utils/wandb_logging PEP8 reformat (#2755)
* wandb_logging PEP8 reformat * Update wandb_utils.py
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@ -1,10 +1,8 @@
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import argparse
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from pathlib import Path
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import yaml
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from wandb_utils import WandbLogger
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from utils.datasets import LoadImagesAndLabels
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WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
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@ -21,6 +19,6 @@ if __name__ == '__main__':
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
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opt = parser.parse_args()
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opt.resume = False # Explicitly disallow resume check for dataset upload Job
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opt.resume = False # Explicitly disallow resume check for dataset upload job
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create_dataset_artifact(opt)
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@ -1,12 +1,9 @@
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import argparse
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import json
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import os
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import shutil
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import sys
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from pathlib import Path
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import torch
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import yaml
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from datetime import datetime
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from pathlib import Path
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from tqdm import tqdm
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sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path
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@ -33,6 +30,7 @@ def check_wandb_config_file(data_config_file):
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return wandb_config
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return data_config_file
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def get_run_info(run_path):
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run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
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run_id = run_path.stem
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@ -40,11 +38,12 @@ def get_run_info(run_path):
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model_artifact_name = 'run_' + run_id + '_model'
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return run_id, project, model_artifact_name
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def check_wandb_resume(opt):
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process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None
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if isinstance(opt.resume, str):
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if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
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if opt.global_rank not in [-1, 0]: # For resuming DDP runs
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if opt.global_rank not in [-1, 0]: # For resuming DDP runs
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run_id, project, model_artifact_name = get_run_info(opt.resume)
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api = wandb.Api()
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artifact = api.artifact(project + '/' + model_artifact_name + ':latest')
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@ -53,6 +52,7 @@ def check_wandb_resume(opt):
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return True
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return None
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def process_wandb_config_ddp_mode(opt):
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with open(opt.data) as f:
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data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
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@ -63,7 +63,7 @@ def process_wandb_config_ddp_mode(opt):
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train_dir = train_artifact.download()
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train_path = Path(train_dir) / 'data/images/'
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data_dict['train'] = str(train_path)
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if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
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api = wandb.Api()
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val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
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@ -71,12 +71,11 @@ def process_wandb_config_ddp_mode(opt):
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val_path = Path(val_dir) / 'data/images/'
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data_dict['val'] = str(val_path)
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if train_dir or val_dir:
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ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
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ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
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with open(ddp_data_path, 'w') as f:
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yaml.dump(data_dict, f)
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opt.data = ddp_data_path
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class WandbLogger():
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def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
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@ -84,7 +83,7 @@ class WandbLogger():
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self.job_type = job_type
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self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
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# It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
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if isinstance(opt.resume, str): # checks resume from artifact
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if isinstance(opt.resume, str): # checks resume from artifact
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if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
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run_id, project, model_artifact_name = get_run_info(opt.resume)
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model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
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@ -98,7 +97,7 @@ class WandbLogger():
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project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
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name=name,
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job_type=job_type,
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id=run_id) if not wandb.run else wandb.run
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id=run_id) if not wandb.run else wandb.run
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if self.wandb_run:
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if self.job_type == 'Training':
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if not opt.resume:
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@ -110,15 +109,15 @@ class WandbLogger():
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if self.job_type == 'Dataset Creation':
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self.data_dict = self.check_and_upload_dataset(opt)
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else:
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print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
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prefix = colorstr('wandb: ')
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print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
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def check_and_upload_dataset(self, opt):
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assert wandb, 'Install wandb to upload dataset'
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check_dataset(self.data_dict)
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config_path = self.log_dataset_artifact(opt.data,
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opt.single_cls,
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'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
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opt.single_cls,
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'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
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print("Created dataset config file ", config_path)
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with open(config_path) as f:
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wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader)
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