REVA-QCAV/eval.py

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2017-08-19 08:59:51 +00:00
import torch
from myloss import dice_coeff
import numpy as np
from torch.autograd import Variable
from data_vis import plot_img_mask
import matplotlib.pyplot as plt
import torch.nn.functional as F
from crf import dense_crf
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 > 0.6))
Q = dense_crf(((X*255).round()).astype(np.uint8), y_pred)
ax4 = fig.add_subplot(1, 4, 4)
print(Q)
ax4.imshow(Q)
plt.show()
return tot / i