Wres: dp training

wresnet-pytorch/src/train.py

@@ -8,6 +8,8 @@ import random
 from utils import json_file_to_pyobj, get_loaders
 from WideResNet import WideResNet
 from tqdm import tqdm
+import opacus
+from opacus.validators import ModuleValidator
 
 
 def set_seed(seed=42):
@@ -21,16 +23,39 @@ def set_seed(seed=42):
 def _train_seed(net, loaders, device, dataset, log=False, checkpoint=False, logfile='', checkpointFile='', epochs=200):
     train_loader, test_loader = loaders
 
+    dp_epsilon = 8
+    if dp_epsilon is not None:
+        print(f"DP epsilon: {dp_epsilon}")
+        #net = ModuleValidator.fix(net, replace_bn_with_in=True)
+        net = ModuleValidator.fix(net)
+        print(net)
+        ModuleValidator.validate(net, strict=True)
+
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=5e-4)
     scheduler = MultiStepLR(optimizer, milestones=[int(elem*epochs) for elem in [0.3, 0.6, 0.8]], gamma=0.2)
 
     best_test_set_accuracy = 0
 
+    privacy_engine = opacus.PrivacyEngine()
+    net, optimizer, train_loader = privacy_engine.make_private_with_epsilon(
+        module=net,
+        optimizer=optimizer,
+        data_loader=train_loader,
+        epochs=epochs,
+        target_epsilon=8,
+        target_delta=1e-5,
+        max_grad_norm=3.0,
+    )
+
+    print(f"Using sigma={optimizer.noise_multiplier} and C={1.0}")
+
     print(f"Training with {epochs} epochs")
-    for epoch in tqdm(range(epochs)):
+    #for epoch in tqdm(range(epochs)):
+    for epoch in range(epochs):
         net.train()
-        for i, data in tqdm(enumerate(train_loader, 0), leave=False):
+        #for i, data in tqdm(enumerate(train_loader, 0), leave=False):
+        for i, data in enumerate(train_loader, 0):
             inputs, labels = data
             inputs = inputs.to(device)
             labels = labels.to(device)
@@ -66,6 +91,7 @@ def _train_seed(net, loaders, device, dataset, log=False, checkpoint=False, logf
             epoch_accuracy = round(100 * epoch_accuracy, 2)
 
             if log:
+                print('Accuracy at epoch {} is {}%\n'.format(epoch + 1, epoch_accuracy))
                 with open(logfile, 'a') as temp:
                     temp.write('Accuracy at epoch {} is {}%\n'.format(epoch + 1, epoch_accuracy))
 
@@ -85,7 +111,7 @@ def train(args):
     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(8), byteorder='big')]
+    seeds = [int.from_bytes(os.urandom(4), byteorder='big')]
     log = True if training_configurations.log.lower() == 'true' else False
 
     if log: