Initial commit: Logo detection test framework
Add DETR+CLIP based logo detection library and test framework: - DetectLogosDETR class for logo detection and matching - Test script with margin-based and multi-ref matching methods - Data preparation script for test database - Documentation for API usage and test methodology
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Rick McEwen
275
test_cuda_support.py
Executable file
275
test_cuda_support.py
Executable file
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#!/usr/bin/env python3
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"""
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CUDA Support Test for Nvidia Jetson Hardware
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This script verifies that OpenCV and PyTorch are properly configured
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with CUDA support on Jetson devices.
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Usage:
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python test_cuda_support.py
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Returns:
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Exit code 0 if CUDA is properly configured
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Exit code 1 if CUDA support is missing or misconfigured
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"""
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import sys
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import platform
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def print_section(title):
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"""Print a section header."""
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print("\n" + "=" * 60)
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print(f" {title}")
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print("=" * 60)
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def test_pytorch_cuda():
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"""Test PyTorch CUDA support."""
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print_section("PyTorch CUDA Support")
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try:
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import torch
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print(f"✓ PyTorch imported successfully")
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print(f" Version: {torch.__version__}")
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# Check CUDA availability
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cuda_available = torch.cuda.is_available()
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print(f"\nCUDA Available: {'✓ YES' if cuda_available else '✗ NO'}")
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if cuda_available:
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print(f" CUDA Version: {torch.version.cuda}")
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print(f" cuDNN Version: {torch.backends.cudnn.version()}")
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print(f" cuDNN Enabled: {torch.backends.cudnn.enabled}")
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# Get device information
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device_count = torch.cuda.device_count()
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print(f"\n GPU Devices: {device_count}")
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for i in range(device_count):
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props = torch.cuda.get_device_properties(i)
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print(f"\n Device {i}: {props.name}")
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print(f" Compute Capability: {props.major}.{props.minor}")
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print(f" Total Memory: {props.total_memory / 1024**3:.2f} GB")
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print(f" Multi-Processor Count: {props.multi_processor_count}")
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# Test tensor operations
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print("\n Testing GPU tensor operations...")
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try:
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x = torch.randn(3, 3).cuda()
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y = torch.randn(3, 3).cuda()
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z = x @ y
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print(f" ✓ GPU tensor operations successful")
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# Check current device
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print(f" Current Device: {torch.cuda.current_device()}")
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print(f" Device Name: {torch.cuda.get_device_name(0)}")
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except Exception as e:
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print(f" ✗ GPU tensor operations failed: {e}")
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return False
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else:
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print("\n ⚠ PyTorch CUDA is NOT available")
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print(" Possible reasons:")
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print(" - PyTorch not built with CUDA support")
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print(" - CUDA drivers not installed")
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print(" - Incompatible CUDA version")
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return False
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return cuda_available
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except ImportError as e:
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print(f"✗ Failed to import PyTorch: {e}")
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return False
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except Exception as e:
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print(f"✗ Error testing PyTorch: {e}")
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return False
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def test_opencv_cuda():
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"""Test OpenCV CUDA support."""
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print_section("OpenCV CUDA Support")
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try:
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import cv2
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print(f"✓ OpenCV imported successfully")
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print(f" Version: {cv2.__version__}")
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# Check build information
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build_info = cv2.getBuildInformation()
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# Parse build info for CUDA
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cuda_enabled = "CUDA:" in build_info and "YES" in build_info.split("CUDA:")[1].split("\n")[0]
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print(f"\nCUDA Support: {'✓ YES' if cuda_enabled else '✗ NO'}")
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if cuda_enabled:
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# Extract CUDA-related information from build info
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lines = build_info.split('\n')
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cuda_section = False
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print("\n CUDA Build Configuration:")
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for line in lines:
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if 'CUDA' in line or cuda_section:
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if 'CUDA' in line:
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cuda_section = True
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if cuda_section:
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# Print relevant CUDA lines
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if any(keyword in line for keyword in ['CUDA', 'cuDNN', 'NVIDIA', 'GPU']):
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print(f" {line.strip()}")
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# Stop at next major section
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if line.strip() and not line.startswith(' ') and 'CUDA' not in line:
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break
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# Check for CUDA device count
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try:
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cuda_device_count = cv2.cuda.getCudaEnabledDeviceCount()
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print(f"\n CUDA Devices: {cuda_device_count}")
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if cuda_device_count > 0:
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# Test CUDA operations
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print("\n Testing CUDA operations...")
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try:
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# Create a simple GPU matrix
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import numpy as np
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test_img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
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gpu_mat = cv2.cuda_GpuMat()
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gpu_mat.upload(test_img)
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result = gpu_mat.download()
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print(f" ✓ CUDA operations successful")
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except Exception as e:
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print(f" ✗ CUDA operations failed: {e}")
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return False
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else:
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print(" ⚠ No CUDA devices detected")
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return False
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except AttributeError:
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print(" ⚠ cv2.cuda module not available")
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print(" OpenCV may not be built with CUDA support")
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return False
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else:
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print("\n ⚠ OpenCV CUDA is NOT available")
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print(" Possible reasons:")
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print(" - OpenCV not built with CUDA support")
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print(" - Need to install opencv-contrib-python with CUDA")
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print(" - For Jetson, may need to build from source")
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return False
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return cuda_enabled
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except ImportError as e:
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print(f"✗ Failed to import OpenCV: {e}")
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return False
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except Exception as e:
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print(f"✗ Error testing OpenCV: {e}")
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return False
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def print_system_info():
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"""Print system information."""
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print_section("System Information")
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print(f"Platform: {platform.platform()}")
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print(f"Python Version: {platform.python_version()}")
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print(f"Architecture: {platform.machine()}")
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print(f"Processor: {platform.processor()}")
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# Try to detect Jetson
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try:
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with open('/etc/nv_tegra_release', 'r') as f:
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jetson_version = f.read().strip()
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print(f"\n✓ Jetson Device Detected")
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print(f" {jetson_version}")
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except FileNotFoundError:
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print("\n Not running on Jetson device (or /etc/nv_tegra_release not found)")
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# Check for CUDA toolkit
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import subprocess
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try:
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result = subprocess.run(['nvcc', '--version'],
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capture_output=True,
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text=True,
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timeout=5)
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if result.returncode == 0:
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print("\n✓ CUDA Toolkit detected:")
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# Print version line
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for line in result.stdout.split('\n'):
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if 'release' in line.lower():
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print(f" {line.strip()}")
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else:
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print("\n⚠ nvcc not found - CUDA toolkit may not be installed")
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except FileNotFoundError:
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print("\n⚠ nvcc not found - CUDA toolkit may not be installed")
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except Exception as e:
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print(f"\n⚠ Error checking CUDA toolkit: {e}")
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def print_memory_info():
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"""Print GPU memory information."""
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print_section("GPU Memory Information")
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try:
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import torch
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if torch.cuda.is_available():
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device = torch.cuda.current_device()
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total_mem = torch.cuda.get_device_properties(device).total_memory
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allocated = torch.cuda.memory_allocated(device)
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cached = torch.cuda.memory_reserved(device)
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print(f"Total GPU Memory: {total_mem / 1024**3:.2f} GB")
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print(f"Allocated: {allocated / 1024**3:.2f} GB")
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print(f"Cached: {cached / 1024**3:.2f} GB")
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print(f"Free: {(total_mem - allocated) / 1024**3:.2f} GB")
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else:
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print("CUDA not available - cannot query GPU memory")
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except Exception as e:
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print(f"Error querying GPU memory: {e}")
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def main():
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"""Main test function."""
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print("\n" + "=" * 60)
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print(" CUDA Support Verification for Nvidia Jetson")
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print("=" * 60)
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# Print system info
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print_system_info()
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# Test PyTorch
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pytorch_cuda = test_pytorch_cuda()
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# Test OpenCV
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opencv_cuda = test_opencv_cuda()
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# Print memory info
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if pytorch_cuda:
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print_memory_info()
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# Print summary
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print_section("Summary")
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print(f"PyTorch CUDA Support: {'✓ ENABLED' if pytorch_cuda else '✗ DISABLED'}")
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print(f"OpenCV CUDA Support: {'✓ ENABLED' if opencv_cuda else '✗ DISABLED'}")
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if pytorch_cuda and opencv_cuda:
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print("\n✓ All CUDA checks passed - system ready for GPU-accelerated processing")
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return 0
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elif pytorch_cuda:
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print("\n⚠ PyTorch CUDA enabled, but OpenCV CUDA disabled")
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print(" Some operations will use GPU, but OpenCV operations will use CPU")
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return 1
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elif opencv_cuda:
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print("\n⚠ OpenCV CUDA enabled, but PyTorch CUDA disabled")
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print(" OpenCV operations will use GPU, but PyTorch models will use CPU")
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return 1
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else:
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print("\n✗ CUDA support not available - will run in CPU mode")
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print(" Performance will be significantly slower")
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return 1
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if __name__ == "__main__":
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exit_code = main()
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print("\n" + "=" * 60 + "\n")
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sys.exit(exit_code)
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