These notes pertain to Frequently Asked Questions (FAQ) for the 2. Intro to Python, Lesson 6. Project: Classify Images that were posted and addressed on AIPND slack. We recommend that you review these notes prior to starting the Classify Images Project to help clarify potential points of confusion regarding the Project.
- GitHub AIPND Repository Link
- Issues with the Project Workspace
- Running the Project on a Local Computer
- Files Required to Run check_images.py Locally
- Running Batch Files on Windows OS Locally
- Hints for this Project
- Eliminating Syntax Errors with Text Editor/Integrated Development Environment
- Cutting and Pasting Code in the Classroom
- Indention of Python Code
- Replacing None and Pass Statements
While it is recommended that you work on the project within the Project Workspace, a few students may have experienced issues trying to work within the Project Workspace. Some students found these issues resolved when they switched their internet browsers. Specifically, some students found that Chrome worked best; while others found that Firefox worked better for them. If you are running into the problem where the files in the workspace don't load and/or running code within the workspace runs extremely slowly; please try the following:
- Quit and exit out of the web browser you are using, then open it back up and restart it.
- Switch to a different web browser.
If you run into issues with the Project Workspace and the above recommendations didn't work; alternatively, you are welcome to complete the project on a local computer using the instructions in the next FAQ.
While it is recommended that you work on the project within the Project Workspace, to run the project on a local computer, you will have needed to have Python 3.6 intalled on your computer.
The easiest way to install python and the appropriate python modules is to install Anaconda. You will also have found the directions to install Anaconda in 2. Intro to Python, Lesson 5. Scripting, Section 3. Install Python Using Anaconda.
For this project you will also need to install the python packages pytorch and torchvision. If your local computer has a Linux, OSX (Mac), or Windows operating system look to Get Started. for installation instructions.
The following files and folders need to be put in the same folder as the check_images.py python program on your local computer. You will find these files and folders within the GitHub AIPND Repository. There are more programs in the repository than you will need, these extra programs are there to provide the code within the lessons in a format that can be copied and pasted from.
- pet_images (folder of 40 pet image)
- uploaded_images (a folder you will have to create to hold your uploaded images in that section of the project)
- classifier.py (classifier function you will be using to classify the images)
- dognames.txt (file that contains all the valid dog names from the classifier function and the pet image files)
- imagenet1000_clsid_to_human.txt (dictionary that converts the classifier function ids to text labels)
- adjust_results4_isadog.py (a program that contains the adjust_results4_isadog function that you will be defining as part of the project)
- calculates_results_stats.py (a program that contains the calculates_results_stats function that you will be defining as part of the project)
- classify_images.py (a program that contains the classify_images function that you will be defining as part of the project)
- get_input_args.py (a program that contains the get_input_args function that you will be defining as part of the project)
- get_pet_labels.py (a program that contains the get_pet_labels function that you will be defining as part of the project)
- print_results.py (a program that contains the print_results function that you will be defining as part of the project)
- run_models_batch.sh (a bash script that will run check_images.py sequentially for all 3 model architectures and output their results to text files - on Unix/Linux/OSX/Project Workspace from a terminal window)
- run_models_batch.bat (a batch script that will run check_images.py sequentially for all 3 model architectures and output their results to text files - on Windows from the Anaconda Prompt window)
- run_models_batch_uploaded.sh (a bash script that will run check_images.py sequentially for all 3 model architectures on the uploaded images folder and output their results to text files - on Unix/Linux/OSX/Project Workspace from a terminal window)
- run_models_batch_uploaded.bat (a batch script that will run check_images.py sequentially for all 3 model architectures on the uploaded images folder and output their results to text files - on Windows from the Anaconda Prompt window)
- test_classifier.py (an example program that demonstrates how to use the classifier function)
- print_functions_for_lab_checks.py (a program that contains functions that will allow you to check your code)
Also be aware that instructor provided hints files are provided for each of the functions used within this project, these files will end with _hints.py. These hints files contain extra code to provide a few hints to help guide students to the solution. You will find these hints files within the Project Workspace and also within the GitHub repository as the following files:
- adjust_results4_isadog_hints.py (a program that contains hints for the adjust_results4_isadog function that you will be defining as part of the project)
- calculates_results_stats_hints.py (a program that contains hints for the calculates_results_stats function that you will be defining as part of the project)
- classify_images_hints.py (a program that contains hints for the classify_images function that you will be defining as part of the project)
- get_input_args_hints.py (a program that contains hints for the get_input_args function that you will be defining as part of the project)
- get_pet_labels_hints.py (a program that contains hints for the get_pet_labels function that you will be defining as part of the project)
- print_results_hints.py (a program that contains hints for the print_results function that you will be defining as part of the project)
To run the files run_models_batch or run_models_batch_uploaded that run all 3 model architectures using check_images.py on a Windows OS locally; you will need to use the files that end with the extention .bat instead of the extension .sh. You will have also needed to have installed Anaconda on your computer (see following FAQ for details on Anaconda installation).
- Open the Anaconda Prompt - either from typing Anaconda Prompt within the search bar and selecting it or by clicking on it once it's found within the Anaconda folder of programs.
- Navigate to the folder within the Anaconda Prompt that contains the Project files including check_images.py and run_models_batch.bat using the command cd.
- Type the command within the Anaconda Prompt:
run_models_batch.bat
If instead you are working with the uploaded images , you will replace all instances of run_models_batch.bat from the directions above with run_models_batch_uploaded.bat.
Instructor provided hints files are provided for each of the functions used within this project, these files will end with _hints.py. These hints files contain extra code to provide a few hints to help guide students to the solution. You will find these hints files within the Project Workspace and also within the GitHub repository as the following files:
- adjust_results4_isadog_hints.py (a program that contains hints for the adjust_results4_isadog function that you will be defining as part of the project)
- calculates_results_stats_hints.py (a program that contains hints for the calculates_results_stats function that you will be defining as part of the project)
- classify_images_hints.py (a program that contains hints for the classify_images function that you will be defining as part of the project)
- get_input_args_hints.py (a program that contains hints for the get_input_args function that you will be defining as part of the project)
- get_pet_labels_hints.py (a program that contains hints for the get_pet_labels function that you will be defining as part of the project)
- print_results_hints.py (a program that contains hints for the print_results function that you will be defining as part of the project)
If you are experiencing a lot of syntax errors with your code, you may consider downloading your code and looking at it with your favorite text editor/IDE to help eliminate the syntax errors from your program. Recall in 2. Intro to Python, Lesson 5. Scripting, Section 6. Programming Environment Setup you were provided with a number of text editors that are available to use with python (like Atom, Sublime Text, Notepad++). Additionally, when you installed Anaconda, the Spyder IDE (Integrated Development Environment) for python should be available through the Anaconda Navigator.
If you cut and paste code directly from the classroom, it is very likely you will generate syntax errors with the single and double quotes. This is because the font type differences. If you are going to cut and paste code from the classroom, you will need to erase and replace any copied double or single quotes. Additionally, cutting and pasting code from the classroom may also result in issues regarding the proper code indention; therefore, it is not recommended to cut and paste code directly from the classroom.
Indention is used within Python to distinquish between blocks of code; whereas, with other programming languages, like Java and C++, they may have used curly brackets. The PEP8 Style guide provides the standard for Python code and is what has been used for the programs within the Github respository and the Project workspace. The PEP8 standard for indention is to use 4 spaces for each indention level. Not using 4 spaces for indention when editing check_images.py, will likely result in syntax errors.
Be aware that using the tab key within most text editors might not guarantee the proper 4 space indention. Additionally, not all text editors (including the Project Workspace) provide the proper 4 space indention as is used in the Python programs within the repository for this project.
When editing the functions provided in check_images.py you will need to replace None or the pass statement with your code for that function. The pass statement does nothing, it's used so that the program will still run even though the functions have not been fully defined. Similarly the value of None represents the absence of a value, it's used so that the program will still run even though the functions have not been fully defined.