in ("class_labels", "boxes"):
                            value = target.get(key)
                            if torch.is_tensor(value) and value.device != device:
                                target[key] = value.to(device)
            return original(targets, *args, **kwargs)

        wrapped._icecream_device_patch = True
        rtdetr_mod.get_contrastive_denoising_training_group = wrapped
    except Exception as exc:
        print(f"Warning: RT-DETR denoising device patch was not installed: {exc}")

def main():
    p=argparse.ArgumentParser()
    p.add_argument("--train-dir",required=True); p.add_argument("--val-dir",required=True)
    p.add_argument("--classes",required=True); p.add_argument("--output-dir",default="model")
    p.add_argument("--epochs",type=int,default=30); p.add_argument("--batch-size",type=int,default=2)
    p.add_argument("--learning-rate",type=float,default=1e-5); p.add_argument("--weight-decay",type=float,default=1e-4)
    p.add_argument("--num-workers",type=int,default=2)
    a=p.parse_args()

    classes=load_classes(a.classes)
    id2label={i:n for i,n in enumerate(classes)}
    label2id={n:i for i,n in enumerate(classes)}

    proc=RTDetrImageProcessor.from_pretrained(BASE_MODEL)
    train=COCODetectionDataset(Path(a.train_dir)/"images",Path(a.train_dir)/"annotations.json",proc)
    val=COCODetectionDataset(Path(a.val_dir)/"images",Path(a.val_dir)/"annotations.json",proc)

    if len(train)==0 or len(val)==0:
        raise ValueError("Training and validation datasets must contain at least one image.")
    if len(train.category_id_to_label)!=len(classes) or len(val.category_id_to_label)!=len(classes):
        raise ValueError("COCO categories do not match classes.txt. Rebuild the dataset after saving the classes.")

    model=RTDetrForObjectDetection.from_pretrained(
        BASE_MODEL,num_labels=len(classes),id2label=id2label,label2id=label2id,
        ignore_mismatched_sizes=True
    )
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    patch_rtdetr_denoising_device()

    tr=DataLoader(train,batch_size=a.batch_size,shuffle=True,num_workers=0,collate_fn=collate_fn)
    va=DataLoader(val,batch_size=a.batch_size,shuffle=False,num_workers=0,collate_fn=collate_fn)
    opt=torch.optim.AdamW(model.parameters(),lr=a.learning_rate,weight_decay=a.weight_decay)

    outdir=Path(a.output_dir); outdir.mkdir(parents=True,exist_ok=True)
    best=float("inf")

    for epoch in range(a.epochs):
        model.train(); running=0
        bar=tqdm(tr,desc=f"epoch {epoch+1}/{a.epochs}")
        for step,batch in enumerate(bar):
            batch=move_to_device(batch, device)
            # RT-DETR's loss matcher uses nested target tensors (boxes/classes).
            # Move every tensor in labels to the same device as the model.
            if "labels" in batch:
                # RT-DETR expects every nested target tensor on the same device as the model.
                for target in batch["labels"]:
                    if isinstance(target, dict):
                        for key, value in list(target.items()):
                            if torch.is_tensor(value):
                                target[key] = value.to(device)
            loss=model(**batch).loss
            loss.backward(); opt.step(); opt.zero_grad(set_to_none=True)
            running += float(loss.item())
            bar.set_postfix(loss=f"{running/(step+1):.4f}")
        vl=evaluate(model,va,device)
        print(f"validation_loss={vl:.4f}")
        if vl<best:
            best=vl
            model.save_pretrained(outdir)
            proc.save_pretrained(outdir)
            (outdir/"classes.json").write_text(json.dumps({"id2label":id2label,"label2id":label2id},indent=2))
    model.save_pretrained(outdir); proc.save_pretrained(outdir)

if __name__=="__main__": main()
"
 TRAINING_DIR = ROOT / "training"
 TRAIN_SCRIPT = TRAINING_DIR / "train.py"
 if not TRAIN_SCRIPT.exists():

requirements.txt

@@ -1,7 +1,7 @@
 gradio>=6.5,<7
 torch>=2.3
 torchvision>=0.18
-transformers>=4.50
+transformers>=4.50,<5
 huggingface_hub>=0.25
 Pillow>=10.0
 numpy>=1.26

training/train.py

@@ -86,6 +86,36 @@ def evaluate(model, loader, device):
     model.train()
     return total/max(n,1)
 
+
+def patch_rtdetr_denoising_device():
+    """Work around RT-DETR denoising code creating CPU index tensors on some Transformers releases."""
+    try:
+        import transformers.models.rt_detr.modeling_rt_detr as rtdetr_mod
+        original = rtdetr_mod.get_contrastive_denoising_training_group
+        if getattr(original, "_icecream_device_patch", False):
+            return
+
+        def wrapped(targets, *args, **kwargs):
+            # class_embed is the 4th positional argument in the supported RT-DETR versions.
+            class_embed = args[2] if len(args) >= 3 else kwargs.get("class_embed")
+            try:
+                device = next(class_embed.parameters()).device
+            except Exception:
+                device = None
+            if device is not None:
+                for target in targets:
+                    if isinstance(target, dict):
+                        for key in ("class_labels", "boxes"):
+                            value = target.get(key)
+                            if torch.is_tensor(value) and value.device != device:
+                                target[key] = value.to(device)
+            return original(targets, *args, **kwargs)
+
+        wrapped._icecream_device_patch = True
+        rtdetr_mod.get_contrastive_denoising_training_group = wrapped
+    except Exception as exc:
+        print(f"Warning: RT-DETR denoising device patch was not installed: {exc}")
+
 def main():
     p=argparse.ArgumentParser()
     p.add_argument("--train-dir",required=True); p.add_argument("--val-dir",required=True)
@@ -114,6 +144,7 @@ def main():
     )
     device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
+    patch_rtdetr_denoising_device()
 
     tr=DataLoader(train,batch_size=a.batch_size,shuffle=True,num_workers=0,collate_fn=collate_fn)
     va=DataLoader(val,batch_size=a.batch_size,shuffle=False,num_workers=0,collate_fn=collate_fn)
@@ -130,7 +161,12 @@ def main():
             # RT-DETR's loss matcher uses nested target tensors (boxes/classes).
             # Move every tensor in labels to the same device as the model.
             if "labels" in batch:
-                batch["labels"] = move_to_device(batch["labels"], device)
+                # RT-DETR expects every nested target tensor on the same device as the model.
+                for target in batch["labels"]:
+                    if isinstance(target, dict):
+                        for key, value in list(target.items()):
+                            if torch.is_tensor(value):
+                                target[key] = value.to(device)
             loss=model(**batch).loss
             loss.backward(); opt.step(); opt.zero_grad(set_to_none=True)
             running += float(loss.item())