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run_lib.py
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run_lib.py
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"""Training and evaluation for score-based generative models. """
import os
import time
import logging
from models import ncsnpp, ddpm
import losses
import sampling
from models import model_utils as mutils
from models.ema import ExponentialMovingAverage
import sde_lib
from absl import flags
import torch
from torch.utils import tensorboard
from utils.utils import *
import utils.datasets as datasets
import tensorflow as tf
FLAGS = flags.FLAGS
def train(config, workdir):
"""Runs the training pipeline.
Args:
config: Configuration to use.
workdir: Working directory for checkpoints and TF summaries. If this
contains checkpoint training will be resumed from the latest checkpoint.
"""
# The directory for saving test results during training
sample_dir = os.path.join(workdir, "samples_in_train")
tf.io.gfile.makedirs(sample_dir)
tb_dir = os.path.join(workdir, "tensorboard")
tf.io.gfile.makedirs(tb_dir)
writer = tensorboard.SummaryWriter(tb_dir)
# Initialize model.
score_model = mutils.create_model(config)
ema = ExponentialMovingAverage(
score_model.parameters(), decay=config.model.ema_rate
)
optimizer = losses.get_optimizer(config, score_model.parameters())
state = dict(optimizer=optimizer, model=score_model, ema=ema, step=0)
# Create checkpoints directory
checkpoint_dir = os.path.join(workdir, "checkpoints")
tf.io.gfile.makedirs(checkpoint_dir)
initial_step = int(state["step"])
# Build pytorch dataloader for training
train_dl = datasets.get_dataset(config, "training")
# Create data scaler and its inverse
scaler = get_data_scaler(config)
# Setup SDEs
if config.training.sde.lower() == "vpsde":
sde = sde_lib.VPSDE(config)
elif config.training.sde.lower() == "subvpsde":
sde = sde_lib.subVPSDE(config)
elif config.training.sde.lower() == "vesde":
sde = sde_lib.VESDE(config)
elif config.training.sde.lower() == "hfssde":
sde = sde_lib.HFS_SDE(config)
else:
raise NotImplementedError(f"SDE {config.training.sde} unknown.")
# Build one-step training and evaluation functions
optimize_fn = losses.optimization_manager(config)
continuous = config.training.continuous
reduce_mean = config.training.reduce_mean
likelihood_weighting = config.training.likelihood_weighting
train_step_fn = losses.get_step_fn(
config,
sde,
train=True,
optimize_fn=optimize_fn,
reduce_mean=reduce_mean,
continuous=continuous,
likelihood_weighting=likelihood_weighting,
)
# In case there are multiple hosts (e.g., TPU pods), only log to host 0
logging.info("Starting training loop at step %d." % (initial_step,))
for epoch in range(config.training.epochs):
loss_sum = 0
for step, batch in enumerate(train_dl):
t0 = time.time()
k0, csm = batch
# TODO: mask condition
label = Emat_xyt_complex(k0, True, csm, 1) # 1x1x320x320
label = c2r(label).type(torch.FloatTensor).to(config.device)
label = scaler(label)
# Execute one training step
loss = train_step_fn(state, label)
loss_sum += loss
param_num = sum(param.numel() for param in state["model"].parameters())
# if step % 10 == 0:
print(
"Epoch",
epoch + 1,
"/",
config.training.epochs,
"Step",
step,
"loss = ",
loss.cpu().data.numpy(),
"loss mean =",
loss_sum.cpu().data.numpy() / (step + 1),
"time",
time.time() - t0,
"param_num",
param_num,
)
# Save a checkpoint for every 5 epochs
if (epoch + 1) % 5 == 0:
save_checkpoint(
os.path.join(checkpoint_dir, f"checkpoint_{epoch + 1}.pth"), state
)
def sample(config, workdir):
"""Generate samples.
Args:
config: Configuration to use.
workdir: Working directory.
"""
# Initialize model
score_model = mutils.create_model(config)
optimizer = losses.get_optimizer(config, score_model.parameters())
ema = ExponentialMovingAverage(
score_model.parameters(), decay=config.model.ema_rate
)
state = dict(optimizer=optimizer, model=score_model, ema=ema, step=0)
checkpoint_dir = os.path.join(workdir, "checkpoints")
ckpt_path = os.path.join(checkpoint_dir, f"checkpoint_{config.sampling.ckpt}.pth")
state = restore_checkpoint(ckpt_path, state, device=config.device)
print("load weights:", ckpt_path)
if FLAGS.config.sampling.datashift == "head":
SAMPLING_FOLDER_ID = "_".join(
[
FLAGS.config.sampling.acc,
FLAGS.config.sampling.acs,
FLAGS.config.sampling.mask_type,
"ckpt",
str(config.sampling.ckpt),
FLAGS.config.sampling.predictor,
FLAGS.config.training.mean_equal,
FLAGS.config.sampling.datashift,
FLAGS.config.sampling.fft,
str(config.sampling.snr),
"predictor_mse",
str(FLAGS.config.sampling.mse),
"corrector_mse",
str(FLAGS.config.sampling.corrector_mse),
str(FLAGS.config.data.centered),
str(
FLAGS.config.sampling.N
if FLAGS.config.sampling.accelerated_sampling
else ""
),
"seed",
str(FLAGS.config.seed),
]
)
test_dl = datasets.get_dataset(
config, "datashift"
) # mode=test:90多张图,modex=sample:一张图,第十张
elif FLAGS.config.sampling.datashift == "photom":
SAMPLING_FOLDER_ID = "_".join(
[
FLAGS.config.sampling.acc,
FLAGS.config.sampling.acs,
FLAGS.config.sampling.mask_type,
"ckpt",
str(config.sampling.ckpt),
FLAGS.config.sampling.predictor,
FLAGS.config.training.mean_equal,
FLAGS.config.sampling.datashift,
FLAGS.config.sampling.fft,
str(config.sampling.snr),
"predictor_mse",
str(FLAGS.config.sampling.mse),
"corrector_mse",
str(FLAGS.config.sampling.corrector_mse),
str(FLAGS.config.data.centered),
str(
FLAGS.config.sampling.N
if FLAGS.config.sampling.accelerated_sampling
else ""
),
"photom",
"seed",
str(FLAGS.config.seed),
]
)
test_dl = datasets.get_dataset(
config, "photom"
) # mode=test:90多张图,modex=sample:一张图,第十张
else:
SAMPLING_FOLDER_ID = "_".join(
[
FLAGS.config.sampling.acc,
FLAGS.config.sampling.acs,
FLAGS.config.sampling.mask_type,
"ckpt",
str(config.sampling.ckpt),
FLAGS.config.sampling.predictor,
FLAGS.config.training.mean_equal,
str(config.sampling.snr),
"predictor_mse",
str(FLAGS.config.sampling.mse),
"corrector_mse",
str(FLAGS.config.sampling.corrector_mse),
str(
FLAGS.config.data.centered,
),
str(
FLAGS.config.sampling.N
if FLAGS.config.sampling.accelerated_sampling
else ""
),
"--",
"seed",
str(FLAGS.config.seed),
]
)
test_dl = datasets.get_dataset(
config, "test"
) # mode=test:90多张图,modex=sample:一张图,第十张
FLAGS.config.sampling.folder = os.path.join(FLAGS.workdir, SAMPLING_FOLDER_ID)
tf.io.gfile.makedirs(FLAGS.config.sampling.folder)
# Create data scaler and its inverse
scaler = get_data_scaler(config)
inverse_scaler = get_data_inverse_scaler(config)
# Setup SDEs
if config.training.sde.lower() == "vpsde":
sde = sde_lib.VPSDE(config)
sampling_eps = 1e-3
elif config.training.sde.lower() == "subvpsde":
sde = sde_lib.subVPSDE(config)
sampling_eps = 1e-3
elif config.training.sde.lower() == "vesde":
sde = sde_lib.VESDE(config)
sampling_eps = 1e-5
elif config.training.sde.lower() == "hfssde":
sde = sde_lib.HFS_SDE(config)
sampling_eps = 1e-3 # TODO
else:
raise NotImplementedError(f"SDE {config.training.sde} unknown.")
atb_mask = get_mask(config, "sample")
train_mask = get_mask(config, "sde")
# Build the sampling function when sampling is enabled
sampling_shape = (
config.sampling.batch_size,
config.data.num_channels,
config.data.image_size,
config.data.image_size,
)
sampling_fn = sampling.get_sampling_fn(
config, sde, sampling_shape, inverse_scaler, sampling_eps, atb_mask, train_mask
)
for index, point in enumerate(test_dl):
print("---------------------------------------------")
print("---------------- point:", index, "------------------")
print("---------------------------------------------")
k0, csm = point
k0 = k0.to(config.device)
csm = csm.to(config.device)
label = Emat_xyt_complex(k0, True, csm, 1.0).to(config.device)
label_dir = os.path.join("results", FLAGS.config.sampling.datashift)
if not tf.io.gfile.exists(label_dir):
tf.io.gfile.makedirs(label_dir)
save_mat(label_dir, label.to(label), "label", index, normalize=False)
atb = k0 * atb_mask
atb_to_image = (
c2r(Emat_xyt_complex(atb, True, csm, 1))
.type(torch.FloatTensor)
.to(config.device)
) # 1x2x320x320
csm = c2r(csm).type(torch.FloatTensor).to(config.device)
recon, n = sampling_fn(score_model, atb, atb_to_image, csm)
recon = r2c(recon)
save_mat(
FLAGS.config.sampling.folder,
recon.to(recon),
"recon",
index,
normalize=False,
)
from utils.calc import Evaluation_metrics
ssim, psnr, nmse = Evaluation_metrics(
label, recon, True if FLAGS.config.sampling.datashift == "photom" else False
)
print(
f"mse_{config.sampling.mse}_snr_{config.sampling.snr}_cmse_{config.sampling.corrector_mse}:"
)
print("nmse:", nmse)
print("ssim:", ssim)
print("psnr:", psnr)