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oneformer_model.py
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# ------------------------------------------------------------------------------
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/maskformer_model.py
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
# ------------------------------------------------------------------------------
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 Boxes, ImageList, Instances, BitMasks
from detectron2.utils.memory import retry_if_cuda_oom
from .modeling.criterion import SetCriterion
from .modeling.matcher import HungarianMatcher
from einops import rearrange
from .modeling.transformer_decoder.text_transformer import TextTransformer
from .modeling.transformer_decoder.oneformer_transformer_decoder import MLP
from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
@META_ARCH_REGISTRY.register()
class OneFormer(nn.Module):
"""
Main class for mask classification semantic segmentation architectures.
"""
@configurable
def __init__(
self,
*,
backbone: Backbone,
sem_seg_head: nn.Module,
task_mlp: nn.Module,
text_encoder: nn.Module,
text_projector: nn.Module,
criterion: nn.Module,
prompt_ctx: nn.Embedding,
num_queries: int,
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],
# inference
semantic_on: bool,
panoptic_on: bool,
instance_on: bool,
detection_on: bool,
test_topk_per_image: int,
task_seq_len: int,
max_seq_len: int,
is_demo: bool,
):
"""
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
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
semantic_on: bool, whether to output semantic segmentation prediction
instance_on: bool, whether to output instance segmentation prediction
panoptic_on: bool, whether to output panoptic segmentation prediction
test_topk_per_image: int, instance segmentation parameter, keep topk instances per image
"""
super().__init__()
self.backbone = backbone
self.sem_seg_head = sem_seg_head
self.task_mlp = task_mlp
self.text_encoder = text_encoder
self.text_projector = text_projector
self.prompt_ctx = prompt_ctx
self.criterion = criterion
self.num_queries = num_queries
self.overlap_threshold = overlap_threshold
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)
# additional args
self.semantic_on = semantic_on
self.instance_on = instance_on
self.panoptic_on = panoptic_on
self.detection_on = detection_on
self.test_topk_per_image = test_topk_per_image
self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
self.is_demo = is_demo
self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()]
if not self.semantic_on:
assert self.sem_seg_postprocess_before_inference
@classmethod
def from_config(cls, cfg):
backbone = build_backbone(cfg)
sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
if cfg.MODEL.IS_TRAIN:
text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH,
width=cfg.MODEL.TEXT_ENCODER.WIDTH,
layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS,
vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE)
text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,
cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS)
if cfg.MODEL.TEXT_ENCODER.N_CTX > 0:
prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH)
else:
prompt_ctx = None
else:
text_encoder = None
text_projector = None
prompt_ctx = None
task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,
cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2)
# Loss parameters:
deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION
no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT
# loss weights
class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT
dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT
mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT
contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT
# building criterion
matcher = HungarianMatcher(
cost_class=class_weight,
cost_mask=mask_weight,
cost_dice=dice_weight,
num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,
)
weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight,
"loss_dice": dice_weight, "loss_contrastive": contrastive_weight}
if deep_supervision:
dec_layers = cfg.MODEL.ONE_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", "contrastive"]
criterion = SetCriterion(
sem_seg_head.num_classes,
matcher=matcher,
weight_dict=weight_dict,
eos_coef=no_object_weight,
contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE,
losses=losses,
num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,
oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO,
importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO,
)
return {
"backbone": backbone,
"sem_seg_head": sem_seg_head,
"task_mlp": task_mlp,
"prompt_ctx": prompt_ctx,
"text_encoder": text_encoder,
"text_projector": text_projector,
"criterion": criterion,
"num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES,
"object_mask_threshold": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD,
"overlap_threshold": cfg.MODEL.TEST.OVERLAP_THRESHOLD,
"metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
"size_divisibility": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY,
"sem_seg_postprocess_before_inference": (
cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE
or cfg.MODEL.TEST.PANOPTIC_ON
or cfg.MODEL.TEST.INSTANCE_ON
),
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
# inference
"semantic_on": cfg.MODEL.TEST.SEMANTIC_ON,
"instance_on": cfg.MODEL.TEST.INSTANCE_ON,
"panoptic_on": cfg.MODEL.TEST.PANOPTIC_ON,
"detection_on": cfg.MODEL.TEST.DETECTION_ON,
"test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
"task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
"max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
"is_demo": cfg.MODEL.IS_DEMO,
}
@property
def device(self):
return self.pixel_mean.device
def encode_text(self, text):
assert text.ndim in [2, 3], text.ndim
b = text.shape[0]
squeeze_dim = False
num_text = 1
if text.ndim == 3:
num_text = text.shape[1]
text = rearrange(text, 'b n l -> (b n) l', n=num_text)
squeeze_dim = True
# [B, C]
x = self.text_encoder(text)
text_x = self.text_projector(x)
if squeeze_dim:
text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)
if self.prompt_ctx is not None:
text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)
text_x = torch.cat([text_x, text_ctx], dim=1)
return {"texts": text_x}
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)
tasks = torch.cat([self.task_tokenizer(x["task"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)
tasks = self.task_mlp(tasks.float())
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, tasks)
if self.training:
texts = torch.cat([self.text_tokenizer(x["text"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)
texts_x = self.encode_text(texts)
outputs = {**outputs, **texts_x}
# 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,
)
del outputs
processed_results = []
for i, data in enumerate(zip(
mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
)):
mask_cls_result, mask_pred_result, input_per_image, image_size = data
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
processed_results.append({})
if self.sem_seg_postprocess_before_inference:
mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
mask_pred_result, image_size, height, width
)
mask_cls_result = mask_cls_result.to(mask_pred_result)
# semantic segmentation inference
if self.semantic_on:
r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result)
if not self.sem_seg_postprocess_before_inference:
r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)
processed_results[-1]["sem_seg"] = r
# panoptic segmentation inference
if self.panoptic_on:
panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)
processed_results[-1]["panoptic_seg"] = panoptic_r
# instance segmentation inference
if self.instance_on:
instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image["task"])
processed_results[-1]["instances"] = instance_r
if self.detection_on:
bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image["task"])
processed_results[-1]["box_instances"] = bbox_r
return processed_results
def prepare_targets(self, targets, images):
h_pad, w_pad = 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_pad, w_pad), 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
def panoptic_inference(self, mask_cls, mask_pred):
scores, labels = F.softmax(mask_cls, dim=-1).max(-1)
mask_pred = mask_pred.sigmoid()
keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)
cur_scores = scores[keep]
cur_classes = labels[keep]
cur_masks = mask_pred[keep]
cur_mask_cls = mask_cls[keep]
cur_mask_cls = cur_mask_cls[:, :-1]
cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks
h, w = cur_masks.shape[-2:]
panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)
segments_info = []
current_segment_id = 0
if cur_masks.shape[0] == 0:
# We didn't detect any mask :(
return panoptic_seg, segments_info
else:
# take argmax
cur_mask_ids = cur_prob_masks.argmax(0)
stuff_memory_list = {}
for k in range(cur_classes.shape[0]):
pred_class = cur_classes[k].item()
isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values()
mask_area = (cur_mask_ids == k).sum().item()
original_area = (cur_masks[k] >= 0.5).sum().item()
mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5)
if mask_area > 0 and original_area > 0 and mask.sum().item() > 0:
if mask_area / original_area < self.overlap_threshold:
continue
# merge stuff regions
if not isthing:
if int(pred_class) in stuff_memory_list.keys():
panoptic_seg[mask] = stuff_memory_list[int(pred_class)]
continue
else:
stuff_memory_list[int(pred_class)] = current_segment_id + 1
current_segment_id += 1
panoptic_seg[mask] = current_segment_id
segments_info.append(
{
"id": current_segment_id,
"isthing": bool(isthing),
"category_id": int(pred_class),
}
)
return panoptic_seg, segments_info
def instance_inference(self, mask_cls, mask_pred, task_type):
# mask_pred is already processed to have the same shape as original input
image_size = mask_pred.shape[-2:]
# [Q, K]
scores = F.softmax(mask_cls, dim=-1)[:, :-1]
labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)
# scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)
scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)
labels_per_image = labels[topk_indices]
topk_indices = topk_indices // self.sem_seg_head.num_classes
# mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)
mask_pred = mask_pred[topk_indices]
# Only consider scores with confidence over [self.object_mask_threshold] for demo
if self.is_demo:
keep = scores_per_image > self.object_mask_threshold
scores_per_image = scores_per_image[keep]
labels_per_image = labels_per_image[keep]
mask_pred = mask_pred[keep]
# if this is panoptic segmentation, we only keep the "thing" classes
if self.panoptic_on:
keep = torch.zeros_like(scores_per_image).bool()
for i, lab in enumerate(labels_per_image):
keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values()
scores_per_image = scores_per_image[keep]
labels_per_image = labels_per_image[keep]
mask_pred = mask_pred[keep]
if 'ade20k' in self.metadata.name and not self.is_demo and "instance" in task_type:
for i in range(labels_per_image.shape[0]):
labels_per_image[i] = self.thing_indices.index(labels_per_image[i].item())
result = Instances(image_size)
# mask (before sigmoid)
result.pred_masks = (mask_pred > 0).float()
if self.detection_on:
# Uncomment the following to get boxes from masks (this is slow)
result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes()
else:
result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4))
# calculate average mask prob
mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6)
result.scores = scores_per_image * mask_scores_per_image
result.pred_classes = labels_per_image
return result