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import torchvision.models as models
import torch
from tqdm import tqdm
import torch.nn as nn
import torchvision.transforms as v2
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
mps_device = torch.device("cuda")
IMAGE_SIZE = 224
mean, std = [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]
transform_test = v2.Compose(
[v2.Resize((IMAGE_SIZE, IMAGE_SIZE)),
v2.ToTensor(),
v2.Normalize(mean, std)])
transform_train = v2.Compose(
[v2.Resize((IMAGE_SIZE, IMAGE_SIZE)),
v2.RandomRotation(20),
v2.RandomHorizontalFlip(0.1),
v2.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
v2.RandomAdjustSharpness(sharpness_factor=2, p=0.1),
v2.ToTensor(),
v2.Normalize(mean, std),
v2.RandomErasing(p=0.75, scale=(0.02, 0.1), value=1.0, inplace=False)])
cifar_trainset = datasets.CIFAR10(
root='./data',
train=True,
download=True,
transform=transform_train)
cifar_testset = datasets.CIFAR10(
root='./data',
train=False,
download=True,
transform=transform_test)
batch_size = 64
trainLoader = DataLoader(
cifar_trainset,
batch_size=batch_size,
shuffle=True,
num_workers=8)
testLoader = DataLoader(
cifar_testset,
batch_size=batch_size,
shuffle=True,
num_workers=8)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
print(next(iter(trainLoader))[0].shape)
resnet = models.resnet50(pretrained=True)
# print(resnet)
# print(resnet.fc)
class JHARMNet(nn.Module):
def __init__(self, pretrained_model, num_classes):
super().__init__()
self.premodel = pretrained_model
self.premodel.fc = nn.Linear(2048, num_classes)
nn.init.xavier_uniform_(self.premodel.fc.weight)
def forward(self, x):
out = self.premodel(x)
return out
model_check = JHARMNet(resnet, 10)
criterion = nn.CrossEntropyLoss()
learning_rate = 0.1
optimizer = torch.optim.SGD(
model_check.parameters(),
lr=learning_rate,
momentum=0.2)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
def train_model(
model,
train_loader,
validation_loader,
optimizer,
n_epochs=20):
# Global variable
N_test = len(cifar_testset)
accuracy_list = []
train_loss_list = []
model = model.to(mps_device)
train_cost_list = []
val_cost_list = []
for epoch in range(n_epochs):
train_COST = 0
print(f"Training Epoch: {epoch}")
for x, y in tqdm(train_loader):
x = x.to(mps_device)
y = y.to(mps_device)
model.train()
optimizer.zero_grad()
z = model(x)
loss = criterion(z, y)
loss.backward()
optimizer.step()
train_COST += loss.item()
train_COST = train_COST / len(train_loader)
train_cost_list.append(train_COST)
correct = 0
print(f"Validation Loop")
# Perform the prediction on the validation data
val_COST = 0
for x_test, y_test in tqdm(validation_loader):
model.eval()
x_test = x_test.to(mps_device)
y_test = y_test.to(mps_device)
z = model(x_test)
val_loss = criterion(z, y_test)
scheduler.step(val_loss)
_, yhat = torch.max(z.data, 1)
correct += (yhat == y_test).sum().item()
val_COST += val_loss.item()
val_COST = val_COST / len(validation_loader)
val_cost_list.append(val_COST)
accuracy = correct / N_test
accuracy_list.append(accuracy)
print("--> Epoch Number : {}".format(epoch + 1),
" | Training Loss : {}".format(round(train_COST, 4)),
" | Validation Loss : {}".format(round(val_COST, 4)),
" | Validation Accuracy : {}%".format(round(accuracy * 100, 2)))
return accuracy_list, train_cost_list, val_cost_list, model
accuracy_list_normalv5, train_cost_listv5, val_cost_listv5, model_to_save = train_model(
model=model_check, n_epochs=100, train_loader=trainLoader, validation_loader=testLoader, optimizer=optimizer)
torch.save(model_to_save.state_dict(), "CIFAR.pt")
model = torch.load("CIFAR.pt")
model.eval()
pred = model(next(iter(testLoader))[0])
print(pred[0][0], next(iter(testLoader))[1][0])
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