Develop Reference

How to import python files in Google Colab

So I have been trying to make this file compatible to Google Colab but I'm not able to find any way to do it.
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EfficientDet-DeepSORT-Tracker is the main folder of this entire package
This picture is from one of the files placed alongside backbone.py
How to fix the fact that the file isn't able to detect backbone.py?
EDIT for more context: I shared the errors I found when trying to run waymo_open_dataset.py which isn't able to detect the other .py files alongside it.

According to this past question you could import 'filename' of the filename.py. So in the main.py file you are trying to run in colab, then import the required files in the main.py file.

Save excel file to local storage from Jupyter using python

I am working with Pandas and I have plenty of Excel files which I need to store in my local storage directly from Jupyter Notebook.
For example,
I create an excel file named "test.xlsx".
Now I want to save this excel file into somewhere in my C:/ drive.
Please help me out with the coding part in python.

How to load image dataset to Jupyter notebook?

I am newbie in ML and Deep Learning and am currently working on Jupyter notebook.
I have an image dataset in the form of a zip file containing nearly 28000 images downloaded on my desktop.
However, I am not being able to find any code that will make Jupyter notebook unzip the file and read it, so I'm able to work with it and develop a model.
Any help is appreciated!

Are these threads from the past helpful?
How to unzip files into a directory in Python
How to unzip files into memory in Python (you may encounter issues with this approach if the images are too large to fit in Jupyter's allocated memory)
How to read images from a directory in Python
If you go the directory route, a friendly reminder that you'll need to update the code in each example to match your directory structure. E.g. if you want to save images to and read images from a directory called "image_data", then change the code examples to unzip files into that directory and read images from that directory.
Lastly, for finding out what files are in a directory (so that you can read them into your program), see this thread answering the same question.

Google Colab - FileNotFoundError

i am trying to train YOLOv3 with custom dataset on Google Colab. I uploaded my folders, weights etc. When I run my train.py, I get path error. I run the code like this:
!python3 "drive/TrainYourOwnYolo/2_Training/Train_YOLO.py"
The error says,
content/TrainYourOwnYolo/2_Training/dataset/img20.jpg is not found.
As I understand on Colab, my all folders are under drive folder. I don't understand why yolo is trying to find my dataset under content folder. Do you have any idea?

As it seems, you have uploaded your data to /drive/TrainYourOwnYolo/, and not to /content/TrainYourOwnYolo/, where your script is looking.
The /content folder is normally used by Colab when saving, in case you don't use Google Drive. But you have mounted your Google Drive under /drive, so your notebook unsurprisingly fails to find the files.
You should change the file paths in your Train_YOLO.py" script to replace references to /content with /drive.
If this is not possible, you can find the /content folder on the file catalogue on the left of your Colab notebook:
and by right-clicling on it, you'll see an option for uploading files there.

How to upload and save large data to Google Colaboratory from local drive?

I have downloaded large image training data as zip from this Kaggle link
https://www.kaggle.com/c/yelp-restaurant-photo-classification/data
How do I efficiently achieve the following?
Create a project folder in Google Colaboratory
Upload zip file to project folder
unzip the files
Thanks
EDIT: I tried the below code but its crashing for my large zip file. Is there a better/efficient way to do this where I can just specify the location of the file in local drive?
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(
name=fn, length=len(uploaded[fn])))

!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config set -n path -v /content
!kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
os.chdir('/content/competitions/jigsaw-toxic-comment-classification-challenge')
for file in os.listdir():
zip_ref = zipfile.ZipFile(file, 'r')
zip_ref.extractall()
zip_ref.close()
There is minor change on line 9, without which was encountering error.
source: https://gist.github.com/jayspeidell/d10b84b8d3da52df723beacc5b15cb27
couldn't add as comment cause rep.

You may refer with these threads:
Import data into Google Colaboratory
Load local data files to Colaboratory
Also check out the I/O example notebook. Example, for access to xls files, you'll want to upload the file to Google Sheets. Then, you can use the gspread recipes in the same I/O example notebook.

You may need to use kaggle-cli module to help with the download.
It’s discussed in this fast.ai thread.

I just wrote this script that downloads and extracts data from the Kaggle API to a Colab notebook. You just need to paste in your username, API key, and competition name.
https://gist.github.com/jayspeidell/d10b84b8d3da52df723beacc5b15cb27
The manual upload function in Colab is kind of buggy now, and it's better to download files via wget or an API service anyway because you start with a fresh VM each time you open the notebook. This way the data will download automatically.

Another option is to upload the data to dropbox (if it can fit), get a download link. Then in the notebook do
!wget link -0 new-name && ls