Metadata-Version: 2.1
Name: torch-tensor-type
Version: 0.0.3
Summary: Practical Pipelining for pyTorch
Home-page: https://github.com/HiiGHoVuTi/TorchTensorTypes
Author: Maxime
Author-email: himyundevacc@gmail.com
License: UNKNOWN
Description: 
        # Torch Tensor Types
        
        This package is a Quality of Life improvement when prototyping and processing Tensor objects from the pyTorch library.
        The TensorType class is a Pipeline for preprocessing tensors automatically, and include multiple utility methods. `my_TensorType<<myData`
        You can add TensorTypes together to have a longer preprocessing pipeline. `myTensorType + myOtherTensorType`
        
        For example, this code 
        ```py
        fake_image = model(torch.unsqueeze(real_image, 0).cuda()).cpu().detach().numpy()[0]
        ```
        can be replaced by
        ```py
        fake_image = SingleDisplayableImage<<model(ModelInputFormat<<real_image)
        ```
        
        ### The list of arguments to the constructor is:
        ```py
        TensorType:
        __init__(shape=None, transforms=[], 
        to_batch=False, device=None, from_single_value=False,
        random_values=False, to_numpy=False, detach=False):
        ```
        All of these should be pretty telling by their name, if you know pyTorch.
        - `TensorType.shape`: the input will be viewed as this shape
        - `TensorType.transforms`: a list of functions that will be applied at the end
        - `TorchType.to_batch`: will unsqueeze the data into a batch with a single sample
        - `TorchType.device`: transfers the tensor to a device
        - `TorchType.from_single_value`: creates a uniform tensor from a single value
        - `TorchType.random_values`: creates a tensor from `torch.rand`
        - `TensorType.to_numpy`: outputs a numpy array
        - `TensorType.detach`: detachs the tensor from the graph
        
        ### Syntax
        ```py
        from TTT import TensorType as TT
        # Creates a uniform image tensor pipeline
        myTensorType = TT(shape=(3, 224, 224), from_single_value=True)
        # A black image
        data = myTensorType<<0
        # "myTensorType" will parse first, then the other TT
        myDisplayableImage = myTensorType + TT(to_numpy=True, transforms=[np.transpose])
        # A white image ready to display
        myDisplayableImage<<1
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
