def load_mod_dl(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
imsize, in_channel, num_classes = 32, 3, 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(
args.batch_size,
val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size, args.test_size])
elif args.dataset == 'cifar100':
imsize, in_channel, num_classes = 32, 3, 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(
args.batch_size,
val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size, args.test_size])
elif args.dataset == 'mnist':
imsize, in_channel, num_classes = 28, 1, 10
num_train = 50000
train_loader, val_loader, test_loader = data_loaders.load_mnist(
args.batch_size,
subset=[args.train_size, args.val_size, args.test_size],
num_train=num_train,
only_split_train=False)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'cbr':
cnn = CBRStudent(in_channel, num_classes)
# This essentially does no mixup.
mixup_mat = -100 * torch.ones([num_classes, num_classes]).cuda()
checkpoint = None
if args.load_checkpoint:
checkpoint = torch.load(args.load_checkpoint)
mixup_mat = checkpoint['mixup_grid']
print(f"loaded mixupmat from {args.load_checkpoint}")
if args.rand_mixup:
# Randomise mixup grid
rng = np.random.RandomState(args.seed)
mixup_mat = rng.uniform(
0.5, 1.0, (num_classes, num_classes)).astype(np.float32)
print("Randomised the mixup mat")
mixup_mat = torch.from_numpy(
mixup_mat.reshape(num_classes, num_classes)).cuda()
model = cnn.cuda()
model.train()
return model, mixup_mat, train_loader, val_loader, test_loader, checkpoint
def load_mod_dl(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
imsize, in_channel, num_classes = 32, 3, 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size,
args.test_size])
elif args.dataset == 'cifar100':
imsize, in_channel, num_classes = 32, 3, 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation,
subset=[args.train_size, args.val_size,
args.test_size])
elif args.dataset == 'mnist':
imsize, in_channel, num_classes = 28, 1, 10
num_train = 50000
train_loader, val_loader, test_loader = data_loaders.load_mnist(args.batch_size,
subset=[args.train_size, args.val_size, args.test_size],
num_train=num_train, only_split_train=False)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes, num_channels=in_channel)
elif args.model == 'cbr':
cnn = CBRStudent(in_channel, num_classes)
mixup_mat = -1*torch.ones([num_classes,num_classes]).cuda()
mixup_mat.requires_grad = True
checkpoint = None
if args.load_baseline_checkpoint:
checkpoint = torch.load(args.load_baseline_checkpoint)
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
return model, mixup_mat, train_loader, val_loader, test_loader, checkpoint
def load_baseline_model(args):
"""
:param args:
:return:
"""
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'mnist':
args.datasize, args.valsize, args.testsize = 100, 100, 100
num_train = args.datasize
if args.datasize == -1:
num_train = 50000
from data_loaders import load_mnist
train_loader, val_loader, test_loader = load_mnist(args.batch_size,
subset=[args.datasize, args.valsize, args.testsize],
num_train=num_train)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=0.3)
checkpoint = None
if args.load_baseline_checkpoint:
checkpoint = torch.load(args.load_baseline_checkpoint)
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
return model, train_loader, val_loader, test_loader, checkpoint
def experiment(exp, arch, loss, double_softmax, confidence_thresh, rampup,
teacher_alpha, fix_ema, unsup_weight, cls_bal_scale,
cls_bal_scale_range, cls_balance, cls_balance_loss,
combine_batches, learning_rate, standardise_samples,
src_affine_std, src_xlat_range, src_hflip, src_intens_flip,
src_intens_scale_range, src_intens_offset_range,
src_gaussian_noise_std, tgt_affine_std, tgt_xlat_range,
tgt_hflip, tgt_intens_flip, tgt_intens_scale_range,
tgt_intens_offset_range, tgt_gaussian_noise_std, num_epochs,
batch_size, epoch_size, seed, log_file, model_file, device):
settings = locals().copy()
import os
import sys
import pickle
import cmdline_helpers
if log_file == '':
log_file = 'output_aug_log_{}.txt'.format(exp)
elif log_file == 'none':
log_file = None
if log_file is not None:
if os.path.exists(log_file):
print('Output log file {} already exists'.format(log_file))
return
use_rampup = rampup > 0
src_intens_scale_range_lower, src_intens_scale_range_upper, src_intens_offset_range_lower, src_intens_offset_range_upper = \
cmdline_helpers.intens_aug_options(src_intens_scale_range, src_intens_offset_range)
tgt_intens_scale_range_lower, tgt_intens_scale_range_upper, tgt_intens_offset_range_lower, tgt_intens_offset_range_upper = \
cmdline_helpers.intens_aug_options(tgt_intens_scale_range, tgt_intens_offset_range)
import time
import math
import numpy as np
from batchup import data_source, work_pool
import data_loaders
import standardisation
import network_architectures
import augmentation
import torch, torch.cuda
from torch import nn
from torch.nn import functional as F
import optim_weight_ema
torch_device = torch.device(device)
pool = work_pool.WorkerThreadPool(2)
n_chn = 0
if exp == 'svhn_mnist':
d_source = data_loaders.load_svhn(zero_centre=False, greyscale=True)
d_target = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
val=False)
elif exp == 'mnist_svhn':
d_source = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True)
d_target = data_loaders.load_svhn(zero_centre=False,
greyscale=True,
val=False)
elif exp == 'svhn_mnist_rgb':
d_source = data_loaders.load_svhn(zero_centre=False, greyscale=False)
d_target = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
val=False,
rgb=True)
elif exp == 'mnist_svhn_rgb':
d_source = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
rgb=True)
d_target = data_loaders.load_svhn(zero_centre=False,
greyscale=False,
val=False)
elif exp == 'cifar_stl':
d_source = data_loaders.load_cifar10(range_01=False)
d_target = data_loaders.load_stl(zero_centre=False, val=False)
elif exp == 'stl_cifar':
d_source = data_loaders.load_stl(zero_centre=False)
d_target = data_loaders.load_cifar10(range_01=False, val=False)
elif exp == 'mnist_usps':
d_source = data_loaders.load_mnist(zero_centre=False)
d_target = data_loaders.load_usps(zero_centre=False,
scale28=True,
val=False)
elif exp == 'usps_mnist':
d_source = data_loaders.load_usps(zero_centre=False, scale28=True)
d_target = data_loaders.load_mnist(zero_centre=False, val=False)
elif exp == 'syndigits_svhn':
d_source = data_loaders.load_syn_digits(zero_centre=False)
d_target = data_loaders.load_svhn(zero_centre=False, val=False)
elif exp == 'synsigns_gtsrb':
d_source = data_loaders.load_syn_signs(zero_centre=False)
d_target = data_loaders.load_gtsrb(zero_centre=False, val=False)
else:
print('Unknown experiment type \'{}\''.format(exp))
return
# Delete the training ground truths as we should not be using them
del d_target.train_y
if standardise_samples:
standardisation.standardise_dataset(d_source)
standardisation.standardise_dataset(d_target)
n_classes = d_source.n_classes
print('Loaded data')
if arch == '':
if exp in {'mnist_usps', 'usps_mnist'}:
arch = 'mnist-bn-32-64-256'
if exp in {'svhn_mnist', 'mnist_svhn'}:
arch = 'grey-32-64-128-gp'
if exp in {
'cifar_stl', 'stl_cifar', 'syndigits_svhn', 'svhn_mnist_rgb',
'mnist_svhn_rgb'
}:
arch = 'rgb-128-256-down-gp'
if exp in {'synsigns_gtsrb'}:
arch = 'rgb40-96-192-384-gp'
net_class, expected_shape = network_architectures.get_net_and_shape_for_architecture(
arch)
if expected_shape != d_source.train_X.shape[1:]:
print(
'Architecture {} not compatible with experiment {}; it needs samples of shape {}, '
'data has samples of shape {}'.format(arch, exp, expected_shape,
d_source.train_X.shape[1:]))
return
student_net = net_class(n_classes).to(torch_device)
teacher_net = net_class(n_classes).to(torch_device)
student_params = list(student_net.parameters())
teacher_params = list(teacher_net.parameters())
for param in teacher_params:
param.requires_grad = False
student_optimizer = torch.optim.Adam(student_params, lr=learning_rate)
if fix_ema:
teacher_optimizer = optim_weight_ema.EMAWeightOptimizer(
teacher_net, student_net, alpha=teacher_alpha)
else:
teacher_optimizer = optim_weight_ema.OldWeightEMA(teacher_net,
student_net,
alpha=teacher_alpha)
classification_criterion = nn.CrossEntropyLoss()
print('Built network')
src_aug = augmentation.ImageAugmentation(
src_hflip,
src_xlat_range,
src_affine_std,
intens_flip=src_intens_flip,
intens_scale_range_lower=src_intens_scale_range_lower,
intens_scale_range_upper=src_intens_scale_range_upper,
intens_offset_range_lower=src_intens_offset_range_lower,
intens_offset_range_upper=src_intens_offset_range_upper,
gaussian_noise_std=src_gaussian_noise_std)
tgt_aug = augmentation.ImageAugmentation(
tgt_hflip,
tgt_xlat_range,
tgt_affine_std,
intens_flip=tgt_intens_flip,
intens_scale_range_lower=tgt_intens_scale_range_lower,
intens_scale_range_upper=tgt_intens_scale_range_upper,
intens_offset_range_lower=tgt_intens_offset_range_lower,
intens_offset_range_upper=tgt_intens_offset_range_upper,
gaussian_noise_std=tgt_gaussian_noise_std)
if combine_batches:
def augment(X_sup, y_src, X_tgt):
X_src_stu, X_src_tea = src_aug.augment_pair(X_sup)
X_tgt_stu, X_tgt_tea = tgt_aug.augment_pair(X_tgt)
return X_src_stu, X_src_tea, y_src, X_tgt_stu, X_tgt_tea
else:
def augment(X_src, y_src, X_tgt):
X_src = src_aug.augment(X_src)
X_tgt_stu, X_tgt_tea = tgt_aug.augment_pair(X_tgt)
return X_src, y_src, X_tgt_stu, X_tgt_tea
rampup_weight_in_list = [0]
cls_bal_fn = network_architectures.get_cls_bal_function(cls_balance_loss)
def compute_aug_loss(stu_out, tea_out):
# Augmentation loss
if use_rampup:
unsup_mask = None
conf_mask_count = None
unsup_mask_count = None
else:
conf_tea = torch.max(tea_out, 1)[0]
unsup_mask = conf_mask = (conf_tea > confidence_thresh).float()
unsup_mask_count = conf_mask_count = conf_mask.sum()
if loss == 'bce':
aug_loss = network_architectures.robust_binary_crossentropy(
stu_out, tea_out)
else:
d_aug_loss = stu_out - tea_out
aug_loss = d_aug_loss * d_aug_loss
# Class balance scaling
if cls_bal_scale:
if use_rampup:
n_samples = float(aug_loss.shape[0])
else:
n_samples = unsup_mask.sum()
avg_pred = n_samples / float(n_classes)
bal_scale = avg_pred / torch.clamp(tea_out.sum(dim=0), min=1.0)
if cls_bal_scale_range != 0.0:
bal_scale = torch.clamp(bal_scale,
min=1.0 / cls_bal_scale_range,
max=cls_bal_scale_range)
bal_scale = bal_scale.detach()
aug_loss = aug_loss * bal_scale[None, :]
aug_loss = aug_loss.mean(dim=1)
if use_rampup:
unsup_loss = aug_loss.mean() * rampup_weight_in_list[0]
else:
unsup_loss = (aug_loss * unsup_mask).mean()
# Class balance loss
if cls_balance > 0.0:
# Compute per-sample average predicated probability
# Average over samples to get average class prediction
avg_cls_prob = stu_out.mean(dim=0)
# Compute loss
equalise_cls_loss = cls_bal_fn(avg_cls_prob,
float(1.0 / n_classes))
equalise_cls_loss = equalise_cls_loss.mean() * n_classes
if use_rampup:
equalise_cls_loss = equalise_cls_loss * rampup_weight_in_list[0]
else:
if rampup == 0:
equalise_cls_loss = equalise_cls_loss * unsup_mask.mean(
dim=0)
unsup_loss += equalise_cls_loss * cls_balance
return unsup_loss, conf_mask_count, unsup_mask_count
if combine_batches:
def f_train(X_src0, X_src1, y_src, X_tgt0, X_tgt1):
X_src0 = torch.tensor(X_src0,
dtype=torch.float,
device=torch_device)
X_src1 = torch.tensor(X_src1,
dtype=torch.float,
device=torch_device)
y_src = torch.tensor(y_src, dtype=torch.long, device=torch_device)
X_tgt0 = torch.tensor(X_tgt0,
dtype=torch.float,
device=torch_device)
X_tgt1 = torch.tensor(X_tgt1,
dtype=torch.float,
device=torch_device)
n_samples = X_src0.size()[0]
n_total = n_samples + X_tgt0.size()[0]
student_optimizer.zero_grad()
student_net.train()
teacher_net.train()
# Concatenate source and target mini-batches
X0 = torch.cat([X_src0, X_tgt0], 0)
X1 = torch.cat([X_src1, X_tgt1], 0)
student_logits_out = student_net(X0)
student_prob_out = F.softmax(student_logits_out, dim=1)
src_logits_out = student_logits_out[:n_samples]
src_prob_out = student_prob_out[:n_samples]
teacher_logits_out = teacher_net(X1)
teacher_prob_out = F.softmax(teacher_logits_out, dim=1)
# Supervised classification loss
if double_softmax:
clf_loss = classification_criterion(src_prob_out, y_src)
else:
clf_loss = classification_criterion(src_logits_out, y_src)
unsup_loss, conf_mask_count, unsup_mask_count = compute_aug_loss(
student_prob_out, teacher_prob_out)
loss_expr = clf_loss + unsup_loss * unsup_weight
loss_expr.backward()
student_optimizer.step()
teacher_optimizer.step()
outputs = [
float(clf_loss) * n_samples,
float(unsup_loss) * n_total
]
if not use_rampup:
mask_count = float(conf_mask_count) * 0.5
unsup_count = float(unsup_mask_count) * 0.5
outputs.append(mask_count)
outputs.append(unsup_count)
return tuple(outputs)
else:
def f_train(X_src, y_src, X_tgt0, X_tgt1):
X_src = torch.tensor(X_src, dtype=torch.float, device=torch_device)
y_src = torch.tensor(y_src, dtype=torch.long, device=torch_device)
X_tgt0 = torch.tensor(X_tgt0,
dtype=torch.float,
device=torch_device)
X_tgt1 = torch.tensor(X_tgt1,
dtype=torch.float,
device=torch_device)
student_optimizer.zero_grad()
student_net.train()
teacher_net.train()
src_logits_out = student_net(X_src)
student_tgt_logits_out = student_net(X_tgt0)
student_tgt_prob_out = F.softmax(student_tgt_logits_out, dim=1)
teacher_tgt_logits_out = teacher_net(X_tgt1)
teacher_tgt_prob_out = F.softmax(teacher_tgt_logits_out, dim=1)
# Supervised classification loss
if double_softmax:
clf_loss = classification_criterion(
F.softmax(src_logits_out, dim=1), y_src)
else:
clf_loss = classification_criterion(src_logits_out, y_src)
unsup_loss, conf_mask_count, unsup_mask_count = compute_aug_loss(
student_tgt_prob_out, teacher_tgt_prob_out)
loss_expr = clf_loss + unsup_loss * unsup_weight
loss_expr.backward()
student_optimizer.step()
teacher_optimizer.step()
n_samples = X_src.size()[0]
outputs = [
float(clf_loss) * n_samples,
float(unsup_loss) * n_samples
]
if not use_rampup:
mask_count = float(conf_mask_count)
unsup_count = float(unsup_mask_count)
outputs.append(mask_count)
outputs.append(unsup_count)
return tuple(outputs)
print('Compiled training function')
def f_pred_src(X_sup):
X_var = torch.tensor(X_sup, dtype=torch.float, device=torch_device)
student_net.eval()
teacher_net.eval()
return (F.softmax(student_net(X_var), dim=1).detach().cpu().numpy(),
F.softmax(teacher_net(X_var), dim=1).detach().cpu().numpy())
def f_pred_tgt(X_sup):
X_var = torch.tensor(X_sup, dtype=torch.float, device=torch_device)
student_net.eval()
teacher_net.eval()
return (F.softmax(student_net(X_var), dim=1).detach().cpu().numpy(),
F.softmax(teacher_net(X_var), dim=1).detach().cpu().numpy())
def f_eval_src(X_sup, y_sup):
y_pred_prob_stu, y_pred_prob_tea = f_pred_src(X_sup)
y_pred_stu = np.argmax(y_pred_prob_stu, axis=1)
y_pred_tea = np.argmax(y_pred_prob_tea, axis=1)
return (float(
(y_pred_stu != y_sup).sum()), float((y_pred_tea != y_sup).sum()))
def f_eval_tgt(X_sup, y_sup):
y_pred_prob_stu, y_pred_prob_tea = f_pred_tgt(X_sup)
y_pred_stu = np.argmax(y_pred_prob_stu, axis=1)
y_pred_tea = np.argmax(y_pred_prob_tea, axis=1)
return (float(
(y_pred_stu != y_sup).sum()), float((y_pred_tea != y_sup).sum()))
print('Compiled evaluation function')
# Setup output
def log(text):
print(text)
if log_file is not None:
with open(log_file, 'a') as f:
f.write(text + '\n')
f.flush()
f.close()
cmdline_helpers.ensure_containing_dir_exists(log_file)
# Report setttings
log('Settings: {}'.format(', '.join([
'{}={}'.format(key, settings[key])
for key in sorted(list(settings.keys()))
])))
# Report dataset size
log('Dataset:')
log('SOURCE Train: X.shape={}, y.shape={}'.format(d_source.train_X.shape,
d_source.train_y.shape))
log('SOURCE Test: X.shape={}, y.shape={}'.format(d_source.test_X.shape,
d_source.test_y.shape))
log('TARGET Train: X.shape={}'.format(d_target.train_X.shape))
log('TARGET Test: X.shape={}, y.shape={}'.format(d_target.test_X.shape,
d_target.test_y.shape))
print('Training...')
sup_ds = data_source.ArrayDataSource([d_source.train_X, d_source.train_y],
repeats=-1)
tgt_train_ds = data_source.ArrayDataSource([d_target.train_X], repeats=-1)
train_ds = data_source.CompositeDataSource([sup_ds,
tgt_train_ds]).map(augment)
train_ds = pool.parallel_data_source(train_ds)
if epoch_size == 'large':
n_samples = max(d_source.train_X.shape[0], d_target.train_X.shape[0])
elif epoch_size == 'small':
n_samples = min(d_source.train_X.shape[0], d_target.train_X.shape[0])
elif epoch_size == 'target':
n_samples = d_target.train_X.shape[0]
n_train_batches = n_samples // batch_size
source_test_ds = data_source.ArrayDataSource(
[d_source.test_X, d_source.test_y])
target_test_ds = data_source.ArrayDataSource(
[d_target.test_X, d_target.test_y])
if seed != 0:
shuffle_rng = np.random.RandomState(seed)
else:
shuffle_rng = np.random
train_batch_iter = train_ds.batch_iterator(batch_size=batch_size,
shuffle=shuffle_rng)
best_teacher_model_state = {
k: v.cpu().numpy()
for k, v in teacher_net.state_dict().items()
}
best_conf_mask_rate = 0.0
best_src_test_err = 1.0
for epoch in range(num_epochs):
t1 = time.time()
if use_rampup:
if epoch < rampup:
p = max(0.0, float(epoch)) / float(rampup)
p = 1.0 - p
rampup_value = math.exp(-p * p * 5.0)
else:
rampup_value = 1.0
rampup_weight_in_list[0] = rampup_value
train_res = data_source.batch_map_mean(f_train,
train_batch_iter,
n_batches=n_train_batches)
train_clf_loss = train_res[0]
if combine_batches:
unsup_loss_string = 'unsup (both) loss={:.6f}'.format(train_res[1])
else:
unsup_loss_string = 'unsup (tgt) loss={:.6f}'.format(train_res[1])
src_test_err_stu, src_test_err_tea = source_test_ds.batch_map_mean(
f_eval_src, batch_size=batch_size * 2)
tgt_test_err_stu, tgt_test_err_tea = target_test_ds.batch_map_mean(
f_eval_tgt, batch_size=batch_size * 2)
if use_rampup:
unsup_loss_string = '{}, rampup={:.3%}'.format(
unsup_loss_string, rampup_value)
if src_test_err_stu < best_src_test_err:
best_src_test_err = src_test_err_stu
best_teacher_model_state = {
k: v.cpu().numpy()
for k, v in teacher_net.state_dict().items()
}
improve = '*** '
else:
improve = ''
else:
conf_mask_rate = train_res[-2]
unsup_mask_rate = train_res[-1]
if conf_mask_rate > best_conf_mask_rate:
best_conf_mask_rate = conf_mask_rate
improve = '*** '
best_teacher_model_state = {
k: v.cpu().numpy()
for k, v in teacher_net.state_dict().items()
}
else:
improve = ''
unsup_loss_string = '{}, conf mask={:.3%}, unsup mask={:.3%}'.format(
unsup_loss_string, conf_mask_rate, unsup_mask_rate)
t2 = time.time()
log('{}Epoch {} took {:.2f}s: TRAIN clf loss={:.6f}, {}; '
'SRC TEST ERR={:.3%}, TGT TEST student err={:.3%}, TGT TEST teacher err={:.3%}'
.format(improve, epoch, t2 - t1, train_clf_loss, unsup_loss_string,
src_test_err_stu, tgt_test_err_stu, tgt_test_err_tea))
# Save network
if model_file != '':
cmdline_helpers.ensure_containing_dir_exists(model_file)
with open(model_file, 'wb') as f:
torch.save(best_teacher_model_state, f)
def experiment(plot_path, ds_name, no_aug, affine_std, scale_u_range,
scale_x_range, scale_y_range, xlat_range, hflip, intens_flip,
intens_scale_range, intens_offset_range, grid_h, grid_w, seed):
settings = locals().copy()
import os
import sys
import cmdline_helpers
intens_scale_range_lower, intens_scale_range_upper = cmdline_helpers.colon_separated_range(
intens_scale_range)
intens_offset_range_lower, intens_offset_range_upper = cmdline_helpers.colon_separated_range(
intens_offset_range)
scale_u_range = cmdline_helpers.colon_separated_range(scale_u_range)
scale_x_range = cmdline_helpers.colon_separated_range(scale_x_range)
scale_y_range = cmdline_helpers.colon_separated_range(scale_y_range)
import numpy as np
# from skimage.util import montage2d
from skimage.util import montage as montage2d
from PIL import Image
from batchup import data_source
import data_loaders
import augmentation
n_chn = 0
if ds_name == 'mnist':
d_source = data_loaders.load_mnist(zero_centre=False)
elif ds_name == 'usps':
d_source = data_loaders.load_usps(zero_centre=False, scale28=True)
elif ds_name == 'svhn_grey':
d_source = data_loaders.load_svhn(zero_centre=False, greyscale=True)
elif ds_name == 'svhn':
d_source = data_loaders.load_svhn(zero_centre=False, greyscale=False)
elif ds_name == 'cifar':
d_source = data_loaders.load_cifar10()
elif ds_name == 'stl':
d_source = data_loaders.load_stl()
elif ds_name == 'syndigits':
d_source = data_loaders.load_syn_digits(zero_centre=False,
greyscale=False)
elif ds_name == 'synsigns':
d_source = data_loaders.load_syn_signs(zero_centre=False,
greyscale=False)
elif ds_name == 'gtsrb':
d_source = data_loaders.load_gtsrb(zero_centre=False, greyscale=False)
else:
print('Unknown dataset \'{}\''.format(ds_name))
return
# Delete the training ground truths as we should not be using them
del d_source.train_y
n_classes = d_source.n_classes
print('Loaded data')
src_aug = augmentation.ImageAugmentation(
hflip,
xlat_range,
affine_std,
intens_flip=intens_flip,
intens_scale_range_lower=intens_scale_range_lower,
intens_scale_range_upper=intens_scale_range_upper,
intens_offset_range_lower=intens_offset_range_lower,
intens_offset_range_upper=intens_offset_range_upper,
scale_u_range=scale_u_range,
scale_x_range=scale_x_range,
scale_y_range=scale_y_range)
def augment(X):
if not no_aug:
X = src_aug.augment(X)
return X,
rampup_weight_in_list = [0]
print('Rendering...')
train_ds = data_source.ArrayDataSource([d_source.train_X],
repeats=-1).map(augment)
n_samples = len(d_source.train_X)
if seed != 0:
shuffle_rng = np.random.RandomState(seed)
else:
shuffle_rng = np.random
batch_size = grid_h * grid_w
display_batch_iter = train_ds.batch_iterator(batch_size=batch_size,
shuffle=shuffle_rng)
best_src_test_err = 1.0
x_batch, = next(display_batch_iter)
montage = []
for chn_i in range(x_batch.shape[1]):
m = montage2d(x_batch[:, chn_i, :, :], grid_shape=(grid_h, grid_w))
montage.append(m[:, :, None])
montage = np.concatenate(montage, axis=2)
if montage.shape[2] == 1:
montage = montage[:, :, 0]
lower = min(0.0, montage.min())
upper = max(1.0, montage.max())
montage = (montage - lower) / (upper - lower)
montage = (np.clip(montage, 0.0, 1.0) * 255.0).astype(np.uint8)
Image.fromarray(montage).save(plot_path)
if args.cuda:
torch.cuda.manual_seed(args.seed)
def half_image_noise(image):
image[0, 14:] = torch.randn(14, 28)
return image
if args.dataset == 'MNIST':
train_loader, val_loader, test_loader = data_loaders.load_mnist(
args.batch_size, val_split=True)
in_channel = 1
fc_shape = 800
elif args.dataset == 'CIFAR10':
train_loader, val_loader, test_loader = data_loaders.load_cifar10(
args.batch_size, val_split=True)
in_channel = 3
fc_shape = 1250
###############################################################################
# Saving
###############################################################################
short_args = {'num_layers': 'l', 'dropout': 'drop', 'input_dropout': 'indrop'}
flags = {}
subdir = "normal_mnist"
files_used = ['mnist/train']
train_labels = ("global_step", "epoch", "batch", "loss")
valid_labels = ("global_step", "loss", "acc")
stats = {"train": train_labels, "valid": valid_labels}
logger = Logger(sys.argv, args, stats)
def experiment(exp, arch, learning_rate, standardise_samples, affine_std,
xlat_range, hflip, intens_flip, intens_scale_range,
intens_offset_range, gaussian_noise_std, num_epochs, batch_size,
seed, log_file, device):
import os
import sys
import cmdline_helpers
if log_file == '':
log_file = 'output_aug_log_{}.txt'.format(exp)
elif log_file == 'none':
log_file = None
if log_file is not None:
if os.path.exists(log_file):
print('Output log file {} already exists'.format(log_file))
return
intens_scale_range_lower, intens_scale_range_upper, intens_offset_range_lower, intens_offset_range_upper = \
cmdline_helpers.intens_aug_options(intens_scale_range, intens_offset_range)
import time
import math
import numpy as np
from batchup import data_source, work_pool
import data_loaders
import standardisation
import network_architectures
import augmentation
import torch, torch.cuda
from torch import nn
from torch.nn import functional as F
with torch.cuda.device(device):
pool = work_pool.WorkerThreadPool(2)
n_chn = 0
if exp == 'svhn_mnist':
d_source = data_loaders.load_svhn(zero_centre=False,
greyscale=True)
d_target = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
val=False)
elif exp == 'mnist_svhn':
d_source = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True)
d_target = data_loaders.load_svhn(zero_centre=False,
greyscale=True,
val=False)
elif exp == 'svhn_mnist_rgb':
d_source = data_loaders.load_svhn(zero_centre=False,
greyscale=False)
d_target = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
val=False,
rgb=True)
elif exp == 'mnist_svhn_rgb':
d_source = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
rgb=True)
d_target = data_loaders.load_svhn(zero_centre=False,
greyscale=False,
val=False)
elif exp == 'cifar_stl':
d_source = data_loaders.load_cifar10(range_01=False)
d_target = data_loaders.load_stl(zero_centre=False, val=False)
elif exp == 'stl_cifar':
d_source = data_loaders.load_stl(zero_centre=False)
d_target = data_loaders.load_cifar10(range_01=False, val=False)
elif exp == 'mnist_usps':
d_source = data_loaders.load_mnist(zero_centre=False)
d_target = data_loaders.load_usps(zero_centre=False,
scale28=True,
val=False)
elif exp == 'usps_mnist':
d_source = data_loaders.load_usps(zero_centre=False, scale28=True)
d_target = data_loaders.load_mnist(zero_centre=False, val=False)
elif exp == 'syndigits_svhn':
d_source = data_loaders.load_syn_digits(zero_centre=False)
d_target = data_loaders.load_svhn(zero_centre=False, val=False)
elif exp == 'svhn_syndigits':
d_source = data_loaders.load_svhn(zero_centre=False, val=False)
d_target = data_loaders.load_syn_digits(zero_centre=False)
elif exp == 'synsigns_gtsrb':
d_source = data_loaders.load_syn_signs(zero_centre=False)
d_target = data_loaders.load_gtsrb(zero_centre=False, val=False)
elif exp == 'gtsrb_synsigns':
d_source = data_loaders.load_gtsrb(zero_centre=False, val=False)
d_target = data_loaders.load_syn_signs(zero_centre=False)
else:
print('Unknown experiment type \'{}\''.format(exp))
return
# Delete the training ground truths as we should not be using them
del d_target.train_y
if standardise_samples:
standardisation.standardise_dataset(d_source)
standardisation.standardise_dataset(d_target)
n_classes = d_source.n_classes
print('Loaded data')
if arch == '':
if exp in {'mnist_usps', 'usps_mnist'}:
arch = 'mnist-bn-32-64-256'
if exp in {'svhn_mnist', 'mnist_svhn'}:
arch = 'grey-32-64-128-gp'
if exp in {
'cifar_stl', 'stl_cifar', 'syndigits_svhn',
'svhn_syndigits', 'svhn_mnist_rgb', 'mnist_svhn_rgb'
}:
arch = 'rgb-48-96-192-gp'
if exp in {'synsigns_gtsrb', 'gtsrb_synsigns'}:
arch = 'rgb40-48-96-192-384-gp'
net_class, expected_shape = network_architectures.get_net_and_shape_for_architecture(
arch)
if expected_shape != d_source.train_X.shape[1:]:
print(
'Architecture {} not compatible with experiment {}; it needs samples of shape {}, '
'data has samples of shape {}'.format(
arch, exp, expected_shape, d_source.train_X.shape[1:]))
return
net = net_class(n_classes).cuda()
params = list(net.parameters())
optimizer = torch.optim.Adam(params, lr=learning_rate)
classification_criterion = nn.CrossEntropyLoss()
print('Built network')
aug = augmentation.ImageAugmentation(
hflip,
xlat_range,
affine_std,
intens_scale_range_lower=intens_scale_range_lower,
intens_scale_range_upper=intens_scale_range_upper,
intens_offset_range_lower=intens_offset_range_lower,
intens_offset_range_upper=intens_offset_range_upper,
intens_flip=intens_flip,
gaussian_noise_std=gaussian_noise_std)
def augment(X_sup, y_sup):
X_sup = aug.augment(X_sup)
return [X_sup, y_sup]
def f_train(X_sup, y_sup):
X_sup = torch.autograd.Variable(torch.from_numpy(X_sup).cuda())
y_sup = torch.autograd.Variable(
torch.from_numpy(y_sup).long().cuda())
optimizer.zero_grad()
net.train(mode=True)
sup_logits_out = net(X_sup)
# Supervised classification loss
clf_loss = classification_criterion(sup_logits_out, y_sup)
loss_expr = clf_loss
loss_expr.backward()
optimizer.step()
n_samples = X_sup.size()[0]
return float(clf_loss.data.cpu().numpy()) * n_samples
print('Compiled training function')
def f_pred_src(X_sup):
X_var = torch.autograd.Variable(torch.from_numpy(X_sup).cuda())
net.train(mode=False)
return F.softmax(net(X_var)).data.cpu().numpy()
def f_pred_tgt(X_sup):
X_var = torch.autograd.Variable(torch.from_numpy(X_sup).cuda())
net.train(mode=False)
return F.softmax(net(X_var)).data.cpu().numpy()
def f_eval_src(X_sup, y_sup):
y_pred_prob = f_pred_src(X_sup)
y_pred = np.argmax(y_pred_prob, axis=1)
return float((y_pred != y_sup).sum())
def f_eval_tgt(X_sup, y_sup):
y_pred_prob = f_pred_tgt(X_sup)
y_pred = np.argmax(y_pred_prob, axis=1)
return float((y_pred != y_sup).sum())
print('Compiled evaluation function')
# Setup output
def log(text):
print(text)
if log_file is not None:
with open(log_file, 'a') as f:
f.write(text + '\n')
f.flush()
f.close()
cmdline_helpers.ensure_containing_dir_exists(log_file)
# Report setttings
log('sys.argv={}'.format(sys.argv))
# Report dataset size
log('Dataset:')
log('SOURCE Train: X.shape={}, y.shape={}'.format(
d_source.train_X.shape, d_source.train_y.shape))
log('SOURCE Test: X.shape={}, y.shape={}'.format(
d_source.test_X.shape, d_source.test_y.shape))
log('TARGET Train: X.shape={}'.format(d_target.train_X.shape))
log('TARGET Test: X.shape={}, y.shape={}'.format(
d_target.test_X.shape, d_target.test_y.shape))
print('Training...')
train_ds = data_source.ArrayDataSource(
[d_source.train_X, d_source.train_y]).map(augment)
source_test_ds = data_source.ArrayDataSource(
[d_source.test_X, d_source.test_y])
target_test_ds = data_source.ArrayDataSource(
[d_target.test_X, d_target.test_y])
if seed != 0:
shuffle_rng = np.random.RandomState(seed)
else:
shuffle_rng = np.random
best_src_test_err = 1.0
for epoch in range(num_epochs):
t1 = time.time()
train_res = train_ds.batch_map_mean(f_train,
batch_size=batch_size,
shuffle=shuffle_rng)
train_clf_loss = train_res[0]
src_test_err, = source_test_ds.batch_map_mean(
f_eval_src, batch_size=batch_size * 4)
tgt_test_err, = target_test_ds.batch_map_mean(
f_eval_tgt, batch_size=batch_size * 4)
t2 = time.time()
if src_test_err < best_src_test_err:
log('*** Epoch {} took {:.2f}s: TRAIN clf loss={:.6f}; '
'SRC TEST ERR={:.3%}, TGT TEST err={:.3%}'.format(
epoch, t2 - t1, train_clf_loss, src_test_err,
tgt_test_err))
best_src_test_err = src_test_err
else:
log('Epoch {} took {:.2f}s: TRAIN clf loss={:.6f}; '
'SRC TEST ERR={:.3%}, TGT TEST err={:.3%}'.format(
epoch, t2 - t1, train_clf_loss, src_test_err,
tgt_test_err))
import os
#import argparse
args = get_args()
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# load data
if args.dataset == 0:
(x_train, y_train), (x_test, y_test) = load_mnist()
elif args.dataset == 1:
(x_train, y_train), (x_test, y_test) = load_fashion_mnist()
elif args.dataset == 2:
(x_train, y_train), (x_test, y_test) = load_svhn()
elif args.dataset == 3:
(x_train, y_train), (x_test, y_test) = load_cifar10()
elif args.dataset == 4:
(x_train, y_train), (x_test, y_test) = load_food101()
x_train = x_train[:args.train_num]
y_train = y_train[:args.train_num]
# define model
if args.dataset != 4:
model, eval_model, manipulate_model = CapsNet(
input_shape=x_train.shape[1:],
n_class=len(np.unique(np.argmax(y_train, 1))),
routings=args.routings,
l1=args.l1)
else:
model, eval_model, manipulate_model = CapsNet_for_big(
input_shape=x_train.shape[1:],
train_transform = transforms.Compose([])
if args.data_augmentation:
train_transform.transforms.append(transforms.RandomCrop(32, padding=4))
train_transform.transforms.append(transforms.RandomHorizontalFlip())
train_transform.transforms.append(transforms.ToTensor())
train_transform.transforms.append(normalize)
if args.cutout:
train_transform.transforms.append(
Cutout(n_holes=args.n_holes, length=args.length))
test_transform = transforms.Compose([transforms.ToTensor(), normalize])
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(
args.batch_size, val_split=True, augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(
args.batch_size, val_split=True, augmentation=args.data_augmentation)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28,
num_classes=num_classes,
widen_factor=10,
dropRate=0.3)
cnn = cnn.cuda()
criterion = nn.CrossEntropyLoss().cuda()
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=cifar_mean, std=cifar_std)
])
tf_test = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(mean=cifar_mean, std=cifar_std)
])
channels = 3
imsize = 32
num_classes = 10
train_loader, test_loader, init_loader = data_loaders.load_cifar10(
args.batch_size, tf, tf_test, shuffle=False)
def run_flow(img):
objective = torch.zeros_like(img[:, 0, 0, 0])
objective += float(-np.log(args.n_bins) * np.prod(img.shape[1:]))
return model(img, objective)
if args.load == '':
print('You need to pass in a checkpoint to load with --load')
sys.exit(0)
load = args.load
checkpoints = [
def cnn_val_loss(config={}, reporter=None, callback=None, return_all=False):
print("Starting cnn_val_loss...")
###############################################################################
# Arguments
###############################################################################
dataset_options = ['cifar10', 'cifar100', 'fashion']
## Tuning parameters: all of the dropouts
parser = argparse.ArgumentParser(description='CNN')
parser.add_argument('--dataset',
default='cifar10',
choices=dataset_options,
help='Choose a dataset (cifar10, cifar100)')
parser.add_argument(
'--model',
default='resnet32',
choices=['resnet32', 'wideresnet', 'simpleconvnet'],
help='Choose a model (resnet32, wideresnet, simpleconvnet)')
#### Optimization hyperparameters
parser.add_argument('--batch_size',
type=int,
default=128,
help='Input batch size for training (default: 128)')
parser.add_argument('--epochs',
type=int,
default=int(config['epochs']),
help='Number of epochs to train (default: 200)')
parser.add_argument('--lr',
type=float,
default=float(config['lr']),
help='Learning rate')
parser.add_argument('--momentum',
type=float,
default=float(config['momentum']),
help='Nesterov momentum')
parser.add_argument('--lr_decay',
type=float,
default=float(config['lr_decay']),
help='Factor by which to multiply the learning rate.')
# parser.add_argument('--weight_decay', type=float, default=float(config['weight_decay']),
# help='Amount of weight decay to use.')
# parser.add_argument('--dropout', type=float, default=config['dropout'] if 'dropout' in config else 0.0,
# help='Amount of dropout for wideresnet')
# parser.add_argument('--dropout1', type=float, default=config['dropout1'] if 'dropout1' in config else -1,
# help='Amount of dropout for wideresnet')
# parser.add_argument('--dropout2', type=float, default=config['dropout2'] if 'dropout2' in config else -1,
# help='Amount of dropout for wideresnet')
# parser.add_argument('--dropout3', type=float, default=config['dropout3'] if 'dropout3' in config else -1,
# help='Amount of dropout for wideresnet')
parser.add_argument('--dropout_type',
type=str,
default=config['dropout_type'],
help='Type of dropout (bernoulli or gaussian)')
# Data augmentation hyperparameters
parser.add_argument(
'--inscale',
type=float,
default=0 if 'inscale' not in config else config['inscale'],
help='defines input scaling factor')
parser.add_argument('--hue',
type=float,
default=0. if 'hue' not in config else config['hue'],
help='hue jitter rate')
parser.add_argument(
'--brightness',
type=float,
default=0. if 'brightness' not in config else config['brightness'],
help='brightness jitter rate')
parser.add_argument(
'--saturation',
type=float,
default=0. if 'saturation' not in config else config['saturation'],
help='saturation jitter rate')
parser.add_argument(
'--contrast',
type=float,
default=0. if 'contrast' not in config else config['contrast'],
help='contrast jitter rate')
# Weight decay and dropout hyperparameters for each layer
parser.add_argument(
'--weight_decays',
type=str,
default='0.0',
help=
'Amount of weight decay to use for each layer, represented as a comma-separated string of floats.'
)
parser.add_argument(
'--dropouts',
type=str,
default='0.0',
help=
'Dropout rates for each layer, represented as a comma-separated string of floats'
)
parser.add_argument(
'--nonmono',
'-nonm',
type=int,
default=60,
help='how many previous epochs to consider for nonmonotonic criterion')
parser.add_argument(
'--patience',
type=int,
default=75,
help=
'How long to wait for the val loss to improve before early stopping.')
parser.add_argument(
'--data_augmentation',
action='store_true',
default=config['data_augmentation'],
help='Augment data by cropping and horizontal flipping')
parser.add_argument(
'--log_interval',
type=int,
default=10,
help='how many steps before logging stats from training set')
parser.add_argument(
'--valid_log_interval',
type=int,
default=50,
help='how many steps before logging stats from validations set')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='enables CUDA training')
parser.add_argument('--save',
action='store_true',
default=False,
help='whether to save current run')
parser.add_argument('--seed',
type=int,
default=11,
help='random seed (default: 11)')
parser.add_argument(
'--save_dir',
default=config['save_dir'],
help=
'subdirectory of logdir/savedir to save in (default changes to date/time)'
)
args, unknown = parser.parse_known_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
cudnn.benchmark = True # Should make training should go faster for large models
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
print(args)
sys.stdout.flush()
# args.dropout1 = args.dropout1 if args.dropout1 != -1 else args.dropout
# args.dropout2 = args.dropout2 if args.dropout2 != -1 else args.dropout
# args.dropout3 = args.dropout3 if args.dropout3 != -1 else args.dropout
###############################################################################
# Saving
###############################################################################
timestamp = '{:%Y-%m-%d}'.format(datetime.datetime.now())
random_hash = random.getrandbits(16)
exp_name = '{}-dset:{}-model:{}-seed:{}-hash:{}'.format(
timestamp, args.dataset, args.model,
args.seed if args.seed else 'None', random_hash)
dropout_rates = [float(value) for value in args.dropouts.split(',')]
weight_decays = [float(value) for value in args.weight_decays.split(',')]
# Create log folder
BASE_SAVE_DIR = 'experiments'
save_dir = os.path.join(BASE_SAVE_DIR, args.save_dir, exp_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Check whether the result.csv file exists already
if os.path.exists(os.path.join(save_dir, 'result.csv')):
if not args.overwrite:
print(
'The result file {} exists! Run with --overwrite to overwrite this experiment.'
.format(os.path.join(save_dir, 'result.csv')))
sys.exit(0)
# Save command-line arguments
with open(os.path.join(save_dir, 'args.yaml'), 'w') as f:
yaml.dump(vars(args), f)
epoch_csv_logger = CSVLogger(
fieldnames=['epoch', 'train_loss', 'train_acc', 'val_loss', 'val_acc'],
filename=os.path.join(save_dir, 'epoch_log.csv'))
###############################################################################
# Data Loading/Model/Optimizer
###############################################################################
if args.dataset == 'cifar10':
train_loader, valid_loader, test_loader = data_loaders.load_cifar10(
args,
args.batch_size,
val_split=True,
augmentation=args.data_augmentation)
num_classes = 10
elif args.dataset == 'cifar100':
train_loader, valid_loader, test_loader = data_loaders.load_cifar100(
args,
args.batch_size,
val_split=True,
augmentation=args.data_augmentation)
num_classes = 100
elif args.dataset == 'fashion':
train_loader, valid_loader, test_loader = data_loaders.load_fashion_mnist(
args.batch_size, val_split=True)
num_classes = 10
if args.model == 'resnet32':
cnn = resnet_cifar.resnet32(dropRates=dropout_rates)
elif args.model == 'wideresnet':
cnn = wide_resnet.WideResNet(depth=16,
num_classes=num_classes,
widen_factor=8,
dropRates=dropout_rates,
dropType=args.dropout_type)
# cnn = wide_resnet.WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=args.dropout)
elif args.model == 'simpleconvnet':
cnn = models.SimpleConvNet(dropType=args.dropout_type,
conv_drop1=args.dropout1,
conv_drop2=args.dropout2,
fc_drop=args.dropout3)
def optim_parameters(model):
module_list = [
m for m in model.modules()
if type(m) == nn.Linear or type(m) == nn.Conv2d
]
weight_decays = [1e-4] * len(module_list)
return [{
'params': layer.parameters(),
'weight_decay': wdecay
} for (layer, wdecay) in zip(module_list, weight_decays)]
cnn = cnn.to(device)
criterion = nn.CrossEntropyLoss()
# cnn_optimizer = torch.optim.SGD(cnn.parameters(),
# lr=args.lr,
# momentum=args.momentum,
# nesterov=True,
# weight_decay=args.weight_decay)
cnn_optimizer = torch.optim.SGD(optim_parameters(cnn),
lr=args.lr,
momentum=args.momentum,
nesterov=True)
###############################################################################
# Training/Evaluation
###############################################################################
def evaluate(loader):
"""Returns the loss and accuracy on the entire validation/test set."""
cnn.eval()
correct = total = loss = 0.
with torch.no_grad():
for images, labels in loader:
images, labels = images.to(device), labels.to(device)
pred = cnn(images)
loss += F.cross_entropy(pred, labels, reduction='sum').item()
hard_pred = torch.max(pred, 1)[1]
total += labels.size(0)
correct += (hard_pred == labels).sum().item()
accuracy = correct / total
mean_loss = loss / total
cnn.train()
return mean_loss, accuracy
epoch = 1
global_step = 0
patience_elapsed = 0
stored_loss = 1e8
best_val_loss = []
start_time = time.time()
# This is based on the schedule used for WideResNets. The gamma (decay factor) can also be 0.2 (= 5x decay)
# Right now we're not using the scheduler because we use nonmonotonic lr decay (based on validation performance)
# scheduler = MultiStepLR(cnn_optimizer, milestones=[60,120,160], gamma=args.lr_decay)
while epoch < args.epochs + 1 and patience_elapsed < args.patience:
running_xentropy = correct = total = 0.
progress_bar = tqdm(train_loader)
for i, (images, labels) in enumerate(progress_bar):
progress_bar.set_description('Epoch ' + str(epoch))
images, labels = images.to(device), labels.to(device)
if args.inscale > 0:
noise = torch.rand(images.size(0), device=device)
scaled_noise = (
(1 + args.inscale) -
(1 / (1 + args.inscale))) * noise + (1 /
(1 + args.inscale))
images = images * scaled_noise[:, None, None, None]
# images = F.dropout(images, p=args.indropout, training=True) # TODO: Incorporate input dropout
cnn.zero_grad()
pred = cnn(images)
xentropy_loss = criterion(pred, labels)
xentropy_loss.backward()
cnn_optimizer.step()
running_xentropy += xentropy_loss.item()
# Calculate running average of accuracy
_, hard_pred = torch.max(pred, 1)
total += labels.size(0)
correct += (hard_pred == labels).sum().item()
accuracy = correct / float(total)
global_step += 1
progress_bar.set_postfix(
xentropy='%.3f' % (running_xentropy / (i + 1)),
acc='%.3f' % accuracy,
lr='%.3e' % cnn_optimizer.param_groups[0]['lr'])
val_loss, val_acc = evaluate(valid_loader)
print('Val loss: {:6.4f} | Val acc: {:6.4f}'.format(val_loss, val_acc))
sys.stdout.flush()
stats = {
'global_step': global_step,
'time': time.time() - start_time,
'loss': val_loss,
'acc': val_acc
}
# logger.write('valid', stats)
if (len(best_val_loss) > args.nonmono
and val_loss > min(best_val_loss[:-args.nonmono])):
cnn_optimizer.param_groups[0]['lr'] *= args.lr_decay
print('Decaying the learning rate to {}'.format(
cnn_optimizer.param_groups[0]['lr']))
sys.stdout.flush()
if val_loss < stored_loss:
with open(os.path.join(save_dir, 'best_checkpoint.pt'), 'wb') as f:
torch.save(cnn.state_dict(), f)
print('Saving model (new best validation)')
sys.stdout.flush()
stored_loss = val_loss
patience_elapsed = 0
else:
patience_elapsed += 1
best_val_loss.append(val_loss)
# scheduler.step(epoch)
avg_xentropy = running_xentropy / (i + 1)
train_acc = correct / float(total)
if callback is not None:
callback(epoch, avg_xentropy, train_acc, val_loss, val_acc, config)
if reporter is not None:
reporter(timesteps_total=epoch, mean_loss=val_loss)
if cnn_optimizer.param_groups[0][
'lr'] < 1e-7: # Another stopping criterion based on decaying the lr
break
epoch += 1
epoch_row = {
'epoch': str(epoch),
'train_loss': avg_xentropy,
'train_acc': str(train_acc),
'val_loss': str(val_loss),
'val_acc': str(val_acc)
}
epoch_csv_logger.writerow(epoch_row)
# Load best model and run on test
with open(os.path.join(save_dir, 'best_checkpoint.pt'), 'rb') as f:
cnn.load_state_dict(torch.load(f))
train_loss = avg_xentropy
train_acc = correct / float(total)
# Run on val and test data.
val_loss, val_acc = evaluate(valid_loader)
test_loss, test_acc = evaluate(test_loader)
print('=' * 89)
print(
'| End of training | trn loss: {:8.5f} | trn acc {:8.5f} | val loss {:8.5f} | val acc {:8.5f} | test loss {:8.5f} | test acc {:8.5f}'
.format(train_loss, train_acc, val_loss, val_acc, test_loss, test_acc))
print('=' * 89)
sys.stdout.flush()
# Save the final val and test performance to a results CSV file
with open(os.path.join(save_dir, 'result_{}.csv'.format(time.time())),
'w') as result_file:
result_writer = csv.DictWriter(result_file,
fieldnames=[
'train_loss', 'train_acc',
'val_loss', 'val_acc', 'test_loss',
'test_acc'
])
result_writer.writeheader()
result_writer.writerow({
'train_loss': train_loss,
'train_acc': train_acc,
'val_loss': val_loss,
'val_acc': val_acc,
'test_loss': test_loss,
'test_acc': test_acc
})
result_file.flush()
if return_all:
print("RETURNING ", train_loss, train_acc, val_loss, val_acc,
test_loss, test_acc)
sys.stdout.flush()
return train_loss, train_acc, val_loss, val_acc, test_loss, test_acc
else:
print("RETURNING ", stored_loss)
sys.stdout.flush()
return stored_loss
def experiment():
parser = argparse.ArgumentParser(description='CNN Hyperparameter Fine-tuning')
parser.add_argument('--dataset', default='cifar10', choices=['cifar10', 'cifar100'],
help='Choose a dataset')
parser.add_argument('--model', default='resnet18', choices=['resnet18', 'wideresnet'],
help='Choose a model')
parser.add_argument('--num_finetune_epochs', type=int, default=200,
help='Number of fine-tuning epochs')
parser.add_argument('--lr', type=float, default=0.1,
help='Learning rate')
parser.add_argument('--optimizer', type=str, default='sgdm',
help='Choose an optimizer')
parser.add_argument('--batch_size', type=int, default=128,
help='Mini-batch size')
parser.add_argument('--data_augmentation', action='store_true', default=True,
help='Whether to use data augmentation')
parser.add_argument('--wdecay', type=float, default=5e-4,
help='Amount of weight decay')
parser.add_argument('--load_checkpoint', type=str,
help='Path to pre-trained checkpoint to load and finetune')
parser.add_argument('--save_dir', type=str, default='finetuned_checkpoints',
help='Save directory for the fine-tuned checkpoint')
args = parser.parse_args()
args.load_checkpoint = '/h/lorraine/PycharmProjects/CG_IFT_test/baseline_checkpoints/cifar10_resnet18_sgdm_lr0.1_wd0.0005_aug0.pt'
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'wideresnet':
cnn = WideResNet(depth=28, num_classes=num_classes, widen_factor=10, dropRate=0.3)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
test_id = '{}_{}_{}_lr{}_wd{}_aug{}'.format(args.dataset, args.model, args.optimizer, args.lr, args.wdecay,
int(args.data_augmentation))
filename = os.path.join(args.save_dir, test_id + '.csv')
csv_logger = CSVLogger(
fieldnames=['epoch', 'train_loss', 'train_acc', 'val_loss', 'val_acc', 'test_loss', 'test_acc'],
filename=filename)
checkpoint = torch.load(args.load_checkpoint)
init_epoch = checkpoint['epoch']
cnn.load_state_dict(checkpoint['model_state_dict'])
model = cnn.cuda()
model.train()
args.hyper_train = 'augment' # 'all_weight' # 'weight'
def init_hyper_train(model):
"""
:return:
"""
init_hyper = None
if args.hyper_train == 'weight':
init_hyper = np.sqrt(args.wdecay)
model.weight_decay = Variable(torch.FloatTensor([init_hyper]).cuda(), requires_grad=True)
model.weight_decay = model.weight_decay.cuda()
elif args.hyper_train == 'all_weight':
num_p = sum(p.numel() for p in model.parameters())
weights = np.ones(num_p) * np.sqrt(args.wdecay)
model.weight_decay = Variable(torch.FloatTensor(weights).cuda(), requires_grad=True)
model.weight_decay = model.weight_decay.cuda()
model = model.cuda()
return init_hyper
if args.hyper_train == 'augment': # Dont do inside the prior function, else scope is wrong
augment_net = UNet(in_channels=3,
n_classes=3,
depth=5,
wf=6,
padding=True,
batch_norm=False,
up_mode='upconv') # TODO(PV): Initialize UNet properly
augment_net = augment_net.cuda()
def get_hyper_train():
"""
:return:
"""
if args.hyper_train == 'weight' or args.hyper_train == 'all_weight':
return [model.weight_decay]
if args.hyper_train == 'augment':
return augment_net.parameters()
def get_hyper_train_flat():
return torch.cat([p.view(-1) for p in get_hyper_train()])
# TODO: Check this size
init_hyper_train(model)
if args.hyper_train == 'all_weight':
wdecay = 0.0
else:
wdecay = args.wdecay
optimizer = optim.SGD(model.parameters(), lr=args.lr * 0.2 * 0.2, momentum=0.9, nesterov=True,
weight_decay=wdecay) # args.wdecay)
# print(checkpoint['optimizer_state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
scheduler = MultiStepLR(optimizer, milestones=[60, 120], gamma=0.2) # [60, 120, 160]
hyper_optimizer = torch.optim.Adam(get_hyper_train(), lr=1e-3) # try 0.1 as lr
# Set random regularization hyperparameters
# data_augmentation_hparams = {} # Random values for hue, saturation, brightness, contrast, rotation, etc.
if args.dataset == 'cifar10':
num_classes = 10
train_loader, val_loader, test_loader = data_loaders.load_cifar10(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
elif args.dataset == 'cifar100':
num_classes = 100
train_loader, val_loader, test_loader = data_loaders.load_cifar100(args.batch_size, val_split=True,
augmentation=args.data_augmentation)
def test(loader):
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
correct = 0.
total = 0.
losses = []
for images, labels in loader:
images = images.cuda()
labels = labels.cuda()
with torch.no_grad():
pred = model(images)
xentropy_loss = F.cross_entropy(pred, labels)
losses.append(xentropy_loss.item())
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels).sum().item()
avg_loss = float(np.mean(losses))
acc = correct / total
model.train()
return avg_loss, acc
def prepare_data(x, y):
"""
:param x:
:param y:
:return:
"""
x, y = x.cuda(), y.cuda()
# x, y = Variable(x), Variable(y)
return x, y
def train_loss_func(x, y):
"""
:param x:
:param y:
:return:
"""
x, y = prepare_data(x, y)
reg_loss = 0.0
if args.hyper_train == 'weight':
pred = model(x)
xentropy_loss = F.cross_entropy(pred, y)
# print(f"weight_decay: {torch.exp(model.weight_decay).shape}")
for p in model.parameters():
# print(f"weight_decay: {torch.exp(model.weight_decay).shape}")
# print(f"shape: {p.shape}")
reg_loss = reg_loss + .5 * (model.weight_decay ** 2) * torch.sum(p ** 2)
# print(f"reg_loss: {reg_loss}")
elif args.hyper_train == 'all_weight':
pred = model(x)
xentropy_loss = F.cross_entropy(pred, y)
count = 0
for p in model.parameters():
reg_loss = reg_loss + .5 * torch.sum(
(model.weight_decay[count: count + p.numel()] ** 2) * torch.flatten(p ** 2))
count += p.numel()
elif args.hyper_train == 'augment':
augmented_x = augment_net(x)
pred = model(augmented_x)
xentropy_loss = F.cross_entropy(pred, y)
return xentropy_loss + reg_loss, pred
def val_loss_func(x, y):
"""
:param x:
:param y:
:return:
"""
x, y = prepare_data(x, y)
pred = model(x)
xentropy_loss = F.cross_entropy(pred, y)
return xentropy_loss
for epoch in range(init_epoch, init_epoch + args.num_finetune_epochs):
xentropy_loss_avg = 0.
total_val_loss = 0.
correct = 0.
total = 0.
progress_bar = tqdm(train_loader)
for i, (images, labels) in enumerate(progress_bar):
progress_bar.set_description('Finetune Epoch ' + str(epoch))
# TODO: Take a hyperparameter step here
optimizer.zero_grad(), hyper_optimizer.zero_grad()
val_loss, weight_norm, grad_norm = hyper_step(1, 1, get_hyper_train, get_hyper_train_flat,
model, val_loss_func,
val_loader, train_loss_func, train_loader,
hyper_optimizer)
# del val_loss
# print(f"hyper: {get_hyper_train()}")
images, labels = images.cuda(), labels.cuda()
# pred = model(images)
# xentropy_loss = F.cross_entropy(pred, labels)
xentropy_loss, pred = train_loss_func(images, labels)
optimizer.zero_grad(), hyper_optimizer.zero_grad()
xentropy_loss.backward()
optimizer.step()
xentropy_loss_avg += xentropy_loss.item()
# Calculate running average of accuracy
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels.data).sum().item()
accuracy = correct / total
progress_bar.set_postfix(
train='%.5f' % (xentropy_loss_avg / (i + 1)),
val='%.4f' % (total_val_loss / (i + 1)),
acc='%.4f' % accuracy,
weight='%.2f' % weight_norm,
update='%.3f' % grad_norm)
val_loss, val_acc = test(val_loader)
test_loss, test_acc = test(test_loader)
tqdm.write('val loss: {:6.4f} | val acc: {:6.4f} | test loss: {:6.4f} | test_acc: {:6.4f}'.format(
val_loss, val_acc, test_loss, test_acc))
scheduler.step(epoch)
row = {'epoch': str(epoch),
'train_loss': str(xentropy_loss_avg / (i + 1)), 'train_acc': str(accuracy),
'val_loss': str(val_loss), 'val_acc': str(val_acc),
'test_loss': str(test_loss), 'test_acc': str(test_acc)}
csv_logger.writerow(row)