Spaces:
Paused
Paused
File size: 10,422 Bytes
a22eb82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 |
from __future__ import division
import datetime
import os
import os.path as osp
import glob
import numpy as np
import cv2
import sys
import onnxruntime
import onnx
import argparse
from onnx import numpy_helper
from insightface.data import get_image
class ArcFaceORT:
def __init__(self, model_path, cpu=False):
self.model_path = model_path
# providers = None will use available provider, for onnxruntime-gpu it will be "CUDAExecutionProvider"
self.providers = ['CPUExecutionProvider'] if cpu else None
#input_size is (w,h), return error message, return None if success
def check(self, track='cfat', test_img = None):
#default is cfat
max_model_size_mb=1024
max_feat_dim=512
max_time_cost=15
if track.startswith('ms1m'):
max_model_size_mb=1024
max_feat_dim=512
max_time_cost=10
elif track.startswith('glint'):
max_model_size_mb=1024
max_feat_dim=1024
max_time_cost=20
elif track.startswith('cfat'):
max_model_size_mb = 1024
max_feat_dim = 512
max_time_cost = 15
elif track.startswith('unconstrained'):
max_model_size_mb=1024
max_feat_dim=1024
max_time_cost=30
else:
return "track not found"
if not os.path.exists(self.model_path):
return "model_path not exists"
if not os.path.isdir(self.model_path):
return "model_path should be directory"
onnx_files = []
for _file in os.listdir(self.model_path):
if _file.endswith('.onnx'):
onnx_files.append(osp.join(self.model_path, _file))
if len(onnx_files)==0:
return "do not have onnx files"
self.model_file = sorted(onnx_files)[-1]
print('use onnx-model:', self.model_file)
try:
session = onnxruntime.InferenceSession(self.model_file, providers=self.providers)
except:
return "load onnx failed"
input_cfg = session.get_inputs()[0]
input_shape = input_cfg.shape
print('input-shape:', input_shape)
if len(input_shape)!=4:
return "length of input_shape should be 4"
if not isinstance(input_shape[0], str):
#return "input_shape[0] should be str to support batch-inference"
print('reset input-shape[0] to None')
model = onnx.load(self.model_file)
model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None'
new_model_file = osp.join(self.model_path, 'zzzzrefined.onnx')
onnx.save(model, new_model_file)
self.model_file = new_model_file
print('use new onnx-model:', self.model_file)
try:
session = onnxruntime.InferenceSession(self.model_file, providers=self.providers)
except:
return "load onnx failed"
input_cfg = session.get_inputs()[0]
input_shape = input_cfg.shape
print('new-input-shape:', input_shape)
self.image_size = tuple(input_shape[2:4][::-1])
#print('image_size:', self.image_size)
input_name = input_cfg.name
outputs = session.get_outputs()
output_names = []
for o in outputs:
output_names.append(o.name)
#print(o.name, o.shape)
if len(output_names)!=1:
return "number of output nodes should be 1"
self.session = session
self.input_name = input_name
self.output_names = output_names
#print(self.output_names)
model = onnx.load(self.model_file)
graph = model.graph
if len(graph.node)<8:
return "too small onnx graph"
input_size = (112,112)
self.crop = None
if track=='cfat':
crop_file = osp.join(self.model_path, 'crop.txt')
if osp.exists(crop_file):
lines = open(crop_file,'r').readlines()
if len(lines)!=6:
return "crop.txt should contain 6 lines"
lines = [int(x) for x in lines]
self.crop = lines[:4]
input_size = tuple(lines[4:6])
if input_size!=self.image_size:
return "input-size is inconsistant with onnx model input, %s vs %s"%(input_size, self.image_size)
self.model_size_mb = os.path.getsize(self.model_file) / float(1024*1024)
if self.model_size_mb > max_model_size_mb:
return "max model size exceed, given %.3f-MB"%self.model_size_mb
input_mean = None
input_std = None
if track=='cfat':
pn_file = osp.join(self.model_path, 'pixel_norm.txt')
if osp.exists(pn_file):
lines = open(pn_file,'r').readlines()
if len(lines)!=2:
return "pixel_norm.txt should contain 2 lines"
input_mean = float(lines[0])
input_std = float(lines[1])
if input_mean is not None or input_std is not None:
if input_mean is None or input_std is None:
return "please set input_mean and input_std simultaneously"
else:
find_sub = False
find_mul = False
for nid, node in enumerate(graph.node[:8]):
print(nid, node.name)
if node.name.startswith('Sub') or node.name.startswith('_minus'):
find_sub = True
if node.name.startswith('Mul') or node.name.startswith('_mul') or node.name.startswith('Div'):
find_mul = True
if find_sub and find_mul:
print("find sub and mul")
#mxnet arcface model
input_mean = 0.0
input_std = 1.0
else:
input_mean = 127.5
input_std = 127.5
self.input_mean = input_mean
self.input_std = input_std
for initn in graph.initializer:
weight_array = numpy_helper.to_array(initn)
dt = weight_array.dtype
if dt.itemsize<4:
return 'invalid weight type - (%s:%s)' % (initn.name, dt.name)
if test_img is None:
test_img = get_image('Tom_Hanks_54745')
test_img = cv2.resize(test_img, self.image_size)
else:
test_img = cv2.resize(test_img, self.image_size)
feat, cost = self.benchmark(test_img)
batch_result = self.check_batch(test_img)
batch_result_sum = float(np.sum(batch_result))
if batch_result_sum in [float('inf'), -float('inf')] or batch_result_sum != batch_result_sum:
print(batch_result)
print(batch_result_sum)
return "batch result output contains NaN!"
if len(feat.shape) < 2:
return "the shape of the feature must be two, but get {}".format(str(feat.shape))
if feat.shape[1] > max_feat_dim:
return "max feat dim exceed, given %d"%feat.shape[1]
self.feat_dim = feat.shape[1]
cost_ms = cost*1000
if cost_ms>max_time_cost:
return "max time cost exceed, given %.4f"%cost_ms
self.cost_ms = cost_ms
print('check stat:, model-size-mb: %.4f, feat-dim: %d, time-cost-ms: %.4f, input-mean: %.3f, input-std: %.3f'%(self.model_size_mb, self.feat_dim, self.cost_ms, self.input_mean, self.input_std))
return None
def check_batch(self, img):
if not isinstance(img, list):
imgs = [img, ] * 32
if self.crop is not None:
nimgs = []
for img in imgs:
nimg = img[self.crop[1]:self.crop[3], self.crop[0]:self.crop[2], :]
if nimg.shape[0] != self.image_size[1] or nimg.shape[1] != self.image_size[0]:
nimg = cv2.resize(nimg, self.image_size)
nimgs.append(nimg)
imgs = nimgs
blob = cv2.dnn.blobFromImages(
images=imgs, scalefactor=1.0 / self.input_std, size=self.image_size,
mean=(self.input_mean, self.input_mean, self.input_mean), swapRB=True)
net_out = self.session.run(self.output_names, {self.input_name: blob})[0]
return net_out
def meta_info(self):
return {'model-size-mb':self.model_size_mb, 'feature-dim':self.feat_dim, 'infer': self.cost_ms}
def forward(self, imgs):
if not isinstance(imgs, list):
imgs = [imgs]
input_size = self.image_size
if self.crop is not None:
nimgs = []
for img in imgs:
nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:]
if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]:
nimg = cv2.resize(nimg, input_size)
nimgs.append(nimg)
imgs = nimgs
blob = cv2.dnn.blobFromImages(imgs, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
net_out = self.session.run(self.output_names, {self.input_name : blob})[0]
return net_out
def benchmark(self, img):
input_size = self.image_size
if self.crop is not None:
nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:]
if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]:
nimg = cv2.resize(nimg, input_size)
img = nimg
blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
costs = []
for _ in range(50):
ta = datetime.datetime.now()
net_out = self.session.run(self.output_names, {self.input_name : blob})[0]
tb = datetime.datetime.now()
cost = (tb-ta).total_seconds()
costs.append(cost)
costs = sorted(costs)
cost = costs[5]
return net_out, cost
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
# general
parser.add_argument('workdir', help='submitted work dir', type=str)
parser.add_argument('--track', help='track name, for different challenge', type=str, default='cfat')
args = parser.parse_args()
handler = ArcFaceORT(args.workdir)
err = handler.check(args.track)
print('err:', err)
|