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f465594 f77800e f465594 210f5d5 f465594 210f5d5 f465594 f77800e f465594 f77800e f465594 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | 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, RandomCrop, RandomHorizontalFlip, Compose
from litetesnormapper import LiteTensorMapper
transform = Compose([
RandomCrop(32, padding=4),
RandomHorizontalFlip(),
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
def check_sizes(image_size, patch_size):
sqrt_num_patches, remainder = divmod(image_size, patch_size)
assert remainder == 0, "`image_size` must be divisibe by `patch_size`"
num_patches = sqrt_num_patches ** 2
return num_patches
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"using {device} device")
class LiteTensorMapperImageClassification(LiteTensorMapper):
def __init__(
self,
image_size=32,
patch_size=4,
in_channels=3,
num_classes=10,
d_model = 256,
num_layers=4,
):
num_patches = check_sizes(image_size, patch_size)
super().__init__(d_model, num_patches,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 = LiteTensorMapperImageClassification().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(train_accuracy)
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
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 Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
test_accuracy = 100*correct
return test_loss, test_accuracy
logname = "/PATH/Experiments_cifar10/logs_litetensormapper/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/Experiments_cifar10/weights_litetensormapper"
model_name = "LiteTensorMapperImageClassification_cifar10"
torch.save(model.state_dict(), f"{path}/{model_name}.pth")
print(f"Saved Model State to {path}/{model_name}.pth ") |