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Wres: dp training

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This commit is contained in:
Akemi Izuko 2024-11-30 20:28:25 -07:00
parent c1561c7d55
commit e4b5998dbb
Signed by: akemi
GPG key ID: 8DE0764E1809E9FC
1 changed files with 29 additions and 3 deletions
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32
wresnet-pytorch/src/train.py
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@ -8,6 +8,8 @@ import random
from utils import json_file_to_pyobj, get_loaders from utils import json_file_to_pyobj, get_loaders
from WideResNet import WideResNet from WideResNet import WideResNet
from tqdm import tqdm from tqdm import tqdm
import opacus
from opacus.validators import ModuleValidator
def set_seed(seed=42): 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): def _train_seed(net, loaders, device, dataset, log=False, checkpoint=False, logfile='', checkpointFile='', epochs=200):
train_loader, test_loader = loaders 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() criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=5e-4) 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) scheduler = MultiStepLR(optimizer, milestones=[int(elem*epochs) for elem in [0.3, 0.6, 0.8]], gamma=0.2)
best_test_set_accuracy = 0 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") 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() 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, labels = data
inputs = inputs.to(device) inputs = inputs.to(device)
labels = labels.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) epoch_accuracy = round(100 * epoch_accuracy, 2)
if log: if log:
print('Accuracy at epoch {} is {}%\n'.format(epoch + 1, epoch_accuracy))
with open(logfile, 'a') as temp: with open(logfile, 'a') as temp:
temp.write('Accuracy at epoch {} is {}%\n'.format(epoch + 1, epoch_accuracy)) 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 wrn_width = training_configurations.wrn_width
dataset = training_configurations.dataset.lower() dataset = training_configurations.dataset.lower()
#seeds = [int(seed) for seed in training_configurations.seeds] #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 log = True if training_configurations.log.lower() == 'true' else False
if log: if log: