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# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
# Modified by Feng Liang from
# https://github.com/MendelXu/zsseg.baseline/blob/master/mask_former/zero_shot_mask_former_model.py
import logging
from typing import Tuple
import numpy as np
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
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.postprocessing import sem_seg_postprocess
from detectron2.structures import ImageList
from detectron2.utils.logger import log_first_n
from .modeling.clip_adapter import (
ClipAdapter,
MaskFormerClipAdapter,
build_text_prompt,
)
from .mask_former_model import MaskFormer
from .utils.misc import get_gt_binary_masks
@META_ARCH_REGISTRY.register()
class OVSeg(MaskFormer):
"""
Main class for zero shot mask classification semantic segmentation architectures.
"""
@configurable
def __init__(
self,
*,
backbone: Backbone,
sem_seg_head: nn.Module,
clip_adapter: 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,
clip_ensemble: bool,
clip_ensemble_weight: float,
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
clip_adapter: adapter for clip-based mask classification
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__(
backbone=backbone,
sem_seg_head=sem_seg_head,
criterion=criterion,
num_queries=num_queries,
panoptic_on=panoptic_on,
object_mask_threshold=object_mask_threshold,
overlap_threshold=overlap_threshold,
metadata=metadata,
size_divisibility=size_divisibility,
sem_seg_postprocess_before_inference=sem_seg_postprocess_before_inference,
pixel_mean=pixel_mean,
pixel_std=pixel_std,
)
self.clip_adapter: ClipAdapter = clip_adapter
self.clip_ensemble: bool = clip_ensemble
self.clip_ensemble_weight: float = clip_ensemble_weight
@classmethod
def from_config(cls, cfg):
init_kwargs = MaskFormer.from_config(cfg)
text_templates = build_text_prompt(cfg.MODEL.CLIP_ADAPTER)
clip_adapter = MaskFormerClipAdapter(
cfg.MODEL.CLIP_ADAPTER.CLIP_MODEL_NAME,
text_templates,
mask_fill=cfg.MODEL.CLIP_ADAPTER.MASK_FILL,
mask_expand_ratio=cfg.MODEL.CLIP_ADAPTER.MASK_EXPAND_RATIO,
mask_thr=cfg.MODEL.CLIP_ADAPTER.MASK_THR,
mask_matting=cfg.MODEL.CLIP_ADAPTER.MASK_MATTING,
region_resized=cfg.MODEL.CLIP_ADAPTER.REGION_RESIZED,
mask_prompt_depth=cfg.MODEL.CLIP_ADAPTER.MASK_PROMPT_DEPTH,
mask_prompt_fwd=cfg.MODEL.CLIP_ADAPTER.MASK_PROMPT_FWD,
)
init_kwargs["clip_adapter"] = clip_adapter
init_kwargs["clip_ensemble"] = cfg.MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE
init_kwargs[
"clip_ensemble_weight"
] = cfg.MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE_WEIGHT
return init_kwargs
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".
"""
dataset_name = [x["meta"]["dataset_name"] for x in batched_inputs]
assert len(set(dataset_name)) == 1
dataset_name = dataset_name[0]
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)
class_names = self.get_class_name_list(dataset_name)
text_features = self.clip_adapter.get_text_features(class_names)
outputs["pred_logits"] = self.clip_adapter.get_sim_logits(
text_features, self.clip_adapter.normalize_feature(outputs["pred_logits"])
)
if self.training:
if "aux_outputs" in outputs.keys():
for i in range(len(outputs["aux_outputs"])):
outputs["aux_outputs"][i][
"pred_logits"
] = self.clip_adapter.get_sim_logits(
text_features,
self.clip_adapter.normalize_feature(
outputs["aux_outputs"][i]["pred_logits"]
),
)
# 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 = image_size[0]
width = image_size[1]
mask_pred_result = sem_seg_postprocess(
mask_pred_result, image_size, height, width
)
image = input_per_image["image"].to(self.device)
r, regions = self.semantic_inference(
mask_cls_result, mask_pred_result, image, class_names
)
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
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 semantic_inference(self, mask_cls, mask_pred, image, class_names):
mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
mask_pred = mask_pred.sigmoid()
regions = None
if self.clip_ensemble:
clip_cls, regions, valid_flag = self.clip_adapter(
image, class_names, mask_pred, normalize=True
)
if clip_cls is None:
clip_cls = torch.empty(0, mask_cls.shape[-1] + 1, device=self.device)
# softmax before index or after?
clip_cls = F.softmax(clip_cls[:, :-1], dim=-1)
if self.clip_ensemble_weight > 0:
map_back_clip_cls = mask_cls.new_ones(mask_cls.shape)
map_back_clip_cls[valid_flag] = clip_cls
mask_cls = torch.pow(mask_cls, 1 - self.clip_ensemble_weight) * \
torch.pow(map_back_clip_cls, self.clip_ensemble_weight)
else:
# only clip model predictions are used
mask_cls = clip_cls
mask_pred = mask_pred[valid_flag]
semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred)
return semseg, regions
def get_class_name_list(self, dataset_name):
class_names = [
c.strip() for c in MetadataCatalog.get(dataset_name).stuff_classes
]
return class_names
@META_ARCH_REGISTRY.register()
class OVSegDEMO(MaskFormer):
"""
Main class for zero shot mask classification semantic segmentation architectures.
"""
@configurable
def __init__(
self,
*,
backbone: Backbone,
sem_seg_head: nn.Module,
clip_adapter: 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,
clip_ensemble: bool,
clip_ensemble_weight: float,
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
clip_adapter: adapter for clip-based mask classification
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__(
backbone=backbone,
sem_seg_head=sem_seg_head,
criterion=criterion,
num_queries=num_queries,
panoptic_on=panoptic_on,
object_mask_threshold=object_mask_threshold,
overlap_threshold=overlap_threshold,
metadata=metadata,
size_divisibility=size_divisibility,
sem_seg_postprocess_before_inference=sem_seg_postprocess_before_inference,
pixel_mean=pixel_mean,
pixel_std=pixel_std,
)
self.clip_adapter: ClipAdapter = clip_adapter
self.clip_ensemble: bool = clip_ensemble
self.clip_ensemble_weight: float = clip_ensemble_weight
@classmethod
def from_config(cls, cfg):
init_kwargs = MaskFormer.from_config(cfg)
text_templates = build_text_prompt(cfg.MODEL.CLIP_ADAPTER)
clip_adapter = MaskFormerClipAdapter(
cfg.MODEL.CLIP_ADAPTER.CLIP_MODEL_NAME,
text_templates,
mask_fill=cfg.MODEL.CLIP_ADAPTER.MASK_FILL,
mask_expand_ratio=cfg.MODEL.CLIP_ADAPTER.MASK_EXPAND_RATIO,
mask_thr=cfg.MODEL.CLIP_ADAPTER.MASK_THR,
mask_matting=cfg.MODEL.CLIP_ADAPTER.MASK_MATTING,
region_resized=cfg.MODEL.CLIP_ADAPTER.REGION_RESIZED,
mask_prompt_depth=cfg.MODEL.CLIP_ADAPTER.MASK_PROMPT_DEPTH,
mask_prompt_fwd=cfg.MODEL.CLIP_ADAPTER.MASK_PROMPT_FWD,
)
init_kwargs["clip_adapter"] = clip_adapter
init_kwargs["clip_ensemble"] = cfg.MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE
init_kwargs[
"clip_ensemble_weight"
] = cfg.MODEL.CLIP_ADAPTER.CLIP_ENSEMBLE_WEIGHT
return init_kwargs
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)
class_names = batched_inputs[0]["class_names"]
if len(class_names) == 1:
# Because classification is performed in a 'contrastive' manner, adding others to represent other concepts
class_names.append('others')
text_features = self.clip_adapter.get_text_features(class_names)
outputs["pred_logits"] = self.clip_adapter.get_sim_logits(
text_features, self.clip_adapter.normalize_feature(outputs["pred_logits"])
)
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 = image_size[0]
width = image_size[1]
mask_pred_result = sem_seg_postprocess(
mask_pred_result, image_size, height, width
)
image = input_per_image["image"].to(self.device)
r, regions = self.demo_inference(mask_cls_result, mask_pred_result, image, class_names)
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
r = sem_seg_postprocess(r, image_size, height, width)
processed_results.append({"sem_seg": r})
return processed_results
def demo_inference(self, mask_cls, mask_pred, image, class_names):
mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
mask_pred = mask_pred.sigmoid()
regions = None
if self.clip_ensemble:
clip_cls, regions, valid_flag = self.clip_adapter(
image, class_names, mask_pred, normalize=True
)
if clip_cls is None:
clip_cls = torch.empty(0, mask_cls.shape[-1] + 1, device=self.device)
# softmax before index or after?
clip_cls = F.softmax(clip_cls[:, :-1], dim=-1)
if self.clip_ensemble_weight > 0:
map_back_clip_cls = mask_cls.new_ones(mask_cls.shape)
map_back_clip_cls[valid_flag] = clip_cls
mask_cls = torch.pow(mask_cls, 1 - self.clip_ensemble_weight) * \
torch.pow(map_back_clip_cls, self.clip_ensemble_weight)
else:
# only clip model predictions are used
mask_cls = clip_cls
mask_pred = mask_pred[valid_flag]
bin_mask = mask_pred > self.clip_adapter.mask_thr
select_cls = torch.zeros(sum(valid_flag), mask_cls.shape[-1], device=self.device)
select_mask = torch.argmax(mask_cls, dim=0)
if len(class_names) == 2 and class_names[-1] == 'others':
select_mask = select_mask[:-1]
for idx in select_mask:
select_cls[idx] = mask_cls[idx]
semseg = torch.einsum("qc,qhw->chw", select_cls, bin_mask.float())
return semseg, regions
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