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eval_util.py
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import torch
import torch.nn.functional as F
import numpy as np
from dice_loss import dice_coeff
def eval_dice(net, dataset, gpu=False):
"""Evaluation without the densecrf with the dice coefficient"""
tot = 0
for i, b in enumerate(dataset):
img = b[0]
true_mask = b[1]
img = torch.from_numpy(img).unsqueeze(0)
true_mask = torch.from_numpy(true_mask).unsqueeze(0)
if gpu:
img = img.cuda()
true_mask = true_mask.cuda()
mask_pred = net(img)[0]
mask_pred = (mask_pred > 0.5).float()
tot += dice_coeff(mask_pred, true_mask).item()
return tot / (i + 1)
def eval_loss(net, criterion, dataset, gpu=False):
tot = 0
for i, b in enumerate(dataset):
img = b[0]
true_mask = b[1]
img = torch.from_numpy(img).unsqueeze(0)
true_mask = torch.from_numpy(true_mask).unsqueeze(0)
if gpu:
img = img.cuda()
true_mask = true_mask.cuda()
masks_pred = net(img)
masks_probs_flat = masks_pred.view(-1)
true_masks_flat = true_mask.view(-1)
loss = criterion(masks_probs_flat, true_masks_flat)
tot += loss.item()
return tot / (i + 1)
def eval_roi(f0, f1, th0 = 0, th1 = 0.5):
'''
f0 is the denominator
'''
f0m = f0.copy()
f1m = f1.copy()
f0m[f0m<=th0] = 0
f0m[f0m>th0] = 1
f1m[f1m<=th1] = 0
f1m[f1m>th1] = 1
num = 0
den = 0
for ich in range(0, f0m.shape[1]):
start = 0
end = 0
for it in range(0, f0m.shape[0]):
if f0m[it,ich] <= 0:
if start < end:
# print(ich, ', ', start, ', ', end, ' : ', np.count_nonzero(f1m[start:end,ich]))
den = den + 1
if np.count_nonzero(f1m[start:end,ich]) > 0:
num = num + 1
start = it
else:
end = it
if den <= 0:
return 0
print("eval_roi: ", num, "/", den, " = ", (num)/den*100, "%")
# return [num, den]
return num/den
def eval_pixel(f0, f1, th0 = 0, th1 = 0.5):
'''
f0 is the denominator
'''
f0m = f0.copy()
f1m = f1.copy()
f0m[f0m<=th0] = 0
f0m[f0m>th0] = 1
f1m[f1m<=th1] = 0
f1m[f1m>th1] = 1
num = np.count_nonzero(np.logical_and(f0m, f1m))
den = np.count_nonzero(f0m)
if den <= 0:
return 0
print("eval_pixel: ", num, "/", den, " = ", (num)/den*100, "%")
# return [num, den]
return num/den
def eval_eff_pur(net, dataset, th=0.5, gpu=False):
eff_pix = 0
pur_pix = 0
eff_roi = 0
pur_roi = 0
for i, b in enumerate(dataset):
img = b[0]
mask_true = b[1]
img = torch.from_numpy(img).unsqueeze(0)
if gpu:
img = img.cuda()
with torch.no_grad():
mask_pred = net(img).squeeze().cpu().numpy()
mask_true = np.transpose(mask_true, [1, 0])
mask_pred = np.transpose(mask_pred, [1, 0])
eff_pix = eff_pix + eval_pixel(mask_true, mask_pred, 0.5, th)
pur_pix = pur_pix + eval_pixel(mask_pred, mask_true, th, 0.5)
eff_roi = eff_roi + eval_roi(mask_true, mask_pred, 0.5, th)
pur_roi = pur_roi + eval_roi(mask_pred, mask_true, th, 0.5)
eff_pix = eff_pix/(i+1)
pur_pix = pur_pix/(i+1)
eff_roi = eff_roi/(i+1)
pur_roi = pur_roi/(i+1)
print('eff_pix: ', eff_pix)
print('pur_pix: ', pur_pix)
print('eff_roi: ', eff_roi)
print('pur_roi: ', pur_roi)
return [eff_pix, pur_pix, eff_roi, pur_roi]