lovely tensors
1.0.0
JAXpip install lovely-tensors或者
mamba install lovely-tensors或者
conda install -c conda-forge lovely-tensors您多久发现自己调试Pytorch代码？您将张量转换为单元格输出，然后查看以下内容：
numbers tensor([[[-0.3541, -0.3369, -0.4054, ..., -0.5596, -0.4739, 2.2489],
[-0.4054, -0.4226, -0.4911, ..., -0.9192, -0.8507, 2.1633],
[-0.4739, -0.4739, -0.5424, ..., -1.0390, -1.0390, 2.1975],
...,
[-0.9020, -0.8335, -0.9363, ..., -1.4672, -1.2959, 2.2318],
[-0.8507, -0.7822, -0.9363, ..., -1.6042, -1.5014, 2.1804],
[-0.8335, -0.8164, -0.9705, ..., -1.6555, -1.5528, 2.1119]],
[[-0.1975, -0.1975, -0.3025, ..., -0.4776, -0.3725, 2.4111],
[-0.2500, -0.2325, -0.3375, ..., -0.7052, -0.6702, 2.3585],
[-0.3025, -0.2850, -0.3901, ..., -0.7402, -0.8102, 2.3761],
...,
[-0.4251, -0.2325, -0.3725, ..., -1.0903, -1.0203, 2.4286],
[-0.3901, -0.2325, -0.4251, ..., -1.2304, -1.2304, 2.4111],
[-0.4076, -0.2850, -0.4776, ..., -1.2829, -1.2829, 2.3410]],
[[-0.6715, -0.9853, -0.8807, ..., -0.9678, -0.6890, 2.3960],
[-0.7238, -1.0724, -0.9678, ..., -1.2467, -1.0201, 2.3263],
[-0.8284, -1.1247, -1.0201, ..., -1.2641, -1.1596, 2.3786],
...,
[-1.2293, -1.4733, -1.3861, ..., -1.5081, -1.2641, 2.5180],
[-1.1944, -1.4559, -1.4210, ..., -1.6476, -1.4733, 2.4308],
[-1.2293, -1.5256, -1.5081, ..., -1.6824, -1.5256, 2.3611]]])
看到所有这些数字对您来说真的很有用吗？
什么形状？大小？
什么是统计数据？
nan还是inf的任何值？
它是一个男人拿着tench的图像吗？
import lovely_tensors as lt lt . monkey_patch () numbers # torch.Tensor tensor[3, 196, 196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
numbers . rgb更好，是吗？
numbers [ 1 ,: 6 , 1 ] # Still shows values if there are not too many. tensor[6] x∈[-0.443, -0.197] μ=-0.311 σ=0.091 [-0.197, -0.232, -0.285, -0.373, -0.443, -0.338]
spicy = numbers [ 0 ,: 12 , 0 ]. 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 # Spicy stuff tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
torch . zeros ( 10 , 10 ) # A zero tensor - make it obvious tensor[10, 10] n=100 all_zeros
spicy . v # Verbose tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01, -5.9380e-01, -5.4243e-01, -5.4243e-01, -5.4243e-01]])
spicy . p # The plain old way tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01, -5.9380e-01, -5.4243e-01, -5.4243e-01, -5.4243e-01]])
named_numbers = numbers . rename ( "C" , "H" , "W" )
named_numbers /home/xl0/mambaforge/envs/lovely-py31-torch25/lib/python3.10/site-packages/torch/_tensor.py:1420: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1925.)
return super().rename(names)
tensor[C=3, H=196, W=196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
.deeper numbers . 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
# You can go deeper if you need to
# And we can use `.deeper` with named dimensions.
named_numbers . deeper ( 2 ) tensor[C=3, H=196, W=196] n=115248 (0.4Mb) x∈[-2.118, 2.640] μ=-0.388 σ=1.073
tensor[H=196, W=196] n=38416 x∈[-2.118, 2.249] μ=-0.324 σ=1.036
tensor[W=196] x∈[-1.912, 2.249] μ=-0.673 σ=0.522
tensor[W=196] x∈[-1.861, 2.163] μ=-0.738 σ=0.418
tensor[W=196] x∈[-1.758, 2.198] μ=-0.806 σ=0.397
tensor[W=196] x∈[-1.656, 2.249] μ=-0.849 σ=0.369
tensor[W=196] x∈[-1.673, 2.198] μ=-0.857 σ=0.357
tensor[W=196] x∈[-1.656, 2.146] μ=-0.848 σ=0.372
tensor[W=196] x∈[-1.433, 2.215] μ=-0.784 σ=0.397
tensor[W=196] x∈[-1.279, 2.249] μ=-0.695 σ=0.486
tensor[W=196] x∈[-1.364, 2.249] μ=-0.637 σ=0.539
...
tensor[H=196, W=196] n=38416 x∈[-1.966, 2.429] μ=-0.274 σ=0.973
tensor[W=196] x∈[-1.861, 2.411] μ=-0.529 σ=0.556
tensor[W=196] x∈[-1.826, 2.359] μ=-0.562 σ=0.473
tensor[W=196] x∈[-1.756, 2.376] μ=-0.622 σ=0.458
tensor[W=196] x∈[-1.633, 2.429] μ=-0.664 σ=0.430
tensor[W=196] x∈[-1.651, 2.376] μ=-0.669 σ=0.399
tensor[W=196] x∈[-1.633, 2.376] μ=-0.701 σ=0.391
tensor[W=196] x∈[-1.563, 2.429] μ=-0.670 σ=0.380
tensor[W=196] x∈[-1.475, 2.429] μ=-0.616 σ=0.386
tensor[W=196] x∈[-1.511, 2.429] μ=-0.593 σ=0.399
...
tensor[H=196, W=196] n=38416 x∈[-1.804, 2.640] μ=-0.567 σ=1.178
tensor[W=196] x∈[-1.717, 2.396] μ=-0.982 σ=0.350
tensor[W=196] x∈[-1.752, 2.326] μ=-1.034 σ=0.314
tensor[W=196] x∈[-1.648, 2.379] μ=-1.086 σ=0.314
tensor[W=196] x∈[-1.630, 2.466] μ=-1.121 σ=0.305
tensor[W=196] x∈[-1.717, 2.448] μ=-1.120 σ=0.302
tensor[W=196] x∈[-1.717, 2.431] μ=-1.166 σ=0.314
tensor[W=196] x∈[-1.560, 2.448] μ=-1.124 σ=0.326
tensor[W=196] x∈[-1.421, 2.431] μ=-1.064 σ=0.383
tensor[W=196] x∈[-1.526, 2.396] μ=-1.047 σ=0.417
...
.rgb颜色为重要的Queston-是我们的男人吗？
numbers . rgb ( numbers ). plt ( numbers + 3 ). plt ( center = "range" ).chans # .chans will map values betwen [-1,1] to colors.
# Make our values fit into that range to avoid clipping.
mean = torch . tensor ( in_stats [ 0 ])[:, None , None ]
std = torch . tensor ( in_stats [ 1 ])[:, None , None ]
numbers_01 = ( numbers * std + mean )
numbers_01 tensor[3, 196, 196] n=115248 (0.4Mb) x∈[0., 1.000] μ=0.361 σ=0.248
numbers_01 . chans # Weights of the second conv layer (64ch -> 128ch) of VGG11,
# grouped per output channel.
weights . chans ( frame_px = 1 , gutter_px = 0 ) lt . chans ( numbers_01 )
numbers . rgb ( in_stats ). fig # matplotlib figure 
( numbers * 0.3 + 0.5 ). chans . fig # matplotlib figure 
numbers . plt . fig . savefig ( 'pretty.svg' ) # Save it !f ile pretty . svg ; rm pretty . svg pretty.svg: SVG Scalable Vector Graphics image
fig = plt . figure ( figsize = ( 8 , 3 ))
fig . set_constrained_layout ( True )
gs = fig . add_gridspec ( 2 , 2 )
ax1 = fig . add_subplot ( gs [ 0 , :])
ax2 = fig . add_subplot ( gs [ 1 , 0 ])
ax3 = fig . add_subplot ( gs [ 1 , 1 :])
ax2 . set_axis_off ()
ax3 . set_axis_off ()
numbers_01 . plt ( ax = ax1 )
numbers_01 . rgb ( ax = ax2 )
numbers_01 . chans ( ax = ax3 );
只是起作用。
def func ( x ):
return x * 2
if torch . __version__ >= "2.0" :
func = torch . compile ( func )
func ( torch . tensor ([ 1 , 2 , 3 ])) tensor[3] i64 x∈[2, 6] μ=4.000 σ=2.000 [2, 4, 6]
可爱的张量安装了导入挂钩。设置LOVELY_TENSORS=1 ，它将自动加载，无需修改代码：>注意：不要在全球设置它，或者您运行的所有Python脚本都会导入LT和Pytorch，这会减慢速度。
import torch
x = torch . randn ( 4 , 16 )
print ( x )LOVELY_TENSORS=1 python test.py x: tensor[4, 16] n=64 x∈[-1.652, 1.813] μ=-0.069 σ=0.844
这对于结合更好的例外是特别有用的：
import torch
x = torch . randn ( 4 , 16 )
print ( f"x: { x } " )
w = torch . randn ( 15 , 8 )
y = torch . matmul ( x , w ) # Dimension mismatch BETTER_EXCEPTIONS=1 LOVELY_TENSORS=1 python test.py x: tensor[4, 16] n=64 x∈[-1.834, 2.421] μ=0.103 σ=0.896
Traceback (most recent call last):
File "/home/xl0/work/projects/lovely-tensors/test.py", line 7, in <module>
y = torch.matmul(x, w)
│ │ └ tensor[15, 8] n=120 x∈[-2.355, 2.165] μ=0.142 σ=0.989
│ └ tensor[4, 16] n=64 x∈[-1.834, 2.421] μ=0.103 σ=0.896
└ <module 'torch' from '/home/xl0/mambaforge/envs/torch25-py313/lib/python3.12/site-packages/torch/__init__.py'>
RuntimeError: mat1 and mat2 shapes cannot be multiplied (4x16 and 15x8)
