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| # The following code is modified from https://github.com/shelhamer/clockwork-fcn | |
| import numpy as np | |
| def get_out_scoremap(net): | |
| return net.blobs['score'].data[0].argmax(axis=0).astype(np.uint8) | |
| def feed_net(net, in_): | |
| """ | |
| Load prepared input into net. | |
| """ | |
| net.blobs['data'].reshape(1, *in_.shape) | |
| net.blobs['data'].data[...] = in_ | |
| def segrun(net, in_): | |
| feed_net(net, in_) | |
| net.forward() | |
| return get_out_scoremap(net) | |
| def fast_hist(a, b, n): | |
| k = np.where((a >= 0) & (a < n))[0] | |
| bc = np.bincount(n * a[k].astype(int) + b[k], minlength=n**2) | |
| if len(bc) != n**2: | |
| # ignore this example if dimension mismatch | |
| return 0 | |
| return bc.reshape(n, n) | |
| def get_scores(hist): | |
| # Mean pixel accuracy | |
| acc = np.diag(hist).sum() / (hist.sum() + 1e-12) | |
| # Per class accuracy | |
| cl_acc = np.diag(hist) / (hist.sum(1) + 1e-12) | |
| # Per class IoU | |
| iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist) + 1e-12) | |
| return acc, np.nanmean(cl_acc), np.nanmean(iu), cl_acc, iu | |