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import logging
from typing import Optional
import numpy as np
import torch
from mivolo.data.misc import prepare_classification_images
from mivolo.model.create_timm_model import create_model
from mivolo.structures import PersonAndFaceCrops, PersonAndFaceResult
from timm.data import resolve_data_config
_logger = logging.getLogger("MiVOLO")
has_compile = hasattr(torch, "compile")
class Meta:
def __init__(self):
self.min_age = None
self.max_age = None
self.avg_age = None
self.num_classes = None
self.in_chans = 3
self.with_persons_model = False
self.disable_faces = False
self.use_persons = True
self.only_age = False
self.num_classes_gender = 2
def load_from_ckpt(self, ckpt_path: str, disable_faces: bool = False, use_persons: bool = True) -> "Meta":
state = torch.load(ckpt_path, map_location="cpu")
self.min_age = state["min_age"]
self.max_age = state["max_age"]
self.avg_age = state["avg_age"]
self.only_age = state["no_gender"]
only_age = state["no_gender"]
self.disable_faces = disable_faces
if "with_persons_model" in state:
self.with_persons_model = state["with_persons_model"]
else:
self.with_persons_model = True if "patch_embed.conv1.0.weight" in state["state_dict"] else False
self.num_classes = 1 if only_age else 3
self.in_chans = 3 if not self.with_persons_model else 6
self.use_persons = use_persons and self.with_persons_model
if not self.with_persons_model and self.disable_faces:
raise ValueError("You can not use disable-faces for faces-only model")
if self.with_persons_model and self.disable_faces and not self.use_persons:
raise ValueError("You can not disable faces and persons together")
return self
def __str__(self):
attrs = vars(self)
attrs.update({"use_person_crops": self.use_person_crops, "use_face_crops": self.use_face_crops})
return ", ".join("%s: %s" % item for item in attrs.items())
@property
def use_person_crops(self) -> bool:
return self.with_persons_model and self.use_persons
@property
def use_face_crops(self) -> bool:
return not self.disable_faces or not self.with_persons_model
class MiVOLO:
def __init__(
self,
ckpt_path: str,
device: str = "cpu",
half: bool = True,
disable_faces: bool = False,
use_persons: bool = True,
verbose: bool = False,
torchcompile: Optional[str] = None,
):
self.verbose = verbose
self.device = torch.device(device)
self.half = half and self.device.type != "cpu"
self.meta: Meta = Meta().load_from_ckpt(ckpt_path, disable_faces, use_persons)
if self.verbose:
_logger.info(f"Model meta:\n{str(self.meta)}")
model_name = "mivolo_d1_224"
self.model = create_model(
model_name=model_name,
num_classes=self.meta.num_classes,
in_chans=self.meta.in_chans,
pretrained=False,
checkpoint_path=ckpt_path,
filter_keys=["fds."],
)
self.param_count = sum([m.numel() for m in self.model.parameters()])
_logger.info(f"Model {model_name} created, param count: {self.param_count}")
self.data_config = resolve_data_config(
model=self.model,
verbose=verbose,
use_test_size=True,
)
self.data_config["crop_pct"] = 1.0
c, h, w = self.data_config["input_size"]
assert h == w, "Incorrect data_config"
self.input_size = w
self.model = self.model.to(self.device)
if torchcompile:
assert has_compile, "A version of torch w/ torch.compile() is required for --compile, possibly a nightly."
torch._dynamo.reset()
self.model = torch.compile(self.model, backend=torchcompile)
self.model.eval()
if self.half:
self.model = self.model.half()
def warmup(self, batch_size: int, steps=10):
if self.meta.with_persons_model:
input_size = (6, self.input_size, self.input_size)
else:
input_size = self.data_config["input_size"]
input = torch.randn((batch_size,) + tuple(input_size)).to(self.device)
for _ in range(steps):
out = self.inference(input) # noqa: F841
if torch.cuda.is_available():
torch.cuda.synchronize()
def inference(self, model_input: torch.tensor) -> torch.tensor:
with torch.no_grad():
if self.half:
model_input = model_input.half()
output = self.model(model_input)
return output
def predict(self, image: np.ndarray, detected_bboxes: PersonAndFaceResult):
if detected_bboxes.n_objects == 0:
return
faces_input, person_input, faces_inds, bodies_inds = self.prepare_crops(image, detected_bboxes)
if self.meta.with_persons_model:
model_input = torch.cat((faces_input, person_input), dim=1)
else:
model_input = faces_input
output = self.inference(model_input)
# write gender and age results into detected_bboxes
self.fill_in_results(output, detected_bboxes, faces_inds, bodies_inds)
def fill_in_results(self, output, detected_bboxes, faces_inds, bodies_inds):
if self.meta.only_age:
age_output = output
gender_probs, gender_indx = None, None
else:
age_output = output[:, 2]
gender_output = output[:, :2].softmax(-1)
gender_probs, gender_indx = gender_output.topk(1)
assert output.shape[0] == len(faces_inds) == len(bodies_inds)
# per face
for index in range(output.shape[0]):
face_ind = faces_inds[index]
body_ind = bodies_inds[index]
# get_age
age = age_output[index].item()
age = age * (self.meta.max_age - self.meta.min_age) + self.meta.avg_age
age = round(age, 2)
detected_bboxes.set_age(face_ind, age)
detected_bboxes.set_age(body_ind, age)
_logger.info(f"\tage: {age}")
if gender_probs is not None:
gender = "male" if gender_indx[index].item() == 0 else "female"
gender_score = gender_probs[index].item()
_logger.info(f"\tgender: {gender} [{int(gender_score * 100)}%]")
detected_bboxes.set_gender(face_ind, gender, gender_score)
detected_bboxes.set_gender(body_ind, gender, gender_score)
def prepare_crops(self, image: np.ndarray, detected_bboxes: PersonAndFaceResult):
if self.meta.use_person_crops and self.meta.use_face_crops:
detected_bboxes.associate_faces_with_persons()
crops: PersonAndFaceCrops = detected_bboxes.collect_crops(image)
(bodies_inds, bodies_crops), (faces_inds, faces_crops) = crops.get_faces_with_bodies(
self.meta.use_person_crops, self.meta.use_face_crops
)
if not self.meta.use_face_crops:
assert all(f is None for f in faces_crops)
faces_input = prepare_classification_images(
faces_crops, self.input_size, self.data_config["mean"], self.data_config["std"], device=self.device
)
if not self.meta.use_person_crops:
assert all(p is None for p in bodies_crops)
person_input = prepare_classification_images(
bodies_crops, self.input_size, self.data_config["mean"], self.data_config["std"], device=self.device
)
_logger.info(
f"faces_input: {faces_input.shape if faces_input is not None else None}, "
f"person_input: {person_input.shape if person_input is not None else None}"
)
return faces_input, person_input, faces_inds, bodies_inds
if __name__ == "__main__":
model = MiVOLO("../pretrained/checkpoint-377.pth.tar", half=True, device="cuda:0")
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