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snsc.py
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snsc.py
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"""(SNSC) Single Node Single GPU Card Training"""
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
BATCH_SIZE = 256
EPOCHS = 5
if __name__ == "__main__":
# 1. define network
device = "cuda"
net = torchvision.models.resnet18(num_classes=10)
net = net.to(device=device)
# 2. define dataloader
trainset = torchvision.datasets.CIFAR10(
root="./data",
train=True,
download=True,
transform=transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
),
]
),
)
train_loader = torch.utils.data.DataLoader(
trainset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=4,
pin_memory=True,
)
# 3. define loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(
net.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=0.0001,
nesterov=True,
)
print(" ======= Training ======= \n")
# 4. start to train
net.train()
for ep in range(1, EPOCHS + 1):
train_loss = correct = total = 0
for idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
total += targets.size(0)
correct += torch.eq(outputs.argmax(dim=1), targets).sum().item()
if (idx + 1) % 50 == 0 or (idx + 1) == len(train_loader):
print(
" == step: [{:3}/{}] [{}/{}] | loss: {:.3f} | acc: {:6.3f}%".format(
idx + 1,
len(train_loader),
ep,
EPOCHS,
train_loss / (idx + 1),
100.0 * correct / total,
)
)
print("\n ======= Training Finished ======= \n")
"""
usage:
>>> python snsc.py
Files already downloaded and verified
======= Training =======
== step: [ 50/196] [1/5] | loss: 1.959 | acc: 28.633%
== step: [100/196] [1/5] | loss: 1.806 | acc: 33.996%
== step: [150/196] [1/5] | loss: 1.718 | acc: 36.987%
== step: [196/196] [1/5] | loss: 1.658 | acc: 39.198%
== step: [ 50/196] [2/5] | loss: 1.393 | acc: 49.578%
== step: [100/196] [2/5] | loss: 1.359 | acc: 50.473%
== step: [150/196] [2/5] | loss: 1.336 | acc: 51.372%
== step: [196/196] [2/5] | loss: 1.317 | acc: 52.200%
== step: [ 50/196] [3/5] | loss: 1.205 | acc: 56.102%
== step: [100/196] [3/5] | loss: 1.185 | acc: 57.254%
== step: [150/196] [3/5] | loss: 1.175 | acc: 57.755%
== step: [196/196] [3/5] | loss: 1.165 | acc: 58.072%
== step: [ 50/196] [4/5] | loss: 1.067 | acc: 60.914%
== step: [100/196] [4/5] | loss: 1.061 | acc: 61.406%
== step: [150/196] [4/5] | loss: 1.058 | acc: 61.643%
== step: [196/196] [4/5] | loss: 1.054 | acc: 62.022%
== step: [ 50/196] [5/5] | loss: 0.988 | acc: 64.852%
== step: [100/196] [5/5] | loss: 0.983 | acc: 64.801%
== step: [150/196] [5/5] | loss: 0.980 | acc: 65.052%
== step: [196/196] [5/5] | loss: 0.977 | acc: 65.076%
======= Training Finished =======
"""