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6f1e1ec 8920d00 6f1e1ec 8920d00 6f1e1ec 8920d00 6f1e1ec 8920d00 6f1e1ec 8920d00 6f1e1ec 8920d00 6f1e1ec 8920d00 | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | import os
import csv
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Normalize, Compose
from adjacencymixer import AdjacencyMixer
transform = Compose([
ToTensor(),
Normalize((0.5, 0.5,0.5),(0.5, 0.5,0.5))
])
training_data = datasets.CIFAR10(
root='data',
train=True,
download=True,
transform=transform
)
test_data = datasets.CIFAR10(
root='data',
train=False,
download=True,
transform=transform
)
batch_size = 128
train_dataloader = DataLoader(training_data, batch_size=batch_size,shuffle=True)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
for X, y in test_dataloader:
print(f"Shape of X [N,C,H,W]:{X.shape}")
print(f"Shape of y:{y.shape}{y.dtype}")
break
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"using {device} device")
class AdjacencyMixerImageClassification(AdjacencyMixer):
def __init__(
self,
image_size=32,
patch_size=4,
in_channels=3,
num_classes=10,
d_model=256,
num_layers=4,
dropout=0.5
):
super().__init__(d_model, num_layers)
self.patcher = nn.Conv2d(
in_channels, d_model, kernel_size=patch_size, stride=patch_size
)
self.classifier = nn.Linear(d_model, num_classes)
def forward(self, x):
patches = self.patcher(x)
batch_size, num_channels, _, _ = patches.shape
patches = patches.permute(0, 2, 3, 1)
patches = patches.view(batch_size, -1, num_channels)
embedding = self.model(patches)
embedding = embedding.mean(dim=1)
out = self.classifier(embedding)
return out
model = AdjacencyMixerImageClassification().to(device)
print(model)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr=1e-3)
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.train()
train_loss = 0
correct = 0
for batch, (X,y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, labels = torch.max(pred.data, 1)
correct += labels.eq(y.data).type(torch.float).sum()
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
train_loss /= num_batches
train_accuracy = 100. * correct.item() / size
print(f"Train Metric: \n Accuracy: {(100*correct):>0.1f}% \n")
return train_loss,train_accuracy
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for X,y in dataloader:
X,y = X.to(device), y.to(device)
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss /= num_batches
correct /= size
print(f"Test Metrics: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
test_accuracy = 100*correct
return test_loss, test_accuracy
logname = "/PATH/Adjacency_Mixer/Experiments_cifar10/logs_adjacencymixer/logs_cifar10.csv"
if not os.path.exists(logname):
with open(logname, 'w') as logfile:
logwriter = csv.writer(logfile, delimiter=',')
logwriter.writerow(['epoch', 'train loss', 'train acc',
'test loss', 'test acc'])
epochs = 100
for epoch in range(epochs):
print(f"Epoch {epoch+1}\n-----------------------------------")
train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer)
test_loss, test_acc = test(test_dataloader, model, loss_fn)
with open(logname, 'a') as logfile:
logwriter = csv.writer(logfile, delimiter=',')
logwriter.writerow([epoch+1, train_loss, train_acc,
test_loss, test_acc])
print("Done!")
path = "/PATH/Adjacency_Mixer/Experiments_cifar10/weights_adjacencymixer"
model_name = "AdjacencyMixerImageClassification_cifar10"
torch.save(model.state_dict(), f"{path}/{model_name}.pth")
print(f"Saved Model State to {path}/{model_name}.pth ")
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