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import os.path as osp
import glob
import logging
import insightface
from insightface.model_zoo.model_zoo import ModelRouter, PickableInferenceSession
from insightface.model_zoo.retinaface import RetinaFace
from insightface.model_zoo.landmark import Landmark
from insightface.model_zoo.attribute import Attribute
from insightface.model_zoo.inswapper import INSwapper
from insightface.model_zoo.arcface_onnx import ArcFaceONNX
from insightface.app import FaceAnalysis
from insightface.utils import DEFAULT_MP_NAME, ensure_available
from insightface.model_zoo import model_zoo
import onnxruntime
import onnx
from onnx import numpy_helper
from scripts.reactor_logger import logger
def patched_get_model(self, **kwargs):
session = PickableInferenceSession(self.onnx_file, **kwargs)
inputs = session.get_inputs()
input_cfg = inputs[0]
input_shape = input_cfg.shape
outputs = session.get_outputs()
if len(outputs) >= 5:
return RetinaFace(model_file=self.onnx_file, session=session)
elif input_shape[2] == 192 and input_shape[3] == 192:
return Landmark(model_file=self.onnx_file, session=session)
elif input_shape[2] == 96 and input_shape[3] == 96:
return Attribute(model_file=self.onnx_file, session=session)
elif len(inputs) == 2 and input_shape[2] == 128 and input_shape[3] == 128:
return INSwapper(model_file=self.onnx_file, session=session)
elif input_shape[2] == input_shape[3] and input_shape[2] >= 112 and input_shape[2] % 16 == 0:
return ArcFaceONNX(model_file=self.onnx_file, session=session)
else:
return None
def patched_faceanalysis_init(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs):
onnxruntime.set_default_logger_severity(3)
self.models = {}
self.model_dir = ensure_available('models', name, root=root)
onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx'))
onnx_files = sorted(onnx_files)
for onnx_file in onnx_files:
model = model_zoo.get_model(onnx_file, **kwargs)
if model is None:
print('model not recognized:', onnx_file)
elif allowed_modules is not None and model.taskname not in allowed_modules:
print('model ignore:', onnx_file, model.taskname)
del model
elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules):
self.models[model.taskname] = model
else:
print('duplicated model task type, ignore:', onnx_file, model.taskname)
del model
assert 'detection' in self.models
self.det_model = self.models['detection']
def patched_faceanalysis_prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)):
self.det_thresh = det_thresh
assert det_size is not None
self.det_size = det_size
for taskname, model in self.models.items():
if taskname == 'detection':
model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh)
else:
model.prepare(ctx_id)
def patched_inswapper_init(self, model_file=None, session=None):
self.model_file = model_file
self.session = session
model = onnx.load(self.model_file)
graph = model.graph
self.emap = numpy_helper.to_array(graph.initializer[-1])
self.input_mean = 0.0
self.input_std = 255.0
if self.session is None:
self.session = onnxruntime.InferenceSession(self.model_file, None)
inputs = self.session.get_inputs()
self.input_names = []
for inp in inputs:
self.input_names.append(inp.name)
outputs = self.session.get_outputs()
output_names = []
for out in outputs:
output_names.append(out.name)
self.output_names = output_names
assert len(self.output_names) == 1
input_cfg = inputs[0]
input_shape = input_cfg.shape
self.input_shape = input_shape
self.input_size = tuple(input_shape[2:4][::-1])
def patch_insightface(get_model, faceanalysis_init, faceanalysis_prepare, inswapper_init):
insightface.model_zoo.model_zoo.ModelRouter.get_model = get_model
insightface.app.FaceAnalysis.__init__ = faceanalysis_init
insightface.app.FaceAnalysis.prepare = faceanalysis_prepare
insightface.model_zoo.inswapper.INSwapper.__init__ = inswapper_init
original_functions = [ModelRouter.get_model, FaceAnalysis.__init__, FaceAnalysis.prepare, INSwapper.__init__]
patched_functions = [patched_get_model, patched_faceanalysis_init, patched_faceanalysis_prepare, patched_inswapper_init]
def apply_logging_patch(console_log_level):
if console_log_level == 0:
patch_insightface(*patched_functions)
logger.setLevel(logging.WARNING)
elif console_log_level == 1:
patch_insightface(*patched_functions)
logger.setLevel(logging.STATUS)
elif console_log_level == 2:
patch_insightface(*original_functions)
logger.setLevel(logging.INFO)
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