linear.py

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
from tqdm import tqdm

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')

    args = parser.parse_args()
    model_path, batch_size, epochs = args.model_path, args.batch_size, args.epochs
    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)

    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)