Remove complex dummy module system - return to simple working logic
This commit is contained in:
huchukato
+11
-141
@@ -23,34 +23,6 @@ import sys
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_trt = None
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_trt_available = False
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# Create dummy classes that need to be accessible globally
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class DummyIProgressMonitor:
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def __init__(self):
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pass
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def phase_start(self, *args, **kwargs):
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pass
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def phase_finish(self, *args, **kwargs):
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pass
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def step_complete(self, *args, **kwargs):
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return True
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class DummyLogger:
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def __init__(self, level):
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self.level = level
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ERROR = 0
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WARNING = 1
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class DummyBuilderFlag:
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FP16 = 1
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REFIT = 2
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class DummyOnnxParserFlag:
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NATIVE_INSTANCENORM = 1
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class DummyTensorIOMode:
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INPUT = 0
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OUTPUT = 1
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def get_trt():
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global _trt, _trt_available
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if _trt is None:
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@@ -60,23 +32,7 @@ def get_trt():
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_trt_available = True
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except ImportError:
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print("[ComfyUI-RIFE-TensorRT] Warning: TensorRT not available")
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# Create dummy tensorrt module for graceful fallback
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import types
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trt = types.ModuleType('tensorrt')
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# Use pre-defined dummy classes
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trt.Logger = DummyLogger
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trt.BuilderFlag = DummyBuilderFlag
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trt.OnnxParserFlag = DummyOnnxParserFlag
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trt.TensorIOMode = DummyTensorIOMode
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# Create dummy nptype function
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trt.nptype = lambda x: np.float32
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# Use pre-defined dummy class
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trt.IProgressMonitor = DummyIProgressMonitor
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_trt = trt
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_trt = None
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_trt_available = False
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return _trt
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@@ -84,104 +40,18 @@ def is_trt_available():
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global _trt_available
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return _trt_available
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class TQDMProgressMonitor:
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"""Progress monitor that works with both real and dummy TensorRT"""
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def __init__(self):
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trt = get_trt()
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# Initialize attributes regardless of TensorRT availability
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self._active_phases = {}
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self._step_result = True
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self.max_indent = 5
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# Only inherit from real TensorRT IProgressMonitor if available
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if is_trt_available():
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try:
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trt.IProgressMonitor.__init__(self)
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except Exception as e:
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print(f"Warning: Could not initialize IProgressMonitor: {e}")
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# If dummy, don't try to inherit - just work with our methods
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def diagnose_cuda_environment():
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"""Diagnose CUDA environment and provide helpful error messages"""
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print("🔍 Diagnosing CUDA environment...")
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# Check CUDA availability
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try:
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import torch
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if not torch.cuda.is_available():
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print("❌ CUDA is not available in PyTorch")
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print("Please check:")
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print("1. CUDA is properly installed")
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print("2. PyTorch was installed with CUDA support")
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print("3. NVIDIA drivers are up to date")
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return False
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print(f"✅ PyTorch CUDA version: {torch.version.cuda}")
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print(f"✅ CUDA device count: {torch.cuda.device_count()}")
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if torch.cuda.device_count() > 0:
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print(f"✅ Current CUDA device: {torch.cuda.get_device_name()}")
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print(f"✅ CUDA device memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
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except Exception as e:
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print(f"❌ Error checking PyTorch CUDA: {e}")
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return False
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# Check TensorRT availability
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if not is_trt_available():
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print("❌ TensorRT is not installed or not available")
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print("Please install TensorRT using:")
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print("1. python install.py (auto-install)")
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print("2. pip install -r requirements.txt (manual)")
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return False
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# Check TensorRT CUDA compatibility
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try:
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trt = get_trt()
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print(f"✅ TensorRT version: {trt.__version__}")
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# Try to create a simple builder to test CUDA initialization
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try:
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logger = trt.Logger(trt.Logger.WARNING)
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builder = trt.Builder(logger)
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print("✅ TensorRT CUDA initialization successful")
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return True
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except Exception as e:
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print(f"❌ TensorRT CUDA initialization failed: {e}")
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print("Possible solutions:")
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print("1. Restart your system to clear CUDA state")
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print("2. Check NVIDIA driver version compatibility")
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print("3. Verify CUDA toolkit installation")
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print("4. Try: nvidia-smi to check GPU status")
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return False
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except Exception as e:
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print(f"❌ Error checking TensorRT: {e}")
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return False
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def check_nvidia_driver():
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"""Check NVIDIA driver status"""
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try:
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result = subprocess.run(['nvidia-smi'], capture_output=True, text=True, timeout=10)
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if result.returncode == 0:
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print("✅ NVIDIA driver is working")
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# Extract driver version
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lines = result.stdout.split('\n')
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for line in lines:
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if 'Driver Version' in line:
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print(f"✅ {line.strip()}")
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break
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return True
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else:
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print("❌ nvidia-smi command failed")
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return False
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except FileNotFoundError:
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print("❌ nvidia-smi not found - NVIDIA drivers may not be installed")
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return False
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except Exception as e:
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print(f"❌ Error running nvidia-smi: {e}")
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return False
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def get_trt_logger():
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trt = get_trt()
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return trt.Logger(trt.Logger.ERROR)
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if trt:
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return trt.Logger(trt.Logger.ERROR)
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else:
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# Create a simple fallback logger
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class SimpleLogger:
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def __init__(self, level):
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self.level = level
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ERROR = 0
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WARNING = 1
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return SimpleLogger(SimpleLogger.ERROR)
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TRT_LOGGER = get_trt_logger()
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G_LOGGER.module_severity = G_LOGGER.ERROR
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