fruit_project.utils.general

Functions

set_seed(→ torch.Generator)	Sets the seed for reproducibility across various libraries.
seed_worker(→ None)	Seeds a worker for multiprocessing to ensure reproducibility.
plot_img(→ None)	Plots an image using matplotlib.
unnormalize(→ torch.Tensor)	Unnormalizes an image tensor by reversing normalization.
is_hf_model(→ bool)	Checks if the given model is a Hugging Face PreTrainedModel.

Module Contents

fruit_project.utils.general.set_seed(SEED: int) → torch.Generator

Sets the seed for reproducibility across various libraries.

Parameters:

SEED (int) – The seed value to use.

Returns:

A PyTorch generator seeded with the given value.

Return type:

torch.Generator

fruit_project.utils.general.seed_worker(worker_id: int, base_seed: int) → None

Seeds a worker for multiprocessing to ensure reproducibility.

Parameters:

• worker_id (int) – The ID of the worker.

• base_seed (int) – The base seed value.

Returns:

None

fruit_project.utils.general.plot_img(img, label: str | None = None) → None

Plots an image using matplotlib.

Parameters:

• img (torch.Tensor) – The image tensor to plot (shape: C x H x W).

• label (str, optional) – The label to display as the title. Defaults to None.

Returns:

None

fruit_project.utils.general.unnormalize(img_tensor: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) → torch.Tensor

Unnormalizes an image tensor by reversing normalization.

Parameters:

• img_tensor (torch.Tensor) – The normalized image tensor (shape: N x C x H x W or C x H x W).

• mean (torch.Tensor) – The mean used for normalization.

• std (torch.Tensor) – The standard deviation used for normalization.

Returns:

The unnormalized image tensor.

Return type:

torch.Tensor

fruit_project.utils.general.is_hf_model(model) → bool

Checks if the given model is a Hugging Face PreTrainedModel.

Parameters:

model – The model to check.

Returns:

True if the model is a Hugging Face PreTrainedModel, False otherwise.

Return type:

bool