def testMultiExtremalPerturbationWithSmoothMask():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
text = "How I want to say hello to Asian people"
model = MMBTGridHMInterfaceOnlyImage(
MMBT.from_pretrained("mmbt.hateful_memes.images"), text)
model = model.to(device)
image_path = "https://img.17qq.com/images/ghhngkfnkwy.jpeg"
image_tensor = model.imageToTensor(image_path)
# if device has some error just comment it
image_tensor = image_tensor.to(device)
_out, out, = multi_extremal_perturbation(model,
torch.unsqueeze(image_tensor, 0),
image_path,
text,
0,
reward_func=contrastive_reward,
debug=True,
max_iter=200,
areas=[0.12],
smooth=0.5,
show_text_result=True)
def main():
image_path = input("enter your image path : ")
text = input("enter your text : ")
model = MMBT.from_pretrained("mmbt.hateful_memes.images")
model.to(torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
image_tensor = image2tensor(image_path)
mask_, hist_, output_tensor, txt_summary, text_explaination = multi_extremal_perturbation(
model,
image_tensor,
image_path,
text,
0, # 0 non hateful 1 hateful
max_iter=50,
areas=[0.12],
)
return output_tensor, txt_summary, text_explaination