W&B: Restructure code to support the new dataset_check() feature (ult…

…ralytics#4197) * Improve docstrings and run names * default wandb login prompt with timeout * return key * Update api_key check logic * Properly support zipped dataset feature * update docstring * Revert tuorial change * extend changes to log_dataset * add run name * bug fix * bug fix * Update comment * fix import check * remove unused import * Hardcore .yaml file extension * reduce code * Reformat using pycharm Co-authored-by: Glenn Jocher <[email protected]>
fhkiel-mlaip · Jul 28, 2021 · 577a5ec · 577a5ec
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Showing 6 changed files with 52 additions and 40 deletions.
diff --git a/README.md b/README.md
diff --git a/train.py b/train.py
@@ -73,24 +73,29 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         yaml.safe_dump(hyp, f, sort_keys=False)
     with open(save_dir / 'opt.yaml', 'w') as f:
         yaml.safe_dump(vars(opt), f, sort_keys=False)
+    data_dict = None
+
+    # Loggers
+    if RANK in [-1, 0]:
+        loggers = Loggers(save_dir, weights, opt, hyp, LOGGER).start()  # loggers dict
+        if loggers.wandb:
+            data_dict = loggers.wandb.data_dict
+            if resume:
+                weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
 
+
     # Config
     plots = not evolve  # create plots
     cuda = device.type != 'cpu'
     init_seeds(1 + RANK)
     with torch_distributed_zero_first(RANK):
-        data_dict = check_dataset(data)  # check
+        data_dict = data_dict or check_dataset(data)  # check if None
     train_path, val_path = data_dict['train'], data_dict['val']
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check
     is_coco = data.endswith('coco.yaml') and nc == 80  # COCO dataset
 
-    # Loggers
-    if RANK in [-1, 0]:
-        loggers = Loggers(save_dir, weights, opt, hyp, data_dict, LOGGER).start()  # loggers dict
-        if loggers.wandb and resume:
-            weights, epochs, hyp, data_dict = opt.weights, opt.epochs, opt.hyp, loggers.wandb.data_dict
 
     # Model
     pretrained = weights.endswith('.pt')

diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py
@@ -1,9 +1,7 @@
 # YOLOv5 experiment logging utils
-
+import torch
 import warnings
 from threading import Thread
-
-import torch
 from torch.utils.tensorboard import SummaryWriter
 
 from utils.general import colorstr, emojis
@@ -23,12 +21,11 @@
 
 class Loggers():
     # YOLOv5 Loggers class
-    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, data_dict=None, logger=None, include=LOGGERS):
+    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
         self.save_dir = save_dir
         self.weights = weights
         self.opt = opt
         self.hyp = hyp
-        self.data_dict = data_dict
         self.logger = logger  # for printing results to console
         self.include = include
         for k in LOGGERS:
@@ -38,9 +35,7 @@ def start(self):
         self.csv = True  # always log to csv
 
         # Message
-        try:
-            import wandb
-        except ImportError:
+        if not wandb:
             prefix = colorstr('Weights & Biases: ')
             s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)"
             print(emojis(s))
@@ -57,7 +52,7 @@ def start(self):
             assert 'wandb' in self.include and wandb
             run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume else None
             self.opt.hyp = self.hyp  # add hyperparameters
-            self.wandb = WandbLogger(self.opt, run_id, self.data_dict)
+            self.wandb = WandbLogger(self.opt, run_id)
         except:
             self.wandb = None
 

diff --git a/utils/loggers/wandb/log_dataset.py b/utils/loggers/wandb/log_dataset.py
@@ -1,5 +1,4 @@
 import argparse
-
 import yaml
 
 from wandb_utils import WandbLogger
@@ -8,9 +7,7 @@
 
 
 def create_dataset_artifact(opt):
-    with open(opt.data, encoding='ascii', errors='ignore') as f:
-        data = yaml.safe_load(f)  # data dict
-    logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')  # TODO: return value unused
+    logger = WandbLogger(opt, None, job_type='Dataset Creation')  # TODO: return value unused
 
 
 if __name__ == '__main__':
@@ -19,6 +16,7 @@ def create_dataset_artifact(opt):
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
     parser.add_argument('--entity', default=None, help='W&B entity')
+    parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
 
     opt = parser.parse_args()
     opt.resume = False  # Explicitly disallow resume check for dataset upload job

diff --git a/utils/loggers/wandb/sweep.py b/utils/loggers/wandb/sweep.py
@@ -1,7 +1,6 @@
 import sys
-from pathlib import Path
-
 import wandb
+from pathlib import Path
 
 FILE = Path(__file__).absolute()
 sys.path.append(FILE.parents[2].as_posix())  # add utils/ to path

diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py
@@ -3,10 +3,9 @@
 import logging
 import os
 import sys
+import yaml
 from contextlib import contextmanager
 from pathlib import Path
-
-import yaml
 from tqdm import tqdm
 
 FILE = Path(__file__).absolute()
@@ -99,7 +98,7 @@ class WandbLogger():
     https://docs.wandb.com/guides/integrations/yolov5
     """
 
-    def __init__(self, opt, run_id, data_dict, job_type='Training'):
+    def __init__(self, opt, run_id, job_type='Training'):
         """
         - Initialize WandbLogger instance
         - Upload dataset if opt.upload_dataset is True
@@ -108,7 +107,6 @@ def __init__(self, opt, run_id, data_dict, job_type='Training'):
         arguments:
         opt (namespace) -- Commandline arguments for this run
         run_id (str) -- Run ID of W&B run to be resumed
-        data_dict (Dict) -- Dictionary conataining info about the dataset to be used
         job_type (str) -- To set the job_type for this run 
 
        """
@@ -119,10 +117,11 @@ def __init__(self, opt, run_id, data_dict, job_type='Training'):
         self.train_artifact_path, self.val_artifact_path = None, None
         self.result_artifact = None
         self.val_table, self.result_table = None, None
-        self.data_dict = data_dict
         self.bbox_media_panel_images = []
         self.val_table_path_map = None
         self.max_imgs_to_log = 16
+        self.wandb_artifact_data_dict = None
+        self.data_dict = None
         # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
         if isinstance(opt.resume, str):  # checks resume from artifact
             if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
@@ -148,11 +147,23 @@ def __init__(self, opt, run_id, data_dict, job_type='Training'):
         if self.wandb_run:
             if self.job_type == 'Training':
                 if not opt.resume:
-                    wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
-                    # Info useful for resuming from artifacts
-                    self.wandb_run.config.update({'opt': vars(opt), 'data_dict': wandb_data_dict},
-                                                 allow_val_change=True)
-                self.data_dict = self.setup_training(opt, data_dict)
+                    if opt.upload_dataset:
+                        self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
+
+                    elif opt.data.endswith('_wandb.yaml'):  # When dataset is W&B artifact
+                        with open(opt.data, encoding='ascii', errors='ignore') as f:
+                            data_dict = yaml.safe_load(f)
+                        self.data_dict = data_dict
+                    else:  # Local .yaml dataset file or .zip file
+                        self.data_dict = check_dataset(opt.data)
+
+                self.setup_training(opt)
+                # write data_dict to config. useful for resuming from artifacts
+                if not self.wandb_artifact_data_dict:
+                    self.wandb_artifact_data_dict = self.data_dict
+                self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},
+                                             allow_val_change=True)
+
             if self.job_type == 'Dataset Creation':
                 self.data_dict = self.check_and_upload_dataset(opt)
 
@@ -167,15 +178,15 @@ def check_and_upload_dataset(self, opt):
         Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
         """
         assert wandb, 'Install wandb to upload dataset'
-        config_path = self.log_dataset_artifact(check_file(opt.data),
+        config_path = self.log_dataset_artifact(opt.data,
                                                 opt.single_cls,
                                                 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
         print("Created dataset config file ", config_path)
         with open(config_path, encoding='ascii', errors='ignore') as f:
             wandb_data_dict = yaml.safe_load(f)
         return wandb_data_dict
 
-    def setup_training(self, opt, data_dict):
+    def setup_training(self, opt):
         """
         Setup the necessary processes for training YOLO models:
           - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
@@ -184,10 +195,7 @@ def setup_training(self, opt, data_dict):
         
         arguments:
         opt (namespace) -- commandline arguments for this run
-        data_dict (Dict) -- Dataset dictionary for this run
         
-        returns:
-        data_dict (Dict) -- contains the updated info about the dataset to be used for training
         """
         self.log_dict, self.current_epoch = {}, 0
         self.bbox_interval = opt.bbox_interval
@@ -198,8 +206,10 @@ def setup_training(self, opt, data_dict):
                 config = self.wandb_run.config
                 opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
                     self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
-                                                                                                       config.opt['hyp']
+                                                                                                       config.hyp
             data_dict = dict(self.wandb_run.config.data_dict)  # eliminates the need for config file to resume
+        else:
+            data_dict = self.data_dict
         if self.val_artifact is None:  # If --upload_dataset is set, use the existing artifact, don't download
             self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
                                                                                            opt.artifact_alias)
@@ -221,7 +231,10 @@ def setup_training(self, opt, data_dict):
                 self.map_val_table_path()
         if opt.bbox_interval == -1:
             self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
-        return data_dict
+        train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
+        # Update the the data_dict to point to local artifacts dir
+        if train_from_artifact:
+            self.data_dict = data_dict
 
     def download_dataset_artifact(self, path, alias):
         """
@@ -299,7 +312,8 @@ def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=
         returns:
         the new .yaml file with artifact links. it can be used to start training directly from artifacts
         """
-        data = check_dataset(data_file)  # parse and check
+        self.data_dict = check_dataset(data_file)  # parse and check
+        data = dict(self.data_dict)
         nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
         names = {k: v for k, v in enumerate(names)}  # to index dictionary
         self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
@@ -310,7 +324,8 @@ def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=
             data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
         if data.get('val'):
             data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
-        path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1))  # updated data.yaml path
+        path = Path(data_file).stem
+        path = (path if overwrite_config else path + '_wandb') + '.yaml'  # updated data.yaml path
         data.pop('download', None)
         data.pop('path', None)
         with open(path, 'w') as f: