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linear.py
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linear.py
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import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import STL10, CIFAR10, CIFAR100
from tqdm import tqdm
import os
import utils
from model import Model
class Net(nn.Module):
def __init__(self, num_class, pretrained_path):
super(Net, self).__init__()
# encoder
model = Model().cuda()
model = nn.DataParallel(model)
model.load_state_dict(torch.load(pretrained_path))
self.f = model.module.f
# classifier
self.fc = nn.Linear(2048, num_class, bias=True)
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}%'
.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))
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('--model_path', type=str, default='results/model_400.pth',
help='The pretrained model path')
parser.add_argument('--batch_size', type=int, default=512, 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('--dataset', type=str, default='STL', help='experiment dataset')
parser.add_argument('--txt', action="store_true", default=False, help='save txt?')
parser.add_argument('--name', type=str, default='None', help='exp name?')
args = parser.parse_args()
if not os.path.exists('downstream/{}/{}'.format(args.dataset, args.name)):
os.makedirs('downstream/{}/{}'.format(args.dataset, args.name))
# seed
utils.set_seed(1234)
model_path, batch_size, epochs = args.model_path, args.batch_size, args.epochs
if args.dataset == 'STL':
train_data = STL10(root='data', split='train', transform=utils.train_transform)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)
test_data = STL10(root='data', split='test', transform=utils.test_transform)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
elif args.dataset == 'CIFAR10':
train_data = utils.CIFAR10(root='data', train=True, transform=utils.train_transform)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
test_data = utils.CIFAR10(root='data', train=False, transform=utils.test_transform)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)
elif args.dataset == 'CIFAR100':
train_data = utils.CIFAR100(root='data', train=True, transform=utils.train_transform)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
test_data = utils.CIFAR100(root='data', train=False, transform=utils.test_transform)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)
model = Net(num_class=len(train_data.classes), pretrained_path=model_path).cuda()
for param in model.f.parameters():
param.requires_grad = False
model = nn.DataParallel(model)
optimizer = optim.Adam(model.module.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': []}
for epoch in range(1, epochs + 1):
train_loss, train_acc_1, train_acc_5 = train_val(model, train_loader, optimizer)
test_loss, test_acc_1, test_acc_5 = train_val(model, test_loader, None)
if args.txt:
txt_write = open("downstream/{}/{}/{}".format(args.dataset, args.name, 'result.txt'), 'a')
txt_write.write('\ntest_loss: {}, test_acc@1: {}, test_acc@5: {}'.format(test_loss, test_acc_1, test_acc_5))