def evaluate(dataloader, model, criterion, accuracy, device=None):
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
losses = AverageMeter()
accs = AverageMeter()
with torch.no_grad():
for i, data in enumerate(tqdm(dataloader)):
inputs = data["input"].to(device)
labels = data["label"].to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
acc = accuracy(outputs, labels)
losses.update(loss.item(), outputs.size(0))
accs.update(acc.item(), outputs.size(0))
print("eval loss %0.5f acc %0.5f " % (losses.avg, accs.avg))
return float(losses.avg), float(accs.avg)
def evaluate(dataloader,
model,
criterion,
accuracy,
static_augmentations=[],
device=None,
random_seed=123):
print("evaluating...")
if random_seed is not None:
utils.make_deterministic(random_seed)
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
losses = AverageMeter()
accs = []
nway = dataloader.batch_sampler.n_way
nshot = dataloader.batch_sampler.n_shot
nquery = dataloader.batch_sampler.n_query
with torch.no_grad():
for i, data in enumerate(tqdm(dataloader)):
inputs = data["input"].to(device)
labels = data["label"].to(device)
inputs_generated = None
if model.mixer is not None or "generated" in static_augmentations:
inputs_generated = data["generated"].to(device)
print_final_nshot = False
if i == 0:
print_final_nshot = True
outputs, query_labels = model(inputs,
labels,
nway,
nshot,
nquery,
inputs_generated=inputs_generated,
print_final_nshot=print_final_nshot,
augmentations=static_augmentations)
loss = criterion(outputs, query_labels)
acc = accuracy(outputs, query_labels)
losses.update(loss.item(), outputs.size(0))
accs.append(acc.item())
print("eval loss: %0.5f " % losses.avg)
acc = float(np.mean(accs))
conf = float(1.96 * np.std(accs) / np.sqrt(len(accs)))
print("eval acc :%0.5f +- %0.5f" % (acc, conf))
return float(losses.avg), acc, conf
def main(args):
device = gpu_setup(args.gpu)
append_args = ["dataset", "model"]
checkpoint_dir = savedir_setup(args.savedir,
args=args,
append_args=append_args,
basedir=args.saveroot)
args.githash = check_githash()
save_args(checkpoint_dir, args)
dataloader = setup_dataloader(
name=args.dataset,
batch_size=args.batch,
num_workers=args.workers,
)
dataset_size = len(dataloader.dataset)
print("number of images (dataset size): ", dataset_size)
model = setup_model(args.model,
dataset_size=dataset_size,
resume=args.resume,
biggan_imagenet_pretrained_model_path=args.pretrained)
model.eval()
#this has to be eval() even if it's training time
#because we want to fix batchnorm running mean and var
#still tune batchnrom scale and bias that is generated by linear layer in biggan
optimizer, scheduler = setup_optimizer(
model,
lr_g_linear=args.lr_g_l,
lr_g_batch_stat=args.lr_g_batch_stat,
lr_bsa_linear=args.lr_bsa_l,
lr_embed=args.lr_embed,
lr_class_cond_embed=args.lr_c_embed,
step=args.step,
step_facter=args.step_facter,
)
criterion = AdaBIGGANLoss(
scale_per=args.loss_per,
scale_emd=args.loss_emd,
scale_reg=args.loss_re,
normalize_img=args.loss_norm_img,
normalize_per=args.loss_norm_per,
dist_per=args.loss_dist_per,
)
#start trainig loop
losses = AverageMeter()
print_freq = args.print_freq
eval_freq = args.eval_freq
save_freq = eval_freq
max_iteration = args.iters
log = {}
log["log"] = []
since = time.time()
iteration = 0
epoch = 0
#prepare model and loss into device
model = model.to(device)
criterion = criterion.to(device)
while (True):
# Iterate over dataset (one epoch).
for data in dataloader:
img = data[0].to(device)
indices = data[1].to(device)
scheduler.step()
#embeddings (i.e. z) + noise (i.e. epsilon)
embeddings = model.embeddings(indices)
embeddings_eps = torch.randn(embeddings.size(),
device=device) * 0.01
#see https://github.com/nogu-atsu/SmallGAN/blob/f604cd17516963d8eec292f3faddd70c227b609a/gen_models/ada_generator.py#L29
#forward
img_generated = model(embeddings + embeddings_eps)
loss = criterion(img_generated, img, embeddings,
model.linear.weight)
losses.update(loss.item(), img.size(0))
#compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if iteration % print_freq == 0:
temp = "train loss: %0.5f " % loss.item()
temp += "| smoothed loss %0.5f " % losses.avg
log["log"].append({
"iteration": iteration,
"epoch": epoch,
"loss": losses.avg
})
print(iteration, temp)
losses = AverageMeter()
if iteration % eval_freq == 0 and iteration > 0:
out_path = os.path.join(checkpoint_dir,
"%d_recon.jpg" % iteration)
generate_samples(model, out_path, dataloader.batch_size)
if iteration % save_freq == 0 and iteration > 0:
save_checkpoint(checkpoint_dir,
device,
model,
iteration=iteration)
if iteration > max_iteration:
break
iteration += 1
if iteration > max_iteration:
break
epoch += 1
log_save_path = os.path.join(checkpoint_dir, "train-log.json")
save_json(log, log_save_path)
def main(args):
device = gpu_setup(args.gpu)
append_args = ["dataset", "data_num", "model"]
if args.mode == 'train':
checkpoint_dir = savedir_setup(args.savedir,
args=args,
append_args=append_args,
basedir=args.saveroot)
args.githash = check_githash()
save_args(checkpoint_dir, args)
dataloader = setup_dataloader(
name=args.dataset,
batch_size=args.batch,
num_workers=args.workers,
data_num=args.data_num,
)
dataset_size = len(dataloader.dataset)
print("number of images (dataset size): ", dataset_size)
if args.model == "biggan128-ada":
model = setup_model(
args.model,
dataset_size=dataset_size,
resume=args.resume,
biggan_imagenet_pretrained_model_path=args.pretrained)
elif args.model == "biggan128-conv1x1":
model = setup_model(
args.model,
dataset_size=dataset_size,
resume=args.resume,
biggan_imagenet_pretrained_model_path=args.pretrained)
elif args.model == 'biggan128-conv1x1-2' or args.model == 'biggan128-conv1x1-3':
model = setup_model(
args.model,
dataset_size=dataset_size,
resume=args.resume,
biggan_imagenet_pretrained_model_path=args.pretrained,
per_groups=args.per_groups)
else:
print('Error: Model not defined')
sys.exit(1)
model.eval()
#this has to be eval() even if it's training time
#because we want to fix batchnorm running mean and var
#still tune batchnrom scale and bias that is generated by linear layer in biggan
optimizer, scheduler = setup_optimizer(
model_name=args.model,
model=model,
lr_g_linear=args.lr_g_l,
lr_g_batch_stat=args.lr_g_batch_stat,
lr_bsa_linear=args.lr_bsa_l,
lr_embed=args.lr_embed,
lr_class_cond_embed=args.lr_c_embed,
step=args.step,
step_facter=args.step_facter,
)
criterion = AdaBIGGANLoss(
scale_per=args.loss_per,
scale_emd=args.loss_emd,
scale_reg=args.loss_re,
normalize_img=args.loss_norm_img,
normalize_per=args.loss_norm_per,
dist_per=args.loss_dist_per,
)
#start trainig loop
losses = AverageMeter()
eval_kmmd = AverageMeter()
print_freq = args.print_freq
eval_freq = args.eval_freq
save_freq = eval_freq
max_epoch = args.epoch
log = {}
log["log"] = []
since = time.time()
min_loss = {
'data': 1000,
'epoch': 0,
}
epoch = 0
#prepare model and loss into device
model = model.to(device)
criterion = criterion.to(device)
while (True):
if args.mode == 'train': scheduler.step()
# Iterate over dataset (one epoch).
for data in dataloader:
define_seed(SEED)
img = data[0].to(device)
indices = data[1].to(device)
#embeddings (i.e. z) + noise (i.e. epsilon)
embeddings = model.embeddings(indices)
embeddings_eps = torch.randn(embeddings.size(),
device=device) * 0.01
#see https://github.com/nogu-atsu/SmallGAN/blob/f604cd17516963d8eec292f3faddd70c227b609a/gen_models/ada_generator.py#L29
#forward
img_generated = model(embeddings + embeddings_eps)
if args.mode == 'train':
loss = criterion(img_generated, img, embeddings,
model.linear.weight)
losses.update(loss.item(), img.size(0))
#compute gradient and do SGD step
# optimizer.zero_grad()
# loss.backward()
# optimizer.step()
elif args.mode == 'eval':
if args.KMMD:
# KMMD
latent_size = embeddings.size(1)
true_sample = torch.randn(args.batch,
latent_size,
requires_grad=False).to(device)
kmmd = KMMD()(true_sample, embeddings)
eval_kmmd.update(kmmd.item(), img.size(0))
if args.mode == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % print_freq == 0:
if min_loss['data'] > losses.avg:
min_loss['data'] = losses.avg
min_loss['epoch'] = epoch
temp = "train loss: %0.5f " % loss.item()
temp += "| smoothed loss %0.5f " % losses.avg
temp += "| min loss %0.5f (%d)" % (min_loss['data'],
min_loss['epoch'])
log["log"].append({"epoch": epoch, "loss": losses.avg})
print(epoch, temp)
losses = AverageMeter()
if epoch % eval_freq == 0:
img_prefix = os.path.join(checkpoint_dir, "%d_" % epoch)
generate_samples(model, img_prefix, dataloader.batch_size)
if epoch % save_freq == 0:
save_checkpoint(checkpoint_dir, device, model, iteration=epoch)
elif args.mode == 'eval':
if epoch * args.data_num > 10000: # eval用のデータ数は10,000
# if epoch * args.batch > 10000: # eval用のデータ数は10,000
if args.KMMD:
print('KMMD:', eval_kmmd.avg)
if args.FID:
out_path = "./outputs/" + args.resume.split('/')[2] + '/'
os.mkdir(out_path)
for i in range(1000):
visualizers.random_eval(model,
out_path,
tmp=0.3,
n=1,
truncate=True,
roop_n=i)
break
if epoch > max_epoch:
break
epoch += 1
if args.mode == 'train':
log_save_path = os.path.join(checkpoint_dir, "train-log.json")
save_json(log, log_save_path)
def train_one_epoch(dataloader,
model,
criterion,
optimizer,
accuracy=accuracy,
device=None,
print_freq=100,
random_seed=None):
if random_seed is not None:
#be careful to use this!
#it's okay to fix seed every time we call evaluate() because we want to have exactly same order of test images
#HOWEVER, for training time, we want to have different orders of training images for each epoch.
#to do this, we can set the seed as epoch, for example.
utils.make_deterministic(random_seed)
since = time.time()
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.train() # Set model to training mode
losses = AverageMeter()
accs = AverageMeter()
suprevised_baseline = False
if hasattr(dataloader.batch_sampler, "n_way"):
nway = dataloader.batch_sampler.n_way
nshot = dataloader.batch_sampler.n_shot
nquery = dataloader.batch_sampler.n_query
else:
suprevised_baseline = True
for i, data in enumerate(tqdm(dataloader)):
inputs = data["input"].to(device)
labels = data["label"].to(device)
if suprevised_baseline:
#this is a baseline without meta-learning
inputs = model.embed_samples(inputs)
outputs = model.classifier(inputs)
query_labels = labels
else:
inputs_generated = None
if model.mixer is not None:
inputs_generated = data["generated"].to(device)
print_final_nshot = False
if i == 0:
print_final_nshot = True
outputs, query_labels = model(inputs,
labels,
nway,
nshot,
nquery,
inputs_generated=inputs_generated,
print_final_nshot=print_final_nshot)
loss = criterion(outputs, query_labels)
acc = accuracy(outputs, query_labels)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure acc and record loss
losses.update(loss.item(), outputs.size(0))
accs.update(acc.item(), outputs.size(0))
if i % print_freq == 0 or i == len(dataloader) - 1:
temp = "current loss: %0.5f " % loss.item()
temp += "acc %0.5f " % acc.item()
temp += "| running average loss %0.5f " % losses.avg
temp += "acc %0.5f " % accs.avg
print(i, temp)
time_elapsed = time.time() - since
print('this epoch took {:.0f}m {:.0f}s'.format(time_elapsed // 60,
time_elapsed % 60))
return float(losses.avg), float(accs.avg)