Merge branch 'main' into main
This commit is contained in:
KevinHuSh
GitHub
commit
f5fa08540e
15 changed files with 37 additions and 18 deletions
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@ -158,6 +158,7 @@ class PaddleInferBenchmark(object):
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return:
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config_status(dict): dict style config info
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"""
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if isinstance(config, paddle_infer.Config):
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config_status = {}
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config_status['runtime_device'] = "gpu" if config.use_gpu(
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@ -15,7 +15,6 @@
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import numpy as np
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import paddle
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import paddle.nn as nn
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from scipy.special import softmax
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from scipy.interpolate import InterpolatedUnivariateSpline
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@ -26,6 +26,7 @@ from infer import Detector, DetectorPicoDet, PredictConfig, print_arguments, get
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from keypoint_infer import KeyPointDetector, PredictConfig_KeyPoint
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from visualize import visualize_pose
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from benchmark_utils import PaddleInferBenchmark
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from utils import get_current_memory_mb
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from keypoint_postprocess import translate_to_ori_images
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@ -19,6 +19,7 @@ import json
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from pathlib import Path
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from functools import reduce
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import cv2
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import numpy as np
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import math
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@ -28,6 +29,7 @@ from paddle.inference import create_predictor
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import sys
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# add deploy path of PaddleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'])))
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sys.path.insert(0, parent_path)
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@ -19,6 +19,7 @@ import glob
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from functools import reduce
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from PIL import Image
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import cv2
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import math
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import numpy as np
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@ -35,6 +36,7 @@ from keypoint_postprocess import HrHRNetPostProcess, HRNetPostProcess
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from visualize import visualize_pose
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from paddle.inference import Config
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from paddle.inference import create_predictor
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from utils import argsparser, Timer, get_current_memory_mb
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from benchmark_utils import PaddleInferBenchmark
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from infer import Detector, get_test_images, print_arguments
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@ -371,6 +371,7 @@ class CenterTrack(Detector):
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online_scores,
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frame_id=frame_id,
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ids2names=ids2names)
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if seq_name is None:
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seq_name = image_list[0].split('/')[-2]
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save_dir = os.path.join(self.output_dir, seq_name)
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@ -442,12 +443,12 @@ class CenterTrack(Detector):
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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if self.save_mot_txts:
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result_filename = os.path.join(
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self.output_dir, video_out_name.split('.')[-2] + '.txt')
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write_mot_results(result_filename, results, data_type, num_classes)
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writer.release()
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@ -114,6 +114,7 @@ class JDE_Detector(Detector):
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tracked_thresh=tracked_thresh,
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metric_type=metric_type)
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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np_boxes = result['pred_dets']
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@ -247,6 +248,7 @@ class JDE_Detector(Detector):
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online_scores,
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frame_id=frame_id,
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ids2names=ids2names)
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if seq_name is None:
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seq_name = image_list[0].split('/')[-2]
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save_dir = os.path.join(self.output_dir, seq_name)
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@ -255,6 +257,7 @@ class JDE_Detector(Detector):
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cv2.imwrite(
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os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
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mot_results.append([online_tlwhs, online_scores, online_ids])
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return mot_results
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@ -16,6 +16,7 @@ import os
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import json
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import cv2
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import math
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import numpy as np
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import paddle
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import yaml
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@ -28,6 +28,7 @@ from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
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from rag.utils import ELASTICSEARCH
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from rag.utils import MINIO
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from rag.utils import rmSpace, findMaxTm
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from rag.nlp import huchunk, huqie, search
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from io import BytesIO
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import pandas as pd
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@ -106,18 +107,18 @@ def build(row, cvmdl):
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set_progress(row["id"], -1, "File size exceeds( <= %dMb )" %
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(int(DOC_MAXIMUM_SIZE / 1024 / 1024)))
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return []
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# If just change the kb for doc
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# res = ELASTICSEARCH.search(Q("term", doc_id=row["id"]), idxnm=search.index_name(row["tenant_id"]))
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# if ELASTICSEARCH.getTotal(res) > 0:
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# ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=row["id"]),
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# scripts="""
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# if(!ctx._source.kb_id.contains('%s'))
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# ctx._source.kb_id.add('%s');
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# """ % (str(row["kb_id"]), str(row["kb_id"])),
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# idxnm=search.index_name(row["tenant_id"])
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# )
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# set_progress(row["id"], 1, "Done")
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# return []
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res = ELASTICSEARCH.search(Q("term", doc_id=row["id"]))
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if ELASTICSEARCH.getTotal(res) > 0:
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ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=row["id"]),
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scripts="""
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if(!ctx._source.kb_id.contains('%s'))
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ctx._source.kb_id.add('%s');
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""" % (str(row["kb_id"]), str(row["kb_id"])),
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idxnm=search.index_name(row["tenant_id"])
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)
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set_progress(row["id"], 1, "Done")
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return []
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random.seed(time.time())
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set_progress(row["id"], random.randint(0, 20) /
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@ -135,7 +136,9 @@ def build(row, cvmdl):
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row["id"], -1, f"Internal server error: %s" %
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str(e).replace(
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"'", ""))
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cron_logger.warn("Chunkking {}/{}: {}".format(row["location"], row["name"], str(e)))
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return []
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if not obj.text_chunks and not obj.table_chunks:
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@ -40,7 +40,7 @@ def findMaxDt(fnm):
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print("WARNING: can't find " + fnm)
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return m
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def findMaxTm(fnm):
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m = 0
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try:
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@ -58,6 +58,7 @@ def findMaxTm(fnm):
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print("WARNING: can't find " + fnm)
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return m
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def num_tokens_from_string(string: str) -> int:
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"""Returns the number of tokens in a text string."""
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encoding = tiktoken.get_encoding('cl100k_base')
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@ -276,4 +276,5 @@ def change_parser():
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return get_json_result(data=True)
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except Exception as e:
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return server_error_response(e)
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return server_error_response(e)
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@ -183,7 +183,9 @@ def rollback_user_registration(user_id):
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except Exception as e:
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pass
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def user_register(user_id, user):
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user_id = get_uuid()
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user["id"] = user_id
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tenant = {
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@ -467,6 +467,7 @@ class Knowledgebase(DataBaseModel):
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doc_num = IntegerField(default=0)
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token_num = IntegerField(default=0)
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chunk_num = IntegerField(default=0)
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parser_id = CharField(max_length=32, null=False, help_text="default parser ID")
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status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted，1: validate)", default="1")
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@ -85,4 +85,5 @@ class DocumentService(CommonService):
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cls.model.id == doc_id).execute()
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if num == 0:raise LookupError("Document not found which is supposed to be there")
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num = Knowledgebase.update(token_num=Knowledgebase.token_num+token_num, chunk_num=Knowledgebase.chunk_num+chunk_num).where(Knowledgebase.id==kb_id).execute()
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return num
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return num
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@ -31,7 +31,6 @@ class LLMService(CommonService):
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model = LLM
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class TenantLLMService(CommonService):
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model = TenantLLM
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@ -51,3 +50,4 @@ class TenantLLMService(CommonService):
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if not objs:return
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return objs[0]
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