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Maelic / SGG-Benchmark

A New Benchmark for Scene Graph Generation, targeting real-world applications
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Unable to get prediction results in coco with yolov8m #35

Open Mingyuan1997 opened 1 week ago

Mingyuan1997 commented 1 week ago

Thanks for the great work and a quick fix on the informative file!

However I still got into trouble when I tried running the training command. Could you help me to see why I cannot get prediction results from the detector?

I used pretrained weight in your link (yolov8 trained at VG)

CUDA_VISIBLE_DEVICES=0 python tools/relation_train_net.py --task sgdet --save-best --config-file "configs/VG150/e2e_relation_yolov8m.yaml" MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 12 TEST.IMS_PER_BATCH 2 DTYPE "float16" SOLVER.MAX_EPOCH 20 MODEL.PRETRAINED_DETECTOR_CKPT ./yolov8m_vg150.pt OUTPUT_DIR ./checkpoints/

2024-10-14 22:40:25.637 | INFO | sgg_benchmark.utils.logger:setup_logger:31 - Using loguru logger with level: INFO 2024-10-14 22:40:25.638 | INFO | main:main:444 - Using 1 GPUs 2024-10-14 22:40:25.638 | INFO | sgg_benchmark.utils.logger:logger_step:15 - #################### Step 1: Collecting environment info... #################### 2024-10-14 22:40:29.360 | INFO | main:main:457 - Saving config into: ./checkpoints/config.yml 2024-10-14 22:40:29.377 | INFO | sgg_benchmark.utils.logger:logger_step:15 - #################### Step 2: Building model... #################### Overriding model.yaml nc=80 with nc=150 2024-10-14 22:40:30.206 | INFO | sgg_benchmark.data.build:get_dataset_statistics:30 - ---------------------------------------------------------------------------------------------------- 2024-10-14 22:40:30.206 | INFO | sgg_benchmark.data.build:get_dataset_statistics:31 - get dataset statistics... 2024-10-14 22:40:30.206 | INFO | sgg_benchmark.data.build:get_dataset_statistics:42 - Loading data statistics from: ./checkpoints/VG150_train_statistics.cache 2024-10-14 22:40:30.206 | INFO | sgg_benchmark.data.build:get_dataset_statistics:43 - ---------------------------------------------------------------------------------------------------- 2024-10-14 22:40:34.091 | INFO | sgg_benchmark.data.build:get_dataset_statistics:30 - ---------------------------------------------------------------------------------------------------- 2024-10-14 22:40:34.091 | INFO | sgg_benchmark.data.build:get_dataset_statistics:31 - get dataset statistics... 2024-10-14 22:40:34.092 | INFO | sgg_benchmark.data.build:get_dataset_statistics:42 - Loading data statistics from: ./checkpoints/VG150_train_statistics.cache 2024-10-14 22:40:34.092 | INFO | sgg_benchmark.data.build:get_dataset_statistics:43 - ---------------------------------------------------------------------------------------------------- loading word vectors from ./glove.6B.200d.pt 2024-10-14 22:40:39.691 | INFO | sgg_benchmark.utils.logger:logger_step:15 - #################### Step 3: Building optimizer and scheduler... #################### Transferred 439/511 items from pretrained weights 2024-10-14 22:40:39.838 | INFO | sgg_benchmark.utils.logger:logger_step:15 - #################### Step 4: Loading Backbone weights from ./yolov8m_vg150.pt #################### 2024-10-14 22:40:39.839 | INFO | sgg_benchmark.utils.logger:logger_step:15 - #################### Step 5: Building checkpointer #################### 108073 /home/haozhen/mingyuan/SGG-Benchmark/datasets/vg/VG_100K 2024-10-14 22:40:42.501 | INFO | sgg_benchmark.utils.miscellaneous:save_labels:50 - Saving labels mapping into ./checkpoints/labels.json 108073 /home/haozhen/mingyuan/SGG-Benchmark/datasets/vg/VG_100K 2024-10-14 22:40:43.736 | INFO | sgg_benchmark.utils.logger:logger_step:15 - #################### Step 6: Building dataloader #################### 2024-10-14 22:40:43.736 | INFO | main:train:203 - Validate before training 2024-10-14 22:40:43.737 | INFO | sgg_benchmark.engine.inference:inference:263 - Start evaluation on VG150_val dataset(5000 images). 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2501/2501 [02:26<00:00, 17.07it/s] 2024-10-14 22:43:10.251 | INFO | sgg_benchmark.engine.inference:inference:280 - Total run time: 0:02:18 (27.661574461897676 ms / img per device, on 1 devices) 2024-10-14 22:43:10.252 | INFO | sgg_benchmark.engine.inference:inference:289 - Average latency per image: 27.661574461897676ms 2024-10-14 22:43:10.252 | INFO | sgg_benchmark.engine.inference:inference:292 - Standard deviation of latency: 24.626527727053297ms 2024-10-14 22:43:11.539 | INFO | sgg_benchmark.data.datasets.evaluation.vg.vg_eval:do_vg_evaluation:70 - Dataset does not have informative_rels, skipping informative evaluation for dataset: VG150 creating index... index created! Loading and preparing results... Converting ndarray to lists... (0, 7) Traceback (most recent call last): File "/home/haozhen/mingyuan/SGG-Benchmark/tools/relation_train_net.py", line 491, in main() File "/home/haozhen/mingyuan/SGG-Benchmark/tools/relation_train_net.py", line 470, in main model, best_checkpoint = train( ^^^^^^ File "/home/haozhen/mingyuan/SGG-Benchmark/tools/relation_train_net.py", line 204, in train run_val(cfg, model, val_data_loaders, args['distributed'], logger, device=device) File "/home/haozhen/mingyuan/SGG-Benchmark/tools/relation_train_net.py", line 323, in run_val dataset_result = inference( ^^^^^^^^^^ File "/home/haozhen/mingyuan/SGG-Benchmark/sgg_benchmark/engine/inference.py", line 335, in inference return evaluate(cfg=cfg, ^^^^^^^^^^^^^^^^^ File "/home/haozhen/mingyuan/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/init.py", line 27, in evaluate return vg_evaluation(**args) ^^^^^^^^^^^^^^^^^^^^^ File "/home/haozhen/mingyuan/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/init.py", line 15, in vg_evaluation return do_vg_evaluation( ^^^^^^^^^^^^^^^^^ File "/home/haozhen/mingyuan/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/vg_eval.py", line 119, in do_vg_evaluation res = fauxcoco.loadRes(cocolike_predictions) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/data/conda/env/scene_graph_benchmark/lib/python3.11/site-packages/pycocotools/coco.py", line 329, in loadRes if 'caption' in anns[0]:


IndexError: list index out of range
Young-Loser commented 1 week ago

I also meet this error.

Maelic commented 1 week ago

It seems that the object detector is not detecting anything, I will investigate this issue.

Maelic commented 1 week ago

I am not able to reproduce the error, can you share your config file please?

Maelic commented 1 week ago

You can copy the content of the .yaml file here

Young-Loser commented 1 week ago

您可以在此处复制 .yaml 文件的内容

This is my config.yml file, but I don't know why the PATHS_DATA path is like this. I have already changed the data loading path in paths_catalog.py, and I am using the VG150 dataset. `DATALOADER: ASPECT_RATIO_GROUPING: true NUM_WORKERS: 4 SIZE_DIVISIBILITY: 32 DATASETS: NAME: '' TEST:

Mingyuan1997 commented 1 week ago

Thanks for your investigation. My yaml as follows:

INPUT: MIN_SIZE_TRAIN: 640 MAX_SIZE_TRAIN: 640 MIN_SIZE_TEST: 640 MAX_SIZE_TEST: 640 PADDING: True MODEL: TEXT_EMBEDDING: glove.6B META_ARCHITECTURE: "GeneralizedYOLO" BOX_HEAD: False WEIGHT: "" PRETRAINED_DETECTOR_CKPT: "/home/haozhen/mingyuan/SGG-Benchmark/yolov8m_vg150.pt" BACKBONE: TYPE: "yolov8" NMS_THRESH: 0.001 FREEZE: True YOLO: SIZE: "yolov8m" OUT_CHANNELS: 192 # 192 for yolov8m, 256 for yolov8l RELATION_ON: True ATTRIBUTE_ON: False FLIP_AUG: False # if there is any left-right relation, FLIP AUG should be false ROI_HEADS: POSITIVE_FRACTION: 0.5 FG_IOU_THRESHOLD: 0.3 BG_IOU_THRESHOLD: 0.1 BATCH_SIZE_PER_IMAGE: 256 DETECTIONS_PER_IMG: 80 NMS_FILTER_DUPLICATES: True NMS: 0.2 ROI_BOX_HEAD: POOLER_SCALES: (0.125, 0.0625, 0.03125) POOLER_RESOLUTION: 7 POOLER_SAMPLING_RATIO: 2 FEATURE_EXTRACTOR: "YOLOV8FeatureExtractor" NUM_CLASSES: 151 # 151 for VG, 1201 for GQA ROI_RELATION_HEAD: EMBED_DIM: 200 USE_GT_BOX: False USE_GT_OBJECT_LABEL: False USE_SPATIAL_FEATURES: True REQUIRE_BOX_OVERLAP: False # for sgdet, during training, only train pairs with overlap ADD_GTBOX_TO_PROPOSAL_IN_TRAIN: True # for sgdet only, in case some gt boxes are missing NUM_CLASSES: 51 # 51 for VG, 201 for GQA (not contain "to the left of" & "to the right of") BATCH_SIZE_PER_IMAGE: 256 # sample as much as possible POSITIVE_FRACTION: 0.25 CONTEXT_POOLING_DIM: 2048 CONTEXT_HIDDEN_DIM: 512 #1024 for VCTree POOLING_ALL_LEVELS: True LABEL_SMOOTHING_LOSS: False FEATURE_EXTRACTOR: "RelationFeatureExtractor" #################### Select Relationship Model ####################

PREDICTOR: "MotifPredictor"

#PREDICTOR: "VCTreePredictor"
#PREDICTOR: "TransformerPredictor"
PREDICTOR: "CausalAnalysisPredictor"
################# Parameters for Motif Predictor ##################
CONTEXT_OBJ_LAYER: 1
CONTEXT_REL_LAYER: 1
############# Parameters for Causal Unbias Predictor ##############
### Implementation for paper "Unbiased Scene Graph Generation from Biased Training"
CAUSAL:
  EFFECT_TYPE: 'TDE'             # candicates: 'TDE', 'NIE', 'TE', 'none'
  FUSION_TYPE: 'sum'              # candicates: 'sum', 'gate'         
  SEPARATE_SPATIAL: False         # separate spatial in union feature
  CONTEXT_LAYER: "motifs"         # candicates: motifs, vctree, vtranse
  EFFECT_ANALYSIS: True
############### Parameters for Transformer Predictor ##############
TRANSFORMER:
  DROPOUT_RATE: 0.1
  OBJ_LAYER: 4
  REL_LAYER: 2
  NUM_HEAD: 8
  KEY_DIM: 64
  VAL_DIM: 64
  INNER_DIM: 2048

DATASETS: TRAIN: ("VG150_train",) VAL: ("VG150_val",) TEST: ("VG150_test",) DATALOADER: SIZE_DIVISIBILITY: 32 SOLVER: MAX_EPOCH: 20 BIAS_LR_FACTOR: 1 BASE_LR: 0.01 WARMUP_FACTOR: 0.1 WEIGHT_DECAY: 0.0001 MOMENTUM: 0.9 GRAD_NORM_CLIP: 5.0 STEPS: (10000, 16000) MAX_ITER: 40000 VAL_PERIOD: 2000 CHECKPOINT_PERIOD: 2000 PRINT_GRAD_FREQ: 4000 SCHEDULE:

the following paramters are only used for WarmupReduceLROnPlateau

TYPE: "WarmupReduceLROnPlateau"    # WarmupMultiStepLR, WarmupReduceLROnPlateau
PATIENCE: 2
THRESHOLD: 0.001
COOLDOWN: 0
FACTOR: 0.1
MAX_DECAY_STEP: 3

OUTPUT_DIR: '/checkpoints' TEST: ALLOW_LOAD_FROM_CACHE: False INFORMATIVE: True RELATION: SYNC_GATHER: True # turn on will slow down the evaluation to solve the sgdet test out of memory problem REQUIRE_OVERLAP: True LATER_NMS_PREDICTION_THRES: 0.5 CUSTUM_EVAL: False # eval SGDet model on custum images, output a json CUSTUM_PATH: '.' # the folder that contains the custum images, only jpg files are allowed

Young-Loser commented 6 days ago

感谢您的调查。我的 yaml 如下：

输入： MIN_SIZE_TRAIN：640 MAX_SIZE_TRAIN：640 MIN_SIZE_TEST：640 MAX_SIZE_TEST：640 PADDING：True 模型： TEXT_EMBEDDING：glove.6B META_ARCHITECTURE：“GeneralizedYOLO” BOX_HEAD：False WEIGHT：“” PRETRAINED_DETECTOR_CKPT：“/home/haozhen/mingyuan/SGG-Benchmark/yolov8m_vg150.pt” BACKBONE： 类型：“yolov8” NMS_THRESH：0.001 FREEZE：True YOLO： SIZE：“yolov8m” OUT_CHANNELS：192 # yolov8m 为 192，yolov8l 为 256 RELATION_ON：True ATTRIBUTE_ON：False FLIP_AUG: False # 如果存在任何左右关系，FLIP AUG 应该为 false ROI_HEADS: POSITIVE_FRACTION: 0.5 FG_IOU_THRESHOLD: 0.3 BG_IOU_THRESHOLD: 0.1 BATCH_SIZE_PER_IMAGE: 256 DETECTIONS_PER_IMG: 80 NMS_FILTER_DUPLICATES: True NMS: 0.2 ROI_BOX_HEAD: POOLER_SCALES: (0.125, 0.0625, 0.03125) POOLER_RESOLUTION: 7 POOLER_SAMPLING_RATIO: 2 FEATURE_EXTRACTOR: "YOLOV8FeatureExtractor" NUM_CLASSES: 151 # 151 表示 VG，1201 表示 GQA ROI_RELATION_HEAD： EMBED_DIM：200 USE_GT_BOX：False USE_GT_OBJECT_LABEL：False USE_SPATIAL_FEATURES：True REQUIRE_BOX_OVERLAP：False # 对于 sgdet，在训练期间，仅训练有重叠的对 ADD_GTBOX_TO_PROPOSAL_IN_TRAIN：True # 仅适用于 sgdet，以防缺少某些 gt 框 NUM_CLASSES：51 # 51 用于 VG，201 用于 GQA（不包含“左侧”和“右侧”） BATCH_SIZE_PER_IMAGE：256 # 尽可能多地采样 POSITIVE_FRACTION：0.25 CONTEXT_POOLING_DIM：2048 CONTEXT_HIDDEN_DIM：512 #1024 用于 VCTree POOLING_ALL_LEVELS：True LABEL_SMOOTHING_LOSS：False FEATURE_EXTRACTOR：“RelationFeatureExtractor” #################### 选择关系模型 #################### #PREDICTOR：“MotifPredictor” #PREDICTOR：“VCTreePredictor” #PREDICTOR：“TransformerPredictor” PREDICTOR：“CausalAnalysisPredictor” ################# Motif 预测器的参数 ################## CONTEXT_OBJ_LAYER：1 CONTEXT_REL_LAYER：1 ############# 因果无偏预测器的参数 ############## ### 论文“从有偏训练中生成无偏场景图”的实现 CAUSAL： EFFECT_TYPE：“TDE” #候选人：“TDE”，'NIE', 'TE', 'none' FUSION_TYPE: 'sum' # 候选: 'sum', 'gate' SEPARATE_SPATIAL: False # 在联合特征中分离空间 CONTEXT_LAYER: "motifs" # 候选者: motifs、vctree、vtranse EFFECT_ANALYSIS: True ############### Transformer Predictor 的参数 ############## TRANSFORMER: DROPOUT_RATE: 0.1 OBJ_LAYER: 4 REL_LAYER: 2 NUM_HEAD: 8 KEY_DIM: 64 VAL_DIM: 64 INNER_DIM: 2048 DATASETS: TRAIN: ("VG150_train",) VAL: ("VG150_val",) TEST: ("VG150_test",) DATALOADER: SIZE_DIVISIBILITY: 32 SOLVER: MAX_EPOCH: 20 BIAS_LR_FACTOR: 1 BASE_LR: 0.01 WARMUP_FACTOR: 0.1 WEIGHT_DECAY: 0.0001 MOMENTUM: 0.9 GRAD_NORM_CLIP: 5.0 STEPS: (10000, 16000) MAX_ITER: 40000 VAL_PERIOD: 2000 CHECKPOINT_PERIOD: 2000 PRINT_GRAD_FREQ: 4000 SCHEDULE: # 以下参数仅用于 WarmupReduceLROnPlateau TYPE: "WarmupReduceLROnPlateau" # WarmupMultiStepLR, WarmupReduceLROnPlateau PATIENCE: 2 THRESHOLD: 0.001 COOLDOWN: 0 FACTOR: 0.1 MAX_DECAY_STEP: 3 OUTPUT_DIR: '/checkpoints' TEST: ALLOW_LOAD_FROM_CACHE: False INFORMATIVE: True RELATION: SYNC_GATHER: True # 开启会减慢评估速度，解决 sgdet 测试内存不足问题 REQUIRE_OVERLAP: True LATER_NMS_PREDICTION_THRES: 0.5 CUSTUM_EVAL: False # 在自定义图片上评估 SGDet 模型，输出一个 json CUSTUM_PATH: '.' # 包含自定义图片的文件夹，只允许 jpg 文件

您好！看到您的回复，我觉得您应该是中国人。我是山东大学研一新生。我有一些关于这个项目复现的问题想向您请教，我是sgg初学者，想和您进行交流。我的邮箱是1339241893@qq.com，我的微信是：XC-992997，希望与您加上好友进行交流。期待得到您的回复，希望与您进行交流学习！（如果可以的话，您可以回复我您的微信，我来加您）

Maelic commented 6 days ago

After a lot of digging I think I found out the issue, it is related to the latest ultralytics implementation. In a recent release, ultralytics changed their codebase for yolov8, and this impacted my implementation of the yolov8 backbone here. As a workaround for now, you can fallback to the previous version of ultralytics as follows: pip install ultralytics==8.2.70 This should solve the issue. I will update the requirements accordingly.

Young-Loser commented 6 days ago

经过大量挖掘，我想我找到了问题所在，它与最新的 ultralytics 实现有关。在最近的一个版本中，ultralytics 更改了 yolov8 的代码库，这影响了我在这里对 yolov8 主干的实现。 作为一种解决方法，您可以按如下方式回退到以前版本的 ultralytics： pip install ultralytics==8.2.70 这应该可以解决问题。我会相应地更新要求。

I tried the methods you suggested, and the previous error is indeed gone. However, now there's a new error: `File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1514, in main() File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1493, in main model, best_checkpoint = train( ^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1253, in train val_result = run_val(cfg, model, val_data_loaders, args['distributed'], logger) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1343, in run_val dataset_result = inference( ^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/engine/inference.py", line 335, in inference return evaluate(cfg=cfg, ^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/init.py", line 27, in evaluate return vg_evaluation(**args) ^^^^^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/init.py", line 15, in vg_evaluation return do_vg_evaluation( ^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/vg_eval.py", line 159, in do_vg_evaluation evaluate_relation_of_one_image(groundtruth, prediction, global_container, evaluator, informative=informative) File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/vg_eval.py", line 287, in evaluate_relation_of_one_image v.calculate(global_container, local_container, mode) File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/sgg_eval.py", line 327, in calculate gt_relationships = local_container['informative_rels']


KeyError: 'informative_rels'
`
Young-Loser commented 6 days ago

我尝试了你建议的方法，之前的错误确实消失了。但是现在又出现了一个新的错误： File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1514, in main() File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1493, in main model, best_checkpoint = train( ^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1253, in train val_result = run_val(cfg, model, val_data_loaders, args['distributed'], logger) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/tools/relation_train_net.py", line 1343, in run_val dataset_result = inference( ^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/engine/inference.py", line 335, in inference return evaluate(cfg=cfg, ^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/init.py", line 27, in evaluate return vg_evaluation(**args) ^^^^^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/init.py", line 15, in vg_evaluation return do_vg_evaluation( ^^^^^^^^^^^^^^^^^ File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/vg_eval.py", line 159, in do_vg_evaluation evaluate_relation_of_one_image(groundtruth, prediction, global_container, evaluator, informative=informative) File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/vg_eval.py", line 287, in evaluate_relation_of_one_image v.calculate(global_container, local_container, mode) File "/Dpan/wyc/realtime_rwsg/SGG-Benchmark/sgg_benchmark/data/datasets/evaluation/vg/sgg_eval.py", line 327, in calculate gt_relationships = local_container['informative_rels'] ~~~^^^^^^^^^^^^^^^^^^^^ KeyError: 'informative_rels'

Mingyuan1997 commented 5 days ago

Thank you so much and I do appreciate tremendous efforts you put in investigating the issue. The detector release is always unstable lol. I am not aware of this since I have not followed the yolo line of work in recent years. Thanks again!

Maelic commented 5 days ago

No worries, hope it helps. By the way, you should put Informative: False in your .yaml config files to not get bothered by the informative_rels etc @Mingyuan1997 @Young-Loser

Young-Loser commented 5 days ago

您好！看到您的回复，我觉得您应该是中国人。我是山东大学研一新生。我有一些关于这个项目复现的问题想向您请教，我是sgg初学者，想和您进行交流。我的邮箱是1339241893@qq.com，我的微信是：XC-992997，希望与您加上好友进行交流。期待得到您的回复，希望与您进行交流学习！（如果可以的话，您可以回复我您的微信，我来加您）