Wres: clean up student a bit

README.md

@@ -1,6 +1,8 @@
 ## Sources
 
  - [cifar10-fast-simple](https://github.com/99991/cifar10-fast-simple)
+ - [lira-pytorch](https://github.com/orientino/lira-pytorch)
+ - [wresnet-pytorch](https://github.com/AlexandrosFerles/Wide-Residual-Networks-PyTorch)
 
 ## Setup
 

cifar10-fast-simple/train.py

@@ -6,52 +6,10 @@ import torchvision
 import model
 
 
-def train(seed=0):
-    # Configurable parameters
-    epochs = 10
-    batch_size = 512
-    momentum = 0.9
-    weight_decay = 0.256
-    weight_decay_bias = 0.004
-    ema_update_freq = 5
-    ema_rho = 0.99 ** ema_update_freq
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    dtype = torch.float16 if device.type != "cpu" else torch.float32
-
-    # First, the learning rate rises from 0 to 0.002 for the first 194 batches.
-    # Next, the learning rate shrinks down to 0.0002 over the next 582 batches.
-    lr_schedule = torch.cat([
-        torch.linspace(0e+0, 2e-3, 194),
-        torch.linspace(2e-3, 2e-4, 582),
-    ])
-
-    lr_schedule_bias = 64.0 * lr_schedule
-
-    # Print information about hardware on first run
-    if seed == 0:
-        if device.type == "cuda":
-            print("Device :", torch.cuda.get_device_name(device.index))
-
-        print("Dtype  :", dtype)
-        print()
-
-    # Start measuring time
-    start_time = time.perf_counter()
-
-    # Set random seed to increase chance of reproducability
-    torch.manual_seed(seed)
-
-    # Setting cudnn.benchmark to True hampers reproducability, but is faster
-    torch.backends.cudnn.benchmark = True
-
-    # Load dataset
-    train_data, train_targets, valid_data, valid_targets = load_cifar10(device, dtype)
-
-    # Compute special weights for first layer
+def load_model(model_path, device, dtype, train_data):
     weights = model.patch_whitening(train_data[:10000, :, 4:-4, 4:-4])
-
-    # Construct the neural network
     train_model = model.Model(weights, c_in=3, c_out=10, scale_out=0.125)
+    train_model.load_state_dict(torch.load(model_path, weights_only=True))
 
     # Convert model weights to half precision
     train_model.to(dtype)
@@ -64,6 +22,75 @@ def train(seed=0):
     # Upload model to GPU
     train_model.to(device)
 
+    return train_model
+
+
+def eval_model(smodel, device, dtype, data, labels, batch_size):
+    smodel.eval()
+    eval_correct = []
+
+    with torch.no_grad():
+        for i in range(0, len(data), batch_size):
+            regular_inputs = data[i : i + batch_size].to(device, dtype)
+            flipped_inputs = torch.flip(regular_inputs, [-1])
+
+            logits1 = smodel(regular_inputs)
+            logits2 = smodel(flipped_inputs)
+
+            # Final logits are average of augmented logits
+            logits = torch.mean(torch.stack([logits1, logits2], dim=0), dim=0)
+
+            # Compute correct predictions
+            correct = logits.max(dim=1)[1] == labels[i : i + batch_size].to(device)
+            eval_correct.append(correct.detach().type(torch.float64))
+
+    # Accuracy is average number of correct predictions
+    eval_acc = torch.mean(torch.cat(eval_correct)).item()
+
+    return eval_acc
+
+
+def run_shadow_model(shadow_path, device, dtype, data, labels, batch_size):
+    smodel = load_model(shadow_path, device, dtype, data)
+    eval_acc = eval_model(smodel, device, dtype, data, labels, batch_size)
+
+    print(f"Evaluation Accuracy: {eval_acc:.4f}")
+
+
+def train_shadow(shadow_path, train_data, train_targets, batch_size):
+    # Configurable parameters
+    epochs = 10
+    momentum = 0.9
+    weight_decay = 0.256
+    weight_decay_bias = 0.004
+    ema_update_freq = 5
+    ema_rho = 0.99**ema_update_freq
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    dtype = torch.float16 if device.type != "cpu" else torch.float32
+
+    # First, the learning rate rises from 0 to 0.002 for the first 194 batches.
+    # Next, the learning rate shrinks down to 0.0002 over the next 582 batches.
+    lr_schedule = torch.cat(
+        [
+            torch.linspace(0e0, 2e-3, 194),
+            torch.linspace(2e-3, 2e-4, 582),
+        ]
+    )
+
+    lr_schedule_bias = 64.0 * lr_schedule
+
+    torch.backends.cudnn.benchmark = True
+
+    weights = model.patch_whitening(train_data[:10000, :, 4:-4, 4:-4])
+    train_model = model.Model(weights, c_in=3, c_out=10, scale_out=0.125)
+    train_model.to(dtype)
+
+    for module in train_model.modules():
+        if isinstance(module, nn.BatchNorm2d):
+            module.float()
+
+    train_model.to(device)
+
     # Collect weights and biases and create nesterov velocity values
     weights = [
         (w, torch.zeros_like(w))
@@ -76,15 +103,15 @@ def train(seed=0):
         if w.requires_grad and len(w.shape) <= 1
     ]
 
-    # Copy the model for validation
-    valid_model = copy.deepcopy(train_model)
-
-    print(f"Preprocessing: {time.perf_counter() - start_time:.2f} seconds")
-
-    # Train and validate
-    print("\nepoch    batch    train time [sec]    validation accuracy")
-    train_time = 0.0
     batch_count = 0
+
+    # Randomly sample half the data per model
+    nb_rows = train_data.shape[0]
+    indices = torch.randperm(nb_rows)[: nb_rows // 2]
+    indices_in = indices[: nb_rows // 2]
+    train_data = train_data[indices_in]
+    train_targets = train_targets[indices_in]
+
     for epoch in range(1, epochs + 1):
         # Flush CUDA pipeline for more accurate time measurement
         if torch.cuda.is_available():
@@ -135,41 +162,8 @@ def train(seed=0):
             update_nesterov(weights, lr, weight_decay, momentum)
             update_nesterov(biases, lr_bias, weight_decay_bias, momentum)
 
-            # Update validation model with exponential moving averages
-            if (i // batch_size % ema_update_freq) == 0:
-                update_ema(train_model, valid_model, ema_rho)
+    torch.save(train_model.state_dict(), shadow_path)
 
-        if torch.cuda.is_available():
-            torch.cuda.synchronize()
-
-        # Add training time
-        train_time += time.perf_counter() - start_time
-
-        valid_correct = []
-        for i in range(0, len(valid_data), batch_size):
-            valid_model.train(False)
-
-            # Test time agumentation: Test model on regular and flipped data
-            regular_inputs = valid_data[i : i + batch_size]
-            flipped_inputs = torch.flip(regular_inputs, [-1])
-
-            logits1 = valid_model(regular_inputs).detach()
-            logits2 = valid_model(flipped_inputs).detach()
-
-            # Final logits are average of augmented logits
-            logits = torch.mean(torch.stack([logits1, logits2], dim=0), dim=0)
-
-            # Compute correct predictions
-            correct = logits.max(dim=1)[1] == valid_targets[i : i + batch_size]
-
-            valid_correct.append(correct.detach().type(torch.float64))
-
-        # Accuracy is average number of correct predictions
-        valid_acc = torch.mean(torch.cat(valid_correct)).item()
-
-        print(f"{epoch:5} {batch_count:8d} {train_time:19.2f} {valid_acc:22.4f}")
-
-    return valid_acc
 
 def preprocess_data(data, device, dtype):
     # Convert to torch float16 tensor
@@ -202,17 +196,6 @@ def load_cifar10(device, dtype, data_dir="~/data"):
     return train_data, train_targets, valid_data, valid_targets
 
 
-def update_ema(train_model, valid_model, rho):
-    # The trained model is not used for validation directly. Instead, the
-    # validation model weights are updated with exponential moving averages.
-    train_weights = train_model.state_dict().values()
-    valid_weights = valid_model.state_dict().values()
-    for train_weight, valid_weight in zip(train_weights, valid_weights):
-        if valid_weight.dtype in [torch.float16, torch.float32]:
-            valid_weight *= rho
-            valid_weight += (1 - rho) * train_weight
-
-
 def update_nesterov(weights, lr, weight_decay, momentum):
     for weight, velocity in weights:
         if weight.requires_grad:
@@ -235,12 +218,14 @@ def random_crop(data, crop_size):
 
 def sha256(path):
     import hashlib
+
     with open(path, "rb") as f:
         return hashlib.sha256(f.read()).hexdigest()
 
 
 def getrelpath(abspath):
     import os
+
     return os.path.relpath(abspath, os.getcwd())
 
 
@@ -254,24 +239,15 @@ def print_info():
 def main():
     print_info()
 
-    accuracies = []
-    threshold = 0.94
-    for run in range(100):
-        valid_acc = train(seed=run)
-        accuracies.append(valid_acc)
+    batch_size = 512
+    shadow_path = "shadow.pt"
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    dtype = torch.float16 if device.type != "cpu" else torch.float32
+    train_data, train_targets, valid_data, valid_targets = load_cifar10(device, dtype)
 
-        # Print accumulated results
-        within_threshold = sum(acc >= threshold for acc in accuracies)
-        acc = threshold * 100.0
-        print()
-        print(f"{within_threshold} of {run + 1} runs >= {acc} % accuracy")
-        mean = sum(accuracies) / len(accuracies)
-        variance = sum((acc - mean)**2 for acc in accuracies) / len(accuracies)
-        std = variance**0.5
-        print(f"Min  accuracy: {min(accuracies)}")
-        print(f"Max  accuracy: {max(accuracies)}")
-        print(f"Mean accuracy: {mean} +- {std}")
-        print()
+    train_shadow(shadow_path, train_data, train_targets, batch_size)
+    run_shadow_model(shadow_path, device, dtype, train_data, train_targets, batch_size)
+    run_shadow_model(shadow_path, device, dtype, valid_data, valid_targets, batch_size)
 
 
 if __name__ == "__main__":

lira-pytorch/.gitignore

@@ -0,0 +1,8 @@
+__pycache__
+exp
+logs
+slurm
+gpu.sh
+*.out
+.psyncup.json
+

lira-pytorch/LICENSE

@@ -0,0 +1,202 @@
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lira-pytorch/README.md

@@ -0,0 +1,42 @@
+# Likelihood Ration Attack (LiRA) in PyTorch
+Implementation of the original [LiRA](https://github.com/tensorflow/privacy/tree/master/research/mi_lira_2021) using PyTorch. To run the code, first create an environment with the `env.yml` file. Then run the following command to train the models and run the LiRA attack:
+
+```
+./run.sh
+```
+
+The output will generate and store a log-scale FPR-TPR curve as `./fprtpr.png` with the TPR@0.1%FPR in the output log.
+
+## Results on CIFAR10
+
+Using 16 shadow models trained with `ResNet18 and 2 augmented queries`:
+
+![roc](figures/fprtpr_resnet18.png)
+```
+Attack Ours (online)
+   AUC 0.6548, Accuracy 0.6015, TPR@0.1%FPR of 0.0068
+Attack Ours (online, fixed variance)
+   AUC 0.6700, Accuracy 0.6042, TPR@0.1%FPR of 0.0464
+Attack Ours (offline)
+   AUC 0.5250, Accuracy 0.5353, TPR@0.1%FPR of 0.0041
+Attack Ours (offline, fixed variance)
+   AUC 0.5270, Accuracy 0.5380, TPR@0.1%FPR of 0.0192
+Attack Global threshold
+   AUC 0.5948, Accuracy 0.5869, TPR@0.1%FPR of 0.0006
+```
+
+Using 16 shadow models trained with `WideResNet28-10 and 2 augmented queries`:
+
+![roc](figures/fprtpr_wideresnet.png)
+```
+Attack Ours (online)
+   AUC 0.6834, Accuracy 0.6152, TPR@0.1%FPR of 0.0240
+Attack Ours (online, fixed variance)
+   AUC 0.7017, Accuracy 0.6240, TPR@0.1%FPR of 0.0704
+Attack Ours (offline)
+   AUC 0.5621, Accuracy 0.5649, TPR@0.1%FPR of 0.0140
+Attack Ours (offline, fixed variance)
+   AUC 0.5698, Accuracy 0.5628, TPR@0.1%FPR of 0.0370
+Attack Global threshold
+   AUC 0.6016, Accuracy 0.5977, TPR@0.1%FPR of 0.0013
+```

lira-pytorch/env.yml

@@ -0,0 +1,35 @@
+# Minimal environment for starting a project using conda/mamba:
+# conda env create -n ENVNAME --file ENV.yml
+
+name: template
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.8.6
+  - pip
+  - pytest
+  - numpy
+  - scipy
+  - scikit-learn
+  - matplotlib
+  - pandas
+  - tqdm
+  - wandb
+  - jupyterlab
+  - jupyter
+  - ipykernel
+  - pytorch
+  - torchvision
+  - torchaudio
+  - pytorch-cuda=12.1
+  - tqdm
+  - pytorch-lightning
+  - lightning-bolts
+  - torchmetrics
+
+  # Install packages with pip
+  # - pip:
+  #  - ray[tune]

lira-pytorch/inference.py

@@ -0,0 +1,58 @@
+# PyTorch implementation of
+# https://github.com/tensorflow/privacy/blob/master/research/mi_lira_2021/inference.py
+#
+# author: Chenxiang Zhang (orientino)
+
+import argparse
+import os
+from pathlib import Path
+
+import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import models, transforms
+from torchvision.datasets import CIFAR10
+from tqdm import tqdm
+
+import student_model 
+from utils import json_file_to_pyobj, get_loaders
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--n_queries", default=2, type=int)
+parser.add_argument("--model", default="resnet18", type=str)
+parser.add_argument("--savedir", default="exp/cifar10", type=str)
+args = parser.parse_args()
+
+
+@torch.no_grad()
+def run():
+    DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps")
+    dataset = "cifar10"
+
+    # Dataset
+    train_dl, test_dl = get_loaders(dataset, 4096)
+
+    # Infer the logits with multiple queries
+    for path in os.listdir(args.savedir):
+        m = student_model.Model(num_classes=10)
+        m.load_state_dict(torch.load(os.path.join(args.savedir, path, "model.pt")))
+        m.to(DEVICE)
+        m.eval()
+
+        logits_n = []
+        for i in range(args.n_queries):
+            logits = []
+            for x, _ in tqdm(train_dl):
+                x = x.to(DEVICE)
+                outputs = m(x)
+                logits.append(outputs.cpu().numpy())
+            logits_n.append(np.concatenate(logits))
+        logits_n = np.stack(logits_n, axis=1)
+        print(logits_n.shape)
+
+        np.save(os.path.join(args.savedir, path, "logits.npy"), logits_n)
+
+
+if __name__ == "__main__":
+    run()

lira-pytorch/plot.py

@@ -0,0 +1,205 @@
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Modified copy by Chenxiang Zhang (orientino) of the original:
+# https://github.com/tensorflow/privacy/tree/master/research/mi_lira_2021
+
+
+import argparse
+import functools
+import os
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats
+from sklearn.metrics import auc, roc_curve
+
+matplotlib.rcParams["pdf.fonttype"] = 42
+matplotlib.rcParams["ps.fonttype"] = 42
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--savedir", default="exp/cifar10", type=str)
+args = parser.parse_args()
+
+
+def sweep(score, x):
+    """
+    Compute a ROC curve and then return the FPR, TPR, AUC, and ACC.
+    """
+    fpr, tpr, _ = roc_curve(x, -score)
+    acc = np.max(1 - (fpr + (1 - tpr)) / 2)
+    return fpr, tpr, auc(fpr, tpr), acc
+
+
+def load_data():
+    """
+    Load our saved scores and then put them into a big matrix.
+    """
+    global scores, keep
+    scores = []
+    keep = []
+
+    for path in os.listdir(args.savedir):
+        scores.append(np.load(os.path.join(args.savedir, path, "scores.npy")))
+        keep.append(np.load(os.path.join(args.savedir, path, "keep.npy")))
+    scores = np.array(scores)
+    keep = np.array(keep)
+
+    return scores, keep
+
+
+def generate_ours(keep, scores, check_keep, check_scores, in_size=100000, out_size=100000, fix_variance=False):
+    """
+    Fit a two predictive models using keep and scores in order to predict
+    if the examples in check_scores were training data or not, using the
+    ground truth answer from check_keep.
+    """
+    dat_in = []
+    dat_out = []
+
+    for j in range(scores.shape[1]):
+        dat_in.append(scores[keep[:, j], j, :])
+        dat_out.append(scores[~keep[:, j], j, :])
+
+    in_size = min(min(map(len, dat_in)), in_size)
+    out_size = min(min(map(len, dat_out)), out_size)
+
+    dat_in = np.array([x[:in_size] for x in dat_in])
+    dat_out = np.array([x[:out_size] for x in dat_out])
+
+    mean_in = np.median(dat_in, 1)
+    mean_out = np.median(dat_out, 1)
+
+    if fix_variance:
+        std_in = np.std(dat_in)
+        std_out = np.std(dat_in)
+    else:
+        std_in = np.std(dat_in, 1)
+        std_out = np.std(dat_out, 1)
+
+    prediction = []
+    answers = []
+    for ans, sc in zip(check_keep, check_scores):
+        pr_in = -scipy.stats.norm.logpdf(sc, mean_in, std_in + 1e-30)
+        pr_out = -scipy.stats.norm.logpdf(sc, mean_out, std_out + 1e-30)
+        score = pr_in - pr_out
+
+        prediction.extend(score.mean(1))
+        answers.extend(ans)
+
+    return prediction, answers
+
+
+def generate_ours_offline(keep, scores, check_keep, check_scores, in_size=100000, out_size=100000, fix_variance=False):
+    """
+    Fit a single predictive model using keep and scores in order to predict
+    if the examples in check_scores were training data or not, using the
+    ground truth answer from check_keep.
+    """
+    dat_in = []
+    dat_out = []
+
+    for j in range(scores.shape[1]):
+        dat_in.append(scores[keep[:, j], j, :])
+        dat_out.append(scores[~keep[:, j], j, :])
+
+    out_size = min(min(map(len, dat_out)), out_size)
+
+    dat_out = np.array([x[:out_size] for x in dat_out])
+
+    mean_out = np.median(dat_out, 1)
+
+    if fix_variance:
+        std_out = np.std(dat_out)
+    else:
+        std_out = np.std(dat_out, 1)
+
+    prediction = []
+    answers = []
+    for ans, sc in zip(check_keep, check_scores):
+        score = scipy.stats.norm.logpdf(sc, mean_out, std_out + 1e-30)
+
+        prediction.extend(score.mean(1))
+        answers.extend(ans)
+    return prediction, answers
+
+
+def generate_global(keep, scores, check_keep, check_scores):
+    """
+    Use a simple global threshold sweep to predict if the examples in
+    check_scores were training data or not, using the ground truth answer from
+    check_keep.
+    """
+    prediction = []
+    answers = []
+    for ans, sc in zip(check_keep, check_scores):
+        prediction.extend(-sc.mean(1))
+        answers.extend(ans)
+
+    return prediction, answers
+
+
+def do_plot(fn, keep, scores, ntest, legend="", metric="auc", sweep_fn=sweep, **plot_kwargs):
+    """
+    Generate the ROC curves by using ntest models as test models and the rest to train.
+    """
+
+    prediction, answers = fn(keep[:-ntest], scores[:-ntest], keep[-ntest:], scores[-ntest:])
+
+    fpr, tpr, auc, acc = sweep_fn(np.array(prediction), np.array(answers, dtype=bool))
+
+    low = tpr[np.where(fpr < 0.001)[0][-1]]
+
+    print("Attack %s   AUC %.4f, Accuracy %.4f, TPR@0.1%%FPR of %.4f" % (legend, auc, acc, low))
+
+    metric_text = ""
+    if metric == "auc":
+        metric_text = "auc=%.3f" % auc
+    elif metric == "acc":
+        metric_text = "acc=%.3f" % acc
+
+    plt.plot(fpr, tpr, label=legend + metric_text, **plot_kwargs)
+    return (acc, auc)
+
+
+def fig_fpr_tpr():
+    plt.figure(figsize=(4, 3))
+
+    do_plot(generate_ours, keep, scores, 1, "Ours (online)\n", metric="auc")
+
+    do_plot(functools.partial(generate_ours, fix_variance=True), keep, scores, 1, "Ours (online, fixed variance)\n", metric="auc")
+
+    do_plot(functools.partial(generate_ours_offline), keep, scores, 1, "Ours (offline)\n", metric="auc")
+
+    do_plot(functools.partial(generate_ours_offline, fix_variance=True), keep, scores, 1, "Ours (offline, fixed variance)\n", metric="auc")
+
+    do_plot(generate_global, keep, scores, 1, "Global threshold\n", metric="auc")
+
+    plt.semilogx()
+    plt.semilogy()
+    plt.xlim(1e-5, 1)
+    plt.ylim(1e-5, 1)
+    plt.xlabel("False Positive Rate")
+    plt.ylabel("True Positive Rate")
+    plt.plot([0, 1], [0, 1], ls="--", color="gray")
+    plt.subplots_adjust(bottom=0.18, left=0.18, top=0.96, right=0.96)
+    plt.legend(fontsize=8)
+    plt.savefig("fprtpr.png")
+    plt.show()
+
+
+if __name__ == "__main__":
+    load_data()
+    fig_fpr_tpr()

lira-pytorch/run.sh

@@ -0,0 +1,21 @@
+python3 train.py --epochs 100 --shadow_id 0 --debug
+python3 train.py --epochs 100 --shadow_id 1 --debug
+python3 train.py --epochs 100 --shadow_id 2 --debug
+python3 train.py --epochs 100 --shadow_id 3 --debug
+python3 train.py --epochs 100 --shadow_id 4 --debug
+python3 train.py --epochs 100 --shadow_id 5 --debug
+python3 train.py --epochs 100 --shadow_id 6 --debug
+python3 train.py --epochs 100 --shadow_id 7 --debug
+python3 train.py --epochs 100 --shadow_id 8 --debug
+python3 train.py --epochs 100 --shadow_id 9 --debug
+python3 train.py --epochs 100 --shadow_id 10 --debug
+python3 train.py --epochs 100 --shadow_id 11 --debug
+python3 train.py --epochs 100 --shadow_id 12 --debug
+python3 train.py --epochs 100 --shadow_id 13 --debug
+python3 train.py --epochs 100 --shadow_id 14 --debug
+python3 train.py --epochs 100 --shadow_id 15 --debug
+
+python3 inference.py --savedir exp/cifar10
+python3 score.py --savedir exp/cifar10
+python3 plot.py --savedir exp/cifar10
+

lira-pytorch/run_distilled.sh

@@ -0,0 +1,16 @@
+python3 student_shadow_train.py --epochs 100 --shadow_id 0 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 1 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 2 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 3 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 4 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 5 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 6 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 7 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 8 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 9 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 10 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 11 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 12 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 13 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 14 --debug
+python3 student_shadow_train.py --epochs 100 --shadow_id 15 --debug

lira-pytorch/score.py

@@ -0,0 +1,70 @@
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Modified copy by Chenxiang Zhang (orientino) of the original:
+# https://github.com/tensorflow/privacy/tree/master/research/mi_lira_2021
+
+
+import argparse
+import multiprocessing as mp
+import os
+from pathlib import Path
+
+import numpy as np
+from torchvision.datasets import CIFAR10
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--savedir", default="exp/cifar10", type=str)
+args = parser.parse_args()
+
+
+def load_one(path):
+    """
+    This loads a logits and converts it to a scored prediction.
+    """
+    opredictions = np.load(os.path.join(path, "logits.npy"))  # [n_examples, n_augs, n_classes]
+
+    # Be exceptionally careful.
+    # Numerically stable everything, as described in the paper.
+    predictions = opredictions - np.max(opredictions, axis=-1, keepdims=True)
+    predictions = np.array(np.exp(predictions), dtype=np.float64)
+    predictions = predictions / np.sum(predictions, axis=-1, keepdims=True)
+
+    labels = get_labels()  # TODO generalize this
+
+    COUNT = predictions.shape[0]
+    y_true = predictions[np.arange(COUNT), :, labels[:COUNT]]
+
+    print("mean acc", np.mean(predictions[:, 0, :].argmax(1) == labels[:COUNT]))
+
+    predictions[np.arange(COUNT), :, labels[:COUNT]] = 0
+    y_wrong = np.sum(predictions, axis=-1)
+
+    logit = np.log(y_true + 1e-45) - np.log(y_wrong + 1e-45)
+    np.save(os.path.join(path, "scores.npy"), logit)
+
+
+def get_labels():
+    datadir = Path().home() / "opt/data/cifar"
+    train_ds = CIFAR10(root=datadir, train=True, download=True)
+    return np.array(train_ds.targets)
+
+
+def load_stats():
+    with mp.Pool(8) as p:
+        p.map(load_one, [os.path.join(args.savedir, x) for x in os.listdir(args.savedir)])
+
+
+if __name__ == "__main__":
+    load_stats()

lira-pytorch/student_model.py

@@ -0,0 +1,30 @@
+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
+

lira-pytorch/student_shadow_train.py

@@ -0,0 +1,246 @@
+# PyTorch implementation of
+# https://github.com/tensorflow/privacy/blob/master/research/mi_lira_2021/train.py
+#
+# author: Chenxiang Zhang (orientino)
+#random stuff
+import os
+import argparse
+import time
+from pathlib import Path
+#torch stuff
+import numpy as np
+import pytorch_lightning as pl
+import torch
+import wandb
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import models, transforms
+from torchvision.datasets import CIFAR10
+from tqdm import tqdm
+from torch.optim.lr_scheduler import MultiStepLR
+import torch.optim as optim
+import torch.nn.functional as F
+import torchvision
+from torchvision import transforms
+
+
+
+#privacy libraries
+import opacus
+from opacus.validators import ModuleValidator
+#cutom modules
+from utils import json_file_to_pyobj, get_loaders 
+from WideResNet import WideResNet
+import student_model 
+
+#suppress warning
+import warnings
+warnings.filterwarnings("ignore")
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--lr", default=0.1, type=float)
+parser.add_argument("--epochs", default=1, type=int)
+parser.add_argument("--n_shadows", default=16, type=int)
+parser.add_argument("--shadow_id", default=1, type=int)
+parser.add_argument("--model", default="resnet18", type=str)
+parser.add_argument("--pkeep", default=0.5, type=float)
+parser.add_argument("--savedir", default="exp/cifar10", type=str)
+parser.add_argument("--debug", action="store_true")
+args = parser.parse_args()
+
+DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps")
+
+def get_trainset(train_batch_size=128, test_batch_size=10):          
+    print(f"Train batch size: {train_batch_size}")
+    normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+
+    train_transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Lambda(lambda x: F.pad(x.unsqueeze(0),
+                                            (4, 4, 4, 4), mode='reflect').squeeze()),
+        transforms.ToPILImage(),
+        transforms.RandomCrop(32),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        normalize,
+    ])
+
+    test_transform = transforms.Compose([
+        transforms.ToTensor(),
+        normalize
+    ])
+
+    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False, transform=train_transform)
+    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False, transform=test_transform)
+
+    return trainset, testset
+
+@torch.no_grad()
+def test(model, test_dl, teacher=False):
+    device = DEVICE
+    model.to(device)
+    model.eval()
+
+    correct = 0
+    total = 0
+
+    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 run(teacher, student):
+    device = DEVICE
+    seed = np.random.randint(0, 1000000000)
+    seed ^= int(time.time())
+    pl.seed_everything(seed)
+
+    args.debug = True
+    wandb.init(project="lira", mode="disabled" if args.debug else "online")
+    wandb.config.update(args)
+
+    # Dataset
+    train_ds, test_ds = get_trainset()
+    # Compute the IN / OUT subset:
+    # If we run each experiment independently then even after a lot of trials
+    # there will still probably be some examples that were always included
+    # or always excluded. So instead, with experiment IDs, we guarantee that
+    # after `args.n_shadows` are done, each example is seen exactly half
+    # of the time in train, and half of the time not in train.
+
+    size = len(train_ds)
+    np.random.seed(seed)
+    if args.n_shadows is not None:
+        np.random.seed(0)
+        keep = np.random.uniform(0, 1, size=(args.n_shadows, size))
+        order = keep.argsort(0)
+        keep = order < int(args.pkeep * args.n_shadows)
+        keep = np.array(keep[args.shadow_id], dtype=bool)
+        keep = keep.nonzero()[0]
+    else:
+        keep = np.random.choice(size, size=int(args.pkeep * size), replace=False)
+        keep.sort()
+    keep_bool = np.full((size), False)
+    keep_bool[keep] = True
+
+    train_ds = torch.utils.data.Subset(train_ds, keep)
+    train_dl = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4)
+    test_dl = DataLoader(test_ds, batch_size=128, shuffle=False, num_workers=4)
+
+
+    # Train 
+    learning_rate=0.001
+    T=2
+    soft_target_loss_weight=0.25
+    ce_loss_weight=0.75
+
+    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(args.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}/{args.epochs}, Loss: {running_loss / len(train_dl)}")
+    accuracy = test(student, test_dl)
+    #saving models
+    print("saving model")
+    savedir = os.path.join(args.savedir, str(args.shadow_id))
+    os.makedirs(savedir, exist_ok=True)
+    np.save(savedir + "/keep.npy", keep_bool)
+    torch.save(student.state_dict(), savedir + "/model.pt")
+
+
+def main():
+    epochs = args.epochs
+    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')
+
+    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 student "shadow model"
+    student = student_model.Model(num_classes=10).to(device)
+    # Check norm of layer for both networks -- student should be smaller?
+    print("Norm of 1st layer for teacher:", torch.norm(teacher.conv1.weight).item())
+    print("Norm of 1st layer for student:", torch.norm(student.features[0].weight).item())
+    #train student shadow model
+    run(teacher=teacher, student=student)
+
+if __name__ == "__main__":
+    main()

lira-pytorch/train.py

@@ -0,0 +1,257 @@
+# PyTorch implementation of
+# https://github.com/tensorflow/privacy/blob/master/research/mi_lira_2021/train.py
+#
+# author: Chenxiang Zhang (orientino)
+
+import argparse
+import os
+import time
+from pathlib import Path
+
+import numpy as np
+import pytorch_lightning as pl
+import torch
+import wandb
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision import models, transforms
+from torchvision.datasets import CIFAR10
+from tqdm import tqdm
+from opacus.validators import ModuleValidator
+from opacus import PrivacyEngine
+from opacus.utils.batch_memory_manager import BatchMemoryManager
+import pyvacy
+#from pyvacy import optim#, analysis, sampling
+
+from wide_resnet import WideResNet
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--lr", default=0.1, type=float)
+parser.add_argument("--epochs", default=1, type=int)
+parser.add_argument("--n_shadows", default=16, type=int)
+parser.add_argument("--shadow_id", default=1, type=int)
+parser.add_argument("--model", default="resnet18", type=str)
+parser.add_argument("--pkeep", default=0.5, type=float)
+parser.add_argument("--savedir", default="exp/cifar10", type=str)
+parser.add_argument("--debug", action="store_true")
+args = parser.parse_args()
+
+DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps")
+EPOCHS = args.epochs
+
+
+class DewisNet(nn.Module):
+    def __init__(self):
+        super(DewisNet, self).__init__()
+        # I started my model from the tutorial: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html,
+        # then modified it.
+
+        # 2 convolutional layers, with pooling after each
+        self.conv1 = nn.Conv2d(3, 12, 5)
+        self.conv2 = nn.Conv2d(12, 32, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+
+        # 3 linear layers
+        self.fc1 = nn.Linear(32 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+class JagielskiNet(nn.Module):
+    def __init__(self, input_shape, num_classes, l2=0.01):
+        super(JagielskiNet, self).__init__()
+        self.flatten = nn.Flatten()
+
+        input_dim = 1
+        for dim in input_shape:
+            input_dim *= dim
+
+        self.dense1 = nn.Linear(input_dim, 32)
+        self.relu1 = nn.ReLU()
+        self.dense2 = nn.Linear(32, num_classes)
+
+        # Initialize weights with Glorot Normal (Xavier Normal)
+        torch.nn.init.xavier_normal_(self.dense1.weight)
+        torch.nn.init.xavier_normal_(self.dense2.weight)
+
+        # L2 regularization (weight decay)
+        self.l2 = l2
+
+    def forward(self, x):
+        x = self.flatten(x)
+        x = self.dense1(x)
+        x = self.relu1(x)
+        x = self.dense2(x)
+        return x
+
+
+def run():
+    seed = np.random.randint(0, 1000000000)
+    seed ^= int(time.time())
+    pl.seed_everything(seed)
+
+    args.debug = True
+    wandb.init(project="lira", mode="disabled" if args.debug else "online")
+    wandb.config.update(args)
+
+    # Dataset
+    train_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]),
+        ]
+    )
+    test_transform = transforms.Compose(
+        [
+            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=train_transform)
+    test_ds = CIFAR10(root=datadir, train=False, download=True, transform=test_transform)
+
+    # Compute the IN / OUT subset:
+    # If we run each experiment independently then even after a lot of trials
+    # there will still probably be some examples that were always included
+    # or always excluded. So instead, with experiment IDs, we guarantee that
+    # after `args.n_shadows` are done, each example is seen exactly half
+    # of the time in train, and half of the time not in train.
+
+    size = len(train_ds)
+    np.random.seed(seed)
+    if args.n_shadows is not None:
+        np.random.seed(0)
+        keep = np.random.uniform(0, 1, size=(args.n_shadows, size))
+        order = keep.argsort(0)
+        keep = order < int(args.pkeep * args.n_shadows)
+        keep = np.array(keep[args.shadow_id], dtype=bool)
+        keep = keep.nonzero()[0]
+    else:
+        keep = np.random.choice(size, size=int(args.pkeep * size), replace=False)
+        keep.sort()
+    keep_bool = np.full((size), False)
+    keep_bool[keep] = True
+
+    train_ds = torch.utils.data.Subset(train_ds, keep)
+    train_dl = DataLoader(train_ds, batch_size=256, shuffle=True, num_workers=4)
+    test_dl = DataLoader(test_ds, batch_size=128, shuffle=False, num_workers=4)
+
+    # Model
+    if args.model == "dewisnet":
+        m = DewisNet()
+    elif args.model == "jnet":
+        m = JagielskiNet((3,32,32), 10)
+    elif args.model == "wresnet28-2":
+        m = WideResNet(28, 2, 0.0, 10)
+    elif args.model == "wresnet28-10":
+        m = WideResNet(28, 10, 0.3, 10)
+    elif args.model == "resnet18":
+        m = models.resnet18(weights=None, num_classes=10)
+        m.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        m.maxpool = nn.Identity()
+    else:
+        raise NotImplementedError
+    m = m.to(DEVICE)
+
+    m = ModuleValidator.fix(m)
+    ModuleValidator.validate(m, strict=True)
+
+    print(f"Device: {DEVICE}")
+    optim = torch.optim.SGD(m.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
+    #optim = pyvacy.DPSGD(
+    #    params=m.parameters(),
+    #    lr=args.lr,
+    #    momentum=0.9,
+    #    weight_decay=5e-4,
+    #)
+    sched = torch.optim.lr_scheduler.CosineAnnealingLR(optim, T_max=args.epochs)
+
+    # Train
+    if False:
+        privacy_engine = PrivacyEngine()
+        m, optim, train_dl = privacy_engine.make_private_with_epsilon(
+            module=m,
+            optimizer=optim,
+            data_loader=train_dl,
+            epochs=args.epochs,
+            target_epsilon=8,
+            target_delta=1e-4,
+            max_grad_norm=1.0,
+            batch_first=True,
+        )
+
+        with BatchMemoryManager(
+            data_loader=train_dl,
+            max_physical_batch_size=1000,
+            optimizer=optim
+        ) as memory_safe_data_loader:
+            for i in tqdm(range(args.epochs)):
+                m.train()
+                loss_total = 0
+                pbar = tqdm(memory_safe_data_loader, leave=False)
+                for itr, (x, y) in enumerate(pbar):
+                    x, y = x.to(DEVICE), y.to(DEVICE)
+                    loss = F.cross_entropy(m(x), y)
+                    loss_total += loss
+
+                    pbar.set_postfix_str(f"loss: {loss:.2f}")
+                    optim.zero_grad()
+                    loss.backward()
+                    optim.step()
+                sched.step()
+
+                wandb.log({"loss": loss_total / len(train_dl)})
+    else:
+        for i in tqdm(range(args.epochs)):
+            m.train()
+            loss_total = 0
+            pbar = tqdm(train_dl, leave=False)
+            for itr, (x, y) in enumerate(pbar):
+                x, y = x.to(DEVICE), y.to(DEVICE)
+                loss = F.cross_entropy(m(x), y)
+                loss_total += loss
+
+                pbar.set_postfix_str(f"loss: {loss:.2f}")
+                optim.zero_grad()
+                loss.backward()
+                optim.step()
+            sched.step()
+
+            wandb.log({"loss": loss_total / len(train_dl)})
+
+    print(f"[test] acc_test: {get_acc(m, test_dl):.4f}")
+    wandb.log({"acc_test": get_acc(m, test_dl)})
+
+    savedir = os.path.join(args.savedir, str(args.shadow_id))
+    os.makedirs(savedir, exist_ok=True)
+    np.save(savedir + "/keep.npy", keep_bool)
+    torch.save(m.state_dict(), savedir + "/model.pt")
+
+
+@torch.no_grad()
+def get_acc(model, dl):
+    acc = []
+    for x, y in dl:
+        x, y = x.to(DEVICE), y.to(DEVICE)
+        acc.append(torch.argmax(model(x), dim=1) == y)
+    acc = torch.cat(acc)
+    acc = torch.sum(acc) / len(acc)
+
+    return acc.item()
+
+
+if __name__ == "__main__":
+    run()

lira-pytorch/wide_resnet.py

@@ -0,0 +1,75 @@
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+
+class wide_basic(nn.Module):
+    def __init__(self, in_planes, planes, dropout_rate, stride=1):
+        super(wide_basic, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1)
+        self.dropout = nn.Dropout(p=dropout_rate)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride),
+            )
+
+    def forward(self, x):
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = self.dropout(out)
+        out = self.conv2(F.relu(self.bn2(out)))
+        out += self.shortcut(x)
+
+        return out
+
+
+class WideResNet(nn.Module):
+    def __init__(self, depth, widen_factor, dropout_rate, n_classes):
+        super(WideResNet, self).__init__()
+        self.in_planes = 16
+
+        assert (depth - 4) % 6 == 0, "Wide-ResNet depth should be 6n+4"
+        n = (depth - 4) // 6
+        k = widen_factor
+        stages = [16, 16 * k, 32 * k, 64 * k]
+
+        self.conv1 = nn.Conv2d(3, stages[0], kernel_size=3, stride=1, padding=1)
+        self.layer1 = self._wide_layer(wide_basic, stages[1], n, dropout_rate, stride=1)
+        self.layer2 = self._wide_layer(wide_basic, stages[2], n, dropout_rate, stride=2)
+        self.layer3 = self._wide_layer(wide_basic, stages[3], n, dropout_rate, stride=2)
+        self.bn1 = nn.BatchNorm2d(stages[3], momentum=0.9)
+        self.linear = nn.Linear(stages[3], n_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
+            elif isinstance(m, nn.Linear):
+                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
+                nn.init.constant_(m.bias, 0)
+
+    def _wide_layer(self, block, planes, n_blocks, dropout_rate, stride):
+        strides = [stride] + [1] * (int(n_blocks) - 1)
+        layers = []
+
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, dropout_rate, stride))
+            self.in_planes = planes
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = F.relu(self.bn1(out))
+        out = F.avg_pool2d(out, 8)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+
+        return out

wresnet-pytorch/README.md

@@ -0,0 +1,34 @@
+# Wide Residual Networks in PyTorch
+
+Implementation of Wide Residual Networks (WRNs) in PyTorch. 
+
+## How to train WRNs
+
+At the moment the CIFAR10 and SVHN datasets are fully supported, with specific augmentations for CIFAR10 drawn from related literature and mean/std normalization for SVHN, and multistep learning rate scheduling for both cases. Training is executed through JSON configuration files, which you can modify or extend to support other configurations of WRNs and/or extend datasets etc.
+
+### Example Runs
+
+Train a WideResNet-16-1 on CIFAR10: 
+```
+python train.py --config configs/WRN-16-1-scratch-CIFAR10.json
+```
+
+Train a WideResNet-40-2 on SVHN: 
+```
+python train.py --config configs/WRN-40-2-scratch-SVHN.json
+```
+
+## Results
+
+This work has been tested with 4 variants of WRNs. When setting the seed generator equal to 0, you should expect a test-set accuracy performance close to the following values:
+
+|Model     | CIFAR10 | SVHN   |
+|:---------|:--------|:-------| 
+| WRN-16-1 |90.97%   | 95.52% |        
+| WRN-16-2 |94.21%   | 96.17% |        
+| WRN-40-1 |93.52%   | 96.07% |        
+| WRN-40-2 |95.14%   | 96.14% |           
+
+## Notes
+
+The motivation for originally implementing WRNs in PyTorch was [this](https://github.com/AlexandrosFerles/NIPS_2019_Reproducibilty_Challenge_Zero-shot_Knowledge_Transfer_via_Adversarial_Belief_Matching) NeurIPS reproducibility project, where WRNs were used as the main framework for few-shot and zero-shot knowledge transfer. 

wresnet-pytorch/src/WideResNet.py

@@ -0,0 +1,143 @@
+import torch
+import torch.nn as nn
+from torchsummary import summary
+import math
+
+
+class IndividualBlock1(nn.Module):
+    def __init__(self, input_features, output_features, stride, subsample_input=True, increase_filters=True):
+        super(IndividualBlock1, self).__init__()
+
+        self.activation = nn.ReLU(inplace=True)
+
+        self.batch_norm1 = nn.BatchNorm2d(input_features)
+        self.batch_norm2 = nn.BatchNorm2d(output_features)
+
+        self.conv1 = nn.Conv2d(input_features, output_features, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.conv2 = nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1, bias=False)
+
+        self.subsample_input = subsample_input
+        self.increase_filters = increase_filters
+        if subsample_input:
+            self.conv_inp = nn.Conv2d(input_features, output_features, kernel_size=1, stride=2, padding=0, bias=False)
+        elif increase_filters:
+            self.conv_inp = nn.Conv2d(input_features, output_features, kernel_size=1, stride=1, padding=0, bias=False)
+
+    def forward(self, x):
+
+        if self.subsample_input or self.increase_filters:
+            x = self.batch_norm1(x)
+            x = self.activation(x)
+            x1 = self.conv1(x)
+        else:
+            x1 = self.batch_norm1(x)
+            x1 = self.activation(x1)
+            x1 = self.conv1(x1)
+        x1 = self.batch_norm2(x1)
+        x1 = self.activation(x1)
+        x1 = self.conv2(x1)
+
+        if self.subsample_input or self.increase_filters:
+            return self.conv_inp(x) + x1
+        else:
+            return x + x1
+
+
+class IndividualBlockN(nn.Module):
+    def __init__(self, input_features, output_features, stride):
+        super(IndividualBlockN, self).__init__()
+
+        self.activation = nn.ReLU(inplace=True)
+
+        self.batch_norm1 = nn.BatchNorm2d(input_features)
+        self.batch_norm2 = nn.BatchNorm2d(output_features)
+
+        self.conv1 = nn.Conv2d(input_features, output_features, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.conv2 = nn.Conv2d(output_features, output_features, kernel_size=3, stride=stride, padding=1, bias=False)
+
+    def forward(self, x):
+        x1 = self.batch_norm1(x)
+        x1 = self.activation(x1)
+        x1 = self.conv1(x1)
+        x1 = self.batch_norm2(x1)
+        x1 = self.activation(x1)
+        x1 = self.conv2(x1)
+
+        return x1 + x
+
+
+class Nblock(nn.Module):
+
+    def __init__(self, N, input_features, output_features, stride, subsample_input=True, increase_filters=True):
+        super(Nblock, self).__init__()
+
+        layers = []
+        for i in range(N):
+            if i == 0:
+                layers.append(IndividualBlock1(input_features, output_features, stride, subsample_input, increase_filters))
+            else:
+                layers.append(IndividualBlockN(output_features, output_features, stride=1))
+
+        self.nblockLayer = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.nblockLayer(x)
+
+
+class WideResNet(nn.Module):
+
+    def __init__(self, d, k, n_classes, input_features, output_features, strides):
+        super(WideResNet, self).__init__()
+
+        self.conv1 = nn.Conv2d(input_features, output_features, kernel_size=3, stride=strides[0], padding=1, bias=False)
+
+        filters = [16 * k, 32 * k, 64 * k]
+        self.out_filters = filters[-1]
+        N = (d - 4) // 6
+        increase_filters = k > 1
+        self.block1 = Nblock(N, input_features=output_features, output_features=filters[0], stride=strides[1], subsample_input=False, increase_filters=increase_filters)
+        self.block2 = Nblock(N, input_features=filters[0], output_features=filters[1], stride=strides[2])
+        self.block3 = Nblock(N, input_features=filters[1], output_features=filters[2], stride=strides[3])
+
+        self.batch_norm = nn.BatchNorm2d(filters[-1])
+        self.activation = nn.ReLU(inplace=True)
+        self.avg_pool = nn.AvgPool2d(kernel_size=8)
+        self.fc = nn.Linear(filters[-1], n_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        attention1 = self.block1(x)
+        attention2 = self.block2(attention1)
+        attention3 = self.block3(attention2)
+        out = self.batch_norm(attention3)
+        out = self.activation(out)
+        out = self.avg_pool(out)
+        out = out.view(-1, self.out_filters)
+
+        return self.fc(out), attention1, attention2, attention3
+
+
+if __name__ == '__main__':
+
+    # change d and k if you want to check a model other than WRN-40-2
+    d = 40
+    k = 2
+    strides = [1, 1, 2, 2]
+    net = WideResNet(d=d, k=k, n_classes=10, input_features=3, output_features=16, strides=strides)
+
+    # verify that an output is produced
+    sample_input = torch.ones(size=(1, 3, 32, 32), requires_grad=False)
+    net(sample_input)
+
+    # Summarize model
+    summary(net, input_size=(3, 32, 32))

wresnet-pytorch/src/configs/WRN-16-1-scratch-CIFAR10.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "CIFAR10",
+    "wrn_depth": 16,
+    "wrn_width": 1,
+    "seeds": "0",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-16-1-scratch-SVHN.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "SVHN",
+    "wrn_depth": 16,
+    "wrn_width": 1,
+    "seeds": "0",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-16-2-scratch-CIFAR10.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "CIFAR10",
+    "wrn_depth": 16,
+    "wrn_width": 2,
+    "seeds": "0",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-16-2-scratch-SVHN.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "SVHN",
+    "wrn_depth": 16,
+    "wrn_width": 2,
+    "seeds": "0",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-40-1-scratch-CIFAR10.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "CIFAR10",
+    "wrn_depth": 40,
+    "wrn_width": 1,
+    "seeds": "0",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-40-1-scratch-SVHN.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "SVHN",
+    "wrn_depth": 40,
+    "wrn_width": 1,
+    "seeds": "0",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-40-2-scratch-CIFAR10.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "CIFAR10",
+    "wrn_depth": 40,
+    "wrn_width": 2,
+    "seeds": "012",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/configs/WRN-40-2-scratch-SVHN.json

@@ -0,0 +1,10 @@
+{
+  "training":{
+    "dataset": "SVHN",
+    "wrn_depth": 40,
+    "wrn_width": 2,
+    "seeds": "012",
+    "checkpoint": "True",
+    "log": "True"
+  }
+}

wresnet-pytorch/src/distillation_train.py

@@ -0,0 +1,168 @@
+from datetime import datetime
+import time
+import argparse
+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, train_dl, epochs, learning_rate, T, soft_target_loss_weight, ce_loss_weight, device):
+    # Dataset
+    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)}")
+
+@torch.no_grad()
+def test(model, device, test_dl, is_teacher=False):
+    model.to(device)
+    model.eval()
+
+    correct = 0
+    total = 0
+
+    for inputs, labels in test_dl:
+        inputs, labels = inputs.to(device), labels.to(device)
+        if is_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
+    return accuracy
+
+def main():
+    parser = argparse.ArgumentParser(description='Student trainer')
+    parser.add_argument('--teacher', type=Path, help='path to saved teacher .pt', required=True)
+    parser.add_argument('--norm', type=float, help='dpsgd norm clip factor', required=True)
+    parser.add_argument('--cuda', type=int, help='gpu index', required=False)
+    parser.add_argument('--epsilon', type=float, help='dp epsilon', required=False, default=None)
+    parser.add_argument('--epochs', type=int, help='student epochs', required=True)
+    args = parser.parse_args()
+
+    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 args.cuda is not None:
+        device = torch.device(f'cuda:{args.cuda}')
+    elif 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
+
+    if args.epsilon is not None:
+        dp_epsilon = args.epsilon
+        dp_delta = 1e-5
+        norm = args.norm
+        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(args.teacher, 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,
+        train_dl=train_loader,
+        epochs=args.epochs,
+        learning_rate=0.001,
+        T=2,
+        soft_target_loss_weight=0.25,
+        ce_loss_weight=0.75,
+        device=device
+    )
+    print(f"Saving student model for time {int(time.time())}")
+    Path('students').mkdir(exist_ok=True)
+    torch.save(student.state_dict(), f"students/studentmodel-{int(time.time())}.pt")
+
+    print("Testing student and teacher")
+    test_student = test(student, device, test_loader)
+    test_teacher = test(teacher, device, test_loader, True)
+    print(f"Teacher accuracy: {test_teacher:.2f}%")
+    print(f"Student accuracy: {test_student:.2f}%")
+
+
+if __name__ == "__main__":
+
+    main()

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

wresnet-pytorch/src/train.py

@@ -0,0 +1,199 @@
+import os
+import time
+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
+from opacus.utils.batch_memory_manager import BatchMemoryManager
+import warnings
+warnings.filterwarnings("ignore")
+
+
+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_no_cap(net, epochs, data_loader, device, optimizer, criterion, scheduler, test_loader, log, logfile, checkpointFile):
+    best_test_set_accuracy = 0
+
+    for epoch in range(epochs):
+        net.train()
+        #for i, data in tqdm(enumerate(train_loader, 0), leave=False):
+        for i, data in enumerate(data_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()
+
+        if epoch % 10 == 0 or epoch == epochs - 1:
+            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 {}%'.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
+                    torch.save(net.state_dict(), checkpointFile)
+
+    return best_test_set_accuracy
+
+
+def _train_seed(net, loaders, device, dataset, log=False, logfile='', epochs=200, norm=1.0, dp_epsilon=None):
+    train_loader, test_loader = loaders
+
+    dp_delta = 1e-5
+    checkpointFile = 'wrn-{}-{}e-{}d-{}n-dict.pt'.format(int(time.time()), dp_epsilon, dp_delta, norm)
+
+    #net = ModuleValidator.fix(net, replace_bn_with_in=True)
+    net = ModuleValidator.fix(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)
+
+    if dp_epsilon is not None:
+        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=dp_epsilon,
+            target_delta=dp_delta,
+            max_grad_norm=norm,
+        )
+
+        print(f"DP epsilon = {dp_epsilon}, delta = {dp_delta}")
+        print(f"Using sigma={optimizer.noise_multiplier} and C = norm = {norm}")
+    else:
+        print("Training without differential privacy")
+
+    print(f"Training with {epochs} epochs")
+
+    if dp_epsilon is not None:
+        with BatchMemoryManager(
+            data_loader=train_loader,
+            max_physical_batch_size=1000,  # Roughly 12gb vram, uses 9.4
+            optimizer=optimizer
+        ) as memory_safe_data_loader:
+            best_test_set_accuracy = train_no_cap(net, epochs, memory_safe_data_loader, device, optimizer, criterion, scheduler, test_loader, log, logfile, checkpointFile)
+    else:
+        best_test_set_accuracy = train_no_cap(net, epochs, train_loader, device, optimizer, criterion, scheduler, test_loader, log, logfile, 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() and args.cuda:
+        device = torch.device(f'cuda:{args.cuda}')
+    elif 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)
+
+        epochs = training_configurations.epochs
+        best_test_set_accuracy = _train_seed(net, loaders, device, dataset, log, logfile, epochs, args.norm, args.epsilon)
+
+        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)
+    parser.add_argument('--norm', type=float, help='dpsgd norm clip factor', required=True)
+    parser.add_argument('--cuda', type=int, help='gpu index', required=False)
+    parser.add_argument('--epsilon', type=float, help='dp epsilon', required=False, default=None)
+
+    args = parser.parse_args()
+
+    train(args)

wresnet-pytorch/src/utils.py

@@ -0,0 +1,61 @@
+import json
+import collections
+import torchvision
+from torchvision import transforms
+from torch.utils.data import DataLoader
+import torch.nn.functional as F
+
+
+# Borrowed from https://github.com/ozan-oktay/Attention-Gated-Networks
+def json_file_to_pyobj(filename):
+    def _json_object_hook(d): return collections.namedtuple('X', d.keys())(*d.values())
+
+    def json2obj(data): return json.loads(data, object_hook=_json_object_hook)
+
+    return json2obj(open(filename).read())
+
+
+def get_loaders(dataset, train_batch_size=128, test_batch_size=10):
+    print(f"Train batch size: {train_batch_size}")
+
+    if dataset == 'cifar10':
+        normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+
+        train_transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Lambda(lambda x: F.pad(x.unsqueeze(0),
+                                              (4, 4, 4, 4), mode='reflect').squeeze()),
+            transforms.ToPILImage(),
+            transforms.RandomCrop(32),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            normalize,
+        ])
+
+        test_transform = transforms.Compose([
+            transforms.ToTensor(),
+            normalize
+        ])
+
+        trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform)
+        trainloader = DataLoader(trainset, batch_size=train_batch_size, shuffle=True, num_workers=4)
+
+        testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform)
+        testloader = DataLoader(testset, batch_size=test_batch_size, shuffle=True, num_workers=4)
+
+    elif dataset == 'svhn':
+        normalize = transforms.Normalize((0.4377, 0.4438, 0.4728), (0.1980, 0.2010, 0.1970))
+
+        transform = transforms.Compose([
+            transforms.ToTensor(),
+            normalize,
+        ])
+
+        trainset = torchvision.datasets.SVHN(root='./data', split='train', download=True, transform=transform)
+        trainloader = DataLoader(trainset, batch_size=train_batch_size, shuffle=True, num_workers=4)
+
+        testset = torchvision.datasets.SVHN(root='./data', split='test', download=True, transform=transform)
+        testloader = DataLoader(testset, batch_size=test_batch_size, shuffle=True, num_workers=4)
+
+    return trainloader, testloader
+

wresnet-pytorch/src/wresnet16-audit-cifar10.json

@@ -0,0 +1,11 @@
+{
+  "training":{
+    "dataset": "CIFAR10",
+    "wrn_depth": 16,
+    "wrn_width": 1,
+    "checkpoint": "True",
+    "log": "True",
+    "batch_size": 4096,
+    "epochs": 200
+  }
+}