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import os
from contextlib import contextmanager, nullcontext
import torch
import wandb
from pytorch_lightning import LightningModule
from torch.nn.functional import mse_loss
from torch.nn.functional import sigmoid
from torch.optim import AdamW
from torch_ema import ExponentialMovingAverage as EMA
from torchmetrics.image.fid import FrechetInceptionDistance
from torchmetrics.image.inception import InceptionScore
from torchvision.transforms.functional import to_pil_image
from torchvision.utils import save_image
from utils.create_arch import create_arch
from huggingface_hub import PyTorchModelHubMixin
class MMSERectifiedFlow(LightningModule,
PyTorchModelHubMixin,
pipeline_tag="image-to-image",
license="mit",
):
def __init__(self,
stage,
arch,
conditional=False,
mmse_model_ckpt_path=None,
mmse_model_arch=None,
lr=5e-4,
weight_decay=1e-3,
betas=(0.9, 0.95),
mmse_noise_std=0.1,
num_flow_steps=50,
ema_decay=0.9999,
eps=0.0,
t_schedule='stratified_uniform',
*args,
**kwargs
):
super().__init__()
self.save_hyperparameters(logger=False)
if stage == 'flow':
if conditional:
condition_channels = 3
else:
condition_channels = 0
if mmse_model_arch is None and 'colorization' in kwargs and kwargs['colorization']:
condition_channels //= 3
self.model = create_arch(arch, condition_channels)
self.mmse_model = create_arch(mmse_model_arch, 0) if mmse_model_arch is not None else None
if mmse_model_ckpt_path is not None:
ckpt = torch.load(mmse_model_ckpt_path, map_location="cpu")
if mmse_model_arch is None:
mmse_model_arch = ckpt['hyper_parameters']['arch']
self.mmse_model = create_arch(mmse_model_arch, 0)
if 'ema' in ckpt:
# ema_decay doesn't affect anything here, because we are doing load_state_dict
mmse_ema = EMA(self.mmse_model.parameters(), decay=ema_decay)
mmse_ema.load_state_dict(ckpt['ema'])
mmse_ema.copy_to()
elif 'params_ema' in ckpt:
self.mmse_model.load_state_dict(ckpt['params_ema'])
else:
state_dict = ckpt['state_dict']
state_dict = {layer_name.replace('model.', ''): weights for layer_name, weights in
state_dict.items()}
state_dict = {layer_name.replace('module.', ''): weights for layer_name, weights in
state_dict.items()}
self.mmse_model.load_state_dict(state_dict)
for param in self.mmse_model.parameters():
param.requires_grad = False
self.mmse_model.eval()
else:
assert stage == 'mmse' or stage == 'naive_flow'
assert not conditional
self.model = create_arch(arch, 0)
self.mmse_model = None
if 'flow' in stage:
self.fid = FrechetInceptionDistance(reset_real_features=True, normalize=True)
self.inception_score = InceptionScore(normalize=True)
self.ema = EMA(self.model.parameters(), decay=ema_decay) if self.ema_wanted else None
self.test_results_path = None
@property
def ema_wanted(self):
return self.hparams.ema_decay != -1
def on_save_checkpoint(self, checkpoint: dict) -> None:
if self.ema_wanted:
checkpoint['ema'] = self.ema.state_dict()
return super().on_save_checkpoint(checkpoint)
def on_load_checkpoint(self, checkpoint: dict) -> None:
if self.ema_wanted:
self.ema.load_state_dict(checkpoint['ema'])
return super().on_load_checkpoint(checkpoint)
def on_before_zero_grad(self, optimizer) -> None:
if self.ema_wanted:
self.ema.update(self.model.parameters())
return super().on_before_zero_grad(optimizer)
def to(self, *args, **kwargs):
if self.ema_wanted:
self.ema.to(*args, **kwargs)
return super().to(*args, **kwargs)
# This will use the contextmanager of ema, to copy the EMA weights to the flow model during validation, and then restore them for training.
@contextmanager
def maybe_ema(self):
ema = self.ema
ctx = nullcontext if ema is None else ema.average_parameters
yield ctx
def forward_mmse(self, y):
return self.model(y).clip(0, 1)
def forward_flow(self, x_t, t, y=None):
if self.hparams.conditional:
if self.mmse_model is not None:
with torch.no_grad():
self.mmse_model.eval()
condition = self.mmse_model(y).clip(0, 1)
else:
condition = y
x_t = torch.cat((x_t, condition), dim=1)
return self.model(x_t, t)
def forward(self, x_t, t, y):
if 'flow' in self.hparams.stage:
return self.forward_flow(x_t, t, y)
else:
return self.forward_mmse(y)
@torch.no_grad()
def create_source_distribution_samples(self, x, y, non_noisy_z0):
with torch.no_grad():
if self.hparams.conditional:
source_dist_samples = torch.randn_like(x)
else:
if self.hparams.stage == 'flow':
if non_noisy_z0 is None:
self.mmse_model.eval()
non_noisy_z0 = self.mmse_model(y).clip(0, 1)
source_dist_samples = non_noisy_z0 + torch.randn_like(non_noisy_z0) * self.hparams.mmse_noise_std
else:
assert self.hparams.stage == 'naive_flow'
if non_noisy_z0 is not None:
source_dist_samples = non_noisy_z0
else:
source_dist_samples = y
if source_dist_samples.shape[1] != x.shape[1]:
assert source_dist_samples.shape[1] == 1 # Colorization
source_dist_samples = source_dist_samples.expand(-1, x.shape[1], -1, -1)
if self.hparams.mmse_noise_std is not None:
source_dist_samples = source_dist_samples + torch.randn_like(source_dist_samples) * self.hparams.mmse_noise_std
return source_dist_samples
@staticmethod
def stratified_uniform(bs, group=0, groups=1, dtype=None, device=None):
if groups <= 0:
raise ValueError(f"groups must be positive, got {groups}")
if group < 0 or group >= groups:
raise ValueError(f"group must be in [0, {groups})")
n = bs * groups
offsets = torch.arange(group, n, groups, dtype=dtype, device=device)
u = torch.rand(bs, dtype=dtype, device=device)
return ((offsets + u) / n).view(bs, 1, 1, 1)
def generate_random_t(self, bs, dtype=None):
if self.hparams.t_schedule == 'logit-normal':
return sigmoid(torch.randn(bs, 1, 1, 1, device=self.device)) * (1.0 - self.hparams.eps) + self.hparams.eps
elif self.hparams.t_schedule == 'uniform':
return torch.rand(bs, 1, 1, 1, device=self.device) * (1.0 - self.hparams.eps) + self.hparams.eps
elif self.hparams.t_schedule == 'stratified_uniform':
return self.stratified_uniform(bs, self.trainer.global_rank, self.trainer.world_size, dtype=dtype,
device=self.device) * (1.0 - self.hparams.eps) + self.hparams.eps
else:
raise NotImplementedError()
def training_step(self, batch, batch_idx):
x = batch['x']
y = batch['y']
non_noisy_z0 = batch['non_noisy_z0'] if 'non_noisy_z0' in batch else None
if 'flow' in self.hparams.stage:
with torch.no_grad():
t = self.generate_random_t(x.shape[0], dtype=x.dtype)
source_dist_samples = self.create_source_distribution_samples(x, y, non_noisy_z0)
x_t = t * x + (1.0 - t) * source_dist_samples
v_t = self(x_t, t.squeeze(), y)
loss = mse_loss(v_t, x - source_dist_samples)
else:
xhat = self(x_t=None, t=None, y=y)
loss = mse_loss(xhat, x)
self.log("train/loss", loss)
return loss
@torch.no_grad()
def generate_reconstructions(self, x, y, non_noisy_z0, num_flow_steps, result_device):
with self.maybe_ema():
if 'flow' in self.hparams.stage:
source_dist_samples = self.create_source_distribution_samples(x, y, non_noisy_z0)
dt = (1.0 / num_flow_steps) * (1.0 - self.hparams.eps)
x_t_next = source_dist_samples.clone()
x_t_seq = [x_t_next]
t_one = torch.ones(x.shape[0], device=self.device)
for i in range(num_flow_steps):
num_t = (i / num_flow_steps) * (1.0 - self.hparams.eps) + self.hparams.eps
v_t_next = self(x_t=x_t_next, t=t_one * num_t, y=y).to(x_t_next.dtype)
x_t_next = x_t_next.clone() + v_t_next * dt
x_t_seq.append(x_t_next.to(result_device))
xhat = x_t_seq[-1].clip(0, 1).to(torch.float32)
source_dist_samples = source_dist_samples.to(result_device)
else:
xhat = self(x_t=None, t=None, y=y).to(torch.float32)
x_t_seq = None
source_dist_samples = None
return xhat.to(result_device), x_t_seq, source_dist_samples
def validation_step(self, batch, batch_idx):
x = batch['x']
y = batch['y']
non_noisy_z0 = batch['non_noisy_z0'] if 'non_noisy_z0' in batch else None
xhat, x_t_seq, source_dist_samples = self.generate_reconstructions(x, y, non_noisy_z0, self.hparams.num_flow_steps,
self.device)
x = x.to(torch.float32)
y = y.to(torch.float32)
self.log_dict({"val_metrics/mse": ((x - xhat) ** 2).mean()}, on_step=False, on_epoch=True, sync_dist=True,
batch_size=x.shape[0])
if 'flow' in self.hparams.stage:
self.fid.update(x, real=True)
self.fid.update(xhat, real=False)
self.inception_score.update(xhat)
if batch_idx == 0:
wandb_logger = self.logger.experiment
wandb_logger.log({'val_images/x': [wandb.Image(to_pil_image(create_grid(x)))],
'val_images/y': [wandb.Image(to_pil_image(create_grid(y.clip(0, 1))))],
'val_images/xhat': [wandb.Image(to_pil_image(create_grid(xhat)))], })
if 'flow' in self.hparams.stage:
wandb_logger.log({'val_images/x_t_seq': [wandb.Image(to_pil_image(create_grid(
torch.cat([elem[0].unsqueeze(0).to(torch.float32) for elem in x_t_seq], dim=0).clip(0, 1),
num_images=len(x_t_seq))))], 'val_images/source_distribution_samples': [
wandb.Image(to_pil_image(create_grid(source_dist_samples.clip(0, 1).to(torch.float32))))]})
if self.mmse_model is not None:
xhat_mmse = self.mmse_model(y).clip(0, 1)
wandb_logger.log({'val_images/xhat_mmse': [
wandb.Image(to_pil_image(create_grid(xhat_mmse.to(torch.float32))))]})
def on_validation_epoch_end(self):
if 'flow' in self.hparams.stage:
inception_score_mean, inception_score_std = self.inception_score.compute()
self.log_dict(
{'val_metrics/fid': self.fid.compute(),
'val_metrics/inception_score_mean': inception_score_mean,
'val_metrics/inception_score_std': inception_score_std},
on_epoch=True, on_step=False, sync_dist=True,
batch_size=1)
self.fid.reset()
self.inception_score.reset()
def test_step(self, batch, batch_idx):
assert self.test_results_path is not None, "Please set test_results_path before testing."
assert os.path.isdir(self.test_results_path), 'Please make sure the test_result_path dir exists.'
def save_image_batch(images, folder, image_file_names):
os.makedirs(folder, exist_ok=True)
for i, img in enumerate(images):
save_image(images[i].clip(0, 1), os.path.join(folder, image_file_names[i]))
os.makedirs(self.test_results_path, exist_ok=True)
x = batch['x']
y = batch['y']
non_noisy_z0 = batch['non_noisy_z0'] if 'non_noisy_z0' in batch else None
y_path = os.path.join(self.test_results_path, 'y')
save_image_batch(y, y_path, batch['img_file_name'])
if 'flow' in self.hparams.stage:
source_dist_samples_to_save = None
for num_flow_steps in self.num_test_flow_steps:
xhat, x_t_seq, source_dist_samples = self.generate_reconstructions(x, y, non_noisy_z0, num_flow_steps,
torch.device("cpu"))
xhat_path = os.path.join(self.test_results_path, f"num_flow_steps={num_flow_steps}", 'xhat')
save_image_batch(xhat, xhat_path, batch['img_file_name'])
if source_dist_samples_to_save is None:
source_dist_samples_to_save = source_dist_samples
source_distribution_samples_path = os.path.join(self.test_results_path, 'source_distribution_samples')
save_image_batch(source_dist_samples_to_save, source_distribution_samples_path, batch['img_file_name'])
if self.mmse_model is not None:
mmse_estimates = self.mmse_model(y).clip(0, 1)
mmse_samples_path = os.path.join(self.test_results_path, 'mmse_samples')
save_image_batch(mmse_estimates, mmse_samples_path, batch['img_file_name'])
else:
xhat, _, _ = self.generate_reconstructions(x, y, non_noisy_z0, None, torch.device('cpu'))
xhat_path = os.path.join(self.test_results_path, 'xhat')
save_image_batch(xhat, xhat_path, batch['img_file_name'])
def configure_optimizers(self):
# Add here a learning rate scheduler if you wish to do so.
optimizer = AdamW(self.model.parameters(),
betas=self.hparams.betas,
eps=1e-8,
lr=self.hparams.lr,
weight_decay=self.hparams.weight_decay)
return optimizer
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