mia_on_model_distillation/wresnet-pytorch/src/train.py

import os
import torch
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
import torch.nn as nn
import numpy as np
import random
from utils import json_file_to_pyobj, get_loaders
from WideResNet import WideResNet
from tqdm import tqdm
import opacus
from opacus.validators import ModuleValidator


def set_seed(seed=42):
    torch.backends.cudnn.deterministic = True
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)


def _train_seed(net, loaders, device, dataset, log=False, checkpoint=False, logfile='', checkpointFile='', epochs=200):
    train_loader, test_loader = loaders

    dp_epsilon = 8
    if dp_epsilon is not None:
        print(f"DP epsilon: {dp_epsilon}")
        #net = ModuleValidator.fix(net, replace_bn_with_in=True)
        net = ModuleValidator.fix(net)
        print(net)
        ModuleValidator.validate(net, strict=True)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=5e-4)
    scheduler = MultiStepLR(optimizer, milestones=[int(elem*epochs) for elem in [0.3, 0.6, 0.8]], gamma=0.2)

    best_test_set_accuracy = 0

    privacy_engine = opacus.PrivacyEngine()
    net, optimizer, train_loader = privacy_engine.make_private_with_epsilon(
        module=net,
        optimizer=optimizer,
        data_loader=train_loader,
        epochs=epochs,
        target_epsilon=8,
        target_delta=1e-5,
        max_grad_norm=3.0,
    )

    print(f"Using sigma={optimizer.noise_multiplier} and C={1.0}")

    print(f"Training with {epochs} epochs")
    #for epoch in tqdm(range(epochs)):
    for epoch in range(epochs):
        net.train()
        #for i, data in tqdm(enumerate(train_loader, 0), leave=False):
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            inputs = inputs.to(device)
            labels = labels.to(device)

            optimizer.zero_grad()

            wrn_outputs = net(inputs)
            outputs = wrn_outputs[0]
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

        scheduler.step()

        with torch.no_grad():

            correct = 0
            total = 0

            net.eval()
            for data in test_loader:
                images, labels = data
                images = images.to(device)
                labels = labels.to(device)

                wrn_outputs = net(images)
                outputs = wrn_outputs[0]
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()

            epoch_accuracy = correct / total
            epoch_accuracy = round(100 * epoch_accuracy, 2)

            if log:
                print('Accuracy at epoch {} is {}%\n'.format(epoch + 1, epoch_accuracy))
                with open(logfile, 'a') as temp:
                    temp.write('Accuracy at epoch {} is {}%\n'.format(epoch + 1, epoch_accuracy))

            if epoch_accuracy > best_test_set_accuracy:
                best_test_set_accuracy = epoch_accuracy
                if checkpoint:
                    torch.save(net.state_dict(), checkpointFile)

    return best_test_set_accuracy


def train(args):
    json_options = json_file_to_pyobj(args.config)
    training_configurations = json_options.training

    wrn_depth = training_configurations.wrn_depth
    wrn_width = training_configurations.wrn_width
    dataset = training_configurations.dataset.lower()
    #seeds = [int(seed) for seed in training_configurations.seeds]
    seeds = [int.from_bytes(os.urandom(4), byteorder='big')]
    log = True if training_configurations.log.lower() == 'true' else False

    if log:
        logfile = 'WideResNet-{}-{}-{}-{}-{}.txt'.format(wrn_depth, wrn_width, training_configurations.dataset, training_configurations.batch_size, training_configurations.epochs)
        with open(logfile, 'w') as temp:
            temp.write('WideResNet-{}-{} on {} {}batch for {} epochs\n'.format(wrn_depth, wrn_width, training_configurations.dataset, training_configurations.batch_size, training_configurations.epochs))
    else:
        logfile = ''

    checkpoint = True if training_configurations.checkpoint.lower() == 'true' else False
    loaders = get_loaders(dataset, training_configurations.batch_size)

    if torch.cuda.is_available():
        device = torch.device('cuda:0')
    else:
        device = torch.device('cpu')

    test_set_accuracies = []

    for seed in seeds:
        set_seed(seed)

        if log:
            with open(logfile, 'a') as temp:
                temp.write('------------------- SEED {} -------------------\n'.format(seed))

        strides = [1, 1, 2, 2]
        net = WideResNet(d=wrn_depth, k=wrn_width, n_classes=10, input_features=3, output_features=16, strides=strides)
        net = net.to(device)

        checkpointFile = 'wrn-{}-{}-seed-{}-{}-dict.pth'.format(wrn_depth, wrn_width, dataset, seed) if checkpoint else ''
        epochs = training_configurations.epochs
        best_test_set_accuracy = _train_seed(net, loaders, device, dataset, log, checkpoint, logfile, checkpointFile, epochs)

        if log:
            with open(logfile, 'a') as temp:
                temp.write('Best test set accuracy of seed {} is {}\n'.format(seed, best_test_set_accuracy))

        test_set_accuracies.append(best_test_set_accuracy)

    mean_test_set_accuracy, std_test_set_accuracy = np.mean(test_set_accuracies), np.std(test_set_accuracies)

    if log:
        with open(logfile, 'a') as temp:
            temp.write('Mean test set accuracy is {} with standard deviation equal to {}\n'.format(mean_test_set_accuracy, std_test_set_accuracy))


if __name__ == '__main__':
    import argparse

    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    os.environ["CUDA_VISIBLE_DEVICES"] = "0, 1, 2, 3"

    parser = argparse.ArgumentParser(description='WideResNet')

    parser.add_argument('-config', '--config', help='Training Configurations', required=True)

    args = parser.parse_args()

    train(args)