def validate(self, net, samples, e):
transforms = generator.TransformationsGenerator([])
dataset = datasets.ImageDataset(samples, settings.train, transforms)
dataloader = DataLoader(
dataset,
num_workers=10,
batch_size=16
)
average_meter_val = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=True, ascii=True) as pbar, torch.no_grad():
net.eval()
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = self.predict(net, images)
self.update_pbar(
masks_predictions,
masks_targets,
pbar,
average_meter_val,
'Validation epoch {}'.format(e)
)
val_stats = {'val_' + k: v for k, v in average_meter_val.get_all().items()}
return val_stats
def test(self, samples_test, dir_test=settings.test, predict=None):
if predict is None:
predict = self.predict
net = DataParallel(self.net).cuda()
transforms = generator.TransformationsGenerator([])
test_dataset = datasets.ImageDataset(samples_test,
dir_test,
transforms,
test=True)
test_dataloader = DataLoader(test_dataset,
num_workers=10,
batch_size=32)
with tqdm(total=len(test_dataloader), leave=True,
ascii=True) as pbar, torch.no_grad():
net.eval()
for images, ids in test_dataloader:
masks_predictions = predict(net, images)
pbar.set_description('Creating test predictions...')
pbar.update()
masks_predictions = masks_predictions.cpu().squeeze().numpy()
for p, id in zip(masks_predictions, ids):
yield p, id
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(
image_size=101,
translation=lambda rs: (rs.randint(-10, 10), rs.randint(-10, 10)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
rotation=lambda rs: rs.randint(-5, 5),
**utils.transformations_options
),
transformations.Padding(((13, 14), (13, 14), (0, 0)))
])
transforms_image = generator.TransformationsGenerator([
random.RandomColorPerturbation(std=1)
])
dataset = datasets.ImageDataset(samples, settings.train, transforms, transforms_image=transforms_image)
dataloader = DataLoader(
dataset,
num_workers=10,
batch_size=16,
shuffle=True
)
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False) as pbar, torch.enable_grad():
net.train()
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions, aux_pam, aux_cam = net(images)
loss_pam = criterion(F.interpolate(aux_pam, size=128, mode='bilinear'), masks_targets)
loss_cam = criterion(F.interpolate(aux_cam, size=128, mode='bilinear'), masks_targets)
loss_segmentation = criterion(masks_predictions, masks_targets)
loss = loss_segmentation + loss_pam + loss_cam
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(
torch.sigmoid(masks_predictions),
masks_targets,
pbar,
average_meter_train,
'Training epoch {}'.format(e)
)
train_stats = {'train_' + k: v for k, v in average_meter_train.get_all().items()}
return train_stats
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((100 - e) / 100))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(image_size=101,
translation=lambda rs:
(rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options),
transformations.Padding(((13, 14), (13, 14), (0, 0)))
])
pseudo_dataset = datasets.SemiSupervisedImageDataset(
samples_test,
settings.test,
transforms,
size=len(samples_test),
test_predictions=self.test_predictions,
momentum=0.0)
dataset = datasets.ImageDataset(samples, settings.train, transforms)
weights = [len(pseudo_dataset) / len(dataset) * 2
] * len(dataset) + [1] * len(pseudo_dataset)
dataloader = DataLoader(ConcatDataset([dataset, pseudo_dataset]),
num_workers=10,
batch_size=16,
sampler=WeightedRandomSampler(
weights=weights, num_samples=3200))
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader),
leave=False) as pbar, torch.enable_grad():
net.train()
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = net(images)
loss = criterion(masks_predictions, masks_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(torch.sigmoid(masks_predictions),
masks_targets, pbar, average_meter_train,
'Training epoch {}'.format(e))
train_stats = {
'train_' + k: v
for k, v in average_meter_train.get_all().items()
}
return train_stats
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(
image_size=101,
translation=lambda rs: (rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options
)
])
dataset = datasets.ImageDataset(samples, settings.train, transforms)
dataloader = DataLoader(
dataset,
num_workers=10,
batch_size=16,
shuffle=True
)
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False, ascii=True) as pbar, torch.enable_grad():
net.train()
padding = tta.Pad((13, 14, 13, 14))
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = padding.transform_backward(net(padding.transform_forward(images))).contiguous()
loss = criterion(masks_predictions, masks_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(
torch.sigmoid(masks_predictions),
masks_targets,
pbar,
average_meter_train,
'Training epoch {}'.format(e)
)
train_stats = {'train_' + k: v for k, v in average_meter_train.get_all().items()}
return train_stats
def validate(args, fixed_z, gen_net: nn.Module, writer_dict):
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
gen_net = gen_net.eval()
global_steps = writer_dict['valid_global_steps']
eval_iter = args.num_eval_imgs // args.eval_batch_size
fid_buffer_dir = os.path.join(args.path_helper['sample_path'],
'fid_buffer')
os.makedirs(fid_buffer_dir)
img_list = list()
for iter_idx in tqdm(range(eval_iter), desc='sample images'):
z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (args.eval_batch_size, args.latent_dim)))
# Generate a batch of images
gen_imgs = gen_net(z).mul_(127.5).add_(127.5).clamp_(
0.0, 255.0).permute(0, 2, 3, 1).to('cpu', torch.uint8).numpy()
for img_idx, img in enumerate(gen_imgs):
file_name = os.path.join(fid_buffer_dir,
f'iter{iter_idx}_b{img_idx}.png')
imsave(file_name, img)
img_list.extend(list(gen_imgs))
# compute IS
inception_score, std = get_inception_score(img_list)
print('------------------------ Inception Score ------------------------')
print(inception_score)
print('------------------------ FID pytorch ------------------------')
print(fid_score)
# Generate a batch of images
sample_dir = os.path.join(args.path_helper['sample_path'], 'sample_dir')
Path(sample_dir).mkdir(exist_ok=True)
sample_imgs = gen_net(fixed_z).mul_(127.5).add_(127.5).clamp_(0.0, 255.0)
img_grid = make_grid(sample_imgs, nrow=5).to('cpu', torch.uint8).numpy()
file_name = os.path.join(
sample_dir, f'final_fid_{fid}_inception_score{inception_score}.png')
imsave(file_name, img_grid.swapaxes(0, 1).swapaxes(1, 2))
writer_dict['valid_global_steps'] = global_steps + 1
return inception_score, fid
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception(MODEL_DIR)
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(
args, weights_init)
# set grow controller
grow_ctrler = GrowCtrler(args.grow_step1, args.grow_step2)
# initial
start_search_iter = 0
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model',
'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file,
map_location={'cuda:0': 'cpu'})
# set controller && its optimizer
cur_stage = checkpoint['cur_stage']
controller, ctrl_optimizer = create_ctrler(args, cur_stage,
weights_init)
start_search_iter = checkpoint['search_iter']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
controller.load_state_dict(checkpoint['ctrl_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
ctrl_optimizer.load_state_dict(checkpoint['ctrl_optimizer'])
prev_archs = checkpoint['prev_archs']
prev_hiddens = checkpoint['prev_hiddens']
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(
f'=> loaded checkpoint {checkpoint_file} (search iteration {start_search_iter})'
)
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
prev_archs = None
prev_hiddens = None
# set controller && its optimizer
cur_stage = 0
controller, ctrl_optimizer = create_ctrler(args, cur_stage,
weights_init)
# set up data_loader
dataset = datasets.ImageDataset(args, 2**(cur_stage + 3),
args.dis_batch_size, args.num_workers)
train_loader = dataset.train
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'controller_steps': start_search_iter * args.ctrl_step
}
g_loss_history = RunningStats(args.dynamic_reset_window)
d_loss_history = RunningStats(args.dynamic_reset_window)
# train loop
for search_iter in tqdm(range(int(start_search_iter),
int(args.max_search_iter)),
desc='search progress'):
logger.info(f"<start search iteration {search_iter}>")
if search_iter == args.grow_step1 or search_iter == args.grow_step2:
# save
cur_stage = grow_ctrler.cur_stage(search_iter)
logger.info(f'=> grow to stage {cur_stage}')
prev_archs, prev_hiddens = get_topk_arch_hidden(
args, controller, gen_net, prev_archs, prev_hiddens)
# grow section
del controller
del ctrl_optimizer
controller, ctrl_optimizer = create_ctrler(args, cur_stage,
weights_init)
dataset = datasets.ImageDataset(args, 2**(cur_stage + 3),
args.dis_batch_size,
args.num_workers)
train_loader = dataset.train
dynamic_reset = train_shared(args,
gen_net,
dis_net,
g_loss_history,
d_loss_history,
controller,
gen_optimizer,
dis_optimizer,
train_loader,
prev_hiddens=prev_hiddens,
prev_archs=prev_archs)
train_controller(args, controller, ctrl_optimizer, gen_net,
prev_hiddens, prev_archs, writer_dict)
if dynamic_reset:
logger.info('re-initialize share GAN')
del gen_net, dis_net, gen_optimizer, dis_optimizer
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(
args, weights_init)
save_checkpoint(
{
'cur_stage': cur_stage,
'search_iter': search_iter + 1,
'gen_model': args.gen_model,
'dis_model': args.dis_model,
'controller': args.controller,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'ctrl_state_dict': controller.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'ctrl_optimizer': ctrl_optimizer.state_dict(),
'prev_archs': prev_archs,
'prev_hiddens': prev_hiddens,
'path_helper': args.path_helper
}, False, args.path_helper['ckpt_path'])
final_archs, _ = get_topk_arch_hidden(args, controller, gen_net,
prev_archs, prev_hiddens)
logger.info(f"discovered archs: {final_archs}")
json.dump(pred, f)
if __name__ == "__main__":
args = get_args()
if args.log_time_and_gpu:
gpu_log_interval = 10 # every k seconds
start_time = time.time()
gpu_check_thread = threading.Thread(
target=log_gpu_util,
args=[gpu_log_interval, (args.gpuid_start, args.gpu)])
gpu_check_thread.daemon = True
gpu_check_thread.start()
img_dataset = datasets.ImageDataset(args, "test", args.img_lst)
dataset_loader = enqueuer_thread.DatasetEnqueuer(
img_dataset,
prefetch=args.prefetch,
shuffle=False,
is_multi_gpu=False,
last_full_batch=True,
start=True,
num_workers=args.num_cpu_worker)
get_batches = dataset_loader.get() # iterator to get batch
if args.out_dir is not None:
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
# 1. load the object detection model
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# import network
gen_net = eval('models.' + args.model + '.Generator')(args=args).cuda()
dis_net = eval('models.' + args.model + '.Discriminator')(args=args).cuda()
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0,
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0,
args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
else:
raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic /
len(train_loader))
# initial
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (25, args.latent_dim)))
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model',
'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
avg_gen_net = deepcopy(gen_net)
avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
gen_avg_param = copy_params(avg_gen_net)
del avg_gen_net
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(
f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)),
desc='total progress'):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer,
gen_avg_param, train_loader, epoch, writer_dict, lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == int(
args.max_epoch) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat,
gen_net, writer_dict)
logger.info(
f'Inception score: {inception_score}, FID score: {fid_score} || @ epoch {epoch}.'
)
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net = deepcopy(gen_net)
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
del avg_gen_net
def main():
args = cfg.parse_args()
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
np.random.seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# import netwo
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net = eval('models.' + args.model + '.Generator')(args=args).cuda()
dis_net = eval('models.' + args.model + '.Discriminator')(args=args).cuda()
gen_net.apply(weights_init)
dis_net.apply(weights_init)
avg_gen_net = deepcopy(gen_net)
initial_gen_net_weight = torch.load(os.path.join(args.init_path,
'initial_gen_net.pth'),
map_location="cpu")
initial_dis_net_weight = torch.load(os.path.join(args.init_path,
'initial_dis_net.pth'),
map_location="cpu")
assert id(initial_dis_net_weight) != id(dis_net.state_dict())
assert id(initial_gen_net_weight) != id(gen_net.state_dict())
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0,
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0,
args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/fid_stats_stl10_train.npz'
else:
raise NotImplementedError('no fid stat for %s' % args.dataset.lower())
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic /
len(train_loader))
# initial
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (25, args.latent_dim)))
start_epoch = 0
best_fid = 1e4
print('=> resuming from %s' % args.load_path)
assert os.path.exists(args.load_path)
checkpoint_file = args.load_path
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
pruning_generate(gen_net, checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
total = 0
total_nonzero = 0
for m in dis_net.modules():
if isinstance(m, nn.Conv2d):
total += m.weight_orig.data.numel()
mask = m.weight_orig.data.abs().clone().gt(0).float().cuda()
total_nonzero += torch.sum(mask)
conv_weights = torch.zeros(total)
index = 0
for m in dis_net.modules():
if isinstance(m, nn.Conv2d):
size = m.weight_orig.data.numel()
conv_weights[index:(
index + size)] = m.weight_orig.data.view(-1).abs().clone()
index += size
y, i = torch.sort(conv_weights)
# thre_index = int(total * args.percent)
# only care about the non zero weights
# e.g: total = 100, total_nonzero = 80, percent = 0.2, thre_index = 36, that means keep 64
thre_index = total - total_nonzero
thre = y[int(thre_index)]
pruned = 0
print('Pruning threshold: {}'.format(thre))
zero_flag = False
masks = OrderedDict()
for k, m in enumerate(dis_net.modules()):
if isinstance(m, nn.Conv2d):
weight_copy = m.weight_orig.data.abs().clone()
mask = weight_copy.gt(thre).float()
masks[k] = mask
pruned = pruned + mask.numel() - torch.sum(mask)
m.weight_orig.data.mul_(mask)
if int(torch.sum(mask)) == 0:
zero_flag = True
print(
'layer index: {:d} \t total params: {:d} \t remaining params: {:d}'
.format(k, mask.numel(), int(torch.sum(mask))))
print('Total conv params: {}, Pruned conv params: {}, Pruned ratio: {}'.
format(total, pruned, pruned / total))
pruning_generate(avg_gen_net, checkpoint['gen_state_dict'])
see_remain_rate(gen_net)
if not args.finetune_G:
gen_weight = gen_net.state_dict()
gen_orig_weight = rewind_weight(initial_gen_net_weight,
gen_weight.keys())
gen_weight.update(gen_orig_weight)
gen_net.load_state_dict(gen_weight)
gen_avg_param = copy_params(gen_net)
if args.finetune_D:
dis_net.load_state_dict(checkpoint['dis_state_dict'])
else:
dis_net.load_state_dict(initial_dis_net_weight)
for k, m in enumerate(dis_net.modules()):
if isinstance(m, nn.Conv2d):
m.weight_orig.data.mul_(masks[k])
orig_dis_net = eval('models.' + args.model +
'.Discriminator')(args=args).cuda()
orig_dis_net.load_state_dict(checkpoint['dis_state_dict'])
orig_dis_net.eval()
args.path_helper = set_log_dir('logs',
args.exp_name + "_{}".format(args.percent))
logger = create_logger(args.path_helper['log_path'])
#logger.info('=> loaded checkpoint %s (epoch %d)' % (checkpoint_file, start_epoch))
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)),
desc='total progress'):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
see_remain_rate(gen_net)
see_remain_rate_orig(dis_net)
if not args.use_kd_D:
train_with_mask(args, gen_net, dis_net, gen_optimizer,
dis_optimizer, gen_avg_param, train_loader, epoch,
writer_dict, masks, lr_schedulers)
else:
train_with_mask_kd(args, gen_net, dis_net, orig_dis_net,
gen_optimizer, dis_optimizer, gen_avg_param,
train_loader, epoch, writer_dict, masks,
lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == int(
args.max_epoch) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat,
gen_net, writer_dict, epoch)
logger.info(
'Inception score: %.4f, FID score: %.4f || @ epoch %d.' %
(inception_score, fid_score, epoch))
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net.load_state_dict(gen_net.state_dict())
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(
image_size=101,
translation=lambda rs: (rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options
),
transformations.Padding(((13, 14), (13, 14), (0, 0)))
])
samples_aux = list(set(samples).intersection(set(utils.get_aux_samples())))
dataset_aux = datasets.ImageDataset(samples_aux, settings.train, transforms)
transforms_mosaic = generator.TransformationsGenerator([
random.RandomCrop(128)
])
pairs_mosaic = utils.get_mosaic_pairs()
samples_mosaic = utils.get_mosaic_samples()
samples_mosaic = [sample for sample, pair in zip(samples_mosaic, pairs_mosaic) if pair[0] in samples and pair[1] in samples]
dataset_mosaic = datasets.ImageDataset(samples_mosaic, './data/mosaic_pairs', transforms_mosaic)
dataset_pseudo = datasets.SemiSupervisedImageDataset(
samples_test,
settings.test,
transforms,
size=len(samples_test),
test_predictions=self.test_predictions,
momentum=0.0
)
dataset = datasets.ImageDataset(samples, settings.train, transforms)
weight_train = len(dataset_pseudo) / len(dataset) * 2
weight_mosaic = weight_train
weight_aux = weight_train / 2
weights = [weight_train] * len(dataset) + [weight_mosaic] * len(dataset_mosaic) + [weight_aux] * len(dataset_aux) + [1] * len(dataset_pseudo)
dataloader = DataLoader(
ConcatDataset([dataset, dataset_aux, dataset_mosaic, dataset_pseudo]),
num_workers=10,
batch_size=16,
sampler=WeightedRandomSampler(weights=weights, num_samples=3200)
)
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False, ascii=True) as pbar, torch.enable_grad():
net.train()
padding = tta.Pad((13, 14, 13, 14))
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_targets = padding.transform_backward(masks_targets).contiguous()
masks_predictions = padding.transform_backward(net(images)).contiguous()
loss = criterion(masks_predictions, masks_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(
torch.sigmoid(masks_predictions),
masks_targets,
pbar,
average_meter_train,
'Training epoch {}'.format(e)
)
train_stats = {'train_' + k: v for k, v in average_meter_train.get_all().items()}
return train_stats
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv2d") != -1:
if args.init_type == "normal":
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == "orth":
nn.init.orthogonal_(m.weight.data)
elif args.init_type == "xavier_uniform":
nn.init.xavier_uniform(m.weight.data, 1.0)
else:
raise NotImplementedError("{} unknown inital type".format(
args.init_type))
elif classname.find("BatchNorm2d") != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(
args, weights_init)
# set grow controller
grow_ctrler = GrowCtrler(args.grow_step1, args.grow_step2)
# initial
start_search_iter = 0
# set writer
if args.load_path:
print(f"=> resuming from {args.load_path}")
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, "Model",
"checkpoint.pth")
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
# set controller && its optimizer
cur_stage = checkpoint["cur_stage"]
controller, ctrl_optimizer = create_ctrler(args, cur_stage,
weights_init)
start_search_iter = checkpoint["search_iter"]
gen_net.load_state_dict(checkpoint["gen_state_dict"])
dis_net.load_state_dict(checkpoint["dis_state_dict"])
controller.load_state_dict(checkpoint["ctrl_state_dict"])
gen_optimizer.load_state_dict(checkpoint["gen_optimizer"])
dis_optimizer.load_state_dict(checkpoint["dis_optimizer"])
ctrl_optimizer.load_state_dict(checkpoint["ctrl_optimizer"])
prev_archs = checkpoint["prev_archs"]
prev_hiddens = checkpoint["prev_hiddens"]
args.path_helper = checkpoint["path_helper"]
logger = create_logger(args.path_helper["log_path"])
logger.info(
f"=> loaded checkpoint {checkpoint_file} (search iteration {start_search_iter})"
)
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir("logs", args.exp_name)
logger = create_logger(args.path_helper["log_path"])
prev_archs = None
prev_hiddens = None
# set controller && its optimizer
cur_stage = 0
controller, ctrl_optimizer = create_ctrler(args, cur_stage,
weights_init)
# set up data_loader
dataset = datasets.ImageDataset(args, 2**(cur_stage + 3))
train_loader = dataset.train
logger.info(args)
writer_dict = {
"writer": SummaryWriter(args.path_helper["log_path"]),
"controller_steps": start_search_iter * args.ctrl_step,
}
g_loss_history = RunningStats(args.dynamic_reset_window)
d_loss_history = RunningStats(args.dynamic_reset_window)
# train loop
for search_iter in tqdm(range(int(start_search_iter),
int(args.max_search_iter)),
desc="search progress"):
logger.info(f"<start search iteration {search_iter}>")
if search_iter == args.grow_step1 or search_iter == args.grow_step2:
# save
cur_stage = grow_ctrler.cur_stage(search_iter)
logger.info(f"=> grow to stage {cur_stage}")
prev_archs, prev_hiddens = get_topk_arch_hidden(
args, controller, gen_net, prev_archs, prev_hiddens)
# grow section
del controller
del ctrl_optimizer
controller, ctrl_optimizer = create_ctrler(args, cur_stage,
weights_init)
dataset = datasets.ImageDataset(args, 2**(cur_stage + 3))
train_loader = dataset.train
dynamic_reset = train_shared(
args,
gen_net,
dis_net,
g_loss_history,
d_loss_history,
controller,
gen_optimizer,
dis_optimizer,
train_loader,
prev_hiddens=prev_hiddens,
prev_archs=prev_archs,
)
train_controller(
args,
controller,
ctrl_optimizer,
gen_net,
prev_hiddens,
prev_archs,
writer_dict,
)
if dynamic_reset:
logger.info("re-initialize share GAN")
del gen_net, dis_net, gen_optimizer, dis_optimizer
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(
args, weights_init)
save_checkpoint(
{
"cur_stage": cur_stage,
"search_iter": search_iter + 1,
"gen_model": args.gen_model,
"dis_model": args.dis_model,
"controller": args.controller,
"gen_state_dict": gen_net.state_dict(),
"dis_state_dict": dis_net.state_dict(),
"ctrl_state_dict": controller.state_dict(),
"gen_optimizer": gen_optimizer.state_dict(),
"dis_optimizer": dis_optimizer.state_dict(),
"ctrl_optimizer": ctrl_optimizer.state_dict(),
"prev_archs": prev_archs,
"prev_hiddens": prev_hiddens,
"path_helper": args.path_helper,
},
False,
args.path_helper["ckpt_path"],
)
final_archs, _ = get_topk_arch_hidden(args, controller, gen_net,
prev_archs, prev_hiddens)
logger.info(f"discovered archs: {final_archs}")
def main(index, args):
device = xm.xla_device()
gen_net = Generator(args).to(device)
dis_net = Discriminator(args).to(device)
enc_net = Encoder(args).to(device)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
enc_net.apply(weights_init)
ae_recon_optimizer = torch.optim.Adam(
itertools.chain(enc_net.parameters(), gen_net.parameters()),
args.ae_recon_lr, (args.beta1, args.beta2))
ae_reg_optimizer = torch.optim.Adam(
itertools.chain(enc_net.parameters(), gen_net.parameters()),
args.ae_reg_lr, (args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(dis_net.parameters(), args.d_lr,
(args.beta1, args.beta2))
gen_optimizer = torch.optim.Adam(gen_net.parameters(), args.g_lr,
(args.beta1, args.beta2))
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
valid_loader = dataset.valid
para_loader = pl.ParallelLoader(train_loader, [device])
fid_stat = str(pathlib.Path(
__file__).parent.absolute()) + '/fid_stat/fid_stat_cifar10_test.npz'
if not os.path.exists(fid_stat):
download_stat_cifar10_test()
is_best = True
args.num_epochs = np.ceil(args.num_iter / len(train_loader))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0,
args.num_iter / 2, args.num_iter)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0,
args.num_iter / 2, args.num_iter)
ae_recon_scheduler = LinearLrDecay(ae_recon_optimizer, args.ae_recon_lr, 0,
args.num_iter / 2, args.num_iter)
ae_reg_scheduler = LinearLrDecay(ae_reg_optimizer, args.ae_reg_lr, 0,
args.num_iter / 2, args.num_iter)
# initial
start_epoch = 0
best_fid = 1e4
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model',
'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
enc_net.load_state_dict(checkpoint['enc_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
ae_recon_optimizer.load_state_dict(checkpoint['ae_recon_optimizer'])
ae_reg_optimizer.load_state_dict(checkpoint['ae_reg_optimizer'])
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(
f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
else:
# create new log dir
assert args.exp_name
logs_dir = str(pathlib.Path(__file__).parent.parent) + '/logs'
args.path_helper = set_log_dir(logs_dir, args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.num_epochs)),
desc='total progress'):
lr_schedulers = (gen_scheduler, dis_scheduler, ae_recon_scheduler,
ae_reg_scheduler)
train(device, args, gen_net, dis_net, enc_net, gen_optimizer,
dis_optimizer, ae_recon_optimizer, ae_reg_optimizer, para_loader,
epoch, writer_dict, lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == args.num_epochs - 1:
fid_score = validate(args, fid_stat, gen_net, writer_dict,
valid_loader)
logger.info(f'FID score: {fid_score} || @ epoch {epoch}.')
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
save_checkpoint(
{
'epoch': epoch + 1,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'enc_state_dict': enc_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'ae_recon_optimizer': ae_recon_optimizer.state_dict(),
'ae_reg_optimizer': ae_reg_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# import network
gen_net = eval("models_search." + args.gen_model +
".Generator")(args=args).cuda()
dis_net = eval("models_search." + args.dis_model +
".Discriminator")(args=args).cuda()
gen_net.set_arch(args.arch, cur_stage=2)
dis_net.cur_stage = 2
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv2d") != -1:
if args.init_type == "normal":
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == "orth":
nn.init.orthogonal_(m.weight.data)
elif args.init_type == "xavier_uniform":
nn.init.xavier_uniform(m.weight.data, 1.0)
else:
raise NotImplementedError("{} unknown inital type".format(
args.init_type))
elif classname.find("BatchNorm2d") != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()),
args.g_lr,
(args.beta1, args.beta2),
)
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()),
args.d_lr,
(args.beta1, args.beta2),
)
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0,
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0,
args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == "cifar10":
fid_stat = "fid_stat/fid_stats_cifar10_train.npz"
elif args.dataset.lower() == "stl10":
fid_stat = "fid_stat/stl10_train_unlabeled_fid_stats_48.npz"
else:
raise NotImplementedError(f"no fid stat for {args.dataset.lower()}")
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic /
len(train_loader))
# initial
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (25, args.latent_dim)))
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
# set writer
if args.load_path:
print(f"=> resuming from {args.load_path}")
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, "Model",
"checkpoint.pth")
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint["epoch"]
best_fid = checkpoint["best_fid"]
gen_net.load_state_dict(checkpoint["gen_state_dict"])
dis_net.load_state_dict(checkpoint["dis_state_dict"])
gen_optimizer.load_state_dict(checkpoint["gen_optimizer"])
dis_optimizer.load_state_dict(checkpoint["dis_optimizer"])
avg_gen_net = deepcopy(gen_net)
avg_gen_net.load_state_dict(checkpoint["avg_gen_state_dict"])
gen_avg_param = copy_params(avg_gen_net)
del avg_gen_net
args.path_helper = checkpoint["path_helper"]
logger = create_logger(args.path_helper["log_path"])
logger.info(
f"=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})")
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir("logs", args.exp_name)
logger = create_logger(args.path_helper["log_path"])
logger.info(args)
writer_dict = {
"writer": SummaryWriter(args.path_helper["log_path"]),
"train_global_steps": start_epoch * len(train_loader),
"valid_global_steps": start_epoch // args.val_freq,
}
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)),
desc="total progress"):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
train(
args,
gen_net,
dis_net,
gen_optimizer,
dis_optimizer,
gen_avg_param,
train_loader,
epoch,
writer_dict,
lr_schedulers,
)
if epoch and epoch % args.val_freq == 0 or epoch == int(
args.max_epoch) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat,
gen_net, writer_dict)
logger.info(
f"Inception score: {inception_score}, FID score: {fid_score} || @ epoch {epoch}."
)
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net = deepcopy(gen_net)
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
"epoch": epoch + 1,
"gen_model": args.gen_model,
"dis_model": args.dis_model,
"gen_state_dict": gen_net.state_dict(),
"dis_state_dict": dis_net.state_dict(),
"avg_gen_state_dict": avg_gen_net.state_dict(),
"gen_optimizer": gen_optimizer.state_dict(),
"dis_optimizer": dis_optimizer.state_dict(),
"best_fid": best_fid,
"path_helper": args.path_helper,
},
is_best,
args.path_helper["ckpt_path"],
)
del avg_gen_net
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set visible GPU ids
if len(args.gpu_ids) > 0:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_ids
# set TensorFlow environment for evaluation (calculate IS and FID)
_init_inception()
inception_path = check_or_download_inception('./tmp/imagenet/')
create_inception_graph(inception_path)
# the first GPU in visible GPUs is dedicated for evaluation (running Inception model)
str_ids = args.gpu_ids.split(',')
args.gpu_ids = []
for id in range(len(str_ids)):
if id >= 0:
args.gpu_ids.append(id)
if len(args.gpu_ids) > 1:
args.gpu_ids = args.gpu_ids[1:]
else:
args.gpu_ids = args.gpu_ids
# genotype G
genotypes_root = os.path.join('exps', args.genotypes_exp, 'Genotypes')
genotype_G = np.load(os.path.join(genotypes_root, 'latest_G.npy'))
# import network from genotype
basemodel_gen = eval('archs.' + args.arch + '.Generator')(args, genotype_G)
gen_net = torch.nn.DataParallel(
basemodel_gen, device_ids=args.gpu_ids).cuda(args.gpu_ids[0])
basemodel_dis = eval('archs.' + args.arch + '.Discriminator')(args)
dis_net = torch.nn.DataParallel(
basemodel_dis, device_ids=args.gpu_ids).cuda(args.gpu_ids[0])
# basemodel_gen = eval('archs.' + args.arch + '.Generator')(args=args)
# gen_net = torch.nn.DataParallel(basemodel_gen, device_ids=args.gpu_ids).cuda(args.gpu_ids[0])
# basemodel_dis = eval('archs.' + args.arch + '.Discriminator')(args=args)
# dis_net = torch.nn.DataParallel(basemodel_dis, device_ids=args.gpu_ids).cuda(args.gpu_ids[0])
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# epoch number for dis_net
args.max_epoch_D = args.max_epoch_G * args.n_critic
if args.max_iter_G:
args.max_epoch_D = np.ceil(args.max_iter_G * args.n_critic /
len(train_loader))
max_iter_D = args.max_epoch_D * len(train_loader)
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0, max_iter_D)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, max_iter_D)
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
else:
raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
assert os.path.exists(fid_stat)
# initial
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
# set writer
if args.checkpoint:
# resuming
print(f'=> resuming from {args.checkpoint}')
assert os.path.exists(os.path.join('exps', args.checkpoint))
checkpoint_file = os.path.join('exps', args.checkpoint, 'Model',
'checkpoint_best.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
avg_gen_net = deepcopy(gen_net)
avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
gen_avg_param = copy_params(avg_gen_net)
del avg_gen_net
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(
f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('exps', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# model size
logger.info('Param size of G = %fMB', count_parameters_in_MB(gen_net))
logger.info('Param size of D = %fMB', count_parameters_in_MB(dis_net))
print_FLOPs(basemodel_gen, (1, args.latent_dim), logger)
print_FLOPs(basemodel_dis, (1, 3, args.img_size, args.img_size), logger)
# for visualization
if args.draw_arch:
from utils.genotype import draw_graph_G
draw_graph_G(genotype_G,
save=True,
file_path=os.path.join(args.path_helper['graph_vis_path'],
'latest_G'))
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (100, args.latent_dim)))
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch_D)),
desc='total progress'):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer,
gen_avg_param, train_loader, epoch, writer_dict, lr_schedulers)
if epoch % args.val_freq == 0 or epoch == int(args.max_epoch_D) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, std, fid_score = validate(args, fixed_z, fid_stat,
gen_net, writer_dict)
logger.info(
f'Inception score mean: {inception_score}, Inception score std: {std}, '
f'FID score: {fid_score} || @ epoch {epoch}.')
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
# save model
avg_gen_net = deepcopy(gen_net)
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.arch,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
del avg_gen_net
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
print(args)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(
args, weights_init)
# initial
start_search_iter = 0
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model',
'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
cur_stage = checkpoint['cur_stage']
start_search_iter = checkpoint['search_iter']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
prev_archs = checkpoint['prev_archs']
prev_hiddens = checkpoint['prev_hiddens']
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(
f'=> loaded checkpoint {checkpoint_file} (search iteration {start_search_iter})'
)
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
prev_archs = None
prev_hiddens = None
# set controller && its optimizer
cur_stage = 0
# set up data_loader
dataset = datasets.ImageDataset(args, 2**(cur_stage + 3))
train_loader = dataset.train
print(args.rl_num_eval_img, "##############################")
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'controller_steps': start_search_iter * args.ctrl_step
}
g_loss_history = RunningStats(args.dynamic_reset_window)
d_loss_history = RunningStats(args.dynamic_reset_window)
# train loop
Agent = SAC(131)
print(Agent.alpha)
memory = ReplayMemory(2560000)
updates = 0
outinfo = {
'rewards': [],
'a_loss': [],
'critic_error': [],
}
Best = False
Z_NUMPY = None
WARMUP = True
update_time = 1
for search_iter in tqdm(range(int(start_search_iter), 100),
desc='search progress'):
logger.info(f"<start search iteration {search_iter}>")
if search_iter >= 1:
WARMUP = False
### Define number of layers, currently only support 1->3
total_layer_num = 3
### Different image size for different layers
ds = [
datasets.ImageDataset(args, 2**(k + 3))
for k in range(total_layer_num)
]
train_loaders = [d.train for d in ds]
last_R = 0. # Initial reward
last_fid = 10000 # Inital reward
last_arch = []
# Set exploration
if search_iter > 69:
update_time = 10
Best = True
else:
Best = False
gen_net.set_stage(-1)
last_R, last_fid, last_state = get_is(args,
gen_net,
args.rl_num_eval_img,
get_is_score=True)
for layer in range(total_layer_num):
cur_stage = layer # This defines which layer to use as output, for example, if cur_stage==0, then the output will be the first layer output. Set it to 2 if you want the output of the last layer.
action = Agent.select_action([layer, last_R, 0.01 * last_fid] +
last_state, Best)
arch = [
action[0][0], action[0][1], action[1][0], action[1][1],
action[1][2], action[2][0], action[2][1], action[2][2],
action[3][0], action[3][1], action[4][0], action[4][1],
action[5][0], action[5][1]
]
# print(arch)
# argmax to get int description of arch
cur_arch = [np.argmax(k) for k in action]
# Pad the skip option 0=False (for only layer 1 and layer2, not layer0, see builing_blocks.py for why)
if layer == 0:
cur_arch = cur_arch[0:4]
elif layer == 1:
cur_arch = cur_arch[0:5]
elif layer == 2:
if cur_arch[4] + cur_arch[5] == 2:
cur_arch = cur_arch[0:4] + [3]
elif cur_arch[4] + cur_arch[5] == 0:
cur_arch = cur_arch[0:4] + [0]
elif cur_arch[4] == 1 and cur_arch[5] == 0:
cur_arch = cur_arch[0:4] + [1]
else:
cur_arch = cur_arch[0:4] + [2]
# Get the network arch with the new architecture attached.
last_arch += cur_arch
gen_net.set_arch(last_arch,
layer) # Set the network, given cur_stage
# Train network
dynamic_reset = train_qin(args,
gen_net,
dis_net,
g_loss_history,
d_loss_history,
gen_optimizer,
dis_optimizer,
train_loaders[layer],
cur_stage,
smooth=False,
WARMUP=WARMUP)
# Get reward, use the jth layer output for generation. (layer 0:j), and the proposed progressive state
R, fid, state = get_is(args,
gen_net,
args.rl_num_eval_img,
z_numpy=Z_NUMPY)
# Print exploitation mark, for better readability of the log.
if Best:
print("arch:", cur_arch, "Exploitation:", Best)
else:
print("arch:", cur_arch, "Exploring...")
# Proxy reward of the up-to-now (0:j) architecture.
print("update times:", updates, "step:", layer + 1, "IS:", R,
"FID:", fid)
mask = 0 if layer == total_layer_num - 1 else 1
if search_iter >= 0: # warm up
memory.push([layer, last_R, 0.01 * last_fid] + last_state,
arch, R - last_R + 0.01 * (last_fid - fid),
[layer + 1, R, 0.01 * fid] + state,
mask) # Append transition to memory
if len(memory) >= 64:
# Number of updates per step in environment
for i in range(update_time):
# Update parameters of all the networks
critic_1_loss, critic_2_loss, policy_loss, ent_loss, alpha = Agent.update_parameters(
memory, min(len(memory), 256), updates)
updates += 1
outinfo['critic_error'] = min(critic_1_loss, critic_2_loss)
outinfo['entropy'] = ent_loss
outinfo['a_loss'] = policy_loss
print("full batch", outinfo, alpha)
last_R = R # next step
last_fid = fid
last_state = state
outinfo['rewards'] = R
critic_1_loss, critic_2_loss, policy_loss, ent_loss, alpha = Agent.update_parameters(
memory, len(memory), updates)
updates += 1
outinfo['critic_error'] = min(critic_1_loss, critic_2_loss)
outinfo['entropy'] = ent_loss
outinfo['a_loss'] = policy_loss
print("full batch", outinfo, alpha)
# Clean up and start a new trajectory from scratch
del gen_net, dis_net, gen_optimizer, dis_optimizer
gen_net, dis_net, gen_optimizer, dis_optimizer = create_shared_gan(
args, weights_init)
print(outinfo, len(memory))
Agent.save_model("test")
WARMUP = False
def main():
args = cfg_train.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# import network
# gen_net = eval('models.' + args.gen_model + '.' + args.gen)(args=args).cuda()
genotype_gen = eval('genotypes.%s' % args.arch_gen)
gen_net = eval('models.' + args.gen_model + '.' + args.gen)(
args, genotype_gen).cuda()
# gen_net = eval('models.' + args.gen_model + '.' + args.gen)(args = args).cuda()
if 'Discriminator' not in args.dis:
genotype_dis = eval('genotypes.%s' % args.arch_dis)
dis_net = eval('models.' + args.dis_model + '.' + args.dis)(
args, genotype_dis).cuda()
else:
dis_net = eval('models.' + args.dis_model + '.' +
args.dis)(args=args).cuda()
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
val_loader = dataset.valid
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, args.g_lr * 0.01,
260 * len(train_loader),
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, args.d_lr * 0.01,
260 * len(train_loader),
args.max_iter * args.n_critic)
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
elif args.dataset.lower() == 'mnist':
fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
else:
raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic /
len(train_loader))
# initial
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (25, args.latent_dim)))
fixed_z_sample = torch.cuda.FloatTensor(
np.random.normal(0, 1, (args.eval_batch_size, args.latent_dim)))
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
best_fid_epoch = 0
is_with_fid = 0
std_with_fid = 0.
best_is = 0
best_is_epoch = 0
fid_with_is = 0
best_dts = 0
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model',
'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
avg_gen_net = deepcopy(gen_net)
avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
gen_avg_param = copy_params(avg_gen_net)
del avg_gen_net
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(
f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# calculate the FLOPs and param count of G
input = torch.randn(args.gen_batch_size, args.latent_dim).cuda()
flops, params = profile(gen_net, inputs=(input, ))
flops, params = clever_format([flops, params], "%.3f")
logger.info('FLOPs is {}, param count is {}'.format(flops, params))
# train loop
dg_list = []
worst_lr = 1e-5
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)),
desc='total progress'):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer,
gen_avg_param, train_loader, epoch, writer_dict, args.consistent,
lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == int(
args.max_epoch) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, std, fid_score = validate(args,
fixed_z,
fid_stat,
gen_net,
writer_dict,
args.path_helper,
search=False)
logger.info(
f'Inception score: {inception_score}, FID score: {fid_score}+-{std} || @ epoch {epoch}.'
)
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
best_fid_epoch = epoch
is_with_fid = inception_score
std_with_fid = std
is_best = True
else:
is_best = False
if inception_score > best_is:
best_is = inception_score
best_std = std
fid_with_is = fid_score
best_is_epoch = epoch
else:
is_best = False
# save generated images
if epoch % args.image_every == 0:
gen_noise = torch.cuda.FloatTensor(
np.random.normal(0, 1,
(args.eval_batch_size, args.latent_dim)))
# gen_images = gen_net(fixed_z_sample)
# gen_images = gen_images.reshape(args.eval_batch_size, 32, 32, 3)
# gen_images = gen_images.cpu().detach()
gen_images = gen_net(fixed_z_sample).mul_(127.5).add_(
127.5).clamp_(0.0, 255.0).permute(0, 2, 3,
1).to('cpu',
torch.uint8).numpy()
fig = plt.figure()
grid = ImageGrid(fig, 111, nrows_ncols=(10, 10), axes_pad=0)
for x in range(args.eval_batch_size):
grid[x].imshow(gen_images[x]) # cmap="gray")
grid[x].set_xticks([])
grid[x].set_yticks([])
plt.savefig(
os.path.join(args.path_helper['sample_path'],
"epoch_{}.png".format(epoch)))
plt.close()
avg_gen_net = deepcopy(gen_net)
# avg_gen_net = eval('models.'+args.gen_model+'.' + args.gen)(args, genotype_gen).cuda()
# avg_gen_net = eval('models.' + args.gen_model + '.' + args.gen)(args=args).cuda()
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
'epoch': epoch + 1,
'gen_model': args.gen_model,
'dis_model': args.dis_model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
del avg_gen_net
logger.info(
'best_is is {}+-{}@{} epoch, fid is {}, best_fid is {}@{}, is is {}+-{}'
.format(best_is, best_std, best_is_epoch, fid_with_is, best_fid,
best_fid_epoch, is_with_fid, std_with_fid))
def main():
args = cfg.parse_args()
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
np.random.seed(args.random_seed)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
os.environ['PYTHONHASHSEED'] = str(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# import network
gen_net = eval('models.'+args.model+'.Generator')(args=args)
dis_net = eval('models.'+args.model+'.Discriminator')(args=args)
initial_gen_net_weight = torch.load(os.path.join(args.init_path, 'initial_gen_net.pth'), map_location="cpu")
initial_dis_net_weight = torch.load(os.path.join(args.init_path, 'initial_dis_net.pth'), map_location="cpu")
gen_net = gen_net.cuda()
dis_net = dis_net.cuda()
gen_net.load_state_dict(initial_gen_net_weight)
dis_net.load_state_dict(initial_dis_net_weight)
# set optimizer
gen_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, gen_net.parameters()),
args.g_lr, (args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, dis_net.parameters()),
args.d_lr, (args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0, args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/fid_stats_stl10_train.npz'
else:
raise NotImplementedError('no fid stat for %s' % args.dataset.lower())
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic / len(train_loader))
# initial
fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
# set writer
if args.load_path:
print('=> resuming from %s' % args.load_path)
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model', 'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
avg_gen_net = deepcopy(gen_net)
avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
gen_avg_param = copy_params(avg_gen_net)
del avg_gen_net
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info('=> loaded checkpoint %s (epoch %d)' % (checkpoint_file, start_epoch))
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# train loop
switch = False
for epoch in range(int(start_epoch), int(args.max_epoch)):
lr_schedulers = (gen_scheduler, dis_scheduler) if args.lr_decay else None
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer, gen_avg_param, train_loader, epoch, writer_dict,
lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == int(args.max_epoch)-1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict, epoch)
logger.info('Inception score: %.4f, FID score: %.4f || @ epoch %d.' % (inception_score, fid_score, epoch))
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net = deepcopy(gen_net)
load_params(avg_gen_net, gen_avg_param)
save_checkpoint({
'epoch': epoch + 1,
'model': args.model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper,
'seed': args.random_seed
}, is_best, args.path_helper['ckpt_path'])
del avg_gen_net
def validate(args, fixed_z, fid_stat, gen_net: nn.Module, writer_dict):
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
gen_net = gen_net.eval()
global_steps = writer_dict['valid_global_steps']
eval_iter = args.num_eval_imgs // args.eval_batch_size
# compute IS
IS_buffer_dir = os.path.join(args.path_helper['sample_path'], 'fid_buffer')
os.makedirs(IS_buffer_dir)
img_list = list()
for iter_idx in tqdm(range(eval_iter), desc='sample images'):
z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (args.eval_batch_size, args.latent_dim)))
# Generate a batch of images
gen_imgs = gen_net(z).mul_(127.5).add_(127.5).clamp_(
0.0, 255.0).permute(0, 2, 3, 1).to('cpu', torch.uint8).numpy()
for img_idx, img in enumerate(gen_imgs):
file_name = os.path.join(IS_buffer_dir,
f'iter{iter_idx}_b{img_idx}.png')
imsave(file_name, img)
img_list.extend(list(gen_imgs))
inception_score, std = get_inception_score(img_list)
print('------------------------Inception Score------------------------')
print(inception_score)
# compute FID
sample_list = []
for i in range(eval_iter):
z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (args.eval_batch_size, args.latent_dim)))
samples = gen_net(z)
sample_list.append(samples.data.cpu().numpy())
new_sample_list = list(chain.from_iterable(sample_list))
fake_image_np = np.concatenate([img[None] for img in new_sample_list], 0)
real_image_np = []
for i, (images, _) in enumerate(train_loader):
real_image_np += [images.data.numpy()]
batch_size = real_image_np[0].shape[0]
if len(real_image_np) * batch_size >= fake_image_np.shape[0]:
break
real_image_np = np.concatenate(real_image_np, 0)[:fake_image_np.shape[0]]
fid_score = calculate_fid(real_image_np, fake_image_np, batch_size=300)
var_fid = fid_score[0][2]
fid = round(fid_score[0][1], 3)
print('------------------------fid_score------------------------')
print(fid_score)
# Generate a batch of images
sample_dir = os.path.join(args.path_helper['sample_path'], 'sample_dir')
Path(sample_dir).mkdir(exist_ok=True)
sample_imgs = gen_net(fixed_z).mul_(127.5).add_(127.5).clamp_(0.0, 255.0)
img_grid = make_grid(sample_imgs, nrow=5).to('cpu', torch.uint8).numpy()
file_name = os.path.join(
sample_dir, f'final_fid_{fid}_inception_score{inception_score}.png')
imsave(file_name, img_grid.swapaxes(0, 1).swapaxes(1, 2))
writer_dict['valid_global_steps'] = global_steps + 1
return inception_score, fid
milestones=[int(args.max_epoch / 2),
int(args.max_epoch * 0.75)])
# set optimizer
W_optimizer = torch.optim.Adam(W_lst, args.lr_w)
W_scheduler = torch.optim.lr_scheduler.LambdaLR(W_optimizer,
lr_lambda=LambdaLR(
args.max_epoch, 0,
args.decay_epoch).step)
gamma_optimizer = torch.optim.SGD(gamma_lst, args.lr_gamma, momentum=0.5)
gamma_scheduler = torch.optim.lr_scheduler.MultiStepLR(
gamma_optimizer,
milestones=[int(args.max_epoch / 2),
int(args.max_epoch * 0.75)])
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
else:
raise NotImplementedError('no fid stat for %s' % args.dataset.lower())
assert os.path.exists(fid_stat)
# initial
fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))
start_epoch = 0
best_fid = 1e4
N = len(train_loader)
def main():
args = cfg.parse_args()
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net = Generator(bottom_width=args.bottom_width,
gf_dim=args.gf_dim,
latent_dim=args.latent_dim).cuda()
dis_net = eval('models.' + args.model + '.Discriminator')(args=args).cuda()
gen_net.apply(weights_init)
dis_net.apply(weights_init)
initial_gen_net_weight = torch.load(os.path.join(args.init_path,
'initial_gen_net.pth'),
map_location="cpu")
initial_dis_net_weight = torch.load(os.path.join(args.init_path,
'initial_dis_net.pth'),
map_location="cpu")
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
exp_str = args.dir
args.load_path = os.path.join('output', exp_str, 'pth',
'epoch{}.pth'.format(args.load_epoch))
# state dict:
assert os.path.exists(args.load_path)
checkpoint = torch.load(args.load_path)
print('=> loaded checkpoint %s' % args.load_path)
state_dict = checkpoint['generator']
gen_net = load_subnet(args, state_dict, initial_gen_net_weight).cuda()
avg_gen_net = deepcopy(gen_net)
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0,
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0,
args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
else:
raise NotImplementedError('no fid stat for %s' % args.dataset.lower())
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic /
len(train_loader))
# initial
np.random.seed(args.random_seed)
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (25, args.latent_dim)))
start_epoch = 0
best_fid = 1e4
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
#logger.info('=> loaded checkpoint %s (epoch %d)' % (checkpoint_file, start_epoch))
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
gen_avg_param = copy_params(gen_net)
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)),
desc='total progress'):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer,
gen_avg_param, train_loader, epoch, writer_dict, lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == int(
args.max_epoch) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat,
gen_net, writer_dict)
logger.info(
'Inception score: %.4f, FID score: %.4f || @ epoch %d.' %
(inception_score, fid_score, epoch))
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net.load_state_dict(gen_net.state_dict())
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
def main():
args = cfg.parse_args()
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# weight init
gen_net = eval('models.' + args.model + '.Generator')(args=args)
dis_net = eval('models.' + args.model + '.Discriminator')(args=args)
# weight init
def weights_init(m):
if isinstance(m, nn.Conv2d):
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif isinstance(m, nn.BatchNorm2d):
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
gen_net = gen_net.cuda()
dis_net = dis_net.cuda()
avg_gen_net = deepcopy(gen_net)
initial_gen_net_weight = deepcopy(gen_net.state_dict())
initial_dis_net_weight = deepcopy(dis_net.state_dict())
assert id(initial_dis_net_weight) != id(dis_net.state_dict())
assert id(initial_gen_net_weight) != id(gen_net.state_dict())
# set optimizer
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0,
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0,
args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/fid_stats_stl10_train.npz'
else:
raise NotImplementedError('no fid stat for %s' % args.dataset.lower())
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic /
len(train_loader))
# initial
np.random.seed(args.random_seed)
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (25, args.latent_dim)))
start_epoch = 0
best_fid = 1e4
args.path_helper = set_log_dir('logs',
args.exp_name + "_{}".format(args.percent))
logger = create_logger(args.path_helper['log_path'])
# logger.info('=> loaded checkpoint %s (epoch %d)' % (checkpoint_file, start_epoch))
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
print('=> resuming from %s' % args.load_path)
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model', 'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
gen_net.load_state_dict(checkpoint['gen_state_dict'])
torch.manual_seed(args.random_seed)
pruning_generate(gen_net, (1 - args.percent), args.pruning_method)
torch.manual_seed(args.random_seed)
pruning_generate(avg_gen_net, (1 - args.percent), args.pruning_method)
see_remain_rate(gen_net)
if args.second_seed:
dis_net.apply(weights_init)
if args.finetune_D:
dis_net.load_state_dict(checkpoint['dis_state_dict'])
else:
dis_net.load_state_dict(initial_dis_net_weight)
gen_weight = gen_net.state_dict()
gen_orig_weight = rewind_weight(initial_gen_net_weight, gen_weight.keys())
assert id(gen_weight) != id(gen_orig_weight)
gen_weight.update(gen_orig_weight)
gen_net.load_state_dict(gen_weight)
gen_avg_param = copy_params(gen_net)
if args.use_kd_D:
orig_dis_net = eval('models.' + args.model +
'.Discriminator')(args=args).cuda()
orig_dis_net.load(checkpoint['dis_state_dict'])
orig_dis_net.eval()
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)),
desc='total progress'):
lr_schedulers = (gen_scheduler,
dis_scheduler) if args.lr_decay else None
see_remain_rate(gen_net)
if not args.use_kd_D:
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer,
gen_avg_param, train_loader, epoch, writer_dict,
lr_schedulers)
else:
train_kd(args, gen_net, dis_net, orig_dis_net, gen_optimizer,
dis_optimizer, gen_avg_param, train_loader, epoch,
writer_dict, lr_schedulers)
if epoch and epoch % args.val_freq == 0 or epoch == int(
args.max_epoch) - 1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat,
gen_net, writer_dict, epoch)
logger.info(
'Inception score: %.4f, FID score: %.4f || @ epoch %d.' %
(inception_score, fid_score, epoch))
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net.load_state_dict(gen_net.state_dict())
load_params(avg_gen_net, gen_avg_param)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
np.random.seed(args.random_seed)
random.seed(args.random_seed)
torch.backends.cudnn.deterministic = True
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# epoch number for dis_net
dataset = datasets.ImageDataset(args, cur_img_size=8)
train_loader = dataset.train
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter / len(train_loader))
else:
args.max_iter = args.max_epoch * len(train_loader)
args.max_epoch = args.max_epoch * args.n_critic
# import network
gen_net = eval('models.' + args.gen_model + '.Generator')(args=args).cuda()
dis_net = eval('models.' + args.dis_model +
'.Discriminator')(args=args).cuda()
gen_net.set_arch(args.arch, cur_stage=2)
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform_(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(
args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
gpu_ids = [i for i in range(int(torch.cuda.device_count()))]
gen_net = torch.nn.DataParallel(gen_net.to("cuda:0"), device_ids=gpu_ids)
dis_net = torch.nn.DataParallel(dis_net.to("cuda:0"), device_ids=gpu_ids)
gen_net.module.cur_stage = 0
dis_net.module.cur_stage = 0
gen_net.module.alpha = 1.
dis_net.module.alpha = 1.
# set optimizer
if args.optimizer == "adam":
gen_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr,
(args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr,
(args.beta1, args.beta2))
elif args.optimizer == "adamw":
gen_optimizer = AdamW(filter(lambda p: p.requires_grad,
gen_net.parameters()),
args.g_lr,
weight_decay=args.wd)
dis_optimizer = AdamW(filter(lambda p: p.requires_grad,
dis_net.parameters()),
args.g_lr,
weight_decay=args.wd)
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0,
args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0,
args.max_iter * args.n_critic)
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
elif args.fid_stat is not None:
fid_stat = args.fid_stat
else:
raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
assert os.path.exists(fid_stat)
# initial
fixed_z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (64, args.latent_dim)))
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path)
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
# avg_gen_net = deepcopy(gen_net)
# avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
gen_avg_param = checkpoint['gen_avg_param']
# del avg_gen_net
cur_stage = cur_stages(start_epoch, args)
gen_net.module.cur_stage = cur_stage
dis_net.module.cur_stage = cur_stage
gen_net.module.alpha = 1.
dis_net.module.alpha = 1.
args.path_helper = checkpoint['path_helper']
else:
# create new log dir
assert args.exp_name
args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
def return_states():
states = {}
states['epoch'] = epoch
states['best_fid'] = best_fid_score
states['gen_state_dict'] = gen_net.state_dict()
states['dis_state_dict'] = dis_net.state_dict()
states['gen_optimizer'] = gen_optimizer.state_dict()
states['dis_optimizer'] = dis_optimizer.state_dict()
states['gen_avg_param'] = gen_avg_param
states['path_helper'] = args.path_helper
return states
# train loop
for epoch in range(start_epoch + 1, args.max_epoch):
train(
args,
gen_net,
dis_net,
gen_optimizer,
dis_optimizer,
gen_avg_param,
train_loader,
epoch,
writer_dict,
fixed_z,
)
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
fid_score = validate(
args,
fixed_z,
fid_stat,
epoch,
gen_net,
writer_dict,
)
logger.info(f'FID score: {fid_score} || @ epoch {epoch}.')
load_params(gen_net, backup_param)
is_best = False
if epoch == 1 or fid_score < best_fid_score:
best_fid_score = fid_score
is_best = True
if is_best or epoch % 1 == 0:
states = return_states()
save_checkpoint(states,
is_best,
args.path_helper['ckpt_path'],
filename=f'checkpoint_epoch_{epoch}.pth')
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
np.random.seed(args.random_seed)
random.seed(args.random_seed)
torch.backends.cudnn.deterministic = True
# import network
# args.gen_model is TransGAN_8_8_1 for example
gen_net = eval('models.'+args.gen_model+'.Generator')(args=args).cuda()
dis_net = eval('models.'+args.dis_model+'.Discriminator')(args=args).cuda()
gen_net.set_arch(args.arch, cur_stage=2)
print("The shit!")
# weight init: Xavier Uniform
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
gpu_ids = [i for i in range(int(torch.cuda.device_count()))]
gen_net = torch.nn.DataParallel(gen_net.to("cuda:0"), device_ids=gpu_ids)
dis_net = torch.nn.DataParallel(dis_net.to("cuda:0"), device_ids=gpu_ids)
# print(gen_net.module.cur_stage)
if args.optimizer == "adam":
gen_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, gen_net.parameters()),
args.g_lr, (args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, dis_net.parameters()),
args.d_lr, (args.beta1, args.beta2))
elif args.optimizer == "adamw":
gen_optimizer = AdamW(filter(lambda p: p.requires_grad, gen_net.parameters()),
args.g_lr, weight_decay=args.wd)
dis_optimizer = AdamW(filter(lambda p: p.requires_grad, dis_net.parameters()),
args.g_lr, weight_decay=args.wd)
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0, args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, args.max_iter * args.n_critic)
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
elif args.fid_stat is not None:
fid_stat = args.fid_stat
else:
raise NotImplementedError # (f"no fid stat for %s"%args.dataset.lower()")
assert os.path.exists(fid_stat)
dataset = datasets.ImageDataset(args, cur_img_size=8)
train_loader = dataset.train
writer=SummaryWriter()
writer_dict = {'writer':writer}
writer_dict["train_global_steps"]=0
writer_dict["valid_global_steps"]=0
best = 1e4
for epoch in range(args.max_epoch):
train(args, gen_net = gen_net, dis_net = dis_net, gen_optimizer = gen_optimizer, dis_optimizer = dis_optimizer, gen_avg_param = None, train_loader = train_loader,
epoch = epoch, writer_dict = writer_dict, fixed_z = None, schedulers=[gen_scheduler, dis_scheduler])
checkpoint = {'epoch':epoch, 'best_fid':best}
checkpoint['gen_state_dict'] = gen_net.state_dict()
checkpoint['dis_state_dict'] = dis_net.state_dict()
score = validate(args, None, fid_stat, epoch, gen_net, writer_dict, clean_dir=True)
# print these scores, is it really the latest
print(f'FID score: {score} - best ID score: {best} || @ epoch {epoch}.')
if epoch == 0 or epoch > 30:
if score < best:
save_checkpoint(checkpoint, is_best=(score<best), output_dir=args.output_dir)
print("Saved Latest Model!")
best = score
checkpoint = {'epoch':epoch, 'best_fid':best}
checkpoint['gen_state_dict'] = gen_net.state_dict()
checkpoint['dis_state_dict'] = dis_net.state_dict()
score = validate(args, None, fid_stat, epoch, gen_net, writer_dict, clean_dir=True)
save_checkpoint(checkpoint, is_best=(score<best), output_dir=args.output_dir)
