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pretraining.py
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pretraining.py
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from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from PyTorch_Src_train.cifar10_models.vgg import vgg11_bn
from PyTorch_Src_train.cifar10_models.resnet import resnet18
from PyTorch_Src_train.cifar10_models.googlenet import GoogLeNet, googlenet
import wandb
from _code.utils import setup_dataset_models_standard as tinyimagenet_load
class Normalize(nn.Module):
def __init__(self, mean, std) :
super(Normalize, self).__init__()
self.register_buffer('mean', torch.Tensor(mean))
self.register_buffer('std', torch.Tensor(std))
def forward(self, input):
# Broadcasting
mean = self.mean.reshape(1, 3, 1, 1)
std = self.std.reshape(1, 3, 1, 1)
return (input - mean) / std
class NormalizeByChannelMeanStd(nn.Module):
def __init__(self, mean, std):
super(NormalizeByChannelMeanStd, self).__init__()
if not isinstance(mean, torch.Tensor):
mean = torch.tensor(mean)
if not isinstance(std, torch.Tensor):
std = torch.tensor(std)
self.register_buffer("mean", mean)
self.register_buffer("std", std)
def forward(self, tensor):
return normalize_fn(tensor, self.mean, self.std)
def extra_repr(self):
return 'mean={}, std={}'.format(self.mean, self.std)
def normalize_fn(tensor, mean, std):
"""Differentiable version of torchvision.functional.normalize"""
# here we assume the color channel is in at dim=1
mean = mean[None, :, None, None]
std = std[None, :, None, None]
return tensor.sub(mean).div(std)
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
wandb.log({f'{args.dataset}/Training Loss': loss.item()})
def test(args,model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
wandb.log({f'{args.dataset}/Test Accuracy': 100. * correct / len(test_loader.dataset)})
def test_train_split(args,model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('Train set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
wandb.log({f'{args.dataset}/Train Accuracy': 100. * correct / len(test_loader.dataset)})
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Training Example')
parser.add_argument('--batch_size', type=int, default=256, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=14, metavar='N',
help='number of epochs to train (default: 14)')
parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--no_cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save_model', action='store_true', default=True,
help='For Saving the current Model')
parser.add_argument('--model_name', default='resnet18', type=str)
parser.add_argument('--dataset',help='cifar10 or tinyimagenet',default='svhn')
parser.add_argument('--wandb',help='wandb',default=0, type=int)
args = parser.parse_args()
print(args)
mode = 'online' if args.wandb else 'disabled'
wandb.init(project='Training_Datasets', entity='vclab', name=f'train_{args.dataset}_{args.model_name}', mode=mode, config=args, tags=[args.dataset,args.model_name])
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
if args.dataset == 'svhn':
norm_layer = Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
train_kwargs = {'batch_size': args.batch_size, 'shuffle': False}
test_kwargs = {'batch_size': args.test_batch_size, 'shuffle': False}
if use_cuda:
cuda_kwargs = {'num_workers': 8,
'pin_memory': True,
'shuffle': False}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
transform=transforms.Compose([
transforms.Resize((32,32)),
transforms.ToTensor(),
])
args.num_classes = 10
dataset1 = datasets.SVHN('data', download=True, transform=transform)
dataset2 = datasets.SVHN('data', transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
if args.dataset == 'tinyimagenet':
norm_layer = NormalizeByChannelMeanStd(mean=[0.4802, 0.4481, 0.3975], std=[0.2302, 0.2265, 0.2262])
args.num_classes = 200
args.data = 'data/tiny-imagenet-200'
train_loader, val_loader, test_loader = tinyimagenet_load(args, shuffle=True)
# test_loader = train_loader
if args.model_name == 'vgg11':
my_model = vgg11_bn(num_classes=args.num_classes, pretrained=False) # automatically picks up weights from PyTorch_Src_train/cifar10_models/state_dicts
model = nn.Sequential(
norm_layer,
my_model).to(device)
if args.model_name == 'resnet18':
my_model = resnet18(num_classes=args.num_classes, pretrained=False) # automatically picks up weights from PyTorch_Src_train/cifar10_models/state_dicts
model = nn.Sequential(
norm_layer,
my_model).to(device)
if args.model_name == 'googlenet':
my_model = GoogLeNet(num_classes=args.num_classes) # automatically picks up weights from PyTorch_Src_train/cifar10_models/state_dicts
model = nn.Sequential(
norm_layer,
my_model).to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test_train_split(args, model, device, train_loader)
test(args, model, device, test_loader)
scheduler.step()
if args.save_model:
save_path = f"_code/checkpoint/{args.dataset}_{args.model_name}_natural.pt"
print('Model Saved at:',save_path)
torch.save(model.state_dict(), save_path)
if __name__ == '__main__':
main()