naxty

import coremltools
model = coremltools.converters.keras.convert(
  model.h5', 
  input_names=['image'], 
  output_names=['output'], 
  class_labels=["0", "1"],
  image_input_names='image',
  red_bias = -1,
  green_bias = -1,
  blue_bias = -1,

naxty

let model = model()
let inputImage = input.resize(to: CGSize(width: 224, height: 224))
guard let features = inputImage?.pixelBuffer() else {
                complete(nil, "Error while creating the pixel buffer.")
                return
           }
guard let result = try? model.prediction(image: features) else {
                complete(nil, "Error while performing the prediction.")
                return
            }

naxty

pip3 install torch torchvision tensorflow onnx onnx-tf

naxty

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)

naxty

from torch.autograd import Variable
trained_model = Net()
trained_model.load_state_dict(torch.load('output/mnist_cnn.pt'))
# Shape of input to the model
dummy_input = Variable(torch.randn(1, 1, 28, 28)) 
# Export the trained model to ONNX
torch.onnx.export(trained_model, dummy_input, "output/mnist.onnx")

naxty

import onnx
from onnx_tf.backend import prepare
model = onnx.load('output/mnist.onnx')
tf_rep = prepare(model)

naxty

import numpy as np
from IPython.display import display
from PIL import Image
img = Image.open('images/five.png').resize((28, 28)).convert('L')
display(img)
output = tf_rep.run(np.asarray(img, dtype=np.float32)[np.newaxis, np.newaxis, :, :])
print('The digit is classified as ', np.argmax(output))

naxty

const session = new onnx.InferenceSession();
await session.loadModel("model.onnx");
const prediction = await session.run([inputTensor]);

naxty

model = FixResNet50(models.resnet.Bottleneck, [3, 4, 6, 3])
from torch.utils.model_zoo import load_url as load_state_dict_from_url
state_dict = load_state_dict_from_url(model_url,
                                    progress=True)
model.load_state_dict(state_dict)
from torch.autograd import Variable
dummy_input = Variable(torch.randn(1, 3, 224, 224))
torch.onnx.export(model, dummy_input, "model.onnx")

naxty

async function base64ToImg(b64string){
    const binaryData = Buffer.from(b64string, 'base64').toString('binary');
    const imgUrl = "/tmp/out.jpg"
    await writeFile(imgUrl, binaryData, "binary");
    return imgUrl;
}