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@nicjac
Last active April 1, 2022 10:47
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An updated WandbCallback for fastai that associates the saved model with its actual metadata (rather than metadata recorded at the end of the training process). To be used with https://gist.github.com/nicjac/b363d2454ea253570a54e5e178e7666a
class WandbCallback(Callback):
"Saves model topology, losses & metrics"
remove_on_fetch,order = True,Recorder.order+1
# Record if watch has been called previously (even in another instance)
_wandb_watch_called = False
def __init__(self, log="gradients", log_preds=True, log_model=True, log_dataset=False, dataset_name=None, valid_dl=None, n_preds=36, seed=12345, reorder=True):
# Check if wandb.init has been called
if wandb.run is None:
raise ValueError('You must call wandb.init() before WandbCallback()')
# W&B log step
self._wandb_step = wandb.run.step - 1 # -1 except if the run has previously logged data (incremented at each batch)
self._wandb_epoch = 0 if not(wandb.run.step) else math.ceil(wandb.run.summary['epoch']) # continue to next epoch
store_attr('log,log_preds,log_model,log_dataset,dataset_name,valid_dl,n_preds,seed,reorder')
def before_fit(self):
"Call watch method to log model topology, gradients & weights"
self.run = not hasattr(self.learn, 'lr_finder') and not hasattr(self, "gather_preds") and rank_distrib()==0
if not self.run: return
# Log config parameters
log_config = self.learn.gather_args()
_format_config(log_config)
try:
wandb.config.update(log_config, allow_val_change=True)
except Exception as e:
print(f'WandbCallback could not log config parameters -> {e}')
if not WandbCallback._wandb_watch_called:
WandbCallback._wandb_watch_called = True
# Logs model topology and optionally gradients and weights
wandb.watch(self.learn.model, log=self.log)
# log dataset
assert isinstance(self.log_dataset, (str, Path, bool)), 'log_dataset must be a path or a boolean'
if self.log_dataset is True:
if Path(self.dls.path) == Path('.'):
print('WandbCallback could not retrieve the dataset path, please provide it explicitly to "log_dataset"')
self.log_dataset = False
else:
self.log_dataset = self.dls.path
if self.log_dataset:
self.log_dataset = Path(self.log_dataset)
assert self.log_dataset.is_dir(), f'log_dataset must be a valid directory: {self.log_dataset}'
metadata = {'path relative to learner': os.path.relpath(self.log_dataset, self.learn.path)}
log_dataset(path=self.log_dataset, name=self.dataset_name, metadata=metadata)
# log model
if self.log_model and not hasattr(self, 'save_model'):
print('WandbCallback requires use of "SaveModelCallback" to log best model')
self.log_model = False
if self.log_preds:
try:
if not self.valid_dl:
#Initializes the batch watched
wandbRandom = random.Random(self.seed) # For repeatability
self.n_preds = min(self.n_preds, len(self.dls.valid_ds))
idxs = wandbRandom.sample(range(len(self.dls.valid_ds)), self.n_preds)
if isinstance(self.dls, TabularDataLoaders):
test_items = getattr(self.dls.valid_ds.items, 'iloc', self.dls.valid_ds.items)[idxs]
self.valid_dl = self.dls.test_dl(test_items, with_labels=True, process=False)
else:
test_items = [getattr(self.dls.valid_ds.items, 'iloc', self.dls.valid_ds.items)[i] for i in idxs]
self.valid_dl = self.dls.test_dl(test_items, with_labels=True)
self.learn.add_cb(FetchPredsCallback(dl=self.valid_dl, with_input=True, with_decoded=True, reorder=self.reorder))
except Exception as e:
self.log_preds = False
print(f'WandbCallback was not able to prepare a DataLoader for logging prediction samples -> {e}')
def after_batch(self):
"Log hyper-parameters and training loss"
if self.training:
self._wandb_step += 1
self._wandb_epoch += 1/self.n_iter
hypers = {f'{k}_{i}':v for i,h in enumerate(self.opt.hypers) for k,v in h.items()}
wandb.log({'epoch': self._wandb_epoch, 'train_loss': to_detach(self.smooth_loss.clone()), 'raw_loss': to_detach(self.loss.clone()), **hypers}, step=self._wandb_step)
def log_predictions(self, preds):
inp,preds,targs,out = preds
b = tuplify(inp) + tuplify(targs)
x,y,its,outs = self.valid_dl.show_results(b, out, show=False, max_n=self.n_preds)
wandb.log(wandb_process(x, y, its, outs), step=self._wandb_step)
def after_epoch(self):
"Log validation loss and custom metrics & log prediction samples"
# Correct any epoch rounding error and overwrite value
self._wandb_epoch = round(self._wandb_epoch)
wandb.log({'epoch': self._wandb_epoch}, step=self._wandb_step)
# Log sample predictions
if self.log_preds:
try:
self.log_predictions(self.learn.fetch_preds.preds)
except Exception as e:
self.log_preds = False
self.remove_cb(FetchPredsCallback)
print(f'WandbCallback was not able to get prediction samples -> {e}')
wandb.log({n:s for n,s in zip(self.recorder.metric_names, self.recorder.log) if n not in ['train_loss', 'epoch', 'time']}, step=self._wandb_step)
def after_fit(self):
if self.log_model:
if self.save_model.last_saved_path is None:
print('WandbCallback could not retrieve a model to upload')
else:
log_model(self.save_model.last_saved_path, metadata=self.save_model.last_saved_metadata)
for metadata_key in self.save_model.last_saved_metadata:
wandb.run.summary[f'best_{metadata_key}'] = self.save_model.last_saved_metadata[metadata_key]
self.run = True
if self.log_preds: self.remove_cb(FetchPredsCallback)
wandb.log({}) # ensure sync of last step
self._wandb_step += 1
@dnth
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dnth commented Apr 1, 2022

Thanks for sharing! Is there a reason why this is not the default behavior in the current wandb callback?

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