def data_unit(net, file_name): n, c, h, w = net.blobs["data"].data.shape plt.subplot(131) plt.title("Original Image") plt.axis("off") vu.visualize_one_channel_images(net.blobs["data"].data.reshape(n, h, w)) plt.subplot(132) plt.title("ST Output") plt.axis("off") vu.visualize_one_channel_images(net.blobs["st_output"].data.reshape(n, h, w)) plt.subplot(133) plt.axis("off") plt.title("Correctness") acc = np.zeros((n, h, w, 3)) gt_label = net.blobs["label"].data est_label = np.argmax(net.blobs["class"].data, axis=1) err = est_label <> gt_label ind = np.array(range(n))[err] for i in ind: acc[i] = np.ones((h, w, 3)) plt.imshow(vu.vis_grid(acc)) plt.gca().axis("off") plt.savefig(file_name + ".jpg", dpi=1000) plt.close()
def data_unit(net, file_name): n, c, h, w = net.blobs['data'].data.shape f = open(file_name+'.txt', 'w') plt.subplot(131) vu.visualize_one_channel_images(net.blobs['data'].data.reshape(n, h, w)) plt.subplot(132) vu.visualize_one_channel_images(net.blobs['st_output'].data.reshape(n, h, w)) plt.subplot(133) acc = np.zeros((n, h, w, 3)) gt_label = net.blobs['label'].data est_label = np.argmax(net.blobs['class'].data, axis=1) err = (est_label <> gt_label) ind = np.array(range(n))[err] for i in ind: x = i/ceil(sqrt(n)) y = i%ceil(sqrt(n)) f.write('Digit at (%d, %d) should be %d, but is classified as %d\n'%(x, y, gt_label[i], est_label[i])) acc[i] = np.ones((h, w, 3)) plt.imshow(vu.vis_grid(acc)) plt.gca().axis('off') plt.savefig(file_name+'.jpg', dpi = 100) plt.close()
def data_unit(net, file_name): n, c, h, w = net.blobs['data'].data.shape plt.subplot(131) plt.title('Original Image') plt.axis('off') vu.visualize_one_channel_images(net.blobs['data'].data.reshape(n, h, w)) plt.subplot(132) plt.title('ST Output') plt.axis('off') vu.visualize_one_channel_images(net.blobs['st_output'].data.reshape(n, h, w)) plt.subplot(133) plt.axis('off') plt.title('Correctness') acc = np.zeros((n, h, w, 3)) gt_label = net.blobs['label'].data est_label = np.argmax(net.blobs['class'].data, axis=1) err = (est_label <> gt_label) ind = np.array(range(n))[err] for i in ind: acc[i] = np.ones((h, w, 3)) plt.imshow(vu.vis_grid(acc)) plt.gca().axis('off') plt.savefig(file_name+'.jpg', dpi = 1000) plt.close()