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atlas.py
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atlas.py
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import torch
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from torch.distributed import all_reduce
from torch.utils.data.distributed import DistributedSampler
import torch.nn.functional as F
from torch.nn.functional import mse_loss
from torch.utils.data import Dataset, DataLoader
import h5py
from utils import tqdm
import numpy as np
import random
from matching import *
import lagomorph as lm
def L2norm(a):
aflat = a.view(-1)
return torch.dot(aflat, aflat)
def affine_atlas(dataset,
As,
Ts,
I=None,
num_epochs=1000,
batch_size=50,
affine_steps=1,
reg_weightA=0e1,
reg_weightT=0e1,
learning_rate_A=1e-3,
learning_rate_T=1e-2,
learning_rate_I=1e5,
loader_workers=8,
gpu=None,
world_size=1,
rank=0):
from torch.utils.data import DataLoader, TensorDataset
if world_size > 1:
sampler = DistributedSampler(dataset,
num_replicas=world_size,
rank=rank)
else:
sampler = None
if gpu is None:
device = 'cpu'
else:
device = f'cuda:{gpu}'
dataloader = DataLoader(dataset, batch_size=batch_size, sampler=sampler,
shuffle=False, num_workers=8, pin_memory=True)
if I is None:
# initialize base image to mean
I = batch_average(dataloader, dim=0)
else:
I = I.clone()
I = I.to(device).view(1,1,*I.squeeze().shape)
image_optimizer = torch.optim.SGD([I],
lr=learning_rate_I,
weight_decay=0.)
eye = torch.eye(3).view(1,3,3).type(I.dtype).to(I.device)
losses = []
iter_losses = []
epbar = range(num_epochs)
if rank == 0:
epbar = tqdm(epbar, desc='epoch', position=0)
for epoch in epbar:
epoch_loss = 0.0
itbar = dataloader
#itbar = tqdm(dataloader, desc='iter', position=1)
image_optimizer.zero_grad()
for it, (ix, img) in enumerate(itbar):
A = As[ix,...].detach().to(device).contiguous()
T = Ts[ix,...].detach().to(device).contiguous()
img = img.to(device)
img.requires_grad_(False)
#A, T, losses_match = affine_matching(I,
# img,
# A=A,
# T=T,
# affine_steps=affine_steps,
# reg_weightA=reg_weightA,
# reg_weightT=reg_weightT,
# learning_rate_A=learning_rate_A,
# learning_rate_T=learning_rate_T,
# progress_bar=False)
for affit in range(affine_steps):
A.requires_grad_(True)
T.requires_grad_(True)
if A.grad is not None:
A.grad.detach_()
A.grad.zero_()
if T.grad is not None:
T.grad.detach_()
T.grad.zero_()
I.requires_grad_(True)
Idef = lm.affine_interp(I, A+eye, T)
regloss = 0
if reg_weightA > 0:
regtermA = L2norm(A)
regloss = regloss + .5*reg_weightA*regtermA
if reg_weightT > 0:
regtermT = L2norm(T)
regloss = regloss + .5*reg_weightT*regtermT
loss = (mse_loss(Idef, img, reduction='sum')*(1./np.prod(I.shape[2:])) + regloss) \
/ (img.shape[0])
loss.backward()
loss.detach_()
iter_losses.append(loss)
with torch.no_grad():
li = (loss*(img.shape[0]/len(dataloader.dataset))).detach()
iter_losses.append(li.item())
A.add_(-learning_rate_A, A.grad)
T.add_(-learning_rate_T, T.grad)
with torch.no_grad():
li = (loss*(img.shape[0]/len(dataloader.dataset))).detach()
epoch_loss = epoch_loss + li
#itbar.set_postfix(minibatch_loss=itloss)
As[ix,...] = A.detach().to(As.device)
Ts[ix,...] = T.detach().to(Ts.device)
with torch.no_grad():
if world_size > 1:
all_reduce(epoch_loss)
all_reduce(I.grad)
I.grad = I.grad/(len(dataloader)*world_size)
image_optimizer.step()
losses.append(epoch_loss.item())
if rank == 0: epbar.set_postfix(epoch_loss=epoch_loss.item())
return I.detach(), As, Ts, losses, iter_losses
class DenseInterp(nn.Module):
"""Very simple module wrapper that enables simple data parallel"""
def __init__(self, I0):
super(DenseInterp, self).__init__()
self.I = nn.Parameter(I0)
def forward(self, h):
return lm.interp(self.I, h)
def lddmm_atlas(dataset,
I0=None,
num_epochs=500,
batch_size=10,
lddmm_steps=1,
lddmm_integration_steps=5,
reg_weight=1e2,
learning_rate_pose = 2e2,
learning_rate_image = 1e4,
fluid_params=[0.1,0.,.01],
device='cuda',
momentum_preconditioning=True,
momentum_pattern='oasis_momenta/momentum_{}.pth',
gpu=None,
world_size=1,
rank=0):
if world_size > 1:
sampler = DistributedSampler(dataset,
num_replicas=world_size,
rank=rank)
else:
sampler = None
dataloader = DataLoader(dataset, sampler=sampler, batch_size=batch_size,
num_workers=8, pin_memory=True, shuffle=False)
if I0 is None:
# initialize base image to mean
I0 = batch_average(dataloader, dim=0)
I0 = I0.view(1,1,*I0.squeeze().shape)
#I = DenseInterp(I0)
#if gpu is not None:
#I = DistributedDataParallel(I, device_ids=[gpu], output_device=gpu)
#I = I.to(f'cuda:{gpu}')
#else:
I = I0.clone()
I = I.to(device)
#image_optimizer = torch.optim.SGD(I.parameters(),
image_optimizer = torch.optim.SGD([I],
lr=learning_rate_image,
weight_decay=0)
metric = lm.FluidMetric(fluid_params)
losses = []
reg_terms = []
iter_losses = []
epbar = range(num_epochs)
if rank == 0:
epbar = tqdm(epbar, desc='epoch')
ms = torch.zeros(len(dataset),3,*I0.shape[-3:], dtype=I0.dtype).pin_memory()
for epoch in epbar:
epoch_loss = 0.0
epoch_reg_term = 0.0
itbar = dataloader
I.requires_grad_(True)
image_optimizer.zero_grad()
for it, (ix, img) in enumerate(itbar):
m = ms[ix,...].detach()
m = m.to(device)
img = img.to(device)
for lit in range(lddmm_steps):
# compute image gradient in last step
I.requires_grad_(lit == lddmm_steps - 1)
# enables taking multiple LDDMM step per image update
m.requires_grad_(True)
if m.grad is not None:
m.grad.detach_()
m.grad.zero_()
h = lm.expmap(metric, m, num_steps=lddmm_integration_steps)
#Idef = I(h)
Idef = lm.interp(I, h)
v = metric.sharp(m)
regterm = reg_weight*(v*m).sum()
loss = (mse_loss(Idef, img, reduction='sum') + regterm) \
/ (img.numel())
loss.backward()
# this makes it so that we can reduce the loss and eventually get
# an accurate MSE for the entire dataset
with torch.no_grad():
li = (loss*(img.shape[0]/len(dataloader.dataset))).detach()
p = m.grad
if momentum_preconditioning:
p = metric.flat(p)
m.add_(-learning_rate_pose, p)
if world_size > 1:
all_reduce(li)
iter_losses.append(li.item())
m = m.detach()
del p
with torch.no_grad():
epoch_loss += li
ri = (regterm*(img.shape[0]/(img.numel()*len(dataloader.dataset)))).detach()
epoch_reg_term += ri
ms[ix,...] = m.detach().cpu()
del m, h, Idef, v, loss, regterm, img
with torch.no_grad():
if world_size > 1:
all_reduce(epoch_loss)
all_reduce(epoch_reg_term)
all_reduce(I.grad)
I.grad = I.grad/world_size
# average over iterations
I.grad = I.grad / len(dataloader)
image_optimizer.step()
losses.append(epoch_loss.item())
reg_terms.append(epoch_reg_term.item())
if rank == 0:
epbar.set_postfix(epoch_loss=epoch_loss.item(),
epoch_reg=epoch_reg_term.item())
#return I.state_dict()['I'].detach(), ms.detach(), losses, iter_losses
return I.detach(), ms.detach(), losses, iter_losses