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)
def forward(self, input, rois):
"""
Args:
input: NCHW images
rois: Bx5 boxes. First column is the index into N.\
The other 4 columns are xyxy.
"""
if self.use_torchvision:
from torchvision.ops import roi_align as tv_roi_align
if 'aligned' in tv_roi_align.__code__.co_varnames:
return tv_roi_align(input, rois, self.output_size,
self.spatial_scale, self.sampling_ratio,
self.aligned)
else:
if self.aligned:
rois -= rois.new_tensor([0.] +
[0.5 / self.spatial_scale] * 4)
return tv_roi_align(input, rois, self.output_size,
self.spatial_scale, self.sampling_ratio)
else:
return roi_align(input, rois, self.output_size, self.spatial_scale,
self.sampling_ratio, self.pool_mode, self.aligned)
def forward(self, input, rois):
"""
Args:
input: NCHW images
rois: Bx5 boxes. First column is the index into N.\
The other 4 columns are xyxy.
"""
if self.use_torchvision:
from torchvision.ops import roi_align as tv_roi_align
return tv_roi_align(input, rois, self.output_size,
self.spatial_scale, self.sampling_ratio)
else:
return roi_align(input, rois, self.output_size, self.spatial_scale,
self.sampling_ratio, self.pool_mode, self.aligned)
def forward(self, input, boxes):
"""
Apply torchvision.roi_align
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)
"""
return tv_roi_align(input, boxes, (self.output_size, self.output_size),
self.spatial_scale, self.sampling_ratio,
self.aligned)
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)
def forward(self, input, rois):
if self.use_torchvision:
from torchvision.ops import roi_align as tv_roi_align
return tv_roi_align(input, rois, _pair(self.out_size), self.spatial_scale, self.sampling_ratio)
else:
return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio)
