mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-08 07:39:33 +01:00
6eeffc64f1
Summary: This is a follow-up PR of https://github.com/pytorch/pytorch/issues/52408 and includes the `pass/` and `fail/` directories. Pull Request resolved: https://github.com/pytorch/pytorch/pull/54234 Reviewed By: walterddr Differential Revision: D27681410 Pulled By: malfet fbshipit-source-id: e6817df77c758f4c1295ea62613106c71cfd3fc3
119 lines
3.1 KiB
Python
119 lines
3.1 KiB
Python
# flake8: noqa
|
|
import torch
|
|
from torch.testing._internal.common_utils import TEST_NUMPY
|
|
if TEST_NUMPY:
|
|
import numpy as np
|
|
|
|
# From the docs, there are quite a few ways to create a tensor:
|
|
# https://pytorch.org/docs/stable/tensors.html
|
|
|
|
# torch.tensor()
|
|
torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
|
|
torch.tensor([0, 1])
|
|
torch.tensor([[0.11111, 0.222222, 0.3333333]],
|
|
dtype=torch.float64,
|
|
device=torch.device('cuda:0'))
|
|
torch.tensor(3.14159)
|
|
|
|
# torch.sparse_coo_tensor
|
|
i = torch.tensor([[0, 1, 1],
|
|
[2, 0, 2]])
|
|
v = torch.tensor([3, 4, 5], dtype=torch.float32)
|
|
torch.sparse_coo_tensor(i, v, [2, 4])
|
|
torch.sparse_coo_tensor(i, v)
|
|
torch.sparse_coo_tensor(i, v, [2, 4],
|
|
dtype=torch.float64,
|
|
device=torch.device('cuda:0'))
|
|
torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])
|
|
torch.sparse_coo_tensor(torch.empty([1, 0]),
|
|
torch.empty([0, 2]), [1, 2])
|
|
|
|
# torch.as_tensor
|
|
a = [1, 2, 3]
|
|
torch.as_tensor(a)
|
|
torch.as_tensor(a, device=torch.device('cuda'))
|
|
|
|
# torch.as_strided
|
|
x = torch.randn(3, 3)
|
|
torch.as_strided(x, (2, 2), (1, 2))
|
|
torch.as_strided(x, (2, 2), (1, 2), 1)
|
|
|
|
# torch.from_numpy
|
|
if TEST_NUMPY:
|
|
torch.from_numpy(np.array([1, 2, 3]))
|
|
|
|
# torch.zeros/zeros_like
|
|
torch.zeros(2, 3)
|
|
torch.zeros((2, 3))
|
|
torch.zeros([2, 3])
|
|
torch.zeros(5)
|
|
torch.zeros_like(torch.empty(2, 3))
|
|
|
|
# torch.ones/ones_like
|
|
torch.ones(2, 3)
|
|
torch.ones((2, 3))
|
|
torch.ones([2, 3])
|
|
torch.ones(5)
|
|
torch.ones_like(torch.empty(2, 3))
|
|
|
|
# torch.arange
|
|
torch.arange(5)
|
|
torch.arange(1, 4)
|
|
torch.arange(1, 2.5, 0.5)
|
|
|
|
# torch.range
|
|
torch.range(1, 4)
|
|
torch.range(1, 4, 0.5)
|
|
|
|
# torch.linspace
|
|
torch.linspace(3, 10, steps=5)
|
|
torch.linspace(-10, 10, steps=5)
|
|
torch.linspace(start=-10, end=10, steps=5)
|
|
torch.linspace(start=-10, end=10, steps=1)
|
|
|
|
# torch.logspace
|
|
torch.logspace(start=-10, end=10, steps=5)
|
|
torch.logspace(start=0.1, end=1.0, steps=5)
|
|
torch.logspace(start=0.1, end=1.0, steps=1)
|
|
torch.logspace(start=2, end=2, steps=1, base=2)
|
|
|
|
# torch.eye
|
|
torch.eye(3)
|
|
|
|
# torch.empty/empty_like/empty_strided
|
|
torch.empty(2, 3)
|
|
torch.empty((2, 3))
|
|
torch.empty([2, 3])
|
|
torch.empty_like(torch.empty(2, 3), dtype=torch.int64)
|
|
torch.empty_strided((2, 3), (1, 2))
|
|
|
|
# torch.full/full_like
|
|
torch.full((2, 3), 3.141592)
|
|
torch.full_like(torch.full((2, 3), 3.141592), 2.71828)
|
|
|
|
# torch.quantize_per_tensor
|
|
torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8)
|
|
|
|
# torch.quantize_per_channel
|
|
x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
|
|
quant = torch.quantize_per_channel(x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8)
|
|
|
|
# torch.dequantize
|
|
torch.dequantize(x)
|
|
|
|
# torch.complex
|
|
real = torch.tensor([1, 2], dtype=torch.float32)
|
|
imag = torch.tensor([3, 4], dtype=torch.float32)
|
|
torch.complex(real, imag)
|
|
|
|
# torch.polar
|
|
abs = torch.tensor([1, 2], dtype=torch.float64)
|
|
pi = torch.acos(torch.zeros(1)).item() * 2
|
|
angle = torch.tensor([pi / 2, 5 * pi / 4], dtype=torch.float64)
|
|
torch.polar(abs, angle)
|
|
|
|
# torch.heaviside
|
|
inp = torch.tensor([-1.5, 0, 2.0])
|
|
values = torch.tensor([0.5])
|
|
torch.heaviside(inp, values)
|