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https://github.com/ultralytics/ultralytics
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Clone reference repos (EUPE, RADIO, UNIC, DUNE), install pinned deps (transformers==5.5.0, webdataset==0.2.111), download teacher weights, and verify all 7 teacher variants load correctly. Also ignore .codex/ and .agents/.
73 lines
2.6 KiB
Bash
Executable file
73 lines
2.6 KiB
Bash
Executable file
#!/bin/bash
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# Setup for YOLO universal encoder distillation (repos + deps + teacher weights)
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# Run this first. Dataset download is separate (get_datacomp12m.sh).
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#
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# Teachers load via native Python (not TorchScript .ts) because EUPE/DINOv3 RoPE
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# position encoding hardcodes device in torch.arange, making traced graphs non-portable.
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set -e
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cd /home/fatih/dev/ultralytics
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echo "=== Step 1: Clone reference repos ==="
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[ -d /home/fatih/dev/eupe ] || git clone https://github.com/facebookresearch/eupe /home/fatih/dev/eupe
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[ -d /home/fatih/dev/RADIO ] || git clone https://github.com/NVlabs/RADIO /home/fatih/dev/RADIO
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[ -d /home/fatih/dev/unic ] || git clone https://github.com/naver/unic /home/fatih/dev/unic
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[ -d /home/fatih/dev/dune ] || git clone https://github.com/naver/dune /home/fatih/dev/dune
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echo "=== Step 2: Install dependencies ==="
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source .venv/bin/activate
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uv pip install \
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"transformers==5.5.0" \
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"webdataset==0.2.111" \
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"img2dataset" \
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"huggingface_hub>=1.8.0"
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echo "=== Step 3: Download EUPE weights (3 variants) ==="
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python -c "
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from huggingface_hub import hf_hub_download
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for repo, fname in [
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('facebook/EUPE-ViT-B', 'EUPE-ViT-B.pt'),
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('facebook/EUPE-ViT-S', 'EUPE-ViT-S.pt'),
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('facebook/EUPE-ConvNeXt-B', 'EUPE-ConvNeXt-B.pt'),
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]:
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path = hf_hub_download(repo, fname)
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print(f' {repo} -> {path}')
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"
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echo "=== Step 4: Pre-download DINOv3 + SigLIP2 weights ==="
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python -c "
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from transformers import AutoModel, SiglipVisionModel
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for name in [
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'facebook/dinov3-vitb16-pretrain-lvd1689m',
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'facebook/dinov3-vitl16-pretrain-lvd1689m',
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'facebook/dinov3-convnext-base-pretrain-lvd1689m',
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]:
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AutoModel.from_pretrained(name)
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print(f' {name} OK')
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SiglipVisionModel.from_pretrained('google/siglip2-giant-opt-patch16-384')
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print(' google/siglip2-giant-opt-patch16-384 OK')
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"
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echo "=== Step 5: Verify all teachers ==="
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python -c "
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import torch
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from ultralytics.nn.teacher_model import build_teacher_model, TEACHER_REGISTRY
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# Skip vit7b (26GB, slow to load) and sam3:l (1024px, needs lots of RAM)
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for variant in ['eupe:vitb16', 'eupe:vits16', 'eupe:convnextb',
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'dinov3:vitb16', 'dinov3:vitl16', 'dinov3:convnextb',
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'siglip2:g']:
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reg = TEACHER_REGISTRY[variant]
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teacher = build_teacher_model(variant, torch.device('cpu'))
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dummy = torch.randn(1, 3, reg['imgsz'], reg['imgsz'])
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with torch.no_grad():
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out = teacher.encode(dummy)
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cls_shape = out.cls.shape if out.cls is not None else None
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print(f' {variant}: cls={cls_shape}, patches={out.patches.shape}')
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del teacher
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print('All teachers verified OK')
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"
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echo "Setup complete! Run get_datacomp12m.sh next for dataset."
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