Use mmcv tensor2imgs (#4010)

mmdet/apis/test.py

@@ -7,9 +7,10 @@ import time
 import mmcv
 import torch
 import torch.distributed as dist
+from mmcv.image import tensor2imgs
 from mmcv.runner import get_dist_info
 
-from mmdet.core import encode_mask_results, tensor2imgs
+from mmdet.core import encode_mask_results
 
 
 def single_gpu_test(model,

mmdet/core/utils/__init__.py

@@ -1,7 +1,4 @@
 from .dist_utils import DistOptimizerHook, allreduce_grads
-from .misc import multi_apply, tensor2imgs, unmap
+from .misc import multi_apply, unmap
 
-__all__ = [
+__all__ = ['allreduce_grads', 'DistOptimizerHook', 'multi_apply', 'unmap']
-    'allreduce_grads', 'DistOptimizerHook', 'tensor2imgs', 'multi_apply',
-    'unmap'
-]

mmdet/core/utils/misc.py

@@ -1,37 +1,9 @@
 from functools import partial
 
-import mmcv
-import numpy as np
 import torch
 from six.moves import map, zip
 
 
-def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
-    """Convert tensor to images.
-
-    Args:
-        tensor (torch.Tensor): Tensor that contains multiple images
-        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
-        std (tuple[float], optional): Standard deviation of images.
-            Defaults to (1, 1, 1).
-        to_rgb (bool, optional): Whether convert the images to RGB format.
-            Defaults to True.
-
-    Returns:
-        list[np.ndarray]: A list that contains multiple images.
-    """
-    num_imgs = tensor.size(0)
-    mean = np.array(mean, dtype=np.float32)
-    std = np.array(std, dtype=np.float32)
-    imgs = []
-    for img_id in range(num_imgs):
-        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
-        img = mmcv.imdenormalize(
-            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
-        imgs.append(np.ascontiguousarray(img))
-    return imgs
-
-
 def multi_apply(func, *args, **kwargs):
     """Apply function to a list of arguments.
 

mmdet/models/detectors/rpn.py

@@ -1,6 +1,7 @@
 import mmcv
+from mmcv.image import tensor2imgs
 
-from mmdet.core import bbox_mapping, tensor2imgs
+from mmdet.core import bbox_mapping
 from ..builder import DETECTORS, build_backbone, build_head, build_neck
 from .base import BaseDetector