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import os
import logging
import time
import glob
import numpy as np
import pandas as pd
import math
import tqdm
import torch
import torch.utils.data as data
from models.diffusion import Model
from models.ema import EMAHelper
from functions import get_optimizer
from functions.losses import loss_registry, calculate_psnr
from datasets import data_transform, inverse_data_transform
from datasets.pmub import PMUB
from datasets.LDFDCT import LDFDCT
from datasets.BRATS import BRATS
from datasets.aariz import Aariz
from functions.ckpt_util import get_ckpt_path
from skimage.metrics import structural_similarity as ssim
import torchvision.utils as tvu
import torchvision
from PIL import Image
def torch2hwcuint8(x, clip=False):
if clip:
x = torch.clamp(x, -1, 1)
x = (x + 1.0) / 2.0
return x
def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
def sigmoid(x):
return 1 / (np.exp(x) + 1)
def tanh(x):
return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))
if beta_schedule == "quad":
betas = (
np.linspace(
beta_start ** 0.5,
beta_end ** 0.5,
num_diffusion_timesteps,
dtype=np.float64,
)
** 2
)
elif beta_schedule == "linear":
betas = np.linspace(
beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
)
elif beta_schedule == "sigmoid":
betas = np.linspace(-6, 6, num_diffusion_timesteps)
betas = sigmoid(betas) * (beta_end - beta_start) + beta_start
elif beta_schedule =='alpha_cosine':
s = 0.008
timesteps = np.arange(0, num_diffusion_timesteps+1, dtype=np.float64)/num_diffusion_timesteps
alphas_cumprod = np.cos((timesteps + s) / (1 + s) * math.pi * 0.5) ** 2
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
betas = np.clip(betas, a_min=None, a_max=0.999)
elif beta_schedule == 'alpha_sigmoid':
x = np.linspace(-6, 6, 1001)
alphas_cumprod = sigmoid(x)
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
betas = np.clip(betas, a_min=None, a_max=0.999)
elif beta_schedule == 'alpha_linear':
timesteps = np.arange(0, num_diffusion_timesteps+1, dtype=np.float64)/num_diffusion_timesteps
alphas_cumprod = -timesteps+1
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
betas = np.clip(betas, a_min=None, a_max=0.999)
else:
raise NotImplementedError(beta_schedule)
assert betas.shape == (num_diffusion_timesteps,)
return betas
class Diffusion(object):
def __init__(self, args, config, device=None):
self.args = args
self.config = config
if device is None:
device = (
torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
self.device = device
self.model_var_type = config.model.var_type
betas = get_beta_schedule(
beta_schedule=config.diffusion.beta_schedule,
beta_start=config.diffusion.beta_start,
beta_end=config.diffusion.beta_end,
num_diffusion_timesteps=config.diffusion.num_diffusion_timesteps,
)
betas = self.betas = torch.from_numpy(betas).float().to(self.device)
self.num_timesteps = betas.shape[0]
alphas = 1.0 - betas
alphas_cumprod = alphas.cumprod(dim=0)
alphas_cumprod_prev = torch.cat(
[torch.ones(1).to(device), alphas_cumprod[:-1]], dim=0
)
posterior_variance = (
betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
)
if self.model_var_type == "fixedlarge":
self.logvar = betas.log()
elif self.model_var_type == "fixedsmall":
self.logvar = posterior_variance.clamp(min=1e-20).log()
# Training Fast-DDPM for tasks that have only one condition: image translation and CT denoising.
def sg_train(self):
args, config = self.args, self.config
tb_logger = self.config.tb_logger
if self.args.dataset=='LDFDCT':
# LDFDCT for CT image denoising
dataset = LDFDCT(self.config.data.train_dataroot, self.config.data.image_size, split='train')
print('Start training your Fast-DDPM model on LDFDCT dataset.')
elif self.args.dataset=='BRATS':
# BRATS for brain image translation
dataset = BRATS(self.config.data.train_dataroot, self.config.data.image_size, split='train')
print('Start training your Fast-DDPM model on BRATS dataset.')
print('The scheduler sampling type is {}. The number of involved time steps is {} out of 1000.'.format(self.args.scheduler_type, self.args.timesteps))
train_loader = data.DataLoader(
dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
pin_memory=True)
model = Model(config)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
optimizer = get_optimizer(self.config, model.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, "ckpt.pth"))
model.load_state_dict(states[0])
states[1]["param_groups"][0]["eps"] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, x in enumerate(train_loader):
n = x['LD'].size(0)
model.train()
step += 1
x_img = x['LD'].to(self.device)
x_gt = x['FD'].to(self.device)
e = torch.randn_like(x_gt)
b = self.betas
if self.args.scheduler_type == 'uniform':
skip = self.num_timesteps // self.args.timesteps
t_intervals = torch.arange(-1, self.num_timesteps, skip)
t_intervals[0] = 0
elif self.args.scheduler_type == 'non-uniform':
t_intervals = torch.tensor([0, 199, 399, 599, 699, 799, 849, 899, 949, 999])
if self.args.timesteps != 10:
num_1 = int(self.args.timesteps*0.4)
num_2 = int(self.args.timesteps*0.6)
stage_1 = torch.linspace(0, 699, num_1+1)[:-1]
stage_2 = torch.linspace(699, 999, num_2)
stage_1 = torch.ceil(stage_1).long()
stage_2 = torch.ceil(stage_2).long()
t_intervals = torch.cat((stage_1, stage_2))
else:
raise Exception("The scheduler type is either uniform or non-uniform.")
# antithetic sampling
idx_1 = torch.randint(0, len(t_intervals), size=(n // 2 + 1,))
idx_2 = len(t_intervals)-idx_1-1
idx = torch.cat([idx_1, idx_2], dim=0)[:n]
t = t_intervals[idx].to(self.device)
loss = loss_registry[config.model.type](model, x_img, x_gt, t, e, b)
tb_logger.add_scalar("loss", loss, global_step=step)
logging.info(
f"step: {step}, loss: {loss.item()}"
)
optimizer.zero_grad()
loss.backward()
try:
torch.nn.utils.clip_grad_norm_(
model.parameters(), config.optim.grad_clip
)
except Exception:
pass
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = [
model.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(
states,
os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)),
)
torch.save(states, os.path.join(self.args.log_path, "ckpt.pth"))
def aariz_train(self):
"""Unconditional Fast-DDPM training on Aariz cephalograms."""
args, config = self.args, self.config
tb_logger = self.config.tb_logger
print("Start training Fast-DDPM on Aariz dataset.")
print(
f"The scheduler sampling type is {self.args.scheduler_type}. "
f"The number of involved time steps is {self.args.timesteps} out of 1000."
)
train_dataset = Aariz(
self.config.data.train_dataroot,
self.config.data.image_size,
split="train",
random_flip=getattr(self.config.data, "random_flip", True),
)
val_loader = None
if getattr(self.config.data, "val_dataroot", None) and os.path.exists(self.config.data.val_dataroot):
val_dataset = Aariz(
self.config.data.val_dataroot,
self.config.data.image_size,
split="val",
random_flip=False,
)
val_loader = data.DataLoader(
val_dataset,
batch_size=config.training.batch_size,
shuffle=False,
num_workers=config.data.num_workers,
pin_memory=True,
)
train_loader = data.DataLoader(
train_dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
pin_memory=True,
)
model = Model(config)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
optimizer = get_optimizer(self.config, model.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, "ckpt.pth"))
model.load_state_dict(states[0])
states[1]["param_groups"][0]["eps"] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
target_iters = self.config.training.n_iters
eval_freq = getattr(self.config.training, "validation_freq", 0)
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, batch in enumerate(train_loader):
n = batch["image"].size(0)
model.train()
step += 1
x0 = batch["image"].to(self.device)
e = torch.randn_like(x0)
b = self.betas
if self.args.scheduler_type == "uniform":
skip = self.num_timesteps // self.args.timesteps
t_intervals = torch.arange(-1, self.num_timesteps, skip)
t_intervals[0] = 0
elif self.args.scheduler_type == "non-uniform":
t_intervals = torch.tensor([0, 199, 399, 599, 699, 799, 849, 899, 949, 999])
if self.args.timesteps != 10:
num_1 = int(self.args.timesteps * 0.4)
num_2 = int(self.args.timesteps * 0.6)
stage_1 = torch.linspace(0, 699, num_1 + 1)[:-1]
stage_2 = torch.linspace(699, 999, num_2)
stage_1 = torch.ceil(stage_1).long()
stage_2 = torch.ceil(stage_2).long()
t_intervals = torch.cat((stage_1, stage_2))
else:
raise Exception("The scheduler type is either uniform or non-uniform.")
idx_1 = torch.randint(0, len(t_intervals), size=(n // 2 + 1,))
idx_2 = len(t_intervals) - idx_1 - 1
idx = torch.cat([idx_1, idx_2], dim=0)[:n]
t = t_intervals[idx].to(self.device)
loss = loss_registry[config.model.type](model, x0, t, e, b)
tb_logger.add_scalar("loss", loss, global_step=step)
logging.info(f"step: {step}, loss: {loss.item()}")
optimizer.zero_grad()
loss.backward()
try:
torch.nn.utils.clip_grad_norm_(model.parameters(), config.optim.grad_clip)
except Exception:
pass
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = [
model.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(states, os.path.join(self.args.log_path, f"ckpt_{step}.pth"))
torch.save(states, os.path.join(self.args.log_path, "ckpt.pth"))
if val_loader and eval_freq and step % eval_freq == 0:
psnr_mean, ssim_mean = self.evaluate_aariz(model, val_loader, ema_helper)
tb_logger.add_scalar("val/psnr", psnr_mean, global_step=step)
tb_logger.add_scalar("val/ssim", ssim_mean, global_step=step)
logging.info(f"Validation at step {step}: PSNR={psnr_mean:.4f}, SSIM={ssim_mean:.4f}")
if step >= target_iters:
break
if step >= target_iters:
break
def evaluate_aariz(self, model, val_loader, ema_helper=None):
"""Compute PSNR/SSIM on a few validation batches."""
eval_model = model
if ema_helper is not None:
eval_model = ema_helper.ema_copy(model)
eval_model.eval()
max_batches = getattr(self.config.training, "max_eval_batches", 1)
psnr_total, ssim_total, count = 0.0, 0.0, 0
with torch.no_grad():
for batch_idx, batch in enumerate(val_loader):
x0 = batch["image"].to(self.device)
n = x0.size(0)
t_scalar = self.args.timesteps - 1
t = torch.full((n,), t_scalar, device=self.device, dtype=torch.long)
a = (1 - self.betas).cumprod(dim=0).index_select(0, t).view(-1, 1, 1, 1)
noise = torch.randn_like(x0)
x_t = x0 * a.sqrt() + noise * (1.0 - a).sqrt()
e_theta = eval_model(x_t, t.float())
x0_pred = (x_t - (1.0 - a).sqrt() * e_theta) / a.sqrt()
x0_pred = torch.clamp(x0_pred, -1.0, 1.0)
x0_np = ((x0.detach().cpu().numpy() + 1.0) * 127.5).astype(np.uint8)
pred_np = ((x0_pred.detach().cpu().numpy() + 1.0) * 127.5).astype(np.uint8)
for j in range(n):
gt_img = np.transpose(x0_np[j], (1, 2, 0))
pred_img = np.transpose(pred_np[j], (1, 2, 0))
psnr_total += calculate_psnr(pred_img, gt_img)
try:
ssim_score = ssim(gt_img, pred_img, channel_axis=2, data_range=255)
except TypeError:
ssim_score = ssim(gt_img, pred_img, multichannel=True, data_range=255)
ssim_total += ssim_score
count += n
if (batch_idx + 1) >= max_batches:
break
eval_model.train()
if count == 0:
return 0.0, 0.0
return psnr_total / count, ssim_total / count
# Training Fast-DDPM for tasks that have two conditions: multi image super-resolution.
def sr_train(self):
args, config = self.args, self.config
tb_logger = self.config.tb_logger
dataset = PMUB(self.config.data.train_dataroot, self.config.data.image_size, split='train')
print('Start training your Fast-DDPM model on PMUB dataset.')
print('The scheduler sampling type is {}. The number of involved time steps is {} out of 1000.'.format(self.args.scheduler_type, self.args.timesteps))
train_loader = data.DataLoader(
dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
pin_memory=True)
model = Model(config)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
optimizer = get_optimizer(self.config, model.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, "ckpt.pth"))
model.load_state_dict(states[0])
states[1]["param_groups"][0]["eps"] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, x in enumerate(train_loader):
n = x['BW'].size(0)
model.train()
step += 1
x_bw = x['BW'].to(self.device)
x_md = x['MD'].to(self.device)
x_fw = x['FW'].to(self.device)
e = torch.randn_like(x_md)
b = self.betas
if self.args.scheduler_type == 'uniform':
skip = self.num_timesteps // self.args.timesteps
t_intervals = torch.arange(-1, self.num_timesteps, skip)
t_intervals[0] = 0
elif self.args.scheduler_type == 'non-uniform':
t_intervals = torch.tensor([0, 199, 399, 599, 699, 799, 849, 899, 949, 999])
if self.args.timesteps != 10:
num_1 = int(self.args.timesteps*0.4)
num_2 = int(self.args.timesteps*0.6)
stage_1 = torch.linspace(0, 699, num_1+1)[:-1]
stage_2 = torch.linspace(699, 999, num_2)
stage_1 = torch.ceil(stage_1).long()
stage_2 = torch.ceil(stage_2).long()
t_intervals = torch.cat((stage_1, stage_2))
else:
raise Exception("The scheduler type is either uniform or non-uniform.")
# antithetic sampling
idx_1 = torch.randint(0, len(t_intervals), size=(n // 2 + 1,))
idx_2 = len(t_intervals)-idx_1-1
idx = torch.cat([idx_1, idx_2], dim=0)[:n]
t = t_intervals[idx].to(self.device)
loss = loss_registry[config.model.type](model, x_bw, x_md, x_fw, t, e, b)
tb_logger.add_scalar("loss", loss, global_step=step)
logging.info(
f"step: {step}, loss: {loss.item()}"
)
optimizer.zero_grad()
loss.backward()
try:
torch.nn.utils.clip_grad_norm_(
model.parameters(), config.optim.grad_clip
)
except Exception:
pass
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = [
model.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(
states,
os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)),
)
torch.save(states, os.path.join(self.args.log_path, "ckpt.pth"))
# Training original DDPM for tasks that have only one condition: image translation and CT denoising.
def sg_ddpm_train(self):
args, config = self.args, self.config
tb_logger = self.config.tb_logger
if self.args.dataset=='LDFDCT':
# LDFDCT for CT image denoising
dataset = LDFDCT(self.config.data.train_dataroot, self.config.data.image_size, split='train')
print('Start training DDPM model on LDFDCT dataset.')
elif self.args.dataset=='BRATS':
# BRATS for brain image translation
dataset = BRATS(self.config.data.train_dataroot, self.config.data.image_size, split='train')
print('Start training DDPM model on BRATS dataset.')
print('The number of involved time steps is {} out of 1000.'.format(self.args.timesteps))
train_loader = data.DataLoader(
dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
pin_memory=True)
model = Model(config)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
optimizer = get_optimizer(self.config, model.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, "ckpt.pth"))
model.load_state_dict(states[0])
states[1]["param_groups"][0]["eps"] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, x in enumerate(train_loader):
n = x['LD'].size(0)
model.train()
step += 1
x_img = x['LD'].to(self.device)
x_gt = x['FD'].to(self.device)
e = torch.randn_like(x_gt)
b = self.betas
t = torch.randint(
low=0, high=self.num_timesteps, size=(n // 2 + 1,)
).to(self.device)
t = torch.cat([t, self.num_timesteps - t - 1], dim=0)[:n]
loss = loss_registry[config.model.type](model, x_img, x_gt, t, e, b)
tb_logger.add_scalar("loss", loss, global_step=step)
logging.info(
f"step: {step}, loss: {loss.item()}"
)
optimizer.zero_grad()
loss.backward()
try:
torch.nn.utils.clip_grad_norm_(
model.parameters(), config.optim.grad_clip
)
except Exception:
pass
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = [
model.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(
states,
os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)),
)
torch.save(states, os.path.join(self.args.log_path, "ckpt.pth"))
# Training original DDPM for tasks that have two conditions: multi image super-resolution.
def sr_ddpm_train(self):
args, config = self.args, self.config
tb_logger = self.config.tb_logger
dataset = PMUB(self.config.data.train_dataroot, self.config.data.image_size, split='train')
print('Start training DDPM model on PMUB dataset.')
print('The number of involved time steps is {} out of 1000.'.format(self.args.timesteps))
train_loader = data.DataLoader(
dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
pin_memory=True)
model = Model(config)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
optimizer = get_optimizer(self.config, model.parameters())
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
else:
ema_helper = None
start_epoch, step = 0, 0
if self.args.resume_training:
states = torch.load(os.path.join(self.args.log_path, "ckpt.pth"))
model.load_state_dict(states[0])
states[1]["param_groups"][0]["eps"] = self.config.optim.eps
optimizer.load_state_dict(states[1])
start_epoch = states[2]
step = states[3]
if self.config.model.ema:
ema_helper.load_state_dict(states[4])
time_start = time.time()
total_time = 0
for epoch in range(start_epoch, self.config.training.n_epochs):
for i, x in enumerate(train_loader):
n = x['BW'].size(0)
model.train()
step += 1
x_bw = x['BW'].to(self.device)
x_md = x['MD'].to(self.device)
x_fw = x['FW'].to(self.device)
e = torch.randn_like(x_md)
b = self.betas
# antithetic sampling
t = torch.randint(
low=0, high=self.num_timesteps, size=(n // 2 + 1,)
).to(self.device)
t = torch.cat([t, self.num_timesteps - t - 1], dim=0)[:n]
loss = loss_registry[config.model.type](model, x_bw, x_md, x_fw, t, e, b)
tb_logger.add_scalar("loss", loss, global_step=step)
logging.info(
f"step: {step}, loss: {loss.item()}"
)
optimizer.zero_grad()
loss.backward()
try:
torch.nn.utils.clip_grad_norm_(
model.parameters(), config.optim.grad_clip
)
except Exception:
pass
optimizer.step()
if self.config.model.ema:
ema_helper.update(model)
if step % self.config.training.snapshot_freq == 0 or step == 1:
states = [
model.state_dict(),
optimizer.state_dict(),
epoch,
step,
]
if self.config.model.ema:
states.append(ema_helper.state_dict())
torch.save(
states,
os.path.join(self.args.log_path, "ckpt_{}.pth".format(step)),
)
torch.save(states, os.path.join(self.args.log_path, "ckpt.pth"))
# Sampling for tasks that have two conditions: multi image super-resolution.
def sr_sample(self):
ckpt_list = self.config.sampling.ckpt_id
for ckpt_idx in ckpt_list:
self.ckpt_idx = ckpt_idx
model = Model(self.config)
print('Start inference on model of {} steps'.format(ckpt_idx))
if not self.args.use_pretrained:
states = torch.load(
os.path.join(
self.args.log_path, f"ckpt_{ckpt_idx}.pth"
),
map_location=self.config.device,
)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
model.load_state_dict(states[0], strict=True)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(model)
else:
ema_helper = None
else:
# This used the pretrained DDPM model, see https://github.com/pesser/pytorch_diffusion
if self.config.data.dataset == "CIFAR10":
name = "cifar10"
elif self.config.data.dataset == "LSUN":
name = f"lsun_{self.config.data.category}"
else:
raise ValueError
ckpt = get_ckpt_path(f"ema_{name}")
print("Loading checkpoint {}".format(ckpt))
model.load_state_dict(torch.load(ckpt, map_location=self.device))
model.to(self.device)
model = torch.nn.DataParallel(model)
model.eval()
if self.args.fid:
self.sr_sample_fid(model)
elif self.args.interpolation:
self.sr_sample_interpolation(model)
elif self.args.sequence:
self.sample_sequence(model)
else:
raise NotImplementedError("Sample procedeure not defined")
# Sampling for tasks that have only one condition: image translation and CT denoising.
def sg_sample(self):
ckpt_list = self.config.sampling.ckpt_id
for ckpt_idx in ckpt_list:
self.ckpt_idx = ckpt_idx
model = Model(self.config)
print('Start inference on model of {} steps'.format(ckpt_idx))
if not self.args.use_pretrained:
states = torch.load(
os.path.join(
self.args.log_path, f"ckpt_{ckpt_idx}.pth"
),
map_location=self.config.device,
)
model = model.to(self.device)
model = torch.nn.DataParallel(model)
model.load_state_dict(states[0], strict=True)
if self.config.model.ema:
ema_helper = EMAHelper(mu=self.config.model.ema_rate)
ema_helper.register(model)
ema_helper.load_state_dict(states[-1])
ema_helper.ema(model)
else:
ema_helper = None
else:
# This used the pretrained DDPM model, see https://github.com/pesser/pytorch_diffusion
if self.config.data.dataset == "CIFAR10":
name = "cifar10"
elif self.config.data.dataset == "LSUN":
name = f"lsun_{self.config.data.category}"
else:
raise ValueError
ckpt = get_ckpt_path(f"ema_{name}")
print("Loading checkpoint {}".format(ckpt))
model.load_state_dict(torch.load(ckpt, map_location=self.device))
model.to(self.device)
model = torch.nn.DataParallel(model)
model.eval()
if self.args.fid:
self.sg_sample_fid(model)
elif self.args.interpolation:
self.sr_sample_interpolation(model)
elif self.args.sequence:
self.sample_sequence(model)
else:
raise NotImplementedError("Sample procedeure not defined")
def sr_sample_fid(self, model):
config = self.config
img_id = len(glob.glob(f"{self.args.image_folder}/*"))
print(f"starting from image {img_id}")
sample_dataset = PMUB(self.config.data.sample_dataroot, self.config.data.image_size, split='calculate')
print('Start sampling model on PMUB dataset.')
print('The inference sample type is {}. The scheduler sampling type is {}. The number of involved time steps is {} out of 1000.'.format(self.args.sample_type, self.args.scheduler_type, self.args.timesteps))
sample_loader = data.DataLoader(
sample_dataset,
batch_size=config.sampling_fid.batch_size,
shuffle=False,
num_workers=config.data.num_workers)
with torch.no_grad():
data_num = len(sample_dataset)
print('The length of test set is:', data_num)
avg_psnr = 0.0
avg_ssim = 0.0
time_list = []
psnr_list = []
ssim_list = []
for batch_idx, img in tqdm.tqdm(enumerate(sample_loader), desc="Generating image samples for FID evaluation."):
n = img['BW'].shape[0]
x = torch.randn(
n,
config.data.channels,
config.data.image_size,
config.data.image_size,
device=self.device,
)
x_bw = img['BW'].to(self.device)
x_md = img['MD'].to(self.device)
x_fw = img['FW'].to(self.device)
case_name = img['case_name'][0]
time_start = time.time()
x = self.sr_sample_image(x, x_bw, x_fw, model)
time_end = time.time()
x = inverse_data_transform(config, x)
x_md = inverse_data_transform(config, x_md)
x_tensor = x
x_md_tensor = x_md
x_md = x_md.squeeze().float().cpu().numpy()
x = x.squeeze().float().cpu().numpy()
x_md = (x_md*255.0).round()
x = (x*255.0).round()
PSNR = 0.0
SSIM = 0.0
for i in range(x.shape[0]):
psnr_temp = calculate_psnr(x[i,:,:], x_md[i,:,:])
ssim_temp = ssim(x_md[i,:,:], x[i,:,:], data_range=255)
PSNR += psnr_temp
SSIM += ssim_temp
psnr_list.append(psnr_temp)
ssim_list.append(ssim_temp)
PSNR_print = PSNR/x.shape[0]
SSIM_print = SSIM/x.shape[0]
case_time = time_end-time_start
time_list.append(case_time)
avg_psnr += PSNR
avg_ssim += SSIM
logging.info('Case {}: PSNR {}, SSIM {}, time {}'.format(case_name, PSNR_print, SSIM_print, case_time))
for i in range(0, n):
# image:(0-1)
tvu.save_image(
x_tensor[i], os.path.join(self.args.image_folder, "{}_{}_pt.png".format(self.ckpt_idx, img_id))
)
tvu.save_image(
x_md_tensor[i], os.path.join(self.args.image_folder, "{}_{}_gt.png".format(self.ckpt_idx, img_id))
)
img_id += 1
avg_psnr = avg_psnr / data_num
avg_ssim = avg_ssim / data_num
# Drop first and last for time calculation.
avg_time = sum(time_list[1:-1])/(len(time_list)-2)
logging.info('Average: PSNR {}, SSIM {}, time {}'.format(avg_psnr, avg_ssim, avg_time))
def sg_sample_fid(self, model):
config = self.config
img_id = len(glob.glob(f"{self.args.image_folder}/*"))
print(f"starting from image {img_id}")
if self.args.dataset=='LDFDCT':
# LDFDCT for CT image denoising
sample_dataset = LDFDCT(self.config.data.sample_dataroot, self.config.data.image_size, split='calculate')
print('Start training model on LDFDCT dataset.')
elif self.args.dataset=='BRATS':
# BRATS for brain image translation
sample_dataset = BRATS(self.config.data.sample_dataroot, self.config.data.image_size, split='calculate')
print('Start training model on BRATS dataset.')
print('The inference sample type is {}. The scheduler sampling type is {}. The number of involved time steps is {} out of 1000.'.format(self.args.sample_type, self.args.scheduler_type, self.args.timesteps))
sample_loader = data.DataLoader(
sample_dataset,
batch_size=config.sampling_fid.batch_size,
shuffle=False,
num_workers=config.data.num_workers)
with torch.no_grad():
data_num = len(sample_dataset)
print('The length of test set is:', data_num)
avg_psnr = 0.0
avg_ssim = 0.0
time_list = []
psnr_list = []
ssim_list = []
for batch_idx, sample in tqdm.tqdm(enumerate(sample_loader), desc="Generating image samples for FID evaluation."):
n = sample['LD'].shape[0]
x = torch.randn(
n,
config.data.channels,
config.data.image_size,
config.data.image_size,
device=self.device,
)
x_img = sample['LD'].to(self.device)
x_gt = sample['FD'].to(self.device)
case_name = sample['case_name']
time_start = time.time()
x = self.sg_sample_image(x, x_img, model)
time_end = time.time()
x = inverse_data_transform(config, x)
x_gt = inverse_data_transform(config, x_gt)
x_tensor = x
x_gt_tensor = x_gt
x_gt = x_gt.squeeze().float().cpu().numpy()
x = x.squeeze().float().cpu().numpy()
x_gt = x_gt*255
x = x*255
PSNR = 0.0
SSIM = 0.0
for i in range(x.shape[0]):
psnr_temp = calculate_psnr(x[i,:,:], x_gt[i,:,:])
ssim_temp = ssim(x_gt[i,:,:], x[i,:,:], data_range=255)
PSNR += psnr_temp
SSIM += ssim_temp
psnr_list.append(psnr_temp)
ssim_list.append(ssim_temp)
PSNR_print = PSNR/x.shape[0]
SSIM_print = SSIM/x.shape[0]
case_time = time_end-time_start
time_list.append(case_time)
avg_psnr += PSNR
avg_ssim += SSIM
logging.info('Case {}: PSNR {}, SSIM {}, time {}'.format(case_name[0], PSNR_print, SSIM_print, case_time))
for i in range(0, n):
# image:(0-1)
tvu.save_image(
x_tensor[i], os.path.join(self.args.image_folder, "{}_{}_pt.png".format(self.ckpt_idx, img_id))
)
tvu.save_image(
x_gt_tensor[i], os.path.join(self.args.image_folder, "{}_{}_gt.png".format(self.ckpt_idx, img_id))
)
img_id += 1
avg_psnr = avg_psnr / data_num
avg_ssim = avg_ssim / data_num
# Drop first and last for time calculation.
avg_time = sum(time_list[1:-1])/(len(time_list)-2)
logging.info('Average: PSNR {}, SSIM {}, time {}'.format(avg_psnr, avg_ssim, avg_time))
def sr_sample_image(self, x, x_bw, x_fw, model, last=True):
try:
skip = self.args.skip
except Exception:
skip = 1
if self.args.sample_type == "generalized":
if self.args.scheduler_type == 'uniform':
skip = self.num_timesteps // self.args.timesteps
seq = range(-1, self.num_timesteps, skip)
seq = list(seq)
seq[0] = 0
elif self.args.scheduler_type == 'non-uniform':
seq = [0, 199, 399, 599, 699, 799, 849, 899, 949, 999]
if self.args.timesteps != 10:
num_1 = int(self.args.timesteps*0.4)
num_2 = int(self.args.timesteps*0.6)
stage_1 = np.linspace(0, 699, num_1+1)[:-1]
stage_2 = np.linspace(699, 999, num_2)
stage_1 = np.ceil(stage_1).astype(int)
stage_2 = np.ceil(stage_2).astype(int)
seq = np.concatenate((stage_1, stage_2))
else:
raise Exception("The scheduler type is either uniform or non-uniform.")
from functions.denoising import generalized_steps, sr_generalized_steps
xs = sr_generalized_steps(x, x_bw, x_fw, seq, model, self.betas, eta=self.args.eta)
x = xs
elif self.args.sample_type == "ddpm_noisy":
skip = self.num_timesteps // self.args.timesteps
seq = range(0, self.num_timesteps, skip)
from functions.denoising import ddpm_steps, sr_ddpm_steps
x = sr_ddpm_steps(x, x_bw, x_fw, seq, model, self.betas)
else:
raise NotImplementedError
if last:
x = x[0][-1]
return x
def sg_sample_image(self, x, x_img, model, last=True):
try:
skip = self.args.skip
except Exception:
skip = 1
if self.args.sample_type == "generalized":
if self.args.scheduler_type == 'uniform':
skip = self.num_timesteps // self.args.timesteps
seq = range(-1, self.num_timesteps, skip)
seq = list(seq)
seq[0] = 0
elif self.args.scheduler_type == 'non-uniform':
seq = [0, 199, 399, 599, 699, 799, 849, 899, 949, 999]
if self.args.timesteps != 10:
num_1 = int(self.args.timesteps*0.4)
num_2 = int(self.args.timesteps*0.6)
stage_1 = np.linspace(0, 699, num_1+1)[:-1]
stage_2 = np.linspace(699, 999, num_2)
stage_1 = np.ceil(stage_1).astype(int)
stage_2 = np.ceil(stage_2).astype(int)
seq = np.concatenate((stage_1, stage_2))
else:
raise Exception("The scheduler type is either uniform or non-uniform.")
from functions.denoising import generalized_steps, sr_generalized_steps, sg_generalized_steps
xs = sg_generalized_steps(x, x_img, seq, model, self.betas, eta=self.args.eta)
x = xs
elif self.args.sample_type == "ddpm_noisy":
skip = self.num_timesteps // self.args.timesteps
seq = range(0, self.num_timesteps, skip)
from functions.denoising import ddpm_steps, sr_ddpm_steps, sg_ddpm_steps
x = sg_ddpm_steps(x, x_img, seq, model, self.betas)
else:
raise NotImplementedError
if last:
x = x[0][-1]
return x
def test(self):
pass
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