📺 View channels

chans


def chans(
    x:Tensor, # Input, shape=([...], H, W)
    cmap:str='twilight', # Use matplotlib colormap by this name
    cm_below:str='blue', # Color for values below -1
    cm_above:str='red', # Color for values above 1
    cm_ninf:str='cyan', # Color for -inf values
    cm_pinf:str='fuchsia', # Color for +inf values
    cm_nan:str='yellow', # Color for NaN values
    view_width:int=966, # Try to produce an image at most this wide
    gutter_px:int=3, # Draw write gutters when tiling the images
    frame_px:int=1, # Draw black frame around each image
    scale:int=1, cl:Any=False, ax:Optional=None
)->ChanProxy:

Map tensor values to colors. RGB[A] color is added as channel-last

in_stats = ( (0.485, 0.456, 0.406), (0.229, 0.224, 0.225) )

image = torch.load("mysteryman.pt")
image = (image * torch.tensor(in_stats[1])[:,None,None])
image += torch.tensor(in_stats[0])[:,None,None]

image.rgb

chans(image)

# In R
image[0,0:32,32:64] = -1.1 # Below min
image[0,0:32,96:128] = 1.1 # Above max
# In G
image[1,0:32,64:96] = float("nan")
# In B
image[2,0:32,0:32] = float("-inf")
image[2,0:32,128:128+32] = float("+inf")

chans(image, cmap="viridis", cm_below="black", cm_above="white")

# 4 images, stacked 2x2
chans(torch.stack([image]*4).view(2,2,3,196,196))