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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import Dict, List
import torch
from torch import nn
from detectron2.config import configurable
from detectron2.structures import ImageList
from ..postprocessing import detector_postprocess, sem_seg_postprocess
from .build import META_ARCH_REGISTRY
from .rcnn import GeneralizedRCNN
from .semantic_seg import build_sem_seg_head
__all__ = ["PanopticFPN"]
@META_ARCH_REGISTRY.register()
class PanopticFPN(GeneralizedRCNN):
"""
Implement the paper :paper:`PanopticFPN`.
"""
@configurable
def __init__(
self,
*,
sem_seg_head: nn.Module,
combine_overlap_thresh: float = 0.5,
combine_stuff_area_thresh: float = 4096,
combine_instances_score_thresh: float = 0.5,
**kwargs,
):
"""
NOTE: this interface is experimental.
Args:
sem_seg_head: a module for the semantic segmentation head.
combine_overlap_thresh: combine masks into one instances if
they have enough overlap
combine_stuff_area_thresh: ignore stuff areas smaller than this threshold
combine_instances_score_thresh: ignore instances whose score is
smaller than this threshold
Other arguments are the same as :class:`GeneralizedRCNN`.
"""
super().__init__(**kwargs)
self.sem_seg_head = sem_seg_head
# options when combining instance & semantic outputs
self.combine_overlap_thresh = combine_overlap_thresh
self.combine_stuff_area_thresh = combine_stuff_area_thresh
self.combine_instances_score_thresh = combine_instances_score_thresh
@classmethod
def from_config(cls, cfg):
ret = super().from_config(cfg)
ret.update(
{
"combine_overlap_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH,
"combine_stuff_area_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT,
"combine_instances_score_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH, # noqa
}
)
ret["sem_seg_head"] = build_sem_seg_head(cfg, ret["backbone"].output_shape())
logger = logging.getLogger(__name__)
if not cfg.MODEL.PANOPTIC_FPN.COMBINE.ENABLED:
logger.warning(
"PANOPTIC_FPN.COMBINED.ENABLED is no longer used. "
" model.inference(do_postprocess=) should be used to toggle postprocessing."
)
if cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT != 1.0:
w = cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT
logger.warning(
"PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT should be replaced by weights on each ROI head."
)
def update_weight(x):
if isinstance(x, dict):
return {k: v * w for k, v in x.items()}
else:
return x * w
roi_heads = ret["roi_heads"]
roi_heads.box_predictor.loss_weight = update_weight(roi_heads.box_predictor.loss_weight)
roi_heads.mask_head.loss_weight = update_weight(roi_heads.mask_head.loss_weight)
return ret
def forward(self, batched_inputs):
"""
Args:
batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
Each item in the list contains the inputs for one image.
For now, each item in the list is a dict that contains:
* "image": Tensor, image in (C, H, W) format.
* "instances": Instances
* "sem_seg": semantic segmentation ground truth.
* Other information that's included in the original dicts, such as:
"height", "width" (int): the output resolution of the model, used in inference.
See :meth:`postprocess` for details.
Returns:
list[dict]:
each dict has the results for one image. The dict contains the following keys:
* "instances": see :meth:`GeneralizedRCNN.forward` for its format.
* "sem_seg": see :meth:`SemanticSegmentor.forward` for its format.
* "panoptic_seg": See the return value of
:func:`combine_semantic_and_instance_outputs` for its format.
"""
if not self.training:
return self.inference(batched_inputs)
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
assert "sem_seg" in batched_inputs[0]
gt_sem_seg = [x["sem_seg"].to(self.device) for x in batched_inputs]
gt_sem_seg = ImageList.from_tensors(
gt_sem_seg,
self.backbone.size_divisibility,
self.sem_seg_head.ignore_value,
self.backbone.padding_constraints,
).tensor
sem_seg_results, sem_seg_losses = self.sem_seg_head(features, gt_sem_seg)
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
proposals, proposal_losses = self.proposal_generator(images, features, gt_instances)
detector_results, detector_losses = self.roi_heads(
images, features, proposals, gt_instances
)
losses = sem_seg_losses
losses.update(proposal_losses)
losses.update(detector_losses)
return losses
def inference(self, batched_inputs: List[Dict[str, torch.Tensor]], do_postprocess: bool = True):
"""
Run inference on the given inputs.
Args:
batched_inputs (list[dict]): same as in :meth:`forward`
do_postprocess (bool): whether to apply post-processing on the outputs.
Returns:
When do_postprocess=True, see docs in :meth:`forward`.
Otherwise, returns a (list[Instances], list[Tensor]) that contains
the raw detector outputs, and raw semantic segmentation outputs.
"""
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
sem_seg_results, sem_seg_losses = self.sem_seg_head(features, None)
proposals, _ = self.proposal_generator(images, features, None)
detector_results, _ = self.roi_heads(images, features, proposals, None)
if do_postprocess:
processed_results = []
for sem_seg_result, detector_result, input_per_image, image_size in zip(
sem_seg_results, detector_results, batched_inputs, images.image_sizes
):
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
sem_seg_r = sem_seg_postprocess(sem_seg_result, image_size, height, width)
detector_r = detector_postprocess(detector_result, height, width)
processed_results.append({"sem_seg": sem_seg_r, "instances": detector_r})
panoptic_r = combine_semantic_and_instance_outputs(
detector_r,
sem_seg_r.argmax(dim=0),
self.combine_overlap_thresh,
self.combine_stuff_area_thresh,
self.combine_instances_score_thresh,
)
processed_results[-1]["panoptic_seg"] = panoptic_r
return processed_results
else:
return detector_results, sem_seg_results
def combine_semantic_and_instance_outputs(
instance_results,
semantic_results,
overlap_threshold,
stuff_area_thresh,
instances_score_thresh,
):
"""
Implement a simple combining logic following
"combine_semantic_and_instance_predictions.py" in panopticapi
to produce panoptic segmentation outputs.
Args:
instance_results: output of :func:`detector_postprocess`.
semantic_results: an (H, W) tensor, each element is the contiguous semantic
category id
Returns:
panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
segments_info (list[dict]): Describe each segment in `panoptic_seg`.
Each dict contains keys "id", "category_id", "isthing".
"""
panoptic_seg = torch.zeros_like(semantic_results, dtype=torch.int32)
# sort instance outputs by scores
sorted_inds = torch.argsort(-instance_results.scores)
current_segment_id = 0
segments_info = []
instance_masks = instance_results.pred_masks.to(dtype=torch.bool, device=panoptic_seg.device)
# Add instances one-by-one, check for overlaps with existing ones
for inst_id in sorted_inds:
score = instance_results.scores[inst_id].item()
if score < instances_score_thresh:
break
mask = instance_masks[inst_id] # H,W
mask_area = mask.sum().item()
if mask_area == 0:
continue
intersect = (mask > 0) & (panoptic_seg > 0)
intersect_area = intersect.sum().item()
if intersect_area * 1.0 / mask_area > overlap_threshold:
continue
if intersect_area > 0:
mask = mask & (panoptic_seg == 0)
current_segment_id += 1
panoptic_seg[mask] = current_segment_id
segments_info.append(
{
"id": current_segment_id,
"isthing": True,
"score": score,
"category_id": instance_results.pred_classes[inst_id].item(),
"instance_id": inst_id.item(),
}
)
# Add semantic results to remaining empty areas
semantic_labels = torch.unique(semantic_results).cpu().tolist()
for semantic_label in semantic_labels:
if semantic_label == 0: # 0 is a special "thing" class
continue
mask = (semantic_results == semantic_label) & (panoptic_seg == 0)
mask_area = mask.sum().item()
if mask_area < stuff_area_thresh:
continue
current_segment_id += 1
panoptic_seg[mask] = current_segment_id
segments_info.append(
{
"id": current_segment_id,
"isthing": False,
"category_id": semantic_label,
"area": mask_area,
}
)
return panoptic_seg, segments_info
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