netron is a JavaScript library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. netron has no vulnerabilities, it has a Permissive License and it has medium support. However netron has 13 bugs. You can install using 'npm i graphview' or download it from GitHub, npm.

Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 and UFF. Netron has experimental support for PyTorch, TensorFlow, TorchScript, OpenVINO, Torch, Vitis AI, Arm NN, BigDL, Chainer, Deeplearning4j, MediaPipe, ML.NET and scikit-learn.

Support

Support

Quality

Quality

Security

Security

License

License

Reuse

Reuse

Support

- netron has a medium active ecosystem.
- It has 18318 star(s) with 2126 fork(s). There are 263 watchers for this library.
- There were 2 major release(s) in the last 12 months.
- There are 17 open issues and 767 have been closed. On average issues are closed in 9 days. There are 1 open pull requests and 0 closed requests.
- It has a neutral sentiment in the developer community.
- The latest version of netron is v5.7.1

netron Support

Best in #Machine Learning

Average in #Machine Learning

netron Support

Best in #Machine Learning

Average in #Machine Learning

Quality

- netron has 13 bugs (2 blocker, 0 critical, 5 major, 6 minor) and 49 code smells.

netron Quality

Best in #Machine Learning

Average in #Machine Learning

netron Quality

Best in #Machine Learning

Average in #Machine Learning

Security

- netron has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
- netron code analysis shows 0 unresolved vulnerabilities.
- There are 3 security hotspots that need review.

netron Security

Best in #Machine Learning

Average in #Machine Learning

netron Security

Best in #Machine Learning

Average in #Machine Learning

License

- netron is licensed under the MIT License. This license is Permissive.
- Permissive licenses have the least restrictions, and you can use them in most projects.

netron License

Best in #Machine Learning

Average in #Machine Learning

netron License

Best in #Machine Learning

Average in #Machine Learning

Reuse

- netron releases are available to install and integrate.
- Deployable package is available in npm.
- Installation instructions are available. Examples and code snippets are not available.

netron Reuse

Best in #Machine Learning

Average in #Machine Learning

netron Reuse

Best in #Machine Learning

Average in #Machine Learning

Top functions reviewed by kandi - BETA

kandi has reviewed netron and discovered the below as its top functions. This is intended to give you an instant insight into netron implemented functionality, and help decide if they suit your requirements.

- Merge a single entry
- handle a writable entry

Get all kandi verified functions for this library.

Get all kandi verified functions for this library.

Visualizer for neural network, deep learning, and machine learning models

See all related Code Snippets

ONNX model checker fails while ONNX runtime works fine when `tf.function` is used to decorate memeber function with loop

CopyDownload

```
tf.function(
input_signature=[
tf.TensorSpec(shape=[None,None], dtype=tf.int32),
tf.TensorSpec(shape=[None,None], dtype=tf.float32),
tf.TensorSpec(shape=[], dtype=tf.float32),
])
```

How to get Tflite model output in c++?

CopyDownload

```
memcpy(input,img.data,32*32*sizeof(float));
```

```
input = inputImg.ptr<float>(0);
```

```
float* output = interpreter->typed_output_tensor<float>(0);
```

```
memcpy(input,img.data,32*32*sizeof(float));
```

```
input = inputImg.ptr<float>(0);
```

```
float* output = interpreter->typed_output_tensor<float>(0);
```

```
memcpy(input,img.data,32*32*sizeof(float));
```

```
input = inputImg.ptr<float>(0);
```

```
float* output = interpreter->typed_output_tensor<float>(0);
```

How to create a TensorFloat for a shape that has unknown component?

CopyDownload

```
int64_t batch_size = 1;
std::vector<int64_t> shape({ batch_size, 224, 224, 3 }); // Note: this doesn't compile since the first component is a string!
binding.Bind(L"Image:0", TensorFloat::Create(shape));
```

This model is not supported: Input tensor 0 does not have a name

CopyDownload

```
input_meta.name = "image_input"
```

Process output data from YOLOv5 TFlite

CopyDownload

```
def classFilter(classdata):
classes = [] # create a list
for i in range(classdata.shape[0]): # loop through all predictions
classes.append(classdata[i].argmax()) # get the best classification location
return classes # return classes (int)
def YOLOdetect(output_data): # input = interpreter, output is boxes(xyxy), classes, scores
output_data = output_data[0] # x(1, 25200, 7) to x(25200, 7)
boxes = np.squeeze(output_data[..., :4]) # boxes [25200, 4]
scores = np.squeeze( output_data[..., 4:5]) # confidences [25200, 1]
classes = classFilter(output_data[..., 5:]) # get classes
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
x, y, w, h = boxes[..., 0], boxes[..., 1], boxes[..., 2], boxes[..., 3] #xywh
xyxy = [x - w / 2, y - h / 2, x + w / 2, y + h / 2] # xywh to xyxy [4, 25200]
return xyxy, classes, scores # output is boxes(x,y,x,y), classes(int), scores(float) [predictions length]
```

```
"""Output data"""
output_data = interpreter.get_tensor(output_details[0]['index']) # get tensor x(1, 25200, 7)
xyxy, classes, scores = YOLOdetect(output_data) #boxes(x,y,x,y), classes(int), scores(float) [25200]
```

```
for i in range(len(scores)):
if ((scores[i] > 0.1) and (scores[i] <= 1.0)):
H = frame.shape[0]
W = frame.shape[1]
xmin = int(max(1,(xyxy[0][i] * W)))
ymin = int(max(1,(xyxy[1][i] * H)))
xmax = int(min(H,(xyxy[2][i] * W)))
ymax = int(min(W,(xyxy[3][i] * H)))
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)
...
```

```
def classFilter(classdata):
classes = [] # create a list
for i in range(classdata.shape[0]): # loop through all predictions
classes.append(classdata[i].argmax()) # get the best classification location
return classes # return classes (int)
def YOLOdetect(output_data): # input = interpreter, output is boxes(xyxy), classes, scores
output_data = output_data[0] # x(1, 25200, 7) to x(25200, 7)
boxes = np.squeeze(output_data[..., :4]) # boxes [25200, 4]
scores = np.squeeze( output_data[..., 4:5]) # confidences [25200, 1]
classes = classFilter(output_data[..., 5:]) # get classes
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
x, y, w, h = boxes[..., 0], boxes[..., 1], boxes[..., 2], boxes[..., 3] #xywh
xyxy = [x - w / 2, y - h / 2, x + w / 2, y + h / 2] # xywh to xyxy [4, 25200]
return xyxy, classes, scores # output is boxes(x,y,x,y), classes(int), scores(float) [predictions length]
```

```
"""Output data"""
output_data = interpreter.get_tensor(output_details[0]['index']) # get tensor x(1, 25200, 7)
xyxy, classes, scores = YOLOdetect(output_data) #boxes(x,y,x,y), classes(int), scores(float) [25200]
```

```
for i in range(len(scores)):
if ((scores[i] > 0.1) and (scores[i] <= 1.0)):
H = frame.shape[0]
W = frame.shape[1]
xmin = int(max(1,(xyxy[0][i] * W)))
ymin = int(max(1,(xyxy[1][i] * H)))
xmax = int(min(H,(xyxy[2][i] * W)))
ymax = int(min(W,(xyxy[3][i] * H)))
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)
...
```

```
def classFilter(classdata):
classes = [] # create a list
for i in range(classdata.shape[0]): # loop through all predictions
classes.append(classdata[i].argmax()) # get the best classification location
return classes # return classes (int)
def YOLOdetect(output_data): # input = interpreter, output is boxes(xyxy), classes, scores
output_data = output_data[0] # x(1, 25200, 7) to x(25200, 7)
boxes = np.squeeze(output_data[..., :4]) # boxes [25200, 4]
scores = np.squeeze( output_data[..., 4:5]) # confidences [25200, 1]
classes = classFilter(output_data[..., 5:]) # get classes
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
x, y, w, h = boxes[..., 0], boxes[..., 1], boxes[..., 2], boxes[..., 3] #xywh
xyxy = [x - w / 2, y - h / 2, x + w / 2, y + h / 2] # xywh to xyxy [4, 25200]
return xyxy, classes, scores # output is boxes(x,y,x,y), classes(int), scores(float) [predictions length]
```

```
"""Output data"""
output_data = interpreter.get_tensor(output_details[0]['index']) # get tensor x(1, 25200, 7)
xyxy, classes, scores = YOLOdetect(output_data) #boxes(x,y,x,y), classes(int), scores(float) [25200]
```

```
for i in range(len(scores)):
if ((scores[i] > 0.1) and (scores[i] <= 1.0)):
H = frame.shape[0]
W = frame.shape[1]
xmin = int(max(1,(xyxy[0][i] * W)))
ymin = int(max(1,(xyxy[1][i] * H)))
xmax = int(min(H,(xyxy[2][i] * W)))
ymax = int(min(W,(xyxy[3][i] * H)))
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)
...
```

Kernel and Recurrent Kernel in Keras LSTMs

CopyDownload

```
1 * 4 * units = kernel
```

```
units * (4 * units) = recurrent kernel
```

```
1 * 4 * units = kernel
```

```
units * (4 * units) = recurrent kernel
```

How to know input/output layer names and sizes for Pytorch model?

CopyDownload

```
import io
import numpy as np
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.onnx
from torchvision import models
model = torch.load('model_final.pth')
model.eval()
print('Finished loading model!')
print(model)
device = torch.device("cpu" if args.cpu else "cuda")
model = model.to(device)
# ------------------------ export -----------------------------
output_onnx = 'super_resolution.onnx'
print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
input_names = ["input0"]
output_names = ["output0","output1"]
inputs = torch.randn(1, 3, 1080, 1920).to(device)
torch_out = torch.onnx._export(model, inputs, output_onnx, export_params=True, verbose=False,
input_names=input_names, output_names=output_names)
```

How to pass image to tflite model in android

CopyDownload

```
ImageProcessor imageProcessor =
new ImageProcessor.Builder()
.add(new ResizeWithCropOrPadOp(cropSize, cropSize))
.add(new ResizeOp(imageSizeX, imageSizeY, ResizeMethod.NEAREST_NEIGHBOR))
.add(new Rot90Op(numRoration))
.add(getPreprocessNormalizeOp())
.build();
return imageProcessor.process(inputImageBuffer);
```

```
tflite.run(inputImageBuffer.getBuffer(), outputProbabilityBuffer.getBuffer().rewind());
```

```
ImageProcessor imageProcessor =
new ImageProcessor.Builder()
.add(new ResizeWithCropOrPadOp(cropSize, cropSize))
.add(new ResizeOp(imageSizeX, imageSizeY, ResizeMethod.NEAREST_NEIGHBOR))
.add(new Rot90Op(numRoration))
.add(getPreprocessNormalizeOp())
.build();
return imageProcessor.process(inputImageBuffer);
```

```
tflite.run(inputImageBuffer.getBuffer(), outputProbabilityBuffer.getBuffer().rewind());
```

Failed to run the tflite model on Interpreter due to Internal Error

CopyDownload

```
import numpy as np
import tensorflow as tf
# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test the model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
```

How to get weights in tflite using c++ api?

CopyDownload

```
TfLiteContext* context; // You would usually have access to this already.
TfLiteNode* node; // <obtain this from the graph>;
for (int i = 0; i < node->inputs->size; ++i) {
TfLiteTensor* input_tensor = GetInput(context, node, i);
// Determine if this is a weight tensor.
// Usually the weights will be memory-mapped read-only tensor
// directly baked in the TFLite model (flatbuffer).
if (input_tensor->allocation_type == kTfLiteMmapRo) {
// Read the values from input_tensor, based on its type.
// For example, if you have float weights,
const float* weights = GetTensorData<float>(input_tensor);
// <read the weight values...>
}
}
```

See all related Code Snippets

QUESTION

PyTorch to ONNX export, ATen operators not supported, onnxruntime hangs out

Asked 2022-Mar-03 at 14:05I want to export `roberta-base`

based language model to `ONNX`

format. The model uses `ROBERTA`

embeddings and performs text classification task.

```
from torch import nn
import torch.onnx
import onnx
import onnxruntime
import torch
import transformers
```

from logs:

```
17: pytorch: 1.10.2+cu113
18: CUDA: False
21: device: cpu
26: onnxruntime: 1.10.0
27: onnx: 1.11.0
```

`PyTorch`

export

```
batch_size = 3
model_input = {
'input_ids': torch.empty(batch_size, 256, dtype=torch.int).random_(32000),
'attention_mask': torch.empty(batch_size, 256, dtype=torch.int).random_(2),
'seq_len': torch.empty(batch_size, 1, dtype=torch.int).random_(256)
}
model_file_path = os.path.join("checkpoints", 'model.onnx')
torch.onnx.export(da_inference.model, # model being run
model_input, # model input (or a tuple for multiple inputs)
model_file_path, # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=11, # the ONNX version to export the model to
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['input_ids', 'attention_mask', 'seq_len'], # the model's input names
output_names = ['output'], # the model's output names
dynamic_axes={'input_ids': {0 : 'batch_size'},
'attention_mask': {0 : 'batch_size'},
'seq_len': {0 : 'batch_size'},
'output' : {0 : 'batch_size'}},
verbose=True)
```

I know there maybe problems converting some operators from ATen (A Tensor Library for C++11), if included in model architecture PyTorch Model Export to ONNX Failed Due to ATen.

Exports succeeds if I set the parameter `operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK`

which means 'leave as is ATen operators if not supported in ONNX'.

PyTorch export function gives me the following warning:

```
Warning: Unsupported operator ATen. No schema registered for this operator.
Warning: Shape inference does not support models with experimental operators: ATen
```

It looks like the only ATen operators in the model that are not converted to ONNX are situated inside layers LayerNorm.weight and LayerNorm.bias (I have several layers like that):

```
%1266 : Float(3, 256, 768, strides=[196608, 768, 1], requires_grad=0, device=cpu) =
onnx::ATen[cudnn_enable=1, eps=1.0000000000000001e-05, normalized_shape=[768], operator="layer_norm"]
(%1265, %model.utterance_rnn.base.encoder.layer.11.output.LayerNorm.weight,
%model.utterance_rnn.base.encoder.layer.11.output.LayerNorm.bias)
# /opt/conda/lib/python3.9/site-packages/torch/nn/functional.py:2347:0
```

Than model check passes OK:

```
model = onnx.load(model_file_path)
# Check that the model is well formed
onnx.checker.check_model(model)
# Print a human readable representation of the graph
print(onnx.helper.printable_graph(model.graph))
```

I also can visualize computation graph using Netron.

But when I try to perform inference using exported ONNX model it stalls with no logs or stdout. So this code will hang the system:

```
model_file_path = os.path.join("checkpoints", "model.onnx")
sess_options = onnxruntime.SessionOptions()
sess_options.log_severity_level = 0
ort_providers: List[str] = ["CUDAExecutionProvider"] if use_gpu else ['CPUExecutionProvider']
session = InferenceSession(model_file_path, providers=ort_providers, sess_options=sess_options)
```

Is there any suggestions to overcome this problem? From official documentation I see that torch.onnx models exported this way are probably runnable only by `Caffe2`

.

This layers are not inside the base frozen roberta model, so this is additional layers that I added by myself. Is it possible to substitute the offending layers with similar ones and retrain the model?

Or `Caffe2`

is the best choice here and onnxruntime will not do the inference?

Update: I retrained the model on the basis of BERT cased embeddings, but the problem persists. The same ATen operators are not converted in ONNX. It looks like the layers LayerNorm.weight and LayerNorm.bias are only in the model above BERT. So, what is your suggestions to change this layers and enable ONNX export?

ANSWER

Answered 2022-Mar-01 at 20:25Have you tried to export after defining the operator for onnx? Something along the lines of the following code by Huawei.

On another note, when loading a model, you can technically override anything you want. Putting a specific layer to equal your modified class that inherits the original, keeps the same behavior (input and output) but execution of it can be modified. You can try to use this to save the model with changed problematic operators, transform it in onnx, and fine tune in such form (or even in pytorch).

This generally seems best solved by the onnx team, so long term solution might be to post a request for that specific operator on the github issues page (but probably slow).

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

No vulnerabilities reported

macOS: Download the .dmg file or run brew install netron. Linux: Download the .AppImage file or run snap install netron. Windows: Download the .exe installer or run winget install -s winget netron. Browser: Start the browser version. Python Server: Run pip install netron and netron [FILE] or netron.start('[FILE]').

For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .

over 430 million Knowledge Items

Save this library and start creating your kit

Explore Related Topics

over 430 million Knowledge Items

Save this library and start creating your kit