prerakmody

import pytorch

use_gpu = torch.cuda.is_available()

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 3, padding=1)
        self.pool  = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 3, padding=1)

prerakmody

Launch an EC2 Instance

0. Check Spot Instance Pricing here
1. Open EC2 console
2. Choose AMI :
   • Deep Learning AMI (Ubuntu) Version 8.0 - ami-dff741a0
     • contains TensorFlow and other famous DL libraries
     • make sure you select the "Ubuntu" version and not "Amazon Linux" version
3. Instance Type : Select an instanct type from the p2/p3 family
   • For inference purposes - p2.xlarge (1 x Nvidia Tesla K80)

• For training purposes - p3.xlarge (1 x Nvidia Tesla V100)

prerakmody

# System Created
*/.ipynb_checkpoints/*
*/__pycache__/*
*.pyc

# Image Formats
*.jpg
*.jpeg
*.png

prerakmody

import sys, time
import requests

def download_file_from_google_drive(id, destination):
    def get_confirm_token(response):
        for key, value in response.cookies.items():
            if key.startswith('download_warning'):
                return value

        return None

prerakmody

PORTS

1. Checking the process running on a port
   • lsof -i :<port_number>
   • sudo netstat -peanut | grep <port_number>
2. Checking processes
   • ps aux | grep <process_name>

LOGS

1. Checking logs of various processes

• tail -f /var/log/<>.log

prerakmody

import matplotlib.pyplot as plt

plt.xlim([-1,20])
plt.ylim([-1,20])
plt.arrow(0, 0, 10, 10,head_width=0.5, head_length=0.5)
plt.arrow(10,10, 8,4,head_width=0.5, head_length=0.5)

prerakmody

"""
Installing Rosbag
        - http://wiki.ros.org/lunar/Installation

RosBag Python API
        - http://wiki.ros.org/rosbag/Cookbook
        - http://wiki.ros.org/rosbag/Code%20API

ROS : Velodyne PointCloud
        - to convert velodyne_msgs/VelodyneScan messages to sensor_msgs/PointCloud2

prerakmody

prerakmody

import os
import numpy as np
from sklearn.metrics import log_loss

"""
 - HARDCODED FORMULAE 
   - In this gist, we shall almost always use probabilities and not unscaled logits
"""
## BINARY CLASS CROSS ENTROPY
y_true = [0,0,0,1] # 4 observations

prerakmody

img_url = '/home/play/data/GOD_DATASET/open_datasets/cityscapes/gtFine_trainvaltest/gtFine/train/aachen/aachen_000000_000019_gtFine_color.png'
# img_url = '/home/play/data/GOD_DATASET/open_datasets/mapillary/mapillary-vistas-dataset_public_v1.0/training/instances/QOJJHmXYAiRVW3rDK3gs0g.png'

import cv2
import skimage.io
import numpy as np

img = cv2.imread(img_url,cv2.IMREAD_UNCHANGED)
print ('\nCV2 : ', np.unique(img))