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train.py
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# coding: utf-8
import matplotlib.pyplot as plt
from pathlib import Path
import pprint
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils import data
from torchvision import transforms
from torchvision.datasets import MNIST, CIFAR10
from ignite.metrics import Accuracy, Loss
from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
import ignite
from torchvision import models
import matplotlib
matplotlib.use("Agg")
def parse_args():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-o", default="result", help="output directory")
parser.add_argument("-d", choices=["mnist", "cifar10"], default="mnist", help="dataset to train on")
parser.add_argument("--cg", action="store_true", help="visualize computational graph (requires torchviz)")
parser.add_argument("-b", type=int, default=64, help="batch size")
parser.add_argument("-e", type=int, default=10, help="epoch")
parser.add_argument("--weight_decay", type=float, default=0, help="weight decay")
parser.add_argument("--lr", type=float, default=0.001, help="learning rate")
parser.add_argument("--betas", type=float, nargs=2, default=(0.9, 0.999), help="beta1 and beta2 of Adam")
args = parser.parse_args()
pprint.pprint(vars(args))
main(args)
def main(args):
log_dir_path = Path(args.o)
try:
log_dir_path.mkdir(parents=True)
except FileExistsError:
pass
if torch.cuda.is_available():
args.device = torch.device("cuda")
print("GPU mode")
else:
args.device = torch.device("cpu")
print("CPU mode")
net = PolyNet(in_channels=(1 if args.d == "mnist" else 3)).to(args.device)
kwds = {"root": ".", "download": True, "transform": transforms.ToTensor()}
dataset_class = {"mnist": MNIST, "cifar10": CIFAR10}[args.d]
train_dataset = dataset_class(train=True, **kwds)
test_dataset = dataset_class(train=False, **kwds)
train_loader = data.DataLoader(train_dataset, batch_size=args.b, shuffle=True)
test_loader = data.DataLoader(test_dataset, batch_size=args.b)
opt = torch.optim.Adam(net.parameters(), lr=args.lr, betas=args.betas, weight_decay=args.weight_decay)
trainer = create_supervised_trainer(net, opt, F.cross_entropy, device=args.device)
metrics = {
"accuracy": Accuracy(),
"loss": Loss(F.cross_entropy)
}
evaluator = create_supervised_evaluator(net, metrics=metrics, device=args.device)
trainer.add_event_handler(Events.EPOCH_COMPLETED, evaluate(evaluator, train_loader, test_loader, log_dir_path))
if args.cg:
trainer.add_event_handler(Events.ITERATION_STARTED(once=1), computational_graph(
net, train_dataset, log_dir_path, device=args.device))
trainer.run(train_loader, max_epochs=args.e)
class PolyNet(nn.Module):
def __init__(self, in_channels=1, n_classes=10):
super().__init__()
N = 16
kwds1 = {"kernel_size": 4, "stride": 2, "padding": 1}
kwds2 = {"kernel_size": 2, "stride": 1, "padding": 0}
kwds3 = {"kernel_size": 3, "stride": 1, "padding": 1}
self.conv11 = nn.Conv2d(in_channels, N, **kwds3)
self.conv12 = nn.Conv2d(in_channels, N, **kwds3)
self.conv21 = nn.Conv2d(N, N * 2, **kwds1)
self.conv22 = nn.Conv2d(N, N * 2, **kwds1)
self.conv31 = nn.Conv2d(N * 2, N * 4, **kwds1)
self.conv32 = nn.Conv2d(N * 2, N * 4, **kwds1)
self.conv41 = nn.Conv2d(N * 4, N * 8, **kwds2)
self.conv42 = nn.Conv2d(N * 4, N * 8, **kwds2)
self.conv51 = nn.Conv2d(N * 8, N * 16, **kwds1)
self.conv52 = nn.Conv2d(N * 8, N * 16, **kwds1)
self.fc = nn.Linear(N * 16 * 3 * 3, n_classes)
def forward(self, x):
h = self.conv11(x) * self.conv12(x)
h = self.conv21(h) * self.conv22(h)
h = self.conv31(h) * self.conv32(h)
h = self.conv41(h) * self.conv42(h)
h = self.conv51(h) * self.conv52(h)
h = self.fc(h.flatten(start_dim=1))
return h
def computational_graph(net, train_dataset, file_dir: Path, device=None):
from torchviz import make_dot
try:
file_dir.mkdir(parents=True)
except FileExistsError:
pass
def _computational_graph(engine):
x, t = train_dataset[0]
x = x.view(1, *x.size()).to(device)
t = torch.tensor([t], dtype=torch.long, device=device)
net.eval()
y = net(x)
loss = F.cross_entropy(y, t)
net.train()
dot = make_dot(loss, params=dict(net.named_parameters()))
dot.render(str(file_dir / "cg.dot"))
print("Computational graph generated")
return _computational_graph
def evaluate(evaluator, train_loader, test_loader, file_dir: Path):
try:
file_dir.mkdir(parents=True)
except FileExistsError:
pass
epochs = []
train_loss, test_loss = [], []
train_accuracy, test_accuracy = [], []
def _evaluate(engine):
evaluator.run(train_loader)
train_metrics = evaluator.state.metrics
evaluator.run(test_loader)
test_metrics = evaluator.state.metrics
epochs.append(engine.state.epoch)
train_loss.append(train_metrics["loss"])
train_accuracy.append(train_metrics["accuracy"])
test_loss.append(test_metrics["loss"])
test_accuracy.append(test_metrics["accuracy"])
print("Epoch {:d}".format(engine.state.epoch))
print(" train: loss={}, accuracy={}".format(train_metrics["loss"], train_metrics["accuracy"]))
print(" test: loss={}, accuracy={}".format(test_metrics["loss"], test_metrics["accuracy"]))
plt.figure()
plt.plot(epochs, train_loss, label="train")
plt.plot(epochs, test_loss, label="test")
plt.legend()
plt.xlabel("epoch")
plt.ylabel("loss")
plt.savefig(str(file_dir / "loss.pdf"))
plt.close()
plt.figure()
plt.plot(epochs, train_accuracy, label="train")
plt.plot(epochs, test_accuracy, label="test")
plt.legend()
plt.xlabel("epoch")
plt.ylabel("accuracy")
plt.savefig(str(file_dir / "accuracy.pdf"))
plt.close()
return _evaluate
if __name__ == "__main__":
parse_args()