def draw_iqa(img, q, target_a, pred_a):
fig, ax = tfplot.subplots(figsize=(6, 6))
ax.imshow(img)
ax.set_title(question2str(q))
ax.set_xlabel(
answer2str(target_a) + answer2str(pred_a, 'Predicted'))
return fig
def draw_iqa(img, q, target_a, pred_a, weights):
d = self.d
H, W = img.shape[:2]
weights = weights.reshape(d*d, d*d)
weights_a2b = np.mean(weights, axis=1).reshape(4,4)
weights_b2a = np.mean(np.transpose(weights), axis=1).reshape(4,4)
mean_w = (weights_a2b + weights_b2a) / 2
mean_w = mean_w / np.max(mean_w)
# print(mean_w.shape, img.shape)
# print("===========")
fig, ax = tfplot.subplots(figsize=(6, 6))
ax.imshow(img, extent=[0,H,0,W])
mid = ax.imshow(mean_w, cmap='jet',
alpha=0.5, extent=[0, H, 0, W])
fig.colorbar(mid)
ax.set_title(question2str(q))
ax.set_xlabel(answer2str(target_a)+answer2str(pred_a, 'Predicted'))
return fig
