DataLoader error - v0 by Vercel

Traceback (most recent call last):
File "G:\Python\pythonProject2\dog_vs_cat\train_catdo.py", line 57, in <module>
train_loader = DataLoader(tran_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\ProgramData\anaconda3\envs\202111612409\Lib\site-packages\torch\utils\data\dataloader.py", line 376, in init
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\ProgramData\anaconda3\envs\202111612409\Lib\site-packages\torch\utils\data\sampler.py", line 164, in init
raise ValueError(
ValueError: num_samples should be a positive integer value, but got num_samples=0

Show full message

434d ago

I understand you're encountering an error while trying to create a DataLoader for your dog vs. cat classification project. The error message suggests that your dataset is empty. Let's go through this step-by-step to identify and fix the issue.

Here's what we can do to troubleshoot and fix this issue:

First, let's check your dataset creation code. Can you show me the part of your code where you create the tran_data dataset?
Make sure your dataset directory structure is correct. Typically, for a dog vs. cat classification task, you might have a structure like this:
data/ ├── train/ │ ├── cat/ │ │ ├── cat1.jpg │ │ ├── cat2.jpg │ │ └── ... │ └── dog/ │ ├── dog1.jpg │ ├── dog2.jpg │ └── ... └── test/ ├── cat/ └── dog/
Verify that your image files are in the correct format (e.g., .jpg, .png) and are not corrupted.
Add some print statements before creating the DataLoader to check the size of your dataset:
print(f"Number of samples in tran_data: {len(tran_data)}")
If you're using a custom Dataset class, make sure your __len__ method is implemented correctly.
Check if you're applying any transforms that might be filtering out all your images inadvertently.

Here's a basic structure of how your code might look:

import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# Define your transforms
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load your dataset
tran_data = datasets.ImageFolder(root='path/to/your/train/data', transform=transform)

# Print the number of samples
print(f"Number of samples in tran_data: {len(tran_data)}")

# Create DataLoader
BATCH_SIZE = 32
train_loader = DataLoader(tran_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)

Can you provide more details about how you're creating your dataset? Once we have that information, we can better diagnose and fix the issue.

434d ago

Show full message

434d ago

Here's what we can do to troubleshoot and fix this issue:

First, let's check your dataset creation code. Can you show me the part of your code where you create the tran_data dataset?
Make sure your dataset directory structure is correct. Typically, for a dog vs. cat classification task, you might have a structure like this:
data/ ├── train/ │ ├── cat/ │ │ ├── cat1.jpg │ │ ├── cat2.jpg │ │ └── ... │ └── dog/ │ ├── dog1.jpg │ ├── dog2.jpg │ └── ... └── test/ ├── cat/ └── dog/
Verify that your image files are in the correct format (e.g., .jpg, .png) and are not corrupted.
Add some print statements before creating the DataLoader to check the size of your dataset:
print(f"Number of samples in tran_data: {len(tran_data)}")
If you're using a custom Dataset class, make sure your __len__ method is implemented correctly.
Check if you're applying any transforms that might be filtering out all your images inadvertently.

Here's a basic structure of how your code might look:

import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# Define your transforms
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load your dataset
tran_data = datasets.ImageFolder(root='path/to/your/train/data', transform=transform)

# Print the number of samples
print(f"Number of samples in tran_data: {len(tran_data)}")

# Create DataLoader
BATCH_SIZE = 32
train_loader = DataLoader(tran_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)

Can you provide more details about how you're creating your dataset? Once we have that information, we can better diagnose and fix the issue.