example/rcnn/rcnn/core/loader.py [121:180]:
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        self.label_name = ['label', 'bbox_target', 'bbox_weight']

        # status variable for synchronization between get_data and get_label
        self.cur = 0
        self.batch = None
        self.data = None
        self.label = None

        # get first batch to fill in provide_data and provide_label
        self.reset()
        self.get_batch()

    @property
    def provide_data(self):
        return [(k, v.shape) for k, v in zip(self.data_name, self.data)]

    @property
    def provide_label(self):
        return [(k, v.shape) for k, v in zip(self.label_name, self.label)]

    def reset(self):
        self.cur = 0
        if self.shuffle:
            if self.aspect_grouping:
                widths = np.array([r['width'] for r in self.roidb])
                heights = np.array([r['height'] for r in self.roidb])
                horz = (widths >= heights)
                vert = np.logical_not(horz)
                horz_inds = np.where(horz)[0]
                vert_inds = np.where(vert)[0]
                inds = np.hstack((np.random.permutation(horz_inds), np.random.permutation(vert_inds)))
                extra = inds.shape[0] % self.batch_size
                inds_ = np.reshape(inds[:-extra], (-1, self.batch_size))
                row_perm = np.random.permutation(np.arange(inds_.shape[0]))
                inds[:-extra] = np.reshape(inds_[row_perm, :], (-1,))
                self.index = inds
            else:
                np.random.shuffle(self.index)

    def iter_next(self):
        return self.cur + self.batch_size <= self.size

    def next(self):
        if self.iter_next():
            self.get_batch()
            self.cur += self.batch_size
            return mx.io.DataBatch(data=self.data, label=self.label,
                                   pad=self.getpad(), index=self.getindex(),
                                   provide_data=self.provide_data, provide_label=self.provide_label)
        else:
            raise StopIteration

    def getindex(self):
        return self.cur / self.batch_size

    def getpad(self):
        if self.cur + self.batch_size > self.size:
            return self.cur + self.batch_size - self.size
        else:
            return 0
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example/rcnn/rcnn/core/loader.py [259:318]:
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        self.label_name = ['label', 'bbox_target', 'bbox_weight']

        # status variable for synchronization between get_data and get_label
        self.cur = 0
        self.batch = None
        self.data = None
        self.label = None

        # get first batch to fill in provide_data and provide_label
        self.reset()
        self.get_batch()

    @property
    def provide_data(self):
        return [(k, v.shape) for k, v in zip(self.data_name, self.data)]

    @property
    def provide_label(self):
        return [(k, v.shape) for k, v in zip(self.label_name, self.label)]

    def reset(self):
        self.cur = 0
        if self.shuffle:
            if self.aspect_grouping:
                widths = np.array([r['width'] for r in self.roidb])
                heights = np.array([r['height'] for r in self.roidb])
                horz = (widths >= heights)
                vert = np.logical_not(horz)
                horz_inds = np.where(horz)[0]
                vert_inds = np.where(vert)[0]
                inds = np.hstack((np.random.permutation(horz_inds), np.random.permutation(vert_inds)))
                extra = inds.shape[0] % self.batch_size
                inds_ = np.reshape(inds[:-extra], (-1, self.batch_size))
                row_perm = np.random.permutation(np.arange(inds_.shape[0]))
                inds[:-extra] = np.reshape(inds_[row_perm, :], (-1,))
                self.index = inds
            else:
                np.random.shuffle(self.index)

    def iter_next(self):
        return self.cur + self.batch_size <= self.size

    def next(self):
        if self.iter_next():
            self.get_batch()
            self.cur += self.batch_size
            return mx.io.DataBatch(data=self.data, label=self.label,
                                   pad=self.getpad(), index=self.getindex(),
                                   provide_data=self.provide_data, provide_label=self.provide_label)
        else:
            raise StopIteration

    def getindex(self):
        return self.cur / self.batch_size

    def getpad(self):
        if self.cur + self.batch_size > self.size:
            return self.cur + self.batch_size - self.size
        else:
            return 0
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