from __future__ import absolute_import import numpy as np import paddle.v2 as paddle import paddle.v2.fluid as fluid from paddle.v2.fluid.framework import program_guard from .base import Model class PaddleModel(Model): """ Create a PaddleModel instance. When you need to generate a adversarial sample, you should construct an instance of PaddleModel. Args: program(paddle.v2.fluid.framework.Program): The program of the model which generate the adversarial sample. input_name(string): The name of the input. logits_name(string): The name of the logits. predict_name(string): The name of the predict. cost_name(string): The name of the loss in the program. """ def __init__(self, program, input_name, logits_name, predict_name, cost_name, bounds, channel_axis=3, preprocess=None): super(PaddleModel, self).__init__( bounds=bounds, channel_axis=channel_axis, preprocess=preprocess) if preprocess is None: preprocess = (0, 1) self._program = program self._place = fluid.CPUPlace() self._exe = fluid.Executor(self._place) self._input_name = input_name self._logits_name = logits_name self._predict_name = predict_name self._cost_name = cost_name # gradient loss = self._program.block(0).var(self._cost_name) param_grads = fluid.backward.append_backward( loss, parameter_list=[self._input_name]) self._gradient = dict(param_grads)[self._input_name] def predict(self, image_batch): """ Predict the label of the image_batch. Args: image_batch(list): The image and label tuple list. Return: numpy.ndarray: predictions of the images with shape (batch_size, num_of_classes). """ feeder = fluid.DataFeeder( feed_list=[self._input_name, self._logits_name], place=self._place, program=self._program) predict_var = self._program.block(0).var(self._predict_name) predict = self._exe.run(self._program, feed=feeder.feed(image_batch), fetch_list=[predict_var]) return predict def num_classes(self): """ Calculate the number of classes of the output label. Return: int: the number of classes """ predict_var = self._program.block(0).var(self._predict_name) assert len(predict_var.shape) == 2 return predict_var.shape[1] def gradient(self, image_batch): """ Calculate the gradient of the loss w.r.t the input. Args: image_batch(list): The image and label tuple list. Return: list: The list of the gradient of the image. """ feeder = fluid.DataFeeder( feed_list=[self._input_name, self._logits_name], place=self._place, program=self._program) grad, = self._exe.run(self._program, feed=feeder.feed(image_batch), fetch_list=[self._gradient]) return grad