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import argparse
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from thop import profile, clever_format
from torch.utils.data import DataLoader
from transfer_datasets import TRANSFER_DATASET
import torchvision.transforms as transforms
from data_statistics import get_data_mean_and_stdev, get_data_nclass
from tqdm import tqdm
import utils
import wandb
import torchvision
def load_transform(dataset, size=32):
mean, std = get_data_mean_and_stdev(dataset)
transform = transforms.Compose([
transforms.Resize((size, size)),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)])
return transform
class Net(nn.Module):
def __init__(self, num_class, pretrained_path, dataset, arch):
super(Net, self).__init__()
if arch=='resnet18':
embedding_size = 512
elif arch=='resnet50':
embedding_size = 2048
else:
raise NotImplementedError
# encoder
from model import Model
self.f = Model(dataset=dataset, arch=arch).f
# classifier
self.fc = nn.Linear(embedding_size, num_class, bias=True)
self.load_state_dict(torch.load(pretrained_path, map_location='cpu'), strict=False)
def forward(self, x):
x = self.f(x)
feature = torch.flatten(x, start_dim=1)
out = self.fc(feature)
return out
# train or test for one epoch
def train_val(net, data_loader, train_optimizer):
is_train = train_optimizer is not None
net.train() if is_train else net.eval()
total_loss, total_correct_1, total_correct_5, total_num, data_bar = 0.0, 0.0, 0.0, 0, tqdm(data_loader)
with (torch.enable_grad() if is_train else torch.no_grad()):
for data, target in data_bar:
data, target = data.cuda(non_blocking=True), target.cuda(non_blocking=True)
out = net(data)
loss = loss_criterion(out, target)
if is_train:
train_optimizer.zero_grad()
loss.backward()
train_optimizer.step()
total_num += data.size(0)
total_loss += loss.item() * data.size(0)
prediction = torch.argsort(out, dim=-1, descending=True)
total_correct_1 += torch.sum((prediction[:, 0:1] == target.unsqueeze(dim=-1)).any(dim=-1).float()).item()
total_correct_5 += torch.sum((prediction[:, 0:5] == target.unsqueeze(dim=-1)).any(dim=-1).float()).item()
data_bar.set_description('{} Epoch: [{}/{}] Loss: {:.4f} ACC@1: {:.2f}% ACC@5: {:.2f}% model: {}'
.format('Train' if is_train else 'Test', epoch, epochs, total_loss / total_num,
total_correct_1 / total_num * 100, total_correct_5 / total_num * 100,
model_path.split('/')[-1]))
return total_loss / total_num, total_correct_1 / total_num * 100, total_correct_5 / total_num * 100
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Linear Evaluation')
parser.add_argument('--dataset', default='cifar10', type=str, help='Pre-trained dataset.', choices=['cifar10', 'cifar100', 'stl10', 'tiny_imagenet'])
parser.add_argument('--transfer_dataset', default='cifar10', type=str, help='Transfer dataset (i.e., testing dataset)', choices=['cifar10', 'cifar100', 'stl-10', 'aircraft', 'cu_birds', 'dtd', 'fashionmnist', 'mnist', 'traffic_sign', 'vgg_flower'])
parser.add_argument('--arch', default='resnet50', type=str, help='Backbone architecture for experiments', choices=['resnet50', 'resnet18'])
parser.add_argument('--model_path', type=str, default='results/Barlow_Twins/0.005_64_128_model.pth',
help='The base string of the pretrained model path')
parser.add_argument('--batch_size', type=int, default=128, help='Number of images in each mini-batch')
parser.add_argument('--epochs', type=int, default=100, help='Number of sweeps over the dataset to train')
parser.add_argument('--screen', type=str, help='screen session id')
# wandb related args
parser.add_argument('--wandb_group', type=str, help='group for wandb')
args = parser.parse_args()
wandb.init(project=f"Barlow-Twins-MixUp-TransferLearn-[{args.dataset}-to-X]-{args.arch}", config=args, dir='/data/wbandar1/projects/ssl-aug-artifacts/wandb_logs/', group=args.wandb_group, name=f'{args.transfer_dataset}')
run_id = wandb.run.id
model_path, batch_size, epochs = args.model_path, args.batch_size, args.epochs
dataset = args.dataset
transfer_dataset = args.transfer_dataset
if dataset in ['cifar10', 'cifar100']:
print("reshaping data into 32x32")
resize = 32
else:
print("reshaping data into 64x64")
resize = 64
train_data = TRANSFER_DATASET[args.transfer_dataset](train=True, image_transforms=load_transform(args.transfer_dataset, resize))
test_data = TRANSFER_DATASET[args.transfer_dataset](train=False, image_transforms=load_transform(args.transfer_dataset, resize))
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=16, pin_memory=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=16, pin_memory=True)
model = Net(num_class=get_data_nclass(args.transfer_dataset), pretrained_path=model_path, dataset=dataset, arch=args.arch).cuda()
for param in model.f.parameters():
param.requires_grad = False
# optimizer with lr sheduler
# lr_start, lr_end = 1e-2, 1e-6
# gamma = (lr_end / lr_start) ** (1 / epochs)
# optimizer = optim.Adam(model.fc.parameters(), lr=lr_start, weight_decay=5e-6)
# scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma)
# adpoted from
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [60, 80], gamma=0.1)
# optimizer with no sheuduler
# optimizer = optim.Adam(model.fc.parameters(), lr=1e-3, weight_decay=1e-6)
loss_criterion = nn.CrossEntropyLoss()
results = {'train_loss': [], 'train_acc@1': [], 'train_acc@5': [],
'test_loss': [], 'test_acc@1': [], 'test_acc@5': []}
save_name = model_path.split('.pth')[0] + '_linear.csv'
best_acc = 0.0
for epoch in range(1, epochs + 1):
train_loss, train_acc_1, train_acc_5 = train_val(model, train_loader, optimizer)
results['train_loss'].append(train_loss)
results['train_acc@1'].append(train_acc_1)
results['train_acc@5'].append(train_acc_5)
test_loss, test_acc_1, test_acc_5 = train_val(model, test_loader, None)
results['test_loss'].append(test_loss)
results['test_acc@1'].append(test_acc_1)
results['test_acc@5'].append(test_acc_5)
# save statistics
# data_frame = pd.DataFrame(data=results, index=range(1, epoch + 1))
# data_frame.to_csv(save_name, index_label='epoch')
if test_acc_1 > best_acc:
best_acc = test_acc_1
wandb.log(
{
"train_loss": train_loss,
"train_acc@1": train_acc_1,
"train_acc@5": train_acc_5,
"test_loss": test_loss,
"test_acc@1": test_acc_1,
"test_acc@5": test_acc_5,
"best_acc": best_acc
}
)
scheduler.step()
wandb.finish()
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