forked from onnx/models
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathretinanet-export.py
151 lines (116 loc) · 5.36 KB
/
retinanet-export.py
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
import os
import torch
import onnxruntime
import numpy as np
from retinanet.model import Model
from PIL import Image
from torchvision import transforms
from onnx import numpy_helper
import urllib
data_dir = 'test_data_set_0'
url, filename = ("https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/retinanet/dependencies/demo.jpg", "demo.jpg")
urllib.request.urlretrieve(url, filename)
def flatten(inputs):
return [[flatten(i) for i in inputs] if isinstance(inputs, (list, tuple)) else inputs]
def update_flatten_list(inputs, res_list):
for i in inputs:
res_list.append(i) if not isinstance(i, (list, tuple)) else update_flatten_list(i, res_list)
return res_list
def to_numpy(x):
if type(x) is not np.ndarray:
x = x.detach().cpu().numpy() if x.requires_grad else x.cpu().numpy()
return x
def save_tensor_proto(file_path, name, data):
tp = numpy_helper.from_array(data)
tp.name = name
with open(file_path, 'wb') as f:
f.write(tp.SerializeToString())
def save_data(test_data_dir, prefix, names, data_list):
if isinstance(data_list, torch.autograd.Variable) or isinstance(data_list, torch.Tensor):
data_list = [data_list]
for i, d in enumerate(data_list):
d = d.data.cpu().numpy()
save_tensor_proto(os.path.join(test_data_dir, '{0}_{1}.pb'.format(prefix, i)), names[i], d)
def save_model(name, model, inputs, outputs, input_names=None, output_names=None, **kwargs):
if hasattr(model, 'train'):
model.train(False)
dir = './'
if not os.path.exists(dir):
os.makedirs(dir)
dir = os.path.join(dir, 'test_' + name)
if not os.path.exists(dir):
os.makedirs(dir)
inputs_flatten = flatten(inputs)
inputs_flatten = update_flatten_list(inputs_flatten, [])
outputs_flatten = flatten(outputs)
outputs_flatten = update_flatten_list(outputs_flatten, [])
if input_names is None:
input_names = []
for i, _ in enumerate(inputs_flatten):
input_names.append('input' + str(i+1))
else:
np.testing.assert_equal(len(input_names), len(inputs_flatten),
"Number of input names provided is not equal to the number of inputs.")
if output_names is None:
output_names = []
for i, _ in enumerate(outputs_flatten):
output_names.append('output' + str(i+1))
else:
np.testing.assert_equal(len(output_names), len(outputs_flatten),
"Number of output names provided is not equal to the number of output.")
model_dir = os.path.join(dir, 'model.onnx')
torch.onnx.export(model, inputs, model_dir, verbose=True, input_names=input_names,
output_names=output_names, example_outputs=outputs, **kwargs)
test_data_dir = os.path.join(dir, data_dir)
if not os.path.exists(test_data_dir):
os.makedirs(test_data_dir)
save_data(test_data_dir, "input", input_names, inputs_flatten)
save_data(test_data_dir, "output", output_names, outputs_flatten)
return model_dir, test_data_dir
def inference(file, inputs, outputs):
inputs_flatten = flatten(inputs)
inputs_flatten = update_flatten_list(inputs_flatten, [])
outputs_flatten = flatten(outputs)
outputs_flatten = update_flatten_list(outputs_flatten, [])
sess = onnxruntime.InferenceSession(file)
ort_inputs = dict((sess.get_inputs()[i].name, to_numpy(input)) for i, input in enumerate(inputs_flatten))
res = sess.run(None, ort_inputs)
if outputs is not None:
print("== Checking model output ==")
[np.testing.assert_allclose(to_numpy(output), res[i], rtol=1e-03, atol=1e-05) for i, output in enumerate(outputs_flatten)]
print("== Done ==")
def torch_inference(model, input):
print("====== Torch Inference ======")
output=model(input)
return output
def ort_inference(file, inputs_flatten, outputs_flatten):
print("====== ORT Inference ======")
ort_sess = onnxruntime.InferenceSession(file)
ort_inputs = dict((ort_sess.get_inputs()[i].name, to_numpy(input)) for i, input in enumerate(inputs_flatten))
ort_outs = ort_sess.run(None, ort_inputs)
if outputs_flatten is not None:
print("== Checking model output ==")
[np.testing.assert_allclose(to_numpy(output), ort_outs[i], rtol=1e-03, atol=1e-05) for i, output in
enumerate(outputs_flatten)]
print("== Done ==")
# Download pretrained model from:
# https://github.com/NVIDIA/retinanet-examples/releases/tag/19.04
model, state = Model.load('retinanet_rn101fpn/retinanet_rn101fpn.pth')
model.eval()
model.exporting = True
input_image = Image.open(filename)
preprocess = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_tensor = input_tensor.unsqueeze(0)
output = torch_inference(model, input_tensor)
# Test exported model with TensorProto data saved in files
inputs_flatten = flatten(input_tensor.detach().cpu().numpy())
inputs_flatten = update_flatten_list(inputs_flatten, [])
outputs_flatten = flatten(output)
outputs_flatten = update_flatten_list(outputs_flatten, [])
model_dir, data_dir = save_model('retinanet_resnet101', model.cpu(), input_tensor, output, input_names=['input'],
opset_version=9)
ort_inference(model_dir, inputs_flatten, outputs_flatten)