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train.py
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"""
This script implements a PyTorch deep learning training pipeline for an eye tracking application.
It includes a main function to pass in arguments, train and validation functions, and uses MLflow as the logging library.
The script also supports fine-grained deep learning hyperparameter tuning using argparse and JSON configuration files.
All hyperparameters are logged with MLflow.
Author: Zuowen Wang
Affiliation: Insitute of Neuroinformatics, University of Zurich and ETH Zurich
Email: [email protected]
"""
import argparse, json, os, mlflow
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from model.BaselineEyeTrackingModel import CNN_GRU
from utils.training_utils import train_epoch, validate_epoch, top_k_checkpoints
from utils.metrics import weighted_MSELoss
from dataset import ThreeETplus_Eyetracking, ScaleLabel, NormalizeLabel, \
TemporalSubsample, NormalizeLabel, SliceLongEventsToShort, \
EventSlicesToVoxelGrid, SliceByTimeEventsTargets
import tonic.transforms as transforms
from tonic import SlicedDataset, DiskCachedDataset
def train(model, train_loader, val_loader, criterion, optimizer, args):
best_val_loss = float("inf")
# Training loop
for epoch in range(args.num_epochs):
model, train_loss, metrics = train_epoch(model, train_loader, criterion, optimizer, args)
mlflow.log_metric("train_loss", train_loss, step=epoch)
mlflow.log_metrics(metrics['tr_p_acc_all'], step=epoch)
mlflow.log_metrics(metrics['tr_p_error_all'], step=epoch)
if args.val_interval > 0 and (epoch + 1) % args.val_interval == 0:
val_loss, val_metrics = validate_epoch(model, val_loader, criterion, args)
if val_loss < best_val_loss:
best_val_loss = val_loss
# save the new best model to MLflow artifact with 3 decimal places of validation loss in the file name
torch.save(model.state_dict(), os.path.join(mlflow.get_artifact_uri(), \
f"model_best_ep{epoch}_val_loss_{val_loss:.4f}.pth"))
# DANGER Zone, this will delete files (checkpoints) in MLflow artifact
top_k_checkpoints(args, mlflow.get_artifact_uri())
print(f"[Validation] at Epoch {epoch+1}/{args.num_epochs}: Val Loss: {val_loss:.4f}")
mlflow.log_metric("val_loss", val_loss, step=epoch)
mlflow.log_metrics(val_metrics['val_p_acc_all'], step=epoch)
mlflow.log_metrics(val_metrics['val_p_error_all'], step=epoch)
# Print progress
print(f"Epoch {epoch+1}/{args.num_epochs}: Train Loss: {train_loss:.4f}")
return model
def main(args):
# Load hyperparameters from JSON configuration file
if args.config_file:
with open(os.path.join('./configs', args.config_file), 'r') as f:
config = json.load(f)
# Overwrite hyperparameters with command-line arguments
for key, value in vars(args).items():
if value is not None:
config[key] = value
args = argparse.Namespace(**config)
else:
raise ValueError("Please provide a JSON configuration file.")
# Set up MLflow logging
mlflow.set_tracking_uri(args.mlflow_path)
mlflow.set_experiment(experiment_name=args.experiment_name)
# Start MLflow run
with mlflow.start_run(run_name=args.run_name):
# dump this training file to MLflow artifact
mlflow.log_artifact(__file__)
# Log all hyperparameters to MLflow
mlflow.log_params(vars(args))
# also dump the args to a JSON file in MLflow artifact
with open(os.path.join(mlflow.get_artifact_uri(), "args.json"), 'w') as f:
json.dump(vars(args), f)
# Define your model, optimizer, and criterion
model = eval(args.architecture)(args).to(args.device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
if args.loss == "mse":
criterion = nn.MSELoss()
elif args.loss == "weighted_mse":
criterion = weighted_MSELoss(weights=torch.tensor((args.sensor_width/args.sensor_height, 1)).to(args.device), \
reduction='mean')
else:
raise ValueError("Invalid loss name")
factor = args.spatial_factor # spatial downsample factor
temp_subsample_factor = args.temporal_subsample_factor # downsampling original 100Hz label to 20Hz
# First we define the label transformations
label_transform = transforms.Compose([
ScaleLabel(factor),
TemporalSubsample(temp_subsample_factor),
NormalizeLabel(pseudo_width=640*factor, pseudo_height=480*factor)
])
# Then we define the raw event recording and label dataset, the raw events spatial coordinates are also downsampled
train_data_orig = ThreeETplus_Eyetracking(save_to=args.data_dir, split="train", \
transform=transforms.Downsample(spatial_factor=factor),
target_transform=label_transform)
val_data_orig = ThreeETplus_Eyetracking(save_to=args.data_dir, split="val", \
transform=transforms.Downsample(spatial_factor=factor),
target_transform=label_transform)
# Then we slice the event recordings into sub-sequences.
# The time-window is determined by the sequence length (train_length, val_length)
# and the temporal subsample factor.
slicing_time_window = args.train_length*int(10000/temp_subsample_factor) #microseconds
train_stride_time = int(10000/temp_subsample_factor*args.train_stride) #microseconds
train_slicer=SliceByTimeEventsTargets(slicing_time_window, overlap=slicing_time_window-train_stride_time, \
seq_length=args.train_length, seq_stride=args.train_stride, include_incomplete=False)
# the validation set is sliced to non-overlapping sequences
val_slicer=SliceByTimeEventsTargets(slicing_time_window, overlap=0, \
seq_length=args.val_length, seq_stride=args.val_stride, include_incomplete=False)
# After slicing the raw event recordings into sub-sequences,
# we make each subsequences into your favorite event representation,
# in this case event voxel-grid
post_slicer_transform = transforms.Compose([
SliceLongEventsToShort(time_window=int(10000/temp_subsample_factor), overlap=0, include_incomplete=True),
EventSlicesToVoxelGrid(sensor_size=(int(640*factor), int(480*factor), 2), \
n_time_bins=args.n_time_bins, per_channel_normalize=args.voxel_grid_ch_normaization)
])
# We use the Tonic SlicedDataset class to handle the collation of the sub-sequences into batches.
train_data = SlicedDataset(train_data_orig, train_slicer, transform=post_slicer_transform, metadata_path=f"./metadata/3et_train_tl_{args.train_length}_ts{args.train_stride}_ch{args.n_time_bins}")
val_data = SlicedDataset(val_data_orig, val_slicer, transform=post_slicer_transform, metadata_path=f"./metadata/3et_val_vl_{args.val_length}_vs{args.val_stride}_ch{args.n_time_bins}")
# cache the dataset to disk to speed up training. The first epoch will be slow, but the following epochs will be fast.
train_data = DiskCachedDataset(train_data, cache_path=f'./cached_dataset/train_tl_{args.train_length}_ts{args.train_stride}_ch{args.n_time_bins}')
val_data = DiskCachedDataset(val_data, cache_path=f'./cached_dataset/val_vl_{args.val_length}_vs{args.val_stride}_ch{args.n_time_bins}')
# Finally we wrap the dataset with pytorch dataloader
train_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, \
num_workers=int(os.cpu_count()-2), pin_memory=True)
val_loader = DataLoader(val_data, batch_size=args.batch_size, shuffle=False, \
num_workers=int(os.cpu_count()-2))
# Train your model
model = train(model, train_loader, val_loader, criterion, optimizer, args)
# Save your model for the last epoch
torch.save(model.state_dict(), os.path.join(mlflow.get_artifact_uri(), f"model_last_epoch{args.num_epochs}.pth"))
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
# training management arguments
parser.add_argument("--mlflow_path", type=str, help="path to MLflow tracking server")
parser.add_argument("--experiment_name", type=str, help="name of the experiment")
parser.add_argument("--run_name", type=str, help="name of the run")
# a config file
parser.add_argument("--config_file", type=str, default=None, help="path to JSON configuration file")
# training hyperparameters
parser.add_argument("--lr", type=float, help="learning rate")
parser.add_argument("--num_epochs", type=int, help="number of epochs")
args = parser.parse_args()
main(args)