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"""
Author: Siyuan Li
Licensed: Apache-2.0 License
"""
import copy
import logging
import re
import warnings
from mmdet.registry import MODELS
from mmengine.logging import MMLogger, print_log
from mmengine.model.weight_init import (PretrainedInit, initialize,
update_init_info)
from .grounding_dino import GroundingDINO
def clean_label_name(name: str) -> str:
name = re.sub(r"\(.*\)", "", name)
name = re.sub(r"_", " ", name)
name = re.sub(r" ", " ", name)
return name
def chunks(lst: list, n: int) -> list:
"""Yield successive n-sized chunks from lst."""
all_ = []
for i in range(0, len(lst), n):
data_index = lst[i : i + n]
all_.append(data_index)
counter = 0
for i in all_:
counter += len(i)
assert counter == len(lst)
return all_
@MODELS.register_module()
class GroundingDINOMasa(GroundingDINO):
"""Implementation of `Grounding DINO: Marrying DINO with Grounded Pre-
Training for Open-Set Object Detection.
<https://arxiv.org/abs/2303.05499>`_
Code is modified from the `official github repo
<https://github.com/IDEA-Research/GroundingDINO>`_.
"""
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.track_text_prompt = None
self.track_text_dict = None
self.token_positive_maps = None
self.track_entities = None
def init_weights(self) -> None:
"""Initialize weights for Transformer and other components."""
if self.init_cfg:
print_log(
f"initialize {self.__class__.__name__} with init_cfg {self.init_cfg}",
logger="current",
level=logging.DEBUG,
)
init_cfgs = self.init_cfg
if isinstance(self.init_cfg, dict):
init_cfgs = [self.init_cfg]
# PretrainedInit has higher priority than any other init_cfg.
# Therefore we initialize `pretrained_cfg` last to overwrite
# the previous initialized weights.
# See details in https://github.com/open-mmlab/mmengine/issues/691 # noqa E501
other_cfgs = []
pretrained_cfg = []
for init_cfg in init_cfgs:
assert isinstance(init_cfg, dict)
if (
init_cfg["type"] == "Pretrained"
or init_cfg["type"] is PretrainedInit
):
pretrained_cfg.append(init_cfg)
else:
other_cfgs.append(init_cfg)
initialize(self, other_cfgs)
else:
super().init_weights()
initialize(self, pretrained_cfg)
def predict(
self, batch_inputs, detection_features, batch_data_samples, rescale: bool = True
):
text_prompts = []
enhanced_text_prompts = []
tokens_positives = []
for data_samples in batch_data_samples:
text_prompts.append(data_samples.text)
if "caption_prompt" in data_samples:
enhanced_text_prompts.append(data_samples.caption_prompt)
else:
enhanced_text_prompts.append(None)
tokens_positives.append(data_samples.get("tokens_positive", None))
if "custom_entities" in batch_data_samples[0]:
# Assuming that the `custom_entities` flag
# inside a batch is always the same. For single image inference
custom_entities = batch_data_samples[0].custom_entities
else:
custom_entities = False
if self.track_text_dict is not None and self.track_text_prompt == text_prompts:
# text feature map layer
is_rec_tasks = []
for i, data_samples in enumerate(batch_data_samples):
if self.token_positive_maps[i] is not None:
is_rec_tasks.append(False)
else:
is_rec_tasks.append(True)
data_samples.token_positive_map = self.token_positive_maps[i]
visual_feats = detection_features
head_inputs_dict = self.forward_transformer(
visual_feats, self.track_text_dict, batch_data_samples
)
results_list = self.bbox_head.predict(
**head_inputs_dict,
rescale=rescale,
batch_data_samples=batch_data_samples,
)
entities = self.track_entities
else:
self.track_text_prompt = text_prompts
if len(text_prompts) == 1:
# All the text prompts are the same,
# so there is no need to calculate them multiple times.
_positive_maps_and_prompts = [
self.get_tokens_positive_and_prompts(
text_prompts[0],
custom_entities,
enhanced_text_prompts[0],
tokens_positives[0],
)
] * len(batch_inputs)
else:
_positive_maps_and_prompts = [
self.get_tokens_positive_and_prompts(
text_prompt,
custom_entities,
enhanced_text_prompt,
tokens_positive,
)
for text_prompt, enhanced_text_prompt, tokens_positive in zip(
text_prompts, enhanced_text_prompts, tokens_positives
)
]
token_positive_maps, text_prompts, _, entities = zip(
*_positive_maps_and_prompts
)
self.token_positive_maps = token_positive_maps
self.track_entities = entities
# image feature extraction
visual_feats = detection_features
if isinstance(text_prompts[0], list):
# chunked text prompts, only bs=1 is supported
assert len(batch_inputs) == 1
count = 0
results_list = []
entities = [[item for lst in entities[0] for item in lst]]
for b in range(len(text_prompts[0])):
text_prompts_once = [text_prompts[0][b]]
token_positive_maps_once = token_positive_maps[0][b]
text_dict = self.language_model(text_prompts_once)
# text feature map layer
if self.text_feat_map is not None:
text_dict["embedded"] = self.text_feat_map(
text_dict["embedded"]
)
batch_data_samples[0].token_positive_map = token_positive_maps_once
head_inputs_dict = self.forward_transformer(
copy.deepcopy(visual_feats), text_dict, batch_data_samples
)
pred_instances = self.bbox_head.predict(
**head_inputs_dict,
rescale=rescale,
batch_data_samples=batch_data_samples,
)[0]
if len(pred_instances) > 0:
pred_instances.labels += count
count += len(token_positive_maps_once)
results_list.append(pred_instances)
results_list = [results_list[0].cat(results_list)]
is_rec_tasks = [False] * len(results_list)
else:
# extract text feats
text_dict = self.language_model(list(text_prompts))
# text feature map layer
if self.text_feat_map is not None:
text_dict["embedded"] = self.text_feat_map(text_dict["embedded"])
is_rec_tasks = []
for i, data_samples in enumerate(batch_data_samples):
if token_positive_maps[i] is not None:
is_rec_tasks.append(False)
else:
is_rec_tasks.append(True)
data_samples.token_positive_map = token_positive_maps[i]
if self.track_text_dict is None:
self.track_text_dict = text_dict
head_inputs_dict = self.forward_transformer(
visual_feats, text_dict, batch_data_samples
)
results_list = self.bbox_head.predict(
**head_inputs_dict,
rescale=rescale,
batch_data_samples=batch_data_samples,
)
for data_sample, pred_instances, entity, is_rec_task in zip(
batch_data_samples, results_list, entities, is_rec_tasks
):
if len(pred_instances) > 0:
label_names = []
for labels in pred_instances.labels:
if is_rec_task:
label_names.append(entity)
continue
if labels >= len(entity):
warnings.warn(
"The unexpected output indicates an issue with "
"named entity recognition. You can try "
"setting custom_entities=True and running "
"again to see if it helps."
)
label_names.append("unobject")
else:
label_names.append(entity[labels])
# for visualization
pred_instances.label_names = label_names
data_sample.pred_instances = pred_instances
return batch_data_samples
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