# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------
# SEEM -- Segment Everything Everywhere All at Once
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------
import random
from typing import Tuple
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from kornia.contrib import distance_transform
from detectron2.structures import Boxes, ImageList, Instances, BitMasks
from detectron2.utils.memory import retry_if_cuda_oom
from detectron2.data import MetadataCatalog
from .build import register_model
from ..utils import configurable, get_class_names, get_iou
from ..vision.backbone import build_backbone, Backbone
from ..body import build_xdecoder_head
from ..modules import sem_seg_postprocess, SetCriterion, HungarianMatcher, bbox_postprocess
from ..language import build_language_encoder
from ..language.loss import vl_similarity
from utilities.prompt_engineering import prompt_engineering
from utilities.constants import COCO_PANOPTIC_CLASSES
class GeneralizedSEEM(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,
interactive_mode: str,
interactive_iter: str,
dilation_kernel: torch.Tensor,
train_max_iter: int,
):
"""
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.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.train_max_iter = train_max_iter
self.test_topk_per_image = test_topk_per_image
self.train_class_names = get_class_names(train_dataset_name)
self.interactive_mode = interactive_mode
self.interactive_iter = interactive_iter
if not self.semantic_on:
assert self.sem_seg_postprocess_before_inference
self.register_buffer("dilation_kernel", dilation_kernel)
@classmethod
def from_config(cls, cfg):
enc_cfg = cfg['MODEL']['ENCODER']
dec_cfg = cfg['MODEL']['DECODER']
# Loss parameters:
deep_supervision = dec_cfg['DEEP_SUPERVISION']
no_object_weight = dec_cfg['NO_OBJECT_WEIGHT']
# loss weights
loss_weights = {'mask': {'ce': dec_cfg['CLASS_WEIGHT'], 'dice': dec_cfg['DICE_WEIGHT'], 'bce': dec_cfg['MASK_WEIGHT']},
'bbox': {'l1': dec_cfg['BBOX_WEIGHT'], 'giou': dec_cfg['GIOU_WEIGHT']},
'spatial': {'ce': dec_cfg['SCLASS_WEIGHT'], 'dice': dec_cfg['SDICE_WEIGHT'], 'bce': dec_cfg['SMASK_WEIGHT']},
'grounding': {'ce': dec_cfg['GCLASS_WEIGHT'], 'dice': dec_cfg['GDICE_WEIGHT'], 'bce': dec_cfg['GMASK_WEIGHT']},
'openimage': {'ce': dec_cfg['OCLASS_WEIGHT'], 'dice': dec_cfg['ODICE_WEIGHT'], 'bce': dec_cfg['OMASK_WEIGHT']}}
openimage_switch = {'grounding': dec_cfg['OPENIMAGE']['GROUNDING'].get('ENABLED', False),
'mask': dec_cfg['OPENIMAGE'].get('ENABLED', False)}
task_switch = {'bbox': dec_cfg.get('DETECTION', False),
'mask': dec_cfg['MASK'].get('ENABLED', True),
'spatial': dec_cfg['SPATIAL'].get('ENABLED', False),
'grounding': dec_cfg['GROUNDING'].get('ENABLED', False),
'openimage': openimage_switch}
top_x_layers = {'mask': dec_cfg.get('TOP_MASK_LAYERS', 10),
'grounding': dec_cfg.get('TOP_GROUNDING_LAYERS', 10),
'openimage': dec_cfg.get('TOP_OPENIMAGE_LAYERS', 10),
'spatial': dec_cfg.get('TOP_SPATIAL_LAYERS', 10)}
spatial_cost = {"class_weight": dec_cfg['COST_SPATIAL']['CLASS_WEIGHT'],
"mask_weight": dec_cfg['COST_SPATIAL']['MASK_WEIGHT'],
"dice_weight": dec_cfg['COST_SPATIAL']['DICE_WEIGHT']}
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=extra)
# building criterion
matcher = HungarianMatcher(
cost_class=loss_weights['mask']['ce'],
cost_mask=loss_weights['mask']['bce'],
cost_dice=loss_weights['mask']['dice'],
num_points=dec_cfg['TRAIN_NUM_POINTS'],
spatial_cost=spatial_cost,
)
# init weight dict and criterion loss functions.
losses = {'seg': [], 'openimage': []}
if task_switch['mask']:
losses['seg'] += ["labels", "masks"]
if task_switch['spatial']:
losses['seg'] += ["spatials"]
if task_switch['grounding']:
losses['seg'] += ["groundings"]
if task_switch['openimage']:
losses['openimage'] += ["labels_openimage", "masks"]
if task_switch['openimage']['grounding']:
losses['openimage'] += ["groundings"]
weight_dict = {}
for key, turn_on in task_switch.items():
if turn_on:
if isinstance(loss_weights[key], dict):
# HACK it should support bbox in the future
for key_, weight in loss_weights[key].items():
weight_dict["loss_{}_{}_0".format(key, key_)] = weight # NOTE: hard code for segmentation that has multiple loss
else:
weight_dict["loss_{}_0".format(key)] = loss_weights[key]
# generate full weight dict and remove not computed layers.
if deep_supervision:
dec_layers = dec_cfg['DEC_LAYERS']
aux_weight_dict = {}
for i in range(dec_layers - 1):
for k, v in weight_dict.items():
if (i+1) > (top_x_layers[k.split('_')[1]] - 1):
continue
aux_weight_dict.update({k.replace('_0', f"_{i+1}"): v})
weight_dict.update(aux_weight_dict)
grd_weight = {'text': dec_cfg['GROUNDING']['TEXT_WEIGHT'], 'class': dec_cfg['GROUNDING']['CLASS_WEIGHT']}
# generate critenrion for loss function.
criterion = SetCriterion(
sem_seg_head.num_classes,
matcher=matcher,
weight_dict=weight_dict,
top_x_layers=top_x_layers,
eos_coef=no_object_weight,
losses=[],
num_points=dec_cfg['TRAIN_NUM_POINTS'],
oversample_ratio=dec_cfg['OVERSAMPLE_RATIO'],
importance_sample_ratio=dec_cfg['IMPORTANCE_SAMPLE_RATIO'],
grounding_weight=grd_weight,
)
# extra logistic
train_dataset_name = cfg['DATASETS']['TRAIN'][0] # HACK for only one training set.
train_max_iter = dec_cfg['SPATIAL'].get('MAX_ITER', 3)
phrase_prob = dec_cfg['CAPTION'].get('PHRASE_PROB', 0.5)
interactive_mode = cfg['STROKE_SAMPLER']['EVAL']['MODE']
interactive_iter = cfg['STROKE_SAMPLER']['EVAL']['MAX_ITER']
dilation = 3
dilation_kernel = torch.ones((1, 1, dilation, dilation), device=torch.cuda.current_device())
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": MetadataCatalog.get(cfg['DATASETS']['TRAIN'][0]),
"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['TEST']['DETECTIONS_PER_IMAGE'],
"train_dataset_name": train_dataset_name,
"interactive_mode": interactive_mode,
"interactive_iter": interactive_iter,
"dilation_kernel": dilation_kernel,
"train_max_iter": train_max_iter,
}
@property
def device(self):
return self.pixel_mean.device
def forward(self, batched_inputs, mode='default'):
"""
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".
"""
if self.training:
losses = {}
if self.task_switch['mask'] or self.task_switch['grounding'] or self.task_switch['spatial']:
losses_seg = self.forward_seg(batched_inputs)
losses.update(losses_seg)
if self.task_switch['openimage'] and self.task_switch['openimage']['mask']:
losses_openimage = self.forward_openimage(batched_inputs['openimage'])
losses_openimage = {key.replace('mask', 'openimage'):value for key, value in losses_openimage.items()}
losses_openimage = {key.replace('grounding', 'grounding_openimage'):value for key, value in losses_openimage.items()}
losses.update(losses_openimage)
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:
if mode == 'interactive':
return self.evaluate_interactive(batched_inputs)
elif mode == 'interactive_grounding':
return self.evaluate_interactive_grounding(batched_inputs)
elif mode == 'grounding_spatial':
return self.evaluate_grounding_sptial(batched_inputs, mode)
elif mode in ['grounding_phrasecut', 'grounding_refcoco']:
return self.evaluate_grounding(batched_inputs, mode)
else:
return self.evaluate(batched_inputs)
def forward_seg(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)
self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(self.train_class_names, is_eval=False)
extra = {}
# mask classification target
if "instances" in batched_inputs[0]:
# input bounding box is checked to be correct.
targets = self.prepare_targets(batched_inputs, images)
if self.task_switch['grounding']:
grounding_tokens = [x['grounding_query_embs'] for x in targets] # need to pad for more than one grounding token
grounding_tokens = nn.utils.rnn.pad_sequence(grounding_tokens, padding_value=-1)
non_zero_query_mask = (grounding_tokens.sum(dim=-1) == -grounding_tokens.shape[-1])
grounding_tokens[non_zero_query_mask] = 0
extra['grounding_tokens'] = grounding_tokens
extra['grounding_nonzero_mask'] = non_zero_query_mask.t()
if self.task_switch['spatial']:
pos_masks = [x['spatial_query']['rand_shape'].to(self.device) for x in batched_inputs]
neg_masks = [(x['spatial_query']['rand_shape'].to(self.device) & False) for x in batched_inputs]
fp_masks = torch.stack([(x['spatial_query']['rand_shape'].to(self.device) & False) for x in batched_inputs])
extra.update({'spatial_query_pos_mask': pos_masks, 'spatial_query_neg_mask': neg_masks, 'false_positive_mask': fp_masks})
features = self.backbone(images.tensor)
mask_features, _, multi_scale_features = self.sem_seg_head.pixel_decoder.forward_features(features)
# forward spatial only without gradient
if self.task_switch['spatial']:
with torch.no_grad():
# generate random integeter between [0,3]
rand_iter_num = random.randint(0, self.train_max_iter)
for i in range(rand_iter_num):
outputs = self.sem_seg_head.predictor(multi_scale_features, mask_features, extra=extra, task='spatial')
extra.update(outputs)
extra.update(self.prepare_next_spaital_mask(extra, batched_inputs))
outputs = self.sem_seg_head.predictor(multi_scale_features, mask_features, extra=extra, task='seg')
extra = {'lang_logit': self.sem_seg_head.predictor.lang_encoder.logit_scale,
'class_embeddings': getattr(self.sem_seg_head.predictor.lang_encoder, '{}_text_embeddings'.format('default')),
'false_positive_mask': extra['false_positive_mask']}
# bipartite matching-based loss
self.criterion.losses = self.losses['seg'] # seg criterion losses
losses = self.criterion(outputs, targets, extra)
del outputs
return losses
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))]
# 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:]
del outputs
processed_results = []
for mask_cls_result, mask_pred_result, box_pred_result, input_per_image, image_size in zip(
mask_cls_results, mask_pred_results, box_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)
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
return processed_results
def evaluate_interactive(self, batched_inputs):
assert self.task_switch['spatial']
assert 'spatial_query' in batched_inputs[0]
assert len(batched_inputs) == 1, "only support batch size equal to 1"
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
extra = {}
features = self.backbone(images.tensor)
mask_features, transformer_encoder_features, multi_scale_features = self.sem_seg_head.pixel_decoder.forward_features(features)
image_sizes = [x["image"].shape[-2:] for x in batched_inputs]
nm = len(batched_inputs[0]['spatial_query']['rand_shape'])
multi_scale_features = [m.repeat(nm,1,1,1) for m in multi_scale_features]
mask_features = mask_features.repeat(nm,1,1,1)
all_batch_shape_iou = []
pred_smask_pointer = None
prev_smask_pointer = None
pred_smask_all = None
# visualization code
# v_pred_mask = []
# v_pos_mask = []
# v_neg_mask = []
# v_gt_mask = batched_inputs[0]['spatial_query']['gt_masks'][0]
query_index = self.sem_seg_head.predictor.query_index
if self.interactive_mode in ['best', 'best_random']:
pos_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)).unbind(0)
pos_masks = ImageList.from_tensors(pos_masks, self.size_divisibility).tensor.unbind(0)
neg_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device) & False).unbind(0)
neg_masks = ImageList.from_tensors(neg_masks, self.size_divisibility).tensor.unbind(0)
extra.update({'spatial_query_pos_mask': pos_masks, 'spatial_query_neg_mask': neg_masks})
elif self.interactive_mode == 'random':
pos_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)==1).unbind(0)
pos_masks = ImageList.from_tensors(pos_masks, self.size_divisibility).tensor
neg_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)==-1).unbind(0)
neg_masks = ImageList.from_tensors(neg_masks, self.size_divisibility).tensor
extra.update({'spatial_query_pos_mask': pos_masks[:,0:1].unbind(), 'spatial_query_neg_mask': neg_masks[:,0:1].unbind()})
else:
assert False, "invalid interactive mode"
for i in range(self.interactive_iter):
# v_pos_mask += [extra['spatial_query_pos_mask'][0][0][:image_sizes[0][0],:image_sizes[0][1]].float().cpu().numpy()]
# v_neg_mask += [extra['spatial_query_neg_mask'][0][0][:image_sizes[0][0],:image_sizes[0][1]].float().cpu().numpy()]
outputs = self.sem_seg_head.predictor(multi_scale_features, mask_features, target_queries=queries_grounding, extra=extra, task='spatial')
extra.update(outputs)
pred_smask = F.interpolate(outputs['prev_mask'], images.tensor.shape[-2:], mode='bilinear')
# v_pred_mask += [(pred_smask[0,0][:image_sizes[0][0],:image_sizes[0][1]].sigmoid() > 0.5).float().cpu().numpy()]
s = image_sizes[0]
b = batched_inputs[0]
pred_smask_all = F.interpolate(pred_smask[:,:,:s[0],:s[1]], (b['height'], b['width']), mode='bilinear')[:,0].sigmoid() > 0.5
gt_smask = b['gt_masks_orisize']
ious = get_iou(gt_smask, pred_smask_all)
all_batch_shape_iou += [ious]
if (ious > 0.9).sum() == len(ious):
all_batch_shape_iou += [ious for j in range(self.interactive_iter-i-1)]
break
if self.interactive_mode in ['best', 'best_random']:
extra.update(self.prepare_next_spaital_mask(extra, batched_inputs, mode=self.interactive_mode))
elif self.interactive_mode == 'random':
extra.update({'spatial_query_pos_mask': pos_masks[:,i+1:i+2].unbind(), 'spatial_query_neg_mask': neg_masks[:,i+1:i+2].unbind()})
else:
assert False, "invalid interactive mode"
all_batch_shape_iou = torch.stack(all_batch_shape_iou)
processed_results = [{"mask_iou": all_batch_shape_iou[:,i]} for i in range(len(all_batch_shape_iou[0]))]
return processed_results
def evaluate_interactive_single(self, batched_inputs, extra={}):
assert self.task_switch['spatial']
assert 'spatial_query' in batched_inputs[0]
assert len(batched_inputs) == 1, "only support batch size equal to 1"
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)
mask_features, transformer_encoder_features, multi_scale_features = self.sem_seg_head.pixel_decoder.forward_features(features)
image_sizes = [x["image"].shape[-2:] for x in batched_inputs]
nm = len(batched_inputs[0]['spatial_query']['rand_shape'])
multi_scale_features = [m.repeat(nm,1,1,1) for m in multi_scale_features]
mask_features = mask_features.repeat(nm,1,1,1)
outputs = self.sem_seg_head.predictor(multi_scale_features, mask_features, target_queries=queries_grounding, extra=extra, task='spatial')
pred_smask = F.interpolate(outputs['prev_mask'], images.tensor.shape[-2:], mode='bicubic')
s = image_sizes[0]
b = batched_inputs[0]
pred_smask_ori = F.interpolate(pred_smask[:,:,:s[0],:s[1]], (b['height'], b['width']), mode='bicubic')[:,0].sigmoid() > 0.5
pred_smask_batch = pred_smask[:,:,:s[0],:s[1]].sigmoid() > 0.5
ious = []
if 'gt_masks_orisize' in b:
gt_smask = b['gt_masks_orisize'].to(pred_smask_ori.device)
ious = get_iou(gt_smask, pred_smask_ori)
processed_results = [{"mask_iou": ious, 'pred_mask_ori': pred_smask_ori, 'pred_mask_batch': pred_smask_batch}]
return processed_results
def evaluate_interactive_grounding(self, batched_inputs):
assert self.task_switch['spatial']
assert 'spatial_query' in batched_inputs[0]
assert len(batched_inputs) == 1, "only support batch size equal to 1"
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
extra = {}
features = self.backbone(images.tensor)
mask_features, transformer_encoder_features, multi_scale_features = self.sem_seg_head.pixel_decoder.forward_features(features)
image_sizes = [x["image"].shape[-2:] for x in batched_inputs]
nm = len(batched_inputs[0]['spatial_query']['rand_shape'])
multi_scale_features = [m.repeat(nm,1,1,1) for m in multi_scale_features]
mask_features = mask_features.repeat(nm,1,1,1)
all_batch_shape_iou = []
pred_smask_pointer = None
prev_smask_pointer = None
pred_smask_all = None
# visualization code
# v_pred_mask = []
# v_pos_mask = []
# v_neg_mask = []
# v_gt_mask = batched_inputs[0]['spatial_query']['gt_masks'][0]
query_index = self.sem_seg_head.predictor.query_index
if self.interactive_mode in ['best', 'best_random']:
pos_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)).unbind(0)
pos_masks = ImageList.from_tensors(pos_masks, self.size_divisibility).tensor.unbind(0)
neg_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device) & False).unbind(0)
neg_masks = ImageList.from_tensors(neg_masks, self.size_divisibility).tensor.unbind(0)
extra.update({'spatial_query_pos_mask': pos_masks, 'spatial_query_neg_mask': neg_masks})
elif self.interactive_mode == 'random':
pos_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)==1).unbind(0)
pos_masks = ImageList.from_tensors(pos_masks, self.size_divisibility).tensor
neg_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)==-1).unbind(0)
neg_masks = ImageList.from_tensors(neg_masks, self.size_divisibility).tensor
extra.update({'spatial_query_pos_mask': pos_masks[:,0:1].unbind(), 'spatial_query_neg_mask': neg_masks[:,0:1].unbind()})
else:
assert False, "invalid interactive mode"
grd_texts = batched_inputs[0]['classes']
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 = nn.utils.rnn.pad_sequence([_token_emb[_tokens.bool()] for _token_emb, _tokens in zip(token_emb, tokens['attention_mask'])], padding_value=-1)
non_zero_query_mask = (query_emb.sum(dim=-1) < 0)
extra['grounding_tokens'] = query_emb
extra['grounding_nonzero_mask'] = non_zero_query_mask.t()
for i in range(self.interactive_iter):
# v_pos_mask += [extra['spatial_query_pos_mask'][0][0][:image_sizes[0][0],:image_sizes[0][1]].float().cpu().numpy()]
# v_neg_mask += [extra['spatial_query_neg_mask'][0][0][:image_sizes[0][0],:image_sizes[0][1]].float().cpu().numpy()]
outputs = self.sem_seg_head.predictor(multi_scale_features, mask_features, target_queries=queries_grounding, extra=extra, task='spatial')
extra.update(outputs)
pred_smask = F.interpolate(outputs['prev_mask'], images.tensor.shape[-2:], mode='bilinear')
# v_pred_mask += [(pred_smask[0,0][:image_sizes[0][0],:image_sizes[0][1]].sigmoid() > 0.5).float().cpu().numpy()]
s = image_sizes[0]
b = batched_inputs[0]
pred_smask_all = F.interpolate(pred_smask[:,:,:s[0],:s[1]], (b['height'], b['width']), mode='bilinear')[:,0].sigmoid() > 0.5
gt_smask = b['gt_masks_orisize']
ious = get_iou(gt_smask, pred_smask_all)
all_batch_shape_iou += [ious]
if (ious > 0.9).sum() == len(ious):
all_batch_shape_iou += [ious for j in range(self.interactive_iter-i-1)]
break
if self.interactive_mode in ['best', 'best_random']:
extra.update(self.prepare_next_spaital_mask(extra, batched_inputs, mode=self.interactive_mode))
elif self.interactive_mode == 'random':
extra.update({'spatial_query_pos_mask': pos_masks[:,i+1:i+2].unbind(), 'spatial_query_neg_mask': neg_masks[:,i+1:i+2].unbind()})
else:
assert False, "invalid interactive mode"
all_batch_shape_iou = torch.stack(all_batch_shape_iou)
processed_results = [{"mask_iou": all_batch_shape_iou[:,i]} for i in range(len(all_batch_shape_iou[0]))]
# visualization
# VL.step()
# import cv2
# v_masks = []
# v_pos_masks = []
# v_neg_masks = []
# txt = []
# img = batched_inputs[0]['image'].permute(1,2,0).cpu().numpy()
# mask_img = VL.overlay_single_mask_to_image(img[:,:,::-1], v_gt_mask.cpu().float().numpy())
# acc_pos_mask = np.zeros(v_pos_mask[0].shape)
# acc_neg_mask = np.zeros(v_neg_mask[0].shape)
# for x,y,z,iou in zip(v_pos_mask, v_neg_mask, v_pred_mask, all_batch_shape_iou):
# # dilate x,y
# x = cv2.dilate(x, np.ones((5,5), np.uint8), iterations=3)
# y = cv2.dilate(y, np.ones((5,5), np.uint8), iterations=3)
# acc_pos_mask += x
# acc_neg_mask += y
# v_masks += [z]
# v_pos_masks += [acc_pos_mask.clip(0,1)]
# v_neg_masks += [acc_neg_mask.clip(0,1)]
# txt += ["pred_{}".format(str(iou[0].item())[0:5])]
# VL.add_image(img[:,:,::-1])
# VL.insert(mask_img, "gt_mask")
# VL.overlay_obj_mask_to_image_withposneg(img[:,:,::-1], v_masks, v_pos_masks, v_neg_masks, txt, max_len=20)
return processed_results
def evaluate_referring_image(self, batched_inputs, extra={}):
assert self.task_switch['spatial']
assert len(batched_inputs) == 1, "only support batch size equal to 1"
assert self.interactive_mode == 'best'
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)
mask_features, transformer_encoder_features, multi_scale_features = self.sem_seg_head.pixel_decoder.forward_features(features)
if 'spatial_query' in batched_inputs[0]:
image_sizes = [x["image"].shape[-2:] for x in batched_inputs]
nm = len(batched_inputs[0]['spatial_query']['rand_shape'])
multi_scale_features = [m.repeat(nm,1,1,1) for m in multi_scale_features]
mask_features = mask_features.repeat(nm,1,1,1)
query_index = self.sem_seg_head.predictor.query_index
pos_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)).unbind(0)
pos_masks = ImageList.from_tensors(pos_masks, self.size_divisibility).tensor.unbind(0)
neg_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device) & False).unbind(0)
neg_masks = ImageList.from_tensors(neg_masks, self.size_divisibility).tensor.unbind(0)
extra.update({'spatial_query_pos_mask': pos_masks, 'spatial_query_neg_mask': neg_masks})
outputs = self.sem_seg_head.predictor(multi_scale_features, mask_features, target_queries=queries_grounding, extra=extra, task='refimg')
return outputs, images.tensor.shape
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)
assert len(images.tensor) == 1, "grounding evaluation only support single batch size now"
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()]
non_zero_query_mask = torch.zeros(query_emb[:,None].shape[:-1], dtype=torch.bool, device=query_emb.device)
extra['grounding_tokens'] = query_emb[:,None]
extra['grounding_nonzero_mask'] = non_zero_query_mask.t()
features = self.backbone(images.tensor)
outputs = self.sem_seg_head(features, extra=extra, task='grounding_eval')
pred_gmasks = outputs['pred_gmasks'][idx]
v_emb = outputs['pred_gtexts'][idx]
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 evaluate_grounding_sptial(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)
assert len(images.tensor) == 1, "grounding evaluation only support single batch size now"
extra = {}
dilation = 3
pos_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device)).unbind(0)
pos_masks = ImageList.from_tensors(pos_masks, self.size_divisibility).tensor
pos_masks = (F.conv2d(pos_masks.float(), self.dilation_kernel, padding=dilation//2) > 0).unbind(0)
neg_masks = (batched_inputs[0]['spatial_query']['rand_shape'].to(self.device) & False).unbind(0)
neg_masks = ImageList.from_tensors(neg_masks, self.size_divisibility).tensor.unbind(0)
mask_pred_results = []
for idx, batch_per_image in enumerate(batched_inputs):
grd_texts = batch_per_image['groundings']['texts']
grd_masks = []
for idx2, anno_text in enumerate(grd_texts):
extra.update({'spatial_query_pos_mask': [pos_masks[idx2]], 'spatial_query_neg_mask': [neg_masks[idx2]]})
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]]
non_zero_query_mask = torch.zeros(grd_emb[:,None].shape[:-1], dtype=torch.bool, device=grd_emb.device)
extra['grounding_tokens'] = grd_emb[:,None]
extra['grounding_nonzero_mask'] = non_zero_query_mask.t()
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_gmasks'][idx]
v_emb = outputs['pred_gtexts'][idx]
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]
grd_masks += [pred_gmasks[matched_id,:,:]]
# grd_masks += [outputs['prev_mask'][0]]
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()]
# non_zero_query_mask = torch.zeros(query_emb[:,None].shape[:-1], dtype=torch.bool, device=query_emb.device)
# extra['grounding_tokens'] = query_emb[:,None]
# extra['grounding_nonzero_mask'] = non_zero_query_mask.t()
# features = self.backbone(images.tensor)
# outputs = self.sem_seg_head(features, extra=extra, task='grounding_eval')
# pred_gmasks = outputs['pred_gmasks'][idx]
# v_emb = outputs['pred_gtexts'][idx]
# 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
return processed_results
def prepare_targets(self, batched_inputs, images):
h_pad, w_pad = images.tensor.shape[-2:]
new_targets = []
for idx, batch_per_image in enumerate(batched_inputs):
targets_per_image = batch_per_image['instances'].to(self.device)
# pad gt
gt_masks = targets_per_image.gt_masks.tensor
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
gt_boxes = targets_per_image.gt_boxes.tensor
ratio = torch.tensor([w_pad,h_pad,w_pad,h_pad]).to(gt_boxes.device)[None,:]
gt_boxes = gt_boxes / ratio
xc,yc,w,h = (gt_boxes[:,0] + gt_boxes[:,2])/2, (gt_boxes[:,1] + gt_boxes[:,3])/2, gt_boxes[:,2] - gt_boxes[:,0], gt_boxes[:,3] - gt_boxes[:,1]
gt_boxes = torch.stack([xc,yc,w,h]).permute(1,0)
target_dict = {
"labels": targets_per_image.gt_classes,
"is_things": targets_per_image.is_things,
"masks": padded_masks,
"boxes": gt_boxes,
}
if self.task_switch['spatial']:
# prepare targets for spatial query
target_dict['gt_spatial_masks'] = batch_per_image['spatial_query']['gt_masks']
if self.task_switch['grounding']:
grd_masks = batch_per_image['groundings']['masks']
grd_texts = batch_per_image['groundings']['texts']
grd_hash = batch_per_image['groundings']['hash']
grd_task = batch_per_image['groundings']['mode']
if len(grd_masks) == 0:
padded_masks = None
else:
padded_masks = torch.zeros((grd_masks.shape[0], h_pad, w_pad), dtype=grd_masks.dtype, device=grd_masks.device)
padded_masks[:, : grd_masks.shape[1], : grd_masks.shape[2]] = grd_masks
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']
unique_hash_id = np.unique(grd_hash, return_index=True)[1]
selected_mask = np.zeros(len(grd_hash)).astype(np.bool)
selected_mask[unique_hash_id] = True
selected_token_emb = token_emb[selected_mask]
selected_attn_mask = tokens['attention_mask'][selected_mask]
query_emb = selected_token_emb[selected_attn_mask.bool()]
class_idx = tokens['attention_mask'].sum(dim=-1) - 1
class_idx = torch.stack((torch.arange(len(class_idx), device=class_idx.device), class_idx)).tolist()
class_emb = token_emb[class_idx]
target_dict['grounding_masks'] = padded_masks
target_dict['grounding_query_embs'] = query_emb
target_dict['grounding_class_embs'] = class_emb
target_dict['grounding_hash'] = grd_hash
target_dict['grounding_task'] = grd_task
new_targets.append(target_dict)
return new_targets
def prepare_next_spaital_mask(self, outputs, batched_inputs, mode='best'):
gt_masks = [batched_inputs[i]['spatial_query']['gt_masks'] for i in range(len(batched_inputs))]
if self.training:
gt_masks = ImageList.from_tensors(gt_masks, self.size_divisibility).tensor
else:
gt_masks = ImageList.from_tensors(gt_masks, self.size_divisibility).tensor.transpose(0,1)
pred_masks = (F.interpolate(outputs['prev_mask'], size=gt_masks.shape[-2:], mode='bilinear', align_corners=False).sigmoid() > 0.5)
prev_masks = torch.stack(outputs['spatial_query_pos_mask']) | torch.stack(outputs['spatial_query_neg_mask'])
fn = gt_masks & (~(gt_masks & pred_masks)) & (~prev_masks) # fn: False Negative, gt:1, pred:0, prev:0
fp = (~gt_masks & pred_masks) & (~prev_masks) # fp: False Positive, gt:0, pred:1, prev:0
# compute iou between gt and pred
iou = (gt_masks & pred_masks).sum(list(range(1,len(fn.shape)))) / ((gt_masks | pred_masks).sum(dim=list(range(1,len(fn.shape)))) + 1e-8)
fn_sum = fn.sum(dim=list(range(1,len(fn.shape))))
fp_sum = fp.sum(dim=list(range(1,len(fp.shape))))
is_postive = fn_sum > fp_sum
# is_postive = torch.ones(len(fn_sum), device=torch.cuda.current_device()).bool()
select_mask = torch.stack([fn[i] if is_postive[i] else fp[i] for i in range(len(fn))])
# conv implementation
n,_,h,w = select_mask.shape
mask_dt = (distance_transform((~F.pad(select_mask, pad=(1, 1, 1, 1), mode='constant', value=0)).float())[:,:,1:-1,1:-1]).reshape(n,-1)
if mode == 'best':
max_xy_idx = torch.stack([torch.arange(n), mask_dt.max(dim=-1)[1].cpu()]).tolist()
elif mode == 'best_random':
max_xy_idx = torch.stack([torch.arange(n), torch.cat([(mask_dt[i] > 0).nonzero()[torch.randint(0, len((mask_dt[i] > 0).nonzero()), (1,))][0] for i in range(len(mask_dt))]).cpu()]).tolist()
next_mask = torch.zeros(gt_masks.shape, device=torch.cuda.current_device()).bool()
next_mask = next_mask.view(n,-1)
next_mask[max_xy_idx] = True
next_mask = next_mask.reshape((n,1,h,w)).float()
dilation = 3
next_mask = F.conv2d(next_mask, self.dilation_kernel, padding=dilation//2) > 0
# determine whether next mask is zero
keep = (iou < 0.925)
next_mask = next_mask & keep.view(-1,1,1,1)
pos_mask = []
neg_mask = []
for idx, ip in enumerate(is_postive):
if ip:
pos_mask += [outputs['spatial_query_pos_mask'][idx] | next_mask[idx]]
neg_mask += [outputs['spatial_query_neg_mask'][idx]]
else:
pos_mask += [outputs['spatial_query_pos_mask'][idx]]
neg_mask += [outputs['spatial_query_neg_mask'][idx] | next_mask[idx]]
if 'false_positive_mask' in outputs:
fp = outputs['false_positive_mask'] | fp
return {'spatial_query_pos_mask': pos_mask, 'spatial_query_neg_mask': neg_mask, 'false_positive_mask': fp}
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, 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:
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 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
def prepare_targets4query(self, targets, images, topk=5):
h_pad, w_pad = images.tensor.shape[-2:]
new_targets = []
new_queries = []
for targets_per_image in targets:
# we randomly sample maximally topk concepts
unique_target_classes = [k for k in set(targets_per_image.gt_classes.tolist())]
selected_target_classes = random.sample(unique_target_classes, min(topk, len(unique_target_classes)))
new_targets_per_image = []
new_queries_per_image = []
for clss in selected_target_classes:
indices = (targets_per_image.gt_classes == clss).nonzero().view(-1)
# pad gt
gt_masks = targets_per_image.gt_masks[indices]
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
# convert class into concept name and then token seq
self.sem_seg_head.predictor.lang_encoder.get_text_embeddings([COCO_PANOPTIC_CLASSES[clss]], name='grounding')
query = getattr(self.sem_seg_head.predictor.lang_encoder, 'grounding_text_embeddings')
new_targets.append(
{
"labels": targets_per_image.gt_classes[indices],
"masks": padded_masks,
}
)
new_queries_per_image.append(query)
new_queries.append(new_queries_per_image)
return new_targets, new_queries
@register_model
def get_seem_model(cfg, **kwargs):
return GeneralizedSEEM(cfg)