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work out accuracy per class

'''

load image into pil format

im = transforms.ToPILImage()(images[0])

convert pil image to bytes

with BytesIO() as output:
im.save(output, 'JPEG')
data = output.getvalue()

url = "http://ml-inference.northeurope.cloudapp.azure.com:5000/inference"

payload = {
'files': (
'1.jpeg',
data,
'image/jpeg'
)
}

r = requests.post(url, files=payload)
r_json = r.json()

outputs = torch.tensor(
r_json['result']['prediction_raw']).unsqueeze(0)

outputs, labels = outputs.to(DEVICE), labels.to(DEVICE)

acc1, acc5 = accuracy(outputs, labels, topk=(1, 5))

top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))

measure elapsed time

batch_time.update(time.time() - end)
end = time.time()

if i % print_freq == 0:
progress.display(i)


This issue has been automatically created by todo-actions based on a TODO comment found in rest_request.py:101. It will automatically be closed when the TODO comment is removed from the default branch (master).

Model evaluation

Create a series of scripts for evaluating and visualizing each model

  • model for evaluating openvino model optimizations
  • visualize results
  • deploy model to cloud and compare with local edge device

model deployment

  • deploy a model to cloud
  • deply model to edge neural compute stick
  • create a module for deployment from onnx to neural compute stick

model architectures

Creating models for evaluation

  • vgg
  • resnet
  • inception
  • mobilenet
  • efficient net
  • yolo
  • alexnet
  • lenet
  • resnext
  • squeezenet
  • retinanet
  • ssd

onnx to OpenVino Conversion

Integrate model conversion from openvino library. This will probably end up calling the mo.py file from the openvino toolkit with the correct flags passed from a file or some other method

  • integrate openvino model optimizer module
  • find out how to call model optimizer from python function

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