generate_image.py

class FilterVisualizer():
    def __init__(self, size=56, upscaling_steps=12, upscaling_factor=1.2):
        self.size, self.upscaling_steps, self.upscaling_factor = size, upscaling_steps, upscaling_factor
        self.model = vgg16(pre=True).cuda().eval()
        set_trainable(self.model, False)

    def visualize(self, layer, filter, lr=0.1, opt_steps=20, blur=None):
        sz = self.size
        img = np.uint8(np.random.uniform(150, 180, (sz, sz, 3)))/255  # generate random image
        activations = SaveFeatures(list(self.model.children())[layer])  # register hook

        for _ in range(self.upscaling_steps):  # scale the image up upscaling_steps times
            train_tfms, val_tfms = tfms_from_model(vgg16, sz)
            img_var = V(val_tfms(img)[None], requires_grad=True)  # convert image to Variable that requires grad
            optimizer = torch.optim.Adam([img_var], lr=lr, weight_decay=1e-6)
            for n in range(opt_steps):  # optimize pixel values for opt_steps times
                optimizer.zero_grad()
                self.model(img_var)
                loss = -activations.features[0, filter].mean()
                loss.backward()
                optimizer.step()
            img = val_tfms.denorm(img_var.data.cpu().numpy()[0].transpose(1,2,0))
            self.output = img
            sz = int(self.upscaling_factor * sz)  # calculate new image size
            img = cv2.resize(img, (sz, sz), interpolation = cv2.INTER_CUBIC)  # scale image up
            if blur is not None: img = cv2.blur(img,(blur,blur))  # blur image to reduce high frequency patterns
        self.save(layer, filter)
        activations.close()
        
    def save(self, layer, filter):
        plt.imsave("layer_"+str(layer)+"_filter_"+str(filter)+".jpg", np.clip(self.output, 0, 1))

Hope to have complete code that can be run directly, including models, demo data and other files that generate_image.py depends on...