Wres: wresnet with audit settings

wresnet-pytorch/src/WideResNet.py

@@ -45,7 +45,6 @@ class IndividualBlock1(nn.Module):
 
 
 class IndividualBlockN(nn.Module):
-
     def __init__(self, input_features, output_features, stride):
         super(IndividualBlockN, self).__init__()
 
@@ -117,7 +116,6 @@ class WideResNet(nn.Module):
                 m.bias.data.zero_()
 
     def forward(self, x):
-
         x = self.conv1(x)
         attention1 = self.block1(x)
         attention2 = self.block2(attention1)

wresnet-pytorch/src/train.py

@@ -7,6 +7,7 @@ import numpy as np
 import random
 from utils import json_file_to_pyobj, get_loaders
 from WideResNet import WideResNet
+from tqdm import tqdm
 
 
 def set_seed(seed=42):
@@ -17,25 +18,19 @@ def set_seed(seed=42):
     torch.cuda.manual_seed(seed)
 
 
-def _train_seed(net, loaders, device, dataset, log=False, checkpoint=False, logfile='', checkpointFile=''):
+def _train_seed(net, loaders, device, dataset, log=False, checkpoint=False, logfile='', checkpointFile='', epochs=200):
-
     train_loader, test_loader = loaders
 
-    if dataset == 'svhn':
-        epochs = 100
-    else:
-        epochs = 200
-
     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
 
-    for epoch in range(epochs):
+    print(f"Training with {epochs} epochs")
-
+    for epoch in tqdm(range(epochs)):
         net.train()
-        for i, data in enumerate(train_loader, 0):
+        for i, data in tqdm(enumerate(train_loader, 0), leave=False):
             inputs, labels = data
             inputs = inputs.to(device)
             labels = labels.to(device)
@@ -89,18 +84,19 @@ def train(args):
     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(seed) for seed in training_configurations.seeds]
+    seeds = [int.from_bytes(os.urandom(8), 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)
+        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 {}\n'.format(wrn_depth, wrn_width, training_configurations.dataset))
+            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)
+    loaders = get_loaders(dataset, training_configurations.batch_size)
 
     if torch.cuda.is_available():
         device = torch.device('cuda:0')
@@ -121,7 +117,8 @@ def train(args):
         net = net.to(device)
 
         checkpointFile = 'wrn-{}-{}-seed-{}-{}-dict.pth'.format(wrn_depth, wrn_width, dataset, seed) if checkpoint else ''
-        best_test_set_accuracy = _train_seed(net, loaders, device, dataset, log, checkpoint, logfile, checkpointFile)
+        epochs = training_configurations.epochs
+        best_test_set_accuracy = _train_seed(net, loaders, device, dataset, log, checkpoint, logfile, checkpointFile, epochs)
 
         if log:
             with open(logfile, 'a') as temp:

wresnet-pytorch/src/utils.py

@@ -16,9 +16,9 @@ def json_file_to_pyobj(filename):
 
 
 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([
@@ -44,7 +44,6 @@ def get_loaders(dataset, train_batch_size=128, test_batch_size=10):
         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([

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
+  }
+}