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from functools import lru_cache
from typing import Iterable
import pytorch_lightning as pl
import torch
import torch.nn as nn
import numpy as np
import wandb
from sklearn import metrics as skl_metrics
import torchvision
import os
from pathlib import Path
import pandas as pd
class TrainingMetric:
def __init__(self, metric_func, metric_name, optimum=None):
self.func = metric_func
self.name = metric_name
self.optimum = optimum
def calc_metric(self, *args, **kwargs):
try:
return self.func(*args, **kwargs)
except ValueError as e:
return np.nan
def __call__(self, y_true, y_pred, labels=None, split=None, step_type=None) -> dict:
# if y_true is empty
if y_true.shape[0] == 0: # TODO: handle other cases
m = {
f"{step_type}_{split}_{l}_{self.name}": self.calc_metric(None, yp)
for yp, l in zip(y_pred.T, labels)
}
return m
# Simple 1:1 y_true and y_pred are either shape=(batch, 1) or shape=(batch,)
if len(y_pred.shape) == 1 or (y_pred.shape[1] == 1 and y_true.shape[1] == 1):
m = {
f"{step_type}_{split}_{self.name}": self.calc_metric(
y_true.flatten(), y_pred.flatten()
)
}
# Multi-binary classification-like y_true and y_pred are shape=(batch, class)
elif y_true.shape[1] != 1 and y_pred.shape[1] != 1:
m = {
f"{step_type}_{split}_{l}_{self.name}": self.calc_metric(yt, yp)
for yt, yp, l in zip(y_true.T, y_pred.T, labels)
}
# Multi-class classification-like y_true is shape=(batch, 1) or shape=(batch,) and y_pred is shape=(batch, class)
elif (len(y_true.shape) == 1 or y_true.shape[1] == 1) and y_pred.shape[1] != 1:
m = {
f"{step_type}_{split}_{l}_{self.name}": self.calc_metric(
y_true.flatten() == i, yp
)
for i, (yp, l) in enumerate(
zip(y_pred.T, labels)
) # turn multi class into binary classification
}
return m
class CumulativeMetric(TrainingMetric):
"""Wraps a metric to apply to every class in output and calculate a cumulative value (like mean AUC)"""
def __init__(
self,
training_metric: TrainingMetric,
metric_func,
metric_name="cumulative",
optimum=None,
):
optimum = optimum or training_metric.optimum
metric_name = f"{metric_name}_{training_metric.name}"
super().__init__(metric_func, metric_name, optimum)
self.base_metric = training_metric
def __call__(self, y_true, y_pred, labels=None, split=None, step_type=None):
vals = list(self.base_metric(y_true, y_pred, labels, split, step_type).values())
m = {f"{step_type}_{split}_{self.name}": self.func(vals)}
return m
r2_metric = TrainingMetric(skl_metrics.r2_score, "r2", optimum="max")
roc_auc_metric = TrainingMetric(skl_metrics.roc_auc_score, "roc_auc", optimum="max")
accuracy_metric = TrainingMetric(skl_metrics.accuracy_score, "accuracy", optimum="max")
mae_metric = TrainingMetric(skl_metrics.mean_absolute_error, "mae", optimum="min")
pred_value_mean_metric = TrainingMetric(
lambda y_true, y_pred: np.mean(y_pred), "pred_value_mean"
)
pred_value_std_metric = TrainingMetric(
lambda y_true, y_pred: np.std(y_pred), "pred_value_std"
)
class TrainingModel(pl.LightningModule):
def __init__(
self,
model,
metrics: Iterable[TrainingMetric] = dict(),
tracked_metric=None,
early_stop_epochs=10,
checkpoint_every_epoch=False,
checkpoint_every_n_steps=None,
index_labels=None,
save_predictions_path=None,
lr=0.01,
):
super().__init__()
self.epoch_preds = {"train": ([], []), "val": ([], [])}
self.epoch_losses = {"train": [], "val": []}
self.metrics = {}
self.metric_funcs = {m.name: m for m in metrics}
self.tracked_metric = f"epoch_val_{tracked_metric}"
self.best_tracked_metric = None
self.early_stop_epochs = early_stop_epochs
self.checkpoint_every_epoch = checkpoint_every_epoch
self.checkpoint_every_n_steps = checkpoint_every_n_steps
self.metrics["epochs_since_last_best"] = 0
self.m = model
self.training_steps = 0
self.steps_since_checkpoint = 0
self.labels = index_labels
if self.labels is not None and isinstance(self.labels, str):
self.labels = [self.labels]
if isinstance(save_predictions_path, str):
save_predictions_path = Path(save_predictions_path)
self.save_predictions_path = save_predictions_path
self.lr = lr
self.step_loss = (None, None)
self.log_path = Path(wandb.run.dir) if wandb.run is not None else None
def configure_optimizers(self):
return torch.optim.AdamW(self.parameters(), self.lr)
def forward(self, x: dict):
# if anything other than 'primary_input' and 'extra_inputs' is used,
# this function must be overridden
if 'extra_inputs' in x:
return self.m((x['primary_input'], x['extra_inputs']))
else:
return self.m(x['primary_input'])
def step(self, batch, step_type='train'):
batch = self.prepare_batch(batch)
y_pred = self.forward(batch)
if step_type != 'predict':
if 'labels' not in batch:
batch['labels'] = torch.empty(0)
loss = self.loss_func(y_pred, batch['labels'])
if torch.isnan(loss):
raise ValueError(loss)
self.log_step(step_type, batch['labels'], y_pred, loss)
return loss
else:
return y_pred
def prepare_batch(self, batch):
return batch
def training_step(self, batch, i):
return self.step(batch, "train")
def validation_step(self, batch, i):
return self.step(batch, "val")
def predict_step(self, batch, *args):
y_pred = self.step(batch, "predict")
return {"filename": batch["filename"], "prediction": y_pred.cpu().numpy()}
def on_predict_epoch_end(self, results):
for i, predict_results in enumerate(results):
filename_df = pd.DataFrame(
{
"filename": np.concatenate(
[batch["filename"] for batch in predict_results]
)
}
)
if self.labels is not None:
columns = [f"{class_name}_preds" for class_name in self.labels]
else:
columns = ["preds"]
outputs_df = pd.DataFrame(
np.concatenate(
[batch["prediction"] for batch in predict_results], axis=0
),
columns=columns,
)
prediction_df = pd.concat([filename_df, outputs_df], axis=1)
dataloader = self.trainer.predict_dataloaders[i]
manifest = dataloader.dataset.manifest
prediction_df = prediction_df.merge(manifest, on="filename", how="outer")
if wandb.run is not None:
prediction_df.to_csv(
Path(wandb.run.dir).parent
/ "data"
/ f"dataloader_{i}_potassium_predictions.csv",
index=False,
)
if self.save_predictions_path is not None:
if ".csv" in self.save_predictions_path.name:
prediction_df.to_csv(
self.save_predictions_path.parent
/ self.save_predictions_path.name.replace(".csv", f"_{i}_.csv"),
index=False,
)
else:
prediction_df.to_csv(
self.save_predictions_path / f"dataloader_{i}_potassium_predictions.csv",
index=False,
)
if wandb.run is None and self.save_predictions_path is None:
print(
"WandB is not active and self.save_predictions_path is None. Predictions will be saved to the directory this script is being run in."
)
prediction_df.to_csv(f"dataloader_{i}_potassium_predictions.csv", index=False)
def log_step(self, step_type, labels, output_tensor, loss):
self.step_loss = (step_type, loss.detach().item())
self.epoch_preds[step_type][0].append(labels.detach().cpu().numpy())
self.epoch_preds[step_type][1].append(output_tensor.detach().cpu().numpy())
self.epoch_losses[step_type].append(loss.detach().item())
if step_type == "train":
self.training_steps += 1
self.steps_since_checkpoint += 1
if (
self.checkpoint_every_n_steps is not None
and self.steps_since_checkpoint > self.checkpoint_every_n_steps
):
self.steps_since_checkpoint = 0
self.checkpoint_weights(f"step_{self.training_steps}")
def checkpoint_weights(self, name=""):
if wandb.run is not None:
weights_path = Path(wandb.run.dir).parent / "weights"
if not weights_path.is_dir():
weights_path.mkdir()
torch.save(self.state_dict(), weights_path / f"model_{name}.pt")
else:
print("Did not checkpoint model. wandb not initialized.")
def validation_epoch_end(self, preds):
# Save weights
self.metrics["epoch"] = self.current_epoch
if self.checkpoint_every_epoch:
self.checkpoint_weights(f"epoch_{self.current_epoch}")
# Calculate metrics
for m_type in ["train", "val"]:
y_true, y_pred = self.epoch_preds[m_type]
if len(y_true) == 0 or len(y_pred) == 0:
continue
y_true, y_pred = np.concatenate(y_true), np.concatenate(y_pred)
self.metrics[f"epoch_{m_type}_loss"] = np.mean(self.epoch_losses[m_type])
for m in self.metric_funcs.values():
self.metrics.update(
m(
y_true,
y_pred,
labels=self.labels,
split=m_type,
step_type="epoch",
)
)
# Reset predictions
self.epoch_losses[m_type] = []
self.epoch_preds[m_type] = ([], [])
# Check if new best epoch
if self.metrics is not None and self.tracked_metric is not None:
if self.tracked_metric == "epoch_val_loss":
metric_optimization = "min"
else:
metric_optimization = self.metric_funcs[
self.tracked_metric.replace("epoch_val_", "")
].optimum
if (
self.metrics[self.tracked_metric] is not None
and (
self.best_tracked_metric is None
or (
metric_optimization == "max"
and self.metrics[self.tracked_metric] > self.best_tracked_metric
)
or (
metric_optimization == "min"
and self.metrics[self.tracked_metric] < self.best_tracked_metric
)
)
and self.current_epoch > 0
):
print(
f"New best epoch! {self.tracked_metric}={self.metrics[self.tracked_metric]}, epoch={self.current_epoch}"
)
self.checkpoint_weights(f"best_{self.tracked_metric}")
self.metrics["epochs_since_last_best"] = 0
self.best_tracked_metric = self.metrics[self.tracked_metric]
else:
self.metrics["epochs_since_last_best"] += 1
if self.metrics["epochs_since_last_best"] >= self.early_stop_epochs:
raise KeyboardInterrupt("Early stopping condition met")
# Log to w&b
if wandb.run is not None:
wandb.log(self.metrics)
class RegressionModel(TrainingModel):
def __init__(
self,
model,
metrics=(r2_metric, mae_metric, pred_value_mean_metric, pred_value_std_metric),
tracked_metric="mae",
early_stop_epochs=10,
checkpoint_every_epoch=False,
checkpoint_every_n_steps=None,
index_labels=None,
save_predictions_path=None,
lr=0.01,
):
super().__init__(
model=model,
metrics=metrics,
tracked_metric=tracked_metric,
early_stop_epochs=early_stop_epochs,
checkpoint_every_epoch=checkpoint_every_epoch,
checkpoint_every_n_steps=checkpoint_every_n_steps,
index_labels=index_labels,
save_predictions_path=save_predictions_path,
lr=lr,
)
self.loss_func = nn.MSELoss()
def prepare_batch(self, batch):
if "labels" in batch and len(batch["labels"].shape) == 1:
batch["labels"] = batch["labels"][:, None]
return batch
class BinaryClassificationModel(TrainingModel):
def __init__(
self,
model,
metrics=(roc_auc_metric, CumulativeMetric(roc_auc_metric, np.nanmean, "mean")),
tracked_metric="mean_roc_auc",
early_stop_epochs=10,
checkpoint_every_epoch=False,
checkpoint_every_n_steps=None,
index_labels=None,
save_predictions_path=None,
lr=0.01,
):
super().__init__(
model=model,
metrics=metrics,
tracked_metric=tracked_metric,
early_stop_epochs=early_stop_epochs,
checkpoint_every_epoch=checkpoint_every_epoch,
checkpoint_every_n_steps=checkpoint_every_n_steps,
index_labels=index_labels,
save_predictions_path=save_predictions_path,
lr=lr,
)
self.loss_func = nn.BCEWithLogitsLoss()
def prepare_batch(self, batch):
if "labels" in batch and len(batch["labels"].shape) == 1:
batch["labels"] = batch["labels"][:, None]
return batch
# Addresses bug caused by labels from a single column in a manifest being delivered as Bx1,
# but nn.CrossEntropyLoss wants a simple list of length B.
class SqueezeCrossEntropyLoss(nn.Module):
def __init__(self):
super().__init__()
self.cross_entropy = nn.CrossEntropyLoss()
def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor):
return self.cross_entropy(y_pred, y_true.squeeze(dim=-1))
class MultiClassificationModel(TrainingModel):
def __init__(
self,
model,
metrics=(roc_auc_metric, CumulativeMetric(roc_auc_metric, np.mean, "mean")),
tracked_metric="mean_roc_auc",
early_stop_epochs=10,
checkpoint_every_epoch=False,
checkpoint_every_n_steps=None,
index_labels=None,
save_predictions_path=None,
lr=0.01,
):
metrics = [*metrics]
super().__init__(
model=model,
metrics=metrics,
tracked_metric=tracked_metric,
early_stop_epochs=early_stop_epochs,
checkpoint_every_epoch=checkpoint_every_epoch,
checkpoint_every_n_steps=checkpoint_every_n_steps,
index_labels=index_labels,
save_predictions_path=save_predictions_path,
lr=lr,
)
self.loss_func = SqueezeCrossEntropyLoss()
def prepare_batch(self, batch):
if "labels" in batch:
batch["labels"] = batch["labels"].long()
batch["primary_input"] = batch["primary_input"].float()
return batch
if __name__ == "__main__":
os.environ["WANDB_MODE"] = "offline"
m = torchvision.models.video.r2plus1d_18()
m.fc = nn.Linear(512, 1)
training_model = RegressionModel(m)
x = torch.randn((4, 3, 8, 112, 112))
y = m(x)
print(y.shape)
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