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import os
import shutil
import torch
import torchvision
from pytorch_fid import fid_score
from torch import distributed
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from tqdm.autonotebook import tqdm, trange
from .DiffAE_support_renderer import *
from .DiffAE_support_config import *
from .DiffAE_diffusion_diffusion import SpacedDiffusionBeatGans as Sampler
import lpips
from ssim import compute_ssim as ssim
def make_subset_loader(conf: TrainConfig,
dataset,
batch_size: int,
shuffle: bool,
parallel: bool,
drop_last=True):
dataset = SubsetDataset(dataset, size=conf.eval_num_images)
if parallel and distributed.is_initialized():
sampler = DistributedSampler(dataset, shuffle=shuffle)
else:
sampler = None
return DataLoader(
dataset,
batch_size=batch_size,
sampler=sampler,
# with sampler, use the sample instead of this option
shuffle=False if sampler else shuffle,
num_workers=conf.num_workers,
pin_memory=True,
drop_last=drop_last,
multiprocessing_context=get_context('fork'),
)
def evaluate_lpips(
sampler: Sampler,
model: Model,
conf: TrainConfig,
device,
val_data,
latent_sampler: Sampler = None,
use_inverted_noise: bool = False,
):
"""
compare the generated images from autoencoder on validation dataset
Args:
use_inversed_noise: the noise is also inverted from DDIM
"""
lpips_fn = lpips.LPIPS(net='alex').to(device)
val_loader = make_subset_loader(conf,
dataset=val_data,
batch_size=conf.batch_size_eval,
shuffle=False,
parallel=True)
model.eval()
with torch.no_grad():
scores = {
'lpips': [],
'mse': [],
'ssim': [],
'psnr': [],
}
for batch in tqdm(val_loader, desc='lpips'):
imgs = batch['img'].to(device)
if use_inverted_noise:
# inverse the noise
# with condition from the encoder
model_kwargs = {}
if conf.model_type.has_autoenc():
with torch.no_grad():
model_kwargs = model.encode(imgs)
x_T = sampler.ddim_reverse_sample_loop(
model=model,
x=imgs,
clip_denoised=True,
model_kwargs=model_kwargs)
x_T = x_T['sample']
else:
x_T = torch.randn((len(imgs), 3, conf.img_size, conf.img_size),
device=device)
if conf.model_type == ModelType.ddpm:
# the case where you want to calculate the inversion capability of the DDIM model
assert use_inverted_noise
pred_imgs = render_uncondition(
conf=conf,
model=model,
x_T=x_T,
sampler=sampler,
latent_sampler=latent_sampler,
)
else:
pred_imgs = render_condition(conf=conf,
model=model,
x_T=x_T,
x_start=imgs,
cond=None,
sampler=sampler)
# # returns {'cond', 'cond2'}
# conds = model.encode(imgs)
# pred_imgs = sampler.sample(model=model,
# noise=x_T,
# model_kwargs=conds)
# (n, 1, 1, 1) => (n, )
scores['lpips'].append(lpips_fn.forward(imgs, pred_imgs).view(-1))
# need to normalize into [0, 1]
norm_imgs = (imgs + 1) / 2
norm_pred_imgs = (pred_imgs + 1) / 2
# (n, )
scores['ssim'].append(
ssim(norm_imgs, norm_pred_imgs, size_average=False))
# (n, )
scores['mse'].append(
(norm_imgs - norm_pred_imgs).pow(2).mean(dim=[1, 2, 3]))
# (n, )
scores['psnr'].append(psnr(norm_imgs, norm_pred_imgs))
# (N, )
for key in scores.keys():
scores[key] = torch.cat(scores[key]).float()
model.train()
barrier()
# support multi-gpu
outs = {
key: [
torch.zeros(len(scores[key]), device=device)
for i in range(get_world_size())
]
for key in scores.keys()
}
for key in scores.keys():
all_gather(outs[key], scores[key])
# final scores
for key in scores.keys():
scores[key] = torch.cat(outs[key]).mean().item()
# {'lpips', 'mse', 'ssim'}
return scores
def psnr(img1, img2):
"""
Args:
img1: (n, c, h, w)
"""
v_max = 1.
# (n,)
mse = torch.mean((img1 - img2)**2, dim=[1, 2, 3])
return 20 * torch.log10(v_max / torch.sqrt(mse))
def evaluate_fid(
sampler: Sampler,
model: Model,
conf: TrainConfig,
device,
train_data,
val_data,
latent_sampler: Sampler = None,
conds_mean=None,
conds_std=None,
remove_cache: bool = True,
clip_latent_noise: bool = False,
):
assert conf.fid_cache is not None
if get_rank() == 0:
# no parallel
# validation data for a comparing FID
val_loader = make_subset_loader(conf,
dataset=val_data,
batch_size=conf.batch_size_eval,
shuffle=False,
parallel=False)
# put the val images to a directory
cache_dir = f'{conf.fid_cache}_{conf.eval_num_images}'
if (os.path.exists(cache_dir)
and len(os.listdir(cache_dir)) < conf.eval_num_images):
shutil.rmtree(cache_dir)
if not os.path.exists(cache_dir):
# write files to the cache
# the images are normalized, hence need to denormalize first
loader_to_path(val_loader, cache_dir, denormalize=True)
# create the generate dir
if os.path.exists(conf.generate_dir):
shutil.rmtree(conf.generate_dir)
os.makedirs(conf.generate_dir)
barrier()
world_size = get_world_size()
rank = get_rank()
batch_size = chunk_size(conf.batch_size_eval, rank, world_size)
def filename(idx):
return world_size * idx + rank
model.eval()
with torch.no_grad():
if conf.model_type.can_sample():
eval_num_images = chunk_size(conf.eval_num_images, rank,
world_size)
desc = "generating images"
for i in trange(0, eval_num_images, batch_size, desc=desc):
batch_size = min(batch_size, eval_num_images - i)
x_T = torch.randn(
(batch_size, 3, conf.img_size, conf.img_size),
device=device)
batch_images = render_uncondition(
conf=conf,
model=model,
x_T=x_T,
sampler=sampler,
latent_sampler=latent_sampler,
conds_mean=conds_mean,
conds_std=conds_std).cpu()
batch_images = (batch_images + 1) / 2
# keep the generated images
for j in range(len(batch_images)):
img_name = filename(i + j)
torchvision.utils.save_image(
batch_images[j],
os.path.join(conf.generate_dir, f'{img_name}.png'))
elif conf.model_type == ModelType.autoencoder:
if conf.train_mode.is_latent_diffusion():
# evaluate autoencoder + latent diffusion (doesn't give the images)
model: BeatGANsAutoencModel
eval_num_images = chunk_size(conf.eval_num_images, rank,
world_size)
desc = "generating images"
for i in trange(0, eval_num_images, batch_size, desc=desc):
batch_size = min(batch_size, eval_num_images - i)
x_T = torch.randn(
(batch_size, 3, conf.img_size, conf.img_size),
device=device)
batch_images = render_uncondition(
conf=conf,
model=model,
x_T=x_T,
sampler=sampler,
latent_sampler=latent_sampler,
conds_mean=conds_mean,
conds_std=conds_std,
clip_latent_noise=clip_latent_noise,
).cpu()
batch_images = (batch_images + 1) / 2
# keep the generated images
for j in range(len(batch_images)):
img_name = filename(i + j)
torchvision.utils.save_image(
batch_images[j],
os.path.join(conf.generate_dir, f'{img_name}.png'))
else:
# evaulate autoencoder (given the images)
# to make the FID fair, autoencoder must not see the validation dataset
# also shuffle to make it closer to unconditional generation
train_loader = make_subset_loader(conf,
dataset=train_data,
batch_size=batch_size,
shuffle=True,
parallel=True)
i = 0
for batch in tqdm(train_loader, desc='generating images'):
imgs = batch['img'].to(device)
x_T = torch.randn(
(len(imgs), 3, conf.img_size, conf.img_size),
device=device)
batch_images = render_condition(
conf=conf,
model=model,
x_T=x_T,
x_start=imgs,
cond=None,
sampler=sampler,
latent_sampler=latent_sampler).cpu()
# model: BeatGANsAutoencModel
# # returns {'cond', 'cond2'}
# conds = model.encode(imgs)
# batch_images = sampler.sample(model=model,
# noise=x_T,
# model_kwargs=conds).cpu()
# denormalize the images
batch_images = (batch_images + 1) / 2
# keep the generated images
for j in range(len(batch_images)):
img_name = filename(i + j)
torchvision.utils.save_image(
batch_images[j],
os.path.join(conf.generate_dir, f'{img_name}.png'))
i += len(imgs)
else:
raise NotImplementedError()
model.train()
barrier()
if get_rank() == 0:
fid = fid_score.calculate_fid_given_paths(
[cache_dir, conf.generate_dir],
batch_size,
device=device,
dims=2048)
# remove the cache
if remove_cache and os.path.exists(conf.generate_dir):
shutil.rmtree(conf.generate_dir)
barrier()
if get_rank() == 0:
# need to float it! unless the broadcasted value is wrong
fid = torch.tensor(float(fid), device=device)
broadcast(fid, 0)
else:
fid = torch.tensor(0., device=device)
broadcast(fid, 0)
fid = fid.item()
print(f'fid ({get_rank()}):', fid)
return fid
def loader_to_path(loader: DataLoader, path: str, denormalize: bool):
# not process safe!
if not os.path.exists(path):
os.makedirs(path)
# write the loader to files
i = 0
for batch in tqdm(loader, desc='copy images'):
imgs = batch['img']
if denormalize:
imgs = (imgs + 1) / 2
for j in range(len(imgs)):
torchvision.utils.save_image(imgs[j],
os.path.join(path, f'{i+j}.png'))
i += len(imgs) |