torch.set_printoptions

設(shè)置pytorch打印張量時(shí)的選項(xiàng)，比如限制打印的元素?cái)?shù)量、設(shè)置精度等。在打印大張量或者需要更精確控制輸出格式時(shí)非常有用。

torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None, sci_mode=None)

• precision – 浮點(diǎn)輸出的精度位數(shù)（默認(rèn)值 = 4）。
• threshold – 輸出的數(shù)組元素總數(shù)（默認(rèn)值 = 1000）。
• edgeitems – 每個(gè)維度開(kāi)頭和結(jié)尾的元素?cái)?shù) （默認(rèn)值 = 3）。
• linewidth – 用于插入換行符的每行字符數(shù)（默認(rèn)值 = 80）。
• profile – 打印選項(xiàng)?？杀簧鲜鋈魏芜x項(xiàng)覆蓋。（三個(gè)選項(xiàng)：default、short、full）
• sci_mode – 啟用 （True） 或禁用 （False） 科學(xué)記數(shù)法。

示例 1：設(shè)置打印的元素?cái)?shù)量

import torch
# 創(chuàng)建一個(gè)大張量
big_tensor = torch.randn(1000, 1000)
# 設(shè)置打印選項(xiàng)，限制每行打印的元素?cái)?shù)量為5
torch.set_printoptions(edgeitems=5)
# 打印大張量
print(big_tensor)
'''output
tensor([[ 7.096e-01, -2.319e-01, -3.508e-01, -1.490e+00, -2.094e+00,  ...,  2.418e+00,2.191e+00, -1.097e+00, -8.462e-03,  1.543e+00],[-3.659e-01, -2.529e+00, -5.387e-01, -9.730e-01,  1.106e+00,  ...,  2.867e-01,-2.998e-01,  1.395e+00,  1.922e-01, -2.857e+00],[-1.248e+00,  9.511e-02,  1.104e+00, -2.893e-01,  2.386e-01,  ..., -4.030e-01,-1.431e+00,  1.698e+00,  7.792e-02,  5.647e-01],[-8.082e-01, -1.269e+00, -9.518e-01, -1.230e+00,  1.623e-01,  ...,  6.505e-01,1.299e-01, -2.172e+00,  9.718e-01,  1.547e-01],[ 3.541e-01, -1.212e+00,  1.333e+00, -8.269e-01,  3.842e-01,  ..., -2.083e-01,2.715e+00,  1.210e+00, -1.783e+00, -7.736e-01],...,[ 3.425e-02, -6.938e-01,  6.390e-02, -1.265e+00,  3.217e-01,  ...,  1.713e+00,5.801e-01,  1.101e+00, -1.443e+00, -2.739e-01],[ 8.133e-01, -1.418e+00, -6.581e-01,  2.327e+00,  1.146e+00,  ...,  6.545e-01,-7.152e-01, -6.270e-01, -9.436e-01, -1.858e+00],[ 1.339e+00,  6.877e-02, -1.111e+00,  8.770e-01,  1.358e+00,  ..., -2.793e+00,-1.135e-01,  1.498e+00,  6.292e-01,  1.196e+00],[-1.095e-01,  6.439e-01, -7.375e-01,  4.185e-01, -1.045e-01,  ..., -1.521e+00,6.631e-01,  2.701e-01,  1.107e-01, -1.860e-01],[-1.281e+00,  3.997e-01,  1.881e-03,  1.170e+00,  4.183e-01,  ...,  3.738e-01,5.330e-01, -2.445e-01,  7.956e-01, -7.097e-01]], device='cuda:0')
'''

示例 2：設(shè)置打印的精度

import torch# 創(chuàng)建一個(gè)包含小數(shù)的張量
tensor = torch.tensor([1.123456789, 2.987654321])# 設(shè)置打印選項(xiàng)，限制打印的浮點(diǎn)數(shù)精度為3位小數(shù)
torch.set_printoptions(precision=3)# 打印張量
print(tensor)
'''output
tensor([1.123, 2.988], device='cuda:0')
'''

示例 3：設(shè)置科學(xué)記數(shù)法的閾值

import torch# 創(chuàng)建一個(gè)包含大數(shù)和小子數(shù)的張量
tensor = torch.tensor([1e-5, 1e5])# 設(shè)置打印選項(xiàng)，當(dāng)絕對(duì)值小于1e-3時(shí)使用科學(xué)記數(shù)法
torch.set_printoptions(sci_mode=True, threshold=1e3)# 打印張量
print(tensor)
'''output
tensor([1.000e-05, 1.000e+05], device='cuda:0')
'''

示例 4：設(shè)置張量的邊距

import torch# 創(chuàng)建一個(gè)張量
tensor = torch.randn(5, 5)# 設(shè)置打印選項(xiàng)，行字符寬度
torch.set_printoptions(edgeitems=3, linewidth=40)
# 打印張量
print(tensor)# 設(shè)置打印選項(xiàng)，行字符寬度
torch.set_printoptions(edgeitems=3, linewidth=50)
# 打印張量
print(tensor)
'''output
tensor([[-0.787,  0.227, -1.878,  0.286,-0.899],[ 0.483,  1.054,  0.770, -0.245,1.599],[ 0.165, -0.034,  1.457, -0.944,0.601],[-0.150,  0.388, -0.811, -0.741,1.286],[-0.657, -0.895,  0.680,  0.946,-1.832]], device='cuda:0')
tensor([[-0.787,  0.227, -1.878,  0.286, -0.899],[ 0.483,  1.054,  0.770, -0.245,  1.599],[ 0.165, -0.034,  1.457, -0.944,  0.601],[-0.150,  0.388, -0.811, -0.741,  1.286],[-0.657, -0.895,  0.680,  0.946, -1.832]],device='cuda:0')'''

示例 5：恢復(fù)默認(rèn)打印選項(xiàng)

import torch# 創(chuàng)建一個(gè)張量
tensor = torch.randn(5, 5)# 恢復(fù)默認(rèn)打印選項(xiàng)
torch.set_printoptions(profile='default')
# 打印張量
print(tensor)# 設(shè)置打印選項(xiàng)為short
torch.set_printoptions(profile='short')
# 打印張量
print(tensor)# 設(shè)置打印選項(xiàng)為full
torch.set_printoptions(profile='full')
# 打印張量
print(tensor)
'''output
tensor([[-0.4571,  0.3753, -1.5940, -0.7643, -2.1443],[ 0.6022, -0.7247, -0.0522,  1.3891,  0.5575],[ 0.6133, -0.4906,  0.8597, -0.7087,  2.1163],[-0.0721,  0.7349,  1.2677,  0.8921, -0.2049],[ 0.3666,  0.1874,  1.7164,  1.0258, -1.8709]], device='cuda:0')
tensor([[-0.46,  0.38, -1.59, -0.76, -2.14],[ 0.60, -0.72, -0.05,  1.39,  0.56],[ 0.61, -0.49,  0.86, -0.71,  2.12],[-0.07,  0.73,  1.27,  0.89, -0.20],[ 0.37,  0.19,  1.72,  1.03, -1.87]], device='cuda:0')
tensor([[-0.4571,  0.3753, -1.5940, -0.7643, -2.1443],[ 0.6022, -0.7247, -0.0522,  1.3891,  0.5575],[ 0.6133, -0.4906,  0.8597, -0.7087,  2.1163],[-0.0721,  0.7349,  1.2677,  0.8921, -0.2049],[ 0.3666,  0.1874,  1.7164,  1.0258, -1.8709]], device='cuda:0')'''