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()
