Python boxlist_ops.remove_small_boxes方法代码示例

# 需要导入模块: from maskrcnn_benchmark.structures import boxlist_ops [as 别名]
# 或者: from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes [as 别名]
def forward_for_single_feature_map(self, anchors, objectness, box_regression):
        """
        Arguments:
            anchors: list[BoxList]
            objectness: tensor of size N, A, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        device = objectness.device
        N, A, H, W = objectness.shape

        # put in the same format as anchors
        objectness = permute_and_flatten(objectness, N, A, 1, H, W).view(N, -1)
        objectness = objectness.sigmoid()

        box_regression = permute_and_flatten(box_regression, N, A, 4, H, W)

        num_anchors = A * H * W

        pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
        objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)

        batch_idx = torch.arange(N, device=device)[:, None]
        box_regression = box_regression[batch_idx, topk_idx]

        image_shapes = [box.size for box in anchors]
        concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
        concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx]

        proposals = self.box_coder.decode(
            box_regression.view(-1, 4), concat_anchors.view(-1, 4)
        )

        proposals = proposals.view(N, -1, 4)

        result = []
        for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
            boxlist = BoxList(proposal, im_shape, mode="xyxy")
            boxlist.add_field("objectness", score)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            boxlist = boxlist_nms(
                boxlist,
                self.nms_thresh,
                max_proposals=self.post_nms_top_n,
                score_field="objectness",
            )
            result.append(boxlist)
        return result 

# 需要导入模块: from maskrcnn_benchmark.structures import boxlist_ops [as 别名]
# 或者: from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes [as 别名]
def forward_for_single_feature_map(self, anchors, objectness, box_regression, angle_cls, angle_regression):
        """
        Arguments:
            anchors: list[BoxList]
            objectness: tensor of size N, A, H, W
            box_regression: tensor of size N, A * 4, H, W
            angle_cls: tensor of size N, 6, H, W
            angle_reg: tensor of size N, 6, H, W
        """
        device = objectness.device
        N, A, H, W = objectness.shape

        # put in the same format as anchors
        objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1)
        objectness = objectness.sigmoid()
        box_regression = box_regression.view(N, -1, 4, H, W).permute(0, 3, 4, 1, 2)
        box_regression = box_regression.reshape(N, -1, 4)



        num_anchors = A * H * W

        pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
        objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)

        batch_idx = torch.arange(N, device=device)[:, None]
        box_regression = box_regression[batch_idx, topk_idx]

        image_shapes = [box.size for box in anchors]
        concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
        concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx]

        proposals = self.box_coder.decode(
            box_regression.view(-1, 4), concat_anchors.view(-1, 4)
        )

        proposals = proposals.view(N, -1, 4)

        result = []
        for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
            boxlist = BoxList(proposal, im_shape, mode="xyxy")
            boxlist.add_field("objectness", score)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            boxlist = boxlist_nms(
                boxlist,
                self.nms_thresh,
                max_proposals=self.post_nms_top_n,
                score_field="objectness",
            )
            result.append(boxlist)
        return result 

# 需要导入模块: from maskrcnn_benchmark.structures import boxlist_ops [as 别名]
# 或者: from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes [as 别名]
def forward_for_single_feature_map(self, anchors, objectness, box_regression):
        """
        Arguments:
            anchors: list[BoxList]
            objectness: tensor of size N, A, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        device = objectness.device
        N, A, H, W = objectness.shape

        # put in the same format as anchors
        objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1)
        objectness = objectness.sigmoid()
        box_regression = box_regression.view(N, -1, 4, H, W).permute(0, 3, 4, 1, 2)
        box_regression = box_regression.reshape(N, -1, 4)

        num_anchors = A * H * W

        pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
        objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)

        batch_idx = torch.arange(N, device=device)[:, None]
        box_regression = box_regression[batch_idx, topk_idx]

        image_shapes = [box.size for box in anchors]
        concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
        concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx]

        proposals = self.box_coder.decode(
            box_regression.view(-1, 4), concat_anchors.view(-1, 4)
        )

        proposals = proposals.view(N, -1, 4)

        result = []
        for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
            boxlist = BoxList(proposal, im_shape, mode="xyxy")
            boxlist.add_field("objectness", score)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            boxlist = boxlist_nms(
                boxlist,
                self.nms_thresh,
                max_proposals=self.post_nms_top_n,
                score_field="objectness",
            )
            result.append(boxlist)
        return result 

# 需要导入模块: from maskrcnn_benchmark.structures import boxlist_ops [as 别名]
# 或者: from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes [as 别名]
def forward_for_single_feature_map_without(self, anchors, box_cls, box_regression,
                                               pre_nms_thresh):
        """
        Arguments:
            anchors: list[BoxList]
            box_cls: tensor of size N, A * C, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        N, _ , H, W = box_cls.shape
        A = int(box_regression.size(1) / 4)
        C = int(box_cls.size(1) / A)

        # put in the same format as anchors
        box_cls = box_cls.view(N, -1, C, H, W).permute(0, 3, 4, 1, 2)
        box_cls = box_cls.reshape(N, -1, C)
        box_cls = box_cls.sigmoid()

        box_regression = box_regression.view(N, -1, 4, H, W)
        box_regression = box_regression.permute(0, 3, 4, 1, 2)
        box_regression = box_regression.reshape(N, -1, 4)

        results = [[] for _ in range(N)]
        candidate_inds = box_cls > pre_nms_thresh

        for batch_idx, (per_box_cls, per_box_regression, per_candidate_inds, per_anchors) in enumerate(zip(
            box_cls,
            box_regression,
            candidate_inds,
            anchors
        )):
            # Sort and select TopN
            per_box_cls = per_box_cls[per_candidate_inds]
            per_candidate_nonzeros = per_candidate_inds.nonzero()
            per_box_loc = per_candidate_nonzeros[:, 0]
            per_class = per_candidate_nonzeros[:, 1]
            per_class += 1

            detections = self.box_coder.decode(
                per_box_regression[per_box_loc, :].view(-1, 4),
                per_anchors.bbox[per_box_loc, :].view(-1, 4)
            )

            boxlist = BoxList(detections, per_anchors.size, mode="xyxy")
            boxlist.add_field("labels", per_class)
            boxlist.add_field("scores", per_box_cls)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            results[batch_idx] = boxlist

        return results 

# 需要导入模块: from maskrcnn_benchmark.structures import boxlist_ops [as 别名]
# 或者: from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes [as 别名]
def forward_for_single_feature_map(self, anchors, objectness, box_regression):
        """
        Arguments:
            anchors: list[BoxList]
            objectness: tensor of size N, A, H, W
            box_regression: tensor of size N, A * 4, H, W
        """
        device = objectness.device
        N, A, H, W = objectness.shape

        # put in the same format as anchors
        objectness = permute_and_flatten(objectness, N, A, 1, H, W).view(N, -1)
        objectness = objectness.sigmoid()

        box_regression = permute_and_flatten(box_regression, N, A, 4, H, W)

        num_anchors = A * H * W

        pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
        objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)

        # if topk_idx.max() >= box_regression.shape[1]:
        #     print()

        batch_idx = torch.arange(N, device=device)[:, None]
        box_regression = box_regression[batch_idx, topk_idx]

        image_shapes = [box.size for box in anchors]
        concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
        concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx]

        proposals = self.box_coder.decode(
            box_regression.view(-1, 4), concat_anchors.view(-1, 4)
        )

        proposals = proposals.view(N, -1, 4)

        result = []
        for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
            boxlist = BoxList(proposal, im_shape, mode="xyxy")
            boxlist.add_field("objectness", score)
            boxlist = boxlist.clip_to_image(remove_empty=False)
            boxlist = remove_small_boxes(boxlist, self.min_size)
            boxlist = boxlist_nms(
                boxlist,
                self.nms_thresh,
                max_proposals=self.post_nms_top_n,
                score_field="objectness",
            )
            result.append(boxlist)
        return result