def __call__(self, probs: Tensor, target: Tensor) -> Tensor:
assert simplex(probs)
assert simplex(target)
assert probs.shape == target.shape
B, K, *xyz = probs.shape # type: ignore
pc = cast(Tensor, probs[:, self.idc, ...].type(torch.float32))
tc = cast(Tensor, target[:, self.idc, ...].type(torch.float32))
assert pc.shape == tc.shape == (B, len(self.idc), *xyz)
target_dm_npy: np.ndarray = np.stack([one_hot2hd_dist(tc[b].cpu().detach().numpy())
for b in range(B)], axis=0)
assert target_dm_npy.shape == tc.shape == pc.shape
tdm: Tensor = torch.tensor(target_dm_npy, device=probs.device, dtype=torch.float32)
pred_segmentation: Tensor = probs2one_hot(probs).cpu().detach()
pred_dm_npy: np.nparray = np.stack([one_hot2hd_dist(pred_segmentation[b, self.idc, ...].numpy())
for b in range(B)], axis=0)
assert pred_dm_npy.shape == tc.shape == pc.shape
pdm: Tensor = torch.tensor(pred_dm_npy, device=probs.device, dtype=torch.float32)
delta = (pc - tc)**2
dtm = tdm**2 + pdm**2
multipled = einsum("bkwh,bkwh->bkwh", delta, dtm)
loss = multipled.mean()
return loss
def compute_metrics(pred_probs, gt, labels):
predicted_mask: Tensor = probs2one_hot(pred_probs)
b, c, _,_ = predicted_mask.shape
dices = dice_coef(predicted_mask.detach(), gt.detach()).cpu().numpy()
baseline_dices = dice_coef(labels.detach(), gt.detach()).cpu().numpy()
haussdorf_res = haussdorf(predicted_mask.detach(), gt.detach(), dtype= pred_probs.dtype).cpu().numpy()
assert haussdorf_res.shape == (b, c)
posim = torch.einsum("bcwh->b", [gt[:, 1:, :, :]]).detach() > 0
posim = posim.cpu().numpy()
return dices, baseline_dices, posim, haussdorf_res
def __call__(self, probs: Tensor, target: Tensor, bounds) -> Tensor:
assert simplex(
probs
) # and simplex(target) # Actually, does not care about second part
b, _, w, h = probs.shape # type: Tuple[int, int, int, int]
predicted_mask = probs2one_hot(probs).detach()
est_prop_mask = self.__fn__(predicted_mask, self.power).squeeze(2)
est_prop: Tensor = self.__fn__(probs, self.power)
if self.curi:
if self.ivd:
bounds = bounds[:, :, 0]
bounds = bounds.unsqueeze(2)
gt_prop = torch.ones_like(est_prop) * bounds / (w * h)
gt_prop = gt_prop[:, :, 0]
else:
gt_prop: Tensor = self.__fn__(
target, self.power
) # the power here is actually useless if we have 0/1 gt labels
if not self.curi:
gt_prop = gt_prop.squeeze(2)
est_prop = est_prop.squeeze(2)
log_est_prop: Tensor = (est_prop + 1e-10).log()
log_gt_prop: Tensor = (gt_prop + 1e-10).log()
log_est_prop_mask: Tensor = (est_prop_mask + 1e-10).log()
loss_cons_prior = -torch.einsum(
"bc,bc->", [est_prop, log_gt_prop]) + torch.einsum(
"bc,bc->", [est_prop, log_est_prop])
# Adding division by batch_size to normalise
loss_cons_prior /= b
log_p: Tensor = (probs + 1e-10).log()
mask: Tensor = probs.type((torch.float32))
mask_weighted = torch.einsum(
"bcwh,c->bcwh", [mask, Tensor(self.weights).to(mask.device)])
loss_se = -torch.einsum("bcwh,bcwh->", [mask_weighted, log_p])
loss_se /= mask.sum() + 1e-10
assert loss_se.requires_grad == probs.requires_grad # Handle the case for validation
return self.lamb_se * loss_se, self.lamb_consprior * loss_cons_prior, est_prop
def __call__(self, probs: Tensor, target: Tensor, bounds: Tensor) -> Tensor:
assert simplex(probs)
assert probs.shape == target.shape
assert len(self.mask_idc) == 1, "Cannot handle more at the time, I guess"
b, c, w, h = probs.shape
fake_probs: Tensor = torch.zeros_like(probs, dtype=torch.float32)
for i in range(len(probs)):
low: Tensor = bounds[i, self.mask_idc][0, 0, 0]
high: Tensor = bounds[i, self.mask_idc][0, 0, 1]
res = self.pathak_generator(probs[i].detach(), target[i].detach(), low, high)
assert simplex(res, axis=0)
assert res.shape == (c, w, h)
fake_probs[i] = res
fake_mask: Tensor = probs2one_hot(fake_probs)
assert fake_mask.shape == probs.shape == target.shape
return super().__call__(probs, fake_mask, bounds)
def pathak_generator(self, probs: Tensor, target: Tensor, bounds) -> Tensor:
_, w, h = probs.shape
# Replace the probabilities with certainty for the few weak labels that we have
weak_labels = target[...]
weak_labels[self.ignore, ...] = 0
assert not simplex(weak_labels) and simplex(target)
lower, upper = bounds[-1]
labeled_pixels = weak_labels.any(axis=0)
assert w * h == (labeled_pixels.sum() + (~labeled_pixels).sum()) # make sure all pixels are covered
scribbled_probs = weak_labels + einsum("cwh,wh->cwh", probs, ~labeled_pixels)
assert simplex(scribbled_probs)
u: Tensor
max_iter: int = 100
lr: float = 0.00005
b: Tensor = Tensor([-lower, upper])
beta: Tensor = torch.zeros(2, torch.float32)
f: Tensor = torch.zeros(2, *probs.shape)
f[0, ...] = -1
f[1, ...] = 1
for i in range(max_iter):
exped = - einsum("i,icwh->cwh", beta, f).exp()
u_star = einsum('cwh,cwh->cwh', probs, exped)
u_star /= u_star.sum(axis=0)
assert simplex(u_star)
d_beta = einsum("cwh,icwh->i", u_star, f) - b
n_beta = torch.max(torch.zeros_like(beta), beta + lr * d_beta)
u = u_star
beta = n_beta
return probs2one_hot(u)
def extra_eval_model(model, dataloaders, log_dir="./log/", logger=None, opt=None):
since = time.time()
if True:
# eval infection segmentation and cls
logger.info("-"*8+"extra eval infection cls"+"-"*8)
model.eval()
val_gt = []
val_cls_pred = []
val_cls_probs = [] # for VOC
val_seg_pred = []
val_seg_probs = [] # for VOC
val_seg_probs_au = []
val_seg_pred_au = [] # for VOC
for batch_idx, (inputs, labels) in enumerate(dataloaders["tgt_cls_extra_val"], 0):
inputs = inputs.to(device)
# adjust label
val_gt.append(labels.cpu().data.numpy())
with torch.set_grad_enabled(False):
annotation = dataloaders["tgt_cls_extra_val"].dataset.annotations[batch_idx]
img_dir = annotation.strip().split(',')[0]
img_name = Path(img_dir).name
if opt.use_aux:
cls_logits, _, seg_logits, _, seg_logits_au = model(inputs)
else:
cls_logits, _, seg_logits, _, _ = model(inputs)
if opt.do_seg:
seg_probs = torch.softmax(seg_logits, dim=1)
val_seg_probs.append(seg_probs[:,-1:].detach().cpu().view(seg_probs.shape[0], 1, -1).max(-1)[0])
predicted_mask_onehot = probs2one_hot(seg_probs.detach())
# for save
predicted_mask = predicted_mask_onehot.squeeze().cpu().numpy() # 3xwxh
mask_inone = (np.zeros_like(predicted_mask[0])+predicted_mask[1]*128+predicted_mask[2]*255).astype(np.uint8)
# save dir:
save_dir = os.path.join(opt.logs, "tgt_cls_extra_val", "eval")
#
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(os.path.join(save_dir, img_name), mask_inone)
# seg2cls
preds_cls_seg = (predicted_mask_onehot[:,-1:].sum(-1).sum(-1) > 0).cpu().numpy().astype(np.uint8)
val_seg_pred.append(preds_cls_seg)
if opt.do_seg and opt.use_aux:
seg_probs_au = torch.softmax(seg_logits_au, dim=1)
val_seg_probs_au.append(seg_probs_au[:,-1:].detach().cpu().view(seg_probs_au.shape[0], 1, -1).max(-1)[0])
predicted_mask_onehot_au = probs2one_hot(seg_probs_au.detach())
# for save
predicted_mask_au = predicted_mask_onehot_au.squeeze().cpu().numpy() # 3xwxh
mask_inone_au = (np.zeros_like(predicted_mask_au[0])+predicted_mask_au[1]*128+predicted_mask_au[2]*255).astype(np.uint8)
# save dir:
save_dir_au = os.path.join(opt.logs, "tgt_cls_extra_val_au", "eval")
#
if not os.path.exists(save_dir_au):
os.makedirs(save_dir_au)
cv2.imwrite(os.path.join(save_dir_au, img_name), mask_inone_au)
# seg2cls
preds_cls_seg_au = (predicted_mask_onehot_au[:,-1:].sum(-1).sum(-1) > 0).cpu().numpy().astype(np.uint8)
val_seg_pred_au.append(preds_cls_seg_au)
# cls
#print(cls_logits)
if opt.do_cls:
probs_cls = torch.softmax(cls_logits, dim=1)
val_cls_probs.append(probs_cls[...,1:].detach().cpu().numpy())
preds_cls = (probs_cls[...,1:] > 0.5).type(torch.long)
val_cls_pred.append(preds_cls.cpu().data.numpy())
if not os.path.exists(os.path.join(opt.logs, "cf")):
os.makedirs(os.path.join(opt.logs, "cf"))
val_gt = np.concatenate(val_gt, axis=0)
if opt.do_cls:
val_cls_pred = np.concatenate(val_cls_pred, axis=0)
val_cls_probs = np.concatenate(val_cls_probs, axis=0)
save_cf_png_dir = os.path.join(opt.logs, "cf", "extra_eval_cls_cf.png")
save_metric_dir = os.path.join(opt.logs, "extra_eval_metric_cls.txt")
result_str = get_results(val_gt, val_cls_pred, val_cls_probs, save_cf_png_dir, save_metric_dir)
logger.info("tgt_cls_extra_val:[cls]: %s" % (result_str))
if opt.do_seg:
val_seg_pred = np.concatenate(val_seg_pred, axis=0)
val_seg_probs = np.concatenate(val_seg_probs, axis=0)
# seg2cls
save_cf_png_dir = os.path.join(opt.logs, "cf", "extra_eval_seg_cf.png")
save_metric_dir = os.path.join(opt.logs, "extra_eval_metric_seg.txt")
result_str = get_results(val_gt, val_seg_pred, val_seg_probs, save_cf_png_dir, save_metric_dir)
logger.info("tgt_seg_extra_val:[seg2cls]: %s" % (result_str))
if opt.do_seg and opt.use_aux:
val_seg_pred_au = np.concatenate(val_seg_pred_au, axis=0)
val_seg_probs_au = np.concatenate(val_seg_probs_au, axis=0)
# seg2cls
save_cf_png_dir_au = os.path.join(opt.logs, "cf", "extra_eval_seg_au_cf.png")
save_metric_dir_au = os.path.join(opt.logs, "extra_eval_metric_seg_au.txt")
result_str_au = get_results(val_gt, val_seg_pred_au, val_seg_probs_au, save_cf_png_dir_au, save_metric_dir_au)
logger.info("tgt_seg_au_extra_val:[seg2cls]: %s" % (result_str_au))
time_elapsed = time.time() - since
logger.info("Extra_Eval complete in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
def eval_model(model, dataloaders, log_dir="./log/", logger=None, opt=None):
since = time.time()
if False:#opt.do_seg:
# eval lung segmentation
logger.info("-"*8+"eval lung segmentation"+"-"*8)
model.eval()
all_dices = []
all_dices_au = []
for batch_idx, (inputs, labels) in enumerate(dataloaders["tgt_lung_seg_val"], 0):
annotation = dataloaders["tgt_lung_seg_val"].dataset.annotations[batch_idx]
img_dir = annotation.strip().split(',')[0]
img_name = Path(img_dir).name
inputs = inputs.to(device)
# adjust labels
labels[labels==opt.xray_mask_value_dict["lung"]] = 1
labels = labels[:,-1].to(device)
labels = torch.stack([labels == c for c in range(2)], dim=1)
with torch.set_grad_enabled(False):
if opt.use_aux:
_, _, seg_logits, _, seg_logits_au = model(inputs)
else:
_, _, seg_logits, _, _ = model(inputs)
seg_probs = torch.softmax(seg_logits, dim=1)
predicted_mask = probs2one_hot(seg_probs.detach())
# change the infection to Lung
predicted_mask_lung = predicted_mask[:,:-1]
predicted_mask_lung[:,-1] += predicted_mask[:,-1]
dices = dice_coef(predicted_mask_lung, labels.detach().type_as(predicted_mask)).cpu().numpy()
all_dices.append(dices) # [(B,C)]
predicted_mask_lung = predicted_mask_lung.squeeze().cpu().numpy() # 3xwxh
mask_inone = (np.zeros_like(predicted_mask_lung[0])+predicted_mask_lung[1]*255).astype(np.uint8)
# save dir:
save_dir = os.path.join(opt.logs, "tgt_lung_seg_val", "eval")
#
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(os.path.join(save_dir, img_name), mask_inone)
###################################################au
if opt.use_aux:
seg_probs_au = torch.softmax(seg_logits_au, dim=1)
predicted_mask_au = probs2one_hot(seg_probs_au.detach())
# change the infection to Lung
predicted_mask_lung_au = predicted_mask_au[:,:-1]
predicted_mask_lung_au[:,-1] += predicted_mask_au[:,-1]
dices_au = dice_coef(predicted_mask_lung_au, labels.detach().type_as(predicted_mask_au)).cpu().numpy()
all_dices_au.append(dices_au) # [(B,C)]
predicted_mask_lung_au = predicted_mask_lung_au.squeeze().cpu().numpy() # 3xwxh
mask_inone_au = (np.zeros_like(predicted_mask_lung_au[0])+predicted_mask_lung_au[1]*255).astype(np.uint8)
# save dir:
save_dir_au = os.path.join(opt.logs, "tgt_lung_seg_val_au", "eval")
#
if not os.path.exists(save_dir_au):
os.makedirs(save_dir_au)
cv2.imwrite(os.path.join(save_dir_au, img_name), mask_inone_au)
avg_dice = np.mean(np.concatenate(all_dices, 0), 0) #
logger.info("tgt_lung_seg_val:[%d/%d],dice0:%.03f,dice1:%.03f,dice:%.03f"
% (batch_idx, len(dataloaders['tgt_lung_seg_val'].dataset)//inputs.shape[0],
avg_dice[0], avg_dice[1], np.mean(np.concatenate(all_dices, 0))))
if opt.use_aux:
avg_dice_au = np.mean(np.concatenate(all_dices_au, 0), 0) #
logger.info("tgt_lung_seg_val_au:[%d/%d],dice0:%.03f,dice1:%.03f,dice:%.03f"
% (batch_idx, len(dataloaders['tgt_lung_seg_val'].dataset)//inputs.shape[0],
avg_dice_au[0], avg_dice_au[1], np.mean(np.concatenate(all_dices_au, 0))))
if True:
# eval infection segmentation and cls
logger.info("-"*8+"eval infection cls"+"-"*8)
model.eval()
val_gt = []
val_cls_pred = []
val_cls_probs = [] # for VOC
val_seg_pred = []
val_seg_probs = [] # for VOC
val_seg_probs_au = []
val_seg_pred_au = [] # for VOC
for batch_idx, (inputs, labels) in enumerate(dataloaders["tgt_cls_val"], 0):
inputs = inputs.to(device)
# adjust label
val_gt.append(labels.cpu().data.numpy())
with torch.set_grad_enabled(False):
annotation = dataloaders["tgt_cls_val"].dataset.annotations[batch_idx]
img_dir = annotation.strip().split(',')[0]
img_name = Path(img_dir).name
if opt.use_aux:
cls_logits, _, seg_logits, _, seg_logits_au = model(inputs)
else:
cls_logits, _, seg_logits, _, _ = model(inputs)
if opt.do_seg:
seg_probs = torch.softmax(seg_logits, dim=1)
val_seg_probs.append(seg_probs[:,-1:].detach().cpu().view(seg_probs.shape[0], 1, -1).max(-1)[0])
predicted_mask_onehot = probs2one_hot(seg_probs.detach())
# for save
predicted_mask = predicted_mask_onehot.squeeze().cpu().numpy() # 3xwxh
mask_inone = (np.zeros_like(predicted_mask[0])+predicted_mask[1]*128+predicted_mask[2]*255).astype(np.uint8)
# save dir:
save_dir = os.path.join(opt.logs, "tgt_cls_val", "eval")
#
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(os.path.join(save_dir, img_name), mask_inone)
# seg2cls
preds_cls_seg = (predicted_mask_onehot[:,-1:].sum(-1).sum(-1) > 0).cpu().numpy().astype(np.uint8)
val_seg_pred.append(preds_cls_seg)
if opt.do_seg and opt.use_aux:
seg_probs_au = torch.softmax(seg_logits_au, dim=1)
val_seg_probs_au.append(seg_probs_au[:,-1:].detach().cpu().view(seg_probs_au.shape[0], 1, -1).max(-1)[0])
predicted_mask_onehot_au = probs2one_hot(seg_probs_au.detach())
# for save
predicted_mask_au = predicted_mask_onehot_au.squeeze().cpu().numpy() # 3xwxh
mask_inone_au = (np.zeros_like(predicted_mask_au[0])+predicted_mask_au[1]*128+predicted_mask_au[2]*255).astype(np.uint8)
# save dir:
save_dir_au = os.path.join(opt.logs, "tgt_cls_val_au", "eval")
#
if not os.path.exists(save_dir_au):
os.makedirs(save_dir_au)
cv2.imwrite(os.path.join(save_dir_au, img_name), mask_inone_au)
# seg2cls
preds_cls_seg_au = (predicted_mask_onehot_au[:,-1:].sum(-1).sum(-1) > 0).cpu().numpy().astype(np.uint8)
val_seg_pred_au.append(preds_cls_seg_au)
# cls
#print(cls_logits)
if opt.do_cls:
probs_cls = torch.softmax(cls_logits, dim=1)
val_cls_probs.append(probs_cls[...,1:].detach().cpu().numpy())
preds_cls = (probs_cls[...,1:] > 0.5).type(torch.long)
val_cls_pred.append(preds_cls.cpu().data.numpy())
if not os.path.exists(os.path.join(opt.logs, "cf")):
os.makedirs(os.path.join(opt.logs, "cf"))
val_gt = np.concatenate(val_gt, axis=0)
if opt.do_cls:
val_cls_pred = np.concatenate(val_cls_pred, axis=0)
val_cls_probs = np.concatenate(val_cls_probs, axis=0)
save_cf_png_dir = os.path.join(opt.logs, "cf", "eval_cls_cf.png")
save_metric_dir = os.path.join(opt.logs, "eval_metric_cls.txt")
result_str = get_results(val_gt, val_cls_pred, val_cls_probs, save_cf_png_dir, save_metric_dir)
logger.info("tgt_cls_val:[cls]: %s" % (result_str))
if opt.do_seg:
val_seg_pred = np.concatenate(val_seg_pred, axis=0)
val_seg_probs = np.concatenate(val_seg_probs, axis=0)
# seg2cls
save_cf_png_dir = os.path.join(opt.logs, "cf", "eval_seg_cf.png")
save_metric_dir = os.path.join(opt.logs, "eval_metric_seg.txt")
result_str = get_results(val_gt, val_seg_pred, val_seg_probs, save_cf_png_dir, save_metric_dir)
logger.info("tgt_seg_val:[seg2cls]: %s" % (result_str))
if opt.do_seg and opt.use_aux:
val_seg_pred_au = np.concatenate(val_seg_pred_au, axis=0)
val_seg_probs_au = np.concatenate(val_seg_probs_au, axis=0)
# seg2cls
save_cf_png_dir_au = os.path.join(opt.logs, "cf", "eval_seg_au_cf.png")
save_metric_dir_au = os.path.join(opt.logs, "eval_metric_seg_au.txt")
result_str_au = get_results(val_gt, val_seg_pred_au, val_seg_probs_au, save_cf_png_dir_au, save_metric_dir_au)
logger.info("tgt_seg_au_val:[seg2cls]: %s" % (result_str_au))
time_elapsed = time.time() - since
logger.info("Eval complete in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
def train_model(model,
dataloaders,
criterion,
optimizer,
num_epochs=25,
is_inception=False,
log_dir="./log/",
scheduler=None,
writer=None,
logger=None,
opt=None):
print(opt)
since = time.time()
val_acc_history = []
best_acc = 0.0
batch_size = dataloaders['train'].batch_size
print_step = 5 # print info per 10 batches
val_losses = []
tgt_cls_train_iter = iter(dataloaders['tgt_cls_train'])
for epoch in range(num_epochs):
logger.info('Epoch {}/{}'.format(epoch, num_epochs - 1))
logger.info('-' * 10)
learning_rate = get_learning_rate(optimizer)
writer.add_scalar("lr", learning_rate, epoch)
epoch_val_preds = []
epoch_val_y = []
epoch_train_preds = []
epoch_train_y = []
# Each epoch has a training and validation phase
model.train() # Set model to training mode
running_loss = 0.0
# Iterate over data.
for batch_idx, (inputs, labels) in enumerate(dataloaders["train"], 0):
inputs = inputs.to(device)
# adjust labels
labels[labels == opt.drr_mask_value_dict["lung"]] = 1
labels[labels == opt.drr_mask_value_dict["infection"]] = 2
labels = labels[:, -1].to(device)
tag_labels = ((labels == 2).sum(-1).sum(-1) > 0).type(
torch.long).to(device) # batch_size, 1
c_labels = tag_labels if opt.do_cls_mmd else None
s_labels = labels if opt.do_seg_mmd else None
if opt.do_cls_mmd or opt.do_seg_mmd:
# tgt_cls
try:
tgt_inputs, _ = tgt_cls_train_iter.next()
except StopIteration:
tgt_cls_train_iter = iter(dataloaders['tgt_cls_train'])
tgt_inputs, _ = tgt_cls_train_iter.next()
tgt_inputs = tgt_inputs.to(device)
else:
tgt_inputs = None
# zero the parameter gradients
optimizer.zero_grad()
model.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(True):
src_cls_logits, loss_cls_lmmd, src_seg_logits, loss_seg_lmmd, _ = model(
inputs,
tgt_img=tgt_inputs,
c_label=c_labels,
s_label=s_labels)
lambd = 2 / (1 + math.exp(-10 * (epoch) / num_epochs)) - 1
if opt.do_cls and opt.do_cls_mmd:
loss_cls_lmmd = lambd * loss_cls_lmmd * opt.lambda_cls_mmd
loss_cls_lmmd_item = loss_cls_lmmd.item()
else:
loss_cls_lmmd = 0
loss_cls_lmmd_item = 0
if opt.do_seg and opt.do_seg_mmd:
loss_seg_lmmd = lambd * loss_seg_lmmd * opt.lmabda_seg_mmd
loss_seg_lmmd_item = loss_seg_lmmd.item()
else:
loss_seg_lmmd = 0
loss_seg_lmmd_item = 0
if opt.do_seg:
loss_seg = criterion(
labels,
src_seg_logits,
class_weights=opt.seg_class_weights) * opt.lambda_seg
loss_seg_item = loss_seg.item()
else:
loss_seg = 0
loss_seg_item = 0
if opt.do_cls:
loss_cls = F.cross_entropy(src_cls_logits,
tag_labels) * opt.lambda_cls
loss_cls_item = loss_cls.item()
else:
loss_cls = 0
loss_cls_item = 0
loss = loss_seg + loss_cls + loss_seg_lmmd + loss_cls_lmmd
loss_item = loss.item()
loss.backward()
optimizer.step()
# statistics
if batch_idx % print_step == 0: # print info
print_loss = running_loss / ((batch_idx + 1) * batch_size)
logger.info(
"Train E{:>03} B{:>05} LR:{:.8f} Loss: {:.4f} LSeg: {:.4f} SegMmd: {:.4f} LCls: {:.4f} ClsMmd: {:.4f}"
.format(epoch, batch_idx, learning_rate, loss_item,
loss_seg_item, loss_seg_lmmd_item, loss_cls_item,
loss_cls_lmmd_item))
scheduler.step()
weight_path = os.path.join(log_dir, "latest.pth")
torch.save(model.state_dict(), weight_path)
if ((epoch + 1) % opt.eval_times == 0
or epoch + 1 == num_epochs) and opt.do_seg:
# eval lung segmentation
logger.info("-" * 8 + "eval lung segmentation" + "-" * 8)
model.eval()
all_dices = []
for batch_idx, (inputs, labels) in enumerate(
dataloaders["tgt_lung_seg_val"], 0):
annotation = dataloaders[
"tgt_lung_seg_val"].dataset.annotations[batch_idx]
img_dir = annotation.strip().split(',')[0]
img_name = Path(img_dir).name
inputs = inputs.to(device)
# adjust labels
labels[labels == opt.xray_mask_value_dict["lung"]] = 1
labels = labels[:, -1].to(device)
labels = torch.stack([labels == c for c in range(2)], dim=1)
with torch.set_grad_enabled(False):
_, _, seg_logits, _, _ = model(inputs)
seg_probs = torch.softmax(seg_logits, dim=1)
predicted_mask = probs2one_hot(seg_probs.detach())
# change the infection to Lung
predicted_mask_lung = predicted_mask[:, :-1]
predicted_mask_lung[:, -1] += predicted_mask[:, -1]
dices = dice_coef(
predicted_mask_lung,
labels.detach().type_as(predicted_mask)).cpu().numpy()
all_dices.append(dices) # [(B,C)]
predicted_mask_lung = predicted_mask_lung.squeeze().cpu(
).numpy() # 3xwxh
mask_inone = (np.zeros_like(predicted_mask_lung[0]) +
predicted_mask_lung[1] * 255).astype(
np.uint8)
# save dir:
save_dir = os.path.join(opt.logs, "tgt_lung_seg_val",
"ep%03d" % epoch)
#
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(os.path.join(save_dir, img_name), mask_inone)
avg_dice = np.mean(np.concatenate(all_dices, 0), 0) #
logger.info(
"tgt_lung_seg_val:EP%03d,[%d/%d],dice0:%.03f,dice1:%.03f,dice:%.03f"
% (epoch, batch_idx,
len(dataloaders['tgt_lung_seg_val'].dataset) //
inputs.shape[0], avg_dice[0], avg_dice[1],
np.mean(np.concatenate(all_dices, 0))))
if ((epoch + 1) % opt.eval_cls_times == 0 or epoch + 1 == num_epochs):
# eval infection segmentation and cls
logger.info("-" * 8 + "eval infection cls" + "-" * 8)
model.eval()
val_gt = []
val_cls_pred = []
val_seg_pred = []
for batch_idx, (inputs,
labels) in enumerate(dataloaders["tgt_cls_val"],
0):
inputs = inputs.to(device)
# adjust label
val_gt.append(labels.cpu().data.numpy())
with torch.set_grad_enabled(False):
annotation = dataloaders[
"tgt_cls_val"].dataset.annotations[batch_idx]
img_dir = annotation.strip().split(',')[0]
img_name = Path(img_dir).name
cls_logits, _, seg_logits, _, _ = model(inputs)
if opt.do_seg:
seg_probs = torch.softmax(seg_logits, dim=1)
predicted_mask_onehot = probs2one_hot(
seg_probs.detach())
# for save
predicted_mask = predicted_mask_onehot.squeeze().cpu(
).numpy() # 3xwxh
mask_inone = (np.zeros_like(predicted_mask[0]) +
predicted_mask[1] * 128 +
predicted_mask[2] * 255).astype(np.uint8)
# save dir:
save_dir = os.path.join(opt.logs, "tgt_cls_val",
"ep%03d" % epoch)
#
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(os.path.join(save_dir, img_name),
mask_inone)
# seg2cls
preds_cls_seg = (
predicted_mask_onehot[:, -1:].sum(-1).sum(-1) >
0).cpu().numpy().astype(np.uint8)
val_seg_pred.append(preds_cls_seg)
# cls
#print(cls_logits)
if opt.do_cls:
probs_cls = torch.softmax(cls_logits, dim=1)
preds_cls = (probs_cls[..., 1:] > 0.5).type(torch.long)
val_cls_pred.append(preds_cls.cpu().data.numpy())
if not os.path.exists(os.path.join(opt.logs, "cf")):
os.makedirs(os.path.join(opt.logs, "cf"))
val_gt = np.concatenate(val_gt, axis=0)
if opt.do_cls:
val_cls_pred = np.concatenate(val_cls_pred, axis=0)
save_cf_png_dir = os.path.join(opt.logs, "cf",
"ep%03d_cls_cf.png" % epoch)
save_metric_dir = os.path.join(opt.logs, "metric_cls.txt")
result_str = get_results(val_gt, val_cls_pred, save_cf_png_dir,
save_metric_dir)
logger.info("tgt_cls_val:EP%03d,[cls]: %s" %
(epoch, result_str))
if opt.do_seg:
val_seg_pred = np.concatenate(val_seg_pred, axis=0)
# seg2cls
save_cf_png_dir = os.path.join(opt.logs, "cf",
"ep%03d_seg_cf.png" % epoch)
save_metric_dir = os.path.join(opt.logs, "metric_seg.txt")
result_str = get_results(val_gt, val_seg_pred, save_cf_png_dir,
save_metric_dir)
logger.info("tgt_seg_val:EP%03d,[seg2cls]: %s" %
(epoch, result_str))
time_elapsed = time.time() - since
logger.info("Training complete in {:.0f}m {:.0f}s".format(
time_elapsed // 60, time_elapsed % 60))
def do_epoch(args, mode: str, net: Any, device: Any, loader: DataLoader, epc: int,
loss_fns: List[Callable], loss_weights: List[float],loss_fns_source: List[Callable],
loss_weights_source: List[float], new_w:int, num_steps:int, C: int, metric_axis:List[int], savedir: str = "",
optimizer: Any = None, target_loader: Any = None):
assert mode in ["train", "val"]
L: int = len(loss_fns)
indices = torch.tensor(metric_axis,device=device)
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
# net.train()
desc = f">> Validation ({epc})"
total_it_s, total_images = len(loader), len(loader.dataset)
total_it_t, total_images_t = len(target_loader), len(target_loader.dataset)
total_iteration = max(total_it_s, total_it_t)
# Lazy add lines below because we will be cycling until the biggest length is reached
total_images = max(total_images, total_images_t)
total_images_t = total_images
pho=1
dtype = eval(args.dtype)
all_dices: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_inter_card: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_card_gt: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_card_pred: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
loss_log: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
loss_inf: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
loss_cons: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
loss_fs: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
posim_log: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
haussdorf_log: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_grp: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
dice_3d_log: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
dice_3d_sd_log: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
if args.source_metrics == True:
all_dices_s: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_inter_card_s: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_card_gt_s: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_card_pred_s: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_grp_s: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
dice_3d_s_log: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
dice_3d_s_sd_log: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
# if len(loader)>len(target_loader):
# tq_iter = tqdm_(enumerate(zip(loader, cycle(target_loader))), total=total_iteration, desc=desc)
# elif len(loader)<len(target_loader):
# tq_iter = tqdm_(enumerate(zip(cycle(loader), target_loader)), total=total_iteration, desc=desc)
# else:
# tq_iter = tqdm_(enumerate(zip(loader, target_loader)), total=total_iteration, desc=desc)
tq_iter = tqdm_(enumerate(zip(loader, target_loader)), total=total_iteration, desc=desc)
#tq_iter = tqdm_(enumerate(target_loader), total=total_iteration, desc=desc)
done: int = 0
#ratio_losses = 0
n_warmup = 0
mult_lw = [pho ** (epc - n_warmup + 1)] * len(loss_weights)
#if epc > 100:
# mult_lw = [pho ** 100] * len(loss_weights)
mult_lw[0] = 1
loss_weights = [a * b for a, b in zip(loss_weights, mult_lw)]
losses_vec, source_vec, target_vec, baseline_target_vec = [], [], [], []
pen_count = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for j, (source_data, target_data) in tq_iter:
#for j, target_data in tq_iter:
source_data[1:] = [e.to(device) for e in source_data[1:]] # Move all tensors to device
filenames_source, source_image, source_gt = source_data[:3]
target_data[1:] = [e.to(device) for e in target_data[1:]] # Move all tensors to device
filenames_target, target_image, target_gt = target_data[:3]
labels = target_data[3:3+L]
labels_source = source_data[3:3 + L]
bounds = target_data[3+L:]
bounds_source = source_data[3+L:]
assert len(labels) == len(bounds), len(bounds)
if args.mix == False:
assert filenames_source == filenames_target
#print(filenames_source,filenames_target)
B = len(target_image)
# Reset gradients
if optimizer:
#adjust_learning_rate(optimizer, 1, args.l_rate, num_steps, args.power)
optimizer.zero_grad()
# Forward
with torch.set_grad_enabled(mode == "train"):
pred_logits: Tensor = net(target_image)
pred_logits_source: Tensor = net(source_image)
pred_probs: Tensor = F.softmax(pred_logits, dim=1)
pred_probs_source: Tensor = F.softmax(pred_logits_source, dim=1)
if new_w > 0:
pred_probs = resize(pred_probs, new_w)
labels = [resize(label, new_w) for label in labels]
target = resize(target, new_w)
predicted_mask: Tensor = probs2one_hot(pred_probs) # Used only for dice computation
predicted_mask_source: Tensor = probs2one_hot(pred_probs_source) # Used only for dice computation
#print(torch.sum(predicted_mask, dim=[2,3]).cpu().numpy())
#print(list(map(lambda n: [int(f) for f in n], np.around(torch.sum(pred_probs, dim=[2,3]).detach().cpu().numpy()))))
assert len(bounds) == len(loss_fns) == len(loss_weights)
if epc < n_warmup:
loss_weights = [0]*len(loss_weights)
loss: Tensor = torch.zeros(1, requires_grad=True).to(device)
loss_vec = []
loss_k = []
for loss_fn,label, w, bound in zip(loss_fns,labels, loss_weights, bounds):
if w > 0:
if args.lin_aug_w:
if epc<70:
w=w*(epc+1)/70
loss_b = loss_fn(pred_probs, label, bound)
loss = loss + w * loss_b
#pen_count += count_b.detach()
#print(count_b.detach())
loss_k.append(w*loss_b.detach())
#for loss_fn, label, w, bound in zip(loss_fns_source, [source_gt], loss_weights_source, torch.randn(1)):
#for loss_fn, label, w, bound in zip(loss_fns_source, labels_source, loss_weights_source, torch.randn(1)):
for loss_fn, label, w, bound in zip(loss_fns_source, labels_source, loss_weights_source, bounds_source):
if w > 0:
loss_b = loss_fn(pred_probs_source, label, bound)
loss = loss+ w * loss_b
loss_k.append(w*loss_b.detach())
#print(loss_k)
# Backward
if optimizer:
loss.backward()
optimizer.step()
# Compute and log metrics
#dices: Tensor = dice_coef(predicted_mask.detach(), target.detach())
# baseline_dices: Tensor = dice_coef(labels[0].detach(), target.detach())
#batch_dice: Tensor = dice_batch(predicted_mask.detach(), target.detach())
# assert batch_dice.shape == (C,) and dices.shape == (B, C), (batch_dice.shape, dices.shape, B, C)
dices, inter_card, card_gt, card_pred = dice_coef(predicted_mask.detach(), target_gt.detach())
assert dices.shape == (B, C), (dices.shape, B, C)
sm_slice = slice(done, done + B) # Values only for current batch
all_dices[sm_slice, ...] = dices
# # for 3D dice
all_grp[sm_slice, ...] = int(re.split('_', filenames_target[0])[1]) * torch.ones([1, C])
all_inter_card[sm_slice, ...] = inter_card
all_card_gt[sm_slice, ...] = card_gt
all_card_pred[sm_slice, ...] = card_pred
# 3D dice on source
if args.source_metrics ==True:
dices_s, inter_card_s, card_gt_s, card_pred_s = dice_coef(predicted_mask_source.detach(), source_gt.detach())
all_grp_s[sm_slice, ...] = int(re.split('_', filenames_source[0])[1]) * torch.ones([1, C])
all_inter_card_s[sm_slice, ...] = inter_card_s
all_card_gt_s[sm_slice, ...] = card_gt_s
all_card_pred_s[sm_slice, ...] = card_pred_s
#loss_log[sm_slice] = loss.detach()
loss_inf[sm_slice] = loss_k[0]
if len(loss_k)>1:
loss_cons[sm_slice] = loss_k[1]
else:
loss_cons[sm_slice] = 0
if len(loss_k)>2:
loss_fs[sm_slice] = loss_k[2]
else:
loss_fs[sm_slice] = 0
#posim_log[sm_slice] = torch.einsum("bcwh->b", [target_gt[:, 1:, :, :]]).detach() > 0
#haussdorf_res: Tensor = haussdorf(predicted_mask.detach(), target_gt.detach(), dtype)
#assert haussdorf_res.shape == (B, C)
#haussdorf_log[sm_slice] = haussdorf_res
# # Save images
if savedir:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
warnings.simplefilter("ignore")
predicted_class: Tensor = probs2class(pred_probs)
#save_images_p(predicted_class, filenames_target, args.dataset, mode, epc, False)
save_images(predicted_class, filenames_target, savedir, mode, epc, True)
# Logging
big_slice = slice(0, done + B) # Value for current and previous batches
stat_dict = {"dice": torch.index_select(all_dices, 1, indices).mean(),
"loss": loss_log[big_slice].mean()}
nice_dict = {k: f"{v:.4f}" for (k, v) in stat_dict.items()}
done += B
tq_iter.set_postfix(nice_dict)
print(f"{desc} " + ', '.join(f"{k}={v}" for (k, v) in nice_dict.items()))
#dice_posim = torch.masked_select(all_dices[:, -1], posim_log.type(dtype=torch.uint8)).mean()
# dice3D gives back the 3d dice mai on images
# if not args.debug:
# dice_3d_log_o, dice_3d_sd_log_o = dice3d(args.workdir, f"iter{epc:03d}", mode, "Subj_\\d+_",args.dataset + mode + '/CT_GT', C)
dice_3d_log, dice_3d_sd_log = dice3dn(all_grp, all_inter_card, all_card_gt, all_card_pred,metric_axis,True)
if args.source_metrics ==True:
dice_3d_s_log, dice_3d_s_sd_log = dice3dn(all_grp_s, all_inter_card_s, all_card_gt_s, all_card_pred_s,metric_axis,True)
print("mean 3d_dice over all patients:",dice_3d_log)
#source_vec = [ dice_3d_s, dice_3d_sd_s, haussdorf_log_s]
dice_2d = torch.index_select(all_dices, 1, indices).mean().cpu().numpy()
target_vec = [ dice_3d_log, dice_3d_sd_log, dice_2d]
if args.source_metrics ==True:
source_vec = [ dice_3d_s_log, dice_3d_s_sd_log]
else:
source_vec = [0,0]
#losses_vec = [loss_log.mean().item()]
losses_vec = [loss_inf.mean().item(),loss_cons.mean().item(),loss_fs.mean().item()]
return losses_vec, target_vec,source_vec
def do_epoch(mode: str, net: Any, device: Any, loader: DataLoader, epc: int,
loss_fns: List[Callable], loss_weights: List[float], C: int,
savedir: str = "", optimizer: Any = None,
metric_axis: List[int] = [1], compute_haussdorf: bool = False) \
-> Tuple[Tensor, Tensor, Tensor, Tensor]:
assert mode in ["train", "val"]
L: int = len(loss_fns)
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
desc = f">> Validation ({epc})"
total_iteration, total_images = len(loader), len(loader.dataset)
all_dices: Tensor = torch.zeros((total_images, C),
dtype=torch.float32,
device=device)
batch_dices: Tensor = torch.zeros((total_iteration, C),
dtype=torch.float32,
device=device)
loss_log: Tensor = torch.zeros((total_iteration),
dtype=torch.float32,
device=device)
haussdorf_log: Tensor = torch.zeros((total_images, C),
dtype=torch.float32,
device=device)
tq_iter = tqdm_(enumerate(loader), total=total_iteration, desc=desc)
done: int = 0
for j, data in tq_iter:
data[1:] = [e.to(device)
for e in data[1:]] # Move all tensors to device
filenames, image, target = data[:3]
labels = data[3:3 + L]
bounds = data[3 + L:]
assert len(labels) == len(bounds)
B = len(image)
# Reset gradients
if optimizer:
optimizer.zero_grad()
# Forward
pred_logits: Tensor = net(image)
pred_probs: Tensor = F.softmax(pred_logits, dim=1)
predicted_mask: Tensor = probs2one_hot(
pred_probs.detach()) # Used only for dice computation
assert len(bounds) == len(loss_fns) == len(loss_weights)
ziped = zip(loss_fns, labels, loss_weights, bounds)
losses = [
w * loss_fn(pred_probs, label, bound)
for loss_fn, label, w, bound in ziped
]
loss = reduce(add, losses)
assert loss.shape == (), loss.shape
# Backward
if optimizer:
loss.backward()
optimizer.step()
# Compute and log metrics
loss_log[j] = loss.detach()
sm_slice = slice(done, done + B) # Values only for current batch
dices: Tensor = dice_coef(predicted_mask, target.detach())
assert dices.shape == (B, C), (dices.shape, B, C)
all_dices[sm_slice, ...] = dices
if B > 1 and mode == "val":
batch_dice: Tensor = dice_batch(predicted_mask, target.detach())
assert batch_dice.shape == (C, ), (batch_dice.shape, B, C)
batch_dices[j] = batch_dice
if compute_haussdorf:
haussdorf_res: Tensor = haussdorf(predicted_mask.detach(),
target.detach())
assert haussdorf_res.shape == (B, C)
haussdorf_log[sm_slice] = haussdorf_res
# Save images
if savedir:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
predicted_class: Tensor = probs2class(pred_probs)
save_images(predicted_class, filenames, savedir, mode, epc)
# Logging
big_slice = slice(0,
done + B) # Value for current and previous batches
dsc_dict = {
f"DSC{n}": all_dices[big_slice, n].mean()
for n in metric_axis
}
hauss_dict = {
f"HD{n}": haussdorf_log[big_slice, n].mean()
for n in metric_axis
} if compute_haussdorf else {}
batch_dict = {
f"bDSC{n}": batch_dices[:j, n].mean()
for n in metric_axis
} if B > 1 and mode == "val" else {}
mean_dict = {
"DSC": all_dices[big_slice, metric_axis].mean(),
"HD": haussdorf_log[big_slice, metric_axis].mean()
} if len(metric_axis) > 1 else {}
stat_dict = {
**dsc_dict,
**hauss_dict,
**mean_dict,
**batch_dict, "loss": loss_log[:j].mean()
}
nice_dict = {k: f"{v:.3f}" for (k, v) in stat_dict.items()}
tq_iter.set_postfix(nice_dict)
done += B
print(f"{desc} " + ', '.join(f"{k}={v}" for (k, v) in nice_dict.items()))
return loss_log, all_dices, batch_dices, haussdorf_log
def do_epoch(mode: str, net: Any, device: Any, loaders: List[DataLoader], epc: int,
list_loss_fns: List[List[Callable]], list_loss_weights: List[List[float]], C: int,
savedir: str = "", optimizer: Any = None,
metric_axis: List[int] = [1], compute_haussdorf: bool = False, compute_miou: bool = False,
temperature: float = 1) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tuple[None, Tensor]]:
assert mode in ["train", "val"]
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
desc = f">> Validation ({epc})"
total_iteration: int = sum(len(loader) for loader in loaders) # U
total_images: int = sum(len(loader.dataset) for loader in loaders) # D
n_loss: int = max(map(len, list_loss_fns))
all_dices: Tensor = torch.zeros((total_images, C), dtype=torch.float32, device=device)
batch_dices: Tensor = torch.zeros((total_iteration, C), dtype=torch.float32, device=device)
loss_log: Tensor = torch.zeros((total_iteration, n_loss), dtype=torch.float32, device=device)
haussdorf_log: Tensor = torch.zeros((total_images, C), dtype=torch.float32, device=device)
iiou_log: Tensor = torch.zeros((total_images, C), dtype=torch.float32, device=device)
intersections: Tensor = torch.zeros((total_images, C), dtype=torch.float32, device=device)
unions: Tensor = torch.zeros((total_images, C), dtype=torch.float32, device=device)
few_axis: bool = len(metric_axis) <= 3
done_img: int = 0
done_batch: int = 0
tq_iter = tqdm_(total=total_iteration, desc=desc)
for i, (loader, loss_fns, loss_weights) in enumerate(zip(loaders, list_loss_fns, list_loss_weights)):
L: int = len(loss_fns)
for data in loader:
data[1:] = [e.to(device) for e in data[1:]] # Move all tensors to device
filenames, image, target = data[:3]
assert not target.requires_grad
labels = data[3:3 + L]
bounds = data[3 + L:]
assert len(labels) == len(bounds)
B = len(image)
# Reset gradients
if optimizer:
optimizer.zero_grad()
# Forward
pred_logits: Tensor = net(image)
pred_probs: Tensor = F.softmax(temperature * pred_logits, dim=1)
predicted_mask: Tensor = probs2one_hot(pred_probs.detach()) # Used only for dice computation
assert not predicted_mask.requires_grad
assert len(bounds) == len(loss_fns) == len(loss_weights) == len(labels)
ziped = zip(loss_fns, labels, loss_weights, bounds)
losses = [w * loss_fn(pred_probs, label, bound) for loss_fn, label, w, bound in ziped]
loss = reduce(add, losses)
assert loss.shape == (), loss.shape
# if epc >= 1 and False:
# import matplotlib.pyplot as plt
# _, axes = plt.subplots(nrows=1, ncols=3)
# axes[0].imshow(image[0, 0].cpu().numpy(), cmap='gray')
# axes[0].contour(target[0, 1].cpu().numpy(), cmap='rainbow')
# pred_np = pred_probs[0, 1].detach().cpu().numpy()
# axes[1].imshow(pred_np)
# bins = np.linspace(0, 1, 50)
# axes[2].hist(pred_np.flatten(), bins)
# print(bounds)
# print(bounds[2].cpu().numpy())
# print(bounds[2][0, 1].cpu().numpy())
# print(pred_np.sum())
# plt.show()
# Backward
if optimizer:
loss.backward()
optimizer.step()
# Compute and log metrics
# loss_log[done_batch] = loss.detach()
for j in range(len(loss_fns)):
loss_log[done_batch, j] = losses[j].detach()
sm_slice = slice(done_img, done_img + B) # Values only for current batch
dices: Tensor = dice_coef(predicted_mask, target)
assert dices.shape == (B, C), (dices.shape, B, C)
all_dices[sm_slice, ...] = dices
if B > 1 and mode == "val":
batch_dice: Tensor = dice_batch(predicted_mask, target)
assert batch_dice.shape == (C,), (batch_dice.shape, B, C)
batch_dices[done_batch] = batch_dice
if compute_haussdorf:
haussdorf_res: Tensor = haussdorf(predicted_mask, target)
assert haussdorf_res.shape == (B, C)
haussdorf_log[sm_slice] = haussdorf_res
if compute_miou:
IoUs: Tensor = iIoU(predicted_mask, target)
assert IoUs.shape == (B, C), IoUs.shape
iiou_log[sm_slice] = IoUs
intersections[sm_slice] = inter_sum(predicted_mask, target)
unions[sm_slice] = union_sum(predicted_mask, target)
# Save images
if savedir:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
predicted_class: Tensor = probs2class(pred_probs)
save_images(predicted_class, filenames, savedir, mode, epc)
# Logging
big_slice = slice(0, done_img + B) # Value for current and previous batches
dsc_dict = {f"DSC{n}": all_dices[big_slice, n].mean() for n in metric_axis} if few_axis else {}
hauss_dict = {f"HD{n}": haussdorf_log[big_slice, n].mean() for n in metric_axis} \
if compute_haussdorf and few_axis else {}
batch_dict = {f"bDSC{n}": batch_dices[:done_batch, n].mean() for n in metric_axis} \
if B > 1 and mode == "val" and few_axis else {}
miou_dict = {f"iIoU": iiou_log[big_slice, metric_axis].mean(),
f"mIoU": (intersections.sum(dim=0) / (unions.sum(dim=0) + 1e-10)).mean()} \
if compute_miou else {}
if len(metric_axis) > 1:
mean_dict = {"DSC": all_dices[big_slice, metric_axis].mean()}
if compute_haussdorf:
mean_dict["HD"] = haussdorf_log[big_slice, metric_axis].mean()
else:
mean_dict = {}
stat_dict = {**miou_dict, **dsc_dict, **hauss_dict, **mean_dict, **batch_dict,
"loss": loss_log[:done_batch].mean()}
nice_dict = {k: f"{v:.3f}" for (k, v) in stat_dict.items()}
done_img += B
done_batch += 1
tq_iter.set_postfix({**nice_dict, "loader": str(i)})
tq_iter.update(1)
tq_iter.close()
print(f"{desc} " + ', '.join(f"{k}={v}" for (k, v) in nice_dict.items()))
if compute_miou:
mIoUs: Tensor = (intersections.sum(dim=0) / (unions.sum(dim=0) + 1e-10))
assert mIoUs.shape == (C,), mIoUs.shape
else:
mIoUs = None
if not few_axis and False:
print(f"DSC: {[f'{all_dices[:, n].mean():.3f}' for n in metric_axis]}")
print(f"iIoU: {[f'{iiou_log[:, n].mean():.3f}' for n in metric_axis]}")
if mIoUs:
print(f"mIoU: {[f'{mIoUs[n]:.3f}' for n in metric_axis]}")
return loss_log, all_dices, batch_dices, haussdorf_log, mIoUs
def do_epoch(
mode: str,
net: Any,
device: Any,
loaders: List[DataLoader],
epc: int,
list_loss_fns: List[List[Callable]],
list_loss_weights: List[List[float]],
K: int,
savedir: str = "",
optimizer: Any = None,
metric_axis: List[int] = [1],
compute_hausdorff: bool = False,
compute_miou: bool = False,
compute_3d_dice: bool = False,
temperature: float = 1
) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor],
Optional[Tensor]]:
assert mode in ["train", "val"]
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
desc = f">> Validation ({epc})"
total_iteration: int = sum(len(loader) for loader in loaders) # U
total_images: int = sum(len(loader.dataset) for loader in loaders) # D
n_loss: int = max(map(len, list_loss_fns))
all_dices: Tensor = torch.zeros((total_images, K),
dtype=torch.float32,
device=device)
loss_log: Tensor = torch.zeros((total_iteration, n_loss),
dtype=torch.float32,
device=device)
iiou_log: Optional[Tensor]
intersections: Optional[Tensor]
unions: Optional[Tensor]
if compute_miou:
iiou_log = torch.zeros((total_images, K),
dtype=torch.float32,
device=device)
intersections = torch.zeros((total_images, K),
dtype=torch.float32,
device=device)
unions = torch.zeros((total_images, K),
dtype=torch.float32,
device=device)
else:
iiou_log = None
intersections = None
unions = None
three_d_dices: Optional[Tensor]
if compute_3d_dice:
three_d_dices = torch.zeros((total_iteration, K),
dtype=torch.float32,
device=device)
else:
three_d_dices = None
hausdorff_log: Optional[Tensor]
if compute_hausdorff:
hausdorff_log = torch.zeros((total_images, K),
dtype=torch.float32,
device=device)
else:
hausdorff_log = None
few_axis: bool = len(metric_axis) <= 3
done_img: int = 0
done_batch: int = 0
tq_iter = tqdm_(total=total_iteration, desc=desc)
for i, (loader, loss_fns, loss_weights) in enumerate(
zip(loaders, list_loss_fns, list_loss_weights)):
for data in loader:
image: Tensor = data["images"].to(device)
target: Tensor = data["gt"].to(device)
spacings: Tensor = data["spacings"] # Keep that one on CPU
assert not target.requires_grad
labels: List[Tensor] = [e.to(device) for e in data["labels"]]
bounds: List[Tensor] = [e.to(device) for e in data["bounds"]]
box_priors: List[List[Tuple[
Tensor, Tensor]]] # one more level for the batch
box_priors = [[(m.to(device), b.to(device)) for (m, b) in B]
for B in data["box_priors"]]
assert len(labels) == len(bounds)
B, C, *_ = image.shape
samplings: List[List[Tuple[slice]]] = data["samplings"]
assert len(samplings) == B
assert len(samplings[0][0]) == len(
image[0, 0].shape), (samplings[0][0], image[0, 0].shape)
probs_receptacle: Tensor = -torch.ones_like(
target, dtype=torch.float32) # -1 for unfilled
mask_receptacle: Tensor = -torch.ones_like(
target, dtype=torch.int32) # -1 for unfilled
# Use the sampling coordinates of the first batch item
assert not (len(samplings[0]) > 1 and
B > 1), samplings # No subsampling if batch size > 1
loss_sub_log: Tensor = torch.zeros(
(len(samplings[0]), len(loss_fns)),
dtype=torch.float32,
device=device)
for k, sampling in enumerate(samplings[0]):
img_sampling = [slice(0, B), slice(0, C)] + list(sampling)
label_sampling = [slice(0, B), slice(0, K)] + list(sampling)
assert len(img_sampling) == len(image.shape), (img_sampling,
image.shape)
sub_img = image[img_sampling]
# Reset gradients
if optimizer:
optimizer.zero_grad()
# Forward
pred_logits: Tensor = net(sub_img)
pred_probs: Tensor = F.softmax(temperature * pred_logits,
dim=1)
predicted_mask: Tensor = probs2one_hot(
pred_probs.detach()) # Used only for dice computation
assert not predicted_mask.requires_grad
probs_receptacle[label_sampling] = pred_probs[...]
mask_receptacle[label_sampling] = predicted_mask[...]
assert len(bounds) == len(loss_fns) == len(
loss_weights) == len(labels)
ziped = zip(loss_fns, labels, loss_weights, bounds)
losses = [
w * loss_fn(pred_probs, label[label_sampling], bound,
box_priors)
for loss_fn, label, w, bound in ziped
]
loss = reduce(add, losses)
assert loss.shape == (), loss.shape
# Backward
if optimizer:
loss.backward()
optimizer.step()
# Compute and log metrics
for j in range(len(loss_fns)):
loss_sub_log[k, j] = losses[j].detach()
reduced_loss_sublog: Tensor = loss_sub_log.sum(dim=0)
assert reduced_loss_sublog.shape == (len(loss_fns), ), (
reduced_loss_sublog.shape, len(loss_fns))
loss_log[done_batch, ...] = reduced_loss_sublog[...]
del loss_sub_log
sm_slice = slice(done_img,
done_img + B) # Values only for current batch
dices: Tensor = dice_coef(mask_receptacle, target)
assert dices.shape == (B, K), (dices.shape, B, K)
all_dices[sm_slice, ...] = dices
if compute_3d_dice:
three_d_DSC: Tensor = dice_batch(mask_receptacle, target)
assert three_d_DSC.shape == (K, )
three_d_dices[done_batch] = three_d_DSC # type: ignore
if compute_hausdorff:
hausdorff_res: Tensor
try:
hausdorff_res = hausdorff(mask_receptacle, target,
spacings)
except RuntimeError:
hausdorff_res = torch.zeros((B, K), device=device)
assert hausdorff_res.shape == (B, K)
hausdorff_log[sm_slice] = hausdorff_res # type: ignore
if compute_miou:
IoUs: Tensor = iIoU(mask_receptacle, target)
assert IoUs.shape == (B, K), IoUs.shape
iiou_log[sm_slice] = IoUs # type: ignore
intersections[sm_slice] = inter_sum(mask_receptacle,
target) # type: ignore
unions[sm_slice] = union_sum(mask_receptacle,
target) # type: ignore
# if False and target[0, 1].sum() > 0: # Useful template for quick and dirty inspection
# import matplotlib.pyplot as plt
# from pprint import pprint
# from mpl_toolkits.axes_grid1 import ImageGrid
# from utils import soft_length
# print(data["filenames"])
# pprint(data["bounds"])
# pprint(soft_length(mask_receptacle))
# fig = plt.figure()
# fig.clear()
# grid = ImageGrid(fig, 211, nrows_ncols=(1, 2))
# grid[0].imshow(data["images"][0, 0], cmap="gray")
# grid[0].contour(data["gt"][0, 1], cmap='jet', alpha=.75, linewidths=2)
# grid[1].imshow(data["images"][0, 0], cmap="gray")
# grid[1].contour(mask_receptacle[0, 1], cmap='jet', alpha=.75, linewidths=2)
# plt.show()
# Save images
if savedir:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
predicted_class: Tensor = probs2class(pred_probs)
save_images(predicted_class, data["filenames"], savedir,
mode, epc)
# Logging
big_slice = slice(0, done_img +
B) # Value for current and previous batches
dsc_dict: Dict
if few_axis:
dsc_dict = {
**{
f"DSC{n}": all_dices[big_slice, n].mean()
for n in metric_axis
},
**({
f"3d_DSC{n}": three_d_dices[:done_batch, n].mean()
for n in metric_axis
} if three_d_dices is not None else {})
}
else:
dsc_dict = {}
# dsc_dict = {f"DSC{n}": all_dices[big_slice, n].mean() for n in metric_axis} if few_axis else {}
hauss_dict = {f"HD{n}": hausdorff_log[big_slice, n].mean() for n in metric_axis} \
if hausdorff_log is not None and few_axis else {}
miou_dict = {f"iIoU": iiou_log[big_slice, metric_axis].mean(),
f"mIoU": (intersections.sum(dim=0) / (unions.sum(dim=0) + 1e-10)).mean()} \
if iiou_log is not None and intersections is not None and unions is not None else {}
if len(metric_axis) > 1:
mean_dict = {"DSC": all_dices[big_slice, metric_axis].mean()}
if hausdorff_log:
mean_dict["HD"] = hausdorff_log[big_slice,
metric_axis].mean()
else:
mean_dict = {}
stat_dict = {
**miou_dict,
**dsc_dict,
**hauss_dict,
**mean_dict, "loss": loss_log[:done_batch].mean()
}
nice_dict = {k: f"{v:.3f}" for (k, v) in stat_dict.items()}
done_img += B
done_batch += 1
tq_iter.set_postfix({**nice_dict, "loader": str(i)})
tq_iter.update(1)
tq_iter.close()
print(f"{desc} " + ', '.join(f"{k}={v}" for (k, v) in nice_dict.items()))
mIoUs: Optional[Tensor]
if intersections and unions:
mIoUs = (intersections.sum(dim=0) / (unions.sum(dim=0) + 1e-10))
assert mIoUs.shape == (K, ), mIoUs.shape
else:
mIoUs = None
if not few_axis and False:
print(f"DSC: {[f'{all_dices[:, n].mean():.3f}' for n in metric_axis]}")
print(f"iIoU: {[f'{iiou_log[:, n].mean():.3f}' for n in metric_axis]}")
if mIoUs:
print(f"mIoU: {[f'{mIoUs[n]:.3f}' for n in metric_axis]}")
return (
loss_log.detach().cpu(), all_dices.detach().cpu(),
hausdorff_log.detach().cpu() if hausdorff_log is not None else None,
mIoUs.detach().cpu() if mIoUs is not None else None,
three_d_dices.detach().cpu() if three_d_dices is not None else None)
def do_epoch(mode: str, args, net, device, use_cuda, loader, optimizer,
num_classes, epoch):
totalImages = len(loader)
if mode == "train":
net.train()
desc = f">> Training ({epoch})"
elif mode == "val":
net.eval()
desc = f">> Validation ({epoch})"
total_iteration, total_images = len(loader), len(loader.dataset)
all_dices: Tensor = torch.zeros((total_images, num_classes),
dtype=eval(args.dtype),
device=device)
batch_dices: Tensor = torch.zeros((total_iteration, num_classes),
dtype=eval(args.dtype),
device=device)
loss_log: Tensor = torch.zeros((total_images),
dtype=eval(args.dtype),
device=device)
entropy_log: Tensor = torch.zeros((total_images),
dtype=eval(args.dtype),
device=device)
KL_log: Tensor = torch.zeros((total_images),
dtype=eval(args.dtype),
device=device)
tq_iter = tqdm_(enumerate(loader), total=total_iteration, desc=desc)
done: int = 0
for j, data in tq_iter:
image_f, image_i, image_d, image_o, image_w, image_c, labels, img_names = data
#image_f=image_f.type(torch.FloatTensor)/65535.
#image_f = image_f.type(torch.FloatTensor)/65535.
#image_i = image_i.type(torch.FloatTensor)/65535.
#image_d = image_d.type(torch.FloatTensor)/65535.
#image_o = image_o.type(torch.FloatTensor)/65535.
#image_w = image_w.type(torch.FloatTensor)/65535.
#image_c = image_c.type(torch.FloatTensor)/65535.
MRI: Tensor = torch.zeros((1, 6, image_f.size()[2], image_f.size()[3]),
dtype=eval(args.dtype))
MRI = torch.cat((image_f, image_i, image_d, image_o, image_w, image_c),
dim=1)
MRI = MRI.type(
torch.FloatTensor
) / 65535.0 #.type(eval(args.dtype)) #.type(torch.FloatTensor)
targets = torch.cat((1 - labels, labels),
dim=1) #.type(torch.LongTensor)
B = len(image_f)
#print(type(labels))
#MRI = torch.cat((image_f,image_i,image_d,image_w),dim=1)
if use_cuda:
MRI, targets = MRI.to(device), targets.to(device)
# forward
outputs = net(MRI)
pred_probs = F.softmax(outputs, dim=1)
predicted_mask = probs2one_hot(pred_probs)
entropy = crossEntropy_f(pred_probs, targets)
pred_probs_aver: Tensor = torch.sum(pred_probs, dim=(2, 3))
pred_probs_aver = pred_probs_aver / torch.sum(targets).float()
target_aver: Tensor = torch.sum(targets, dim=(2, 3)).float()
target_aver = target_aver / torch.sum(targets).float()
KL_loss = args.lam * kl(target_aver, pred_probs_aver)
loss = entropy + KL_loss
if mode == "train":
# zero the parameter gradients8544
optimizer.zero_grad()
# backward + optimize
loss.backward()
optimizer.step()
# Compute and log metrics
dices: Tensor = dice_coef(predicted_mask.detach(),
targets.type(torch.cuda.IntTensor).detach())
batch_dice: Tensor = dice_batch(
predicted_mask.detach(),
targets.type(torch.cuda.IntTensor).detach())
assert batch_dice.shape == (num_classes, ) and dices.shape == (
B, num_classes), (batch_dice.shape, dices.shape, B, num_classes)
sm_slice = slice(done, done + B) # Values only for current batch
all_dices[sm_slice, ...] = dices
entropy_log[sm_slice] = entropy.detach()
loss_log[sm_slice] = loss.detach()
KL_log[sm_slice] = KL_loss.detach()
batch_dices[j] = batch_dice
# Logging
big_slice = slice(0,
done + B) # Value for current and previous batches
stat_dict = {
"dice": all_dices[big_slice, -1].mean(),
"total loss": loss_log[big_slice].mean(),
"entropy loss": entropy_log[big_slice].mean(),
"KL loss": KL_log[big_slice].mean(),
"b dice": batch_dices[:j + 1, -1].mean()
}
nice_dict = {k: f"{v:.4f}" for (k, v) in stat_dict.items()}
done += B
tq_iter.set_postfix(nice_dict)
return loss_log, entropy_log, KL_log, all_dices, batch_dices
def do_epoch(args, mode: str, net: Any, device: Any, epc: int,
loss_fns: List[Callable], loss_weights: List[float],
new_w:int, C: int, metric_axis:List[int], savedir: str = "",
optimizer: Any = None, target_loader: Any = None, best_dice3d_val:Any=None):
assert mode in ["train", "val"]
L: int = len(loss_fns)
indices = torch.tensor(metric_axis,device=device)
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
# net.train()
desc = f">> Validation ({epc})"
total_it_t, total_images_t = len(target_loader), len(target_loader.dataset)
total_iteration = total_it_t
total_images = total_images_t
if args.debug:
total_iteration = 10
pho=1
dtype = eval(args.dtype)
all_dices: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_sizes: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_sizes: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_gt_sizes: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_sizes2: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_inter_card: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_card_gt: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_card_pred: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_gt = []
all_pred = []
if args.do_hd:
all_gt: Tensor = torch.zeros((total_images, args.wh, args.wh), dtype=dtype)
all_pred: Tensor = torch.zeros((total_images, args.wh, args.wh), dtype=dtype)
loss_log: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
loss_cons: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
loss_se: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
loss_tot: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
posim_log: Tensor = torch.zeros((total_images), dtype=dtype, device=device)
haussdorf_log: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_grp: Tensor = torch.zeros((total_images, C), dtype=dtype, device=device)
all_pnames = np.zeros([total_images]).astype('U256')
#dice_3d_log: Tensor = torch.zeros((1, C), dtype=dtype, device=device)
dice_3d_log, dice_3d_sd_log = 0, 0
#dice_3d_sd_log: Tensor = torch.zeros((1, C), dtype=dtype, device=device)
hd_3d_log, asd_3d_log, hd_3d_sd_log, asd_3d_sd_log= 0, 0, 0, 0
tq_iter = tqdm_(enumerate(target_loader), total=total_iteration, desc=desc)
done: int = 0
n_warmup = args.n_warmup
mult_lw = [pho ** (epc - n_warmup + 1)] * len(loss_weights)
mult_lw[0] = 1
loss_weights = [a * b for a, b in zip(loss_weights, mult_lw)]
losses_vec, source_vec, target_vec, baseline_target_vec = [], [], [], []
pen_count = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore")
count_losses = 0
for j, target_data in tq_iter:
target_data[1:] = [e.to(device) for e in target_data[1:]] # Move all tensors to device
filenames_target, target_image, target_gt = target_data[:3]
#print("target", filenames_target)
labels = target_data[3:3+L]
bounds = target_data[3+L:]
filenames_target = [f.split('.nii')[0] for f in filenames_target]
assert len(labels) == len(bounds), len(bounds)
B = len(target_image)
# Reset gradients
if optimizer:
optimizer.zero_grad()
# Forward
with torch.set_grad_enabled(mode == "train"):
pred_logits: Tensor = net(target_image)
pred_probs: Tensor = F.softmax(pred_logits, dim=1)
if new_w > 0:
pred_probs = resize(pred_probs, new_w)
labels = [resize(label, new_w) for label in labels]
target = resize(target, new_w)
predicted_mask: Tensor = probs2one_hot(pred_probs) # Used only for dice computation
assert len(bounds) == len(loss_fns) == len(loss_weights)
if epc < n_warmup:
loss_weights = [0]*len(loss_weights)
loss: Tensor = torch.zeros(1, requires_grad=True).to(device)
loss_vec = []
loss_kw = []
for loss_fn,label, w, bound in zip(loss_fns,labels, loss_weights, bounds):
if w > 0:
if eval(args.target_losses)[0][0]=="EntKLProp":
loss_1, loss_cons_prior,est_prop = loss_fn(pred_probs, label, bound)
loss = loss_1 + loss_cons_prior
else:
loss = loss_fn(pred_probs, label, bound)
loss = w*loss
loss_1 = loss
loss_kw.append(loss_1.detach())
# Backward
if optimizer:
loss.backward()
optimizer.step()
dices, inter_card, card_gt, card_pred = dice_coef(predicted_mask.detach(), target_gt.detach())
assert dices.shape == (B, C), (dices.shape, B, C)
sm_slice = slice(done, done + B) # Values only for current batch
all_dices[sm_slice, ...] = dices
if eval(args.target_losses)[0][0] in ["EntKLProp"]:
all_sizes[sm_slice, ...] = torch.round(est_prop.detach()*target_image.shape[2]*target_image.shape[3])
all_sizes2[sm_slice, ...] = torch.sum(predicted_mask,dim=(2,3))
all_gt_sizes[sm_slice, ...] = torch.sum(target_gt,dim=(2,3))
all_grp[sm_slice, ...] = torch.FloatTensor(get_subj_nb(filenames_target)).unsqueeze(1).repeat(1,C)
all_pnames[sm_slice] = filenames_target
all_inter_card[sm_slice, ...] = inter_card
all_card_gt[sm_slice, ...] = card_gt
all_card_pred[sm_slice, ...] = card_pred
if args.do_hd:
all_pred[sm_slice, ...] = probs2class(predicted_mask[:,:,:,:]).cpu().detach()
all_gt[sm_slice, ...] = probs2class(target_gt).detach()
loss_se[sm_slice] = loss_kw[0]
if len(loss_kw)>1:
loss_cons[sm_slice] = loss_kw[1]
loss_tot[sm_slice] = loss_kw[1]+loss_kw[0]
else:
loss_cons[sm_slice] = 0
loss_tot[sm_slice] = loss_kw[0]
# # Save images
if savedir and args.saveim and mode =="val":
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
warnings.simplefilter("ignore")
predicted_class: Tensor = probs2class(pred_probs)
save_images(predicted_class, filenames_target, savedir, mode, epc, False)
if args.entmap:
ent_map = torch.einsum("bcwh,bcwh->bwh", [-pred_probs, (pred_probs+1e-10).log()])
save_images_ent(ent_map, filenames_target, savedir,'ent_map', epc)
# Logging
big_slice = slice(0, done + B) # Value for current and previous batches
stat_dict = {**{f"DSC{n}": all_dices[big_slice, n].mean() for n in metric_axis},
**{f"SZ{n}": all_sizes[big_slice, n].mean() for n in metric_axis},
**({f"DSC_source{n}": all_dices_s[big_slice, n].mean() for n in metric_axis}
if args.source_metrics else {})}
size_dict = {**{f"SZ{n}": all_sizes[big_slice, n].mean() for n in metric_axis}}
nice_dict = {k: f"{v:.4f}" for (k, v) in stat_dict.items()}
done += B
tq_iter.set_postfix(nice_dict)
if args.dice_3d and (mode == 'val'):
dice_3d_log, dice_3d_sd_log,asd_3d_log, asd_3d_sd_log,hd_3d_log, hd_3d_sd_log = dice3d(all_grp, all_inter_card, all_card_gt, all_card_pred,all_pred,all_gt,all_pnames,metric_axis,args.pprint,args.do_hd,args.do_asd,best_dice3d_val)
dice_2d = torch.index_select(all_dices, 1, indices).mean().cpu().numpy().item()
target_vec = [dice_3d_log, dice_3d_sd_log,asd_3d_log, asd_3d_sd_log,hd_3d_log,hd_3d_sd_log,dice_2d]
size_mean = torch.index_select(all_sizes2, 1, indices).mean(dim=0).cpu().numpy()
size_gt_mean = torch.index_select(all_gt_sizes, 1, indices).mean(dim=0).cpu().numpy()
mask_pos = torch.index_select(all_sizes2, 1, indices)!=0
gt_pos = torch.index_select(all_gt_sizes, 1, indices)!=0
size_mean_pos = torch.index_select(all_sizes2, 1, indices).sum(dim=0).cpu().numpy()/mask_pos.sum(dim=0).cpu().numpy()
gt_size_mean_pos = torch.index_select(all_gt_sizes, 1, indices).sum(dim=0).cpu().numpy()/gt_pos.sum(dim=0).cpu().numpy()
size_mean2 = torch.index_select(all_sizes2, 1, indices).mean(dim=0).cpu().numpy()
losses_vec = [loss_se.mean().item(),loss_cons.mean().item(),loss_tot.mean().item(),size_mean.mean(),size_mean_pos.mean(),size_gt_mean.mean(),gt_size_mean_pos.mean()]
if not epc%50:
df_t = pd.DataFrame({
"val_ids":all_pnames,
"proposal_size":all_sizes2.cpu()})
df_t.to_csv(Path(savedir,mode+str(epc)+"sizes.csv"), float_format="%.4f", index_label="epoch")
return losses_vec, target_vec,source_vec
def for_back_step_comb(optimizer, mode, source_image, target_image, gt_source, labels,
net, loss_fns,loss_weights, loss_fns_source, loss_weights_source, new_w, device, bounds,
model_D, optimizer_D, lambda_adv_target):
source_label = 0
target_label = 1
# Reset gradients
if optimizer:
optimizer.zero_grad()
optimizer_D.zero_grad()
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# Forward
with torch.set_grad_enabled(mode == "train"):
#Forward
pred_logits_source: Tensor = net(source_image)
probs_source: Tensor = F.softmax(pred_logits_source, dim=1)
pred_logits_target: Tensor = net(target_image)
probs_target: Tensor = F.softmax(pred_logits_target, dim=1)
predicted_mask_target: Tensor = probs2one_hot(probs_target)
if new_w > 0:
probs_source = resize(probs_source, new_w)
probs_target = resize(probs_target, new_w)
if labels[0].shape[3]!= new_w:
labels = [resize(label, new_w) for label in labels ]
gt_source = resize(gt_source, new_w)
assert len(bounds) == len(loss_fns) == len(loss_weights)
loss_adv_target = torch.zeros(1, requires_grad=True).to(device)
loss_vec = []
# Losses on source
ziped = zip(loss_fns_source, [gt_source], loss_weights_source)
losses = [w * loss_fn(probs_source, label, torch.randn(1)) for loss_fn, label, w in ziped]
loss_vec.extend([loss.item() for loss in losses])
loss_source = reduce(add, losses)
# add adversarial loss of target im if not a pair of negative images
if lambda_adv_target > 0 and max(predicted_mask_target[0,1,...].sum(),predicted_mask_target[0,1,...].sum()).item()>0:
D_out = model_D(probs_target)
loss_adv_target = d_loss_calc(D_out, source_label).to(device=device)*lambda_adv_target
loss_vec.append(loss_adv_target.item())
else:
loss_vec.append(0)
# Constraint loss on target images (and eventually also cross entropy with FGT)
ziped = zip(loss_fns, labels, loss_weights, bounds)
losses = [w * loss_fn(probs_target, label, bound) for loss_fn, label, w, bound in ziped]
loss_vec.extend([loss.item() for loss in losses])
loss_target = reduce(add, losses)
loss = loss_source + loss_adv_target + loss_target
# Backward
if optimizer:
loss.backward()
optimizer.step()
# train D
if lambda_adv_target > 0 and max(predicted_mask_target[0,1,...].sum(),predicted_mask_target[0,1,...].sum()).item()>0:
# bring back requires_grad
for param in model_D.parameters():
param.requires_grad = True
# train with source
probs_source = probs_source.detach()
D_out = model_D(probs_source)
loss_D_s = d_loss_calc(D_out, source_label).to(device=device)/2
if optimizer:
loss_D_s.backward()
# train with target
probs_target = probs_target.detach()
D_out_t = model_D(probs_target)
loss_D_t = d_loss_calc(D_out_t, target_label).to(device=device)/2
if optimizer:
loss_D_t.backward()
optimizer_D.step()
loss_vec.append(loss_D_s.item()+loss_D_t.item())
else:
loss_vec.append(0)
return probs_source, probs_target, loss, loss_vec[0], loss_vec[1], loss_vec[2], loss_vec[3], loss_vec[4]
def do_epoch(
mode: str,
net: Any,
device: Any,
loaders: list[DataLoader],
epc: int,
list_loss_fns: list[list[Callable]],
list_loss_weights: list[list[float]],
K: int,
savedir: str = "",
optimizer: Any = None,
metric_axis: list[int] = [1],
compute_3d_dice: bool = False,
temperature: float = 1) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
assert mode in ["train", "val", "dual"]
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
desc = f">> Validation ({epc})"
total_iteration: int = sum(len(loader) for loader in loaders) # U
total_images: int = sum(len(loader.dataset) for loader in loaders) # D
n_loss: int = max(map(len, list_loss_fns))
all_dices: Tensor = torch.zeros((total_images, K),
dtype=torch.float32,
device=device)
loss_log: Tensor = torch.zeros((total_iteration, n_loss),
dtype=torch.float32,
device=device)
three_d_dices: Optional[Tensor]
if compute_3d_dice:
three_d_dices = torch.zeros((total_iteration, K),
dtype=torch.float32,
device=device)
else:
three_d_dices = None
done_img: int = 0
done_batch: int = 0
tq_iter = tqdm_(total=total_iteration, desc=desc)
for i, (loader, loss_fns, loss_weights) in enumerate(
zip(loaders, list_loss_fns, list_loss_weights)):
for data in loader:
# t0 = time()
image: Tensor = data["images"].to(device)
target: Tensor = data["gt"].to(device)
filenames: list[str] = data["filenames"]
assert not target.requires_grad
labels: list[Tensor] = [e.to(device) for e in data["labels"]]
B, C, *_ = image.shape
# Reset gradients
if optimizer:
optimizer.zero_grad()
# Forward
pred_logits: Tensor = net(image)
pred_probs: Tensor = F.softmax(temperature * pred_logits, dim=1)
predicted_mask: Tensor = probs2one_hot(
pred_probs.detach()) # Used only for dice computation
assert not predicted_mask.requires_grad
assert len(loss_fns) == len(loss_weights) == len(labels)
ziped = zip(loss_fns, labels, loss_weights)
losses = [
w * loss_fn(pred_probs, label) for loss_fn, label, w in ziped
]
loss = reduce(add, losses)
assert loss.shape == (), loss.shape
# Backward
if optimizer:
loss.backward()
optimizer.step()
# Compute and log metrics
for j in range(len(loss_fns)):
loss_log[done_batch, j] = losses[j].detach()
sm_slice = slice(done_img,
done_img + B) # Values only for current batch
dices: Tensor = dice_coef(predicted_mask, target)
assert dices.shape == (B, K), (dices.shape, B, K)
all_dices[sm_slice, ...] = dices
if compute_3d_dice:
three_d_DSC: Tensor = dice_batch(predicted_mask, target)
assert three_d_DSC.shape == (K, )
three_d_dices[done_batch] = three_d_DSC # type: ignore
# Save images
if savedir:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
predicted_class: Tensor = probs2class(pred_probs)
save_images(predicted_class, filenames, savedir, mode, epc)
# Logging
big_slice = slice(0, done_img +
B) # Value for current and previous batches
dsc_dict: dict = {f"DSC{n}": all_dices[big_slice, n].mean() for n in metric_axis} | \
({f"3d_DSC{n}": three_d_dices[:done_batch, n].mean() for n in metric_axis}
if three_d_dices is not None else {})
loss_dict = {
f"loss_{i}": loss_log[:done_batch].mean(dim=0)[i]
for i in range(n_loss)
}
stat_dict = dsc_dict | loss_dict
nice_dict = {k: f"{v:.3f}" for (k, v) in stat_dict.items()}
done_img += B
done_batch += 1
tq_iter.set_postfix({**nice_dict, "loader": str(i)})
tq_iter.update(1)
tq_iter.close()
print(f"{desc} " + ', '.join(f"{k}={v}" for (k, v) in nice_dict.items()))
return (loss_log.detach().cpu(), all_dices.detach().cpu(),
three_d_dices.detach().cpu()
if three_d_dices is not None else None)
fold_all_H2.write(f"file, dice, haussdorf,connecterror \n")
fold_clean_H2.write(f"file, dice, haussdorf,connecterror \n")
for _,_,files in os.walk(os.path.join(root, 'in_npy')):
for file in files:
image = np.load(os.path.join(root,'in_npy', file))
gt = np.load(os.path.join(root,'gt_npy', file))
#print('infering {} of shape {} and classes {}, max {} and min {} '.format( file, image.shape, np.unique(gt), image.max(), image.min()))
image = image.reshape(-1, 1, 256, 256)
image = torch.tensor(image, dtype=torch.float)
image = Variable(image, requires_grad=True)
pred = net(image)
pred = F.softmax(pred, dim=1)
predicted_output = probs2one_hot(pred.detach())
#np.save(os.path.join(path, 'predictions', '{}'.format(file)), pred.detach().numpy())
dice = dice_coef(predicted_output, class2one_hot(torch.tensor(gt), n_classes))
hauss = haussdorf(predicted_output, class2one_hot(torch.tensor(gt), n_classes))
#pred_label = len(np.unique(label(np.array(pred.argmax(axis = 1).detach().numpy()))))
#gt_label = len(np.unique(label(gt)))
pred_label = len(np.unique(label(predicted_output[0][1])))
gt_label = len(np.unique(label(class2one_hot(torch.tensor(gt), n_classes)[0][1])))
error = np.abs(pred_label - gt_label)
pred_label2 = len(np.unique(label(predicted_output[0][2])))
gt_label2 = len(np.unique(label(class2one_hot(torch.tensor(gt), n_classes)[0][2])))
error2 = np.abs(pred_label2 - gt_label2)
print(f"{file}, {np.float(dice[0][1])}, {np.float(hauss[0][1])},{np.float(error)} \n")
print(f"{file}, {np.float(dice[0][2])}, {np.float(hauss[0][2])},{np.float(error2)} \n")
def do_epoch(mode: str, net: Any, device: Any, loaders: list[DataLoader], epc: int,
list_loss_fns: list[list[Callable]], list_loss_weights: list[list[float]], K: int,
savedir: Path = None, optimizer: Any = None,
metric_axis: list[int] = [1], requested_metrics: list[str] = None,
temperature: float = 1) -> dict[str, Tensor]:
assert mode in ["train", "val", "dual"]
if requested_metrics is None:
requested_metrics = []
if mode == "train":
net.train()
desc = f">> Training ({epc})"
elif mode == "val":
net.eval()
desc = f">> Validation ({epc})"
elif mode == "dual":
net.eval()
desc = f">> Dual ({epc})"
total_iteration: int = sum(len(loader) for loader in loaders) # U
total_images: int = sum(len(loader.dataset) for loader in loaders) # D
n_loss: int = max(map(len, list_loss_fns))
epoch_metrics: dict[str, Tensor]
epoch_metrics = {"dice": torch.zeros((total_images, K), dtype=torch.float32, device=device),
"loss": torch.zeros((total_iteration, n_loss), dtype=torch.float32, device=device)}
if "3d_dsc" in requested_metrics:
epoch_metrics["3d_dsc"] = torch.zeros((total_iteration, K), dtype=torch.float32, device=device)
few_axis: bool = len(metric_axis) <= 4
# time_log: np.ndarray = np.ndarray(total_iteration, dtype=np.float32)
done_img: int = 0
done_batch: int = 0
tq_iter = tqdm_(total=total_iteration, desc=desc)
for i, (loader, loss_fns, loss_weights) in enumerate(zip(loaders, list_loss_fns, list_loss_weights)):
for data in loader:
# t0 = time()
image: Tensor = data["images"].to(device)
target: Tensor = data["gt"].to(device)
filenames: list[str] = data["filenames"]
assert not target.requires_grad
labels: list[Tensor] = [e.to(device) for e in data["labels"]]
bounds: list[Tensor] = [e.to(device) for e in data["bounds"]]
assert len(labels) == len(bounds)
B, C, *_ = image.shape
samplings: list[list[Tuple[slice]]] = data["samplings"]
assert len(samplings) == B
assert len(samplings[0][0]) == len(image[0, 0].shape), (samplings[0][0], image[0, 0].shape)
probs_receptacle: Tensor = - torch.ones_like(target, dtype=torch.float32) # -1 for unfilled
mask_receptacle: Tensor = - torch.ones_like(target, dtype=torch.int32) # -1 for unfilled
# Use the sampling coordinates of the first batch item
assert not (len(samplings[0]) > 1 and B > 1), samplings # No subsampling if batch size > 1
loss_sub_log: Tensor = torch.zeros((len(samplings[0]), len(loss_fns)),
dtype=torch.float32, device=device)
for k, sampling in enumerate(samplings[0]):
img_sampling = [slice(0, B), slice(0, C)] + list(sampling)
label_sampling = [slice(0, B), slice(0, K)] + list(sampling)
assert len(img_sampling) == len(image.shape), (img_sampling, image.shape)
sub_img = image[img_sampling]
# Reset gradients
if optimizer:
optimizer.zero_grad()
# Forward
pred_logits: Tensor = net(sub_img)
pred_probs: Tensor = F.softmax(temperature * pred_logits, dim=1)
# Used only for dice computation:
predicted_mask: Tensor = probs2one_hot(pred_probs.detach())
assert not predicted_mask.requires_grad
probs_receptacle[label_sampling] = pred_probs[...]
mask_receptacle[label_sampling] = predicted_mask[...]
assert len(bounds) == len(loss_fns) == len(loss_weights) == len(labels)
ziped = zip(loss_fns, labels, loss_weights, bounds)
losses = [w * loss_fn(pred_probs, label[label_sampling], bound, filenames)
for loss_fn, label, w, bound in ziped]
loss = reduce(add, losses)
assert loss.shape == (), loss.shape
# Backward
if optimizer:
loss.backward()
optimizer.step()
# Compute and log metrics
for j in range(len(loss_fns)):
loss_sub_log[k, j] = losses[j].detach()
reduced_loss_sublog: Tensor = loss_sub_log.sum(dim=0)
assert reduced_loss_sublog.shape == (len(loss_fns),), (reduced_loss_sublog.shape, len(loss_fns))
epoch_metrics["loss"][done_batch, ...] = reduced_loss_sublog[...]
del loss_sub_log
sm_slice = slice(done_img, done_img + B) # Values only for current batch
dices: Tensor = dice_coef(mask_receptacle, target)
assert dices.shape == (B, K), (dices.shape, B, K)
epoch_metrics["dice"][sm_slice, ...] = dices
if "3d_dsc" in requested_metrics:
three_d_DSC: Tensor = dice_batch(mask_receptacle, target)
assert three_d_DSC.shape == (K,)
epoch_metrics["3d_dsc"][done_batch] = three_d_DSC # type: ignore
# Save images
if savedir:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
predicted_class: Tensor = probs2class(pred_probs)
save_images(predicted_class,
data["filenames"],
savedir / f"iter{epc:03d}" / mode)
# Logging
big_slice = slice(0, done_img + B) # Value for current and previous batches
stat_dict: dict[str, Any] = {}
# The order matters for the final display -- it is easy to change
if few_axis:
stat_dict |= {f"DSC{n}": epoch_metrics["dice"][big_slice, n].mean()
for n in metric_axis}
if "3d_dsc" in requested_metrics:
stat_dict |= {f"3d_DSC{n}": epoch_metrics["3d_dsc"][:done_batch, n].mean()
for n in metric_axis}
if len(metric_axis) > 1:
stat_dict |= {"DSC": epoch_metrics["dice"][big_slice, metric_axis].mean()}
stat_dict |= {f"loss_{i}": epoch_metrics["loss"][:done_batch].mean(dim=0)[i]
for i in range(n_loss)}
nice_dict = {k: f"{v:.3f}" for (k, v) in stat_dict.items()}
# t1 = time()
# time_log[done_batch] = (t1 - t0)
done_img += B
done_batch += 1
tq_iter.set_postfix({**nice_dict, "loader": str(i)})
tq_iter.update(1)
tq_iter.close()
print(f"{desc} " + ', '.join(f"{k}={v}" for (k, v) in nice_dict.items()))
return {k: v.detach().cpu() for (k, v) in epoch_metrics.items()}
