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https://github.com/gmalivenko/onnx2keras
Convert ONNX model graph to Keras model format.
https://github.com/gmalivenko/onnx2keras
deep-convolutional-networks deep-learning keras onnx onnx2keras tensorflow tensorflow-models
Last synced: 5 days ago
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Convert ONNX model graph to Keras model format.
- Host: GitHub
- URL: https://github.com/gmalivenko/onnx2keras
- Owner: gmalivenko
- License: mit
- Created: 2019-06-21T15:02:28.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-06-20T09:42:46.000Z (5 months ago)
- Last Synced: 2024-10-14T03:05:49.589Z (23 days ago)
- Topics: deep-convolutional-networks, deep-learning, keras, onnx, onnx2keras, tensorflow, tensorflow-models
- Language: Python
- Size: 175 KB
- Stars: 195
- Watchers: 8
- Forks: 116
- Open Issues: 113
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# onnx2keras
ONNX to Keras deep neural network converter.
[](https://opensource.org/licenses/MIT)
[](https://github.com/gmalivenko/onnx2keras)
[](https://pepy.tech/project/onnx2keras)
## Requirements
TensorFlow 2.0
## API
`onnx_to_keras(onnx_model, input_names, input_shapes=None, name_policy=None, verbose=True, change_ordering=False) -> {Keras model}`
`onnx_model`: ONNX model to convert
`input_names`: list with graph input names
`input_shapes`: override input shapes (experimental)
`name_policy`: ['renumerate', 'short', 'default'] override layer names (experimental)
`verbose`: detailed output
`change_ordering:` change ordering to HWC (experimental)
## Getting started
### ONNX model
```python
import onnx
from onnx2keras import onnx_to_keras
# Load ONNX model
onnx_model = onnx.load('resnet18.onnx')
# Call the converter (input - is the main model input name, can be different for your model)
k_model = onnx_to_keras(onnx_model, ['input'])
```
Keras model will be stored to the `k_model` variable. So simple, isn't it?
### PyTorch model
Using ONNX as intermediate format, you can convert PyTorch model as well.
```python
import numpy as np
import torch
from torch.autograd import Variable
from pytorch2keras.converter import pytorch_to_keras
import torchvision.models as models
if __name__ == '__main__':
input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
input_var = Variable(torch.FloatTensor(input_np))
model = models.resnet18()
model.eval()
k_model = \
pytorch_to_keras(model, input_var, [(3, 224, 224,)], verbose=True, change_ordering=True)
for i in range(3):
input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
input_var = Variable(torch.FloatTensor(input_np))
output = model(input_var)
pytorch_output = output.data.numpy()
keras_output = k_model.predict(np.transpose(input_np, [0, 2, 3, 1]))
error = np.max(pytorch_output - keras_output)
print('error -- ', error) # Around zero :)
```
### Deplying model as frozen graph
You can try using the snippet below to convert your onnx / PyTorch model to frozen graph. It may be useful for deploy for Tensorflow.js / for Tensorflow for Android / for Tensorflow C-API.
```python
import numpy as np
import torch
from pytorch2keras.converter import pytorch_to_keras
from torch.autograd import Variable
import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
# Create and load model
model = Model()
model.load_state_dict(torch.load('model-checkpoint.pth'))
model.eval()
# Make dummy variables (and checking if the model works)
input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
input_var = Variable(torch.FloatTensor(input_np))
output = model(input_var)
# Convert the model!
k_model = \
pytorch_to_keras(model, input_var, (3, 224, 224),
verbose=True, name_policy='short',
change_ordering=True)
# Save model to SavedModel format
tf.saved_model.save(k_model, "./models")
# Convert Keras model to ConcreteFunction
full_model = tf.function(lambda x: k_model(x))
full_model = full_model.get_concrete_function(
tf.TensorSpec(k_model.inputs[0].shape, k_model.inputs[0].dtype))
# Get frozen ConcreteFunction
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
print("-" * 50)
print("Frozen model layers: ")
for layer in [op.name for op in frozen_func.graph.get_operations()]:
print(layer)
print("-" * 50)
print("Frozen model inputs: ")
print(frozen_func.inputs)
print("Frozen model outputs: ")
print(frozen_func.outputs)
# Save frozen graph from frozen ConcreteFunction to hard drive
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
logdir="./frozen_models",
name="frozen_graph.pb",
as_text=False)
```
## License
This software is covered by MIT License.