def main(args):
check_inputs(args)
t1 = dt.now()
## set the device
use_cuda = torch.cuda.is_available()
log('Use cuda {}'.format(use_cuda))
if use_cuda:
torch.set_default_tensor_type(torch.cuda.FloatTensor)
if args.config is not None:
args = config.load_config(args.config, args)
log(args)
if args.downsample:
assert args.downsample % 2 == 0, "Boxsize must be even"
assert args.downsample < args.D, "Must be smaller than original box size"
D = args.D + 1
lattice = Lattice(D, extent=args.l_extent)
if args.enc_mask:
args.enc_mask = lattice.get_circular_mask(args.enc_mask)
in_dim = args.enc_mask.sum()
else:
in_dim = lattice.D**2
model = HetOnlyVAE(lattice,
args.qlayers,
args.qdim,
args.players,
args.pdim,
in_dim,
args.zdim,
encode_mode=args.encode_mode,
enc_mask=args.enc_mask,
enc_type=args.pe_type,
enc_dim=args.pe_dim,
domain=args.domain)
log('Loading weights from {}'.format(args.weights))
checkpoint = torch.load(args.weights)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
### Multiple z ###
if args.z_start or args.zfile:
### Get z values
if args.z_start:
args.z_start = np.array(args.z_start)
args.z_end = np.array(args.z_end)
z = np.repeat(np.arange(args.n, dtype=np.float32),
args.zdim).reshape((args.n, args.zdim))
z *= ((args.z_end - args.z_start) / (args.n - 1))
z += args.z_start
else:
z = np.loadtxt(args.zfile).reshape(-1, args.zdim)
if not os.path.exists(args.o):
os.makedirs(args.o)
log(f'Generating {len(z)} volumes')
for i, zz in enumerate(z):
log(zz)
if args.downsample:
extent = lattice.extent * (args.downsample / args.D)
vol = model.decoder.eval_volume(
lattice.get_downsample_coords(args.downsample + 1),
args.downsample + 1, extent, args.norm, zz)
else:
vol = model.decoder.eval_volume(lattice.coords, lattice.D,
lattice.extent, args.norm, zz)
out_mrc = '{}/{}{:03d}.mrc'.format(args.o, args.prefix, i)
if args.flip:
vol = vol[::-1]
mrc.write(out_mrc, vol.astype(np.float32), Apix=args.Apix)
### Single z ###
else:
z = np.array(args.z)
log(z)
if args.downsample:
extent = lattice.extent * (args.downsample / args.D)
vol = model.decoder.eval_volume(
lattice.get_downsample_coords(args.downsample + 1),
args.downsample + 1, extent, args.norm, z)
else:
vol = model.decoder.eval_volume(lattice.coords, lattice.D,
lattice.extent, args.norm, z)
if args.flip:
vol = vol[::-1]
mrc.write(args.o, vol.astype(np.float32), Apix=args.Apix)
td = dt.now() - t1
log('Finsihed in {}'.format(td))
def main(args):
t1 = dt.now()
# set the device
use_cuda = torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
log('Use cuda {}'.format(use_cuda))
if use_cuda:
torch.set_default_tensor_type(torch.cuda.FloatTensor)
if args.config is not None:
args = config.load_config(args.config, args)
log(args)
beta = args.beta
# load the particles
if args.ind is not None:
log('Filtering image dataset with {}'.format(args.ind))
ind = pickle.load(open(args.ind, 'rb'))
else:
ind = None
if args.tilt is None:
if args.encode_mode == 'conv':
args.use_real = True
if args.lazy:
data = dataset.LazyMRCData(args.particles,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
keepreal=args.use_real,
window=args.window,
datadir=args.datadir)
else:
data = dataset.MRCData(args.particles,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
keepreal=args.use_real,
window=args.window,
datadir=args.datadir)
tilt = None
else:
assert args.encode_mode == 'tilt'
if args.lazy: raise NotImplementedError
data = dataset.TiltMRCData(args.particles,
args.tilt,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
window=args.window,
keepreal=args.use_real,
datadir=args.datadir)
tilt = torch.tensor(utils.xrot(args.tilt_deg).astype(np.float32))
Nimg = data.N
D = data.D
if args.encode_mode == 'conv':
assert D - 1 == 64, "Image size must be 64x64 for convolutional encoder"
# load poses
posetracker = PoseTracker.load(args.poses, Nimg, D, None, ind)
# load ctf
if args.ctf is not None:
if args.use_real:
raise NotImplementedError(
"Not implemented with real-space encoder. Use phase-flipped images instead"
)
log('Loading ctf params from {}'.format(args.ctf))
ctf_params = ctf.load_ctf_for_training(D - 1, args.ctf)
if args.ind is not None: ctf_params = ctf_params[ind]
ctf_params = torch.tensor(ctf_params)
else:
ctf_params = None
# instantiate model
lattice = Lattice(D, extent=0.5)
if args.enc_mask is None:
args.enc_mask = D // 2
if args.enc_mask > 0:
assert args.enc_mask <= D // 2
enc_mask = lattice.get_circular_mask(args.enc_mask)
in_dim = enc_mask.sum()
elif args.enc_mask == -1:
enc_mask = None
in_dim = lattice.D**2 if not args.use_real else (lattice.D - 1)**2
else:
raise RuntimeError(
"Invalid argument for encoder mask radius {}".format(
args.enc_mask))
model = HetOnlyVAE(lattice,
args.qlayers,
args.qdim,
args.players,
args.pdim,
in_dim,
args.zdim,
encode_mode=args.encode_mode,
enc_mask=enc_mask,
enc_type=args.pe_type,
enc_dim=args.pe_dim,
domain=args.domain)
log('Loading weights from {}'.format(args.weights))
checkpoint = torch.load(args.weights)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
z_mu_all = []
z_logvar_all = []
gen_loss_accum = 0
kld_accum = 0
loss_accum = 0
batch_it = 0
data_generator = DataLoader(data,
batch_size=args.batch_size,
shuffle=False)
for minibatch in data_generator:
ind = minibatch[-1].to(device)
y = minibatch[0].to(device)
yt = minibatch[1].to(device) if tilt is not None else None
B = len(ind)
batch_it += B
yr = torch.from_numpy(
data.particles_real[ind]).to(device) if args.use_real else None
rot, tran = posetracker.get_pose(ind)
ctf_param = ctf_params[ind] if ctf_params is not None else None
z_mu, z_logvar, loss, gen_loss, kld = eval_batch(model,
lattice,
y,
yt,
rot,
tran,
beta,
tilt,
ctf_params=ctf_param,
yr=yr)
z_mu_all.append(z_mu)
z_logvar_all.append(z_logvar)
# logging
gen_loss_accum += gen_loss * B
kld_accum += kld * B
loss_accum += loss * B
if batch_it % args.log_interval == 0:
log('# [{}/{} images] gen loss={:.4f}, kld={:.4f}, beta={:.4f}, loss={:.4f}'
.format(batch_it, Nimg, gen_loss, kld, beta, loss))
log('# =====> Average gen loss = {:.6}, KLD = {:.6f}, total loss = {:.6f}'.
format(gen_loss_accum / Nimg, kld_accum / Nimg, loss_accum / Nimg))
z_mu_all = np.vstack(z_mu_all)
z_logvar_all = np.vstack(z_logvar_all)
with open(args.o, 'wb') as f:
pickle.dump(z_mu_all, f)
pickle.dump(z_logvar_all, f)
pickle.dump([loss_accum, gen_loss_accum, kld_accum], f)
log('Finsihed in {}'.format(dt.now() - t1))
def main(args):
t1 = dt.now()
if args.outdir is not None and not os.path.exists(args.outdir):
os.makedirs(args.outdir)
LOG = f'{args.outdir}/run.log'
def flog(msg): # HACK: switch to logging module
return utils.flog(msg, LOG)
if args.load == 'latest':
args = get_latest(args, flog)
flog(' '.join(sys.argv))
flog(args)
# set the random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# set the device
use_cuda = torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
flog('Use cuda {}'.format(use_cuda))
if not use_cuda:
log('WARNING: No GPUs detected')
# set beta schedule
if args.beta is None:
args.beta = 1. / args.zdim
try:
args.beta = float(args.beta)
except ValueError:
assert args.beta_control, "Need to set beta control weight for schedule {}".format(
args.beta)
beta_schedule = get_beta_schedule(args.beta)
# load index filter
if args.ind is not None:
flog('Filtering image dataset with {}'.format(args.ind))
ind = pickle.load(open(args.ind, 'rb'))
else:
ind = None
# load dataset
flog(f'Loading dataset from {args.particles}')
if args.tilt is None:
tilt = None
args.use_real = args.encode_mode == 'conv'
if args.lazy:
data = dataset.LazyMRCData(args.particles,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
keepreal=args.use_real,
window=args.window,
datadir=args.datadir,
window_r=args.window_r,
flog=flog)
elif args.preprocessed:
flog(
f'Using preprocessed inputs. Ignoring any --window/--invert-data options'
)
data = dataset.PreprocessedMRCData(args.particles,
norm=args.norm,
ind=ind,
flog=flog)
else:
data = dataset.MRCData(args.particles,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
keepreal=args.use_real,
window=args.window,
datadir=args.datadir,
max_threads=args.max_threads,
window_r=args.window_r,
flog=flog)
# Tilt series data -- lots of unsupported features
else:
assert args.encode_mode == 'tilt'
if args.lazy: raise NotImplementedError
if args.preprocessed: raise NotImplementedError
data = dataset.TiltMRCData(args.particles,
args.tilt,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
window=args.window,
keepreal=args.use_real,
datadir=args.datadir,
window_r=args.window_r,
flog=flog)
tilt = torch.tensor(utils.xrot(args.tilt_deg).astype(np.float32),
device=device)
Nimg = data.N
D = data.D
if args.encode_mode == 'conv':
assert D - 1 == 64, "Image size must be 64x64 for convolutional encoder"
# load poses
if args.do_pose_sgd:
assert args.domain == 'hartley', "Need to use --domain hartley if doing pose SGD"
do_pose_sgd = args.do_pose_sgd
posetracker = PoseTracker.load(args.poses,
Nimg,
D,
's2s2' if do_pose_sgd else None,
ind,
device=device)
pose_optimizer = torch.optim.SparseAdam(
list(posetracker.parameters()),
lr=args.pose_lr) if do_pose_sgd else None
# load ctf
if args.ctf is not None:
if args.use_real:
raise NotImplementedError(
"Not implemented with real-space encoder. Use phase-flipped images instead"
)
flog('Loading ctf params from {}'.format(args.ctf))
ctf_params = ctf.load_ctf_for_training(D - 1, args.ctf)
if args.ind is not None: ctf_params = ctf_params[ind]
assert ctf_params.shape == (Nimg, 8)
ctf_params = torch.tensor(ctf_params, device=device)
else:
ctf_params = None
# instantiate model
lattice = Lattice(D, extent=0.5, device=device)
if args.enc_mask is None:
args.enc_mask = D // 2
if args.enc_mask > 0:
assert args.enc_mask <= D // 2
enc_mask = lattice.get_circular_mask(args.enc_mask)
in_dim = enc_mask.sum()
elif args.enc_mask == -1:
enc_mask = None
in_dim = lattice.D**2 if not args.use_real else (lattice.D - 1)**2
else:
raise RuntimeError(
"Invalid argument for encoder mask radius {}".format(
args.enc_mask))
activation = {"relu": nn.ReLU, "leaky_relu": nn.LeakyReLU}[args.activation]
model = HetOnlyVAE(lattice,
args.qlayers,
args.qdim,
args.players,
args.pdim,
in_dim,
args.zdim,
encode_mode=args.encode_mode,
enc_mask=enc_mask,
enc_type=args.pe_type,
enc_dim=args.pe_dim,
domain=args.domain,
activation=activation,
feat_sigma=args.feat_sigma)
model.to(device)
flog(model)
flog('{} parameters in model'.format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
flog('{} parameters in encoder'.format(
sum(p.numel() for p in model.encoder.parameters() if p.requires_grad)))
flog('{} parameters in deoder'.format(
sum(p.numel() for p in model.decoder.parameters() if p.requires_grad)))
# save configuration
out_config = '{}/config.pkl'.format(args.outdir)
save_config(args, data, lattice, model, out_config)
optim = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
# Mixed precision training
scaler = None
if args.amp:
assert args.batch_size % 8 == 0, "Batch size must be divisible by 8 for AMP training"
assert (
D -
1) % 8 == 0, "Image size must be divisible by 8 for AMP training"
assert args.pdim % 8 == 0, "Decoder hidden layer dimension must be divisible by 8 for AMP training"
assert args.qdim % 8 == 0, "Encoder hidden layer dimension must be divisible by 8 for AMP training"
# Also check zdim, enc_mask dim? Add them as warnings for now.
if args.zdim % 8 != 0:
log('Warning: z dimension is not a multiple of 8 -- AMP training speedup is not optimized'
)
if in_dim % 8 != 0:
log('Warning: Masked input image dimension is not a mutiple of 8 -- AMP training speedup is not optimized'
)
try: # Mixed precision with apex.amp
model, optim = amp.initialize(model, optim, opt_level='O1')
except: # Mixed precision with pytorch (v1.6+)
scaler = torch.cuda.amp.GradScaler()
# restart from checkpoint
if args.load:
flog('Loading checkpoint from {}'.format(args.load))
checkpoint = torch.load(args.load)
model.load_state_dict(checkpoint['model_state_dict'])
optim.load_state_dict(checkpoint['optimizer_state_dict'])
start_epoch = checkpoint['epoch'] + 1
model.train()
else:
start_epoch = 0
# parallelize
if args.multigpu and torch.cuda.device_count() > 1:
log(f'Using {torch.cuda.device_count()} GPUs!')
args.batch_size *= torch.cuda.device_count()
log(f'Increasing batch size to {args.batch_size}')
model = nn.DataParallel(model)
elif args.multigpu:
log(f'WARNING: --multigpu selected, but {torch.cuda.device_count()} GPUs detected'
)
# training loop
data_generator = DataLoader(data, batch_size=args.batch_size, shuffle=True)
num_epochs = args.num_epochs
for epoch in range(start_epoch, num_epochs):
t2 = dt.now()
gen_loss_accum = 0
loss_accum = 0
kld_accum = 0
eq_loss_accum = 0
batch_it = 0
for minibatch in data_generator:
ind = minibatch[-1].to(device)
y = minibatch[0].to(device)
yt = minibatch[1].to(device) if tilt is not None else None
B = len(ind)
batch_it += B
global_it = Nimg * epoch + batch_it
beta = beta_schedule(global_it)
yr = torch.from_numpy(data.particles_real[ind.numpy()]).to(
device) if args.use_real else None
if do_pose_sgd:
pose_optimizer.zero_grad()
rot, tran = posetracker.get_pose(ind)
ctf_param = ctf_params[ind] if ctf_params is not None else None
loss, gen_loss, kld = train_batch(model,
lattice,
y,
yt,
rot,
tran,
optim,
beta,
args.beta_control,
tilt,
ctf_params=ctf_param,
yr=yr,
use_amp=args.amp,
scaler=scaler)
if do_pose_sgd and epoch >= args.pretrain:
pose_optimizer.step()
# logging
gen_loss_accum += gen_loss * B
kld_accum += kld * B
loss_accum += loss * B
if batch_it % args.log_interval == 0:
log('# [Train Epoch: {}/{}] [{}/{} images] gen loss={:.6f}, kld={:.6f}, beta={:.6f}, loss={:.6f}'
.format(epoch + 1, num_epochs, batch_it, Nimg, gen_loss,
kld, beta, loss))
flog(
'# =====> Epoch: {} Average gen loss = {:.6}, KLD = {:.6f}, total loss = {:.6f}; Finished in {}'
.format(epoch + 1, gen_loss_accum / Nimg, kld_accum / Nimg,
loss_accum / Nimg,
dt.now() - t2))
if args.checkpoint and epoch % args.checkpoint == 0:
out_weights = '{}/weights.{}.pkl'.format(args.outdir, epoch)
out_z = '{}/z.{}.pkl'.format(args.outdir, epoch)
model.eval()
with torch.no_grad():
z_mu, z_logvar = eval_z(model, lattice, data, args.batch_size,
device, posetracker.trans, tilt
is not None, ctf_params, args.use_real)
save_checkpoint(model, optim, epoch, z_mu, z_logvar,
out_weights, out_z)
if args.do_pose_sgd and epoch >= args.pretrain:
out_pose = '{}/pose.{}.pkl'.format(args.outdir, epoch)
posetracker.save(out_pose)
# save model weights, latent encoding, and evaluate the model on 3D lattice
out_weights = '{}/weights.pkl'.format(args.outdir)
out_z = '{}/z.pkl'.format(args.outdir)
model.eval()
with torch.no_grad():
z_mu, z_logvar = eval_z(model, lattice, data, args.batch_size, device,
posetracker.trans, tilt is not None,
ctf_params, args.use_real)
save_checkpoint(model, optim, epoch, z_mu, z_logvar, out_weights,
out_z)
if args.do_pose_sgd and epoch >= args.pretrain:
out_pose = '{}/pose.pkl'.format(args.outdir)
posetracker.save(out_pose)
td = dt.now() - t1
flog('Finished in {} ({} per epoch)'.format(
td, td / (num_epochs - start_epoch)))
def main(args):
t1 = dt.now()
if args.outdir is not None and not os.path.exists(args.outdir):
os.makedirs(args.outdir)
LOG = f'{args.outdir}/run.log'
def flog(msg): # HACK: switch to logging module
return utils.flog(msg, LOG)
if args.load == 'latest':
args = get_latest(args)
flog(' '.join(sys.argv))
flog(args)
# set the random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# set the device
use_cuda = torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
flog('Use cuda {}'.format(use_cuda))
if use_cuda:
torch.set_default_tensor_type(torch.cuda.FloatTensor)
# set beta schedule
try:
args.beta = float(args.beta)
except ValueError:
assert args.beta_control, "Need to set beta control weight for schedule {}".format(
args.beta)
beta_schedule = get_beta_schedule(args.beta)
# load the particles
if args.ind is not None:
flog('Filtering image dataset with {}'.format(args.ind))
ind = pickle.load(open(args.ind, 'rb'))
else:
ind = None
if args.tilt is None:
if args.encode_mode == 'conv':
args.use_real = True
if args.lazy:
data = dataset.LazyMRCData(args.particles,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
keepreal=args.use_real,
window=args.window,
datadir=args.datadir,
relion31=args.relion31)
else:
data = dataset.MRCData(args.particles,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
keepreal=args.use_real,
window=args.window,
datadir=args.datadir,
relion31=args.relion31)
tilt = None
else:
assert args.encode_mode == 'tilt'
if args.lazy: raise NotImplementedError
if args.relion31: raise NotImplementedError
data = dataset.TiltMRCData(args.particles,
args.tilt,
norm=args.norm,
invert_data=args.invert_data,
ind=ind,
window=args.window,
keepreal=args.use_real,
datadir=args.datadir)
tilt = torch.tensor(utils.xrot(args.tilt_deg).astype(np.float32))
Nimg = data.N
D = data.D
if args.encode_mode == 'conv':
assert D - 1 == 64, "Image size must be 64x64 for convolutional encoder"
# load poses
if args.do_pose_sgd:
assert args.domain == 'hartley', "Need to use --domain hartley if doing pose SGD"
do_pose_sgd = args.do_pose_sgd
posetracker = PoseTracker.load(args.poses, Nimg, D,
's2s2' if do_pose_sgd else None, ind)
pose_optimizer = torch.optim.SparseAdam(
posetracker.parameters(), lr=args.pose_lr) if do_pose_sgd else None
# load ctf
if args.ctf is not None:
if args.use_real:
raise NotImplementedError(
"Not implemented with real-space encoder. Use phase-flipped images instead"
)
flog('Loading ctf params from {}'.format(args.ctf))
ctf_params = ctf.load_ctf_for_training(D - 1, args.ctf)
if args.ind is not None: ctf_params = ctf_params[ind]
ctf_params = torch.tensor(ctf_params)
else:
ctf_params = None
# instantiate model
lattice = Lattice(D, extent=0.5)
if args.enc_mask is None:
args.enc_mask = D // 2
if args.enc_mask > 0:
assert args.enc_mask <= D // 2
enc_mask = lattice.get_circular_mask(args.enc_mask)
in_dim = enc_mask.sum()
elif args.enc_mask == -1:
enc_mask = None
in_dim = lattice.D**2 if not args.use_real else (lattice.D - 1)**2
else:
raise RuntimeError(
"Invalid argument for encoder mask radius {}".format(
args.enc_mask))
model = HetOnlyVAE(lattice,
args.qlayers,
args.qdim,
args.players,
args.pdim,
in_dim,
args.zdim,
encode_mode=args.encode_mode,
enc_mask=enc_mask,
enc_type=args.pe_type,
enc_dim=args.pe_dim,
domain=args.domain)
flog(model)
flog('{} parameters in model'.format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
# save configuration
out_config = '{}/config.pkl'.format(args.outdir)
save_config(args, data, lattice, model, out_config)
optim = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
# restart from checkpoint
if args.load:
flog('Loading checkpoint from {}'.format(args.load))
checkpoint = torch.load(args.load)
model.load_state_dict(checkpoint['model_state_dict'])
optim.load_state_dict(checkpoint['optimizer_state_dict'])
start_epoch = checkpoint['epoch'] + 1
model.train()
else:
start_epoch = 0
# training loop
data_generator = DataLoader(data, batch_size=args.batch_size, shuffle=True)
num_epochs = args.num_epochs
for epoch in range(start_epoch, num_epochs):
t2 = dt.now()
gen_loss_accum = 0
loss_accum = 0
kld_accum = 0
eq_loss_accum = 0
batch_it = 0
for minibatch in data_generator:
ind = minibatch[-1].to(device)
y = minibatch[0].to(device)
yt = minibatch[1].to(device) if tilt is not None else None
B = len(ind)
batch_it += B
global_it = Nimg * epoch + batch_it
beta = beta_schedule(global_it)
yr = torch.from_numpy(data.particles_real[ind.numpy()]).to(
device) if args.use_real else None
if do_pose_sgd:
pose_optimizer.zero_grad()
rot, tran = posetracker.get_pose(ind)
ctf_param = ctf_params[ind] if ctf_params is not None else None
loss, gen_loss, kld = train_batch(model,
lattice,
y,
yt,
rot,
tran,
optim,
beta,
args.beta_control,
tilt,
ctf_params=ctf_param,
yr=yr)
if do_pose_sgd and epoch >= args.pretrain:
pose_optimizer.step()
# logging
gen_loss_accum += gen_loss * B
kld_accum += kld * B
loss_accum += loss * B
if batch_it % args.log_interval == 0:
log('# [Train Epoch: {}/{}] [{}/{} images] gen loss={:.6f}, kld={:.6f}, beta={:.6f}, loss={:.6f}'
.format(epoch + 1, num_epochs, batch_it, Nimg, gen_loss,
kld, beta, loss))
flog(
'# =====> Epoch: {} Average gen loss = {:.6}, KLD = {:.6f}, total loss = {:.6f}; Finished in {}'
.format(epoch + 1, gen_loss_accum / Nimg, kld_accum / Nimg,
loss_accum / Nimg,
dt.now() - t2))
if args.checkpoint and epoch % args.checkpoint == 0:
out_weights = '{}/weights.{}.pkl'.format(args.outdir, epoch)
out_z = '{}/z.{}.pkl'.format(args.outdir, epoch)
model.eval()
with torch.no_grad():
z_mu, z_logvar = eval_z(model, lattice, data, args.batch_size,
device, posetracker.trans, tilt
is not None, ctf_params, args.use_real)
save_checkpoint(model, optim, epoch, z_mu, z_logvar,
out_weights, out_z)
if args.do_pose_sgd and epoch >= args.pretrain:
out_pose = '{}/pose.{}.pkl'.format(args.outdir, epoch)
posetracker.save(out_pose)
# save model weights, latent encoding, and evaluate the model on 3D lattice
out_weights = '{}/weights.pkl'.format(args.outdir)
out_z = '{}/z.pkl'.format(args.outdir)
model.eval()
with torch.no_grad():
z_mu, z_logvar = eval_z(model, lattice, data, args.batch_size, device,
posetracker.trans, tilt is not None,
ctf_params, args.use_real)
save_checkpoint(model, optim, epoch, z_mu, z_logvar, out_weights,
out_z)
if args.do_pose_sgd and epoch >= args.pretrain:
out_pose = '{}/pose.pkl'.format(args.outdir)
posetracker.save(out_pose)
td = dt.now() - t1
flog('Finsihed in {} ({} per epoch)'.format(
td, td / (num_epochs - start_epoch)))