utils_dataRV.py 文件源码

def do_training(solver, step_size, nb_step=0):
        solver.step(step_size)

        heat_map = solver.test_nets[0].blobs["score-final"].data[0,:,:,:].transpose(1,2,0)
        heat_map_normalize = normalize_heatmap(heat_map)
#         heat_map_normalize = heat_map
        minimum = np.min(heat_map[:,:,0])

        nb_subplot = 4
        plt.figure(figsize=(10,10))
        image_test = solver.test_nets[0].blobs["data"].data[0,0,:,:]
        image_test_label = solver.test_nets[0].blobs["label"].data[0,0,:,:]
        plt.subplot(1,nb_subplot,1)
        plt.imshow(image_test)
        plt.title("image test")
        plt.subplot(1,nb_subplot,2)
        plt.imshow(image_test_label)
        plt.title("Label of the test image")
        plt.subplot(1,nb_subplot,3)
        plt.imshow(np.append(heat_map_normalize, np.zeros((heat_map_normalize.shape[0], heat_map_normalize.shape[1],1)), 2))
        plt.title("Heat map")
        # plt.subplot(1,nb_subplot,4)
        # plt.imshow(np.append(heat_map_normalize, np.zeros(heat_map_normalize.shape[0], heat_map_normalize.shape[1],1), 3))
        # plt.title("score")
        plt.subplot(1,nb_subplot,4)
        plt.imshow(solver.test_nets[0].blobs["score-final"].data[0,:,:,:].transpose(1,2,0).argmax(2), vmin=0, vmax=1)
        plt.title("After : " + str(nb_step+step_size) + " itterations")
        display.display(plt.gcf())
        display.clear_output(wait=True)
        time.sleep(1)

###
    # save_image : place where to save the image
###