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Copy pathLA_utils.py
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317 lines (245 loc) · 9.26 KB
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import os
import sys
import shutil
import time
import random
import torch
import logging
from pathlib import Path
import numpy as np
from torch import multiprocessing
from torch.nn import functional as F
import nibabel as nib
from tensorboardX import SummaryWriter
from skimage.measure import label
def mkdir(path, level=2, create_self=True):
""" Make directory for this path,
level is how many parent folders should be created.
create_self is whether create path(if it is a file, it should not be created)
e.g. : mkdir('/home/parent1/parent2/folder', level=3, create_self=False),
it will first create parent1, then parent2, then folder.
:param path: string
:param level: int
:param create_self: True or False
:return:
"""
p = Path(path)
if create_self:
paths = [p]
else:
paths = []
level -= 1
while level != 0:
p = p.parent
paths.append(p)
level -= 1
for p in paths[::-1]:
p.mkdir(exist_ok=True)
def seed_reproducer(seed=2022):
"""Reproducer for pytorch experiment.
Parameters
----------
seed: int, optional (default = 2020)
Radnom seed.
Example
-------
seed_reproducer(seed=2020).
"""
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # set all gpus seed
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False # if input data type and channels' changes arent' large use it improve train efficient
torch.backends.cudnn.enabled = True
def cutmix_config_log(save_path, tensorboard=False):
writer = SummaryWriter(str(save_path), filename_suffix=time.strftime('_%Y-%m-%d_%H-%M-%S')) if tensorboard else None
save_path = str(Path(save_path) / 'log.txt')
formatter = logging.Formatter('%(levelname)s [%(asctime)s] %(message)s')
logger = logging.getLogger(save_path.split('\\')[-2])
logger.setLevel(logging.INFO)
handler = logging.FileHandler(save_path)
handler.setFormatter(formatter)
logger.addHandler(handler)
sh = logging.StreamHandler(sys.stdout)
handler.setFormatter(formatter)
logger.addHandler(sh)
return logger, writer
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
return self
def update(self, val, n=1):
self.val = val
self.sum += val
self.count += n
self.avg = self.sum / self.count
return self
class Measures():
def __init__(self, keys, writer, logger):
self.keys = keys
self.measures = {k: AverageMeter() for k in self.keys}
self.writer = writer
self.logger = logger
def reset(self):
[v.reset() for v in self.measures.values()]
class CutPreMeasures(Measures):
def __init__(self, writer, logger):
keys = ['ce_loss', 'dice_loss', 'loss_all', 'train_dice']
super(CutPreMeasures, self).__init__(keys, writer, logger)
def update(self, out, lab, ce_loss, dice_loss, loss):
masks = get_mask(out)
train_dice1 = statistic.dice_ratio(masks, lab)
self.logger.info("ce loss: %.4f, dice loss: %.4f, total loss: %.4f, train_dice: %.4f" %
(ce_loss.item(), dice_loss.item(), loss.item(), train_dice1))
# args.append(train_dice1)
# dict_variables = dict(zip(self.keys, args))
# for k, v in dict_variables.items():
# self.measures[k].update(v)
def log(self, epoch, step):
# self.logger.info('epoch : %d, step : %d, train_loss: %.4f, train_dice: %.4f' % (
# epoch, step, self.measures['loss_all'].avg, self.measures['train_dice'].avg))
log_string, params = 'Epoch : {}', []
for k in self.keys:
log_string += ', ' + k + ': {:.4f}'
params.append(self.measures[k].val)
self.logger.info(log_string.format(epoch, *params))
for k, measure in self.measures.items():
k = 'pretrain/' + k
self.writer.add_scalar(k, measure.avg, step)
self.writer.flush()
def get_mask(out, thres=0.5):
probs = F.softmax(out, 1)
masks = (probs >= thres).float()
masks = masks[:, 1, :, :].contiguous()
return masks
def save_net_opt(net, optimizer, path, epoch):
state = {
'net': net.state_dict(),
'opt': optimizer.state_dict(),
'epoch': epoch,
}
torch.save(state, str(path))
def load_net_opt(net, optimizer, path):
state = torch.load(str(path))
net.load_state_dict(state['net'])
optimizer.load_state_dict(state['opt'])
def save_net(net, path):
state = {
'net': net.state_dict(),
}
torch.save(state, str(path))
def load_net(net, path):
state = torch.load(str(path))
net.load_state_dict(state['net'])
def generate_mask(img, patch_size):
batch_l = img.shape[0]
# batch_unlab = unimg.shape[0]
loss_mask = torch.ones(batch_l, 96, 96, 96).cuda()
# loss_mask_unlab = torch.ones(batch_unlab, 96, 96, 96).cuda()
mask = torch.ones(96, 96, 96).cuda()
w = np.random.randint(0, 96 - patch_size)
h = np.random.randint(0, 96 - patch_size)
z = np.random.randint(0, 96 - patch_size)
mask[w:w + patch_size, h:h + patch_size, z:z + patch_size] = 0
loss_mask[:, w:w + patch_size, h:h + patch_size, z:z + patch_size] = 0
# loss_mask_unlab[:, w:w+patch_size, h:h+patch_size, z:z+patch_size] = 0
# cordi = [w, h, z]
return mask.long(), loss_mask.long()
def config_log(save_path, tensorboard=False):
writer = SummaryWriter(str(save_path), filename_suffix=time.strftime('_%Y-%m-%d_%H-%M-%S')) if tensorboard else None
save_path = str(Path(save_path) / 'log.txt')
formatter = logging.Formatter('%(levelname)s [%(asctime)s] %(message)s')
logger = logging.getLogger(save_path.split('\\')[-2])
logger.setLevel(logging.INFO)
handler = logging.FileHandler(save_path)
handler.setFormatter(formatter)
logger.addHandler(handler)
sh = logging.StreamHandler(sys.stdout)
handler.setFormatter(formatter)
logger.addHandler(sh)
return logger, writer
class CutmixFTMeasures(Measures):
def __init__(self, writer, logger):
keys = ['mix_loss_lab', 'mix_loss_unlab']
super(CutmixFTMeasures, self).__init__(keys, writer, logger)
def update(self, *args):
args = list(args)
# masks = get_mask(out[0])
# train_dice = statistic.dice_ratio(masks, lab)
# args.append(train_dice)
dict_variables = dict(zip(self.keys, args))
for k, v in dict_variables.items():
self.measures[k].update(v)
def log(self, epoch, step):
# self.logger.info('epoch : %d, step : %d, train_loss: %.4f, train_dice: %.4f' % (
# epoch, step, self.measures['loss_all'].avg, self.measures['train_dice'].avg))
log_string, params = 'Epoch : {}', []
for k in self.keys:
log_string += ', ' + k + ': {:.4f}'
params.append(self.measures[k].val)
self.logger.info(log_string.format(epoch, *params))
for k, measure in self.measures.items():
k = 'pretrain/' + k
self.writer.add_scalar(k, measure.avg, step)
self.writer.flush()
def to_cuda(tensors, device=None):
res = []
if isinstance(tensors, (list, tuple)):
for t in tensors:
res.append(to_cuda(t, device))
return res
elif isinstance(tensors, (dict,)):
res = {}
for k, v in tensors.items():
res[k] = to_cuda(v, device)
return res
else:
if isinstance(tensors, torch.Tensor):
if device is None:
return tensors.cuda()
else:
return tensors.to(device)
else:
return tensors
def get_cut_mask(out, thres=0.5, nms=True, connect_mode=1):
probs = F.softmax(out, 1)
masks = (probs >= thres).type(torch.int64)
masks = masks[:, 1, :, :].contiguous()
if nms == True:
masks = LargestCC_pancreas(masks, connect_mode=connect_mode)
return masks
def get_cut_mask_two(out1, out2, thres=0.5, nms=True, connect_mode=1):
probs1 = F.softmax(out1, 1)
probs2 = F.softmax(out2, 1)
probs = (probs1 + probs2) / 2
masks = (probs >= thres).type(torch.int64)
masks = masks[:, 1, :, :].contiguous()
if nms == True:
masks = LargestCC_pancreas(masks, connect_mode=connect_mode)
return masks
def LargestCC_pancreas(segmentation, connect_mode=1):
N = segmentation.shape[0]
batch_list = []
for n in range(N):
n_prob = segmentation[n].detach().cpu().numpy()
labels = label(n_prob, connectivity=connect_mode)
if labels.max() != 0:
largestCC = labels == np.argmax(np.bincount(labels.flat)[1:]) + 1
else:
largestCC = n_prob
batch_list.append(largestCC)
return torch.Tensor(batch_list).cuda()
@torch.no_grad()
def update_ema_variables(model, ema_model, alpha):
for ema_param, param in zip(ema_model.parameters(), model.parameters()):
ema_param.data.mul_(alpha).add_((1 - alpha) * param.data)