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custom dataset pytorch

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Beautiful Buffalo answered on May 21, 2022 Popularity 10/10 Helpfulness 3/10

answer custom dataset pytorch

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custom dataset pytorch

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class FaceLandmarksDataset(Dataset):
    """Face Landmarks dataset."""

    def __init__(self, csv_file, root_dir, transform=None):
        """
        Args:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all the images.
            transform (callable, optional): Optional transform to be applied
                on a sample.
        """
        self.landmarks_frame = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.landmarks_frame)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        img_name = os.path.join(self.root_dir,
                                self.landmarks_frame.iloc[idx, 0])
        image = io.imread(img_name)
        landmarks = self.landmarks_frame.iloc[idx, 1:]
        landmarks = np.array([landmarks])
        landmarks = landmarks.astype('float').reshape(-1, 2)
        sample = {'image': image, 'landmarks': landmarks}

        if self.transform:
            sample = self.transform(sample)

        return sample

Popularity 10/10 Helpfulness 3/10 Language python

Source: pytorch.org

Tags: custom-dataset python pytorch

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Contributed on May 21 2022

Beautiful Buffalo

0 Answers Avg Quality 2/10

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

# Custom dataset class
class MyDataset(Dataset):
    def __init__(self, data,transform=None):
        self.data = data
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        return self.data[index]
      
# Generating a random dataset
data = torch.randn(100, 3)  # Example: 100 samples, each with 3 features

# Creating an instance of the custom dataset
dataset = MyDataset(data)

# Custom transform class for normalization
class CustomNormalize(object):
    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

    def __call__(self, sample):
        # Perform the normalization operation
        normalized_sample = (sample - self.mean) / self.std
        return normalized_sample
      
# Applying transformations
transform = transforms.Compose([
    transforms.ToTensor(),  # Convert data to tensor
    CustomNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # Custom normalization
])

transformed_dataset = MyDataset(data,transform=transform)
print(transformed_dataset)
print(*transformed_dataset)

# Creating a data loader for batch processing
dataloader = DataLoader(transformed_dataset, batch_size=4, shuffle=True)

# Iterating through the data loader
for batch in dataloader:
    # Accessing the batched data
    print(batch)

Popularity 10/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: dataset

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Contributed on May 30 2023

Innocent Iguana

0 Answers Avg Quality 2/10

generate dataset pytorch

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# Create custom dataset objectfor image
class Dataset(Dataset):
  
    def __init__(self, csv_file, data_dir, transform=None):   # Constructor        
        self.data_dir=data_dir         # Image directory        
        self.transform = transform # The transform is goint to be used on image
        data_dircsv_file=os.path.join(self.data_dir,csv_file)
        self.data_name= pd.read_csv(data_dircsv_file)    # Load the CSV file contians image info    
        self.len=self.data_name.shape[0] # Number of images in dataset
    
    def __len__(self): # Get the length
        return self.len
    
    def __getitem__(self, idx):   # Getter 
        img_name=os.path.join(self.data_dir,self.data_name.iloc[idx, 1]) # Image file path
        image = Image.open(img_name)        # Open image file
        y = self.data_name.iloc[idx, 0]     # The class label for the image    
        if self.transform: # If there is any transform method, apply it onto the image
            image = self.transform(image)
        return image, y # getter will return both image and label

# Create the dataset objects
dataset = Dataset(csv_file=csv_file, data_dir=directory)

image=dataset[0][0] # First image in dataset
y=dataset[0][1] # First image's label in dataset

import torchvision.transforms as transforms
# Combine two transforms: crop and convert to tensor. Apply the compose to MNIST dataset
croptensor_data_transform = transforms.Compose([transforms.CenterCrop(20), transforms.ToTensor()])
dataset = Dataset(csv_file=csv_file , data_dir=directory,transform=croptensor_data_transform )
print("The shape of the first element tensor: ", dataset[0][0].shape)

def show_data(data_sample, shape = (28, 28)): # Helper func to show image
    plt.imshow(data_sample[0].numpy().reshape(shape), cmap='gray')
    plt.title('y = ' + data_sample[1])
    
show_data(dataset[0],shape = (20, 20)) # Plot the first element in the dataset

# Construct the compose for multiple transformations back to back.
fliptensor_data_transform = transforms.Compose([transforms.RandomVerticalFlip(p=1),transforms.ToTensor()])
dataset = Dataset(csv_file=csv_file , data_dir=directory,transform=fliptensor_data_transform )
show_data(dataset[1])

Popularity 9/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: dataset

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Contributed on May 30 2023

Innocent Iguana

0 Answers Avg Quality 2/10

dataset pytorch

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class MyDataset(torch.utils.data.Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: dataset

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Contributed on Dec 04 2022

Dizzy Dormouse

0 Answers Avg Quality 2/10

custom dataset pytorch

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