From 0eb26f89797e6ab192c9e323cf6327f4bf042907 Mon Sep 17 00:00:00 2001 From: Akemi Izuko Date: Sun, 1 Dec 2024 13:38:32 -0700 Subject: [PATCH] Wres: add distillation code --- wresnet-pytorch/src/distillation_train.py | 170 ++++++++++++++++++++++ wresnet-pytorch/src/student_model.py | 29 ++++ 2 files changed, 199 insertions(+) create mode 100644 wresnet-pytorch/src/distillation_train.py create mode 100644 wresnet-pytorch/src/student_model.py diff --git a/wresnet-pytorch/src/distillation_train.py b/wresnet-pytorch/src/distillation_train.py new file mode 100644 index 0000000..f0a5cbb --- /dev/null +++ b/wresnet-pytorch/src/distillation_train.py @@ -0,0 +1,170 @@ +from utils import json_file_to_pyobj, get_loaders +from WideResNet import WideResNet +from opacus.validators import ModuleValidator +import os +from pathlib import Path +from torch.optim.lr_scheduler import MultiStepLR +from torchvision.datasets import CIFAR10 +from torch.utils.data import DataLoader +import os +import torch +import torch.nn as nn +from torchvision import models, transforms +import student_model +import torch.optim as optim +import torch.nn.functional as F +import opacus +import warnings +warnings.filterwarnings("ignore") + + +def train_knowledge_distillation(teacher, student, epochs, learning_rate, T, soft_target_loss_weight, ce_loss_weight, device): + # Dataset + transform = transforms.Compose( + [ + transforms.RandomHorizontalFlip(), + transforms.RandomCrop(32, padding=4), + transforms.ToTensor(), + transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2470, 0.2435, 0.2616]), + ] + ) + datadir = Path().home() / "opt/data/cifar" + train_ds = CIFAR10(root=datadir, train=True, download=True, transform=transform) + train_dl = DataLoader(train_ds, batch_size=128, shuffle=False, num_workers=4) + + + ce_loss = nn.CrossEntropyLoss() + optimizer = optim.Adam(student.parameters(), lr=learning_rate) + + teacher.eval() # Teacher set to evaluation mode + student.train() # Student to train mode + + for epoch in range(epochs): + running_loss = 0.0 + for inputs, labels in train_dl: + inputs, labels = inputs.to(device), labels.to(device) + + optimizer.zero_grad() + + # Forward pass with the teacher model - do not save gradients here as we do not change the teacher's weights + with torch.no_grad(): + teacher_logits, _, _, _ = teacher(inputs) + + # Forward pass with the student model + student_logits = student(inputs) + #Soften the student logits by applying softmax first and log() second + soft_targets = nn.functional.softmax(teacher_logits / T, dim=-1) + soft_prob = nn.functional.log_softmax(student_logits / T, dim=-1) + + # Calculate the soft targets loss. Scaled by T**2 as suggested by the authors of the paper "Distilling the knowledge in a neural network" + soft_targets_loss = torch.sum(soft_targets * (soft_targets.log() - soft_prob)) / soft_prob.size()[0] * (T**2) + + # Calculate the true label loss + label_loss = ce_loss(student_logits, labels) + + # Weighted sum of the two losses + loss = soft_target_loss_weight * soft_targets_loss + ce_loss_weight * label_loss + + loss.backward() + optimizer.step() + + running_loss += loss.item() + + print(f"Epoch {epoch+1}/{epochs}, Loss: {running_loss / len(train_dl)}") + + + +def test(model, device, teacher=False): + transform = transforms.Compose( + [ + transforms.RandomHorizontalFlip(), + transforms.RandomCrop(32, padding=4), + transforms.ToTensor(), + transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2470, 0.2435, 0.2616]), + ] + ) + datadir = Path().home() / "opt/data/cifar" + test_ds = CIFAR10(root=datadir, train=True, download=False, transform=transform) + test_dl = DataLoader(test_ds, batch_size=128, shuffle=False, num_workers=4 + ) + model.to(device) + model.eval() + + correct = 0 + total = 0 + + with torch.no_grad(): + for inputs, labels in test_dl: + inputs, labels = inputs.to(device), labels.to(device) + if teacher: + outputs, _, _, _ = model(inputs) + else: + outputs = model(inputs) + _, predicted = torch.max(outputs.data, 1) + + total += labels.size(0) + correct += (predicted == labels).sum().item() + + accuracy = 100 * correct / total + print(f"Test Accuracy: {accuracy:.2f}%") + return accuracy + + +def main(): + json_options = json_file_to_pyobj("wresnet16-audit-cifar10.json") + training_configurations = json_options.training + + wrn_depth = training_configurations.wrn_depth + wrn_width = training_configurations.wrn_width + dataset = training_configurations.dataset.lower() + + if torch.cuda.is_available(): + device = torch.device('cuda:0') + else: + device = torch.device('cpu') + epochs=10 + + print("Load the teacher model") + # instantiate teacher model + strides = [1, 1, 2, 2] + teacher = WideResNet(d=wrn_depth, k=wrn_width, n_classes=10, input_features=3, output_features=16, strides=strides) + teacher = ModuleValidator.fix(teacher) + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(teacher.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) + train_loader, test_loader = get_loaders(dataset, training_configurations.batch_size) + best_test_set_accuracy = 0 + dp_epsilon = 8 + dp_delta = 1e-5 + norm = 1.0 + privacy_engine = opacus.PrivacyEngine() + teacher, optimizer, train_loader = privacy_engine.make_private_with_epsilon( + module=teacher, + optimizer=optimizer, + data_loader=train_loader, + epochs=epochs, + target_epsilon=dp_epsilon, + target_delta=dp_delta, + max_grad_norm=norm, + ) + + + + teacher.load_state_dict(torch.load(os.path.join("wrn-1733078278-8e-1e-05d-12.0n-dict.pt"), weights_only=True)) + teacher.to(device) + teacher.eval() + #instantiate istudent + student = student_model.Model(num_classes=10).to(device) + + + print("Training student") + #train_knowledge_distillation(teacher=teacher, student=student, epochs=100, learning_rate=0.001, T=2, soft_target_loss_weight=0.25, ce_loss_weight=0.75, device=device) + #test_student = test(student, device) + test_teacher = test(teacher, device, True) + print(f"Teacher accuracy: {test_teacher:.2f}%") + #print(f"Student accuracy: {test_student:.2f}%") + + +if __name__ == "__main__": + main() + diff --git a/wresnet-pytorch/src/student_model.py b/wresnet-pytorch/src/student_model.py new file mode 100644 index 0000000..fa6d723 --- /dev/null +++ b/wresnet-pytorch/src/student_model.py @@ -0,0 +1,29 @@ +import torch +import torch.nn as nn + +# Create a similar student class where we return a tuple. We do not apply pooling after flattening. +class ModifiedLightNNCosine(nn.Module): + def __init__(self, num_classes=10): + super(ModifiedLightNNCosine, self).__init__() + self.features = nn.Sequential( + nn.Conv2d(3, 16, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.Conv2d(16, 16, kernel_size=3, padding=1), + nn.ReLU(), + nn.MaxPool2d(kernel_size=2, stride=2), + ) + self.classifier = nn.Sequential( + nn.Linear(1024, 256), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(256, num_classes) + ) + + def forward(self, x): + x = self.features(x) + flattened_conv_output = torch.flatten(x, 1) + x = self.classifier(flattened_conv_output) + return x + +Model = ModifiedLightNNCosine