PyTorch 설치 및 Jupyter 등록
환경은 Windows 10, Anaconda를 사용하고 있습니다.
conda create -y -n pytorch ipykernel
activate pytorch
PyTorch 링크를 보고 자신한테 맞는 환경을 골라 명령어를 입력한다.
conda install pytorch cuda90 -c pytorch
pip install torchvision
설치가 완료되면 예제 코드를 다운받아 실행 시킨다.
python tensor_tutorial.py
제대로 실행되면 pytorch 설치가 완료된 것이다.
주피터에 등록을 하기 위해선 다음 명령을 입력한다.
python -m ipykernel install --user --name pytorch --display-name "PyTorch"
tensor_tutorial.py
# -*- coding: utf-8 -*-
"""
What is PyTorch?
================
It’s a Python-based scientific computing package targeted at two sets of
audiences:
- A replacement for NumPy to use the power of GPUs
- a deep learning research platform that provides maximum flexibility
and speed
Getting Started
---------------
Tensors
^^^^^^^
Tensors are similar to NumPy’s ndarrays, with the addition being that
Tensors can also be used on a GPU to accelerate computing.
"""
from __future__ import print_function
import torch
###############################################################
# Construct a 5x3 matrix, uninitialized:
x = torch.empty(5, 3)
print(x)
###############################################################
# Construct a randomly initialized matrix:
x = torch.rand(5, 3)
print(x)
###############################################################
# Construct a matrix filled zeros and of dtype long:
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
###############################################################
# Construct a tensor directly from data:
x = torch.tensor([5.5, 3])
print(x)
###############################################################
# or create a tensor based on an existing tensor. These methods
# will reuse properties of the input tensor, e.g. dtype, unless
# new values are provided by user
x = x.new_ones(5, 3, dtype=torch.double) # new_* methods take in sizes
print(x)
x = torch.randn_like(x, dtype=torch.float) # override dtype!
print(x) # result has the same size
###############################################################
# Get its size:
print(x.size())
###############################################################
# .. note::
# ``torch.Size`` is in fact a tuple, so it supports all tuple operations.
#
# Operations
# ^^^^^^^^^^
# There are multiple syntaxes for operations. In the following
# example, we will take a look at the addition operation.
#
# Addition: syntax 1
y = torch.rand(5, 3)
print(x + y)
###############################################################
# Addition: syntax 2
print(torch.add(x, y))
###############################################################
# Addition: providing an output tensor as argument
result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)
###############################################################
# Addition: in-place
# adds x to y
y.add_(x)
print(y)
###############################################################
# .. note::
# Any operation that mutates a tensor in-place is post-fixed with an ``_``.
# For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.
#
# You can use standard NumPy-like indexing with all bells and whistles!
print(x[:, 1])
###############################################################
# Resizing: If you want to resize/reshape tensor, you can use ``torch.view``:
x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8) # the size -1 is inferred from other dimensions
print(x.size(), y.size(), z.size())
###############################################################
# If you have a one element tensor, use ``.item()`` to get the value as a
# Python number
x = torch.randn(1)
print(x)
print(x.item())
###############################################################
# **Read later:**
#
#
# 100+ Tensor operations, including transposing, indexing, slicing,
# mathematical operations, linear algebra, random numbers, etc.,
# are described
# `here <http://pytorch.org/docs/torch>`_.
#
# NumPy Bridge
# ------------
#
# Converting a Torch Tensor to a NumPy array and vice versa is a breeze.
#
# The Torch Tensor and NumPy array will share their underlying memory
# locations, and changing one will change the other.
#
# Converting a Torch Tensor to a NumPy Array
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
a = torch.ones(5)
print(a)
###############################################################
#
b = a.numpy()
print(b)
###############################################################
# See how the numpy array changed in value.
a.add_(1)
print(a)
print(b)
###############################################################
# Converting NumPy Array to Torch Tensor
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# See how changing the np array changed the Torch Tensor automatically
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)
###############################################################
# All the Tensors on the CPU except a CharTensor support converting to
# NumPy and back.
#
# CUDA Tensors
# ------------
#
# Tensors can be moved onto any device using the ``.to`` method.
# let us run this cell only if CUDA is available
# We will use ``torch.device`` objects to move tensors in and out of GPU
if torch.cuda.is_available():
device = torch.device("cuda") # a CUDA device object
y = torch.ones_like(x, device=device) # directly create a tensor on GPU
x = x.to(device) # or just use strings ``.to("cuda")``
z = x + y
print(z)
print(z.to("cpu", torch.double)) # ``.to`` can also change dtype together!