本文整理汇总了Python中torchvision.ops.roi_align方法的典型用法代码示例。如果您正苦于以下问题：Python ops.roi_align方法的具体用法？Python ops.roi_align怎么用？Python ops.roi_align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torchvision.ops的用法示例。
在下文中一共展示了ops.roi_align方法的6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
# 需要导入模块: from torchvision import ops [as 别名]
# 或者: from torchvision.ops import roi_align [as 别名]
def forward(self, features, rois):
"""
Args:
features: NCHW images
rois: Bx5 boxes. First column is the index into N. The other 4
columns are xyxy.
"""
assert rois.dim() == 2 and rois.size(1) == 5
if self.use_torchvision:
from torchvision.ops import roi_align as tv_roi_align
return tv_roi_align(features, rois, self.out_size,
self.spatial_scale, self.sample_num)
else:
return roi_align(features, rois, self.out_size, self.spatial_scale,
self.sample_num, self.aligned) 示例2: forward
# 需要导入模块: from torchvision import ops [as 别名]
# 或者: from torchvision.ops import roi_align [as 别名]
def forward(self, x):
x, rois, sequences = x
_, _, input_h, input_w = x.shape
x_l1, x_l2 = self.base(x)
dtype = x_l1.dtype
rois = [roi.to(dtype) for roi in rois]
del x
x_l1 = roi_align(
x_l1, rois,
output_size=(self.res_l1, self.res_l1),
spatial_scale=x_l1.shape[3] / input_w,
)
x_l2 = roi_align(
x_l2, rois,
output_size=(self.res_l2, self.res_l2),
spatial_scale=x_l2.shape[3] / input_w,
)
x = torch.cat(
[x_l1.flatten(start_dim=1),
x_l2.flatten(start_dim=1)],
dim=1)
x, x_features = self.head(x)
if self.use_sequences: # unused
x_features = self._apply_lstm(x_features, rois, sequences)
x = self.head.apply_fc_out(x_features)
return x, x_features, rois 示例3: get_yolo_feature_vec
# 需要导入模块: from torchvision import ops [as 别名]
# 或者: from torchvision.ops import roi_align [as 别名]
def get_yolo_feature_vec(self, coords):
feature_map = self.get_feature_map()
ratio = self.img_size/feature_map.size()[2]
#coords = (10,10,100,100)
coords = torch.cat((torch.Tensor([0]),torch.Tensor(coords))).view(1,5).cuda()
#coords = torch.Tensor(coords).view(1,4).cuda()
#print(feature_map.shape)
#print(coords.shape)
#print(coords.shape)
with torch.no_grad():
roi = roi_align( feature_map, coords,(3,3) , spatial_scale=1/ratio)
#print(roi)
vec = F.adaptive_avg_pool2d(roi, (1, 1))
return np.squeeze(vec.cpu().detach().numpy()) 示例4: project_masks_on_boxes
# 需要导入模块: from torchvision import ops [as 别名]
# 或者: from torchvision.ops import roi_align [as 别名]
def project_masks_on_boxes(gt_masks, boxes, matched_idxs, M):
"""
Given segmentation masks and the bounding boxes corresponding
to the location of the masks in the image, this function
crops and resizes the masks in the position defined by the
boxes. This prepares the masks for them to be fed to the
loss computation as the targets.
"""
matched_idxs = matched_idxs.to(boxes)
rois = torch.cat([matched_idxs[:, None], boxes], dim=1)
gt_masks = gt_masks[:, None].to(rois)
return roi_align(gt_masks, rois, (M, M), 1)[:, 0] 示例5: forward
# 需要导入模块: from torchvision import ops [as 别名]
# 或者: from torchvision.ops import roi_align [as 别名]
def forward(self, features, rois):
if self.use_torchvision:
from torchvision.ops import roi_align as tv_roi_align
return tv_roi_align(features, rois, self.out_size,
self.spatial_scale, self.sample_num)
else:
return roi_align(features, rois, self.out_size, self.spatial_scale,
self.sample_num) 示例6: forward
# 需要导入模块: from torchvision import ops [as 别名]
# 或者: from torchvision.ops import roi_align [as 别名]
def forward(self, features, rois):
if self.use_torchvision:
from torchvision.ops import roi_align as tv_roi_align
return tv_roi_align(features, rois, _pair(self.out_size),
self.spatial_scale, self.sample_num)
else:
return roi_align(features, rois, self.out_size, self.spatial_scale,
self.sample_num)