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update examples/community/face_adversarial_attack/test.py.

Signed-off-by: 君君臣臣君 <mingjun@isrc.iscas.ac.cn>
pull/425/head
君君臣臣君 Gitee 2 years ago
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1 changed files with 7 additions and 8 deletions
  1. +7
    -8
      examples/community/face_adversarial_attack/test.py

+ 7
- 8
examples/community/face_adversarial_attack/test.py View File

@@ -21,21 +21,21 @@ from mindspore.dataset.vision.py_transforms import ToTensor
import mindspore.dataset.vision.py_transforms as P import mindspore.dataset.vision.py_transforms as P
from mindspore.dataset.vision.py_transforms import ToPIL as ToPILImage from mindspore.dataset.vision.py_transforms import ToPIL as ToPILImage
from FaceRecognition.eval import get_net from FaceRecognition.eval import get_net
import AFR
import adversarial_attack


context.set_context(mode=context.GRAPH_MODE, device_target="GPU") context.set_context(mode=context.GRAPH_MODE, device_target="GPU")




imageize = ToPILImage()


if __name__ == '__main__': if __name__ == '__main__':
""" """
The input image, target image and adversarial image are tested using the FaceRecognition model. The input image, target image and adversarial image are tested using the FaceRecognition model.
""" """


image = AFR.load_data('photos/adv_input/')
inputs = AFR.load_data('photos/input/')
targets = AFR.load_data('photos/target/')
image = adversarial_attack.load_data('photos/adv_input/')
inputs = adversarial_attack.load_data('photos/input/')
targets = adversarial_attack.load_data('photos/target/')


tensorize = ToTensor() tensorize = ToTensor()
normalize = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) normalize = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
@@ -45,9 +45,8 @@ if __name__ == '__main__':


resnet = get_net() resnet = get_net()


image = mp.imread("./outputs/adversarial_example.jpg")
adv = Tensor(normalize(tensorize(image)))

adv = Tensor(normalize(tensorize(image[0])))
input_tensor = Tensor(normalize(tensorize(inputs[0]))) input_tensor = Tensor(normalize(tensorize(inputs[0])))
target_tensor = Tensor(normalize(tensorize(targets[0]))) target_tensor = Tensor(normalize(tensorize(targets[0])))




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