daniil-lyakhov

from transformers import AutoTokenizer, AutoModelForCausalLM
from optimum.intel.openvino import OVModelForCausalLM, OVWeightQuantizationConfig
import whowhatbench

model_dir = "state-spaces/mamba-130m-hf"
tokenizer = AutoTokenizer.from_pretrained(model_dir)

ov_model = AutoModelForCausalLM.from_pretrained(model_dir)

daniil-lyakhov

for f in *.MTS; do
  ffmpeg  -ss 0.07 -i "$f" \
    -vf yadif=mode=1:parity=auto:deint=all \
    -c:v libx264 -crf 18 -preset slow \
    -c:a copy \
    -movflags +faststart \
    "${f%.mp4}_deint.mp4"
done

daniil-lyakhov

# torch==2.3.1
# ultralytics==8.2.35
import torch
from ultralytics.models.yolo import YOLO


torch.manual_seed(42)

def run_yolo(torch_fx, inputs):
    yolo_model = YOLO("yolov8n")

daniil-lyakhov

# torch==2.3.1
# ultralytics==8.2.35
from typing import Dict, Tuple

import numpy as np
import torch
from tqdm import tqdm
from ultralytics.data.utils import check_det_dataset
from ultralytics.engine.validator import BaseValidator as Validator
from ultralytics.models.yolo import YOLO

daniil-lyakhov

import sys
import json
from collections import defaultdict
import numpy as np

def main():
    path = sys.argv[1]
    with open(path, 'r') as f:
        data = json.load(f)

daniil-lyakhov

import os
import sys
import json
import xml.etree.ElementTree as ET

def get_json_name_map(model_path_before, model_path_after, output_path):
    attrs_to_check = ['type']
    attr_to_map = 'name'

    root_before = ET.parse(model_path_before).getroot()

daniil-lyakhov

import xml.etree.ElementTree as ET
root = ET.parse('openvino.xml').getroot()
layers = root[0]
for idx, l in enumerate(layers):
    if l.get('type') == 'Interpolate':
        if layers[idx + 1].get('type') in ['Add', 'Multiply']:
            print(layers[idx + 1].get('name'))#, layers[idx + 1].get('type'))

daniil-lyakhov

# Openvino==2022.2.0
import sys
import numpy as np

from openvino.runtime import Core


if len(sys.argv) < 3:
    print("Please provide path to model xml file as a first arg and"
          " path to output text file to dump model constants.")

daniil-lyakhov

import sys
  
from openvino.frontend import FrontEndManager
from openvino.offline_transformations import apply_pruning_transformation
from openvino.runtime import serialize

print(f'input model path {sys.argv[1]} \n output model path {sys.argv[2]}.xml \n output model weights {sys.argv[2]}.bin')
input_model = sys.argv[1]
fem = FrontEndManager()

daniil-lyakhov

# Openvino==2022.1.0
import sys

from openvino.runtime import Core

DELIMITER = ' | '

if len(sys.argv) < 3:
    print("Please provide path to model xml file as a first arg and"
          " path to output text file to dump model constants.")