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__pycache__/train.cpython-37.pyc
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__pycache__/train.cpython-37.pyc
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59
ieemoo-ai-isempty.py
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59
ieemoo-ai-isempty.py
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@ -13,6 +13,23 @@ from torchvision import transforms
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from models.modeling import VisionTransformer, CONFIGS
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sys.path.insert(0, ".")
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import logging.config
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from skywalking import agent, config
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SW_SERVER = os.environ.get('SW_AGENT_COLLECTOR_BACKEND_SERVICES')
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SW_SERVICE_NAME = os.environ.get('SW_AGENT_NAME')
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if SW_SERVER and SW_SERVICE_NAME:
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config.init() #采集服务的地址，给自己的服务起个名称
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#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
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agent.start()
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def setup_logging(path):
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if os.path.exists(path):
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with open(path, 'r') as f:
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config = json.load(f)
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logging.config.dictConfig(config)
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logger = logging.getLogger("root")
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return logger
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logger = setup_logging('utils/logging.json')
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app = Flask(__name__)
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app.use_reloader=False
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@ -34,7 +51,7 @@ class Predictor(object):
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def __init__(self, args):
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self.args = args
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self.args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(self.args.device)
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#print(self.args.device)
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self.args.nprocs = torch.cuda.device_count()
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self.cls_dict = {}
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self.num_classes = 0
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@ -49,7 +66,7 @@ class Predictor(object):
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config.split = self.args.split
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config.slide_step = self.args.slide_step
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model_name = os.path.basename(self.args.pretrained_model).replace("_checkpoint.bin", "")
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print("use model_name: ", model_name)
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#print("use model_name: ", model_name)
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self.num_classes = 5
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self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
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self.model = VisionTransformer(config, self.args.img_size, zero_head=True, num_classes=self.num_classes, smoothing_value=self.args.smoothing_value)
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@ -67,7 +84,8 @@ class Predictor(object):
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def normal_predict(self, img_data, result):
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# img = Image.open(img_path)
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if img_data is None:
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print('error, img data is None')
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#print('error, img data is None')
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logger.warning('error, img data is None')
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return result
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else:
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with torch.no_grad():
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@ -79,7 +97,7 @@ class Predictor(object):
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topN = torch.argsort(probs, dim=-1, descending=True).tolist()
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clas_ids = topN[0][0]
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clas_ids = 0 if 0==int(clas_ids) or 2 == int(clas_ids) or 3 == int(clas_ids) else 1
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print("cur_img result: class id: %d, score: %0.3f" % (clas_ids, probs[0, clas_ids].item()))
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#print("cur_img result: class id: %d, score: %0.3f" % (clas_ids, probs[0, clas_ids].item()))
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result["success"] = "true"
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result["rst_cls"] = str(clas_ids)
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return result
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@ -94,14 +112,15 @@ predictor = Predictor(args)
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@app.route("/isempty", methods=['POST'])
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def get_isempty():
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start = time.time()
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print('--------------------EmptyPredict-----------------')
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#print('--------------------EmptyPredict-----------------')
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data = request.get_data()
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ip = request.remote_addr
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print('------ ip = %s ------' % ip)
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#print('------ ip = %s ------' % ip)
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logger.info(ip)
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json_data = json.loads(data.decode("utf-8"))
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getdateend = time.time()
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print('get date use time: {0:.2f}s'.format(getdateend - start))
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#print('get date use time: {0:.2f}s'.format(getdateend - start))
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pic = json_data.get("pic")
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result = {"success": "false",
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@ -113,29 +132,11 @@ def get_isempty():
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img_src = cv2.imdecode(imgdata_np, cv2.IMREAD_COLOR)
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img_data = Image.fromarray(np.uint8(img_src))
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result = predictor.normal_predict(img_data, result) # 1==empty, 0==nonEmpty
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except:
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except Exception as e:
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logger.warning(e)
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return repr(result)
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logger.info(repr(result))
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return repr(result)
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if __name__ == "__main__":
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app.run()
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# http_server = WSGIServer(('0.0.0.0',8000), app)
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# http_server.serve_forever()
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app.run(host='192.168.1.142', port=8000)
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0
requirements.txt
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0
requirements.txt
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8
train.py
8
train.py
@ -20,10 +20,11 @@ from models.modeling import VisionTransformer, CONFIGS
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from utils.scheduler import WarmupLinearSchedule, WarmupCosineSchedule
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from utils.data_utils import get_loader
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from utils.dist_util import get_world_size
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import pdb
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logger = logging.getLogger(__name__)
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os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2"
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self):
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@ -92,12 +93,14 @@ def setup(args):
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if args.pretrained_model is not None:
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pretrained_model = torch.load(args.pretrained_model)['model']
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model.load_state_dict(pretrained_model)
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model.to(args.device)
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#model.to(args.device)
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#pdb.set_trace()
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num_params = count_parameters(model)
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logger.info("{}".format(config))
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logger.info("Training parameters %s", args)
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logger.info("Total Parameter: \t%2.1fM" % num_params)
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model = torch.nn.DataParallel(model, device_ids=[0,1]).cuda()
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return args, model
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@ -351,6 +354,7 @@ def main():
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count())
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args.n_gpu = torch.cuda.device_count()
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print('torch.cuda.device_count()>>>>>>>>>>>>>>>>>>>>>>>>>', torch.cuda.device_count())
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else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
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torch.cuda.set_device(args.local_rank)
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device = torch.device("cuda", args.local_rank)
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46
utils/logging.json
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46
utils/logging.json
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@ -0,0 +1,46 @@
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{
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"version":1,
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"disable_existing_loggers":false,
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"formatters":{
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"simple":{
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"format":"%(asctime)s - %(module)s - %(thread)d - %(levelname)s : %(message)s"
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}
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},
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"handlers":{
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"console":{
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"class":"logging.StreamHandler",
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"level":"DEBUG",
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"formatter":"simple",
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"stream":"ext://sys.stdout"
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},
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"info_file_handler":{
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"class":"logging.handlers.RotatingFileHandler",
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"level":"INFO",
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"formatter":"simple",
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"filename":"../log/ieemoo-ai-isempty-biz.log",
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"maxBytes":10485760,
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"backupCount":20,
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"encoding":"utf8"
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},
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"error_file_handler":{
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"class":"logging.handlers.RotatingFileHandler",
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"level":"ERROR",
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"formatter":"simple",
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"filename":"../log/ieemoo-ai-isempty-biz.log",
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"maxBytes":10485760,
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"backupCount":20,
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"encoding":"utf8"
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}
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},
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"loggers":{
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"my_module":{
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"level":"ERROR",
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"handlers":["info_file_handler"],
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"propagate":"no"}
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},
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"root":{
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"level":"INFO",
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"handlers":["console","info_file_handler","error_file_handler"]
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}
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}
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