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Merge remote-tracking branch 'origin/gan_lab' into gan_lab

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James Betker 2020-10-27 20:59:59 -06:00
commit 10da206db6
5 changed files with 88 additions and 76 deletions
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4
codes/models/ExtensibleTrainer.py
View File

@ -30,7 +30,9 @@ class ExtensibleTrainer(BaseModel):
self.env = {'device': self.device,
'rank': self.rank,
'opt': opt,
'step': 0}
'step': 0,
'dist': opt['dist']
}
if opt['path']['models'] is not None:
self.env['base_path'] = os.path.join(opt['path']['models'])

8
codes/models/archs/SPSR_arch.py
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@ -94,12 +94,12 @@ class SPSRNet(nn.Module):
n_upscale = int(math.log(upscale, 2))
self.scale=n_upscale
self.scale=upscale
if upscale == 3:
n_upscale = 1
fea_conv = ConvGnLelu(in_nc, nf//2, kernel_size=7, norm=False, activation=False)
self.ref_conv = ConvGnLelu(in_nc, nf//2, stride=n_upscale, kernel_size=7, norm=False, activation=False)
self.ref_conv = ConvGnLelu(in_nc, nf//2, stride=upscale, kernel_size=7, norm=False, activation=False)
self.join_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
rb_blocks = [RRDB(nf) for _ in range(nb)]
@ -118,7 +118,7 @@ class SPSRNet(nn.Module):
*upsampler, self.HR_conv0_new)
self.b_fea_conv = ConvGnLelu(in_nc, nf//2, kernel_size=3, norm=False, activation=False)
self.b_ref_conv = ConvGnLelu(in_nc, nf//2, stride=n_upscale, kernel_size=3, norm=False, activation=False)
self.b_ref_conv = ConvGnLelu(in_nc, nf//2, stride=upscale, kernel_size=3, norm=False, activation=False)
self.b_join_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
self.b_concat_1 = ConvGnLelu(2 * nf, nf, kernel_size=3, norm=False, activation=False)
@ -665,4 +665,4 @@ class SwitchedSpsr(nn.Module):
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
return val

79
codes/models/steps/steps.py
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@ -126,49 +126,48 @@ class ConfigurableStep(Module):
self.env['current_step_optimizers'] = self.optimizers
self.env['training'] = train
with self.get_network_for_name(self.get_networks_trained()[0]).join():
# Inject in any extra dependencies.
for inj in self.injectors:
# Don't do injections tagged with eval unless we are not in train mode.
if train and 'eval' in inj.opt.keys() and inj.opt['eval']:
continue
# Likewise, don't do injections tagged with train unless we are not in eval.
if not train and 'train' in inj.opt.keys() and inj.opt['train']:
continue
# Don't do injections tagged with 'after' or 'before' when we are out of spec.
if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
'before' in inj.opt.keys() and self.env['step'] > inj.opt['before']:
continue
injected = inj(local_state)
local_state.update(injected)
new_state.update(injected)
# Inject in any extra dependencies.
for inj in self.injectors:
# Don't do injections tagged with eval unless we are not in train mode.
if train and 'eval' in inj.opt.keys() and inj.opt['eval']:
continue
# Likewise, don't do injections tagged with train unless we are not in eval.
if not train and 'train' in inj.opt.keys() and inj.opt['train']:
continue
# Don't do injections tagged with 'after' or 'before' when we are out of spec.
if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
'before' in inj.opt.keys() and self.env['step'] > inj.opt['before']:
continue
injected = inj(local_state)
local_state.update(injected)
new_state.update(injected)
if train and len(self.losses) > 0:
# Finally, compute the losses.
total_loss = 0
for loss_name, loss in self.losses.items():
# Some losses only activate after a set number of steps. For example, proto-discriminator losses can
# be very disruptive to a generator.
if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step']:
continue
l = loss(self.training_net, local_state)
total_loss += l * self.weights[loss_name]
# Record metrics.
if isinstance(l, torch.Tensor):
self.loss_accumulator.add_loss(loss_name, l)
for n, v in loss.extra_metrics():
self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
loss.clear_metrics()
if train and len(self.losses) > 0:
# Finally, compute the losses.
total_loss = 0
for loss_name, loss in self.losses.items():
# Some losses only activate after a set number of steps. For example, proto-discriminator losses can
# be very disruptive to a generator.
if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step']:
continue
l = loss(self.training_net, local_state)
total_loss += l * self.weights[loss_name]
# Record metrics.
if isinstance(l, torch.Tensor):
self.loss_accumulator.add_loss(loss_name, l)
for n, v in loss.extra_metrics():
self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
loss.clear_metrics()
# In some cases, the loss could not be set (e.g. all losses have 'after')
if isinstance(total_loss, torch.Tensor):
self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss)
# Scale the loss down by the accumulation factor.
total_loss = total_loss / self.env['mega_batch_factor']
# In some cases, the loss could not be set (e.g. all losses have 'after')
if isinstance(total_loss, torch.Tensor):
self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss)
# Scale the loss down by the accumulation factor.
total_loss = total_loss / self.env['mega_batch_factor']
# Get dem grads!
self.scaler.scale(total_loss).backward()
self.grads_generated = True
# Get dem grads!
self.scaler.scale(total_loss).backward()
self.grads_generated = True
# Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
# we must release the gradients.

15
codes/multi_modal_train.py
View File

@ -15,15 +15,29 @@ import yaml
import train
import utils.options as option
from utils.util import OrderedYaml
import torch
def main(master_opt, launcher):
trainers = []
all_networks = {}
shared_networks = []
if launcher != 'none':
train.init_dist('nccl')
for i, sub_opt in enumerate(master_opt['trainer_options']):
sub_opt_parsed = option.parse(sub_opt, is_train=True)
trainer = train.Trainer()
#### distributed training settings
if launcher == 'none': # disabled distributed training
sub_opt_parsed['dist'] = False
trainer.rank = -1
print('Disabled distributed training.')
else:
sub_opt_parsed['dist'] = True
trainer.world_size = torch.distributed.get_world_size()
trainer.rank = torch.distributed.get_rank()
trainer.init(sub_opt_parsed, launcher, all_networks)
train_gen = trainer.create_training_generator(i)
model = next(train_gen)
@ -44,6 +58,7 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.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()
Loader, Dumper = OrderedYaml()

58
codes/train.py
View File

@ -13,43 +13,26 @@ from data import create_dataloader, create_dataset
from models.ExtensibleTrainer import ExtensibleTrainer
from time import time
class Trainer:
def init_dist(self, backend, **kwargs):
# These packages have globals that screw with Windows, so only import them if needed.
import torch.distributed as dist
import torch.multiprocessing as mp
def init_dist(backend, **kwargs):
# These packages have globals that screw with Windows, so only import them if needed.
import torch.distributed as dist
import torch.multiprocessing as mp
"""initialization for distributed training"""
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn')
self.rank = int(os.environ['RANK'])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device(self.rank % num_gpus)
dist.init_process_group(backend=backend, **kwargs)
"""initialization for distributed training"""
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn')
rank = int(os.environ['RANK'])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device(rank % num_gpus)
dist.init_process_group(backend=backend, **kwargs)
class Trainer:
def init(self, opt, launcher, all_networks={}):
self._profile = False
self.val_compute_psnr = opt['eval']['compute_psnr'] if 'compute_psnr' in opt['eval'] else True
self.val_compute_fea = opt['eval']['compute_fea'] if 'compute_fea' in opt['eval'] else True
#### distributed training settings
if len(opt['gpu_ids']) == 1 and torch.cuda.device_count() > 1:
gpu = input(
'I noticed you have multiple GPUs. Starting two jobs on the same GPU sucks. Please confirm which GPU'
'you want to use. Press enter to use the specified one [%s]' % (opt['gpu_ids']))
if gpu:
opt['gpu_ids'] = [int(gpu)]
if launcher == 'none': # disabled distributed training
opt['dist'] = False
self.rank = -1
print('Disabled distributed training.')
else:
opt['dist'] = True
self.init_dist('nccl')
world_size = torch.distributed.get_world_size()
self.rank = torch.distributed.get_rank()
#### loading resume state if exists
if opt['path'].get('resume_state', None):
# distributed resuming: all load into default GPU
@ -117,7 +100,7 @@ class Trainer:
total_iters = int(opt['train']['niter'])
self.total_epochs = int(math.ceil(total_iters / train_size))
if opt['dist']:
self.train_sampler = DistIterSampler(self.train_set, world_size, self.rank, dataset_ratio)
self.train_sampler = DistIterSampler(self.train_set, self.world_size, self.rank, dataset_ratio)
self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
else:
self.train_sampler = None
@ -288,5 +271,18 @@ if __name__ == '__main__':
args = parser.parse_args()
opt = option.parse(args.opt, is_train=True)
trainer = Trainer()
#### distributed training settings
if args.launcher == 'none': # disabled distributed training
opt['dist'] = False
trainer.rank = -1
print('Disabled distributed training.')
else:
opt['dist'] = True
init_dist('nccl')
trainer.world_size = torch.distributed.get_world_size()
trainer.rank = torch.distributed.get_rank()
trainer.init(opt, args.launcher)
trainer.do_training()