Spaces:
Runtime error
Runtime error
File size: 10,786 Bytes
583456e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 |
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
from typing import Tuple
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.postprocessing import sem_seg_postprocess
from detectron2.structures import ImageList
from .modeling.criterion import SetCriterion
from .modeling.matcher import HungarianMatcher
@META_ARCH_REGISTRY.register()
class MaskFormer(nn.Module):
"""
Main class for mask classification semantic segmentation architectures.
"""
@configurable
def __init__(
self,
*,
backbone: Backbone,
sem_seg_head: nn.Module,
criterion: nn.Module,
num_queries: int,
panoptic_on: bool,
object_mask_threshold: float,
overlap_threshold: float,
metadata,
size_divisibility: int,
sem_seg_postprocess_before_inference: bool,
pixel_mean: Tuple[float],
pixel_std: Tuple[float],
):
"""
Args:
backbone: a backbone module, must follow detectron2's backbone interface
sem_seg_head: a module that predicts semantic segmentation from backbone features
criterion: a module that defines the loss
num_queries: int, number of queries
panoptic_on: bool, whether to output panoptic segmentation prediction
object_mask_threshold: float, threshold to filter query based on classification score
for panoptic segmentation inference
overlap_threshold: overlap threshold used in general inference for panoptic segmentation
metadata: dataset meta, get `thing` and `stuff` category names for panoptic
segmentation inference
size_divisibility: Some backbones require the input height and width to be divisible by a
specific integer. We can use this to override such requirement.
sem_seg_postprocess_before_inference: whether to resize the prediction back
to original input size before semantic segmentation inference or after.
For high-resolution dataset like Mapillary, resizing predictions before
inference will cause OOM error.
pixel_mean, pixel_std: list or tuple with #channels element, representing
the per-channel mean and std to be used to normalize the input image
"""
super().__init__()
self.backbone = backbone
self.sem_seg_head = sem_seg_head
self.criterion = criterion
self.num_queries = num_queries
self.overlap_threshold = overlap_threshold
self.panoptic_on = panoptic_on
self.object_mask_threshold = object_mask_threshold
self.metadata = metadata
if size_divisibility < 0:
# use backbone size_divisibility if not set
size_divisibility = self.backbone.size_divisibility
self.size_divisibility = size_divisibility
self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference
self.register_buffer(
"pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False
)
self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
@classmethod
def from_config(cls, cfg):
backbone = build_backbone(cfg)
sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
# Loss parameters:
deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT
dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT
mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT
# building criterion
matcher = HungarianMatcher(
cost_class=1,
cost_mask=mask_weight,
cost_dice=dice_weight,
)
weight_dict = {"loss_ce": 1, "loss_mask": mask_weight, "loss_dice": dice_weight}
if deep_supervision:
dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS
aux_weight_dict = {}
for i in range(dec_layers - 1):
aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
weight_dict.update(aux_weight_dict)
losses = ["labels", "masks"]
criterion = SetCriterion(
sem_seg_head.num_classes,
matcher=matcher,
weight_dict=weight_dict,
eos_coef=no_object_weight,
losses=losses,
)
return {
"backbone": backbone,
"sem_seg_head": sem_seg_head,
"criterion": criterion,
"num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES,
"panoptic_on": cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON,
"object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD,
"overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD,
"metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
"size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY,
"sem_seg_postprocess_before_inference": (
cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE
or cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON
),
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
}
@property
def device(self):
return self.pixel_mean.device
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": per-region ground truth
* Other information that's included in the original dicts, such as:
"height", "width" (int): the output resolution of the model (may be different
from input resolution), used in inference.
Returns:
list[dict]:
each dict has the results for one image. The dict contains the following keys:
* "sem_seg":
A Tensor that represents the
per-pixel segmentation prediced by the head.
The prediction has shape KxHxW that represents the logits of
each class for each pixel.
* "panoptic_seg":
A tuple that represent panoptic output
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".
"""
images = [x["image"].to(self.device) for x in batched_inputs]
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
images = ImageList.from_tensors(images, self.size_divisibility)
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features)
if self.training:
# mask classification target
if "instances" in batched_inputs[0]:
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
targets = self.prepare_targets(gt_instances, images)
else:
targets = None
# bipartite matching-based loss
losses = self.criterion(outputs, targets)
for k in list(losses.keys()):
if k in self.criterion.weight_dict:
losses[k] *= self.criterion.weight_dict[k]
else:
# remove this loss if not specified in `weight_dict`
losses.pop(k)
return losses
else:
mask_cls_results = outputs["pred_logits"]
mask_pred_results = outputs["pred_masks"]
# upsample masks
mask_pred_results = F.interpolate(
mask_pred_results,
size=(images.tensor.shape[-2], images.tensor.shape[-1]),
mode="bilinear",
align_corners=False,
)
processed_results = []
for mask_cls_result, mask_pred_result, input_per_image, image_size in zip(
mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
):
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
if self.sem_seg_postprocess_before_inference:
mask_pred_result = sem_seg_postprocess(
mask_pred_result, image_size, height, width
)
# semantic segmentation inference
r = self.semantic_inference(mask_cls_result, mask_pred_result)
if not self.sem_seg_postprocess_before_inference:
r = sem_seg_postprocess(r, image_size, height, width)
processed_results.append({"sem_seg": r})
# panoptic segmentation inference
if self.panoptic_on:
panoptic_r = self.panoptic_inference(
mask_cls_result, mask_pred_result
)
processed_results[-1]["panoptic_seg"] = panoptic_r
return processed_results
def prepare_targets(self, targets, images):
h, w = images.tensor.shape[-2:]
new_targets = []
for targets_per_image in targets:
# pad gt
gt_masks = targets_per_image.gt_masks
padded_masks = torch.zeros(
(gt_masks.shape[0], h, w), dtype=gt_masks.dtype, device=gt_masks.device
)
padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks
new_targets.append(
{
"labels": targets_per_image.gt_classes,
"masks": padded_masks,
}
)
return new_targets
def semantic_inference(self, mask_cls, mask_pred):
mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
mask_pred = mask_pred.sigmoid()
semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred)
return semseg
|