Sat 03 June 2023
I have thoughts I'd like to share from time-to-time. I have been testing my Pelican-based blog generator for a while and got it working, but a lack of fully-formed blogpost ideas to upload has consequently kept me from updating my website for months. This has become a problem for me as news and results have accumulated. Thus at this point, I have no choice but to push a placeholder blogpost such as this. Looking forward to making a more substantial post soon 🫡
Check out this code! You can pass in any Callback object from pytorch lightning, to track the training process of a model in pytorch lightning. This automatically collects the training dynamics statistics and writes them to a given output csv file for future processing and analysis!
class CartographyCallback(Callback):
def __init__(self, output_base):
super().__init__()
self.output_base = output_base
def init_buffers(self, trainer):
key_nums = {
"train" : len(trainer.datamodule.train),
"val" : len(trainer.datamodule.valid),
"test" : len(trainer.datamodule.test)
}
self.confidences = define_samplewise_metric(key_nums)
self.correctnesses = define_samplewise_metric(key_nums)
def cartography_save(self, epoch, key):
np.save(f"{self.output_base}/conf_{key}_{epoch}.npy", self.confidences[key])
np.save(f"{self.output_base}/corr_{key}_{epoch}.npy", self.correctnesses[key])
def batch_end_accumulate(self, batch, outputs, key):
targets = batch['labels'].squeeze()
logits = outputs['logits']
preds = torch.max(logits, dim=-1).indices
batch_idces = batch['idx'].cpu().numpy().squeeze()
self.confidences[key][batch_idces] = confidence_elementwise(targets, logits).squeeze()
self.correctnesses[key][batch_idces] = correct_elementwise(targets, preds).squeeze()
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
self.batch_end_accumulate(batch, outputs, "train")
def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self.batch_end_accumulate(batch, outputs, "val")
def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self.batch_end_accumulate(batch, outputs, "test")
def on_train_epoch_end(self, trainer, pl_module):
self.cartography_save(trainer.current_epoch, "train")
def on_validation_epoch_end(self, trainer, pl_module):
self.cartography_save(trainer.current_epoch, "val")
def on_test_epoch_end(self, trainer, pl_module):
self.cartography_save(trainer.current_epoch, "test")
def on_train_start(self, trainer, pl_module):
self.init_buffers(trainer)
def on_sanity_check_start(self, trainer, pl_module):
self.init_buffers(trainer)
I wrote this code so that I could replicate the Dataset Cartography features of confidence and correctness to analyze the effectiveness of my PECO tool for analyzing datasets. You can see the code used in-context here!