Package Compatibility

[ChloeCarbonniere]

Hello,

I would like to work on H-DETR, but I am struggling to find compatible versions of the required packages.

Currently, my machine runs CUDA 12.0, so I tried upgrading the requirements accordingly, but without success.

As a second option, I attempted to set up a Docker environment with Python 3.7, PyTorch 1.10.1, and CUDA 10.2, but still ran into compatibility issues with mmcv-full and mmdet.

Would it be possible to get a list of the packages used along with their compatible versions?

[asadsehto]

hyy bro. I ran into the same problem working with this. I was working on Kaggle, and I applied these patches to solve the problem apply these patches you need to build wheel for mmcv and compile cuda to get this to work atleast this was the way it worked for me

#!/usr/bin/env python3


import os
import sys
import subprocess


# ============================================================
# STEP 1: Install Compatible Packages
# ============================================================
print("\n Installing compatible packages...")
commands = [
    "pip install -q opencv-python pycocotools scipy wandb timm openmim tensorboard",
    "pip uninstall -y mmcv mmcv-full mmdet 2>/dev/null || true",
    "mim install mmcv-full==1.7.2",
    "pip install -q 'mmdet<3.0.0'"
]
for cmd in commands:
    subprocess.run(cmd, shell=True)

# ============================================================
# STEP 2: Clone Repository 
# ============================================================
DETR_DIR = "./H-Deformable-DETR"
if not os.path.exists(DETR_DIR):
    print("\n Cloning H-Deformable-DETR...")
    subprocess.run("git clone https://github.com/HDETR/H-Deformable-DETR.git", shell=True)

# ============================================================
# STEP 3: Set CUDA Environment
# ============================================================
print("\n🔧 Setting CUDA environment...")
os.environ['CUDA_HOME'] = '/usr/local/cuda'
os.environ['TORCH_CUDA_ARCH_LIST'] = '7.5'  # Adjust: 7.5=RTX2000/3000, 8.6=RTX3090, 8.9=RTX4000
os.environ['FORCE_CUDA'] = '1'

# ============================================================
# STEP 4: Apply Code Patches
# ============================================================
print("\n  Applying compatibility patches...")

# Patch 1: Fix setup.py - Add CUDA include path
print("  → Patching setup.py...")
with open(f"{DETR_DIR}/models/ops/setup.py", 'r') as f:
    content = f.read()
content = content.replace(
    'include_dirs = [extensions_dir]',
    'include_dirs = [extensions_dir, "/usr/local/cuda/include"]'
)
with open(f"{DETR_DIR}/models/ops/setup.py", 'w') as f:
    f.write(content)

# Patch 2: Fix CUDA kernel - Update deprecated PyTorch API
print("  → Patching ms_deform_attn_cuda.cu...")
with open(f"{DETR_DIR}/models/ops/src/cuda/ms_deform_attn_cuda.cu", 'r') as f:
    content = f.read()

patches = [
    ('value.type(), "ms_deform_attn_forward_cuda"', 'value.scalar_type(), "ms_deform_attn_forward_cuda"'),
    ('value.type(), "ms_deform_attn_backward_cuda"', 'value.scalar_type(), "ms_deform_attn_backward_cuda"'),
    ('value.type().is_cuda()', 'value.is_cuda()'),
    ('spatial_shapes.type().is_cuda()', 'spatial_shapes.is_cuda()'),
    ('level_start_index.type().is_cuda()', 'level_start_index.is_cuda()'),
    ('sampling_loc.type().is_cuda()', 'sampling_loc.is_cuda()'),
    ('attn_weight.type().is_cuda()', 'attn_weight.is_cuda()'),
    ('grad_output.type().is_cuda()', 'grad_output.is_cuda()'),
]

for old, new in patches:
    content = content.replace(old, new)

with open(f"{DETR_DIR}/models/ops/src/cuda/ms_deform_attn_cuda.cu", 'w') as f:
    f.write(content)

# Patch 3: Fix header file
print("  → Patching ms_deform_attn.h...")
with open(f"{DETR_DIR}/models/ops/src/ms_deform_attn.h", 'r') as f:
    content = f.read()
content = content.replace('.type().is_cuda()', '.is_cuda()')
with open(f"{DETR_DIR}/models/ops/src/ms_deform_attn.h", 'w') as f:
    f.write(content)

# Patch 4: Fix main.py - Add weights_only=False
print("  → Patching main.py...")
with open(f"{DETR_DIR}/main.py", 'r') as f:
    content = f.read()

content = content.replace(
    'torch.load(args.resume, map_location="cpu")',
    'torch.load(args.resume, map_location="cpu", weights_only=False)'
)
content = content.replace(
    'torch.load(checkpoint_path, map_location="cpu")',
    'torch.load(checkpoint_path, map_location="cpu", weights_only=False)'
)
content = content.replace(
    'torch.load(args.frozen_weights, map_location="cpu")',
    'torch.load(args.frozen_weights, map_location="cpu", weights_only=False)'
)

with open(f"{DETR_DIR}/main.py", 'w') as f:
    f.write(content)

# Patch 5: Fix engine.py - DataParallel compatibility
print("  → Patching engine.py...")
with open(f"{DETR_DIR}/engine.py", 'r') as f:
    content = f.read()

content = content.replace(
    "save_num_queries = model.module.num_queries",
    "save_num_queries = model.module.num_queries if hasattr(model, 'module') else model.num_queries"
)

content = content.replace(
    "save_two_stage_num_proposals = model.module.transformer.two_stage_num_proposals",
    "save_two_stage_num_proposals = (model.module.transformer if hasattr(model, 'module') else model.transformer).two_stage_num_proposals"
)

content = content.replace(
    "    model.module.num_queries = model.module.num_queries_one2one",
    """    if hasattr(model, 'module'):
        model.module.num_queries = model.module.num_queries_one2one
    else:
        model.num_queries = model.num_queries_one2one"""
)

content = content.replace(
    "    model.module.transformer.two_stage_num_proposals = model.module.num_queries",
    """    if hasattr(model, 'module'):
        model.module.transformer.two_stage_num_proposals = model.module.num_queries
    else:
        model.transformer.two_stage_num_proposals = model.num_queries"""
)

content = content.replace(
    "    model.module.num_queries = save_num_queries",
    "    if hasattr(model, 'module'):\n        model.module.num_queries = save_num_queries\n    else:\n        model.num_queries = save_num_queries"
)

content = content.replace(
    "    model.module.transformer.two_stage_num_proposals = save_two_stage_num_proposals",
    "    if hasattr(model, 'module'):\n        model.module.transformer.two_stage_num_proposals = save_two_stage_num_proposals\n    else:\n        model.transformer.two_stage_num_proposals = save_two_stage_num_proposals"
)

with open(f"{DETR_DIR}/engine.py", 'w') as f:
    f.write(content)

print("\n All patches applied!")

# ============================================================
# STEP 5: Compile CUDA Operators
# ============================================================
print("\n Compiling CUDA operators...")
os.chdir(f"{DETR_DIR}/models/ops")
subprocess.run("rm -rf build dist *.egg-info", shell=True)
result = subprocess.run("python setup.py build", shell=True)

if result.returncode == 0:
    subprocess.run("pip install -e .", shell=True)
    print("\n" + "="*60)
    print("H-DETR is now compatible with PyTorch 2.6")
    print("="*60)
else:
    print("\n Compilation failed. Check errors above.")
    sys.exit(1)

fixes

• mmcv/mmdet version conflicts
• CUDA compilation errors with PyTorch 2.6
• Deprecated .type() API calls
• torch.load() weights_only warnings
• DataParallel compatibility issues

Tested Environment

• PyTorch 2.6
• CUDA 12.0 / 12.1 / 12.4
• Python 3.10+
• Ubuntu 22.04 / Kaggle notebooks

i ran this as cell 1 on Kaggle before training it. worked hope this helps