satyajitghana

import gradio as gr
import torch
import requests

from PIL import Image
from torchvision import transforms

model = torch.hub.load('pytorch/vision:v0.6.0',
                       'resnet18', pretrained=True).eval()

satyajitghana

import argparse
import subprocess
from pathlib import Path

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Just an example",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(

satyajitghana

{
  "data": [
	"data:image/jpeg;base64,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

satyajitghana

satyajitghana

satyajitghana

SSH git clone url format on gitlab

General syntax:

<protocol>://[<user>[:<password>]@]<hostname>[:<port>][:][/]<path>[#<commit-ish> | #semver:<semver>]
                                    prefer the slash here ^

These URLs work:

satyajitghana

service: tejas-service
org: tensorclan

frameworkVersion: '2'

provider:
  name: aws
  runtime: python3.8
  stage: dev
  region: ap-south-1

satyajitghana

import onnxruntime

ort_session = onnxruntime.InferenceSession("simple_pose_estimation.quantized.onnx")

def to_numpy(tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()

# compute ONNX Runtime output prediction
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(tr_img.unsqueeze(0))}
ort_outs = ort_session.run(None, ort_inputs)

satyajitghana

from onnxruntime.quantization import quantize
from onnxruntime.quantization import QuantizationMode

quantized_model = quantize(onnx_model, quantization_mode=QuantizationMode.IntegerOps, static=False)

onnx.save(quantized_model, 'simple_pose_estimation.quantized.onnx')

print_size_of_onnx_model(quantized_model)

satyajitghana

import io
import numpy as np
import torch.onnx

# Input to the model
torch_model = new_model
batch_size = 1
x = torch.randn(batch_size, 3, 256, 256, requires_grad=True)
torch_out = torch_model(x)