import logging, os, psutil, torch, platform, cpuinfo, yaml #py-cpuinfo from vouchervision.general_utils import get_datetime, print_main_warn, print_main_info class SanitizingFileHandler(logging.FileHandler): def __init__(self, filename, mode='a', encoding=None, delay=False): super().__init__(filename, mode, encoding, delay) def emit(self, record): try: record.msg = record.msg.encode('utf-8', 'replace').decode('utf-8') except Exception as e: record.msg = f'[Error encoding text: {e}]' super().emit(record) def start_logging(Dirs, cfg): run_name = cfg['leafmachine']['project']['run_name'] path_log = os.path.join(Dirs.path_log, '__'.join(['LM2-log', str(get_datetime()), run_name]) + '.log') # Disable default StreamHandler logging.getLogger().handlers = [] # create logger logger = logging.getLogger('Hardware Components') logger.setLevel(logging.DEBUG) # create custom sanitizing file handler and set level to debug sanitizing_fh = SanitizingFileHandler(path_log, encoding='utf-8') sanitizing_fh.setLevel(logging.DEBUG) # create console handler and set level to debug ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) # create formatter formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(name)s - %(message)s') # add formatter to handlers sanitizing_fh.setFormatter(formatter) ch.setFormatter(formatter) # add handlers to logger logger.addHandler(sanitizing_fh) logger.addHandler(ch) # Create a logger for the file handler file_logger = logging.getLogger('file_logger') file_logger.setLevel(logging.DEBUG) file_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') file_handler = logging.FileHandler(path_log) file_handler.setLevel(logging.DEBUG) file_handler.setFormatter(file_formatter) file_logger.addHandler(file_handler) # Disable propagation of log messages to the root logger file_logger.propagate = False # 'application' code # logger.debug('debug message') # logger.info('info message') # logger.warning('warn message') # logger.error('error message') # logger.critical('critical message') # Get CPU information logger.info(f"CPU: {find_cpu_info()}") # Get GPU information (using PyTorch) if torch.cuda.is_available(): num_gpus = torch.cuda.device_count() if num_gpus == 1: gpu = torch.cuda.get_device_properties(0) logger.info(f"GPU: {gpu.name} ({gpu.total_memory // (1024 * 1024)} MB)") else: for i in range(num_gpus): gpu = torch.cuda.get_device_properties(i) logger.info(f"GPU {i}: {gpu.name} ({gpu.total_memory // (1024 * 1024)} MB)") else: logger.info("No GPU found") logger.info("LeafMachine2 image cropping and embedding search will be extremely slow or not possible.") print_main_info("No GPU found!") print_main_info("LeafMachine2 image cropping and embedding search will be extremely slow or not possible.") # Get memory information mem_info = psutil.virtual_memory() logger.info(f"Memory: {mem_info.total // (1024 * 1024)} MB") logger.info(LM2_banner()) logger.info(f"Config added to log file") file_logger.info('Config:\n{}'.format(yaml.dump(cfg))) return logger def find_cpu_info(): cpu_info = [] cpu_info.append(platform.processor()) try: with open('/proc/cpuinfo') as f: for line in f: if line.startswith('model name'): cpu_info.append(line.split(':')[1].strip()) break return ' / '.join(cpu_info) except: try: info = cpuinfo.get_cpu_info() cpu_info = [] cpu_info.append(info['brand_raw']) cpu_info.append(f"{info['hz_actual_friendly']}") return ' / '.join(cpu_info) except: return "CPU: UNKNOWN" def LM2_banner(): logo = """ _ __ __ __ _ _ ___ | | / _| \/ | | | (_) |__ \ | | ___ __ _| |_| \ / | __ _ ___| |__ _ _ __ ___ ) | | | / _ \/ _` | _| |\/| |/ _` |/ __| '_ \| | '_ \ / _ \ / / | |___| __/ (_| | | | | | | (_| | (__| | | | | | | | __// /_ |______\___|\__,_|_| |_| |_|\__,_|\___|_| |_|_|_| |_|\___|____| __ __ _ _| |_ __ ___ _ \ \ / / | | |_ _| \ \ / (_) (_) \ \ / /__ _ _ ___| |__ |_|_ _ _\ \ / / _ ___ _ ___ _ __ \ \/ / _ \| | | |/ __| '_ \ / _ \ '__\ \/ / | / __| |/ _ \| '_ \ \ / (_) | |_| | (__| | | | __/ | \ / | \__ \ | (_) | | | | \/ \___/ \__,_|\___|_| |_|\___|_| \/ |_|___/_|\___/|_| |_|""" return logo