diff --git a/__pycache__/train.cpython-37.pyc b/__pycache__/train.cpython-37.pyc new file mode 100644 index 0000000..315ba3f Binary files /dev/null and b/__pycache__/train.cpython-37.pyc differ diff --git a/ieemoo-ai-isempty.py b/ieemoo-ai-isempty.py old mode 100644 new mode 100755 index 26eb0ba..06c1248 --- a/ieemoo-ai-isempty.py +++ b/ieemoo-ai-isempty.py @@ -13,6 +13,23 @@ from torchvision import transforms from models.modeling import VisionTransformer, CONFIGS sys.path.insert(0, ".") +import logging.config +from skywalking import agent, config +SW_SERVER = os.environ.get('SW_AGENT_COLLECTOR_BACKEND_SERVICES') +SW_SERVICE_NAME = os.environ.get('SW_AGENT_NAME') +if SW_SERVER and SW_SERVICE_NAME: + config.init() #采集服务的地址，给自己的服务起个名称 + #config.init(collector="123.60.56.51:11800", service='ieemoo-ai-search') #采集服务的地址，给自己的服务起个名称 + agent.start() +def setup_logging(path): + if os.path.exists(path): + with open(path, 'r') as f: + config = json.load(f) + logging.config.dictConfig(config) + logger = logging.getLogger("root") + return logger +logger = setup_logging('utils/logging.json') + app = Flask(__name__) app.use_reloader=False @@ -34,7 +51,7 @@ class Predictor(object): def __init__(self, args): self.args = args self.args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - print(self.args.device) + #print(self.args.device) self.args.nprocs = torch.cuda.device_count() self.cls_dict = {} self.num_classes = 0 @@ -49,7 +66,7 @@ class Predictor(object): config.split = self.args.split config.slide_step = self.args.slide_step model_name = os.path.basename(self.args.pretrained_model).replace("_checkpoint.bin", "") - print("use model_name: ", model_name) + #print("use model_name: ", model_name) self.num_classes = 5 self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"} self.model = VisionTransformer(config, self.args.img_size, zero_head=True, num_classes=self.num_classes, smoothing_value=self.args.smoothing_value) @@ -67,7 +84,8 @@ class Predictor(object): def normal_predict(self, img_data, result): # img = Image.open(img_path) if img_data is None: - print('error, img data is None') + #print('error, img data is None') + logger.warning('error, img data is None') return result else: with torch.no_grad(): @@ -79,7 +97,7 @@ class Predictor(object): topN = torch.argsort(probs, dim=-1, descending=True).tolist() clas_ids = topN[0][0] clas_ids = 0 if 0==int(clas_ids) or 2 == int(clas_ids) or 3 == int(clas_ids) else 1 - print("cur_img result: class id: %d, score: %0.3f" % (clas_ids, probs[0, clas_ids].item())) + #print("cur_img result: class id: %d, score: %0.3f" % (clas_ids, probs[0, clas_ids].item())) result["success"] = "true" result["rst_cls"] = str(clas_ids) return result @@ -94,14 +112,15 @@ predictor = Predictor(args) @app.route("/isempty", methods=['POST']) def get_isempty(): start = time.time() - print('--------------------EmptyPredict-----------------') + #print('--------------------EmptyPredict-----------------') data = request.get_data() ip = request.remote_addr - print('------ ip = %s ------' % ip) + #print('------ ip = %s ------' % ip) + logger.info(ip) json_data = json.loads(data.decode("utf-8")) getdateend = time.time() - print('get date use time: {0:.2f}s'.format(getdateend - start)) + #print('get date use time: {0:.2f}s'.format(getdateend - start)) pic = json_data.get("pic") result = {"success": "false", @@ -113,29 +132,11 @@ def get_isempty(): img_src = cv2.imdecode(imgdata_np, cv2.IMREAD_COLOR) img_data = Image.fromarray(np.uint8(img_src)) result = predictor.normal_predict(img_data, result) # 1==empty, 0==nonEmpty - except: + except Exception as e: + logger.warning(e) return repr(result) - + logger.info(repr(result)) return repr(result) - if __name__ == "__main__": - app.run() -# http_server = WSGIServer(('0.0.0.0',8000), app) -# http_server.serve_forever() - - - - - - - - - - - - - - - - + app.run(host='192.168.1.142', port=8000) diff --git a/init.sh b/init.sh old mode 100644 new mode 100755 diff --git a/requirements.txt b/requirements.txt old mode 100644 new mode 100755 diff --git a/start.sh b/start.sh old mode 100644 new mode 100755 diff --git a/stop.sh b/stop.sh old mode 100644 new mode 100755 diff --git a/train.py b/train.py index fb444c7..bf63476 100755 --- a/train.py +++ b/train.py @@ -20,10 +20,11 @@ from models.modeling import VisionTransformer, CONFIGS from utils.scheduler import WarmupLinearSchedule, WarmupCosineSchedule from utils.data_utils import get_loader from utils.dist_util import get_world_size +import pdb logger = logging.getLogger(__name__) - +os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2" class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): @@ -92,12 +93,14 @@ def setup(args): if args.pretrained_model is not None: pretrained_model = torch.load(args.pretrained_model)['model'] model.load_state_dict(pretrained_model) - model.to(args.device) + #model.to(args.device) + #pdb.set_trace() num_params = count_parameters(model) logger.info("{}".format(config)) logger.info("Training parameters %s", args) logger.info("Total Parameter: \t%2.1fM" % num_params) + model = torch.nn.DataParallel(model, device_ids=[0,1]).cuda() return args, model @@ -351,6 +354,7 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count()) args.n_gpu = torch.cuda.device_count() + print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count()) else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) diff --git a/utils/logging.json b/utils/logging.json new file mode 100644 index 0000000..886d62b --- /dev/null +++ b/utils/logging.json @@ -0,0 +1,46 @@ +{ + "version":1, + "disable_existing_loggers":false, + "formatters":{ + "simple":{ + "format":"%(asctime)s - %(module)s - %(thread)d - %(levelname)s : %(message)s" + } + }, + "handlers":{ + "console":{ + "class":"logging.StreamHandler", + "level":"DEBUG", + "formatter":"simple", + "stream":"ext://sys.stdout" + }, + "info_file_handler":{ + "class":"logging.handlers.RotatingFileHandler", + "level":"INFO", + "formatter":"simple", + "filename":"../log/ieemoo-ai-isempty-biz.log", + "maxBytes":10485760, + "backupCount":20, + "encoding":"utf8" + }, + "error_file_handler":{ + "class":"logging.handlers.RotatingFileHandler", + "level":"ERROR", + "formatter":"simple", + "filename":"../log/ieemoo-ai-isempty-biz.log", + "maxBytes":10485760, + "backupCount":20, + "encoding":"utf8" + } + }, + "loggers":{ + "my_module":{ + "level":"ERROR", + "handlers":["info_file_handler"], + "propagate":"no"} + }, + "root":{ + "level":"INFO", + "handlers":["console","info_file_handler","error_file_handler"] + } +} +