🙉 Monkey-patching

monkey_patch

 monkey_patch (cls=<class 'torch.Tensor'>)

Monkey-patch lovely features into cls

monkey_patch()

image = torch.load("mysteryman.pt")

/tmp/ipykernel_172230/672345934.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  image = torch.load("mysteryman.pt")

spicy = image.flatten()[:12].clone()

spicy[0] *= 10000
spicy[1] /= 10000
spicy[2] = float('inf')
spicy[3] = float('-inf')
spicy[4] = float('nan')

spicy = spicy.reshape((2,6))
spicy

tensor[2, 6] n=12 x∈[-3.541e+03, -3.369e-05] μ=-393.776 σ=1.180e+03 +Inf! -Inf! NaN!

spicy.v

tensor[2, 6] n=12 x∈[-3.541e+03, -3.369e-05] μ=-393.776 σ=1.180e+03 +Inf! -Inf! NaN!
tensor([[-3.5405e+03, -3.3693e-05,         inf,        -inf,         nan,
         -4.0543e-01],
        [-4.2255e-01, -4.9105e-01, -5.0818e-01, -5.5955e-01, -5.4243e-01,
         -5.0818e-01]])

spicy.p

tensor([[-3.5405e+03, -3.3693e-05,         inf,        -inf,         nan,
         -4.0543e-01],
        [-4.2255e-01, -4.9105e-01, -5.0818e-01, -5.5955e-01, -5.4243e-01,
         -5.0818e-01]])

image.deeper

tensor[3, 196, 196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
  tensor[196, 196] n=38416 x∈[-2.118, 2.249] μ=-0.324 σ=1.036
  tensor[196, 196] n=38416 x∈[-1.966, 2.429] μ=-0.274 σ=0.973
  tensor[196, 196] n=38416 x∈[-1.804, 2.640] μ=-0.567 σ=1.178

image[:3,:3,:5].deeper(depth=2)

tensor[3, 3, 5] n=45 x∈[-1.316, -0.197] μ=-0.593 σ=0.306
  tensor[3, 5] n=15 x∈[-0.765, -0.337] μ=-0.492 σ=0.124
    tensor[5] x∈[-0.440, -0.337] μ=-0.385 σ=0.041 [-0.354, -0.337, -0.405, -0.440, -0.388]
    tensor[5] x∈[-0.662, -0.405] μ=-0.512 σ=0.108 [-0.405, -0.423, -0.491, -0.577, -0.662]
    tensor[5] x∈[-0.765, -0.474] μ=-0.580 σ=0.125 [-0.474, -0.474, -0.542, -0.645, -0.765]
  tensor[3, 5] n=15 x∈[-0.513, -0.197] μ=-0.321 σ=0.099
    tensor[5] x∈[-0.303, -0.197] μ=-0.243 σ=0.055 [-0.197, -0.197, -0.303, -0.303, -0.215]
    tensor[5] x∈[-0.408, -0.232] μ=-0.327 σ=0.084 [-0.250, -0.232, -0.338, -0.408, -0.408]
    tensor[5] x∈[-0.513, -0.285] μ=-0.394 σ=0.102 [-0.303, -0.285, -0.390, -0.478, -0.513]
  tensor[3, 5] n=15 x∈[-1.316, -0.672] μ=-0.964 σ=0.176
    tensor[5] x∈[-0.985, -0.672] μ=-0.846 σ=0.123 [-0.672, -0.985, -0.881, -0.776, -0.916]
    tensor[5] x∈[-1.212, -0.724] μ=-0.989 σ=0.179 [-0.724, -1.072, -0.968, -0.968, -1.212]
    tensor[5] x∈[-1.316, -0.828] μ=-1.058 σ=0.179 [-0.828, -1.125, -1.020, -1.003, -1.316]

# Paramster is printed in a slingle line with the class name.
torch.nn.Parameter(torch.zeros(10, 10))

Parameter[10, 10] n=100 all_zeros grad

t = torch.empty(3, 3, device="meta")
t

tensor[3, 3] n=9 meta meta

image.rgb

in_stats = ( (0.485, 0.456, 0.406),     # mean
             (0.229, 0.224, 0.225) )    # std
image.rgb(in_stats)

mean = torch.tensor(in_stats[0])[:,None,None]
std = torch.tensor(in_stats[1])[:,None,None]

(image*std + mean).chans # all pixels in [0, 1] range

(image*0.3+0.5) # Slightly outside of [0, 1] range

tensor[3, 196, 196] n=115248 x∈[-0.135, 1.292] μ=0.384 σ=0.322

(image*0.3+0.5).chans # shows clipping (bright blue/red)

image.plt

image.plt(center="mean")

fig, ax = plt.subplots(figsize=(6, 2))
plt.close(fig)
image.plt(ax=ax)
fig