mxnet.gluon.data.vision.transforms.ToTensor

import mxnet
print(help(mxnet.gluon.data.vision.transforms.ToTensor))

由下可见，小小的ToTensor竟实现了这么多转换：

1、数值类型：uint8 ——> float32

2、数值范围：[0, 255] ——> [0,  1]

3、矩阵格式：(H x W x C) or (N x H x W x C)  ——>(C x H x W) or (N x H x W x C)  PS：H为高，W为宽，C为通道，N为batch

至于做这么多转换的原因是，这是MXNet处理图像时指定的格式，便于处理

输出：

Help on class ToTensor in module mxnet.gluon.data.vision.transforms:

class ToTensor(mxnet.gluon.block.HybridBlock)
 |  Converts an image NDArray or batch of image NDArray to a tensor NDArray.
 |  
 |  Converts an image NDArray of shape (H x W x C) in the range
 |  [0, 255] to a float32 tensor NDArray of shape (C x H x W) in
 |  the range [0, 1].
 |  
 |  If batch input, converts a batch image NDArray of shape (N x H x W x C) in the
 |  range [0, 255] to a float32 tensor NDArray of shape (N x C x H x W).
 |  
 |  Inputs:
 |      - **data**: input tensor with (H x W x C) or (N x H x W x C) shape and uint8 type.
 |  
 |  Outputs:
 |      - **out**: output tensor with (C x H x W) or (N x H x W x C) shape and float32 type.
 |  
 |  Examples
 |  --------
 |  >>> transformer = vision.transforms.ToTensor()
 |  >>> image = mx.nd.random.uniform(0, 255, (4, 2, 3)).astype(dtype=np.uint8)
 |  >>> transformer(image)
 |  [[[ 0.85490197  0.72156864]
 |    [ 0.09019608  0.74117649]
 |    [ 0.61960787  0.92941177]
 |    [ 0.96470588  0.1882353 ]]
 |   [[ 0.6156863   0.73725492]
 |    [ 0.46666667  0.98039216]
 |    [ 0.44705883  0.45490196]
 |    [ 0.01960784  0.8509804 ]]
 |   [[ 0.39607844  0.03137255]
 |    [ 0.72156864  0.52941179]
 |    [ 0.16470589  0.7647059 ]
 |    [ 0.05490196  0.70588237]]]
 |