mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
fix: audit fixes for cross-platform correctness
- Fix NameError in restore.py: face enhancement loop used undefined variable `i`, now uses enumerate() - Fix gpu.py ONNX fallback: previous smoke-test with empty bytes always raised, making GPU detection unreachable via the ONNX path. Now uses nvidia-smi hardware check after confirming CUDA EP is compiled in — works on Linux, Windows, and gracefully fails on macOS - Fix cpu_fallback_packages stripping CUDA-specific index URLs when replacing paddlepaddle-gpu with paddlepaddle for CPU-only systems Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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+14
-20
@@ -1,7 +1,7 @@
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"""Runtime GPU/CUDA detection utility."""
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import ctypes
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import functools
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import os
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import subprocess
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import sys
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@@ -28,31 +28,25 @@ def gpu_available():
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except ImportError as e:
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print(f"[gpu] torch not importable: {e}", file=sys.stderr, flush=True)
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# Fallback: check if onnxruntime's CUDA provider can actually load.
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# get_available_providers() only reports *compiled-in* backends, not whether
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# the required libraries (cuDNN, etc.) are present at runtime. We verify
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# by creating a minimal CUDA session — this transitively checks that cuDNN
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# and all required libraries are present.
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# Fallback: check if onnxruntime-gpu is installed and CUDA EP is available,
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# then verify an actual NVIDIA GPU is present via nvidia-smi.
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try:
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import onnxruntime as _ort
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providers = _ort.get_available_providers()
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if "CUDAExecutionProvider" not in providers:
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return False
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# Smoke-test: create a session with CUDA to verify libraries load
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import numpy as _np
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_ort.InferenceSession(
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_np.zeros(0, dtype=_np.uint8).tobytes(),
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providers=["CUDAExecutionProvider"],
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# CUDA EP is compiled in — verify hardware is actually present.
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# nvidia-smi is the most reliable cross-platform check.
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
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capture_output=True, text=True, timeout=5,
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)
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# If we get here without error, CUDA provider is functional
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# (the empty model will fail, but the provider DLLs loaded)
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return True
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except Exception as e:
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# CUDA provider may report as available but fail to load (missing cuDNN, etc.)
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# Any error here means CUDA isn't usable — fall back to CPU
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err_str = str(e).lower()
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if "cuda" in err_str or "cudnn" in err_str or "provider" in err_str:
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print(f"[gpu] ONNX CUDA provider not functional: {e}", file=sys.stderr, flush=True)
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if result.returncode == 0 and result.stdout.strip():
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print(f"[gpu] CUDA available via ONNX Runtime + nvidia-smi: {result.stdout.strip()}",
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file=sys.stderr, flush=True)
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return True
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return False
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except (ImportError, FileNotFoundError, subprocess.TimeoutExpired):
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return False
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@@ -90,8 +90,11 @@ def cpu_fallback_packages(packages: list[str]) -> list[str]:
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name = pkg.split("==")[0].split(">=")[0].split("[")[0].strip()
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if name in replacements:
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version = pkg[len(name):] # e.g. "==1.20.1"
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result.append(replacements[name] + version)
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# Extract only the version spec, drop any inline flags
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# (e.g. "--extra-index-url https://...cu126/" is GPU-specific)
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tokens = pkg.split()
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version_token = tokens[0][len(name):] # e.g. ">=3.2.1"
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result.append(replacements[name] + version_token)
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else:
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result.append(pkg)
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return result
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@@ -331,7 +331,7 @@ def enhance_faces(img_bgr, fidelity=0.7):
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result = img_bgr.copy()
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faces_enhanced = 0
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for face_box in face_boxes:
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for i, face_box in enumerate(face_boxes):
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x = face_box["x"]
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y = face_box["y"]
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w = face_box["w"]
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