def train(args):
json_options = json_file_to_pyobj(args.config)
no_teacher_configurations = json_options.training
wrn_depth = no_teacher_configurations.wrn_depth
wrn_width = no_teacher_configurations.wrn_width
M = no_teacher_configurations.M
dataset = no_teacher_configurations.dataset
seeds = [int(seed) for seed in no_teacher_configurations.seeds]
log = True if no_teacher_configurations.log.lower() == 'True' else False
if log:
net_str = "WideResNet-{}-{}".format(wrn_depth, wrn_width)
logfile = "No_Teacher-{}-{}-M-{}.txt".format(
net_str, no_teacher_configurations.dataset, M)
with open(os.path.join('./', logfile), "w") as temp:
temp.write('No teacher {} in {} with M={}\n'.format(
net_str, no_teacher_configurations.dataset, M))
else:
logfile = ''
checkpoint = bool(no_teacher_configurations.checkpoint)
if 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 dataset.lower() == 'cifar10':
# Full data
if M == 5000:
from utils import cifar10loaders
loaders = cifar10loaders()
# No data
elif M == 0:
from utils import cifar10loaders
_, test_loader = cifar10loaders()
else:
from utils import cifar10loadersM
loaders = cifar10loadersM(M)
elif dataset.lower() == 'svhn':
# Full data
if M == 5000:
from utils import svhnLoaders
loaders = svhnLoaders()
# No data
elif M == 0:
from utils import svhnLoaders
_, test_loader = svhnLoaders()
else:
from utils import svhnloadersM
loaders = svhnloadersM(M)
else:
raise ValueError('Datasets to choose from: CIFAR10 and SVHN')
if log:
with open(os.path.join('./', 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)
checkpointFile = 'No_teacher_wrn-{}-{}-M-{}-seed-{}-{}-dict.pth'.format(
wrn_depth, wrn_width, M, seed, dataset) if checkpoint else ''
best_test_set_accuracy = _train_seed_no_teacher(
net, M, loaders, device, dataset, log, checkpoint, logfile,
checkpointFile)
if log:
with open(os.path.join('./', 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)
if log:
with open(os.path.join('./', logfile), "a") as temp:
temp.write('Best test set accuracy of seed {} is {}\n'.format(
seed, 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(os.path.join('./', 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))
def train(args):
json_options = json_file_to_pyobj(args.config)
kd_att_configurations = json_options.training
wrn_depth_teacher = kd_att_configurations.wrn_depth_teacher
wrn_width_teacher = kd_att_configurations.wrn_width_teacher
wrn_depth_student = kd_att_configurations.wrn_depth_student
wrn_width_student = kd_att_configurations.wrn_width_student
M = kd_att_configurations.M
dataset = kd_att_configurations.dataset
seeds = [int(seed) for seed in kd_att_configurations.seeds]
log = True if kd_att_configurations.log.lower() == 'True' else False
if log:
teacher_str = "WideResNet-{}-{}".format(wrn_depth_teacher,
wrn_width_teacher)
student_str = "WideResNet-{}-{}".format(wrn_depth_student,
wrn_width_student)
logfile = "Teacher-{}-Student-{}-{}-M-{}-seeds-1-2.txt".format(
teacher_str, student_str, kd_att_configurations.dataset, M)
print(logfile)
with open(os.path.join('./', logfile), "w") as temp:
temp.write(
'KD_ATT with teacher {} and student {} in {} with M={}\n'.
format(teacher_str, student_str, kd_att_configurations.dataset,
M))
else:
logfile = ''
checkpoint = bool(kd_att_configurations.checkpoint)
if 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 dataset.lower() == 'cifar10':
# Full data
if M == 5000:
from utils import cifar10loaders
loaders = cifar10loaders()
# No data
elif M == 0:
from utils import cifar10loaders
_, test_loader = cifar10loaders()
else:
from utils import cifar10loadersM
loaders = cifar10loadersM(M)
elif dataset.lower() == 'svhn':
# Full data
if M == 5000:
from utils import svhnLoaders
loaders = svhnLoaders()
# No data
elif M == 0:
from utils import svhnLoaders
_, test_loader = svhnLoaders()
else:
from utils import svhnloadersM
loaders = svhnloadersM(M)
else:
raise ValueError('Datasets to choose from: CIFAR10 and SVHN')
if log:
with open(os.path.join('./', logfile), "a") as temp:
temp.write(
'------------------- SEED {} -------------------\n'.format(
seed))
strides = [1, 1, 2, 2]
teacher_net = WideResNet(d=wrn_depth_teacher,
k=wrn_width_teacher,
n_classes=10,
input_features=3,
output_features=16,
strides=strides)
teacher_net = teacher_net.to(device)
if dataset.lower() == 'cifar10':
torch_checkpoint = torch.load(
'./PreTrainedModels/PreTrainedScratches/CIFAR10/wrn-{}-{}-seed-{}-dict.pth'
.format(wrn_depth_teacher, wrn_width_teacher, seed),
map_location=device)
else:
torch_checkpoint = torch.load(
'./PreTrainedModels/PreTrainedScratches/SVHN/wrn-{}-{}-seed-svhn-{}-dict.pth'
.format(wrn_depth_teacher, wrn_width_teacher, seed),
map_location=device)
teacher_net.load_state_dict(torch_checkpoint)
student_net = WideResNet(d=wrn_depth_student,
k=wrn_width_student,
n_classes=10,
input_features=3,
output_features=16,
strides=strides)
student_net = student_net.to(device)
checkpointFile = 'kd_att_teacher_wrn-{}-{}_student_wrn-{}-{}-M-{}-seed-{}-{}-dict.pth'.format(
wrn_depth_teacher, wrn_width_teacher, wrn_depth_student,
wrn_width_student, M, seed, dataset) if checkpoint else ''
if M != 0:
best_test_set_accuracy = _train_seed_kd_att(
teacher_net, student_net, M, loaders, device, dataset, log,
checkpoint, logfile, checkpointFile)
if log:
with open(os.path.join('./', 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)
if log:
with open(os.path.join('./', logfile), "a") as temp:
temp.write(
'Best test set accuracy of seed {} is {}\n'.format(
seed, best_test_set_accuracy))
else:
best_test_set_accuracy = _test_set_eval(student_net, device,
test_loader)
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(os.path.join('./', 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))
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]
log = bool(training_configurations.checkpoint)
if log:
logfile = training_configurations.logfile
with open(logfile, 'w') as temp:
temp.write('WideResNet-{}-{} scratch in {}\n'.format(
wrn_depth, wrn_width, training_configurations.dataset))
else:
logfile = ''
checkpoint = bool(training_configurations.checkpoint)
test_set_accuracies = []
for seed in seeds:
if dataset == 'cifar10':
from tf_utils import cifar10loaders
loaders = cifar10loaders(seed=seed)
elif dataset == 'svhn':
from utils import svhnLoaders
loaders = svhnLoaders()
else:
ValueError('Datasets to choose from: CIFAR10 and SVHN')
set_seed(seed)
if log:
with open(logfile, 'a') as temp:
temp.write(
'------------------- SEED {} -------------------\n'.format(
seed))
strides = [1, 1, 2, 2]
model = WideResNet(d=wrn_depth,
k=wrn_width,
n_classes=10,
output_features=16,
strides=strides)
checkpointFile = '_wrn-{}-{}-seed-{}-{}-dict.pth'.format(
wrn_depth, wrn_width, dataset, seed) if checkpoint else ''
best_test_set_accuracy = _train_seed(model, loaders, log, checkpoint,
logfile, checkpointFile)
# best_test_set_accuracy = _train_seed_amateur(model, loaders, log, checkpoint, logfile, checkpointFile)
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)
if log:
with open(logfile, 'a') as temp:
temp.write('Best test set accuracy of seed {} is {}\n'.format(
seed, 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))
def train(args):
json_options = json_file_to_pyobj(args.config)
modified_zero_shot_configurations = json_options.training
wrn_depth_teacher = modified_zero_shot_configurations.wrn_depth_teacher
wrn_width_teacher = modified_zero_shot_configurations.wrn_width_teacher
wrn_depth_student = modified_zero_shot_configurations.wrn_depth_student
wrn_width_student = modified_zero_shot_configurations.wrn_width_student
M = modified_zero_shot_configurations.M
dataset = modified_zero_shot_configurations.dataset
seeds = [int(seed) for seed in modified_zero_shot_configurations.seeds]
log = True if modified_zero_shot_configurations.log.lower() == 'True' else False
if log:
teacher_str = 'WideResNet-{}-{}'.format(wrn_depth_teacher, wrn_width_teacher)
student_str = 'WideResNet-{}-{}'.format(wrn_depth_student, wrn_width_student)
logfile = 'Teacher-{}-Student-{}-{}-M-{}-Zero-Shot.txt'.format(teacher_str, student_str, modified_zero_shot_configurations.dataset, M)
with open(logfile, 'w') as temp:
temp.write('Zero-Shot with teacher {} and student {} in {} with M-{}\n'.format(teacher_str, student_str, modified_zero_shot_configurations.dataset, M))
else:
logfile = ''
checkpoint = bool(modified_zero_shot_configurations.checkpoint)
if 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 dataset.lower() == 'cifar10':
from utils import cifar10loaders
_, test_loader = cifar10loaders()
elif dataset.lower() == 'svhn':
from utils import svhnLoaders
_, test_loader = svhnLoaders()
else:
raise ValueError('Datasets to choose from: CIFAR10 and SVHN')
if log:
with open(logfile, 'a') as temp:
temp.write('------------------- SEED {} -------------------\n'.format(seed))
strides = [1, 1, 2, 2]
teacher_net = WideResNet(d=wrn_depth_teacher, k=wrn_width_teacher, n_classes=10, input_features=3, output_features=16, strides=strides)
teacher_net = teacher_net.to(device)
if dataset.lower() == 'cifar10':
torch_checkpoint = torch.load('./PreTrainedModels/PreTrainedScratches/CIFAR10/wrn-{}-{}-seed-{}-dict.pth'.format(wrn_depth_teacher, wrn_width_teacher, seed), map_location=device)
elif dataset.lower() == 'svhn':
torch_checkpoint = torch.load('./PreTrainedModels/PreTrainedScratches/SVHN/wrn-{}-{}-seed-svhn-{}-dict.pth'.format(wrn_depth_teacher, wrn_width_teacher, seed), map_location=device)
else:
raise ValueError('Dataset not found')
teacher_net.load_state_dict(torch_checkpoint)
student_net = WideResNet(d=wrn_depth_student, k=wrn_width_student, n_classes=10, input_features=3, output_features=16, strides=strides)
student_net = student_net.to(device)
generator_net = Generator()
generator_net = generator_net.to(device)
checkpointFile = 'zero_shot_teacher_wrn-{}-{}_student_wrn-{}-{}-M-{}-seed-{}-{}-dict.pth'.format(wrn_depth_teacher, wrn_width_teacher, wrn_depth_student, wrn_width_student, M, seed, dataset) if checkpoint else ''
finalCheckpointFile = 'zero_shot_teacher_wrn-{}-{}_student_wrn-{}-{}-M-{}-seed-{}-{}-final-dict.pth'.format(wrn_depth_teacher, wrn_width_teacher, wrn_depth_student, wrn_width_student, M, seed, dataset) if checkpoint else ''
genCheckpointFile = 'zero_shot_teacher_wrn-{}-{}_student_wrn-{}-{}-M-{}-seed-{}-{}-generator-dict.pth'.format(wrn_depth_teacher, wrn_width_teacher, wrn_depth_student, wrn_width_student, M, seed, dataset) if checkpoint else ''
best_test_set_accuracy = _train_seed_zero_shot(teacher_net, student_net, generator_net, test_loader, device, log, checkpoint, logfile, checkpointFile, finalCheckpointFile, genCheckpointFile)
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)
if log:
with open(logfile, 'a') as temp:
temp.write('Best test set accuracy of seed {} is {}\n'.format(seed, 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))
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]
log = True if training_configurations.log.lower() == 'True' else False
if log:
logfile = training_configurations.logfile
with open(logfile, 'w') as temp:
temp.write('WideResNet-{}-{} scratch in {}\n'.format(
wrn_depth, wrn_width, training_configurations.dataset))
else:
logfile = ''
checkpoint = True if training_configurations.checkpoint.lower(
) == 'true' else False
if dataset.lower() == 'cifar10':
from utils import cifar10loaders
loaders = cifar10loaders()
elif dataset.lower() == 'svhn':
from utils import svhnLoaders
loaders = svhnLoaders()
else:
ValueError('Datasets to choose from: CIFAR10 and SVHN')
if 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)
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)
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)
if log:
with open(logfile, 'a') as temp:
temp.write('Best test set accuracy of seed {} is {}\n'.format(
seed, 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))