Laurent/REVA-QCAV
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Move the sigmoid activation to the model itself

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Former-commit-id: e3f8ca7b1ac7c5e9694637a81be260e9b48973b9
This commit is contained in:
rht 2018-11-10 22:42:16 +00:00
parent 46d1db3115
commit 81234be7da
4 changed files with 7 additions and 7 deletions
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2
eval.py
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@ -20,7 +20,7 @@ def eval_net(net, dataset, gpu=False):
true_mask = true_mask.cuda() true_mask = true_mask.cuda()
mask_pred = net(img)[0] mask_pred = net(img)[0]
mask_pred = (F.sigmoid(mask_pred) > 0.5).float() mask_pred = (mask_pred > 0.5).float()
tot += dice_coeff(mask_pred, true_mask).item() tot += dice_coeff(mask_pred, true_mask).item()
return tot / i return tot / i

4
predict.py
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@ -43,8 +43,8 @@ def predict_img(net,
output_left = net(X_left) output_left = net(X_left)
output_right = net(X_right) output_right = net(X_right)
left_probs = F.sigmoid(output_left).squeeze(0) left_probs = output_left.squeeze(0)
right_probs = F.sigmoid(output_right).squeeze(0) right_probs = output_right.squeeze(0)
tf = transforms.Compose( tf = transforms.Compose(
[ [

4
train.py
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@ -6,7 +6,6 @@ import numpy as np
import torch import torch
import torch.backends.cudnn as cudnn import torch.backends.cudnn as cudnn
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F
from torch import optim from torch import optim
from eval import eval_net from eval import eval_net
@ -74,8 +73,7 @@ def train_net(net,
true_masks = true_masks.cuda() true_masks = true_masks.cuda()
masks_pred = net(imgs) masks_pred = net(imgs)
masks_probs = F.sigmoid(masks_pred) masks_probs_flat = masks_pred.view(-1)
masks_probs_flat = masks_probs.view(-1)
true_masks_flat = true_masks.view(-1) true_masks_flat = true_masks.view(-1)

4
unet/unet_model.py
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@ -1,5 +1,7 @@
# full assembly of the sub-parts to form the complete net # full assembly of the sub-parts to form the complete net
import torch.nn.functional as F
from .unet_parts import * from .unet_parts import *
class UNet(nn.Module): class UNet(nn.Module):
@ -27,4 +29,4 @@ class UNet(nn.Module):
x = self.up3(x, x2) x = self.up3(x, x2)
x = self.up4(x, x1) x = self.up4(x, x1)
x = self.outc(x) x = self.outc(x)
return x return F.sigmoid(x)