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W&B: Refactor the wandb_utils.py file (ultralytics#4496)
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* 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

* Remove redundant try catch

* More refactoring and bug fixes

* retry

* Reformat using pycharm

* respect LOGGERS include list

* Fix

* fix

* refactor constructor

* refactor

* refactor

* refactor

* PyCharm reformat

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@AyushExel @glenn-jocher
AyushExel and glenn-jocher authored Aug 23, 2021
1 parent 02594b4 commit 94a33e3
Showing 1 changed file with 43 additions and 34 deletions.
77 changes: 43 additions & 34 deletions utils/loggers/wandb/wandb_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,19 @@ def check_wandb_config_file(data_config_file):
return data_config_file


def check_wandb_dataset(data_file):
is_wandb_artifact = False
if check_file(data_file) and data_file.endswith('.yaml'):
with open(data_file, errors='ignore') as f:
data_dict = yaml.safe_load(f)
is_wandb_artifact = (data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX) or
data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX))
if is_wandb_artifact:
return data_dict
else:
return check_dataset(data_file)


def get_run_info(run_path):
run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
run_id = run_path.stem
Expand Down Expand Up @@ -104,7 +117,7 @@ def __init__(self, opt, run_id, job_type='Training'):
- Initialize WandbLogger instance
- Upload dataset if opt.upload_dataset is True
- Setup trainig processes if job_type is 'Training'
arguments:
opt (namespace) -- Commandline arguments for this run
run_id (str) -- Run ID of W&B run to be resumed
Expand Down Expand Up @@ -147,37 +160,35 @@ def __init__(self, opt, run_id, job_type='Training'):
allow_val_change=True) if not wandb.run else wandb.run
if self.wandb_run:
if self.job_type == 'Training':
if not opt.resume:
if opt.upload_dataset:
if opt.upload_dataset:
if not opt.resume:
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, 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)
if opt.resume:
# resume from artifact
if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
self.data_dict = dict(self.wandb_run.config.data_dict)
else: # local resume
self.data_dict = check_wandb_dataset(opt.data)
else:
self.data_dict = check_dataset(opt.data)
self.data_dict = check_wandb_dataset(opt.data)
self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict

self.setup_training(opt)
if not self.wandb_artifact_data_dict:
self.wandb_artifact_data_dict = self.data_dict
# write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
if not opt.resume:
# write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},
allow_val_change=True)
self.setup_training(opt)

if self.job_type == 'Dataset Creation':
self.data_dict = self.check_and_upload_dataset(opt)

def check_and_upload_dataset(self, opt):
"""
Check if the dataset format is compatible and upload it as W&B artifact
arguments:
opt (namespace)-- Commandline arguments for current run
returns:
Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
"""
Expand All @@ -196,10 +207,10 @@ def setup_training(self, opt):
- Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
- Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
- Setup log_dict, initialize bbox_interval
arguments:
opt (namespace) -- commandline arguments for this run
"""
self.log_dict, self.current_epoch = {}, 0
self.bbox_interval = opt.bbox_interval
Expand All @@ -211,9 +222,7 @@ def setup_training(self, opt):
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.hyp
data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume
else:
data_dict = self.data_dict
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)
Expand Down Expand Up @@ -243,11 +252,11 @@ def setup_training(self, opt):
def download_dataset_artifact(self, path, alias):
"""
download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
arguments:
path -- path of the dataset to be used for training
alias (str)-- alias of the artifact to be download/used for training
returns:
(str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
is found otherwise returns (None, None)
Expand All @@ -263,7 +272,7 @@ def download_dataset_artifact(self, path, alias):
def download_model_artifact(self, opt):
"""
download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
arguments:
opt (namespace) -- Commandline arguments for this run
"""
Expand All @@ -281,7 +290,7 @@ def download_model_artifact(self, opt):
def log_model(self, path, opt, epoch, fitness_score, best_model=False):
"""
Log the model checkpoint as W&B artifact
arguments:
path (Path) -- Path of directory containing the checkpoints
opt (namespace) -- Command line arguments for this run
Expand All @@ -305,14 +314,14 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
"""
Log the dataset as W&B artifact and return the new data file with W&B links
arguments:
data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
single_class (boolean) -- train multi-class data as single-class
project (str) -- project name. Used to construct the artifact path
overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
file with _wandb postfix. Eg -> data_wandb.yaml
returns:
the new .yaml file with artifact links. it can be used to start training directly from artifacts
"""
Expand Down Expand Up @@ -359,12 +368,12 @@ def map_val_table_path(self):
def create_dataset_table(self, dataset, class_to_id, name='dataset'):
"""
Create and return W&B artifact containing W&B Table of the dataset.
arguments:
dataset (LoadImagesAndLabels) -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
class_to_id (dict(int, str)) -- hash map that maps class ids to labels
name (str) -- name of the artifact
returns:
dataset artifact to be logged or used
"""
Expand Down Expand Up @@ -401,7 +410,7 @@ def create_dataset_table(self, dataset, class_to_id, name='dataset'):
def log_training_progress(self, predn, path, names):
"""
Build evaluation Table. Uses reference from validation dataset table.
arguments:
predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
path (str): local path of the current evaluation image
Expand Down Expand Up @@ -431,7 +440,7 @@ def log_training_progress(self, predn, path, names):
def val_one_image(self, pred, predn, path, names, im):
"""
Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
arguments:
pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
Expand All @@ -453,7 +462,7 @@ def val_one_image(self, pred, predn, path, names, im):
def log(self, log_dict):
"""
save the metrics to the logging dictionary
arguments:
log_dict (Dict) -- metrics/media to be logged in current step
"""
Expand All @@ -464,7 +473,7 @@ def log(self, log_dict):
def end_epoch(self, best_result=False):
"""
commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
arguments:
best_result (boolean): Boolean representing if the result of this evaluation is best or not
"""
Expand Down

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