168 lines
6 KiB
Python
168 lines
6 KiB
Python
from datetime import datetime
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import time
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import argparse
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from utils import json_file_to_pyobj, get_loaders
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from WideResNet import WideResNet
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from opacus.validators import ModuleValidator
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import os
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from pathlib import Path
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from torch.optim.lr_scheduler import MultiStepLR
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from torchvision.datasets import CIFAR10
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from torch.utils.data import DataLoader
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import os
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import torch
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import torch.nn as nn
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from torchvision import models, transforms
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import student_model
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import torch.optim as optim
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import torch.nn.functional as F
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import opacus
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import warnings
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warnings.filterwarnings("ignore")
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def train_knowledge_distillation(teacher, student, train_dl, epochs, learning_rate, T, soft_target_loss_weight, ce_loss_weight, device):
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# Dataset
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ce_loss = nn.CrossEntropyLoss()
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optimizer = optim.Adam(student.parameters(), lr=learning_rate)
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teacher.eval() # Teacher set to evaluation mode
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student.train() # Student to train mode
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for epoch in range(epochs):
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running_loss = 0.0
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for inputs, labels in train_dl:
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inputs, labels = inputs.to(device), labels.to(device)
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optimizer.zero_grad()
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# Forward pass with the teacher model - do not save gradients here as we do not change the teacher's weights
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with torch.no_grad():
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teacher_logits, _, _, _ = teacher(inputs)
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# Forward pass with the student model
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student_logits = student(inputs)
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#Soften the student logits by applying softmax first and log() second
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soft_targets = nn.functional.softmax(teacher_logits / T, dim=-1)
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soft_prob = nn.functional.log_softmax(student_logits / T, dim=-1)
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# Calculate the soft targets loss. Scaled by T**2 as suggested by the authors of the paper "Distilling the knowledge in a neural network"
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soft_targets_loss = torch.sum(soft_targets * (soft_targets.log() - soft_prob)) / soft_prob.size()[0] * (T**2)
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# Calculate the true label loss
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label_loss = ce_loss(student_logits, labels)
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# Weighted sum of the two losses
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loss = soft_target_loss_weight * soft_targets_loss + ce_loss_weight * label_loss
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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print(f"Epoch {epoch+1}/{epochs}, Loss: {running_loss / len(train_dl)}")
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@torch.no_grad()
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def test(model, device, test_dl, is_teacher=False):
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model.to(device)
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model.eval()
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correct = 0
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total = 0
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for inputs, labels in test_dl:
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inputs, labels = inputs.to(device), labels.to(device)
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if is_teacher:
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outputs, _, _, _ = model(inputs)
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else:
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outputs = model(inputs)
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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accuracy = 100 * correct / total
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return accuracy
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def main():
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parser = argparse.ArgumentParser(description='Student trainer')
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parser.add_argument('--teacher', type=Path, help='path to saved teacher .pt', required=True)
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parser.add_argument('--norm', type=float, help='dpsgd norm clip factor', required=True)
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parser.add_argument('--cuda', type=int, help='gpu index', required=False)
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parser.add_argument('--epsilon', type=float, help='dp epsilon', required=False, default=None)
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parser.add_argument('--epochs', type=int, help='student epochs', required=True)
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args = parser.parse_args()
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json_options = json_file_to_pyobj("wresnet16-audit-cifar10.json")
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training_configurations = json_options.training
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wrn_depth = training_configurations.wrn_depth
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wrn_width = training_configurations.wrn_width
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dataset = training_configurations.dataset.lower()
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if args.cuda is not None:
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device = torch.device(f'cuda:{args.cuda}')
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elif torch.cuda.is_available():
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device = torch.device('cuda:0')
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else:
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device = torch.device('cpu')
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epochs=10
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print("Load the teacher model")
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# instantiate teacher model
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strides = [1, 1, 2, 2]
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teacher = WideResNet(d=wrn_depth, k=wrn_width, n_classes=10, input_features=3, output_features=16, strides=strides)
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teacher = ModuleValidator.fix(teacher)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(teacher.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=5e-4)
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scheduler = MultiStepLR(optimizer, milestones=[int(elem*epochs) for elem in [0.3, 0.6, 0.8]], gamma=0.2)
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train_loader, test_loader = get_loaders(dataset, training_configurations.batch_size)
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best_test_set_accuracy = 0
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if args.epsilon is not None:
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dp_epsilon = args.epsilon
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dp_delta = 1e-5
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norm = args.norm
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privacy_engine = opacus.PrivacyEngine()
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teacher, optimizer, train_loader = privacy_engine.make_private_with_epsilon(
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module=teacher,
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optimizer=optimizer,
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data_loader=train_loader,
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epochs=epochs,
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target_epsilon=dp_epsilon,
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target_delta=dp_delta,
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max_grad_norm=norm,
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)
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teacher.load_state_dict(torch.load(args.teacher, weights_only=True))
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teacher.to(device)
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teacher.eval()
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#instantiate istudent
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student = student_model.Model(num_classes=10).to(device)
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print("Training student")
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train_knowledge_distillation(
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teacher=teacher,
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student=student,
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train_dl=train_loader,
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epochs=args.epochs,
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learning_rate=0.001,
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T=2,
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soft_target_loss_weight=0.25,
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ce_loss_weight=0.75,
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device=device
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)
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print(f"Saving student model for time {int(time.time())}")
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Path('students').mkdir(exist_ok=True)
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torch.save(student.state_dict(), f"students/studentmodel-{int(time.time())}.pt")
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print("Testing student and teacher")
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test_student = test(student, device, test_loader)
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test_teacher = test(teacher, device, test_loader, True)
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print(f"Teacher accuracy: {test_teacher:.2f}%")
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print(f"Student accuracy: {test_student:.2f}%")
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if __name__ == "__main__":
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main()
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