How to use the mmcv.imresize function in mmcv

mask_targets = []
    if num_pos > 0:
        proposals_np = pos_proposals.cpu().numpy()
        _, maxh, maxw = gt_masks.shape
        proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw - 1)
        proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh - 1)
        pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
        for i in range(num_pos):
            gt_mask = gt_masks[pos_assigned_gt_inds[i]]
            bbox = proposals_np[i, :].astype(np.int32)
            x1, y1, x2, y2 = bbox
            w = np.maximum(x2 - x1 + 1, 1)
            h = np.maximum(y2 - y1 + 1, 1)
            # mask is uint8 both before and after resizing
            # mask_size (h, w) to (w, h)
            target = mmcv.imresize(gt_mask[y1:y1 + h, x1:x1 + w],
                                   mask_size[::-1])
            mask_targets.append(target)
        mask_targets = torch.from_numpy(np.stack(mask_targets)).float().to(
            pos_proposals.device)
    else:
        mask_targets = pos_proposals.new_zeros((0, ) + mask_size)
    return mask_targets

def _resize_masks(self, results):
        for key in results.get('mask_fields', []):
            if results[key] is None:
                continue
            if self.keep_ratio:
                masks = [
                    mmcv.imrescale(
                        mask, results['scale_factor'], interpolation='nearest')
                    for mask in results[key]
                ]
            else:
                mask_size = (results['img_shape'][1], results['img_shape'][0])
                masks = [
                    mmcv.imresize(mask, mask_size, interpolation='nearest')
                    for mask in results[key]
                ]
            if masks:
                results[key] = np.stack(masks)
            else:
                results[key] = np.empty(
                    (0, ) + results['img_shape'], dtype=np.uint8)

scale_factor = 1.0

        for i in range(bboxes.shape[0]):
            if not isinstance(scale_factor, (float, np.ndarray)):
                scale_factor = scale_factor.cpu().numpy()
            bbox = (bboxes[i, :] / scale_factor).astype(np.int32)
            label = labels[i]
            w = max(bbox[2] - bbox[0] + 1, 1)
            h = max(bbox[3] - bbox[1] + 1, 1)

            if not self.class_agnostic:
                mask_pred_ = mask_pred[i, label, :, :]
            else:
                mask_pred_ = mask_pred[i, 0, :, :]

            bbox_mask = mmcv.imresize(mask_pred_, (w, h))
            bbox_mask = (bbox_mask > rcnn_test_cfg.mask_thr_binary).astype(
                np.uint8)

            if rcnn_test_cfg.get('crop_mask', False):
                im_mask = bbox_mask
            else:
                im_mask = np.zeros((img_h, img_w), dtype=np.uint8)
                im_mask[bbox[1]:bbox[1] + h, bbox[0]:bbox[0] + w] = bbox_mask

            if rcnn_test_cfg.get('rle_mask_encode', True):
                rle = mask_util.encode(
                    np.array(im_mask[:, :, np.newaxis], order='F'))[0]
                cls_segms[label - 1].append(rle)
            else:
                cls_segms[label - 1].append(im_mask)

def __call__(self, img, scale, flip=False, keep_ratio=True):
        if keep_ratio:
            img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
        else:
            img, w_scale, h_scale = mmcv.imresize(
                img, scale, return_scale=True)
            scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
                                    dtype=np.float32)
        img_shape = img.shape
        img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
        if flip:
            img = mmcv.imflip(img)
        if self.size_divisor is not None:
            img = mmcv.impad_to_multiple(img, self.size_divisor)
            pad_shape = img.shape
        else:
            pad_shape = img_shape
        img = img.transpose(2, 0, 1)
        return img, img_shape, pad_shape, scale_factor

def __call__(self, img, scale, flip=False, keep_ratio=True):
        if keep_ratio:
            img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
        else:
            img, w_scale, h_scale = mmcv.imresize(
                img, scale, return_scale=True)
            scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
                                    dtype=np.float32)
        img_shape = img.shape
        img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
        if flip:
            img = mmcv.imflip(img)
        if self.size_divisor is not None:
            img = mmcv.impad_to_multiple(img, self.size_divisor)
            pad_shape = img.shape
        else:
            pad_shape = img_shape
        img = img.transpose(2, 0, 1)
        return img, img_shape, pad_shape, scale_factor

"""
    mask_size = cfg.mask_size
    num_pos = pos_proposals.size(0)
    mask_targets = []
    if num_pos > 0:
        proposals_np = pos_proposals.cpu().numpy()
        pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
        for i in range(num_pos):
            gt_mask = gt_masks[pos_assigned_gt_inds[i]]    # gt_mask原本为整张图大小的数组(1216,800), 包含1和0，其中1代表有物体，0代表没物体
                                                           # 这里提取出第i个gt_mask作为target
            bbox = proposals_np[i, :].astype(np.int32)
            x1, y1, x2, y2 = bbox
            w = np.maximum(x2 - x1 + 1, 1)
            h = np.maximum(y2 - y1 + 1, 1)
            # mask is uint8 both before and after resizing
            target = mmcv.imresize(gt_mask[y1:y1 + h, x1:x1 + w],   # 取出gt_mask中proposal大小的一块，然后缩放到目标特征大小(28,28)
                                   (mask_size, mask_size))
            mask_targets.append(target)
        mask_targets = torch.from_numpy(np.stack(mask_targets)).float().to(
            pos_proposals.device)
    else:
        mask_targets = pos_proposals.new_zeros((0, mask_size, mask_size))
    return mask_targets

# aspect ratio unchanged
        if isinstance(scale_factor, float):
            masks = [
                mmcv.imrescale(mask, scale_factor, interpolation='nearest')
                for mask in masks
            ]
        # aspect ratio changed
        else:
            w_ratio, h_ratio = scale_factor[:2]
            if masks:
                h, w = masks[0].shape[:2]
                new_h = int(np.round(h * h_ratio))
                new_w = int(np.round(w * w_ratio))
                new_size = (new_w, new_h)
                masks = [
                    mmcv.imresize(mask, new_size, interpolation='nearest')
                    for mask in masks
                ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks

def _resize_seg(self, results):
        for key in results.get('seg_fields', []):
            if self.keep_ratio:
                gt_seg = mmcv.imrescale(
                    results[key], results['scale'], interpolation='nearest')
            else:
                gt_seg = mmcv.imresize(
                    results[key], results['scale'], interpolation='nearest')
            results['gt_semantic_seg'] = gt_seg

for i in range(bboxes.shape[0]):
            if not isinstance(scale_factor, (float, np.ndarray)):
                scale_factor = scale_factor.cpu().numpy()
            bbox = (bboxes[i, :] / scale_factor).astype(np.int32)
            label = labels[i]
            w = max(bbox[2] - bbox[0] + 1, 1)
            h = max(bbox[3] - bbox[1] + 1, 1)

            if not self.class_agnostic:
                mask_pred_ = mask_pred[i, label, :, :]
            else:
                mask_pred_ = mask_pred[i, 0, :, :]
            im_mask = np.zeros((img_h, img_w), dtype=np.uint8)

            bbox_mask = mmcv.imresize(mask_pred_, (w, h))
            bbox_mask = (bbox_mask > rcnn_test_cfg.mask_thr_binary).astype(
                np.uint8)
            im_mask[bbox[1]:bbox[1] + h, bbox[0]:bbox[0] + w] = bbox_mask
            rle = mask_util.encode(
                np.array(im_mask[:, :, np.newaxis], order='F'))[0]
            cls_segms[label - 1].append(rle)

        return cls_segms

img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32)
            scale_factor = 1.0

        for i in range(bboxes.shape[0]):
            bbox = (bboxes[i, :] / scale_factor).astype(np.int32)
            label = labels[i]
            w = max(bbox[2] - bbox[0] + 1, 1)
            h = max(bbox[3] - bbox[1] + 1, 1)

            if not self.class_agnostic:
                mask_pred_ = mask_pred[i, label, :, :]
            else:
                mask_pred_ = mask_pred[i, 0, :, :]
            im_mask = np.zeros((img_h, img_w), dtype=np.uint8)

            bbox_mask = mmcv.imresize(mask_pred_, (w, h))
            bbox_mask = (bbox_mask > rcnn_test_cfg.mask_thr_binary).astype(
                np.uint8)
            im_mask[bbox[1]:bbox[1] + h, bbox[0]:bbox[0] + w] = bbox_mask
            rle = mask_util.encode(
                np.array(im_mask[:, :, np.newaxis], order='F'))[0]
            cls_segms[label - 1].append(rle)

        return cls_segms