mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-09-19 11:35:28 +00:00
56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import torch
|
|
from myloss import dice_coeff
|
|
import numpy as np
|
|
from torch.autograd import Variable
|
|
import matplotlib.pyplot as plt
|
|
import torch.nn.functional as F
|
|
|
|
from utils import dense_crf, plot_img_mask
|
|
|
|
|
|
def eval_net(net, dataset, gpu=False):
|
|
tot = 0
|
|
for i, b in enumerate(dataset):
|
|
X = b[0]
|
|
y = b[1]
|
|
|
|
X = torch.FloatTensor(X).unsqueeze(0)
|
|
y = torch.ByteTensor(y).unsqueeze(0)
|
|
|
|
if gpu:
|
|
X = Variable(X, volatile=True).cuda()
|
|
y = Variable(y, volatile=True).cuda()
|
|
else:
|
|
X = Variable(X, volatile=True)
|
|
y = Variable(y, volatile=True)
|
|
|
|
y_pred = net(X)
|
|
|
|
y_pred = (F.sigmoid(y_pred) > 0.6).float()
|
|
# y_pred = F.sigmoid(y_pred).float()
|
|
|
|
dice = dice_coeff(y_pred, y.float()).data[0]
|
|
tot += dice
|
|
|
|
if 0:
|
|
X = X.data.squeeze(0).cpu().numpy()
|
|
X = np.transpose(X, axes=[1, 2, 0])
|
|
y = y.data.squeeze(0).cpu().numpy()
|
|
y_pred = y_pred.data.squeeze(0).squeeze(0).cpu().numpy()
|
|
print(y_pred.shape)
|
|
|
|
fig = plt.figure()
|
|
ax1 = fig.add_subplot(1, 4, 1)
|
|
ax1.imshow(X)
|
|
ax2 = fig.add_subplot(1, 4, 2)
|
|
ax2.imshow(y)
|
|
ax3 = fig.add_subplot(1, 4, 3)
|
|
ax3.imshow((y_pred > 0.5))
|
|
|
|
Q = dense_crf(((X*255).round()).astype(np.uint8), y_pred)
|
|
ax4 = fig.add_subplot(1, 4, 4)
|
|
print(Q)
|
|
ax4.imshow(Q > 0.5)
|
|
plt.show()
|
|
return tot / i
|