本文整理汇总了Python中core.config.cfg.PYTORCH_VERSION_LESS_THAN_040属性的典型用法代码示例。如果您正苦于以下问题：Python cfg.PYTORCH_VERSION_LESS_THAN_040属性的具体用法？Python cfg.PYTORCH_VERSION_LESS_THAN_040怎么用？Python cfg.PYTORCH_VERSION_LESS_THAN_040使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类core.config.cfg的用法示例。
在下文中一共展示了cfg.PYTORCH_VERSION_LESS_THAN_040属性的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: check_inference
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def check_inference(net_func):
@wraps(net_func)
def wrapper(self, *args, **kwargs):
if not self.training:
if cfg.PYTORCH_VERSION_LESS_THAN_040:
return net_func(self, *args, **kwargs)
else:
with torch.no_grad():
return net_func(self, *args, **kwargs)
else:
raise ValueError('You should call this function only on inference.'
'Set the network in inference mode by net.eval().')
return wrapper 示例2: forward
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def forward(self, data, im_info, roidb=None, **rpn_kwargs):
if cfg.PYTORCH_VERSION_LESS_THAN_040:
return self._forward(data, im_info, roidb, **rpn_kwargs)
else:
with torch.set_grad_enabled(self.training):
return self._forward(data, im_info, roidb, **rpn_kwargs) 示例3: im_conv_body_only
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def im_conv_body_only(model, im, target_scale, target_max_size):
inputs, im_scale = _get_blobs(im, None, target_scale, target_max_size)
if cfg.PYTORCH_VERSION_LESS_THAN_040:
inputs['data'] = Variable(torch.from_numpy(inputs['data']), volatile=True).cuda()
else:
inputs['data'] = torch.from_numpy(inputs['data']).cuda()
inputs.pop('im_info')
blob_conv = model.module.convbody_net(**inputs)
return blob_conv, im_scale 示例4: forward
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def forward(self, data, rois, labels):
if cfg.PYTORCH_VERSION_LESS_THAN_040:
return self._forward(data, rois, labels)
else:
with torch.set_grad_enabled(self.training):
return self._forward(data, rois, labels) 示例5: im_detect_bbox
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def im_detect_bbox(model, im, target_scale, target_max_size, boxes=None):
"""Prepare the bbox for testing"""
inputs, im_scale = _get_blobs(im, boxes, target_scale, target_max_size)
if cfg.DEDUP_BOXES > 0:
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
hashes = np.round(inputs['rois'] * cfg.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(
hashes, return_index=True, return_inverse=True
)
inputs['rois'] = inputs['rois'][index, :]
boxes = boxes[index, :]
if cfg.PYTORCH_VERSION_LESS_THAN_040:
inputs['data'] = [Variable(torch.from_numpy(inputs['data']), volatile=True)]
inputs['rois'] = [Variable(torch.from_numpy(inputs['rois']), volatile=True)]
inputs['labels'] = [Variable(torch.from_numpy(inputs['labels']), volatile=True)]
else:
inputs['data'] = [torch.from_numpy(inputs['data'])]
inputs['rois'] = [torch.from_numpy(inputs['rois'])]
inputs['labels'] = [torch.from_numpy(inputs['labels'])]
return_dict = model(**inputs)
# cls prob (activations after softmax)
scores = return_dict['refine_score'][0].data.cpu().numpy().squeeze()
for i in range(1, cfg.REFINE_TIMES):
scores += return_dict['refine_score'][i].data.cpu().numpy().squeeze()
scores /= cfg.REFINE_TIMES
# In case there is 1 proposal
scores = scores.reshape([-1, scores.shape[-1]])
pred_boxes = boxes
if cfg.DEDUP_BOXES > 0:
# Map scores and predictions back to the original set of boxes
scores = scores[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
return scores, pred_boxes, im_scale, return_dict['blob_conv'] 示例6: forward
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def forward(self, data, im_info, roidb=None, rois=None, **rpn_kwargs):
if cfg.PYTORCH_VERSION_LESS_THAN_040:
return self._forward(data, im_info, roidb, rois, **rpn_kwargs)
else:
with torch.set_grad_enabled(self.training):
return self._forward(data, im_info, roidb, rois, **rpn_kwargs) 示例7: forward
# 需要导入模块: from core.config import cfg [as 别名]
# 或者: from core.config.cfg import PYTORCH_VERSION_LESS_THAN_040 [as 别名]
def forward(self, data, im_info, dataset_name=None, roidb=None, use_gt_labels=False, **rpn_kwargs):
if cfg.PYTORCH_VERSION_LESS_THAN_040:
return self._forward(data, im_info, dataset_name, roidb, use_gt_labels, **rpn_kwargs)
else:
with torch.set_grad_enabled(self.training):
return self._forward(data, im_info, dataset_name, roidb, use_gt_labels, **rpn_kwargs)