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mnist_with_tensorboard_logger.py
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mnist_with_tensorboard_logger.py
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"""
MNIST example with training and validation monitoring using TensorboardX and Tensorboard.
Requirements:
Optionally TensorboardX (https://github.com/lanpa/tensorboard-pytorch): `pip install tensorboardX`
Tensorboard: `pip install tensorflow` (or just install tensorboard without the rest of tensorflow)
Usage:
Start tensorboard:
```bash
tensorboard --logdir=/tmp/tensorboard_logs/
```
Run the example:
```bash
python mnist_with_tensorboard_logger.py --log_dir=/tmp/tensorboard_logs
```
"""
import sys
from argparse import ArgumentParser
import torch
import torch.nn.functional as F
from torch import nn
from torch.optim import SGD
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, Normalize, ToTensor
from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
from ignite.handlers import ModelCheckpoint
from ignite.handlers.tensorboard_logger import (
global_step_from_engine,
GradsHistHandler,
GradsScalarHandler,
TensorboardLogger,
WeightsHistHandler,
WeightsScalarHandler,
)
from ignite.metrics import Accuracy, Loss
from ignite.utils import setup_logger
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=-1)
def get_data_loaders(train_batch_size, val_batch_size):
data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
train_loader = DataLoader(
MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True
)
val_loader = DataLoader(
MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False
)
return train_loader, val_loader
def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_dir):
train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size)
model = Net()
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
model.to(device) # Move model before creating optimizer
optimizer = SGD(model.parameters(), lr=lr, momentum=momentum)
criterion = nn.CrossEntropyLoss()
trainer = create_supervised_trainer(model, optimizer, criterion, device=device)
trainer.logger = setup_logger("Trainer")
if sys.version_info > (3,):
from ignite.metrics.gpu_info import GpuInfo
try:
GpuInfo().attach(trainer)
except RuntimeError:
print(
"INFO: By default, in this example it is possible to log GPU information (used memory, utilization). "
"As there is no pynvml python package installed, GPU information won't be logged. Otherwise, please "
"install it : `pip install pynvml`"
)
metrics = {"accuracy": Accuracy(), "loss": Loss(criterion)}
train_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)
train_evaluator.logger = setup_logger("Train Evaluator")
validation_evaluator = create_supervised_evaluator(model, metrics=metrics, device=device)
validation_evaluator.logger = setup_logger("Val Evaluator")
@trainer.on(Events.EPOCH_COMPLETED)
def compute_metrics(engine):
train_evaluator.run(train_loader)
validation_evaluator.run(val_loader)
tb_logger = TensorboardLogger(log_dir=log_dir)
tb_logger.attach_output_handler(
trainer,
event_name=Events.ITERATION_COMPLETED(every=100),
tag="training",
output_transform=lambda loss: {"batchloss": loss},
metric_names="all",
)
for tag, evaluator in [("training", train_evaluator), ("validation", validation_evaluator)]:
tb_logger.attach_output_handler(
evaluator,
event_name=Events.EPOCH_COMPLETED,
tag=tag,
metric_names=["loss", "accuracy"],
global_step_transform=global_step_from_engine(trainer),
)
tb_logger.attach_opt_params_handler(trainer, event_name=Events.ITERATION_COMPLETED(every=100), optimizer=optimizer)
tb_logger.attach(
trainer,
log_handler=WeightsScalarHandler(model, whitelist=["fc1"]),
event_name=Events.ITERATION_COMPLETED(every=100),
)
def is_conv(n, _):
return "conv" in n
tb_logger.attach(
trainer,
log_handler=WeightsHistHandler(model, whitelist=is_conv),
event_name=Events.ITERATION_COMPLETED(every=100),
)
tb_logger.attach(trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100))
tb_logger.attach(
trainer,
log_handler=GradsHistHandler(model, whitelist=["fc2.weight"]),
event_name=Events.ITERATION_COMPLETED(every=100),
)
def score_function(engine):
return engine.state.metrics["accuracy"]
model_checkpoint = ModelCheckpoint(
log_dir,
n_saved=2,
filename_prefix="best",
score_function=score_function,
score_name="validation_accuracy",
global_step_transform=global_step_from_engine(trainer),
)
validation_evaluator.add_event_handler(Events.COMPLETED, model_checkpoint, {"model": model})
# kick everything off
trainer.run(train_loader, max_epochs=epochs)
tb_logger.close()
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--batch_size", type=int, default=64, help="input batch size for training (default: 64)")
parser.add_argument(
"--val_batch_size", type=int, default=1000, help="input batch size for validation (default: 1000)"
)
parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train (default: 10)")
parser.add_argument("--lr", type=float, default=0.01, help="learning rate (default: 0.01)")
parser.add_argument("--momentum", type=float, default=0.5, help="SGD momentum (default: 0.5)")
parser.add_argument(
"--log_dir", type=str, default="tensorboard_logs", help="log directory for Tensorboard log output"
)
args = parser.parse_args()
run(args.batch_size, args.val_batch_size, args.epochs, args.lr, args.momentum, args.log_dir)