onnx2keras
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Convert ONNX model graph to Keras model format.
Hello, This is a part of an ONNX model (a MobileNet-V2 residual block, which has a depthwise convolution with padding):  After using the tool to convert the model into...
Hello, I'm trying to convert my pytorch model to keras and I have ready onnx file for It. When I started to converting onnx to keras, I've got next error:...
I found that the added `ZeroPadding2D` layer is not correct when the paddings are not symmetric, as two of the four elements in `pads` of ONNX data are ignored. below...
@nerox8664 In TF 2.x, its suppose to use tensorflow.python.keras.layers.normalization_v2.BatchNormalization but looks like this conversion is using older tensorflow.python.keras.layers.normalization.BatchNormalization. Tf version: 2.1.0
``` pip3 install --upgrade onnx2keras Looking in indexes: https://bytedpypi.byted.org/simple, https://mirrors.aliyun.com/pypi/simple Collecting onnx2keras Downloading https://bytedpypi.byted.org/tos/pkg/pypi/onnx2keras/onnx2keras-0.0.17.tar.gz / 40kB 35.5MB/s Collecting tensorflow>=2.0 (from onnx2keras) ``` I get an error saying that tensorflow2.0 is...
Hello @nerox8664, below is a minimal working example for the Error I got. I want to convert a UNet to Keras and I need to concat some outputs. The concatenation...
Can you suggest how I can fix this error?
Hello Grigory, Thank you for the library, using this to convert a pytorch model to tfjs via onnx and using this library, Here is a link to onnx file, https://drive.google.com/open?id=1BJM5R1uQ4fW3RwWikvjdmop-NEwMo9RF,...
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...
Commit 322af56ba03e79c87a049f6d67534ff04efc8c79 introduced a bug in onnx2keras/reshape_layers.py if a shape contains None. The following change fixes it: 51c51,55 < layers[node_name] = np.array([i.value for i in input_0.shape]) --- > aa =...