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# !!!! MODEL TRAINED BY ME (@KristofGaming39)!!!!
import torch
import torchvision
from torchvision.models import resnet50
from torchvision.transforms import transforms
from torch.utils.data import DataLoader, Dataset
from PIL import Image
class RobloxDataset(Dataset):
def __init__(self, root_dir, transform=None):
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return 15
def __getitem__(self, idx):
img_path = f'{self.root_dir}/Character/human{str(idx+1).zfill(2)}.png'
image = Image.open(img_path).convert('RGB')
if self.transform:
image = self.transform(image)
return image
data_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
dataset = RobloxDataset('/content/dataset', transform=data_transform)
data_loader = DataLoader(dataset, batch_size=1, shuffle=True)
model = resnet50(pretrained=True)
model.fc = torch.nn.Linear(in_features=2048, out_features=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 10
for epoch in range(num_epochs):
for images in data_loader:
images = images.to(device)
labels = torch.ones((images.size(0), 1)).to(device)
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}') |