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ShufflenetV2-export.py
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ShufflenetV2-export.py
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# SPDX-License-Identifier: BSD-3-Clause
import torch
import onnxruntime
import onnx
from onnx import numpy_helper
from PIL import Image
from torchvision import transforms
import numpy as np
import os
import urllib
# GitHub Repo | Model
MODELS = [
('pytorch/vision:v0.5.0', 'shufflenet_v2_x0_5'),
('pytorch/vision:v0.5.0', 'shufflenet_v2_x1_0'),
]
data_dir = 'test_data_set_0'
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.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 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')
if isinstance(model, torch.jit.ScriptModule):
torch.onnx._export(model, inputs, model_dir, verbose=True, input_names=input_names,
output_names=output_names, example_outputs=outputs, **kwargs)
else:
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 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 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, [])
# Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
# other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
# based on the build flags) when instantiating InferenceSession.
# For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
# onnxruntime.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
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 shufflenetv2_test():
for github_repo, model in MODELS:
# Load pretrained model
model = torch.hub.load(github_repo, model, pretrained=True)
model.eval()
input_image = Image.open(filename)
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_1 = input_tensor.unsqueeze(0)
output_1 = model(input_1)
model_dir, data_dir = save_model('shufflenetv2', model.cpu(), input_1, output_1,
opset_version=10,
input_names=['input'],
output_names=['output'],
dynamic_axes={"input": {0: 'batch_size'}, "output": {0: 'batch_size'}})
# Test exported model with TensorProto data saved in files
inputs_flatten = flatten(input_1)
inputs_flatten = update_flatten_list(inputs_flatten, [])
outputs_flatten = flatten(output_1)
outputs_flatten = update_flatten_list(outputs_flatten, [])
inputs = []
for i, _ in enumerate(inputs_flatten):
f_ = os.path.join(data_dir, '{0}_{1}.pb'.format("input", i))
tensor = onnx.TensorProto()
with open(f_, 'rb') as file:
tensor.ParseFromString(file.read())
inputs.append(numpy_helper.to_array(tensor))
outputs = []
for i, _ in enumerate(outputs_flatten):
f_ = os.path.join(data_dir, '{0}_{1}.pb'.format("output", i))
tensor = onnx.TensorProto()
with open(f_, 'rb') as file:
tensor.ParseFromString(file.read())
outputs.append(numpy_helper.to_array(tensor))
inference(model_dir, inputs, outputs)
# Test model with different input
input_2 = torch.randn(6, 3, 224, 224)
output_2 = model(input_2)
inference(model_dir, input_2, output_2)
shufflenetv2_test()