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https://github.com/Laurent2916/REVA-QCAV.git
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refactor: moved the table logging in a callback
Former-commit-id: 37fa7b0da4556417ee1665d9f0375e71ce075958 [formerly 9314a3dfee09b085bb0d125d178c9f589532c6f5] Former-commit-id: 9c8bef3f5c6560f26512bc6586f1337b9d7985f3
This commit is contained in:
Laurent Fainsin
parent
edcc3c7bb2
commit
4015dad491
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@ -36,6 +36,19 @@ class Spheres(pl.LightningDataModule):
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pin_memory=wandb.config.PIN_MEMORY,
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)
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# dataset = LabeledDataset(image_dir="/home/lilian/data_disk/lfainsin/prerender/")
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# dataset = LabeledDataset(image_dir=wandb.config.DIR_VALID_IMG)
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# dataset = Subset(dataset, list(range(0, len(dataset), len(dataset) // 100 + 1)))
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# return DataLoader(
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# dataset,
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# shuffle=True,
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# batch_size=8,
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# prefetch_factor=8,
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# num_workers=wandb.config.WORKERS,
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# pin_memory=wandb.config.PIN_MEMORY,
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# )
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def val_dataloader(self):
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dataset = LabeledDataset(image_dir=wandb.config.DIR_VALID_IMG)
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dataset = Subset(dataset, list(range(0, len(dataset), len(dataset) // 100 + 1)))
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22
src/train.py
22
src/train.py
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@ -1,17 +1,19 @@
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import logging
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import pytorch_lightning as pl
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import torch
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from pytorch_lightning.callbacks import ModelCheckpoint, RichProgressBar
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from pytorch_lightning.loggers import WandbLogger
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import wandb
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from data import Spheres
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from unet import UNetModule
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from utils import TableLog
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CONFIG = {
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"DIR_TRAIN_IMG": "/home/lilian/data_disk/lfainsin/train/",
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"DIR_VALID_IMG": "//home/lilian/data_disk/lfainsin/test_split/",
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"DIR_SPHERE": "/home/lilian/data_disk/lfainsin/spheres+real_split/",
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"DIR_VALID_IMG": "/home/lilian/data_disk/lfainsin/test_batched_fast/",
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"DIR_SPHERE": "/home/lilian/data_disk/lfainsin/spheres+real/",
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"FEATURES": [8, 16, 32, 64],
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"N_CHANNELS": 3,
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"N_CLASSES": 1,
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@ -19,9 +21,9 @@ CONFIG = {
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"PIN_MEMORY": True,
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"BENCHMARK": True,
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"DEVICE": "gpu",
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"WORKERS": 14,
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"EPOCHS": 1,
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"BATCH_SIZE": 16 * 3,
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"WORKERS": 8,
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"EPOCHS": 10,
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"BATCH_SIZE": 16,
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"LEARNING_RATE": 1e-4,
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"WEIGHT_DECAY": 1e-8,
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"MOMENTUM": 0.9,
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@ -54,12 +56,18 @@ if __name__ == "__main__":
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features=CONFIG["FEATURES"],
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)
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# load checkpoint
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state_dict = torch.load("checkpoints/synth.pth")
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state_dict = dict([(f"model.{key}", value) for key, value in state_dict.items()])
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model.load_state_dict(state_dict)
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# log gradients and weights regularly
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logger.watch(model, log="all")
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# create checkpoint callback
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checkpoint_callback = ModelCheckpoint(
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dirpath="checkpoints",
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filename="model.ckpt",
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monitor="val/dice",
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)
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@ -75,8 +83,8 @@ if __name__ == "__main__":
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# precision=16,
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logger=logger,
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log_every_n_steps=1,
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val_check_interval=100,
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callbacks=RichProgressBar(),
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val_check_interval=25,
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callbacks=[RichProgressBar(), checkpoint_callback, TableLog()],
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)
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trainer.fit(model=model, datamodule=datamodule)
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@ -52,105 +52,30 @@ class UNetModule(pl.LightningModule):
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}
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# wrap tensors in dictionnary
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tensors = {
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"data": data,
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"ground_truth": ground_truth,
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"prediction": prediction,
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predictions = {
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"linear": prediction,
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"binary": binary,
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}
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return metrics, tensors
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return metrics, predictions
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def training_step(self, batch, batch_idx):
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# compute metrics
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metrics, tensors = self.shared_step(batch)
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metrics, _ = self.shared_step(batch)
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# log metrics
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self.log_dict(dict([(f"train/{key}", value) for key, value in metrics.items()]))
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if batch_idx == 5000:
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rows = []
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columns = ["ID", "image", "ground truth", "prediction", "dice", "dice_bin"]
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for i, (img, mask, pred, pred_bin) in enumerate(
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zip( # TODO: use comprehension list to zip the dictionnary
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tensors["data"].cpu(),
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tensors["ground_truth"].cpu(),
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tensors["prediction"].cpu(),
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tensors["binary"]
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.cpu()
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.squeeze(1)
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.int()
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.numpy(), # TODO: check if .functions can be moved elsewhere
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)
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):
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rows.append(
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[
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i,
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wandb.Image(img),
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wandb.Image(mask),
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wandb.Image(
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pred,
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masks={
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"predictions": {
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"mask_data": pred_bin,
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"class_labels": class_labels,
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},
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},
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),
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metrics["dice"],
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metrics["dice_bin"],
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]
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)
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# log table
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wandb.log(
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{
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"train/predictions": wandb.Table(
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columns=columns,
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data=rows,
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)
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}
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)
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return metrics["dice"]
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def validation_step(self, batch, batch_idx):
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metrics, tensors = self.shared_step(batch)
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# compute metrics
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metrics, predictions = self.shared_step(batch)
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rows = []
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if batch_idx % 50 == 0 or metrics["dice"] > 0.9:
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for i, (img, mask, pred, pred_bin) in enumerate(
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zip( # TODO: use comprehension list to zip the dictionnary
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tensors["data"].cpu(),
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tensors["ground_truth"].cpu(),
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tensors["prediction"].cpu(),
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tensors["binary"]
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.cpu()
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.squeeze(1)
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.int()
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.numpy(), # TODO: check if .functions can be moved elsewhere
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)
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):
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rows.append(
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[
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i,
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wandb.Image(img),
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wandb.Image(mask),
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wandb.Image(
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pred,
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masks={
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"predictions": {
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"mask_data": pred_bin,
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"class_labels": class_labels,
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},
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},
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),
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metrics["dice"],
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metrics["dice_bin"],
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]
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)
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# log metrics
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self.log_dict(dict([(f"val/{key}", value) for key, value in metrics.items()]))
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return metrics, rows
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return metrics, predictions
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def validation_epoch_end(self, validation_outputs):
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# unpacking
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@ -1 +1,2 @@
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from .callback import TableLog
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from .paste import RandomPaste
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64
src/utils/callback.py
Normal file
64
src/utils/callback.py
Normal file
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@ -0,0 +1,64 @@
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from pytorch_lightning.callbacks import Callback
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from torch import tensor
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import wandb
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columns = [
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"ID",
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"image",
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"ground truth",
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"prediction",
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"dice",
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"dice_bin",
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]
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class_labels = {
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1: "sphere",
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}
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class TableLog(Callback):
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def on_validation_epoch_start(self, trainer, pl_module):
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self.rows = []
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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# unpacking
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images, ground_truth = batch
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metrics, predictions = outputs
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for i, (img, mask, pred, pred_bin) in enumerate(
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zip(
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images.cpu(),
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ground_truth.cpu(),
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predictions["linear"].cpu(),
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predictions["binary"].cpu().squeeze(1).int().numpy(),
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)
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):
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self.rows.append(
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[
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i,
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wandb.Image(img),
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wandb.Image(mask),
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wandb.Image(
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pred,
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masks={
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"predictions": {
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"mask_data": pred_bin,
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"class_labels": class_labels,
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},
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},
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),
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metrics["dice"],
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metrics["dice_bin"],
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]
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)
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def on_validation_epoch_end(self, trainer, pl_module):
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# log table
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wandb.log(
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{
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"val/predictions": wandb.Table(
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columns=columns,
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data=self.rows,
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)
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}
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)
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