🙉 Monkey-patching

monkey_patch

 monkey_patch (cls=<class 'tinygrad.tensor.Tensor'>)

Monkey-patch lovely features into cls

monkey_patch()

Tensor.randn(2,2)

Tensor[2, 2] n=4 x∈[-0.253, 0.685] μ=0.168 σ=0.458 CPU Realized MUL [[-0.178, 0.685], [-0.253, 0.419]]

np_image = np.load("mysteryman.npy")
image = Tensor(np_image)

spicy = np_image.flatten()[:12] #.clone()

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

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

Tensor[2, 6] n=12 x∈[-3.541e+03, -3.369e-05] μ=-393.776 σ=1.113e+03 +Inf! -Inf! NaN! CPU

spicy.v

<Tensor <LB (2, 6) dtypes.float op=buffer<12, dtypes.float, 140393040282048> st=ShapeTracker(views=(View(shape=(2, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),))> on CPU with grad None>
Tensor[2, 6] n=12 x∈[-3.541e+03, -3.369e-05] μ=-393.776 σ=1.113e+03 +Inf! -Inf! NaN! CPU

spicy.p

<Tensor <LB (2, 6) dtypes.float op=buffer<12, dtypes.float, 140393040282048> st=ShapeTracker(views=(View(shape=(2, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),))> on CPU with grad None>

image.deeper

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

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

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

image.rgb

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

mean = Tensor(in_stats[0])[:,None,None]
std = 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 CPU Realized ADD

(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