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utils.py
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utils.py
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import torch
import torch.nn as nn
import torchvision
import numpy as np
from PIL import Image
def sum_tensor(inp, axes, keepdim=False):
# copy from: https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/utilities/tensor_utilities.py
axes = np.unique(axes).astype(int)
if keepdim:
for ax in axes:
inp = inp.sum(int(ax), keepdim=True)
else:
for ax in sorted(axes, reverse=True):
inp = inp.sum(int(ax))
return inp
def get_tp_fp_fn(net_output, gt, axes=None, mask=None, square=False):
"""
net_output must be (b, c, x, y(, z)))
gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
if mask is provided it must have shape (b, 1, x, y(, z)))
:param net_output:
:param gt:
:param axes:
:param mask: mask must be 1 for valid pixels and 0 for invalid pixels
:param square: if True then fp, tp and fn will be squared before summation
:return:
"""
if axes is None:
axes = tuple(range(2, len(net_output.size())))
shp_x = net_output.shape
shp_y = gt.shape
with torch.no_grad():
if len(shp_x) != len(shp_y):
gt = gt.view((shp_y[0], 1, *shp_y[1:]))
if all([i == j for i, j in zip(net_output.shape, gt.shape)]):
# if this is the case then gt is probably already a one hot encoding
y_onehot = gt
else:
gt = gt.long()
y_onehot = torch.zeros(shp_x)
if net_output.device.type == "cuda":
y_onehot = y_onehot.cuda(net_output.device.index)
y_onehot.scatter_(1, gt, 1)
tp = net_output * y_onehot
fp = net_output * (1 - y_onehot)
fn = (1 - net_output) * y_onehot
if mask is not None:
tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp, dim=1)), dim=1)
fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp, dim=1)), dim=1)
fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn, dim=1)), dim=1)
if square:
tp = tp ** 2
fp = fp ** 2
fn = fn ** 2
tp = sum_tensor(tp, axes, keepdim=False)
fp = sum_tensor(fp, axes, keepdim=False)
fn = sum_tensor(fn, axes, keepdim=False)
return tp, fp, fn
class SoftDiceLoss(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True, smooth=1.,
square=False):
"""
paper: https://arxiv.org/pdf/1606.04797.pdf
"""
super(SoftDiceLoss, self).__init__()
self.square = square
self.do_bg = do_bg
self.batch_dice = batch_dice
self.apply_nonlin = apply_nonlin
self.smooth = smooth
def forward(self, x, y, loss_mask=None):
shp_x = x.shape
if self.batch_dice:
axes = [0] + list(range(2, len(shp_x)))
else:
axes = list(range(2, len(shp_x)))
if self.apply_nonlin is not None:
x = self.apply_nonlin(x)
tp, fp, fn = get_tp_fp_fn(x, y, axes, loss_mask, self.square)
dc = (2 * tp + self.smooth) / (2 * tp + fp + fn + self.smooth)
if not self.do_bg:
if self.batch_dice:
dc = dc[1:]
else:
dc = dc[:, 1:]
dc = dc.mean()
return -dc
class IoULoss(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True, smooth=1.,
square=False):
"""
paper: https://link.springer.com/chapter/10.1007/978-3-319-50835-1_22
"""
super(IoULoss, self).__init__()
self.square = square
self.do_bg = do_bg
self.batch_dice = batch_dice
self.apply_nonlin = apply_nonlin
self.smooth = smooth
def forward(self, x, y, loss_mask=None):
shp_x = x.shape
if self.batch_dice:
axes = [0] + list(range(2, len(shp_x)))
else:
axes = list(range(2, len(shp_x)))
if self.apply_nonlin is not None:
x = self.apply_nonlin(x)
tp, fp, fn = get_tp_fp_fn(x, y, axes, loss_mask, self.square)
iou = (tp + self.smooth) / (tp + fp + fn + self.smooth)
if not self.do_bg:
if self.batch_dice:
iou = iou[1:]
else:
iou = iou[:, 1:]
iou = iou.type(torch.float64)
iou = iou.mean()
return -iou
def snapshot(model, optimizer, loss, dir_type ,suffix = 1):
"""This fun saves the model and optimizer state dictionaries.
Keep in mind to change the 'map_location' argument in torch.load while loading the model on cpu."""
path = 'checkpoints/{}/'.format(dir_type)
checkpoint = {"model": model,
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict(),
'loss':loss}
torch.save(checkpoint, path + ('epoch_' +str(suffix) +'.pth'))
def load_model(epoch_number, dir_type):
path = 'checkpoints/{}/'.format(dir_type)
checkpoint = torch.load( path + 'epoch_' + str(epoch_number) + '.pth')
model = checkpoint['model']
model.load_state_dict(checkpoint['state_dict'])
optimizer = checkpoint['optimizer']
return model, optimizer
def save_mask(im, filename, dir_type):
'''Save as image from pytorch tensor'''
path = 'outputs/{}/'.format(dir_type)
if im.dtype is not torch.uint8:
im.type(torch.uint8)
im = im.cpu().numpy()
im = np.transpose(im, (1,2,0))
im = Image.fromarray(im)
im.save(path + filename)
def save_image(im, filename, dir_type):
path = 'outputs/{}/'.format(dir_type)
torchvision.utils.save_image(im.squeeze(), path + filename )