def forward(self, input, rois):
if self.use_torchvision:
from torchvision.ops import roi_pool as tv_roi_pool
return tv_roi_pool(input, rois, _pair(self.out_size),
self.spatial_scale)
else:
return roi_pool(input, rois, self.output_size, self.spatial_scale)
def forward(self, features, rois):
if self.use_torchvision:
from torchvision.ops import roi_pool as tv_roi_pool
return tv_roi_pool(features, rois, self.out_size,
self.spatial_scale)
else:
return roi_pool(features, rois, self.out_size, self.spatial_scale)
def forward(self, input, boxes):
"""
Apply torchvision.roi_pool
Args:
input (Tensor): shape (N x C x H x W)
boxes (Tensor): boxes in pooling format (image_index, x1, y1, x2, y2), shape (M x 5)
Returns:
output (Tensor): pooled roi feature map, shape (M x C x out_size x out_size)
"""
output = tv_roi_pool(input, boxes, self.output_size,
self.spatial_scale)
return output
