From 9a84f4bb4bcb10a674ba402555b83d34cd1ae78c Mon Sep 17 00:00:00 2001
From: "lee.lcy" <lee.lcy@alibaba-inc.com>
Date: Mon, 13 Mar 2023 11:20:26 +0800
Subject: [PATCH] feat(thirdparty): add ADADET && add thirdparty arg for
 damoyolo trainer.

feat(thirdparty): add ADADET && add thirdparty arg for damoyolo trainer.
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11957959

    * feat(thirdparty): add ADADET && add thirdparty arg for damoyolo trainer.

(cherry picked from commit 104213e4bf6817b54aa3b7609e237eadd3c03769)
---
 .../trainers/cv/image_detection_damoyolo_trainer.py  | 12 +++++++++---
 modelscope/utils/constant.py                         |  1 +
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/modelscope/trainers/cv/image_detection_damoyolo_trainer.py b/modelscope/trainers/cv/image_detection_damoyolo_trainer.py
index c8081ee0..8d8b32ae 100644
--- a/modelscope/trainers/cv/image_detection_damoyolo_trainer.py
+++ b/modelscope/trainers/cv/image_detection_damoyolo_trainer.py
@@ -28,7 +28,8 @@ from modelscope.msdatasets.dataset_cls.custom_datasets.damoyolo import (
 from modelscope.trainers.base import BaseTrainer
 from modelscope.trainers.builder import TRAINERS
 from modelscope.utils.checkpoint import save_checkpoint
-from modelscope.utils.constant import DEFAULT_MODEL_REVISION, ModelFile
+from modelscope.utils.constant import (DEFAULT_MODEL_REVISION, ModelFile,
+                                       ThirdParty)
 from modelscope.utils.logger import get_logger
 from modelscope.utils.metric import MeterBuffer
 from modelscope.utils.torch_utils import get_rank, synchronize
@@ -62,14 +63,19 @@ class ImageDetectionDamoyoloTrainer(BaseTrainer):
             train_ann: the path of train set annotation file.
             val_ann: the path of val set annotation file.
             num_classes: class number.
-            base_lr_per_img: learning rate per image. The final learning rate is base_lr_per_img*batch_size.
+            base_lr_per_img: learning rate per image.
+                The final learning rate is base_lr_per_img*batch_size.
             pretrain_model: the path of pretrained model.
             work_dir: the directory of work folder.
             exp_name: the name of experiment.
+            third_party: in which third party library this function is called.
         """
         if model is not None:
+            third_party = kwargs.get(ThirdParty.KEY)
+            if third_party is not None:
+                kwargs.pop(ThirdParty.KEY)
             self.cache_path = self.get_or_download_model_dir(
-                model, model_revision)
+                model, model_revision, third_party)
             if cfg_file is None:
                 self.cfg_file = os.path.join(self.cache_path,
                                              ModelFile.CONFIGURATION)
diff --git a/modelscope/utils/constant.py b/modelscope/utils/constant.py
index 6400c468..68630c81 100644
--- a/modelscope/utils/constant.py
+++ b/modelscope/utils/constant.py
@@ -399,6 +399,7 @@ class ThirdParty(object):
     KEY = 'third_party'
     EASYCV = 'easycv'
     ADASEQ = 'adaseq'
+    ADADET = 'adadet'
 
 
 class ConfigFields(object):