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Add support for training an EMA network alongside the main networks

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This commit is contained in:
James Betker 2021-06-12 21:01:41 -06:00
parent 696f320820
commit 3e3ad7825f
6 changed files with 40 additions and 30 deletions
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2
codes/test.py
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@ -58,7 +58,7 @@ if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
want_metrics = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_unet.yml')
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_unet_sm.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt

2
codes/train.py
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@ -298,7 +298,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_resnet_cifar.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_unet_diffusion_sm.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()

36
codes/trainer/ExtensibleTrainer.py
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@ -1,3 +1,4 @@
import copy
import logging
import os
@ -42,6 +43,7 @@ class ExtensibleTrainer(BaseModel):
self.mega_batch_factor = train_opt['mega_batch_factor']
self.env['mega_batch_factor'] = self.mega_batch_factor
self.batch_factor = self.mega_batch_factor
self.ema_rate = opt_get(train_opt, ['ema_rate'], .999)
self.checkpointing_cache = opt['checkpointing_enabled']
self.netsG = {}
@ -123,8 +125,9 @@ class ExtensibleTrainer(BaseModel):
dnet.eval()
dnets.append(dnet)
# Backpush the wrapped networks into the network dicts..
# Backpush the wrapped networks into the network dicts. Also build the EMA parameters.
self.networks = {}
self.emas = {}
found = 0
for dnet in dnets:
for net_dict in [self.netsD, self.netsG]:
@ -132,6 +135,8 @@ class ExtensibleTrainer(BaseModel):
if v == dnet.module:
net_dict[k] = dnet
self.networks[k] = dnet
if self.is_train:
self.emas[k] = copy.deepcopy(v)
found += 1
assert found == len(self.netsG) + len(self.netsD)
@ -140,7 +145,7 @@ class ExtensibleTrainer(BaseModel):
self.env['discriminators'] = self.netsD
self.print_network() # print network
self.load() # load G and D if needed
self.load() # load networks from save states as needed
# Load experiments
self.experiments = []
@ -248,12 +253,17 @@ class ExtensibleTrainer(BaseModel):
# And finally perform optimization.
[e.before_optimize(state) for e in self.experiments]
s.do_step(step)
# Some networks have custom steps, for example EMA
for net in self.networks:
# Call into custom step hooks as well as update EMA params.
for name, net in self.networks.items():
if hasattr(net, "custom_optimizer_step"):
net.custom_optimizer_step(step)
ema_params = self.emas[name].parameters()
net_params = net.parameters()
for ep, np in zip(ema_params, net_params):
ep.detach().mul_(self.ema_rate).add_(np, alpha=1 - self.ema_rate)
[e.after_optimize(state) for e in self.experiments]
# Record visual outputs for usage in debugging and testing.
if 'visuals' in self.opt['logger'].keys() and self.rank <= 0 and step % self.opt['logger']['visual_debug_rate'] == 0:
def fix_image(img):
@ -360,10 +370,19 @@ class ExtensibleTrainer(BaseModel):
if not self.opt['networks'][name]['trainable']:
continue
load_path = self.opt['path']['pretrain_model_%s' % (name,)]
if load_path is not None:
if self.rank <= 0:
logger.info('Loading model for [%s]' % (load_path,))
self.load_network(load_path, net, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
if load_path is None:
return
if self.rank <= 0:
logger.info('Loading model for [%s]' % (load_path,))
self.load_network(load_path, net, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
load_path_ema = load_path.replace('.pth', '_ema.pth')
if self.is_train:
ema_model = self.emas[name]
if os.path.exists(load_path_ema):
self.load_network(load_path_ema, ema_model, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
else:
print("WARNING! Unable to find EMA network! Starting a new EMA from given model parameters.")
self.emas[name] = copy.deepcopy(net)
if hasattr(net.module, 'network_loaded'):
net.module.network_loaded()
@ -372,6 +391,7 @@ class ExtensibleTrainer(BaseModel):
# Don't save non-trainable networks.
if self.opt['networks'][name]['trainable']:
self.save_network(net, name, iter_step)
self.save_network(self.emas[name], f'{name}_ema', iter_step)
def force_restore_swapout(self):
# Legacy method. Do nothing.

1
codes/trainer/base_model.py
View File

@ -151,4 +151,5 @@ class BaseModel():
for i, s in enumerate(resume_schedulers):
self.schedulers[i].load_state_dict(s)
if load_amp and 'amp' in resume_state.keys():
from apex import amp
amp.load_state_dict(resume_state['amp'])

4
codes/trainer/steps.py
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@ -53,9 +53,9 @@ class ConfigurableStep(Module):
# This default implementation defines a single optimizer for all Generator parameters.
# Must be called after networks are initialized and wrapped.
def define_optimizers(self):
opt_configs = opt_get(self.step_opt, ['optimizer_params'], None)
opt_configs = [opt_get(self.step_opt, ['optimizer_params'], None)]
self.optimizers = []
if opt_configs is None:
if opt_configs[0] is None:
return
training = self.step_opt['training']
training_net = self.get_network_for_name(training)

25
codes/utils/options.py
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@ -100,21 +100,10 @@ def check_resume(opt, resume_iter):
'pretrain_model_D', None) is not None:
logger.warning('pretrain_model path will be ignored when resuming training.')
if opt['model'] == 'extensibletrainer':
for k in opt['networks'].keys():
pt_key = 'pretrain_model_%s' % (k,)
opt['path'][pt_key] = osp.join(opt['path']['models'],
'{}_{}.pth'.format(resume_iter, k))
logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))
else:
opt['path']['pretrain_model_G'] = osp.join(opt['path']['models'],
'{}_G.pth'.format(resume_iter))
logger.info('Set [pretrain_model_G] to ' + opt['path']['pretrain_model_G'])
if 'gan' in opt['model'] or 'spsr' in opt['model']:
opt['path']['pretrain_model_D'] = osp.join(opt['path']['models'],
'{}_D.pth'.format(resume_iter))
logger.info('Set [pretrain_model_D] to ' + opt['path']['pretrain_model_D'])
if 'spsr' in opt['model']:
opt['path']['pretrain_model_D_grad'] = osp.join(opt['path']['models'],
'{}_D_grad.pth'.format(resume_iter))
logger.info('Set [pretrain_model_D_grad] to ' + opt['path']['pretrain_model_D_grad'])
# Automatically fill in the network paths for a given resume iteration.
for k in opt['networks'].keys():
pt_key = 'pretrain_model_%s' % (k,)
assert pt_key not in opt['path'].keys() # There's no real reason to load from a training_state AND a model.
opt['path'][pt_key] = osp.join(opt['path']['models'],
'{}_{}.pth'.format(resume_iter, k))
logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))