Python rpn.get_rpn_batch方法代碼示例

# 需要導入模塊: from rpn import rpn [as 別名]
# 或者: from rpn.rpn import get_rpn_batch [as 別名]
def parfetch(self, iroidb):
        # get testing data for multigpu
        data, label = get_rpn_batch(iroidb, self.cfg)
        data_shape = {k: v.shape for k, v in data.items()}
        del data_shape['im_info']
        _, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
        feat_shape = [int(i) for i in feat_shape[0]]

        # add gt_boxes to data for e2e
        data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]

        # assign anchor for label
        label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
                              self.feat_stride, self.anchor_scales,
                              self.anchor_ratios, self.allowed_border)
        return {'data': data, 'label': label} 

# 需要導入模塊: from rpn import rpn [as 別名]
# 或者: from rpn.rpn import get_rpn_batch [as 別名]
def parfetch(self, iroidb):
        # get testing data for multigpu
        data, label = get_rpn_batch(iroidb, self.cfg)
        data_shape = {k: v.shape for k, v in data.items()}
        del data_shape['im_info']
        _, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
        feat_shape = [int(i) for i in feat_shape[0]]

        # add gt_boxes to data for e2e
        data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]

        # assign anchor for label
        label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
                              self.feat_stride, self.anchor_scales,
                              self.anchor_ratios, self.allowed_border,
                              self.normalize_target, self.bbox_mean, self.bbox_std)
        return {'data': data, 'label': label} 

# 需要導入模塊: from rpn import rpn [as 別名]
# 或者: from rpn.rpn import get_rpn_batch [as 別名]
def parfetch(self, iroidb):
        # get testing data for multigpu
        data, label = get_rpn_batch(iroidb, self.cfg)
        data_shape = {k: v.shape for k, v in data.items()}
        del data_shape['im_info']
        _, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
        feat_shape = [int(i) for i in feat_shape[0]]

        # add gt_boxes to data for e2e
        data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]

        # assign anchor for label
        label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
                              self.feat_stride, self.anchor_scales,
                              self.anchor_ratios, self.allowed_border)
        return {'data': data, 'label': label}

# TODO test this dataloader for quadrangle 

# 需要導入模塊: from rpn import rpn [as 別名]
# 或者: from rpn.rpn import get_rpn_batch [as 別名]
def par_assign_anchor_wrapper(cfg, iroidb, feat_sym, feat_strides, anchor_scales, anchor_ratios, allowed_border):
    # get testing data for multigpu
    data, rpn_label = get_rpn_batch(iroidb, cfg)
    data_shape = {k: v.shape for k, v in data.items()}
    del data_shape['im_info']

    # add gt_boxes to data for e2e
    data['gt_boxes'] = rpn_label['gt_boxes'][np.newaxis, :, :]

    feat_shape = [y[1] for y in [x.infer_shape(**data_shape) for x in feat_sym]]
    label = assign_pyramid_anchor(feat_shape, rpn_label['gt_boxes'], data['im_info'], cfg,
                                  feat_strides, anchor_scales, anchor_ratios, allowed_border)
    return {'data': data, 'label': label} 

# 需要導入模塊: from rpn import rpn [as 別名]
# 或者: from rpn.rpn import get_rpn_batch [as 別名]
def get_batch(self):
        # slice roidb
        cur_from = self.cur
        cur_to = min(cur_from + self.batch_size, self.size)
        roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]

        # decide multi device slice
        work_load_list = self.work_load_list
        ctx = self.ctx
        if work_load_list is None:
            work_load_list = [1] * len(ctx)
        assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
            "Invalid settings for work load. "
        slices = _split_input_slice(self.batch_size, work_load_list)

        # get testing data for multigpu
        data_list = []
        label_list = []
        for islice in slices:
            iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
            data, label = get_rpn_batch(iroidb, self.cfg)
            data_list.append(data)
            label_list.append(label)

        # pad data first and then assign anchor (read label)
        data_tensor = tensor_vstack([batch['data'] for batch in data_list])
        for data, data_pad in zip(data_list, data_tensor):
            data['data'] = data_pad[np.newaxis, :]

        new_label_list = []
        for data, label in zip(data_list, label_list):
            # infer label shape
            data_shape = {k: v.shape for k, v in data.items()}
            del data_shape['im_info']
            _, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
            feat_shape = [int(i) for i in feat_shape[0]]

            # add gt_boxes to data for e2e
            data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]

            # assign anchor for label
            label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
                                  self.feat_stride, self.anchor_scales,
                                  self.anchor_ratios, self.allowed_border)
            new_label_list.append(label)

        all_data = dict()
        for key in self.data_name:
            all_data[key] = tensor_vstack([batch[key] for batch in data_list])

        all_label = dict()
        for key in self.label_name:
            pad = -1 if key == 'label' else 0
            all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)

        self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
        self.label = [mx.nd.array(all_label[key]) for key in self.label_name] 

# 需要導入模塊: from rpn import rpn [as 別名]
# 或者: from rpn.rpn import get_rpn_batch [as 別名]
def get_batch(self):
        # slice roidb
        cur_from = self.cur
        cur_to = min(cur_from + self.batch_size, self.size)
        roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]

        # decide multi device slice
        work_load_list = self.work_load_list
        ctx = self.ctx
        if work_load_list is None:
            work_load_list = [1] * len(ctx)
        assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
            "Invalid settings for work load. "
        slices = _split_input_slice(self.batch_size, work_load_list)

        # get testing data for multigpu
        data_list = []
        label_list = []
        for islice in slices:
            iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
            data, label = get_rpn_batch(iroidb, self.cfg)
            data_list.append(data)
            label_list.append(label)

        # pad data first and then assign anchor (read label)
        data_tensor = tensor_vstack([batch['data'] for batch in data_list])
        for data, data_pad in zip(data_list, data_tensor):
            data['data'] = data_pad[np.newaxis, :]

        new_label_list = []
        for data, label in zip(data_list, label_list):
            # infer label shape
            data_shape = {k: v.shape for k, v in data.items()}
            del data_shape['im_info']
            _, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
            feat_shape = [int(i) for i in feat_shape[0]]

            # add gt_boxes to data for e2e
            data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]

            # assign anchor for label
            label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
                                  self.feat_stride, self.anchor_scales,
                                  self.anchor_ratios, self.allowed_border,
                                  self.normalize_target, self.bbox_mean, self.bbox_std)
            new_label_list.append(label)

        all_data = dict()
        for key in self.data_name:
            all_data[key] = tensor_vstack([batch[key] for batch in data_list])

        all_label = dict()
        for key in self.label_name:
            pad = -1 if key == 'label' else 0
            all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)

        self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
        self.label = [mx.nd.array(all_label[key]) for key in self.label_name]