package torch

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in-package search v0.2.0

torch
- Torch
  - Checkpointing
  - Cifar_helper
  - Cuda
  - Dataset_helper
  - Device
  - Ivalue
  - Layer
    
    Lstm
  - Mnist_helper
  - Module
  - Optimizer
    
    Clip_grad
    
    Linear_interpolation
  - Scalar
  - Serialize
  - Tensor
  - Text_helper
  - Var_store
    
    Init
torch.core
- Torch_core
  - Device
  - Kind
  - Reduction
  - Torch_bindings
    
    C
    
    Cuda
    
    Ivalue
    
    Module
    
    Optimizer
    
    Scalar
    
    Serialize
    
    Tensor
    
    TensorG
  - Torch_bindings_generated
    
    C
  - Torch_generated
  - Wrapper
    
    Cuda
    
    Ivalue
    
    Tag
    
    Module
    
    Optimizer
    
    Scalar
    
    Serialize
    
    Tensor
  - Wrapper_generated
    
    C
    
    Cuda
    
    Ivalue
    
    Module
    
    Optimizer
    
    Scalar
    
    Serialize
    
    Tensor
    
    TensorG
  - Wrapper_generated_intf
    
    S
torch.toplevel
- Torch_toplevel
torch.vision
- Torch_vision
  - Alexnet
  - Densenet
  - Image
    
    Loader
  - Imagenet
    
    Classes
    
    Loader
  - Inception
  - Mobilenet
  - Resnet
  - Squeezenet
  - Stb_image_write
  - Vgg

Legend:
Library
Module
Module type
Parameter
Class
Class type

val load_image : 
  ?resize:(Base.int * Base.int) ->
  Base.string ->
  Torch.Tensor.t Base.Or_error.t

load_image ?resize filename returns a tensor containing the pixels for the image in filename. Supported image formats are JPEG and PNG. The resulting tensor has dimensions NCHW (with N = 1). When resize is set, the image is first resized preserving its original ratio then a center crop is taken.

val load_images : 
  ?resize:(Base.int * Base.int) ->
  Base.string ->
  Torch.Tensor.t

load_images ?resize dir_name is similar to applying load_image to all the images in dir_name. The resulting tensor has dimensions NCHW where N is the number of images.

val load_dataset : 
  dir:Base.string ->
  classes:Base.string Base.list ->
  with_cache:Base.string Base.option ->
  resize:(Base.int * Base.int) ->
  Torch.Dataset_helper.t

val write_image : Torch.Tensor.t -> filename:Base.string -> Base.unit

module Loader : sig ... end