本文整理汇总了Python中mmdet.core.tensor2imgs方法的典型用法代码示例。如果您正苦于以下问题：Python core.tensor2imgs方法的具体用法？Python core.tensor2imgs怎么用？Python core.tensor2imgs使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmdet.core的用法示例。
在下文中一共展示了core.tensor2imgs方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: forward_train
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def forward_train(self,
img,
img_metas,
gt_bboxes=None,
gt_bboxes_ignore=None):
"""
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
img_metas (list[dict]): A List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
:class:`mmdet.datasets.pipelines.Collect`.
gt_bboxes (list[Tensor]): Each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
if self.train_cfg.rpn.get('debug', False):
self.rpn_head.debug_imgs = tensor2imgs(img)
x = self.extract_feat(img)
losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,
gt_bboxes_ignore)
return losses 示例2: show_result
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def show_result(self, data, result, dataset=None, top_k=20):
"""Show RPN proposals on the image.
Although we assume batch size is 1, this method supports arbitrary
batch size.
"""
img_tensor = data['img'][0]
img_metas = data['img_metas'][0].data[0]
imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
assert len(imgs) == len(img_metas)
for img, img_meta in zip(imgs, img_metas):
h, w, _ = img_meta['img_shape']
img_show = img[:h, :w, :]
mmcv.imshow_bboxes(img_show, result, top_k=top_k) 示例3: forward_train
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def forward_train(self,
img,
img_meta,
gt_bboxes=None,
gt_bboxes_ignore=None):
if self.train_cfg.rpn.get('debug', False):
self.rpn_head.debug_imgs = tensor2imgs(img)
x = self.extract_feat(img)
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta, self.train_cfg.rpn)
losses = self.rpn_head.loss(
*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses 示例4: show_result
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def show_result(self, data, result, img_norm_cfg, dataset=None, top_k=20):
"""Show RPN proposals on the image.
Although we assume batch size is 1, this method supports arbitrary
batch size.
"""
img_tensor = data['img'][0]
img_metas = data['img_meta'][0].data[0]
imgs = tensor2imgs(img_tensor, **img_norm_cfg)
assert len(imgs) == len(img_metas)
for img, img_meta in zip(imgs, img_metas):
h, w, _ = img_meta['img_shape']
img_show = img[:h, :w, :]
mmcv.imshow_bboxes(img_show, result, top_k=top_k) 示例5: show_result
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def show_result(self, data, result, dataset=None, top_k=20):
"""Show RPN proposals on the image.
Although we assume batch size is 1, this method supports arbitrary
batch size.
"""
img_tensor = data['img'][0]
img_metas = data['img_meta'][0].data[0]
imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
assert len(imgs) == len(img_metas)
for img, img_meta in zip(imgs, img_metas):
h, w, _ = img_meta['img_shape']
img_show = img[:h, :w, :]
mmcv.imshow_bboxes(img_show, result, top_k=top_k) 示例6: forward_train
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def forward_train(self, img, img_meta, gt_bboxes=None):
if self.train_cfg.rpn.get('debug', False):
self.rpn_head.debug_imgs = tensor2imgs(img)
x = self.extract_feat(img)
rpn_outs = self.rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta, self.train_cfg.rpn)
losses = self.rpn_head.loss(*rpn_loss_inputs)
return losses 示例7: show_result
# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import tensor2imgs [as 别名]
def show_result(self, data, result, img_norm_cfg):
"""Show RPN proposals on the image.
Although we assume batch size is 1, this method supports arbitrary
batch size.
"""
img_tensor = data['img'][0]
img_metas = data['img_meta'][0].data[0]
imgs = tensor2imgs(img_tensor, **img_norm_cfg)
assert len(imgs) == len(img_metas)
for img, img_meta in zip(imgs, img_metas):
h, w, _ = img_meta['img_shape']
img_show = img[:h, :w, :]
mmcv.imshow_bboxes(img_show, result, top_k=20)