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Improve docstrings and run names (PaddlePaddle#4174)
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@AyushExel
AyushExel authored Jul 27, 2021
1 parent 0ad6301 commit 63a1971
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Showing 2 changed files with 133 additions and 14 deletions.
2 changes: 1 addition & 1 deletion utils/loggers/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ 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, s.stem, run_id, self.data_dict)
self.wandb = WandbLogger(self.opt, run_id, self.data_dict)
except:
self.wandb = None

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145 changes: 132 additions & 13 deletions utils/loggers/wandb/wandb_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,19 @@ class WandbLogger():
https://docs.wandb.com/guides/integrations/yolov5
"""

def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
def __init__(self, opt, run_id, data_dict, 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
data_dict (Dict) -- Dictionary conataining info about the dataset to be used
job_type (str) -- To set the job_type for this run
'''
# Pre-training routine --
self.job_type = job_type
self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
Expand Down Expand Up @@ -129,7 +141,7 @@ def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
resume="allow",
project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
entity=opt.entity,
name=name,
name=opt.name if opt.name != 'exp' else None,
job_type=job_type,
id=run_id,
allow_val_change=True) if not wandb.run else wandb.run
Expand All @@ -145,6 +157,15 @@ def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
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.
'''
assert wandb, 'Install wandb to upload dataset'
config_path = self.log_dataset_artifact(check_file(opt.data),
opt.single_cls,
Expand All @@ -155,6 +176,19 @@ def check_and_upload_dataset(self, opt):
return wandb_data_dict

def setup_training(self, opt, data_dict):
'''
Setup the necessary processes for training YOLO models:
- 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
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
if isinstance(opt.resume, str):
Expand Down Expand Up @@ -185,12 +219,22 @@ def setup_training(self, opt, data_dict):
self.val_table = self.val_artifact.get("val")
if self.val_table_path_map is None:
self.map_val_table_path()
wandb.log({"validation dataset": self.val_table})
if opt.bbox_interval == -1:
self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
return data_dict

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)
'''
if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
Expand All @@ -200,6 +244,12 @@ def download_dataset_artifact(self, path, alias):
return None, None

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
'''
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
Expand All @@ -212,6 +262,16 @@ def download_model_artifact(self, opt):
return None, None

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
epoch (int) -- Current epoch number
fitness_score (float) -- fitness score for current epoch
best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
'''
model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
'original_url': str(path),
'epochs_trained': epoch + 1,
Expand All @@ -226,6 +286,19 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
print("Saving model artifact on epoch ", epoch + 1)

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
'''
with open(data_file, encoding='ascii', errors='ignore') as f:
data = yaml.safe_load(f) # data dict
check_dataset(data)
Expand Down Expand Up @@ -257,12 +330,27 @@ def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=
return path

def map_val_table_path(self):
'''
Map the validation dataset Table like name of file -> it's id in the W&B Table.
Useful for - referencing artifacts for evaluation.
'''
self.val_table_path_map = {}
print("Mapping dataset")
for i, data in enumerate(tqdm(self.val_table.data)):
self.val_table_path_map[data[3]] = data[0]

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
'''
# TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
artifact = wandb.Artifact(name=name, type="dataset")
img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
Expand Down Expand Up @@ -294,6 +382,14 @@ def create_dataset_table(self, dataset, class_to_id, name='dataset'):
return artifact

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
names (dict(int, str)): hash map that maps class ids to labels
'''
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
box_data = []
total_conf = 0
Expand All @@ -316,25 +412,45 @@ 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]
path (str): local path of the current evaluation image
'''
if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
self.log_training_progress(predn, path, names)
else: # Default to bbox media panelif Val artifact not found
if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
if self.current_epoch % self.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))

if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
if self.current_epoch % self.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))

def log(self, log_dict):
'''
save the metrics to the logging dictionary
arguments:
log_dict (Dict) -- metrics/media to be logged in current step
'''
if self.wandb_run:
for key, value in log_dict.items():
self.log_dict[key] = value

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
'''
if self.wandb_run:
with all_logging_disabled():
if self.bbox_media_panel_images:
Expand All @@ -352,6 +468,9 @@ def end_epoch(self, best_result=False):
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")

def finish_run(self):
'''
Log metrics if any and finish the current W&B run
'''
if self.wandb_run:
if self.log_dict:
with all_logging_disabled():
Expand Down

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