Fixing 'FileNotFoundError: Missing Floor_shapes.npy' In Flodiff

Nov 9, 2025 by Admin 64 views

Hey guys! Ever stumbled upon the dreaded FileNotFoundError: Missing floor_shapes.npy while trying to get Flodiff up and running? Yeah, it's a real head-scratcher. This error pops up during dataset initialization in training/train.py, and it's all about a missing file called floor_shapes.npy. This file is super important because it tells the script the original floorplan size for each scene. The problem? It's not included in the repo or the dataset package, and there's no clear way to generate it. Let's dive into how to fix this, shall we?

The Root of the Problem: Why `floor_shapes.npy` Matters

Alright, so what's the deal with this floor_shapes.npy file, anyway? Well, in the grand scheme of things, it's a critical component for Flodiff's training process. It lives under the trav_maps directory within your dataset structure. This file contains the dimensions of the floorplans used in the dataset. The code, particularly in model/flona_dataset.py, tries to load this file to figure out the original size of the floorplans. Without it, the model doesn't know how to properly process the scene data, and boom – the FileNotFoundError rears its ugly head.

The error message itself is pretty clear: FileNotFoundError: [Errno 2] No such file or directory: '/data/user/flodiff/datasets/trav_maps/floor_shapes.npy'. This tells us that the script is looking for floor_shapes.npy in the trav_maps folder, but it can't find it. This usually happens when the dataset isn't set up correctly or when the necessary preprocessing steps haven't been completed. The np.load() function, which is trying to load the file, throws this error. It's like trying to bake a cake without the flour – you're just not gonna get anywhere.

So, why isn't this file there? Well, that's where the confusion comes in. The creators of the model haven't provided the file directly, and there's no documented script or clear instructions on how to generate it. This leaves us, the users, in a bit of a pickle. We need to figure out how to create this file to continue with the training. The absence of this file creates a significant roadblock, preventing users from effectively training the Flodiff model on their datasets. This necessitates a workaround to ensure the model functions as intended. The file's role is to provide the critical dimensions, and its absence halts the training process, thereby emphasizing the importance of solving this issue for a successful model implementation.

The Code's Perspective: Where the Error Occurs

To really understand what's happening, let's zoom in on the code snippets where this error originates. The traceback tells us the error happens in model/flona_dataset.py at line 53, and it's called from training/train.py at line 28. This means the dataset loading process in flona_dataset.py is trying to access floor_shapes.npy, and because it can't find it, the error bubbles up to train.py. The use of np.load() indicates that the file should be a NumPy array, which stores numerical data efficiently. The allow_pickle=True argument in the np.load() function suggests that the file might contain pickled Python objects, which could include the floorplan sizes stored in a dictionary or other data structures. The absence of this file completely breaks the initialization process, and the training simply cannot begin until the issue is addressed. This makes the creation or acquisition of this file a critical first step. This part of the process is essentially the gatekeeper to starting the training, and it needs to be resolved before further progress is possible.

Potential Solutions: How to Get `floor_shapes.npy`

Now for the good stuff: How do we actually solve this FileNotFoundError? Since the file isn't provided, we've got a few options:

Recreate the file: This involves figuring out how the original data was processed to generate the file. You might need to examine the dataset creation scripts (if any are available) or the code that uses this file to understand its format and how to create it. This usually means digging into the dataset preprocessing steps, which might involve loading the raw scene data, extracting the floorplan dimensions, and saving them into a NumPy array.
Look for pre-generated files: It is always a good idea to check if someone has already done the work for you. See if you can find pre-generated floor_shapes.npy files from the research community. This could be in the form of a pre-trained model or a shared dataset. Check the project's issue tracker (like on GitHub) or forums related to the project. Sometimes, other users have already faced the same problem and shared their solutions.
Manual creation: If you have access to the original scene data (e.g., raw floorplan images or 3D models), you might be able to manually create the floor_shapes.npy file. You'll need to write a script that loads each scene, extracts the floorplan size, and saves it in a NumPy array. This approach requires understanding the data format and potentially some image processing or 3D modeling knowledge.
Check for alternative data sources: In some cases, the dataset might be derived from other sources or databases. You could look into the original sources and see if they contain the necessary information. It is always a good idea to ensure that the dataset aligns with the expected format.

Detailed Steps: Recreating the file

If you opt to recreate the file, here's a general approach:

Understand the dataset: Examine the dataset structure and data formats. You'll need to know where the floorplan data is stored and how it's represented (e.g., as images or 3D models). Figure out where the floorplan dimensions are encoded in the available scene data. This might involve looking at image sizes, model extents, or other metadata.
Write a script: Create a Python script using libraries like NumPy, OpenCV (for image processing), or libraries that can load 3D models. The script should: Loop through each scene in your dataset, load the floorplan data for the scene, extract the floorplan dimensions (width and height, for instance), and store these dimensions in a Python dictionary or a similar structure. Use the dimensions as the data for your floor_shapes.npy file.
Save the file: Use np.save() to save your data as a .npy file. Ensure that the file is saved in the trav_maps directory within the dataset structure, as this is where the training script expects to find it.

Troubleshooting Tips and Common Pitfalls

Even with these steps, you might run into some roadblocks. Here's a quick guide to help you navigate them:

Incorrect dataset path: Double-check that the trav_maps directory is in the correct location and that your dataset path is configured properly in train.py. The training script needs to know where to find the data. Verify that the dataset path is set up correctly in your environment and that the path matches the structure where you have placed floor_shapes.npy. Incorrect paths are a common source of file not found errors.
Data format issues: Make sure you understand the data format expected by the training script. If the format of your floor_shapes.npy doesn't match what the script expects, you'll still encounter errors. Ensure that the data structure within the floor_shapes.npy file is compatible with how the training script loads and uses it. Mismatched data formats often lead to unexpected behavior and errors during model training.
Environment setup: Ensure you have the necessary libraries installed (NumPy, OpenCV, etc.). Missing dependencies can lead to import errors. Review your environment and install any missing packages using pip or conda before running the training script. A properly configured environment is essential for smooth execution of the code and prevents library-related issues.
Permissions: Make sure your script has the necessary read/write permissions for the dataset directory. Permission issues often prevent the script from accessing the files it needs. Ensure that your user account has the required access rights to the dataset folder to prevent permission-related errors. Correct permissions are essential for successful data loading and saving operations.
Debugging: Use print statements or a debugger to inspect the data and the flow of your script. This can help you identify where errors are occurring and what's going wrong. Add debug messages to key sections of your code to track variable values and program flow. This is super helpful when you're trying to figure out what's going on.

Addressing the issue

Example Code (Conceptual)

Let's get into some example code to illustrate the process of generating floor_shapes.npy (This is a conceptual example and might need adjustments based on your specific dataset and how its structure):

import os
import numpy as np
from PIL import Image # or any library used to load your floorplan images.

# Assuming your dataset structure is like this:
# dataset_root/
#   ├── scene1/
#   │   └── floorplan.png
#   ├── scene2/
#   │   └── floorplan.png
#   └── ...

def generate_floor_shapes(dataset_root, output_path='trav_maps/floor_shapes.npy'):
    floor_shapes = {}
    for scene_name in os.listdir(dataset_root):
        scene_path = os.path.join(dataset_root, scene_name)
        if os.path.isdir(scene_path):
            floorplan_path = os.path.join(scene_path, 'floorplan.png') # Or whatever your floorplan image name is
            if os.path.exists(floorplan_path):
                try:
                    # Load the image and get its dimensions
                    img = Image.open(floorplan_path)
                    width, height = img.size
                    floor_shapes[scene_name] = (width, height)  # Store width and height
                except Exception as e:
                    print(f"Error processing {scene_name}: {e}")
                    continue

    # Convert the dictionary to a numpy array for saving
    np.save(output_path, floor_shapes)
    print(f"floor_shapes.npy generated at {output_path}")

# Example usage. Replace with your actual dataset path
dataset_root = '/data/user/flodiff/datasets'
generate_floor_shapes(dataset_root)

Key Points:

Adjust Paths: Modify the dataset_root to match your actual dataset location.
Image Format: The code assumes floorplans are images (.png). Adjust the file loading and processing as needed based on your actual data formats.
Error Handling: The try...except block is very important to prevent the script from crashing if it encounters a problematic image or a missing floorplan.
Directory Creation: Make sure the directory trav_maps exists inside your dataset root before running the script. You can use os.makedirs(os.path.dirname(output_path), exist_ok=True) to create the directory if it doesn't exist.
File Extension: Adjust the floorplan file extension (floorplan.png) according to your actual files. If you're using a different image type or 3D model format, you will need to replace the Image.open() and the way the image sizes are captured.

Practical Considerations

Before running the code, it's very important to: Make sure you have the required libraries installed (e.g., Pillow for image loading). Double-check the dataset path to ensure that it accurately points to the location of your data. Inspect the file structure to locate the floorplan images or the relevant floorplan data. Adjust the code to suit the format of the floorplan data. Test the script on a small subset of your dataset to confirm that it correctly extracts the dimensions. Verify that the output floor_shapes.npy file has been created in the correct location.

Conclusion: Getting Past the `FileNotFoundError`

So, there you have it, guys! The FileNotFoundError: Missing floor_shapes.npy can be a pain, but with a bit of detective work and some coding, you can overcome it. Remember to understand your dataset, write a script to generate the missing file (if necessary), and always double-check your paths and dependencies. By following these steps, you should be able to get your Flodiff training up and running in no time. Good luck, and happy training!

This guide offers comprehensive insights into the FileNotFoundError, equipping users with practical solutions and a thorough understanding of the issue. By recreating the file, looking for pre-generated ones, or manually creating the floor_shapes.npy, the training process can be successfully initiated, paving the way for further model development. Remember to adapt the given code snippets to fit your dataset's specific characteristics, ensuring the training workflow progresses without interruption.