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