def save(self, model_url="", model_name=None):
mname = model_name or self.model_name
if os.path.isfile(self.model_save_path):
self.model_save_path, mname = os.path.split(self.model_save_path)
FileOps.clean_folder([self.model_save_path], clean=False)
model_path = FileOps.join_path(self.model_save_path, mname)
self.estimator.save(model_path)
if model_url and FileOps.exists(model_path):
FileOps.upload(model_path, model_url)
model_path = model_url
return model_path
def __init__(self, estimator, hard_example_mining: dict = None):
super(IncrementalLearning, self).__init__(estimator=estimator)
self.model_urls = self.get_parameters(
"MODEL_URLS") # use in evaluation
self.job_kind = K8sResourceKind.INCREMENTAL_JOB.value
FileOps.clean_folder([self.config.model_url], clean=False)
self.hard_example_mining_algorithm = None
if not hard_example_mining:
hard_example_mining = self.get_hem_algorithm_from_config()
if hard_example_mining:
hem = hard_example_mining.get("method", "IBT")
hem_parameters = hard_example_mining.get("param", {})
self.hard_example_mining_algorithm = ClassFactory.get_cls(
ClassType.HEM, hem)(**hem_parameters)
def register(self, timeout=300):
"""
Deprecated, Client proactively subscribes to the aggregation service.
Parameters
----------
timeout: int, connect timeout. Default: 300
"""
self.log.info(
f"Node {self.worker_name} connect to : {self.config.agg_uri}")
self.node = AggregationClient(
url=self.config.agg_uri,
client_id=self.worker_name,
ping_timeout=timeout
)
FileOps.clean_folder([self.config.model_url], clean=False)
self.aggregation = self.aggregation()
self.log.info(f"{self.worker_name} model prepared")
if callable(self.estimator):
self.estimator = self.estimator()
def __init__(self,
host: str,
http_port: int = 8080,
workers: int = 1,
save_dir=""):
servername = "knowledgebase"
super(KBServer, self).__init__(servername=servername,
host=host,
http_port=http_port,
workers=workers)
self.save_dir = FileOps.clean_folder([save_dir], clean=False)[0]
self.url = f"{self.url}/{servername}"
self.kb_index = KBResourceConstant.KB_INDEX_NAME.value
self.app = FastAPI(
routes=[
APIRoute(
f"/{servername}/update",
self.update,
methods=["POST"],
),
APIRoute(
f"/{servername}/update/status",
self.update_status,
methods=["POST"],
),
APIRoute(
f"/{servername}/query",
self.query,
response_model=TaskItem,
response_class=JSONResponse,
methods=["POST"],
),
APIRoute(
f"/{servername}/file/download",
self.file_download,
methods=["GET"],
),
APIRoute(
f"/{servername}/file/upload",
self.file_upload,
methods=["POST"],
),
],
log_level="trace",
timeout=600,
)
import time
import warnings
import cv2
import numpy as np
from sedna.common.config import Context
from sedna.common.file_ops import FileOps
from sedna.core.incremental_learning import IncrementalLearning
from interface import Estimator
he_saved_url = Context.get_parameters("HE_SAVED_URL", '/tmp')
rsl_saved_url = Context.get_parameters("RESULT_SAVED_URL", '/tmp')
class_names = ['person', 'helmet', 'helmet_on', 'helmet_off']
FileOps.clean_folder([he_saved_url, rsl_saved_url], clean=False)
def draw_boxes(img, labels, scores, bboxes, class_names, colors):
line_type = 2
text_thickness = 1
box_thickness = 1
# get color code
colors = colors.split(",")
colors_code = []
for color in colors:
if color == 'green':
colors_code.append((0, 255, 0))
elif color == 'blue':
colors_code.append((255, 0, 0))
elif color == 'yellow':