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