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Support gaussian diffusion models

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Adds support for GD models, courtesy of some maths from openai.

Also:
- Fixes requirement for eval{} even when it isn't being used
- Adds support for denormalizing an imagenet norm
This commit is contained in:
James Betker 2021-06-02 21:47:32 -06:00
parent 45bc76ba92
commit 6084915af8
13 changed files with 1602 additions and 20 deletions
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12
codes/data/image_folder_dataset.py
View File

@ -34,6 +34,7 @@ class ImageFolderDataset:
# from the same video source. Search for 'fetch_alt_image' for more info.
self.skip_lq = opt_get(opt, ['skip_lq'], False)
self.disable_flip = opt_get(opt, ['disable_flip'], False)
self.rgb_n1_to_1 = opt_get(opt, ['rgb_n1_to_1'], False)
if 'normalize' in opt.keys():
if opt['normalize'] == 'stylegan2_norm':
self.normalize = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
@ -143,8 +144,6 @@ class ImageFolderDataset:
# Convert to torch tensor
hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
if self.normalize:
hq = self.normalize(hq)
out_dict = {'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item], 'has_alt': False}
@ -202,6 +201,15 @@ class ImageFolderDataset:
out_dict['labels'] = lbls
out_dict['labels_mask'] = lbl_masks
out_dict['label_strings'] = lblstrings
for k, v in out_dict.items():
if isinstance(v, torch.Tensor) and len(v.shape) == 3:
if self.normalize:
v = self.normalize(v)
if self.rgb_n1_to_1:
v = v * 2 - 1
out_dict[k] = v
return out_dict
if __name__ == '__main__':

0
codes/models/diffusion/__init__.py Normal file
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901
codes/models/diffusion/gaussian_diffusion.py Normal file
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@ -0,0 +1,901 @@
"""
This code started out as a PyTorch port of Ho et al's diffusion models:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
"""
import enum
import math
import numpy as np
import torch as th
from tqdm import tqdm
from .nn import mean_flat
from .losses import normal_kl, discretized_gaussian_log_likelihood
def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
"""
Get a pre-defined beta schedule for the given name.
The beta schedule library consists of beta schedules which remain similar
in the limit of num_diffusion_timesteps.
Beta schedules may be added, but should not be removed or changed once
they are committed to maintain backwards compatibility.
"""
if schedule_name == "linear":
# Linear schedule from Ho et al, extended to work for any number of
# diffusion steps.
scale = 1000 / num_diffusion_timesteps
beta_start = scale * 0.0001
beta_end = scale * 0.02
return np.linspace(
beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
)
elif schedule_name == "cosine":
return betas_for_alpha_bar(
num_diffusion_timesteps,
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
)
else:
raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas)
class ModelMeanType(enum.Enum):
"""
Which type of output the model predicts.
"""
PREVIOUS_X = 'previous_x' # the model predicts x_{t-1}
START_X = 'start_x' # the model predicts x_0
EPSILON = 'epsilon' # the model predicts epsilon
class ModelVarType(enum.Enum):
"""
What is used as the model's output variance.
The LEARNED_RANGE option has been added to allow the model to predict
values between FIXED_SMALL and FIXED_LARGE, making its job easier.
"""
LEARNED = 'learned'
FIXED_SMALL = 'fixed_small'
FIXED_LARGE = 'fixed_large'
LEARNED_RANGE = 'learned_range'
class LossType(enum.Enum):
MSE = 'mse' # use raw MSE loss (and KL when learning variances)
RESCALED_MSE = 'rescaled_mse' # use raw MSE loss (with RESCALED_KL when learning variances)
KL = 'kl' # use the variational lower-bound
RESCALED_KL = 'rescaled_kl' # like KL, but rescale to estimate the full VLB
def is_vb(self):
return self == LossType.KL or self == LossType.RESCALED_KL
class GaussianDiffusion:
"""
Utilities for training and sampling diffusion models.
Ported directly from here, and then adapted over time to further experimentation.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
:param betas: a 1-D numpy array of betas for each diffusion timestep,
starting at T and going to 1.
:param model_mean_type: a ModelMeanType determining what the model outputs.
:param model_var_type: a ModelVarType determining how variance is output.
:param loss_type: a LossType determining the loss function to use.
:param rescale_timesteps: if True, pass floating point timesteps into the
model so that they are always scaled like in the
original paper (0 to 1000).
"""
def __init__(
self,
*,
betas,
model_mean_type,
model_var_type,
loss_type,
rescale_timesteps=False,
):
self.model_mean_type = ModelMeanType(model_mean_type)
self.model_var_type = ModelVarType(model_var_type)
self.loss_type = LossType(loss_type)
self.rescale_timesteps = rescale_timesteps
# Use float64 for accuracy.
betas = np.array(betas, dtype=np.float64)
self.betas = betas
assert len(betas.shape) == 1, "betas must be 1-D"
assert (betas > 0).all() and (betas <= 1).all()
self.num_timesteps = int(betas.shape[0])
alphas = 1.0 - betas
self.alphas_cumprod = np.cumprod(alphas, axis=0)
self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
# calculations for posterior q(x_{t-1} | x_t, x_0)
self.posterior_variance = (
betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
# log calculation clipped because the posterior variance is 0 at the
# beginning of the diffusion chain.
self.posterior_log_variance_clipped = np.log(
np.append(self.posterior_variance[1], self.posterior_variance[1:])
)
self.posterior_mean_coef1 = (
betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
self.posterior_mean_coef2 = (
(1.0 - self.alphas_cumprod_prev)
* np.sqrt(alphas)
/ (1.0 - self.alphas_cumprod)
)
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (
_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
)
variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = _extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def q_sample(self, x_start, t, noise=None):
"""
Diffuse the data for a given number of diffusion steps.
In other words, sample from q(x_t | x_0).
:param x_start: the initial data batch.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:param noise: if specified, the split-out normal noise.
:return: A noisy version of x_start.
"""
if noise is None:
noise = th.randn_like(x_start)
assert noise.shape == x_start.shape
return (
_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def q_posterior_mean_variance(self, x_start, x_t, t):
"""
Compute the mean and variance of the diffusion posterior:
q(x_{t-1} | x_t, x_0)
"""
assert x_start.shape == x_t.shape
posterior_mean = (
_extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = _extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
assert (
posterior_mean.shape[0]
== posterior_variance.shape[0]
== posterior_log_variance_clipped.shape[0]
== x_start.shape[0]
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(
self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
):
"""
Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
the initial x, x_0.
:param model: the model, which takes a signal and a batch of timesteps
as input.
:param x: the [N x C x ...] tensor at time t.
:param t: a 1-D Tensor of timesteps.
:param clip_denoised: if True, clip the denoised signal into [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample. Applies before
clip_denoised.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict with the following keys:
- 'mean': the model mean output.
- 'variance': the model variance output.
- 'log_variance': the log of 'variance'.
- 'pred_xstart': the prediction for x_0.
"""
if model_kwargs is None:
model_kwargs = {}
B, C = x.shape[:2]
assert t.shape == (B,)
model_output = model(x, self._scale_timesteps(t), **model_kwargs)
if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
assert model_output.shape == (B, C * 2, *x.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
if self.model_var_type == ModelVarType.LEARNED:
model_log_variance = model_var_values
model_variance = th.exp(model_log_variance)
else:
min_log = _extract_into_tensor(
self.posterior_log_variance_clipped, t, x.shape
)
max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
# The model_var_values is [-1, 1] for [min_var, max_var].
frac = (model_var_values + 1) / 2
model_log_variance = frac * max_log + (1 - frac) * min_log
model_variance = th.exp(model_log_variance)
else:
model_variance, model_log_variance = {
# for fixedlarge, we set the initial (log-)variance like so
# to get a better decoder log likelihood.
ModelVarType.FIXED_LARGE: (
np.append(self.posterior_variance[1], self.betas[1:]),
np.log(np.append(self.posterior_variance[1], self.betas[1:])),
),
ModelVarType.FIXED_SMALL: (
self.posterior_variance,
self.posterior_log_variance_clipped,
),
}[self.model_var_type]
model_variance = _extract_into_tensor(model_variance, t, x.shape)
model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
def process_xstart(x):
if denoised_fn is not None:
x = denoised_fn(x)
if clip_denoised:
return x.clamp(-1, 1)
return x
if self.model_mean_type == ModelMeanType.PREVIOUS_X:
pred_xstart = process_xstart(
self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
)
model_mean = model_output
elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
if self.model_mean_type == ModelMeanType.START_X:
pred_xstart = process_xstart(model_output)
else:
pred_xstart = process_xstart(
self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
)
model_mean, _, _ = self.q_posterior_mean_variance(
x_start=pred_xstart, x_t=x, t=t
)
else:
raise NotImplementedError(self.model_mean_type)
assert (
model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
)
return {
"mean": model_mean,
"variance": model_variance,
"log_variance": model_log_variance,
"pred_xstart": pred_xstart,
}
def _predict_xstart_from_eps(self, x_t, t, eps):
assert x_t.shape == eps.shape
return (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
)
def _predict_xstart_from_xprev(self, x_t, t, xprev):
assert x_t.shape == xprev.shape
return ( # (xprev - coef2*x_t) / coef1
_extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
- _extract_into_tensor(
self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
)
* x_t
)
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- pred_xstart
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _scale_timesteps(self, t):
if self.rescale_timesteps:
return t.float() * (1000.0 / self.num_timesteps)
return t
def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute the mean for the previous step, given a function cond_fn that
computes the gradient of a conditional log probability with respect to
x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
condition on y.
This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
"""
gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
new_mean = (
p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
)
return new_mean
def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute what the p_mean_variance output would have been, should the
model's score function be conditioned by cond_fn.
See condition_mean() for details on cond_fn.
Unlike condition_mean(), this instead uses the conditioning strategy
from Song et al (2020).
"""
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
x, self._scale_timesteps(t), **model_kwargs
)
out = p_mean_var.copy()
out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
out["mean"], _, _ = self.q_posterior_mean_variance(
x_start=out["pred_xstart"], x_t=x, t=t
)
return out
def p_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
):
"""
Sample x_{t-1} from the model at the given timestep.
:param model: the model to sample from.
:param x: the current tensor at x_{t-1}.
:param t: the value of t, starting at 0 for the first diffusion step.
:param clip_denoised: if True, clip the x_start prediction to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- 'sample': a random sample from the model.
- 'pred_xstart': a prediction of x_0.
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
noise = th.randn_like(x)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
if cond_fn is not None:
out["mean"] = self.condition_mean(
cond_fn, out, x, t, model_kwargs=model_kwargs
)
sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def p_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model.
:param model: the model module.
:param shape: the shape of the samples, (N, C, H, W).
:param noise: if specified, the noise from the encoder to sample.
Should be of the same shape as `shape`.
:param clip_denoised: if True, clip x_start predictions to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param device: if specified, the device to create the samples on.
If not specified, use a model parameter's device.
:param progress: if True, show a tqdm progress bar.
:return: a non-differentiable batch of samples.
"""
final = None
for sample in self.p_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
):
final = sample
return final["sample"]
def p_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model and yield intermediate samples from
each timestep of diffusion.
Arguments are the same as p_sample_loop().
Returns a generator over dicts, where each dict is the return value of
p_sample().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
for i in tqdm(indices):
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.p_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
)
yield out
img = out["sample"]
def ddim_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t-1} from the model using DDIM.
Same usage as p_sample().
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
if cond_fn is not None:
out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
sigma = (
eta
* th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
* th.sqrt(1 - alpha_bar / alpha_bar_prev)
)
# Equation 12.
noise = th.randn_like(x)
mean_pred = (
out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+ th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
sample = mean_pred + nonzero_mask * sigma * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def ddim_reverse_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t+1} from the model using DDIM reverse ODE.
"""
assert eta == 0.0, "Reverse ODE only for deterministic path"
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
- out["pred_xstart"]
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
# Equation 12. reversed
mean_pred = (
out["pred_xstart"] * th.sqrt(alpha_bar_next)
+ th.sqrt(1 - alpha_bar_next) * eps
)
return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
def ddim_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Generate samples from the model using DDIM.
Same usage as p_sample_loop().
"""
final = None
for sample in self.ddim_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
eta=eta,
):
final = sample
return final["sample"]
def ddim_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Use DDIM to sample from the model and yield intermediate samples from
each timestep of DDIM.
Same usage as p_sample_loop_progressive().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.ddim_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
eta=eta,
)
yield out
img = out["sample"]
def _vb_terms_bpd(
self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
):
"""
Get a term for the variational lower-bound.
The resulting units are bits (rather than nats, as one might expect).
This allows for comparison to other papers.
:return: a dict with the following keys:
- 'output': a shape [N] tensor of NLLs or KLs.
- 'pred_xstart': the x_0 predictions.
"""
true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)
out = self.p_mean_variance(
model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
)
kl = normal_kl(
true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
)
kl = mean_flat(kl) / np.log(2.0)
decoder_nll = -discretized_gaussian_log_likelihood(
x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
)
assert decoder_nll.shape == x_start.shape
decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
# At the first timestep return the decoder NLL,
# otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
output = th.where((t == 0), decoder_nll, kl)
return {"output": output, "pred_xstart": out["pred_xstart"]}
def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
"""
Compute training losses for a single timestep.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param t: a batch of timestep indices.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param noise: if specified, the specific Gaussian noise to try to remove.
:return: a dict with the key "loss" containing a tensor of shape [N].
Some mean or variance settings may also have other keys.
"""
if model_kwargs is None:
model_kwargs = {}
if noise is None:
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start, t, noise=noise)
terms = {}
if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
terms["loss"] = self._vb_terms_bpd(
model=model,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
model_kwargs=model_kwargs,
)["output"]
if self.loss_type == LossType.RESCALED_KL:
terms["loss"] *= self.num_timesteps
elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
if self.model_var_type in [
ModelVarType.LEARNED,
ModelVarType.LEARNED_RANGE,
]:
B, C = x_t.shape[:2]
assert model_output.shape == (B, C * 2, *x_t.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
# Learn the variance using the variational bound, but don't let
# it affect our mean prediction.
frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
terms["vb"] = self._vb_terms_bpd(
model=lambda *args, r=frozen_out: r,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
)["output"]
if self.loss_type == LossType.RESCALED_MSE:
# Divide by 1000 for equivalence with initial implementation.
# Without a factor of 1/1000, the VB term hurts the MSE term.
terms["vb"] *= self.num_timesteps / 1000.0
target = {
ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)[0],
ModelMeanType.START_X: x_start,
ModelMeanType.EPSILON: noise,
}[self.model_mean_type]
assert model_output.shape == target.shape == x_start.shape
terms["mse"] = mean_flat((target - model_output) ** 2)
if "vb" in terms:
terms["loss"] = terms["mse"] + terms["vb"]
else:
terms["loss"] = terms["mse"]
else:
raise NotImplementedError(self.loss_type)
return terms
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(
mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
)
return mean_flat(kl_prior) / np.log(2.0)
def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
"""
Compute the entire variational lower-bound, measured in bits-per-dim,
as well as other related quantities.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param clip_denoised: if True, clip denoised samples.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- total_bpd: the total variational lower-bound, per batch element.
- prior_bpd: the prior term in the lower-bound.
- vb: an [N x T] tensor of terms in the lower-bound.
- xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
- mse: an [N x T] tensor of epsilon MSEs for each timestep.
"""
device = x_start.device
batch_size = x_start.shape[0]
vb = []
xstart_mse = []
mse = []
for t in list(range(self.num_timesteps))[::-1]:
t_batch = th.tensor([t] * batch_size, device=device)
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
# Calculate VLB term at the current timestep
with th.no_grad():
out = self._vb_terms_bpd(
model,
x_start=x_start,
x_t=x_t,
t=t_batch,
clip_denoised=clip_denoised,
model_kwargs=model_kwargs,
)
vb.append(out["output"])
xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
mse.append(mean_flat((eps - noise) ** 2))
vb = th.stack(vb, dim=1)
xstart_mse = th.stack(xstart_mse, dim=1)
mse = th.stack(mse, dim=1)
prior_bpd = self._prior_bpd(x_start)
total_bpd = vb.sum(dim=1) + prior_bpd
return {
"total_bpd": total_bpd,
"prior_bpd": prior_bpd,
"vb": vb,
"xstart_mse": xstart_mse,
"mse": mse,
}
def _extract_into_tensor(arr, timesteps, broadcast_shape):
"""
Extract values from a 1-D numpy array for a batch of indices.
:param arr: the 1-D numpy array.
:param timesteps: a tensor of indices into the array to extract.
:param broadcast_shape: a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
while len(res.shape) < len(broadcast_shape):
res = res[..., None]
return res.expand(broadcast_shape)

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"""
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""
import numpy as np
import torch as th
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, th.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for th.exp().
logvar1, logvar2 = [
x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ th.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * th.exp(-logvar2)
)
def approx_standard_normal_cdf(x):
"""
A fast approximation of the cumulative distribution function of the
standard normal.
"""
return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
def discretized_gaussian_log_likelihood(x, *, means, log_scales):
"""
Compute the log-likelihood of a Gaussian distribution discretizing to a
given image.
:param x: the target images. It is assumed that this was uint8 values,
rescaled to the range [-1, 1].
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
assert x.shape == means.shape == log_scales.shape
centered_x = x - means
inv_stdv = th.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
cdf_plus = approx_standard_normal_cdf(plus_in)
min_in = inv_stdv * (centered_x - 1.0 / 255.0)
cdf_min = approx_standard_normal_cdf(min_in)
log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = th.where(
x < -0.999,
log_cdf_plus,
th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
)
assert log_probs.shape == x.shape
return log_probs

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"""
Various utilities for neural networks.
"""
import math
import torch as th
import torch.nn as nn
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * th.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
"""
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = th.exp(
-math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
if dim % 2:
embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs)
class CheckpointFunction(th.autograd.Function):
@staticmethod
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with th.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with th.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = th.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads

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from abc import ABC, abstractmethod
import numpy as np
import torch as th
import torch.distributed as dist
def create_named_schedule_sampler(name, diffusion):
"""
Create a ScheduleSampler from a library of pre-defined samplers.
:param name: the name of the sampler.
:param diffusion: the diffusion object to sample for.
"""
if name == "uniform":
return UniformSampler(diffusion)
elif name == "loss-second-moment":
return LossSecondMomentResampler(diffusion)
else:
raise NotImplementedError(f"unknown schedule sampler: {name}")
class ScheduleSampler(ABC):
"""
A distribution over timesteps in the diffusion process, intended to reduce
variance of the objective.
By default, samplers perform unbiased importance sampling, in which the
objective's mean is unchanged.
However, subclasses may override sample() to change how the resampled
terms are reweighted, allowing for actual changes in the objective.
"""
@abstractmethod
def weights(self):
"""
Get a numpy array of weights, one per diffusion step.
The weights needn't be normalized, but must be positive.
"""
def sample(self, batch_size, device):
"""
Importance-sample timesteps for a batch.
:param batch_size: the number of timesteps.
:param device: the torch device to save to.
:return: a tuple (timesteps, weights):
- timesteps: a tensor of timestep indices.
- weights: a tensor of weights to scale the resulting losses.
"""
w = self.weights()
p = w / np.sum(w)
indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
indices = th.from_numpy(indices_np).long().to(device)
weights_np = 1 / (len(p) * p[indices_np])
weights = th.from_numpy(weights_np).float().to(device)
return indices, weights
class UniformSampler(ScheduleSampler):
def __init__(self, diffusion):
self.diffusion = diffusion
self._weights = np.ones([diffusion.num_timesteps])
def weights(self):
return self._weights
class LossAwareSampler(ScheduleSampler):
def update_with_local_losses(self, local_ts, local_losses):
"""
Update the reweighting using losses from a model.
Call this method from each rank with a batch of timesteps and the
corresponding losses for each of those timesteps.
This method will perform synchronization to make sure all of the ranks
maintain the exact same reweighting.
:param local_ts: an integer Tensor of timesteps.
:param local_losses: a 1D Tensor of losses.
"""
batch_sizes = [
th.tensor([0], dtype=th.int32, device=local_ts.device)
for _ in range(dist.get_world_size())
]
dist.all_gather(
batch_sizes,
th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
)
# Pad all_gather batches to be the maximum batch size.
batch_sizes = [x.item() for x in batch_sizes]
max_bs = max(batch_sizes)
timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
dist.all_gather(timestep_batches, local_ts)
dist.all_gather(loss_batches, local_losses)
timesteps = [
x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
]
losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
self.update_with_all_losses(timesteps, losses)
@abstractmethod
def update_with_all_losses(self, ts, losses):
"""
Update the reweighting using losses from a model.
Sub-classes should override this method to update the reweighting
using losses from the model.
This method directly updates the reweighting without synchronizing
between workers. It is called by update_with_local_losses from all
ranks with identical arguments. Thus, it should have deterministic
behavior to maintain state across workers.
:param ts: a list of int timesteps.
:param losses: a list of float losses, one per timestep.
"""
class LossSecondMomentResampler(LossAwareSampler):
def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
self.diffusion = diffusion
self.history_per_term = history_per_term
self.uniform_prob = uniform_prob
self._loss_history = np.zeros(
[diffusion.num_timesteps, history_per_term], dtype=np.float64
)
self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
def weights(self):
if not self._warmed_up():
return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
weights /= np.sum(weights)
weights *= 1 - self.uniform_prob
weights += self.uniform_prob / len(weights)
return weights
def update_with_all_losses(self, ts, losses):
for t, loss in zip(ts, losses):
if self._loss_counts[t] == self.history_per_term:
# Shift out the oldest loss term.
self._loss_history[t, :-1] = self._loss_history[t, 1:]
self._loss_history[t, -1] = loss
else:
self._loss_history[t, self._loss_counts[t]] = loss
self._loss_counts[t] += 1
def _warmed_up(self):
return (self._loss_counts == self.history_per_term).all()

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import torch
import torch.nn as nn
import torch.nn.functional as F
from models.arch_util import ConvGnLelu, default_init_weights, make_layer
from models.diffusion.nn import timestep_embedding
from trainer.networks import register_model
from utils.util import checkpoint
# Conditionally uses torch's checkpoint functionality if it is enabled in the opt file.
class ResidualDenseBlock(nn.Module):
"""Residual Dense Block.
Used in RRDB block in ESRGAN.
Args:
mid_channels (int): Channel number of intermediate features.
growth_channels (int): Channels for each growth.
"""
def __init__(self, mid_channels=64, growth_channels=32, embedding=False, init_weight=.1):
super(ResidualDenseBlock, self).__init__()
self.embedding = embedding
if embedding:
self.first_conv = ConvGnLelu(mid_channels, mid_channels, activation=True, norm=False, bias=True)
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
mid_channels*4,
mid_channels,
),
)
for i in range(5):
out_channels = mid_channels if i == 4 else growth_channels
self.add_module(
f'conv{i + 1}',
nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
1, 1))
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
for i in range(4):
default_init_weights(getattr(self, f'conv{i + 1}'), init_weight)
default_init_weights(self.conv5, 0)
self.normalize = nn.GroupNorm(num_groups=8, num_channels=mid_channels)
def forward(self, x, emb):
"""Forward function.
Args:
x (Tensor): Input tensor with shape (n, c, h, w).
Returns:
Tensor: Forward results.
"""
if self.embedding:
x0 = self.first_conv(x)
emb_out = self.emb_layers(emb).type(x0.dtype)
while len(emb_out.shape) < len(x0.shape):
emb_out = emb_out[..., None]
x0 = x0 + emb_out
else:
x0 = x
x1 = self.lrelu(self.conv1(x0))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return self.normalize(x5 * .2 + x)
class RRDB(nn.Module):
"""Residual in Residual Dense Block.
Used in RRDB-Net in ESRGAN.
Args:
mid_channels (int): Channel number of intermediate features.
growth_channels (int): Channels for each growth.
"""
def __init__(self, mid_channels, growth_channels=32):
super(RRDB, self).__init__()
self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels, embedding=True)
self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
self.normalize = nn.GroupNorm(num_groups=8, num_channels=mid_channels)
self.residual_mult = nn.Parameter(torch.FloatTensor([.1]))
def forward(self, x, emb):
"""Forward function.
Args:
x (Tensor): Input tensor with shape (n, c, h, w).
Returns:
Tensor: Forward results.
"""
out = self.rdb1(x, emb)
out = self.rdb2(out, emb)
out = self.rdb3(out, emb)
return self.normalize(out * self.residual_mult + x)
class RRDBNet(nn.Module):
"""Networks consisting of Residual in Residual Dense Block, which is used
in ESRGAN.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
Currently, it supports x4 upsampling scale factor.
Args:
in_channels (int): Channel number of inputs.
out_channels (int): Channel number of outputs.
mid_channels (int): Channel number of intermediate features.
Default: 64
num_blocks (int): Block number in the trunk network. Defaults: 23
growth_channels (int): Channels for each growth. Default: 32.
"""
def __init__(self,
in_channels,
out_channels,
mid_channels=64,
num_blocks=23,
growth_channels=32,
body_block=RRDB,
):
super(RRDBNet, self).__init__()
self.num_blocks = num_blocks
self.in_channels = in_channels
self.mid_channels = mid_channels
# The diffusion RRDB starts with a full resolution image and downsamples into a .25 working space
self.input_block = ConvGnLelu(in_channels, mid_channels, kernel_size=7, stride=1, activation=True, norm=True, bias=True)
self.down1 = ConvGnLelu(mid_channels, mid_channels, kernel_size=3, stride=2, activation=True, norm=True, bias=True)
self.down2 = ConvGnLelu(mid_channels, mid_channels, kernel_size=3, stride=2, activation=True, norm=True, bias=True)
# Guided diffusion uses a time embedding.
time_embed_dim = mid_channels * 4
self.time_embed = nn.Sequential(
nn.Linear(mid_channels, time_embed_dim),
nn.SiLU(),
nn.Linear(time_embed_dim, time_embed_dim),
)
self.body = make_layer(
body_block,
num_blocks,
mid_channels=mid_channels,
growth_channels=growth_channels)
self.conv_body = nn.Conv2d(self.mid_channels, self.mid_channels, 3, 1, 1)
# upsample
self.conv_up1 = nn.Conv2d(self.mid_channels, self.mid_channels, 3, 1, 1)
self.conv_up2 = nn.Conv2d(self.mid_channels*2, self.mid_channels, 3, 1, 1)
self.conv_up3 = None
self.conv_hr = nn.Conv2d(self.mid_channels*2, self.mid_channels, 3, 1, 1)
self.conv_last = nn.Conv2d(self.mid_channels, out_channels, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.normalize = nn.GroupNorm(num_groups=8, num_channels=self.mid_channels)
for m in [
self.conv_body, self.conv_up1,
self.conv_up2, self.conv_hr
]:
if m is not None:
default_init_weights(m, 1.0)
default_init_weights(self.conv_last, 0)
def forward(self, x, timesteps, low_res=None):
emb = self.time_embed(timestep_embedding(timesteps, self.mid_channels))
_, _, new_height, new_width = x.shape
upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
x = torch.cat([x, upsampled], dim=1)
d1 = self.input_block(x)
d2 = self.down1(d1)
feat = self.down2(d2)
for bl in self.body:
feat = checkpoint(bl, feat, emb)
feat = feat[:, :self.mid_channels]
body_feat = self.conv_body(feat)
feat = self.normalize(feat + body_feat)
# upsample
out = torch.cat([self.lrelu(
self.normalize(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))),
d2], dim=1)
out = torch.cat([self.lrelu(
self.normalize(self.conv_up2(F.interpolate(out, scale_factor=2, mode='nearest')))),
d1], dim=1)
out = self.conv_last(self.normalize(self.lrelu(self.conv_hr(out))))
return out
@register_model
def register_rrdb_diffusion(opt_net, opt):
return RRDBNet(**opt_net['args'])
if __name__ == '__main__':
model = RRDBNet(6,6)
x = torch.randn(1,3,128,128)
l = torch.randn(1,3,32,32)
t = torch.LongTensor([555])
y = model(x, t, l)
print(y.shape, y.mean(), y.std(), y.min(), y.max())

11
codes/train.py
View File

@ -14,6 +14,9 @@ from data import create_dataloader, create_dataset
from trainer.ExtensibleTrainer import ExtensibleTrainer
from time import time
from utils.util import opt_get
def init_dist(backend, **kwargs):
# These packages have globals that screw with Windows, so only import them if needed.
import torch.distributed as dist
@ -31,8 +34,8 @@ class Trainer:
def init(self, opt, launcher, all_networks={}):
self._profile = False
self.val_compute_psnr = opt['eval']['compute_psnr'] if 'compute_psnr' in opt['eval'].keys() else True
self.val_compute_fea = opt['eval']['compute_fea'] if 'compute_fea' in opt['eval'].keys() else True
self.val_compute_psnr = opt_get(opt, ['eval', 'compute_psnr'], False)
self.val_compute_fea = opt_get(opt, ['eval', 'compute_fea'], False)
#### loading resume state if exists
if opt['path'].get('resume_state', None):
@ -133,7 +136,7 @@ class Trainer:
### Evaluators
self.evaluators = []
if 'evaluators' in opt['eval'].keys():
if 'eval' in opt.keys() and 'evaluators' in opt['eval'].keys():
for ev_key, ev_opt in opt['eval']['evaluators'].items():
self.evaluators.append(create_evaluator(self.model.networks[ev_opt['for']],
ev_opt, self.model.env))
@ -295,7 +298,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imagenet_resnet50_yt_pretrained.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_rrdb_diffusion.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()

26
codes/trainer/ExtensibleTrainer.py
View File

@ -13,7 +13,7 @@ from trainer.steps import ConfigurableStep
from trainer.experiments.experiments import get_experiment_for_name
import torchvision.utils as utils
from utils.util import opt_get
from utils.util import opt_get, denormalize
logger = logging.getLogger('base')
@ -259,10 +259,15 @@ class ExtensibleTrainer(BaseModel):
# Record visual outputs for usage in debugging and testing.
if 'visuals' in self.opt['logger'].keys() and self.rank <= 0 and step % self.opt['logger']['visual_debug_rate'] == 0:
denorm = 'image_normalization_range' in self.opt.keys()
denorm_range = opt_get(self.opt, ['image_normalization_range'], None)
if denorm_range:
denorm_range = tuple(denorm_range)
def fix_image(img):
if img.shape[1] > 3:
img = img[:, :3, :, :]
if opt_get(self.opt, ['logger', 'reverse_n1_to_1'], False):
img = (img + 1) / 2
if opt_get(self.opt, ['logger', 'reverse_imagenet_norm'], False):
img = denormalize(img)
return img
sample_save_path = os.path.join(self.opt['path']['models'], "..", "visual_dbg")
for v in self.opt['logger']['visuals']:
if v not in state.keys():
@ -270,16 +275,13 @@ class ExtensibleTrainer(BaseModel):
for i, dbgv in enumerate(state[v]):
if 'recurrent_visual_indices' in self.opt['logger'].keys() and len(dbgv.shape)==5:
for rvi in self.opt['logger']['recurrent_visual_indices']:
rdbgv = dbgv[:, rvi]
if rdbgv.shape[1] > 3:
rdbgv = rdbgv[:, :3, :, :]
rdbgv = fix_image(dbgv[:, rvi])
os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
utils.save_image(rdbgv.float(), os.path.join(sample_save_path, v, "%05i_%02i_%02i.png" % (step, rvi, i)), normalize=denorm, range=denorm_range)
utils.save_image(rdbgv.float(), os.path.join(sample_save_path, v, "%05i_%02i_%02i.png" % (step, rvi, i)))
else:
if dbgv.shape[1] > 3:
dbgv = dbgv[:,:3,:,:]
dbgv = fix_image(dbgv)
os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
utils.save_image(dbgv.float(), os.path.join(sample_save_path, v, "%05i_%02i.png" % (step, i)), normalize=denorm, range=denorm_range)
utils.save_image(dbgv.float(), os.path.join(sample_save_path, v, "%05i_%02i.png" % (step, i)))
# Some models have their own specific visual debug routines.
for net_name, net in self.networks.items():
if hasattr(net.module, "visual_dbg"):

2
codes/trainer/inject.py
View File

@ -55,7 +55,7 @@ class CreateInjectorError(Exception):
f'{available}')
# Injectors are a way to sythesize data within a step that can then be used (and reused) by loss functions.
# Injectors are a way to synthesize data within a step that can then be used (and reused) by loss functions.
def create_injector(opt_inject, env):
injectors = find_registered_injectors()
type = opt_inject['type']

45
codes/trainer/injectors/gaussian_diffusion_injector.py Normal file
View File

@ -0,0 +1,45 @@
import torch
from models.diffusion.gaussian_diffusion import GaussianDiffusion, get_named_beta_schedule
from models.diffusion.resample import create_named_schedule_sampler
from trainer.inject import Injector
from utils.util import opt_get
# Injects a gaussian diffusion loss as described by OpenAIs "Improved Denoising Diffusion Probabilistic Models" paper.
# Largely uses OpenAI's own code to do so (all code from models.diffusion.*)
class GaussianDiffusionInjector(Injector):
def __init__(self, opt, env):
super().__init__(opt, env)
self.generator = opt['generator']
opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule'])
self.diffusion = GaussianDiffusion(**opt['diffusion_args'])
self.schedule_sampler = create_named_schedule_sampler(opt['sampler_type'], self.diffusion)
self.model_input_keys = opt_get(opt, ['model_input_keys'], [])
def forward(self, state):
gen = self.env['generators'][self.opt['generator']]
hq = state[self.input]
model_inputs = {k: state[v] for k, v in self.model_input_keys.items()}
t, weights = self.schedule_sampler.sample(hq.shape[0], hq.device)
return {self.output: self.diffusion.training_losses(gen, hq, t, model_kwargs=model_inputs)['loss'] * weights}
# Performs inference using a network trained to predict a reverse diffusion process, which nets a image.
class GaussianDiffusionInferenceInjector(Injector):
def __init__(self, opt, env):
super().__init__(opt, env)
self.generator = opt['generator']
self.output_shape = opt['output_shape']
opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule'])
self.diffusion = GaussianDiffusion(**opt['diffusion_args'])
self.model_input_keys = opt_get(opt, ['model_input_keys'], [])
def forward(self, state):
gen = self.env['generators'][self.opt['generator']]
batch_size = self.output_shape[0]
model_inputs = {k: state[v][:batch_size] for k, v in self.model_input_keys.items()}
gen.eval()
with torch.no_grad():
gen = self.diffusion.p_sample_loop(gen, self.output_shape, model_kwargs=model_inputs)
return {self.output: gen}

2
codes/trainer/losses.py
View File

@ -179,7 +179,7 @@ class SrPixLoss(ConfigurableLoss):
return loss.mean()
# Loss defined by averaging the input tensor across all dimensions an optionally inverting it.
# Loss defined by averaging the input tensor across all dimensions and optionally inverting it.
class DirectLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(DirectLoss, self).__init__(opt, env)

7
codes/utils/util.py
View File

@ -401,3 +401,10 @@ def opt_get(opt, keys, default=None):
if ret is None:
return default
return ret
def denormalize(x, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
ten = x.clone().permute(1, 2, 3, 0)
for t, m, s in zip(ten, mean, std):
t.mul_(s).add_(m)
return torch.clamp(ten, 0, 1).permute(3, 0, 1, 2)