# -*- coding: utf-8 -*-
"""
Created on Tue Sep 4 08:47:56 2018
@author: www
"""
import sys
sys.path.append('...')
import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
from torchvision.datasets import CIFAR10
#我们可以定义一个 vgg 的 block，传入三个参数，第一个是模型层数，第二个是输入的通道数，第三个是
#输出的通道数，第一层卷积接受的输入通道就是图片输入的通道数，然后输出最后的输出通道数，后面的卷积
#接受的通道数就是最后的输出通道数
def vgg_block(num_convs, in_channels, out_channels):
net = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.ReLU(True)]
for i in range(num_convs-1):#定义后面的许多层
net.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))
net.append(nn.ReLU(True))
net.append(nn.MaxPool2d(2, 2))#定义池化层
return nn.Sequential(*net)
#将模型打印出来看一下结构
block_demo = vgg_block(3, 64, 128)
print(block_demo)
#首先定义输入为（1， 64， 300， 300）
input_demo = Variable(torch.zeros(1, 64, 300, 300))
output_demo = block_demo(input_demo)
print(output_demo.shape)
#可以看到输出就变为了 (1, 128, 150, 150)，可以看到经过了这一个 vgg block，输入大小被减半，通道数变成了 128
#下面我们定义一个函数对这个 vgg block 进行堆叠
def vgg_stack(num_convs, channels):
net = []
for n, c in zip(num_convs, channels):
in_c = c[0]
out_c = c[1]
net.append(vgg_block(n, in_c, out_c))
return nn.Sequential(*net)
#作为实例，我们定义一个稍微简单一点的 vgg 结构，其中有 8 个卷积层
vgg_net = vgg_stack((1,1,2,2,2), ((3,64), (64,128), (128,256), (256, 512), (512,512)))
print(vgg_net)
#我们可以看到网络结构中有个 5 个 最大池化，说明图片的大小会减少 5 倍，我们可以验证一下，输入一张 256 x 256
#的图片看看结果是什么
test_x = Variable(torch.zeros(1, 3, 256, 256))
test_y = vgg_net(test_x)
print(test_y.shape)
#看到图片减小了 $2^5$ 倍，最后再加上几层全连接，就能够得到我们想要的分类输出
class vgg(nn.Module):
def __init__(self):
super(vgg, self).__init__()
self.feature = vgg_net
self.fc = nn.Sequential(
nn.Linear(512, 1000),
nn.ReLU(True),
nn.Linear(100, 10)
)
def forward(self, x):
x = self.feature(x)
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x
#然后我们可以训练我们的模型看看在 cifar10 上的效果
def data_tf(x):
x = np.array(x, dtype='float32') / 255
x = (x - 0.5) / 0.5
x = x.transpose((2,0,1)) ## 将 channel 放到第一维，只是 pytorch 要求的输入方式
x = torch.from_numpy(x)
return x
train_set = CIFAR10('./data', train=True, transform=data_tf, download=True)
train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = CIFAR10('./data', train=False, transform=data_tf, download=True)
test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)
net = vgg()
optimizer = torch.optim.SGD(net.parameters(), lr=1e-1)
criterion = nn.CrossEntropyLoss()
from datetime import datetime
def get_acc(output, label):
total = output.shape[0]
_, pred_label = output.max(1)
num_correct = (pred_label == label).sum().data[0]
return num_correct / total
def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
if torch.cuda.is_available():
net = net.cuda()
prev_time = datetime.now()
for epoch in range(num_epochs):
train_loss = 0
train_acc = 0
net = net.train()
for im, label in train_data:
if torch.cuda.is_available():
im = Variable(im.cuda())
label = Variable(label.cuda())
else:
im = Variable(im)
label = Variable(label)
#forward
output = net(im)
loss = criterion(output, label)
#forward
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.data[0]
train_acc += get_acc(output, label)
cur_time = datetime.now()
h, remainder = divmod((cur_time-prev_time).seconds, 3600)
m, s = divmod(remainder, 60)
time_str = "Time %02d:%02d:%02d" % (h, m, s)
if valid_data is not None:
valid_loss = 0
valid_acc = 0
net = net.eval()
for im, label in valid_data:
if torch.cuda.is_available():
im = Variable(im.cuda(), volatile=True)
label = Variable(label.cuda(), volatile=True)
else:
im = Variable(im, volatile=True)
label = Variable(label, volatile=True)
output = net(im)
loss = criterion(output, label)
valid_loss += loss.item()
valid_acc += get_acc(output, label)
epoch_str = (
"Epoch %d. Train Loss: %f, Train Acc: %f, Valid Loss: %f, Valid Acc: %f, "
% (epoch, train_loss / len(train_data),
train_acc / len(train_data), valid_loss / len(valid_data),
valid_acc / len(valid_data)))
else:
epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " %
(epoch, train_loss / len(train_data),
train_acc / len(train_data)))
prev_time = cur_time
print(epoch_str + time_str)
train(net, train_data, test_data, 20, optimizer, criterion)
