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184 | def train():
"""
Train function
Sample output :
```.bash
(venv) me@host:~$ python mnist.py
Logging to ./logs/CMNIST_0
>> Training
100%|██████| 844/844 [00:17<00:00, 48.61it/s]
>> Testing
[Step 0] Train : CE 0.20 Acc 0.94 | Valid : CE 0.08 Acc 0.97 | Test : CE 0.06 Acc 0.98[>> BETTER <<]
>> Training
100%|██████| 844/844 [00:16<00:00, 51.69it/s]
>> Testing
[Step 1] Train : CE 0.06 Acc 0.98 | Valid : CE 0.06 Acc 0.98 | Test : CE 0.05 Acc 0.98[>> BETTER <<]
>> Training
100%|██████| 844/844 [00:15<00:00, 53.47it/s]
>> Testing
[Step 2] Train : CE 0.04 Acc 0.99 | Valid : CE 0.04 Acc 0.99 | Test : CE 0.04 Acc 0.99[>> BETTER <<]
[...]
```
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
valid_ratio = 0.1
batch_size = 64
epochs = 10
cdtype = torch.complex64
# Dataloading
train_valid_dataset = torchvision.datasets.MNIST(
root="./data",
train=True,
download=True,
transform=v2_transforms.Compose([v2_transforms.PILToTensor(), torch.fft.fft]),
)
test_dataset = torchvision.datasets.MNIST(
root="./data",
train=False,
download=True,
transform=v2_transforms.Compose([v2_transforms.PILToTensor(), torch.fft.fft]),
)
all_indices = list(range(len(train_valid_dataset)))
random.shuffle(all_indices)
split_idx = int(valid_ratio * len(train_valid_dataset))
valid_indices, train_indices = all_indices[:split_idx], all_indices[split_idx:]
# Train dataloader
train_dataset = torch.utils.data.Subset(train_valid_dataset, train_indices)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, shuffle=True
)
# Valid dataloader
valid_dataset = torch.utils.data.Subset(train_valid_dataset, valid_indices)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=batch_size, shuffle=False
)
# Test dataloader
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=batch_size, shuffle=False
)
# Model
conv_model = nn.Sequential(
*conv_block(1, 16, cdtype),
*conv_block(16, 16, cdtype),
*conv_block(16, 32, cdtype),
*conv_block(32, 32, cdtype),
nn.Flatten(),
)
with torch.no_grad():
conv_model.eval()
dummy_input = torch.zeros((64, 1, 28, 28), dtype=cdtype, requires_grad=False)
out_conv = conv_model(dummy_input).view(64, -1)
lin_model = nn.Sequential(
nn.Linear(out_conv.shape[-1], 124, dtype=cdtype),
c_nn.Cardioid(),
nn.Linear(124, 10, dtype=cdtype),
c_nn.Mod(),
)
model = nn.Sequential(conv_model, lin_model)
model.to(device)
# Loss, optimizer, callbacks
f_loss = nn.CrossEntropyLoss()
optim = torch.optim.Adam(model.parameters(), lr=3e-4)
logpath = utils.generate_unique_logpath("./logs", "CMNIST")
print(f"Logging to {logpath}")
checkpoint = utils.ModelCheckpoint(model, logpath, 4, min_is_best=True)
# Training loop
for e in range(epochs):
print(">> Training")
train_loss, train_acc = utils.train_epoch(
model, train_loader, f_loss, optim, device
)
print(">> Testing")
valid_loss, valid_acc = utils.test_epoch(model, valid_loader, f_loss, device)
test_loss, test_acc = utils.test_epoch(model, test_loader, f_loss, device)
updated = checkpoint.update(valid_loss)
better_str = "[>> BETTER <<]" if updated else ""
print(
f"[Step {e}] Train : CE {train_loss:5.2f} Acc {train_acc:5.2f} | Valid : CE {valid_loss:5.2f} Acc {valid_acc:5.2f} | Test : CE {test_loss:5.2f} Acc {test_acc:5.2f}"
+ better_str
)
|