pytorch demo 实践

   1、基本概念Tensors、Variable、Numpy等
   2、如何搭建神经网络模型（包括卷积神经网络）
   3、如何定义损失函数和优化器（包括不同分类器和优化器的含义）
   4、如何训练（包括如何读取数据、如何在GPU上训练）
   5、如何测试（定义好评价指标，比如准确率和loss）

#coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

# Training settings
batch_size = 64

# MNIST Dataset
train_dataset = datasets.MNIST(root='./mnist_data/',
                               train=True,
                               transform=transforms.ToTensor(),
                               download=True)

test_dataset = datasets.MNIST(root='./mnist_data/',
                              train=False,
                              transform=transforms.ToTensor())

# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.l1 = nn.Linear(784, 520)
        self.l2 = nn.Linear(520, 320)
        self.l3 = nn.Linear(320, 240)
        self.l4 = nn.Linear(240, 120)
        self.l5 = nn.Linear(120, 10)Variable

    def forward(self, x):
        # Flatten the data (n, 1, 28, 28) --> (n, 784)
        x = x.view(-1, 784)
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        return F.log_softmax(self.l5(x))
        #return self.l5(x)

model = Net()

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

def train(epoch):
    # 每次输入barch_idx个数据
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = Variable(data), Variable(target)

        optimizer.zero_grad()
        output = model(data)
        # loss
        loss = F.nll_loss(output, target)
        loss.backward()
        # update
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))

def test():
    test_loss = 0
    correct = 0
    # 测试集
    for data, target in test_loader:
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        # sum up batch loss
        test_loss += F.nll_loss(output, target).data[0]
        # get the index of the max
        #pred = output.data.max(1, keepdim=True)[1]
        pred = output.data.max(1)[1]
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

for epoch in range(1,6):
    train(epoch)
    test()

"""
Optional: Data Parallelism
==========================
**Authors**: `Sung Kim <https://github.com/hunkim>`_ and `Jenny Kang <https://github.com/jennykang>`_

In this tutorial, we will learn how to use multiple GPUs using ``DataParallel``.

It's very easy to use GPUs with PyTorch. You can put the model on a GPU:

.. code:: python

    model.gpu()

Then, you can copy all your tensors to the GPU:

.. code:: python

    mytensor = my_tensor.gpu()

Please note that just calling ``mytensor.gpu()`` won't copy the tensor
to the GPU. You need to assign it to a new tensor and use that tensor on the GPU.

It's natural to execute your forward, backward propagations on multiple GPUs. 
However, Pytorch will only use one GPU by default. You can easily run your 
operations on multiple GPUs by making your model run parallelly using 
``DataParallel``:

.. code:: python

    model = nn.DataParallel(model)

That's the core behind this tutorial. We will explore it in more detail below.
"""


######################################################################
# Imports and parameters
# ----------------------
# 
# Import PyTorch modules and define parameters.
# 

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader

# Parameters and DataLoaders
input_size = 5
output_size = 2

batch_size = 30
data_size = 100


######################################################################
# Dummy DataSet
# -------------
# 
# Make a dummy (random) dataset. You just need to implement the
# getitem 
#

class RandomDataset(Dataset):

    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len

rand_loader = DataLoader(dataset=RandomDataset(input_size, 100),
                         batch_size=batch_size, shuffle=True)


######################################################################
# Simple Model
# ------------
# 
# For the demo, our model just gets an input, performs a linear operation, and 
# gives an output. However, you can use ``DataParallel`` on any model (CNN, RNN,
# Capsule Net etc.) 
#
# We've placed a print statement inside the model to monitor the size of input
# and output tensors. 
# Please pay attention to what is printed at batch rank 0.
# 

class Model(nn.Module):
    # Our model

    def __init__(self, input_size, output_size):
        super(Model, self).__init__()
        self.fc = nn.Linear(input_size, output_size)

    def forward(self, input):
        output = self.fc(input)
        print("  In Model: input size", input.size(), 
              "output size", output.size())

        return output


######################################################################
# Create Model and DataParallel
# -----------------------------
# 
# This is the core part of the tutorial. First, we need to make a model instance
# and check if we have multiple GPUs. If we have multiple GPUs, we can wrap 
# our model using ``nn.DataParallel``. Then we can put our model on GPUs by
# ``model.gpu()`` 
# 

model = Model(input_size, output_size)
if torch.cuda.device_count() >= 1:
  print("Let's use", torch.cuda.device_count(), "GPUs!")
  # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
  model = nn.DataParallel(model)

if torch.cuda.is_available():
   print "cuda cuda cuda"
   model.cuda()


######################################################################
# Run the Model
# -------------
# 
# Now we can see the sizes of input and output tensors.
# 

for data in rand_loader:
    if torch.cuda.is_available():
        input_var = Variable(data.cuda())
    else:
        input_var = Variable(data)

    output = model(input_var)
    print("Outside: input size", input_var.size(),
          "output_size", output.size())


######################################################################
# Results
# -------
# 
# When we batch 30 inputs and 30 outputs, the model gets 30 and outputs 30 as
# expected. But if you have GPUs, then you can get results like this.
# 
# 2 GPUs
# ~~~~~~
#
# If you have 2, you will see:
# 
# .. code:: bash
# 
#     # on 2 GPUs
#     Let's use 2 GPUs!
#         In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
#         In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
#         In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
#         In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
#         In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
#     Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])
# 
# 3 GPUs
# ~~~~~~
# 
# If you have 3 GPUs, you will see:
# 
# .. code:: bash
# 
#     Let's use 3 GPUs!
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#         In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#     Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])
# 
# 8 GPUs
# ~~~~~~~~~~~~~~
# 
# If you have 8, you will see:
# 
# .. code:: bash
# 
#     Let's use 8 GPUs!
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#     Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#         In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
#     Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])
# 


######################################################################
# Summary
# -------
# 
# DataParallel splits your data automatically and sends job orders to multiple
# models on several GPUs. After each model finishes their job, DataParallel
# collects and merges the results before returning it to you.
# 
# For more information, please check out
# http://pytorch.org/tutorials/beginner/former\_torchies/parallelism\_tutorial.html.
# 

RuntimeError: cublas runtime error : library not initialized at /b/wheel/pytorch-src/torch/lib/THC/THCGeneral.c:387

尝试执行 sudo rm -rf ~/.nv 

MNIST
COCO (Captioning and Detection)
LSUN Classification
ImageFolder
Imagenet-12
CIFAR10 and CIFAR100
STL10

#coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torchvision
from torch.autograd import Vari上边的minist实战的数据读取就是用这种方式able
import matplotlib.pyplot as plt
import numpy as np
import cv2
# Training settings
batch_size = 2

# MNIST Dataset
train_dataset = datasets.MNIST(root='./mnist_data/',
                               train=True,
                               transform=transforms.ToTensor(),
                               download=True)

test_dataset = datasets.MNIST(root='./mnist_data/',
                              transform=transforms.ToTensor())

# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

dataiter = iter(train_loader)
image,lables = dataiter.next()

print image[0] #打印第一张图片的矩阵
print lables[0] #打印第一张图片的标签值

#分别用opencv matlabplot读取和显示本地图片
im = cv2.imread("1.jpg")
win = cv2.namedWindow('opencv test win', flags=0)
cv2.imshow("opencv test win",im)
cv2.waitKey(5)
plt.imshow(im)
plt.show("matplotlib")
#plt.imshow(image.reshape(image.shape[0], image.shape[1]), cmap=plt.cm.Greys)

0.jpg  1 2 3 4 5 6
1.jpg  2 3 4 5 6 7
2.jpg  6 7 8 9 0 1

import os  
import torch  
import torch.utils.data as data  
from PIL import Image  
  
def default_loader(path):  
    return Image.open(path).convert('RGB')  
  
class myImageFloder(data.Dataset):  
    def __init__(self, root, label, transform = None, target_transform=None, loader=default_loader):  
        fh = open(label)  
        c=0  
        imgs=[]  
        class_names=[]  
        for line in  fh.readlines():  
            if c==0:  
                class_names=[n.strip() for n in line.rstrip().split('   ')]  
            else:  
                cls = line.split()   
                fn = cls.pop(0)  
                if os.path.isfile(os.path.join(root, fn)):  
                    imgs.append((fn, tuple([float(v) for v in cls])))  
            c=c+1  
        self.root = root  
        self.imgs = imgs  
        self.classes = class_names  
        self.transform = transform  
        self.target_transform = target_transform  
        self.loader = loader  
  
    def __getitem__(self, index):  
        fn, label = self.imgs[index]  
        img = self.loader(os.path.join(self.root, fn))  
        if self.transform is not None:  
            img = self.transform(img)  
        return img, torch.Tensor(label)  
  
    def __len__(self):  
        return len(self.imgs)  
      
    def getName(self):  
        return self.classes 

    mytransform = transforms.Compose([  
        transforms.ToTensor()  
        ]  
    )  
      
    # torch.utils.data.DataLoader  
    imgLoader = torch.utils.data.DataLoader(  
             myFloder.myImageFloder(root = "../data/testImages/images", label = "../data/testImages/test.txt", transform = mytransform ),   
             batch_size= 2, shuffle= False, num_workers= 2)  
      
    for i, data in enumerate(imgLoader, 0):  
        print(data[i][0])  
        # opencv  
        img2 = data[i][0].numpy()*255  
        img2 = img2.astype('uint8')  
        img2 = np.transpose(img2, (1,2,0))  
        img2=img2[:,:,::-1]#RGB->BGR  
        cv2.imshow('img2', img2)  
        cv2.waitKey()  
        break