humandotlearning

import cv2
import os
import time
import queue
import threading
import multiprocessing

def capture_frames(url, fps, frame_save_path, video_duration=5):
    try:
        cap = cv2.VideoCapture(url)

humandotlearning

#####################################################################################################

                          # Snippet for pytorch distributed training

######################################################################################################

import torch
from torch import nn

model = Model(input_size, output_size)

humandotlearning

# save docker in .tar.gz to save space
# this line loads a docker image from some_docker.tar.gz
sudo docker load < some_docker.tar.gz

# you can detach from a container and leave it running using the  CTRL-p CTRL-q key sequence.
 CTRL-p CTRL-q 



####################################################################################################################

humandotlearning

# https://github.com/utkuozbulak/pytorch-custom-dataset-examples

from torch.utils.data.dataset import Dataset

class ABCDataset(Dataset):
    def __init__(self, csv_file_pth, img_dir):
        """
        Args:
            csv_file_pth (string): Path to the csv file with annotations.
            img_dir (string): Directory with all the images.

humandotlearning

# use any FTP like fillezilla for transferring files between your comp to aws
# or use cli
sudo scp -i "aws_vpn_test.pem" -r ubuntu@ec2-3-14-73-32.us-east-2.compute.amazonaws.com:~/FR/FaceRecognition/lightning_logs/default/version_27/ ~/

humandotlearning

# to limit the gpu usuage of tf, use the following commands
###################################################################################################
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  # Restrict TensorFlow to only allocate 3GB of memory on the first GPU
  try:
    tf.config.experimental.set_virtual_device_configuration(
        gpus[0],
        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=3000)])

humandotlearning

# draw rectangle
bb_img = cv2.rectangle(img , (5, 5), (220, 220), (255, 0, 0) , 2)  #image, start_point, end_point, color, thickness

humandotlearning

#!/bin/bash
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl2_2.1.4-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl-dev_2.1.4-1+cuda9.0_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libnccl2_2.1.4-1+cuda9.0_amd64.deb

humandotlearning

def foo(bar):
    print 'hello {0}'.format(bar)
    return "foo"
    
# for python 2
class ThreadWithReturnValue(Thread):
    def __init__(self, group=None, target=None, name=None,
                 args=(), kwargs={}, Verbose=None):
        Thread.__init__(self, group, target, name, args, kwargs, Verbose)
        self._return = None

humandotlearning

# to download file from gdrive create a shareable link and follow the instruction in the link
https://github.com/circulosmeos/gdown.pl