ci: add Ruff config, pre-commit hooks, and GitHub Actions lint workflow

.github/workflows/lint.yml

@@ -0,0 +1,17 @@
+name: Lint
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v5
+      - run: uv python install 3.10
+      - run: uv pip install ruff
+      - run: uv run ruff check .
+      - run: uv run ruff format --check .

.pre-commit-config.yaml

@@ -0,0 +1,7 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.12.2
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format

baselines/da_v2.py

@@ -1,8 +1,8 @@
 # Reference: https://github.com/DepthAnything/Depth-Anything-V2
 import os
 import sys
-from typing import *
 from pathlib import Path
+from typing import *
 
 import click
 import torch

baselines/da_v2_metric.py

@@ -1,15 +1,14 @@
 # Reference https://github.com/DepthAnything/Depth-Anything-V2/metric_depth
 import os
 import sys
-from typing import *
 from pathlib import Path
+from typing import *
 
 import click
 import torch
 import torch.nn.functional as F
 import torchvision.transforms as T
 import torchvision.transforms.functional as TF
-import cv2
 
 from moge.test.baseline import MGEBaselineInterface
 

baselines/metric3d_v2.py

@@ -1,12 +1,10 @@
 # Reference: https://github.com/YvanYin/Metric3D
-import os
-import sys
 from typing import *
 
 import click
+import cv2
 import torch
 import torch.nn.functional as F
-import cv2
 
 from moge.test.baseline import MGEBaselineInterface
 

baselines/moge.py

@@ -1,7 +1,4 @@
-import os
-import sys
 from typing import *
-import importlib
 
 import click
 import torch

moge/model/dinov2/hub/utils.py

@@ -10,7 +10,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-
 _DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
 
 

moge/model/dinov2/layers/__init__.py

@@ -3,9 +3,9 @@
 # This source code is licensed under the Apache License, Version 2.0
 # found in the LICENSE file in the root directory of this source tree.
 
+from .attention import MemEffAttention
+from .block import NestedTensorBlock
 from .dino_head import DINOHead
 from .mlp import Mlp
 from .patch_embed import PatchEmbed
 from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
-from .block import NestedTensorBlock
-from .attention import MemEffAttention

moge/model/dinov2/layers/attention.py

@@ -9,12 +9,9 @@
 
 import logging
 import os
-import warnings
 
 import torch.nn.functional as F
-from torch import Tensor
-from torch import nn
-
+from torch import Tensor, nn
 
 logger = logging.getLogger("dinov2")
 

moge/model/dinov2/layers/block.py

@@ -9,25 +9,23 @@
 
 import logging
 import os
-from typing import Callable, List, Any, Tuple, Dict
-import warnings
+from typing import Any, Callable, Dict, List, Tuple
 
 import torch
-from torch import nn, Tensor
+from torch import Tensor, nn
 
 from .attention import Attention, MemEffAttention
 from .drop_path import DropPath
 from .layer_scale import LayerScale
 from .mlp import Mlp
 
-
 logger = logging.getLogger("dinov2")
 
 
 XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
 try:
     if XFORMERS_ENABLED:
-        from xformers.ops import fmha, scaled_index_add, index_select_cat
+        from xformers.ops import fmha, index_select_cat, scaled_index_add
 
         XFORMERS_AVAILABLE = True
         # warnings.warn("xFormers is available (Block)")

moge/model/dinov2/layers/layer_scale.py

@@ -8,8 +8,7 @@
 from typing import Union
 
 import torch
-from torch import Tensor
-from torch import nn
+from torch import Tensor, nn
 
 
 class LayerScale(nn.Module):

moge/model/dinov2/layers/patch_embed.py

@@ -9,8 +9,8 @@
 
 from typing import Callable, Optional, Tuple, Union
 
-from torch import Tensor
 import torch.nn as nn
+from torch import Tensor
 
 
 def make_2tuple(x):

moge/model/dinov2/layers/swiglu_ffn.py

@@ -5,10 +5,9 @@
 
 import os
 from typing import Callable, Optional
-import warnings
 
-from torch import Tensor, nn
 import torch.nn.functional as F
+from torch import Tensor, nn
 
 
 class SwiGLUFFN(nn.Module):

moge/model/dinov2/models/__init__.py

@@ -7,7 +7,6 @@
 
 from . import vision_transformer as vits
 
-
 logger = logging.getLogger("dinov2")
 
 

moge/model/dinov2/models/vision_transformer.py

@@ -7,18 +7,18 @@
 #   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
 #   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
 
-from functools import partial
-import math
 import logging
-from typing import Sequence, Tuple, Union, Callable, Optional, List
+import math
+from functools import partial
+from typing import Callable, Sequence, Tuple, Union
 
 import torch
 import torch.nn as nn
 import torch.utils.checkpoint
 from torch.nn.init import trunc_normal_
 
-from ..layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
-
+from ..layers import MemEffAttention, Mlp, PatchEmbed, SwiGLUFFNFused
+from ..layers import NestedTensorBlock as Block
 
 logger = logging.getLogger("dinov2")
 

moge/model/dinov2/utils/cluster.py

@@ -3,8 +3,8 @@
 # This source code is licensed under the Apache License, Version 2.0
 # found in the LICENSE file in the root directory of this source tree.
 
-from enum import Enum
 import os
+from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, Optional
 

moge/model/dinov2/utils/config.py

@@ -3,17 +3,15 @@
 # This source code is licensed under the Apache License, Version 2.0
 # found in the LICENSE file in the root directory of this source tree.
 
-import math
 import logging
+import math
 import os
 
-from omegaconf import OmegaConf
-
 import dinov2.distributed as distributed
+from dinov2.configs import dinov2_default_config
 from dinov2.logging import setup_logging
 from dinov2.utils import utils
-from dinov2.configs import dinov2_default_config
-
+from omegaconf import OmegaConf
 
 logger = logging.getLogger("dinov2")
 

moge/model/dinov2/utils/dtype.py

@@ -9,7 +9,6 @@
 import numpy as np
 import torch
 
-
 TypeSpec = Union[str, np.dtype, torch.dtype]
 
 

moge/model/dinov2/utils/param_groups.py

@@ -3,9 +3,8 @@
 # This source code is licensed under the Apache License, Version 2.0
 # found in the LICENSE file in the root directory of this source tree.
 
-from collections import defaultdict
 import logging
-
+from collections import defaultdict
 
 logger = logging.getLogger("dinov2")
 

moge/model/dinov2/utils/utils.py

@@ -13,7 +13,6 @@
 import torch
 from torch import nn
 
-
 logger = logging.getLogger("dinov2")
 
 

moge/model/modules.py

@@ -1,18 +1,17 @@
-from typing import *
-from numbers import Number
+import functools
 import importlib
 import itertools
-import functools
-import sys
+from typing import *
 
 import torch
-from torch import Tensor
 import torch.nn as nn
 import torch.nn.functional as F
 
 from .dinov2.models.vision_transformer import DinoVisionTransformer
-from .utils import wrap_dinov2_attention_with_sdpa, wrap_module_with_gradient_checkpointing, unwrap_module_with_gradient_checkpointing
-from ..utils.geometry_torch import normalized_view_plane_uv
+from .utils import (
+    wrap_dinov2_attention_with_sdpa,
+    wrap_module_with_gradient_checkpointing,
+)
 
 
 class ResidualConvBlock(nn.Module):  

moge/model/utils.py

@@ -4,6 +4,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
+
 def wrap_module_with_gradient_checkpointing(module: nn.Module):
     from torch.utils.checkpoint import checkpoint
     class _CheckpointingWrapper(module.__class__):

moge/model/v1.py

@@ -1,10 +1,8 @@
-from typing import *
-from numbers import Number
-from functools import partial
-from pathlib import Path
 import importlib
 import warnings
-import json
+from numbers import Number
+from pathlib import Path
+from typing import *
 
 import torch
 import torch.nn as nn
@@ -15,10 +13,10 @@
 import utils3d
 from huggingface_hub import hf_hub_download
 
-
-from ..utils.geometry_torch import normalized_view_plane_uv, recover_focal_shift, gaussian_blur_2d, dilate_with_mask
-from .utils import wrap_dinov2_attention_with_sdpa, wrap_module_with_gradient_checkpointing, unwrap_module_with_gradient_checkpointing
-from ..utils.tools import timeit
+from ..utils.geometry_torch import normalized_view_plane_uv, recover_focal_shift
+from .utils import (
+    wrap_module_with_gradient_checkpointing,
+)
 
 
 class ResidualConvBlock(nn.Module):  

moge/model/v2.py

@@ -1,24 +1,22 @@
-from typing import *
+import warnings
 from numbers import Number
-from functools import partial
 from pathlib import Path
-import warnings
+from typing import *
 
 import torch
+import torch.amp
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils
 import torch.utils.checkpoint
-import torch.amp
 import torch.version
 import utils3d
 from huggingface_hub import hf_hub_download
 
-from ..utils.geometry_torch import normalized_view_plane_uv, recover_focal_shift, angle_diff_vec3
-from .utils import wrap_dinov2_attention_with_sdpa, wrap_module_with_gradient_checkpointing, unwrap_module_with_gradient_checkpointing
-from .modules import DINOv2Encoder, MLP, ConvStack
+from ..utils.geometry_torch import normalized_view_plane_uv, recover_focal_shift
+from .modules import MLP, ConvStack, DINOv2Encoder
+
 
-
 class MoGeModel(nn.Module):
     encoder: DINOv2Encoder
     neck: ConvStack