MaureenZOU
init
fcc479d
raw history blame
No virus
27.9 kB
# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------
import random
from typing import Tuple
from unicodedata import name
import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
from .registry import register_model
from ..utils import configurable
from ..backbone import build_backbone, Backbone
from ..body import build_xdecoder_head
from ..modules import sem_seg_postprocess, bbox_postprocess
from ..language import build_language_encoder
from ..language.loss import vl_similarity
from timm.models.layers import trunc_normal_
from nltk.stem.lancaster import LancasterStemmer
from detectron2.structures import Boxes, ImageList, Instances, BitMasks, BoxMode
from detectron2.utils.memory import retry_if_cuda_oom
from detectron2.data import MetadataCatalog
from utils.misc import prompt_engineering
st = LancasterStemmer()
class X_Decoder_Model(nn.Module):
@configurable
def __init__(
self,
*,
backbone: Backbone,
sem_seg_head: nn.Module,
criterion: nn.Module,
losses: dict,
num_queries: int,
object_mask_threshold: float,
overlap_threshold: float,
metadata,
task_switch: dict,
phrase_prob: float,
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,
test_topk_per_image: int,
train_dataset_name: str,
retrieval_emsemble: bool,
backbone_dim: int,
dim_proj: int,
):
super().__init__()
self.backbone = backbone
self.sem_seg_head = sem_seg_head
self.criterion = criterion
self.losses = losses
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
# caption argument
self.task_switch = task_switch
self.phrase_prob = phrase_prob
self.test_topk_per_image = test_topk_per_image
self.train_class_names = None
self.retrieval_emsemble = retrieval_emsemble
# backbone itc loss
if task_switch['retrieval'] and retrieval_emsemble:
self.backbone_proj = nn.Parameter(torch.empty(backbone_dim, dim_proj))
trunc_normal_(self.backbone_proj, std=.02)
if not self.semantic_on:
assert self.sem_seg_postprocess_before_inference
@classmethod
def from_config(cls, cfg):
enc_cfg = cfg['MODEL']['ENCODER']
dec_cfg = cfg['MODEL']['DECODER']
task_switch = {'bbox': dec_cfg.get('DETECTION', False),
'mask': dec_cfg.get('MASK', True),
'caption': dec_cfg['CAPTION'].get('ENABLED', False),
'captioning': dec_cfg['CAPTIONING'].get('ENABLED', False),
'retrieval': dec_cfg['RETRIEVAL'].get('ENABLED', False),
'grounding': dec_cfg['GROUNDING'].get('ENABLED', False)}
# build model
extra = {'task_switch': task_switch}
backbone = build_backbone(cfg)
lang_encoder = build_language_encoder(cfg)
sem_seg_head = build_xdecoder_head(cfg, backbone.output_shape(), lang_encoder, extra)
# Training Settings.
loss_weights = {}
matcher = None
losses = {}
weight_dict = {}
grd_weight = {}
top_x_layers = {}
criterion = None
train_dataset_name = None
phrase_prob = None
# Loss parameters:
deep_supervision = None
no_object_weight = None
return {
"backbone": backbone,
"sem_seg_head": sem_seg_head,
"criterion": criterion,
"losses": losses,
"num_queries": dec_cfg['NUM_OBJECT_QUERIES'],
"object_mask_threshold": dec_cfg['TEST']['OBJECT_MASK_THRESHOLD'],
"overlap_threshold": dec_cfg['TEST']['OVERLAP_THRESHOLD'],
"metadata": None,
"size_divisibility": dec_cfg['SIZE_DIVISIBILITY'],
"sem_seg_postprocess_before_inference": (
dec_cfg['TEST']['SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE']
or dec_cfg['TEST']['PANOPTIC_ON']
or dec_cfg['TEST']['INSTANCE_ON']
),
"pixel_mean": cfg['INPUT']['PIXEL_MEAN'],
"pixel_std": cfg['INPUT']['PIXEL_STD'],
"task_switch": task_switch,
"phrase_prob": phrase_prob,
# inference
"semantic_on": dec_cfg['TEST']['SEMANTIC_ON'],
"instance_on": dec_cfg['TEST']['INSTANCE_ON'],
"panoptic_on": dec_cfg['TEST']['PANOPTIC_ON'],
"test_topk_per_image": cfg['MODEL']['DECODER']['TEST']['DETECTIONS_PER_IMAGE'],
"train_dataset_name": train_dataset_name,
"retrieval_emsemble": dec_cfg['RETRIEVAL']['ENSEMBLE'],
"backbone_dim": cfg['MODEL']['BACKBONE_DIM'],
"dim_proj": cfg['MODEL']['DIM_PROJ'],
}
@property
def device(self):
return self.pixel_mean.device
def forward(self, batched_inputs, mode=None):
if self.training:
assert False, "Not support trianing mode."
else:
if mode == 'retrieval':
return self.evaluate_retrieval(batched_inputs)
elif mode == 'captioning':
return self.evaluate_captioning(batched_inputs)
elif mode == 'classification':
return self.evaluate_classification(batched_inputs)
elif mode in ['grounding_phrasecut', 'grounding_refcoco']:
return self.evaluate_grounding(batched_inputs, mode)
else:
return self.evaluate(batched_inputs)
def evaluate(self, batched_inputs):
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)
img_bs = images.tensor.shape[0]
targets = targets_grounding = queries_grounding = None
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, target_queries=queries_grounding)
mask_cls_results = outputs["pred_logits"]
mask_pred_results = outputs["pred_masks"]
box_pred_results = outputs["pred_boxes"] if self.task_switch['bbox'] else [None for i in range(len(mask_pred_results))]
caption_pred_results = outputs["pred_captions"] if self.task_switch['caption'] else [None for i in range(len(mask_pred_results))]
# upsample masks
mask_pred_results = F.interpolate(
mask_pred_results,
size=(images.tensor.shape[-2], images.tensor.shape[-1]),
mode="bilinear",
align_corners=False,
)
input_size = mask_pred_results.shape[-2:]
keep_sem_bgd = self.metadata.keep_sem_bgd if hasattr(self.metadata, 'keep_sem_bgd') else False
del outputs
processed_results = []
for mask_cls_result, mask_pred_result, box_pred_result, caption_pred_result, input_per_image, image_size in zip(
mask_cls_results, mask_pred_results, box_pred_results, caption_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])
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, keep_sem_bgd)
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:
if self.task_switch['bbox']:
box_pred_result = bbox_postprocess(box_pred_result, input_size, image_size, height, width)
instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, box_pred_result)
processed_results[-1]["instances"] = instance_r
if self.task_switch['caption']:
processed_results[-1]["captions"] = caption_pred_result
processed_results[-1]["masks"] = mask_pred_result
return processed_results
def evaluate_retrieval(self, batched_inputs):
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)
img_bs = images.tensor.shape[0]
targets = targets_grounding = queries_grounding = None
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, target_queries=queries_grounding)
v_emb_it = outputs['pred_captions'][:,-1]
# compute backbone score
if self.task_switch['retrieval'] and self.retrieval_emsemble:
_v_emb_it = features['res5']
bs,nc,_,_ = _v_emb_it.shape
_v_emb_it = _v_emb_it.reshape(bs,nc,-1)
_v_emb_it = F.adaptive_avg_pool1d(_v_emb_it, 1).reshape(bs,nc) @ self.backbone_proj
processed_results = []
for idx, batch_data in enumerate(batched_inputs):
caption_ids = []
t_emb_its = []
processed_results.append({})
for caption in batch_data['captions']:
lang_results = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings(caption)
t_emb_it = lang_results['class_emb']
caption_ids.append(batch_data['image_id'])
t_emb_its.append(t_emb_it)
t_emb_it = torch.cat(t_emb_its, dim=0)
image_embeds = [v_emb_it[idx].unsqueeze(0)]
if self.task_switch['retrieval'] and self.retrieval_emsemble:
image_embeds += [_v_emb_it[idx].unsqueeze(0)]
caption_results = {
'image_embeds': image_embeds,
'text_embeds': t_emb_it,
'caption_ids': caption_ids,
'image_ids': batch_data['image_id'],
}
processed_results[-1]["caption"] = caption_results
return processed_results
def evaluate_captioning(self, batched_inputs, extra={}):
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)
img_bs = images.tensor.shape[0]
if not hasattr(self, 'start_token'):
self.start_token = torch.tensor([[49406]*77], device=self.device)
targets = targets_grounding = queries_grounding = None
features = self.backbone(images.tensor)
captioning_mask = None
if 'captioning_mask' in batched_inputs[-1]:
captioning_mask = torch.cat([x['captioning_mask'] for x in batched_inputs])
extra.update({'start_token': self.start_token, 'captioning_mask': captioning_mask})
outputs = self.sem_seg_head(features, target_queries=queries_grounding, task='captioning_infer', extra=extra)
processed_results = []
for idx, batch_data in enumerate(batched_inputs):
processed_results.append({})
processed_results[-1]["captioning_token"] = outputs['pred_captionings'][idx]
processed_results[-1]["captioning_text"] = outputs['pred_texts'][idx].split('.')[0]
processed_results[-1]["image_id"] = batched_inputs[idx]['image_id']
return processed_results
def evaluate_classification(self, batched_inputs):
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)
img_bs = images.tensor.shape[0]
targets = targets_grounding = queries_grounding = None
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, target_queries=queries_grounding)
processed_results = []
for idx, batch_data in enumerate(batched_inputs):
processed_results.append({})
processed_results[-1]["pred_class"] = outputs['pred_logits'][idx,-1]
return processed_results
def evaluate_grounding_baseline(self, batched_inputs, mode):
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)
img_bs = images.tensor.shape[0]
targets = targets_grounding = queries_grounding = None
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, target_queries=queries_grounding)
mask_pred_results = outputs["pred_masks"]
caption_pred_results = outputs["pred_captions"] if self.task_switch['caption'] else [None for i in range(len(mask_pred_results))]
# 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_pred_result, caption_pred_result, input_per_image, image_size in zip(
mask_pred_results, caption_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])
processed_results.append({})
mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
mask_pred_result, image_size, height, width
)[:-1]
texts_all = input_per_image['groundings']['texts']
grd_masks = []
for texts in texts_all:
if mode == 'grounding_refcoco':
self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(texts, name='grounding', prompt=False, is_eval=True)
elif mode == 'grounding_phrasecut':
self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(texts, name='grounding', prompt=True, is_eval=False)
t_emb = getattr(self.sem_seg_head.predictor.lang_encoder, "{}_text_embeddings".format('grounding')).t()
v_emb = caption_pred_result[:-1]
v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
vt_sim = v_emb @ t_emb
max_id = vt_sim.max(0)[1][0]
grd_masks += [mask_pred_result[max_id]]
processed_results[-1]['grounding_mask'] = torch.stack(grd_masks)
return processed_results
def evaluate_grounding(self, batched_inputs, mode):
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)
extra = {}
# mask_pred_results = []
# for idx, batch_per_image in enumerate(batched_inputs):
# grd_texts = batch_per_image['groundings']['texts']
# grd_masks = []
# for anno_text in grd_texts:
# gtext = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings([anno_text[0]], name='grounding', token=False, norm=False)
# token_emb = gtext['token_emb']
# tokens = gtext['tokens']
# grd_emb = token_emb[0][tokens['attention_mask'].bool()[0]]
# extra['grounding_tokens'] = grd_emb[:,None]
# assert len(images.tensor) == 1, "grounding evaluation only support single batch size now"
# features = self.backbone(images.tensor)
# outputs = self.sem_seg_head(features, extra=extra, task='grounding_eval')
# pred_gmasks = outputs['pred_masks'][idx,self.num_queries:2*self.num_queries-1]
# v_emb = outputs['pred_captions'][idx,self.num_queries:2*self.num_queries-1]
# t_emb = grd_emb[-1:]
# t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7)
# v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
# temperature = self.sem_seg_head.predictor.lang_encoder.logit_scale
# out_prob = vl_similarity(v_emb, t_emb, temperature=temperature)
# matched_id = out_prob.max(0)[1]
# grd_masks += [pred_gmasks[matched_id,:,:]]
# mask_pred_results += [torch.cat(grd_masks)]
# comment for multi object inference.
mask_pred_results = []
for idx, batch_per_image in enumerate(batched_inputs):
grd_texts = batch_per_image['groundings']['texts']
grd_texts = [x[0] for x in grd_texts]
gtext = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings(grd_texts, name='grounding', token=False, norm=False)
token_emb = gtext['token_emb']
tokens = gtext['tokens']
query_emb = token_emb[tokens['attention_mask'].bool()]
extra['grounding_tokens'] = query_emb[:,None]
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, extra=extra, task='grounding_eval')
pred_gmasks = outputs['pred_masks'][idx,self.num_queries:2*self.num_queries-1]
v_emb = outputs['pred_captions'][idx,self.num_queries:2*self.num_queries-1]
t_emb = gtext['class_emb']
t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7)
v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
temperature = self.sem_seg_head.predictor.lang_encoder.logit_scale
out_prob = vl_similarity(v_emb, t_emb, temperature=temperature)
matched_id = out_prob.max(0)[1]
mask_pred_results += [pred_gmasks[matched_id,:,:]]
for i in range(len(mask_pred_results)):
# upsample masks
mask_pred_results[i] = F.interpolate(
mask_pred_results[i][None,],
size=(images.tensor.shape[-2], images.tensor.shape[-1]),
mode="bilinear",
align_corners=False,
)[0]
processed_results = []
for mask_pred_result, input_per_image, image_size in zip(
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])
processed_results.append({})
mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
mask_pred_result, image_size, height, width
)
processed_results[-1]['grounding_mask'] = mask_pred_result
# compute bbox
# bbox = BitMasks(mask_pred_result > 0).get_bounding_boxes()
# bbox = BoxMode.convert(bbox.tensor, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
# processed_results[-1]['grounding_box'] = bbox
return processed_results
def prepare_vlp_targets(self, batched_inputs, device):
input_ids = []
attention_mask = []
for cnt, x in enumerate(batched_inputs):
captions = x['captions']
randid = random.randint(0, len(captions)-1)
input_ids += x['tokens']['input_ids'][randid:randid+1]
attention_mask += x['tokens']['attention_mask'][randid:randid+1]
input_ids = torch.stack(input_ids)
attention_mask = torch.stack(attention_mask)
tokens = {"input_ids": input_ids, "attention_mask": attention_mask}
lang_results = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings(tokens, token=True)
target_vlp = []
for cnt, x in enumerate(batched_inputs):
target_dict = {}
target_dict["caption_tokens"] = lang_results['token_emb'][cnt:cnt+1]
target_dict["caption_proj"] = lang_results['class_emb'][cnt:cnt+1]
target_dict["caption_tokenids"] = lang_results['tokens']['input_ids'][cnt:cnt+1]
target_dict["caption_mask"] = lang_results['tokens']['attention_mask'][cnt:cnt+1]
target_vlp.append(target_dict)
return target_vlp
def semantic_inference(self, mask_cls, mask_pred, keep_sem_bgd=False):
if keep_sem_bgd:
mask_cls = F.softmax(mask_cls, dim=-1)
else:
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 = {}
thing_dataset_id_to_contiguous_id = self.metadata.thing_dataset_id_to_contiguous_id if hasattr(self.metadata, 'thing_dataset_id_to_contiguous_id') else {}
for k in range(cur_classes.shape[0]):
pred_class = cur_classes[k].item()
isthing = pred_class in 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, box_pred):
# 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]
if box_pred is not None:
box_pred = box_pred[topk_indices]
# if this is panoptic segmentation, we only keep the "thing" classes
if self.panoptic_on:
thing_dataset_id_to_contiguous_id = self.metadata.thing_dataset_id_to_contiguous_id if hasattr(self.metadata, 'thing_dataset_id_to_contiguous_id') else {}
keep = torch.zeros_like(scores_per_image).bool()
for i, lab in enumerate(labels_per_image):
keep[i] = lab in 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 box_pred is not None:
box_pred = box_pred[keep]
result = Instances(image_size)
# mask (before sigmoid)
result.pred_masks = (mask_pred > 0).float()
# result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4))
# Uncomment the following to get boxes from masks (this is slow)
if box_pred is not None:
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
@register_model
def get_segmentation_model(cfg, **kwargs):
return X_Decoder_Model(cfg)