186 lines
7.0 KiB
Python
186 lines
7.0 KiB
Python
import numpy as np
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from .utils import str2bool, str2int, str2float, clip_ary
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class runner(object):
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def __init__(self):
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self.set = {
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'general': {
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'print_info':'no',
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'model_size':[0,0],
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'numerical_type':'floating',
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'type': 'kneron'
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},
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'floating':{
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"scale": 1,
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"bias": 0,
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"mean": "",
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"std": "",
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},
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'hw':{
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"radix":8,
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"shift":"",
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"sub":""
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}
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}
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return
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def update(self, **kwargs):
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#
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self.set.update(kwargs)
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#
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if self.set['general']['numerical_type'] == '520':
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if self.set['general']['type'].lower() in ['TF', 'Tf', 'tf']:
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self.fun_normalize = self._chen_520
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self.shift = 7 - self.set['hw']['radix']
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self.sub = 128
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elif self.set['general']['type'].lower() in ['YOLO', 'Yolo', 'yolo']:
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self.fun_normalize = self._chen_520
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self.shift = 8 - self.set['hw']['radix']
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self.sub = 0
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elif self.set['general']['type'].lower() in ['KNERON', 'Kneron', 'kneron']:
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self.fun_normalize = self._chen_520
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self.shift = 8 - self.set['hw']['radix']
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self.sub = 128
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else:
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self.fun_normalize = self._chen_520
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self.shift = 0
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self.sub = 0
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elif self.set['general']['numerical_type'] == '720':
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self.fun_normalize = self._chen_720
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self.shift = 0
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self.sub = 0
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else:
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if self.set['general']['type'].lower() in ['TORCH', 'Torch', 'torch']:
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self.fun_normalize = self._normalize_torch
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self.set['floating']['scale'] = 255.
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self.set['floating']['mean'] = [0.485, 0.456, 0.406]
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self.set['floating']['std'] = [0.229, 0.224, 0.225]
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elif self.set['general']['type'].lower() in ['TF', 'Tf', 'tf']:
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self.fun_normalize = self._normalize_tf
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self.set['floating']['scale'] = 127.5
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self.set['floating']['bias'] = -1.
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elif self.set['general']['type'].lower() in ['CAFFE', 'Caffe', 'caffe']:
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self.fun_normalize = self._normalize_caffe
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self.set['floating']['mean'] = [103.939, 116.779, 123.68]
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elif self.set['general']['type'].lower() in ['YOLO', 'Yolo', 'yolo']:
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self.fun_normalize = self._normalize_yolo
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self.set['floating']['scale'] = 255.
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elif self.set['general']['type'].lower() in ['KNERON', 'Kneron', 'kneron']:
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self.fun_normalize = self._normalize_kneron
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self.set['floating']['scale'] = 256.
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self.set['floating']['bias'] = -0.5
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else:
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self.fun_normalize = self._normalize_customized
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self.set['floating']['scale'] = str2float(self.set['floating']['scale'])
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self.set['floating']['bias'] = str2float(self.set['floating']['bias'])
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if self.set['floating']['mean'] != None:
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if len(self.set['floating']['mean']) != 3:
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self.set['floating']['mean'] = None
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if self.set['floating']['std'] != None:
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if len(self.set['floating']['std']) != 3:
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self.set['floating']['std'] = None
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def print_info(self):
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if self.set['general']['numerical_type'] == '520':
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print("<normalize>",
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'numerical_type', self.set['general']['numerical_type'],
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", type:", self.set['general']['type'],
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', shift:',self.shift,
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', sub:', self.sub)
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else:
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print("<normalize>",
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'numerical_type', self.set['general']['numerical_type'],
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", type:", self.set['general']['type'],
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', scale:',self.set['floating']['scale'],
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', bias:', self.set['floating']['bias'],
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', mean:', self.set['floating']['mean'],
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', std:',self.set['floating']['std'])
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def run(self, image_data):
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# print info
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if str2bool(self.set['general']['print_info']):
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self.print_info()
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# norm
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image_data = self.fun_normalize(image_data)
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# output
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info = {}
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return image_data, info
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def _normalize_torch(self, x):
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if len(x.shape) != 3:
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return x
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x = x.astype('float')
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x = x / self.set['floating']['scale']
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x[..., 0] -= self.set['floating']['mean'][0]
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x[..., 1] -= self.set['floating']['mean'][1]
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x[..., 2] -= self.set['floating']['mean'][2]
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x[..., 0] /= self.set['floating']['std'][0]
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x[..., 1] /= self.set['floating']['std'][1]
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x[..., 2] /= self.set['floating']['std'][2]
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return x
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def _normalize_tf(self, x):
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# print('_normalize_tf')
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x = x.astype('float')
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x = x / self.set['floating']['scale']
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x = x + self.set['floating']['bias']
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return x
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def _normalize_caffe(self, x):
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if len(x.shape) != 3:
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return x
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x = x.astype('float')
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x = x[..., ::-1]
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x[..., 0] -= self.set['floating']['mean'][0]
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x[..., 1] -= self.set['floating']['mean'][1]
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x[..., 2] -= self.set['floating']['mean'][2]
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return x
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def _normalize_yolo(self, x):
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# print('_normalize_yolo')
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x = x.astype('float')
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x = x / self.set['floating']['scale']
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return x
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def _normalize_kneron(self, x):
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# print('_normalize_kneron')
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x = x.astype('float')
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x = x/self.set['floating']['scale']
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x = x + self.set['floating']['bias']
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return x
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def _normalize_customized(self, x):
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# print('_normalize_customized')
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x = x.astype('float')
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if self.set['floating']['scale'] != 0:
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x = x/ self.set['floating']['scale']
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x = x + self.set['floating']['bias']
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if self.set['floating']['mean'] is not None:
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x[..., 0] -= self.set['floating']['mean'][0]
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x[..., 1] -= self.set['floating']['mean'][1]
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x[..., 2] -= self.set['floating']['mean'][2]
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if self.set['floating']['std'] is not None:
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x[..., 0] /= self.set['floating']['std'][0]
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x[..., 1] /= self.set['floating']['std'][1]
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x[..., 2] /= self.set['floating']['std'][2]
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return x
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def _chen_520(self, x):
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# print('_chen_520')
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x = (x - self.sub).astype('uint8')
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x = (np.right_shift(x,self.shift))
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x=x.astype('uint8')
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return x
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def _chen_720(self, x):
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# print('_chen_720')
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if self.shift == 1:
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x = x + np.array([[self.sub], [self.sub], [self.sub]])
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else:
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x = x + np.array([[self.sub], [self.sub], [self.sub]])
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return x |