The Classification Data Class#
The ClassificationData data class represents a CV classification task in deepchecks.
It is a subclass of the VisionData class and is used to load and preprocess data for a
classification task.
The ClassificationData class contains additional data and general methods intended for easy access to relevant metadata
for classification ML models validation.
For more info, please visit the API reference page: ClassificationData
Accepted Image Format#
All checks in deepchecks require images in the same format. They use the batch_to_images() function in order to get
the images in the correct format. For more info on the accepted formats, please visit the
VisionData User Guide.
Accepted Label Format#
Deepchecks’ checks use the batch_to_labels() function in order to get the labels in the correct format.
The accepted label format for the ClassificationData is a tensor of shape (N,), when N is the number of samples.
Each element is an integer representing the class index. For example, for a task with 3 different classes, the label
tensor could be: [0, 1, 2].
Accepted Prediction Format#
Deepchecks’ checks use the infer_on_batch() function in order to get the predictions of the model in the correct format.
The accepted prediction format for classification is a tensor of shape (N, n_classes), where N is the number of
samples. Each element is an array of length n_classes that represent the probability of each class. For example, for a
task with 3 different classes, the prediction tensor could be: [[0.1, 0.2, 0.7], [0.2, 0.3, 0.5], [0.3, 0.4, 0.1]].
Examples#
from deepchecks.vision import ClassificationData
import torch.nn.functional as F
class MyClassificationTaskData(ClassificationData)
"""Implement a ClassificationData class for a classification task."""
def batch_to_images(self, batch):
"""Convert a batch of images to a list of PIL images.
Parameters
----------
batch : torch.Tensor
The batch of images to convert.
Returns
-------
list
A list of PIL images.
"""
# Assuming batch[0] is a batch of (N, C, H, W) images, we convert it to (N, H, W, C)/
imgs = batch[0].detach().numpy().transpose((0, 2, 3, 1))
# The images are normalized to [0, 1] range based on the mean and std of the ImageNet dataset, so we need to
# convert them back to [0, 255] range.
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
imgs = std * imgs + mean
imgs = np.clip(imgs, 0, 1)
imgs *= 255
return imgs
def batch_to_labels(self, batch):
"""Convert a batch of labels to a tensor.
Parameters
----------
batch : torch.Tensor
The batch of labels to convert.
Returns
-------
torch.Tensor
A tensor of shape (N,).
"""
return batch[1]
def infer_on_batch(self, batch, model, device):
"""Get the predictions of the model on a batch of images.
Parameters
----------
batch : torch.Tensor
The batch of data.
model : torch.nn.Module
The model to use for inference.
device : torch.device
The device to use for inference.
Returns
-------
torch.Tensor
A tensor of shape (N, n_classes).
"""
# Assuming the model returns the logits of the predictions, we need to convert them to probabilities.
logits = model.to(device)(batch[0].to(device))
return F.softmax(logits, dim=1)
# Now, in order to test the class, we can create an instance of it:
data = MyClassificationTaskData(your_dataloader)
# And validate the implementation:
data.validate()