1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
# mypy: ignore-errors
import os
from torchvision import datasets, transforms
import torch
import torch._lazy
import torch._lazy.metrics
import torch._lazy.ts_backend
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
torch._lazy.ts_backend.init()
class Net(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
def train(log_interval, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad(set_to_none=True)
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
torch._lazy.mark_step()
if batch_idx % log_interval == 0:
print(
f"Train Epoch: {epoch} "
f"[{batch_idx * len(data)}/{len(train_loader.dataset)} ({100.0 * batch_idx / len(train_loader):.0f}%)]"
f"\tLoss: {loss.item():.6f}"
)
if __name__ == "__main__":
bsz = 64
device = "lazy"
epochs = 14
log_interval = 10
lr = 1
gamma = 0.7
train_kwargs = {"batch_size": bsz}
# if we want to use CUDA
if "LTC_TS_CUDA" in os.environ:
cuda_kwargs = {
"num_workers": 1,
"pin_memory": True,
"shuffle": True,
"batch_size": bsz,
}
train_kwargs.update(cuda_kwargs)
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
)
dataset1 = datasets.MNIST("./data", train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=lr)
scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
for epoch in range(1, epochs + 1):
train(log_interval, model, device, train_loader, optimizer, epoch)
scheduler.step()
|
