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import copy
import json
import os
import re
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
from numpy.lib.function_base import flip
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.callbacks import *
from torch import nn
from torch.cuda import amp
from torch.distributions import Categorical
from torch.optim.optimizer import Optimizer
from torch.utils.data.dataset import ConcatDataset, TensorDataset
from torchvision.utils import make_grid, save_image
from config import *
from dataset import *
from dist_utils import *
from lmdb_writer import *
from metrics import *
from renderer import *
class LitModel(pl.LightningModule):
def __init__(self, conf: TrainConfig):
super().__init__()
assert conf.train_mode != TrainMode.manipulate
if conf.seed is not None:
pl.seed_everything(conf.seed)
self.save_hyperparameters(conf.as_dict_jsonable())
self.conf = conf
self.model = conf.make_model_conf().make_model()
self.ema_model = copy.deepcopy(self.model)
self.ema_model.requires_grad_(False)
self.ema_model.eval()
model_size = 0
for param in self.model.parameters():
model_size += param.data.nelement()
print('Model params: %.2f M' % (model_size / 1024 / 1024))
self.sampler = conf.make_diffusion_conf().make_sampler()
self.eval_sampler = conf.make_eval_diffusion_conf().make_sampler()
# this is shared for both model and latent
self.T_sampler = conf.make_T_sampler()
if conf.train_mode.use_latent_net():
self.latent_sampler = conf.make_latent_diffusion_conf(
).make_sampler()
self.eval_latent_sampler = conf.make_latent_eval_diffusion_conf(
).make_sampler()
else:
self.latent_sampler = None
self.eval_latent_sampler = None
# initial variables for consistent sampling
self.register_buffer(
'x_T',
torch.randn(conf.sample_size, 3, conf.img_size, conf.img_size))
if conf.pretrain is not None:
print(f'loading pretrain ... {conf.pretrain.name}')
state = torch.load(conf.pretrain.path, map_location='cpu')
print('step:', state['global_step'])
self.load_state_dict(state['state_dict'], strict=False)
if conf.latent_infer_path is not None:
print('loading latent stats ...')
state = torch.load(conf.latent_infer_path)
self.conds = state['conds']
self.register_buffer('conds_mean', state['conds_mean'][None, :])
self.register_buffer('conds_std', state['conds_std'][None, :])
else:
self.conds_mean = None
self.conds_std = None
def normalize(self, cond):
cond = (cond - self.conds_mean.to(self.device)) / self.conds_std.to(
self.device)
return cond
def denormalize(self, cond):
cond = (cond * self.conds_std.to(self.device)) + self.conds_mean.to(
self.device)
return cond
def sample(self, N, device, T=None, T_latent=None):
if T is None:
sampler = self.eval_sampler
latent_sampler = self.latent_sampler
else:
sampler = self.conf._make_diffusion_conf(T).make_sampler()
latent_sampler = self.conf._make_latent_diffusion_conf(T_latent).make_sampler()
noise = torch.randn(N,
3,
self.conf.img_size,
self.conf.img_size,
device=device)
pred_img = render_uncondition(
self.conf,
self.ema_model,
noise,
sampler=sampler,
latent_sampler=latent_sampler,
conds_mean=self.conds_mean,
conds_std=self.conds_std,
)
pred_img = (pred_img + 1) / 2
return pred_img
def render(self, noise, cond=None, T=None):
if T is None:
sampler = self.eval_sampler
else:
sampler = self.conf._make_diffusion_conf(T).make_sampler()
if cond is not None:
pred_img = render_condition(self.conf,
self.ema_model,
noise,
sampler=sampler,
cond=cond)
else:
pred_img = render_uncondition(self.conf,
self.ema_model,
noise,
sampler=sampler,
latent_sampler=None)
pred_img = (pred_img + 1) / 2
return pred_img
def encode(self, x):
# TODO:
assert self.conf.model_type.has_autoenc()
cond = self.ema_model.encoder.forward(x)
return cond
def encode_stochastic(self, x, cond, T=None):
if T is None:
sampler = self.eval_sampler
else:
sampler = self.conf._make_diffusion_conf(T).make_sampler()
out = sampler.ddim_reverse_sample_loop(self.ema_model,
x,
model_kwargs={'cond': cond})
return out['sample']
def forward(self, noise=None, x_start=None, ema_model: bool = False):
with amp.autocast(False):
if ema_model:
model = self.ema_model
else:
model = self.model
gen = self.eval_sampler.sample(model=model,
noise=noise,
x_start=x_start)
return gen
def setup(self, stage=None) -> None:
"""
make datasets & seeding each worker separately
"""
##############################################
# NEED TO SET THE SEED SEPARATELY HERE
if self.conf.seed is not None:
seed = self.conf.seed * get_world_size() + self.global_rank
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
print('local seed:', seed)
##############################################
self.train_data = self.conf.make_dataset()
print('train data:', len(self.train_data))
self.val_data = self.train_data
print('val data:', len(self.val_data))
def _train_dataloader(self, drop_last=True):
"""
really make the dataloader
"""
# make sure to use the fraction of batch size
# the batch size is global!
conf = self.conf.clone()
conf.batch_size = self.batch_size
dataloader = conf.make_loader(self.train_data,
shuffle=True,
drop_last=drop_last)
return dataloader
def train_dataloader(self):
"""
return the dataloader, if diffusion mode => return image dataset
if latent mode => return the inferred latent dataset
"""
print('on train dataloader start ...')
if self.conf.train_mode.require_dataset_infer():
if self.conds is None:
# usually we load self.conds from a file
# so we do not need to do this again!
self.conds = self.infer_whole_dataset()
# need to use float32! unless the mean & std will be off!
# (1, c)
self.conds_mean.data = self.conds.float().mean(dim=0,
keepdim=True)
self.conds_std.data = self.conds.float().std(dim=0,
keepdim=True)
print('mean:', self.conds_mean.mean(), 'std:',
self.conds_std.mean())
# return the dataset with pre-calculated conds
conf = self.conf.clone()
conf.batch_size = self.batch_size
data = TensorDataset(self.conds)
return conf.make_loader(data, shuffle=True)
else:
return self._train_dataloader()
@property
def batch_size(self):
"""
local batch size for each worker
"""
ws = get_world_size()
assert self.conf.batch_size % ws == 0
return self.conf.batch_size // ws
@property
def num_samples(self):
"""
(global) batch size * iterations
"""
# batch size here is global!
# global_step already takes into account the accum batches
return self.global_step * self.conf.batch_size_effective
def is_last_accum(self, batch_idx):
"""
is it the last gradient accumulation loop?
used with gradient_accum > 1 and to see if the optimizer will perform "step" in this iteration or not
"""
return (batch_idx + 1) % self.conf.accum_batches == 0
def infer_whole_dataset(self,
with_render=False,
T_render=None,
render_save_path=None):
"""
predicting the latents given images using the encoder
Args:
both_flips: include both original and flipped images; no need, it's not an improvement
with_render: whether to also render the images corresponding to that latent
render_save_path: lmdb output for the rendered images
"""
data = self.conf.make_dataset()
if isinstance(data, CelebAlmdb) and data.crop_d2c:
# special case where we need the d2c crop
data.transform = make_transform(self.conf.img_size,
flip_prob=0,
crop_d2c=True)
else:
data.transform = make_transform(self.conf.img_size, flip_prob=0)
# data = SubsetDataset(data, 21)
loader = self.conf.make_loader(
data,
shuffle=False,
drop_last=False,
batch_size=self.conf.batch_size_eval,
parallel=True,
)
model = self.ema_model
model.eval()
conds = []
if with_render:
sampler = self.conf._make_diffusion_conf(
T=T_render or self.conf.T_eval).make_sampler()
if self.global_rank == 0:
writer = LMDBImageWriter(render_save_path,
format='webp',
quality=100)
else:
writer = nullcontext()
else:
writer = nullcontext()
with writer:
for batch in tqdm(loader, total=len(loader), desc='infer'):
with torch.no_grad():
# (n, c)
# print('idx:', batch['index'])
cond = model.encoder(batch['img'].to(self.device))
# used for reordering to match the original dataset
idx = batch['index']
idx = self.all_gather(idx)
if idx.dim() == 2:
idx = idx.flatten(0, 1)
argsort = idx.argsort()
if with_render:
noise = torch.randn(len(cond),
3,
self.conf.img_size,
self.conf.img_size,
device=self.device)
render = sampler.sample(model, noise=noise, cond=cond)
render = (render + 1) / 2
# print('render:', render.shape)
# (k, n, c, h, w)
render = self.all_gather(render)
if render.dim() == 5:
# (k*n, c)
render = render.flatten(0, 1)
# print('global_rank:', self.global_rank)
if self.global_rank == 0:
writer.put_images(render[argsort])
# (k, n, c)
cond = self.all_gather(cond)
if cond.dim() == 3:
# (k*n, c)
cond = cond.flatten(0, 1)
conds.append(cond[argsort].cpu())
# break
model.train()
# (N, c) cpu
conds = torch.cat(conds).float()
return conds
def training_step(self, batch, batch_idx):
"""
given an input, calculate the loss function
no optimization at this stage.
"""
with amp.autocast(False):
# batch size here is local!
# forward
if self.conf.train_mode.require_dataset_infer():
# this mode as pre-calculated cond
cond = batch[0]
if self.conf.latent_znormalize:
cond = (cond - self.conds_mean.to(
self.device)) / self.conds_std.to(self.device)
else:
imgs, idxs = batch['img'], batch['index']
# print(f'(rank {self.global_rank}) batch size:', len(imgs))
x_start = imgs
if self.conf.train_mode == TrainMode.diffusion:
"""
main training mode!!!
"""
# with numpy seed we have the problem that the sample t's are related!
t, weight = self.T_sampler.sample(len(x_start), x_start.device)
losses = self.sampler.training_losses(model=self.model,
x_start=x_start,
t=t)
elif self.conf.train_mode.is_latent_diffusion():
"""
training the latent variables!
"""
# diffusion on the latent
t, weight = self.T_sampler.sample(len(cond), cond.device)
latent_losses = self.latent_sampler.training_losses(
model=self.model.latent_net, x_start=cond, t=t)
# train only do the latent diffusion
losses = {
'latent': latent_losses['loss'],
'loss': latent_losses['loss']
}
else:
raise NotImplementedError()
loss = losses['loss'].mean()
# divide by accum batches to make the accumulated gradient exact!
for key in ['loss', 'vae', 'latent', 'mmd', 'chamfer', 'arg_cnt']:
if key in losses:
losses[key] = self.all_gather(losses[key]).mean()
if self.global_rank == 0:
self.logger.experiment.add_scalar('loss', losses['loss'],
self.num_samples)
for key in ['vae', 'latent', 'mmd', 'chamfer', 'arg_cnt']:
if key in losses:
self.logger.experiment.add_scalar(
f'loss/{key}', losses[key], self.num_samples)
return {'loss': loss}
def on_train_batch_end(self, outputs, batch, batch_idx: int,
dataloader_idx: int) -> None:
"""
after each training step ...
"""
if self.is_last_accum(batch_idx):
# only apply ema on the last gradient accumulation step,
# if it is the iteration that has optimizer.step()
if self.conf.train_mode == TrainMode.latent_diffusion:
# it trains only the latent hence change only the latent
ema(self.model.latent_net, self.ema_model.latent_net,
self.conf.ema_decay)
else:
ema(self.model, self.ema_model, self.conf.ema_decay)
# logging
if self.conf.train_mode.require_dataset_infer():
imgs = None
else:
imgs = batch['img']
self.log_sample(x_start=imgs)
self.evaluate_scores()
def on_before_optimizer_step(self, optimizer: Optimizer,
optimizer_idx: int) -> None:
# fix the fp16 + clip grad norm problem with pytorch lightinng
# this is the currently correct way to do it
if self.conf.grad_clip > 0:
# from trainer.params_grads import grads_norm, iter_opt_params
params = [
p for group in optimizer.param_groups for p in group['params']
]
# print('before:', grads_norm(iter_opt_params(optimizer)))
torch.nn.utils.clip_grad_norm_(params,
max_norm=self.conf.grad_clip)
# print('after:', grads_norm(iter_opt_params(optimizer)))
def log_sample(self, x_start):
"""
put images to the tensorboard
"""
def do(model,
postfix,
use_xstart,
save_real=False,
no_latent_diff=False,
interpolate=False):
model.eval()
with torch.no_grad():
all_x_T = self.split_tensor(self.x_T)
batch_size = min(len(all_x_T), self.conf.batch_size_eval)
# allow for superlarge models
loader = DataLoader(all_x_T, batch_size=batch_size)
Gen = []
for x_T in loader:
if use_xstart:
_xstart = x_start[:len(x_T)]
else:
_xstart = None
if self.conf.train_mode.is_latent_diffusion(
) and not use_xstart:
# diffusion of the latent first
gen = render_uncondition(
conf=self.conf,
model=model,
x_T=x_T,
sampler=self.eval_sampler,
latent_sampler=self.eval_latent_sampler,
conds_mean=self.conds_mean,
conds_std=self.conds_std)
else:
if not use_xstart and self.conf.model_type.has_noise_to_cond(
):
model: BeatGANsAutoencModel
# special case, it may not be stochastic, yet can sample
cond = torch.randn(len(x_T),
self.conf.style_ch,
device=self.device)
cond = model.noise_to_cond(cond)
else:
if interpolate:
with amp.autocast(self.conf.fp16):
cond = model.encoder(_xstart)
i = torch.randperm(len(cond))
cond = (cond + cond[i]) / 2
else:
cond = None
gen = self.eval_sampler.sample(model=model,
noise=x_T,
cond=cond,
x_start=_xstart)
Gen.append(gen)
gen = torch.cat(Gen)
gen = self.all_gather(gen)
if gen.dim() == 5:
# (n, c, h, w)
gen = gen.flatten(0, 1)
if save_real and use_xstart:
# save the original images to the tensorboard
real = self.all_gather(_xstart)
if real.dim() == 5:
real = real.flatten(0, 1)
if self.global_rank == 0:
grid_real = (make_grid(real) + 1) / 2
self.logger.experiment.add_image(
f'sample{postfix}/real', grid_real,
self.num_samples)
if self.global_rank == 0:
# save samples to the tensorboard
grid = (make_grid(gen) + 1) / 2
sample_dir = os.path.join(self.conf.logdir,
f'sample{postfix}')
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
path = os.path.join(sample_dir,
'%d.png' % self.num_samples)
save_image(grid, path)
self.logger.experiment.add_image(f'sample{postfix}', grid,
self.num_samples)
model.train()
if self.conf.sample_every_samples > 0 and is_time(
self.num_samples, self.conf.sample_every_samples,
self.conf.batch_size_effective):
if self.conf.train_mode.require_dataset_infer():
do(self.model, '', use_xstart=False)
do(self.ema_model, '_ema', use_xstart=False)
else:
if self.conf.model_type.has_autoenc(
) and self.conf.model_type.can_sample():
do(self.model, '', use_xstart=False)
do(self.ema_model, '_ema', use_xstart=False)
# autoencoding mode
do(self.model, '_enc', use_xstart=True, save_real=True)
do(self.ema_model,
'_enc_ema',
use_xstart=True,
save_real=True)
elif self.conf.train_mode.use_latent_net():
do(self.model, '', use_xstart=False)
do(self.ema_model, '_ema', use_xstart=False)
# autoencoding mode
do(self.model, '_enc', use_xstart=True, save_real=True)
do(self.model,
'_enc_nodiff',
use_xstart=True,
save_real=True,
no_latent_diff=True)
do(self.ema_model,
'_enc_ema',
use_xstart=True,
save_real=True)
else:
do(self.model, '', use_xstart=True, save_real=True)
do(self.ema_model, '_ema', use_xstart=True, save_real=True)
def evaluate_scores(self):
"""
evaluate FID and other scores during training (put to the tensorboard)
For, FID. It is a fast version with 5k images (gold standard is 50k).
Don't use its results in the paper!
"""
def fid(model, postfix):
score = evaluate_fid(self.eval_sampler,
model,
self.conf,
device=self.device,
train_data=self.train_data,
val_data=self.val_data,
latent_sampler=self.eval_latent_sampler,
conds_mean=self.conds_mean,
conds_std=self.conds_std)
if self.global_rank == 0:
self.logger.experiment.add_scalar(f'FID{postfix}', score,
self.num_samples)
if not os.path.exists(self.conf.logdir):
os.makedirs(self.conf.logdir)
with open(os.path.join(self.conf.logdir, 'eval.txt'),
'a') as f:
metrics = {
f'FID{postfix}': score,
'num_samples': self.num_samples,
}
f.write(json.dumps(metrics) + "\n")
def lpips(model, postfix):
if self.conf.model_type.has_autoenc(
) and self.conf.train_mode.is_autoenc():
# {'lpips', 'ssim', 'mse'}
score = evaluate_lpips(self.eval_sampler,
model,
self.conf,
device=self.device,
val_data=self.val_data,
latent_sampler=self.eval_latent_sampler)
if self.global_rank == 0:
for key, val in score.items():
self.logger.experiment.add_scalar(
f'{key}{postfix}', val, self.num_samples)
if self.conf.eval_every_samples > 0 and self.num_samples > 0 and is_time(
self.num_samples, self.conf.eval_every_samples,
self.conf.batch_size_effective):
print(f'eval fid @ {self.num_samples}')
lpips(self.model, '')
fid(self.model, '')
if self.conf.eval_ema_every_samples > 0 and self.num_samples > 0 and is_time(
self.num_samples, self.conf.eval_ema_every_samples,
self.conf.batch_size_effective):
print(f'eval fid ema @ {self.num_samples}')
fid(self.ema_model, '_ema')
# it's too slow
# lpips(self.ema_model, '_ema')
def configure_optimizers(self):
out = {}
if self.conf.optimizer == OptimizerType.adam:
optim = torch.optim.Adam(self.model.parameters(),
lr=self.conf.lr,
weight_decay=self.conf.weight_decay)
elif self.conf.optimizer == OptimizerType.adamw:
optim = torch.optim.AdamW(self.model.parameters(),
lr=self.conf.lr,
weight_decay=self.conf.weight_decay)
else:
raise NotImplementedError()
out['optimizer'] = optim
if self.conf.warmup > 0:
sched = torch.optim.lr_scheduler.LambdaLR(optim,
lr_lambda=WarmupLR(
self.conf.warmup))
out['lr_scheduler'] = {
'scheduler': sched,
'interval': 'step',
}
return out
def split_tensor(self, x):
"""
extract the tensor for a corresponding "worker" in the batch dimension
Args:
x: (n, c)
Returns: x: (n_local, c)
"""
n = len(x)
rank = self.global_rank
world_size = get_world_size()
# print(f'rank: {rank}/{world_size}')
per_rank = n // world_size
return x[rank * per_rank:(rank + 1) * per_rank]
def test_step(self, batch, *args, **kwargs):
"""
for the "eval" mode.
We first select what to do according to the "conf.eval_programs".
test_step will only run for "one iteration" (it's a hack!).
We just want the multi-gpu support.
"""
# make sure you seed each worker differently!
self.setup()
# it will run only one step!
print('global step:', self.global_step)
"""
"infer" = predict the latent variables using the encoder on the whole dataset
"""
if 'infer' in self.conf.eval_programs:
if 'infer' in self.conf.eval_programs:
print('infer ...')
conds = self.infer_whole_dataset().float()
# NOTE: always use this path for the latent.pkl files
save_path = f'checkpoints/{self.conf.name}/latent.pkl'
else:
raise NotImplementedError()
if self.global_rank == 0:
conds_mean = conds.mean(dim=0)
conds_std = conds.std(dim=0)
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
torch.save(
{
'conds': conds,
'conds_mean': conds_mean,
'conds_std': conds_std,
}, save_path)
"""
"infer+render" = predict the latent variables using the encoder on the whole dataset
THIS ALSO GENERATE CORRESPONDING IMAGES
"""
# infer + reconstruction quality of the input
for each in self.conf.eval_programs:
if each.startswith('infer+render'):
m = re.match(r'infer\+render([0-9]+)', each)
if m is not None:
T = int(m[1])
self.setup()
print(f'infer + reconstruction T{T} ...')
conds = self.infer_whole_dataset(
with_render=True,
T_render=T,
render_save_path=
f'latent_infer_render{T}/{self.conf.name}.lmdb',
)
save_path = f'latent_infer_render{T}/{self.conf.name}.pkl'
conds_mean = conds.mean(dim=0)
conds_std = conds.std(dim=0)
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
torch.save(
{
'conds': conds,
'conds_mean': conds_mean,
'conds_std': conds_std,
}, save_path)
# evals those "fidXX"
"""
"fid<T>" = unconditional generation (conf.train_mode = diffusion).
Note: Diff. autoenc will still receive real images in this mode.
"fid<T>,<T_latent>" = unconditional generation for latent models (conf.train_mode = latent_diffusion).
Note: Diff. autoenc will still NOT receive real images in this made.
but you need to make sure that the train_mode is latent_diffusion.
"""
for each in self.conf.eval_programs:
if each.startswith('fid'):
m = re.match(r'fid\(([0-9]+),([0-9]+)\)', each)
clip_latent_noise = False
if m is not None:
# eval(T1,T2)
T = int(m[1])
T_latent = int(m[2])
print(f'evaluating FID T = {T}... latent T = {T_latent}')
else:
m = re.match(r'fidclip\(([0-9]+),([0-9]+)\)', each)
if m is not None:
# fidclip(T1,T2)
T = int(m[1])
T_latent = int(m[2])
clip_latent_noise = True
print(
f'evaluating FID (clip latent noise) T = {T}... latent T = {T_latent}'
)
else:
# evalT
_, T = each.split('fid')
T = int(T)
T_latent = None
print(f'evaluating FID T = {T}...')
self.train_dataloader()
sampler = self.conf._make_diffusion_conf(T=T).make_sampler()
if T_latent is not None:
latent_sampler = self.conf._make_latent_diffusion_conf(
T=T_latent).make_sampler()
else:
latent_sampler = None
conf = self.conf.clone()
conf.eval_num_images = 50_000
score = evaluate_fid(
sampler,
self.ema_model,
conf,
device=self.device,
train_data=self.train_data,
val_data=self.val_data,
latent_sampler=latent_sampler,
conds_mean=self.conds_mean,
conds_std=self.conds_std,
remove_cache=False,
clip_latent_noise=clip_latent_noise,
)
if T_latent is None:
self.log(f'fid_ema_T{T}', score)
else:
name = 'fid'
if clip_latent_noise:
name += '_clip'
name += f'_ema_T{T}_Tlatent{T_latent}'
self.log(name, score)
"""
"recon<T>" = reconstruction & autoencoding (without noise inversion)
"""
for each in self.conf.eval_programs:
if each.startswith('recon'):
self.model: BeatGANsAutoencModel
_, T = each.split('recon')
T = int(T)
print(f'evaluating reconstruction T = {T}...')
sampler = self.conf._make_diffusion_conf(T=T).make_sampler()
conf = self.conf.clone()
# eval whole val dataset
conf.eval_num_images = len(self.val_data)
# {'lpips', 'mse', 'ssim'}
score = evaluate_lpips(sampler,
self.ema_model,
conf,
device=self.device,
val_data=self.val_data,
latent_sampler=None)
for k, v in score.items():
self.log(f'{k}_ema_T{T}', v)
"""
"inv<T>" = reconstruction with noise inversion
"""
for each in self.conf.eval_programs:
if each.startswith('inv'):
self.model: BeatGANsAutoencModel
_, T = each.split('inv')
T = int(T)
print(
f'evaluating reconstruction with noise inversion T = {T}...'
)
sampler = self.conf._make_diffusion_conf(T=T).make_sampler()
conf = self.conf.clone()
# eval whole val dataset
conf.eval_num_images = len(self.val_data)
# {'lpips', 'mse', 'ssim'}
score = evaluate_lpips(sampler,
self.ema_model,
conf,
device=self.device,
val_data=self.val_data,
latent_sampler=None,
use_inverted_noise=True)
for k, v in score.items():
self.log(f'{k}_inv_ema_T{T}', v)
def ema(source, target, decay):
source_dict = source.state_dict()
target_dict = target.state_dict()
for key in source_dict.keys():
target_dict[key].data.copy_(target_dict[key].data * decay +
source_dict[key].data * (1 - decay))
class WarmupLR:
def __init__(self, warmup) -> None:
self.warmup = warmup
def __call__(self, step):
return min(step, self.warmup) / self.warmup
def is_time(num_samples, every, step_size):
closest = (num_samples // every) * every
return num_samples - closest < step_size
def train(conf: TrainConfig, gpus, nodes=1, mode: str = 'train'):
print('conf:', conf.name)
# assert not (conf.fp16 and conf.grad_clip > 0
# ), 'pytorch lightning has bug with amp + gradient clipping'
model = LitModel(conf)
if not os.path.exists(conf.logdir):
os.makedirs(conf.logdir)
checkpoint = ModelCheckpoint(dirpath=f'{conf.logdir}',
save_last=True,
save_top_k=1,
every_n_train_steps=conf.save_every_samples //
conf.batch_size_effective)
checkpoint_path = f'{conf.logdir}/last.ckpt'
print('ckpt path:', checkpoint_path)
if os.path.exists(checkpoint_path):
resume = checkpoint_path
print('resume!')
else:
if conf.continue_from is not None:
# continue from a checkpoint
resume = conf.continue_from.path
else:
resume = None
tb_logger = pl_loggers.TensorBoardLogger(save_dir=conf.logdir,
name=None,
version='')
# from pytorch_lightning.
plugins = []
if len(gpus) == 1 and nodes == 1:
accelerator = None
else:
accelerator = 'ddp'
from pytorch_lightning.plugins import DDPPlugin
# important for working with gradient checkpoint
plugins.append(DDPPlugin(find_unused_parameters=False))
trainer = pl.Trainer(
max_steps=conf.total_samples // conf.batch_size_effective,
resume_from_checkpoint=resume,
gpus=gpus,
num_nodes=nodes,
accelerator=accelerator,
precision=16 if conf.fp16 else 32,
callbacks=[
checkpoint,
LearningRateMonitor(),
],
# clip in the model instead
# gradient_clip_val=conf.grad_clip,
replace_sampler_ddp=True,
logger=tb_logger,
accumulate_grad_batches=conf.accum_batches,
plugins=plugins,
)
if mode == 'train':
trainer.fit(model)
elif mode == 'eval':
# load the latest checkpoint
# perform lpips
# dummy loader to allow calling "test_step"
dummy = DataLoader(TensorDataset(torch.tensor([0.] * conf.batch_size)),
batch_size=conf.batch_size)
eval_path = conf.eval_path or checkpoint_path
# conf.eval_num_images = 50
print('loading from:', eval_path)
state = torch.load(eval_path, map_location='cpu')
print('step:', state['global_step'])
model.load_state_dict(state['state_dict'])
# trainer.fit(model)
out = trainer.test(model, dataloaders=dummy)
# first (and only) loader
out = out[0]
print(out)
if get_rank() == 0:
# save to tensorboard
for k, v in out.items():
tb_logger.experiment.add_scalar(
k, v, state['global_step'] * conf.batch_size_effective)
# # save to file
# # make it a dict of list
# for k, v in out.items():
# out[k] = [v]
tgt = f'evals/{conf.name}.txt'
dirname = os.path.dirname(tgt)
if not os.path.exists(dirname):
os.makedirs(dirname)
with open(tgt, 'a') as f:
f.write(json.dumps(out) + "\n")
# pd.DataFrame(out).to_csv(tgt)
else:
raise NotImplementedError()
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