RomanSteinberg

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Input, Dropout 


def imgs_input_fn(filenames, labels=None, perform_shuffle=False, repeat_count=1, batch_size=1):
    """
    Creates tf.data.Dataset object.
    Args:
        filenames (list: 
        labels (list): 

RomanSteinberg

import numpy as np

import tensorflow as tf

experiment_folder = '/output/'
input_shape = [299, 299, 3]


def imgs_input_fn(filenames, labels=None, perform_shuffle=False, repeat_count=1, batch_size=1):
    """

RomanSteinberg

import git
repo = git.Repo(search_parent_directories=True)
opt_file.write('branch: %s\n' % repo.active_branch)
opt_file.write('sha: %s\n' % repo.head.object.hexsha)

RomanSteinberg

class Heap {
  constructor() {
    this.container = [null];
  }
  
  isRoot(ind) {
    return ind == 1;
  }
  
  swap(ind1, ind2) {

RomanSteinberg

import pandas as pd
import json

# input parameters
quiz_res_file = '/home/roman/temp/pre_survey/quiz_result.csv'
quiz_descr_file = '/home/roman/temp/pre_survey/quiz_description.json'

# main
df = pd.read_csv(quiz_res_file)
description = json.load(open(quiz_descr_file))

RomanSteinberg

# Description:
#   This script is a minimal example of a freeing buffer strange behavior. Originally it contains error diagnosed 
#   by PyTorch:
#   "RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed.
#   Specify retain_graph=True when calling backward the first time."
#
#   One can find statements which can be changed to remove error.

import torch
from torch import nn, cuda

RomanSteinberg

from multiprocessing import Process, Queue, Event
from queue import Empty as QueueEmpty
from random import randint, seed
from time import monotonic as now
from datetime import timedelta

TASKS_COUNT = 12
ARRAY_SIZE = 2_000_000
POOL_SIZE = 2

RomanSteinberg

import yaml


def get_option():
    # get option from config or env or something
    return yaml.load(open('config.yaml'))['production']


def measure(method):
    # measures metrics for method

RomanSteinberg

class Cat:
    def __init__(self):
        self.start_position = np.array([1, 1])
        self.velocity = 7
        self.reached = False
        self.room = np.array([100, 100])  # выход за границы комнаты не критичен

    def move_generator(self):
        pos = self.start_position
        while True:

RomanSteinberg

import tensorrt as trt
import os
import torch
import onnx

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)


def convert_to_trt(image_width, image_height):
    onnx_file_path = 'model.onnx'