🖌️ View as RGB images

rgb

 rgb (x:torch.Tensor, denorm:Any=None, cl:Any=False, gutter_px:int=3,
      frame_px:int=1, scale:int=1, view_width:int=966,
      ax:Optional[matplotlib.axes._axes.Axes]=None)

	Type	Default	Details
x	Tensor		Tensor to display. [[…], C,H,W] or [[…], H,W,C]
denorm	Any	None	Reverse per-channel normalizatoin
cl	Any	False	Channel-last
gutter_px	int	3	If more than one tensor -> tile with this gutter width
frame_px	int	1	If more than one tensor -> tile with this frame width
scale	int	1	Scale up. Can’t scale down.
view_width	int	966	target width of the image
ax	Optional	None	Use this Axes
Returns	RGBProxy

rgb(image)

rgb(image, scale=2)

two_images = torch.stack([image]*2)
two_images

tensor[2, 3, 196, 196] n=230496 x∈[-2.118, 2.640] μ=-0.388 σ=1.073

in_stats = (    (0.485, 0.456, 0.406),  # Mean
                (0.229, 0.224, 0.225) ) # std
rgb(two_images, denorm=in_stats)

# Make 8 images with progressively higher brightness and stack them 2x2x2.
eight_images = (torch.stack([image]*8) + torch.linspace(-2, 2, 8)[:,None,None,None])
eight_images = (eight_images
                    .mul(torch.tensor(in_stats[1])[:,None,None])
                    .add(torch.tensor(in_stats[0])[:,None,None])
                    .clamp(0,1)
                    .view(2,2,2,3,196,196)
)
eight_images

tensor[2, 2, 2, 3, 196, 196] n=921984 x∈[0., 1.000] μ=0.382 σ=0.319

rgb(eight_images)

# You can do channel-last too:
rgb(image.permute(1, 2, 0), cl=True)