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losses_and_metrics_for_mesh.py
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losses_and_metrics_for_mesh.py
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import torch
import numpy as np
def weighting_DSC(y_pred, y_true, class_weights, smooth = 1.0):
'''
inputs:
y_pred [n_classes, x, y, z] probability
y_true [n_classes, x, y, z] one-hot code
class_weights
smooth = 1.0
'''
smooth = 1.
mdsc = 0.0
n_classes = y_pred.shape[-1]
# convert probability to one-hot code
max_idx = torch.argmax(y_pred, dim=-1, keepdim=True)
one_hot = torch.zeros_like(y_pred)
one_hot.scatter_(-1, max_idx, 1)
for c in range(0, n_classes):
pred_flat = one_hot[:, :, c].reshape(-1)
true_flat = y_true[:, :, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
w = class_weights[c]/class_weights.sum()
mdsc += w*((2. * intersection + smooth) / (pred_flat.sum() + true_flat.sum() + smooth))
return mdsc
def weighting_SEN(y_pred, y_true, class_weights, smooth = 1.0):
'''
inputs:
y_pred [n_classes, x, y, z] probability
y_true [n_classes, x, y, z] one-hot code
class_weights
smooth = 1.0
'''
smooth = 1.
msen = 0.0
n_classes = y_pred.shape[-1]
# convert probability to one-hot code
max_idx = torch.argmax(y_pred, dim=-1, keepdim=True)
one_hot = torch.zeros_like(y_pred)
one_hot.scatter_(-1, max_idx, 1)
for c in range(0, n_classes):
pred_flat = one_hot[:, :, c].reshape(-1)
true_flat = y_true[:, :, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
w = class_weights[c]/class_weights.sum()
msen += w*((intersection + smooth) / (true_flat.sum() + smooth))
return msen
def weighting_PPV(y_pred, y_true, class_weights, smooth = 1.0):
'''
inputs:
y_pred [n_classes, x, y, z] probability
y_true [n_classes, x, y, z] one-hot code
class_weights
smooth = 1.0
'''
smooth = 1.
mppv = 0.0
n_classes = y_pred.shape[-1]
# convert probability to one-hot code
max_idx = torch.argmax(y_pred, dim=-1, keepdim=True)
one_hot = torch.zeros_like(y_pred)
one_hot.scatter_(-1, max_idx, 1)
for c in range(0, n_classes):
pred_flat = one_hot[:, :, c].reshape(-1)
true_flat = y_true[:, :, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
w = class_weights[c]/class_weights.sum()
mppv += w*((intersection + smooth) / (pred_flat.sum() + smooth))
return mppv
def Generalized_Dice_Loss(y_pred, y_true, class_weights, smooth = 1.0):
'''
inputs:
y_pred [n_classes, x, y, z] probability
y_true [n_classes, x, y, z] one-hot code
class_weights
smooth = 1.0
'''
smooth = 1.
loss = 0.
n_classes = y_pred.shape[-1]
for c in range(0, n_classes):
pred_flat = y_pred[:, :, c].reshape(-1)
true_flat = y_true[:, :, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
# with weight
w = class_weights[c]/class_weights.sum()
loss += w*(1 - ((2. * intersection + smooth) /
(pred_flat.sum() + true_flat.sum() + smooth)))
return loss
def DSC(y_pred, y_true, ignore_background=True, smooth = 1.0):
'''
inputs:
y_pred [npts, n_classes] one-hot code
y_true [npts, n_classes] one-hot code
'''
smooth = 1.
n_classes = y_pred.shape[-1]
dsc = []
if ignore_background:
for c in range(1, n_classes): #pass 0 because 0 is background
pred_flat = y_pred[:, c].reshape(-1)
true_flat = y_true[:, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
dsc.append(((2. * intersection + smooth) / (pred_flat.sum() + true_flat.sum() + smooth)))
dsc = np.asarray(dsc)
else:
for c in range(0, n_classes):
pred_flat = y_pred[:, c].reshape(-1)
true_flat = y_true[:, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
dsc.append(((2. * intersection + smooth) / (pred_flat.sum() + true_flat.sum() + smooth)))
dsc = np.asarray(dsc)
return dsc
def SEN(y_pred, y_true, ignore_background=True, smooth = 1.0):
'''
inputs:
y_pred [npts, n_classes] one-hot code
y_true [npts, n_classes] one-hot code
'''
smooth = 1.
n_classes = y_pred.shape[-1]
sen = []
if ignore_background:
for c in range(1, n_classes): #pass 0 because 0 is background
pred_flat = y_pred[:, c].reshape(-1)
true_flat = y_true[:, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
sen.append(((intersection + smooth) / (true_flat.sum() + smooth)))
sen = np.asarray(sen)
else:
for c in range(0, n_classes):
pred_flat = y_pred[:, c].reshape(-1)
true_flat = y_true[:, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
sen.append(((intersection + smooth) / (true_flat.sum() + smooth)))
sen = np.asarray(sen)
return sen
def PPV(y_pred, y_true, ignore_background=True, smooth = 1.0):
'''
inputs:
y_pred [npts, n_classes] one-hot code
y_true [npts, n_classes] one-hot code
'''
smooth = 1.
n_classes = y_pred.shape[-1]
ppv = []
if ignore_background:
for c in range(1, n_classes): #pass 0 because 0 is background
pred_flat = y_pred[:, c].reshape(-1)
true_flat = y_true[:, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
ppv.append(((intersection + smooth) / (pred_flat.sum() + smooth)))
ppv = np.asarray(ppv)
else:
for c in range(0, n_classes):
pred_flat = y_pred[:, c].reshape(-1)
true_flat = y_true[:, c].reshape(-1)
intersection = (pred_flat * true_flat).sum()
ppv.append(((intersection + smooth) / (pred_flat.sum() + smooth)))
ppv = np.asarray(ppv)
return ppv