search

MIV-XJTU / ARTrack

Apache License 2.0
228 stars 33 forks source link

dependency conflicts #68

Closed sunqimin closed 4 months ago

sunqimin commented 4 months ago

When setting up the environment, executing the commands you provided results in many dependency conflicts among packages. How can this be resolved

AlexDotHam commented 4 months ago

I change a server and reproduced the enviroment, there maybe only numpy is conflicts with others, but not influence the execution of training and inference. Can you show me the detail of the problem you meet?

sunqimin commented 4 months ago

Sorry, I have turned off , because there are conflicts between many dependencies. Parts of it like this： Found conflicts! Looking for incompatible packages. This can take several minutes. Press CTRL-C to abort. Examining conflict for argon2-cffi-bindings brotlipy cffi gevent _anaconda_depends anaconda pycparser cryptography: : 37Examining conflict for harfbuzz cairo: : 374it [7:43:59, 82.24s/it] Examining conflict for pytest spyder atomicwrites _anaconda_depends anaconda: : 382it [7:46:49, 50.48s/i But it failed in the end. Now I am using pip to install packages one by one.

AlexDotHam commented 4 months ago

Sorry for that, i will retry and rebuild the environment in another server, and update the new environment support file.

sunqimin commented 4 months ago

I'm gonna bother you one more time，because I have a new problem like this：

$ sh tracking/run_video_demo.sh test config: {'MODEL': {'PRETRAIN_FILE': 'mae_pretrain_vit_base.pth', 'PRETRAIN _PTH': 'D:\SQM\tracking\ARTrack\pretrained_models\mae_pretrain_vit_base.pth ', 'PRENUM': 7, 'EXTRA_MERGER': False, 'RETURN_INTER': False, 'RETURN_STAGES': [ 2, 5, 8, 11], 'BACKBONE': {'TYPE': 'vit_base_patch16_224', 'STRIDE': 16, 'MID_PE ': False, 'SEP_SEG': False, 'CAT_MODE': 'direct', 'MERGE_LAYER': 0, 'ADD_CLS_TOK EN': False, 'CLS_TOKEN_USE_MODE': 'ignore', 'CE_LOC': [], 'CE_KEEP_RATIO': [], ' CE_TEMPLATE_RANGE': 'ALL'}, 'BINS': 400, 'RANGE': 2, 'ENCODER_LAYER': 3, 'NUM_HE ADS': 12, 'MLP_RATIO': 4, 'QKV_BIAS': True, 'DROP_RATE': 0.1, 'ATTN_DROP': 0.0, 'DROP_PATH': 0.0, 'DECODER_LAYER': 6, 'HEAD': {'TYPE': 'PIX', 'NUM_CHANNELS': 76 8}}, 'TRAIN': {'LR': 4e-06, 'WEIGHT_DECAY': 0.05, 'EPOCH': 60, 'LR_DROP_EPOCH': 999, 'BATCH_SIZE': 8, 'NUM_WORKER': 4, 'OPTIMIZER': 'ADAMW', 'BACKBONE_MULTIPLIE R': 0.1, 'GIOU_WEIGHT': 2.0, 'L1_WEIGHT': 0.0, 'FREEZE_LAYERS': [0], 'PRINT_INTE RVAL': 1, 'VAL_EPOCH_INTERVAL': 10, 'GRAD_CLIP_NORM': 0.1, 'AMP': False, 'CE_STA RT_EPOCH': 20, 'CE_WARM_EPOCH': 80, 'DROP_PATH_RATE': 0.1, 'SCHEDULER': {'TYPE': 'step', 'DECAY_RATE': 0.1}}, 'DATA': {'SAMPLER_MODE': 'causal', 'MEAN': [0.485, 0.456, 0.406], 'STD': [0.229, 0.224, 0.225], 'MAX_SAMPLE_INTERVAL': 200, 'MAX_G AP': 300, 'MAX_INTERVAL': 5, 'INTERVAL_PROB': 0.0, 'TEMP': 2, 'TRAIN': {'DATASET S_NAME': ['LASOT', 'GOT10K_vottrain', 'TRACKINGNET'], 'DATASETS_RATIO': [1, 1, 1 ], 'SAMPLE_PER_EPOCH': 1000}, 'VAL': {'DATASETS_NAME': ['GOT10K_official_val'], 'DATASETS_RATIO': [1], 'SAMPLE_PER_EPOCH': 10000}, 'SEARCH': {'SIZE': 256, 'FACT OR': 4.0, 'CENTER_JITTER': 3, 'SCALE_JITTER': 0.25, 'NUMBER': 36}, 'TEMPLATE': { 'NUMBER': 1, 'SIZE': 128, 'FACTOR': 2.0, 'CENTER_JITTER': 0, 'SCALE_JITTER': 0}} , 'TEST': {'TEMPLATE_FACTOR': 2.0, 'TEMPLATE_SIZE': 128, 'SEARCH_FACTOR': 4.0, ' SEARCH_SIZE': 256, 'EPOCH': 60}} 4 4 4 Traceback (most recent call last): File "D:\SQM\tracking\ARTrack\tracking\video_demo.py", line 43, in main() File "D:\SQM\tracking\ARTrack\tracking\video_demo.py", line 38, in main run_video(args.tracker_name, args.tracker_param, args.videofile, args.option al_box, args.debug, File "D:\SQM\tracking\ARTrack\tracking\video_demo.py", line 21, in run_video tracker.run_video(videofilepath=videofile, optional_box=optional_box, debug= debug, save_results=save_results) File "D:\SQM\tracking\ARTrack\tracking..\lib\test\evaluation\tracker.py", lin e 174, in run_video tracker = self.create_tracker(params) File "D:\SQM\tracking\ARTrack\tracking..\lib\test\evaluation\tracker.py", lin e 63, in create_tracker tracker = self.tracker_class(params, self.dataset_name) File "D:\SQM\tracking\ARTrack\tracking..\lib\test\tracker\artrack_seq.py", li ne 22, in init network = build_artrack_seq(params.cfg, training=False) File "D:\SQM\tracking\ARTrack\tracking..\lib\models\artrack_seq\artrack_seq.p y", line 102, in build_artrack_seq missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], str ict=False) KeyError: 'net' What should i do？

AlexDotHam commented 4 months ago

The PRETRAIN_PTH in your file is wrong, it should be the Checkpoint we provide in https://drive.google.com/drive/folders/1KsH_MIZIdgjZpUZBmR4P88yeYDqM8yNW but not the mae_pretrain you used.

sunqimin commented 4 months ago

Thank you！It‘s ok.

sunqimin commented 4 months ago

May I ask what is the difference between artrack and artrack_seq？

AlexDotHam commented 4 months ago

In our paper, artrack represents the first-stage template matching training, this processing is for the quick converge of our sequential training, which is called artrack_seq, this training process is different from a conventional tracking pipeline, sampling time-continuous frames from a video, then conduct tracking frame-by-frame then training our artrack_seq.