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)