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import os
import clip
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import criteria.clip_loss as clip_loss
from criteria import id_loss
from mapper.datasets.latents_dataset import LatentsDataset
from mapper.styleclip_mapper import StyleCLIPMapper
from mapper.training.ranger import Ranger
from mapper.training import train_utils
class Coach:
def __init__(self, opts):
self.opts = opts
self.global_step = 0
self.device = 'cuda:0'
self.opts.device = self.device
# Initialize network
self.net = StyleCLIPMapper(self.opts).to(self.device)
# Initialize loss
if self.opts.id_lambda > 0:
self.id_loss = id_loss.IDLoss(self.opts).to(self.device).eval()
if self.opts.clip_lambda > 0:
self.clip_loss = clip_loss.CLIPLoss(opts)
if self.opts.latent_l2_lambda > 0:
self.latent_l2_loss = nn.MSELoss().to(self.device).eval()
# Initialize optimizer
self.optimizer = self.configure_optimizers()
# Initialize dataset
self.train_dataset, self.test_dataset = self.configure_datasets()
self.train_dataloader = DataLoader(self.train_dataset,
batch_size=self.opts.batch_size,
shuffle=True,
num_workers=int(self.opts.workers),
drop_last=True)
self.test_dataloader = DataLoader(self.test_dataset,
batch_size=self.opts.test_batch_size,
shuffle=False,
num_workers=int(self.opts.test_workers),
drop_last=True)
self.text_inputs = torch.cat([clip.tokenize(self.opts.description)]).cuda()
# Initialize logger
log_dir = os.path.join(opts.exp_dir, 'logs')
os.makedirs(log_dir, exist_ok=True)
self.log_dir = log_dir
self.logger = SummaryWriter(log_dir=log_dir)
# Initialize checkpoint dir
self.checkpoint_dir = os.path.join(opts.exp_dir, 'checkpoints')
os.makedirs(self.checkpoint_dir, exist_ok=True)
self.best_val_loss = None
if self.opts.save_interval is None:
self.opts.save_interval = self.opts.max_steps
def train(self):
self.net.train()
while self.global_step < self.opts.max_steps:
for batch_idx, batch in enumerate(self.train_dataloader):
self.optimizer.zero_grad()
w = batch
w = w.to(self.device)
with torch.no_grad():
x, _ = self.net.decoder([w], input_is_latent=True, randomize_noise=False, truncation=1)
w_hat = w + 0.1 * self.net.mapper(w)
x_hat, w_hat = self.net.decoder([w_hat], input_is_latent=True, return_latents=True, randomize_noise=False, truncation=1)
loss, loss_dict = self.calc_loss(w, x, w_hat, x_hat)
loss.backward()
self.optimizer.step()
# Logging related
if self.global_step % self.opts.image_interval == 0 or (
self.global_step < 1000 and self.global_step % 1000 == 0):
self.parse_and_log_images(x, x_hat, title='images_train')
if self.global_step % self.opts.board_interval == 0:
self.print_metrics(loss_dict, prefix='train')
self.log_metrics(loss_dict, prefix='train')
# Validation related
val_loss_dict = None
if self.global_step % self.opts.val_interval == 0 or self.global_step == self.opts.max_steps:
val_loss_dict = self.validate()
if val_loss_dict and (self.best_val_loss is None or val_loss_dict['loss'] < self.best_val_loss):
self.best_val_loss = val_loss_dict['loss']
self.checkpoint_me(val_loss_dict, is_best=True)
if self.global_step % self.opts.save_interval == 0 or self.global_step == self.opts.max_steps:
if val_loss_dict is not None:
self.checkpoint_me(val_loss_dict, is_best=False)
else:
self.checkpoint_me(loss_dict, is_best=False)
if self.global_step == self.opts.max_steps:
print('OMG, finished training!')
break
self.global_step += 1
def validate(self):
self.net.eval()
agg_loss_dict = []
for batch_idx, batch in enumerate(self.test_dataloader):
if batch_idx > 200:
break
w = batch
with torch.no_grad():
w = w.to(self.device).float()
x, _ = self.net.decoder([w], input_is_latent=True, randomize_noise=True, truncation=1)
w_hat = w + 0.1 * self.net.mapper(w)
x_hat, _ = self.net.decoder([w_hat], input_is_latent=True, randomize_noise=True, truncation=1)
loss, cur_loss_dict = self.calc_loss(w, x, w_hat, x_hat)
agg_loss_dict.append(cur_loss_dict)
# Logging related
self.parse_and_log_images(x, x_hat, title='images_val', index=batch_idx)
# For first step just do sanity test on small amount of data
if self.global_step == 0 and batch_idx >= 4:
self.net.train()
return None # Do not log, inaccurate in first batch
loss_dict = train_utils.aggregate_loss_dict(agg_loss_dict)
self.log_metrics(loss_dict, prefix='test')
self.print_metrics(loss_dict, prefix='test')
self.net.train()
return loss_dict
def checkpoint_me(self, loss_dict, is_best):
save_name = 'best_model.pt' if is_best else 'iteration_{}.pt'.format(self.global_step)
save_dict = self.__get_save_dict()
checkpoint_path = os.path.join(self.checkpoint_dir, save_name)
torch.save(save_dict, checkpoint_path)
with open(os.path.join(self.checkpoint_dir, 'timestamp.txt'), 'a') as f:
if is_best:
f.write('**Best**: Step - {}, Loss - {:.3f} \n{}\n'.format(self.global_step, self.best_val_loss, loss_dict))
else:
f.write('Step - {}, \n{}\n'.format(self.global_step, loss_dict))
def configure_optimizers(self):
params = list(self.net.mapper.parameters())
if self.opts.optim_name == 'adam':
optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate)
else:
optimizer = Ranger(params, lr=self.opts.learning_rate)
return optimizer
def configure_datasets(self):
if self.opts.latents_train_path:
train_latents = torch.load(self.opts.latents_train_path)
else:
train_latents_z = torch.randn(self.opts.train_dataset_size, 512).cuda()
train_latents = []
for b in range(self.opts.train_dataset_size // self.opts.batch_size):
with torch.no_grad():
_, train_latents_b = self.net.decoder([train_latents_z[b: b + self.opts.batch_size]],
truncation=0.7, truncation_latent=self.net.latent_avg, return_latents=True)
train_latents.append(train_latents_b)
train_latents = torch.cat(train_latents)
if self.opts.latents_test_path:
test_latents = torch.load(self.opts.latents_test_path)
else:
test_latents_z = torch.randn(self.opts.train_dataset_size, 512).cuda()
test_latents = []
for b in range(self.opts.test_dataset_size // self.opts.test_batch_size):
with torch.no_grad():
_, test_latents_b = self.net.decoder([test_latents_z[b: b + self.opts.test_batch_size]],
truncation=0.7, truncation_latent=self.net.latent_avg, return_latents=True)
test_latents.append(test_latents_b)
test_latents = torch.cat(test_latents)
train_dataset_celeba = LatentsDataset(latents=train_latents.cpu(),
opts=self.opts)
test_dataset_celeba = LatentsDataset(latents=test_latents.cpu(),
opts=self.opts)
train_dataset = train_dataset_celeba
test_dataset = test_dataset_celeba
print("Number of training samples: {}".format(len(train_dataset)))
print("Number of test samples: {}".format(len(test_dataset)))
return train_dataset, test_dataset
def calc_loss(self, w, x, w_hat, x_hat):
loss_dict = {}
loss = 0.0
if self.opts.id_lambda > 0:
loss_id, sim_improvement = self.id_loss(x_hat, x)
loss_dict['loss_id'] = float(loss_id)
loss_dict['id_improve'] = float(sim_improvement)
loss = loss_id * self.opts.id_lambda
if self.opts.clip_lambda > 0:
loss_clip = self.clip_loss(x_hat, self.text_inputs).mean()
loss_dict['loss_clip'] = float(loss_clip)
loss += loss_clip * self.opts.clip_lambda
if self.opts.latent_l2_lambda > 0:
loss_l2_latent = self.latent_l2_loss(w_hat, w)
loss_dict['loss_l2_latent'] = float(loss_l2_latent)
loss += loss_l2_latent * self.opts.latent_l2_lambda
loss_dict['loss'] = float(loss)
return loss, loss_dict
def log_metrics(self, metrics_dict, prefix):
for key, value in metrics_dict.items():
#pass
print(f"step: {self.global_step} \t metric: {prefix}/{key} \t value: {value}")
self.logger.add_scalar('{}/{}'.format(prefix, key), value, self.global_step)
def print_metrics(self, metrics_dict, prefix):
print('Metrics for {}, step {}'.format(prefix, self.global_step))
for key, value in metrics_dict.items():
print('\t{} = '.format(key), value)
def parse_and_log_images(self, x, x_hat, title, index=None):
if index is None:
path = os.path.join(self.log_dir, title, f'{str(self.global_step).zfill(5)}.jpg')
else:
path = os.path.join(self.log_dir, title, f'{str(self.global_step).zfill(5)}_{str(index).zfill(5)}.jpg')
os.makedirs(os.path.dirname(path), exist_ok=True)
torchvision.utils.save_image(torch.cat([x.detach().cpu(), x_hat.detach().cpu()]), path,
normalize=True, scale_each=True, range=(-1, 1), nrow=self.opts.batch_size)
def __get_save_dict(self):
save_dict = {
'state_dict': self.net.state_dict(),
'opts': vars(self.opts)
}
return save_dict |