def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'RetinaFace', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'RetinaFace', 1)
inference_helper.add_image_input('INPUT__0', (512, 512, 3),
'识别用的图像', ([0, 0, 0], [1, 1, 1]))
inference_helper.add_output('OUTPUT__0', (1, 16128, 2),
'face classification')
inference_helper.add_output('OUTPUT__1', (1, 16128, 4),
'box predict')
inference_helper.add_output('OUTPUT__2', (1, 16128, 10),
'landmark')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for retina face detect not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'QRCodeDetect', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'QRCodeDetect', 1)
inference_helper.add_image_input('INPUT__0', (-1, -1, 1),
'检测用灰度的图像', ([
0,
], [
255,
]))
inference_helper.add_output('OUTPUT__0', (-1), 'anchor location')
inference_helper.add_output('OUTPUT__1', (-1), 'anchor score')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for qrcode detect not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'UltraLightFaceDetect', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'UltraLightFaceDetect',
1)
inference_helper.add_image_input(
'INPUT__0', (320, 240, 3), '识别用的图像',
([127, 127, 127], [128, 128, 128]))
inference_helper.add_output('OUTPUT__0', (1, 4420, 2),
'detect score')
inference_helper.add_output('OUTPUT__1', (1, 4420, 4),
'box predict')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for ultra light face detect not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper('Landmark106p',
self.inference_config['triton_url'],
self.inference_config['triton_port'],
'Landmark106p', 1)
inference_helper.add_image_input('INPUT__0', (192, 192, 3), '检测用rgb的图像',
([0, 0, 0], [1, 1, 1]))
inference_helper.add_output('OUTPUT__0', (1, 212), '回归的坐标')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for face alignment 106p not implement")
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'AsiaFaceEmbedding', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'AsiaFaceEmbedding', 1)
inference_helper.add_image_input(
'INPUT__0', (112, 112, 3), '人脸图像',
([127.5, 127.5, 127.5], [127.5, 127.5, 127.5]))
inference_helper.add_output('OUTPUT__0', (1, 512), '人脸特征向量')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for asian face embedding not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'MiniFASNetV2', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'MiniFASNetV2', 1)
inference_helper.add_image_input('INPUT__0', (80, 80, 3), '人脸图像',
([0, 0, 0], [1, 1, 1]))
inference_helper.add_output('OUTPUT__0', (1, 3),
'三个类别的分类情况，0，2均为非真实人脸')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for MiniFASNetV2 not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'HumanMattingUnet', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'HumanMattingUnet', 1)
inference_helper.add_image_input(
'INPUT__0', (1280, 720, 3), '分割用rgb图像',
([123.675, 116.28, 103.53], [58.395, 57.12, 57.375]))
inference_helper.add_output('OUTPUT__0', (1, 720, 1280), '分割结果')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for unet human matting not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'FaceParsing', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'FaceParsing', 1)
inference_helper.add_image_input(
'INPUT__0', (512, 512, 3), '检测用rgb的图像',
([103.53, 116.28, 123.675], [57.375, 57.12, 58.395]))
inference_helper.add_output('OUTPUT__0', (19, 512, 512), '每个类别的区域')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for face parsing not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'Fair', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'Fair', 1)
inference_helper.add_image_input(
'INPUT__0', (224, 224, 3), '人脸图像',
([123.675, 116.28, 103.53], [58.395, 57.12, 57.375]))
inference_helper.add_output('OUTPUT__0', (18, ),
'race[:7] sexual[7:9] age[9:18]')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for fair not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'HumanMattingBiSe', self.inference_config['triton_url'],
self.inference_config['triton_port'], 'HumanMattingBiSe', 1)
inference_helper.add_image_input(
'INPUT__0', (512, 512, 3), '分割用bgr图像',
([123.675, 116.28, 103.53], [58.395, 57.12, 57.375]))
inference_helper.add_output('OUTPUT__0', (4, 512, 512),
'分割结果，最后一个通道为alpha信息')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for bise human matting not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
support_backbone_type = {'mbv3', 'res34'}
backbone_type = 'res34'
if 'backbone_type' in self.inference_config and \
self.inference_config['backbone_type'] in support_backbone_type:
backbone_type = self.inference_config['backbone_type']
else:
print(f'crnn use the default backbone:{backbone_type}')
inference_helper = TritonInferenceHelper(
f'CRNN_{backbone_type}', self.inference_config['triton_url'],
self.inference_config['triton_port'], f'CRNN_{backbone_type}',
1)
inference_helper.add_image_input(
'INPUT__0', (32, -1, 3), '识别用的图像',
([127.5, 127.5, 127.5], [127.5, 127.5, 127.5]))
inference_helper.add_output('OUTPUT__0', (-1, 1), '识别的max')
inference_helper.add_output('OUTPUT__1', (-1, 1), '识别的argmax的结果')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for crnn text recognize not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
inference_helper = TritonInferenceHelper(
'TextOrientationClassification',
self.inference_config['triton_url'],
self.inference_config['triton_port'],
'TextOrientationClassification', 1)
inference_helper.add_image_input(
'INPUT__0', (192, 48, 3), '识别用的图像',
([127.5, 127.5, 127.5], [127.5, 127.5, 127.5]))
inference_helper.add_output('OUTPUT__0', (2, ), '方向的分类')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for text orientation classification not implement"
)
def get_inference_helper(self):
if self.inference_config['name'] == 'triton':
support_backbone_type = {'mbv3', 'res18'}
backbone_type = 'res18'
if 'backbone_type' in self.inference_config and \
self.inference_config['backbone_type'] in support_backbone_type:
backbone_type = self.inference_config['backbone_type']
else:
print(f'db use the default backbone:{backbone_type}')
inference_helper = TritonInferenceHelper(
f'DB_{backbone_type}', self.inference_config['triton_url'],
self.inference_config['triton_port'], f'DB_{backbone_type}', 1)
inference_helper.add_image_input(
'INPUT__0', (-1, -1, 3), '识别用的图像',
([123.675, 116.28, 103.53], [58.395, 57.12, 57.375]))
inference_helper.add_output('OUTPUT__0', (1, -1, -1),
'detect score')
self.inference_helper = inference_helper
else:
raise NotImplementedError(
f"{self.inference_config['name']} helper for db text detect not implement"
)