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EPEExperiment.py
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EPEExperiment.py
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import warnings
warnings.filterwarnings('ignore', message='numpy.dtype size changed')
warnings.filterwarnings('ignore', message='numpy.ufunc size changed')
warnings.filterwarnings("ignore", category=DeprecationWarning)
from argparse import ArgumentParser
import datetime
import logging
from pathlib import Path
import random
import imageio
import numpy as np
from skimage.transform import resize
import torch
import torch.utils.data
from torch import autograd
import kornia as K
import epe.utils
import epe.dataset as ds
import epe.network as nw
import epe.experiment as ee
from epe.matching import MatchedCrops, IndependentCrops
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
logger = logging.getLogger('main')
gan_losses = {\
'ls':nw.LSLoss,
'ns':nw.NSLoss,
'hinge':nw.HingeLoss}
vgg_losses = {\
'lpips_alex': lambda vgg: nw.LPIPSLoss(net='alex'),
'lpips_squeeze': lambda vgg: nw.LPIPSLoss(net='squeeze'),
'lpips_vgg': lambda vgg: nw.LPIPSLoss(net='vgg'),
'munit': lambda vgg: nw.VGGLoss(vgg, 'munit'),
'johnson': lambda vgg: nw.VGGLoss(vgg, 'johnson'),
}
fake_datasets = {\
'GTA':ds.PfDDataset,
}
dataset_pairings = [\
'matching',
'matchinghd',
'independent_256',
'independent_256',
'independent_400',
]
def tee_loss(x, y):
return x+y, y.detach()
def accuracy(pred):
return (pred > 0.5).float().mean()
def real_penalty(loss, real_img):
''' Compute penalty on real images. '''
b = real_img.shape[0]
grad_out = autograd.grad(outputs=loss, inputs=[real_img], create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)
logger.debug(f'real_penalty: g:{grad_out[0].shape}')
reg_loss = torch.cat([g.pow(2).reshape(b, -1).sum(dim=1, keepdim=True) for g in grad_out if g is not None], 1).mean()
return reg_loss
class PassthruGenerator(torch.nn.Module):
def __init__(self, network):
super(PassthruGenerator, self).__init__()
self.network = network
pass
def forward(self, epe_batch):
return self.network(epe_batch)
class EPEExperiment(ee.GANExperiment):
def __init__(self, args):
super(EPEExperiment, self).__init__(args)
self.collate_fn_train = ds.JointEPEBatch.collate_fn
self.collate_fn_val = ds.EPEBatch.collate_fn
pass
def _parse_config(self):
super()._parse_config()
# fake dataset
fake_cfg = dict(self.cfg.get('fake_dataset', {}))
self.fake_name = str(fake_cfg.get('name'))
self.fake_train_path = Path(fake_cfg.get('train_filelist', None))
self.fake_val_path = Path(fake_cfg.get('val_filelist', None))
self.fake_test_path = Path(fake_cfg.get('test_filelist', None))
self._log.debug(f' Fake dataset {self.fake_name} in {self.fake_train_path}[train], {self.fake_val_path}[val], {self.fake_test_path}[test].')
# real dataset
real_cfg = dict(self.cfg.get('real_dataset', {}))
self.real_name = str(real_cfg.get('name'))
self.real_basepath = Path(real_cfg.get('filelist', None))
self._log.debug(f' Real dataset {self.real_name} in {self.real_basepath}.')
# sampling
self.sample_cfg = dict(fake_cfg.get('sampling', {}))
self.sampling = str(self.sample_cfg.get('type', 'matching'))
# networks
self.vgg = nw.VGG16().to(self.device)
gen_cfg = dict(self.cfg.get('generator', {}))
self.gen_cfg = dict(gen_cfg.get('config', {}))
disc_cfg = dict(self.cfg.get('discriminator', {}))
self.disc_cfg = dict(disc_cfg.get('config', {}))
# loss functions
loss_cfg = dict(self.cfg.get('objectives', {}))
self.gan_loss = gan_losses[str(loss_cfg.get('gan', 'ls'))]().to(self.device)
perc_cfg = dict(loss_cfg.get('perceptual', {}))
vgg_type = str(perc_cfg.get('type', 'lpips_vgg'))
self.vgg_loss = vgg_losses[vgg_type](self.vgg).to(self.device)
self.vgg_weight = float(perc_cfg.get('weight', 1.0))
reg_cfg = dict(loss_cfg.get('reg', {}))
self.reg_weight = float(reg_cfg.get('weight', 1.0))
pass
def _init_dataset(self):
self._log.debug('Initializing datasets ...')
# validation
if self.no_validation:
self.dataset_fake_val = None
elif self.action == 'test':
self.dataset_fake_val = fake_datasets[self.fake_name](ds.utils.read_filelist(self.fake_test_path, 4, True))
else:
self.dataset_fake_val = fake_datasets[self.fake_name](ds.utils.read_filelist(self.fake_val_path, 4, True))
pass
# training
if self.action == 'train':
source_dataset = fake_datasets[self.fake_name](ds.utils.read_filelist(self.fake_train_path, 4, True))
target_dataset = ds.RobustlyLabeledDataset(self.real_name, ds.utils.read_filelist(self.real_basepath, 2, True))
if self.sampling == 'matching':
self.dataset_train = MatchedCrops(source_dataset, target_dataset, self.sample_cfg)
elif self.sampling.startswith('independent_'):
crop_size = int(self.sampling[len('independent_'):])
self.dataset_train = IndependentCrops(source_dataset, target_dataset, self.sample_cfg)
else:
raise NotImplementedError
pass
else:
self.dataset_train = None
pass
def _init_network(self):
self._log.debug('Initializing networks ...')
# network arch depends on dataset
if self.dataset_train is not None:
self.gen_cfg['num_classes'] = self.dataset_train.source.num_classes
self.gen_cfg['num_gbuffer_channels'] = self.dataset_train.source.num_gbuffer_channels
self.gen_cfg['cls2gbuf'] = self.dataset_train.source.cls2gbuf
else:
self.gen_cfg['num_classes'] = self.dataset_fake_val.num_classes
self.gen_cfg['num_gbuffer_channels'] = self.dataset_fake_val.num_gbuffer_channels
self.gen_cfg['cls2gbuf'] = self.dataset_fake_val.cls2gbuf
self._log.debug(f'Fake dataset has {self.gen_cfg["num_classes"]} classes and {self.gen_cfg["num_gbuffer_channels"]} G-buffers.')
self._log.debug(f'Classes are mapped to G-Buffers via {self.gen_cfg["cls2gbuf"]}.')
generator_type = self.cfg['generator']['type']
discriminator_type = self.cfg['discriminator']['type']
run_disc_always = bool(self.cfg['discriminator'].get('run_always', False))
self.check_fake_for_backprop = bool(self.cfg['discriminator'].get('check_fake_for_backprop', True))
backprop_target = float(self.cfg['discriminator'].get('backprop_target', 0.6))
if generator_type == 'hr':
generator = nw.ResidualGenerator(nw.make_ienet(self.gen_cfg))
pass
elif generator_type == 'hr_new':
generator = PassthruGenerator(nw.make_ienet2(self.gen_cfg))
pass
discriminator = {\
'patchgan':nw.PatchGANDiscriminator,
'pde':nw.PerceptualDiscEnsemble,
'ppde':nw.PerceptualProjectionDiscEnsemble,
}[discriminator_type](self.disc_cfg)
self.network = nw.GAN(generator, discriminator).to(self.device)
self.adaptive_backprop = epe.utils.AdaptiveBackprop(len(self.network.discriminator), self.device, backprop_target) if not run_disc_always else None
self._log.debug(f'AdaptiveBackprop is [{"on" if self.adaptive_backprop else "off"}].')
self._log.debug(f' check fake performance : [{"on" if self.check_fake_for_backprop else "off"}].')
self._log.debug(f' target : {backprop_target}')
self._log.debug('Networks are initialized.')
self._log.info(f'{self.network}')
pass
def _run_generator(self, batch_fake, batch_real, batch_id):
rec_fake = self.network.generator(batch_fake)
realism_maps = self.network.discriminator.forward(\
vgg=self.vgg, img=rec_fake, robust_labels=batch_fake.robust_labels,
fix_input=False, run_discs=True)
loss = 0
log_info = {}
for i, rm in enumerate(realism_maps):
loss, log_info[f'gs{i}'] = tee_loss(loss, self.gan_loss.forward_gen(rm[0,:,:,:].unsqueeze(0)).mean())
pass
loss, log_info['vgg'] = tee_loss(loss, self.vgg_weight * self.vgg_loss.forward_fake(batch_fake.img, rec_fake)[0])
loss.backward()
return log_info, \
{'rec_fake':rec_fake.detach(), 'fake':batch_fake.img.detach(), 'real':batch_real.img.detach()}
def _forward_generator_fake(self, batch_fake):
""" Run the generator without any loss computation. """
rec_fake = self.network.generator(batch_fake)
return {'rec_fake':rec_fake.detach(), 'fake':batch_fake.img.detach()}
def _run_discriminator(self, batch_fake, batch_real, batch_id:int):
log_scalar = {}
log_img = {}
# sample probability of running certain discriminator
if self.adaptive_backprop is not None:
run_discs = self.adaptive_backprop.sample()
else:
run_discs = [True] * len(self.network.discriminator)
pass
if not any(run_discs):
return log_scalar, log_img
with torch.no_grad():
rep_fake = self.network.generator(batch_fake)
pass
log_img['fake'] = batch_fake.img.detach()
log_img['rec_fake'] = rep_fake.detach()
rec_fake = rep_fake.detach()
rec_fake.requires_grad_()
# forward fake images
realism_maps = self.network.discriminator.forward(\
vgg=self.vgg, img=rec_fake, robust_labels=batch_fake.robust_labels,
fix_input=True, run_discs=run_discs)
loss = 0
pred_labels = {} # for adaptive backprop
for i, rm in enumerate(realism_maps):
if rm is None:
continue
if self._log.isEnabledFor(logging.DEBUG):
log_img[f'realism_fake_{i}'] = rm.detach()
pass
# for getting probability of back
if self.check_fake_for_backprop:
pred_labels[i] = [(rm.detach() < 0.5).float().reshape(1,-1)]
pass
log_scalar[f'rdf{i}'] = accuracy(rm.detach()) # percentage of fake predicted as real
loss, log_scalar[f'ds{i}'] = tee_loss(loss, self.gan_loss.forward_fake(rm).mean())
pass
del rm
del realism_maps
loss.backward()
log_img['real'] = batch_real.img.detach()
batch_real.img.requires_grad_()
# forward real images
realism_maps = self.network.discriminator.forward(\
vgg=self.vgg, img=batch_real.img, robust_labels=batch_real.robust_labels, robust_img=batch_real.img,
fix_input=(self.reg_weight <= 0), run_discs=run_discs)
loss = 0
for i, rm in enumerate(realism_maps):
if rm is None:
continue
if self._log.isEnabledFor(logging.DEBUG):
log_img[f'realism_real_{i}'] = rm.detach()
pass
if i in pred_labels:
# predicted correctly, here real as real
pred_labels[i].append((rm.detach() > 0.5).float().reshape(1,-1))
else:
pred_labels[i] = [(rm.detach() > 0.5).float().reshape(1,-1)]
pass
log_scalar[f'rdr{i}'] = accuracy(rm.detach()) # percentage of real predicted as real
loss += self.gan_loss.forward_real(rm).mean()
pass
del rm
del realism_maps
# compute gradient penalty on real images
if self.reg_weight > 0:
loss.backward(retain_graph=True)
self._log.debug(f'Computing penalty on real: {loss} from i:{batch_real.img.shape}.')
reg_loss, log_scalar['reg'] = tee_loss(0, real_penalty(loss, batch_real.img))
(self.reg_weight * reg_loss).backward()
else:
loss.backward()
pass
pass
# update disc probabilities
if self.adaptive_backprop is not None:
self.adaptive_backprop.update(pred_labels)
pass
return log_scalar, log_img
def evaluate_test(self, batch_fake, batch_id):
new_img = self.network.generator(batch_fake)
return new_img, batch_fake.img, batch_fake.path[0].stem
def results_exists(self, id):
return (self.args.dbg_dir / self.args.weight_save / img_name).exists()
def save_result(self, results, id):
new_img, old_img, filename = results
# img_name = self.dataset_fake_val.fakes[id]
# if type(img_name) is tuple:
# img_name = [str(t) for t in img_name]
# img_name = '_'.join(img_name)+'.png'
# pass
img = (new_img[0,...].clamp(min=0,max=1).permute(1,2,0).cpu().numpy() * 255.0).astype(np.uint8)
imageio.imwrite(str(self.dbg_dir / self.weight_save / f'{filename}{self.result_ext}'), img[:,:,:3])
pass
pass
if __name__ == '__main__':
parser = ArgumentParser()
EPEExperiment.add_arguments(parser)
args = parser.parse_args()
ee.init_logging(args)
experiment = EPEExperiment(args)
experiment.run()