Kaggle Dogs vs. Cats Redux: Kernels Edition （猫狗大战）by Pytorch
比赛简介
链接如下：https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition
比赛内容很简单，就是跟一些猫、狗图片，要求对其进行分类。
解法
在这里参考了这位兄台的解法https://ypw.io/dogs-vs-cats-2/，我使用了resnet50、vgg16、densenet161三个已经在imagenet数据集上提前训练好的模型，去掉最后的分类全连接层，产出产出图片的特征向量，进行拼接，最后再接入2个全连接层进行分类。算法使用pytorch实现，最终再取得了Public Score 0.18336的得分，还是有较大改进空间。
代码如下：
# encoding=utf-8
import sys
import argparse
import numpy as np
import re
import math
import os
import datetime
import logging
import logging.handlers
import redis
import traceback
import operator
import requests
import bisect
import json
import hashlib
import random
import inspect
import itertools
import numpy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
import time
import csv
import torchvision.models as models
from torch.autograd import Variable
from PIL import Image
from conf import *
from operator import itemgetter
from tqdm import *
from subprocess import Popen
from subprocess import PIPE
from threading import Lock
from threading import Thread
from urllib import urlencode
from Queue import Queue
from conf import *
from gensim.models import KeyedVectors
from torch.autograd import Variable
from torchvision import transforms, datasets
'''
./train
dogs/
1.jpg
2.jpg
.
.
.
cats/
1.jpg
2.jpg
./val
dogs/
1.jpg
2.jpg
.
.
.
cats/
1.jpg
2.jpg
'''
def lp(content):
content = content.__str__()
file_path = os.path.abspath(__file__)
noline = str(inspect.currentframe().f_back.f_lineno)
print file_path + ':' + noline + ' ' + content
logging.basicConfig(
format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
TRAIN_ROOT = './train'
VAL_ROOT = './val'
TEST_ROOT = './test'
MODEL_PATH='./output/model_9'
RESULT_PATH='result'
EPOCH_NUM = 100
# PREDICT = False
PREDICT = True
MODEL_SAVE_INTERVAL = 1
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.resnet50 = models.resnet50(pretrained=True)
self.resnet50.fc = nn.Linear(2048, 2048)
self.resnet50.fc.weight.data = torch.eye(2048)
for p in self.resnet50.parameters():
p.requires_grad = False
self.vgg16 = models.vgg16(pretrained=True)
mod = list(self.vgg16.classifier.children())
mod.pop()
self.vgg16.classifier = torch.nn.Sequential(*mod)
for p in self.vgg16.parameters():
p.requires_grad = False
self.densenet161 = models.densenet161(pretrained=True)
self.densenet161.classifier = nn.Linear(2208, 2208)
self.densenet161.classifier.weight.data = torch.eye(2208)
for p in self.densenet161.parameters():
p.requires_grad = False
self.fc1 = nn.Linear(8352, 1000)
self.dp1 = nn.Dropout()
self.fc2 = nn.Linear(1000, 2)
def forward(self, x):
x1 = self.resnet50(x)
x2 = self.vgg16(x)
x3 = self.densenet161(x)
x = torch.cat([x1, x3, x4], 1)
x = F.relu(self.fc1(x))
x = self.dp1(x)
x = self.fc2(x)
return x
def train():
data_transform = transforms.Compose([
transforms.Scale((224, 224), interpolation=2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
0.229, 0.224, 0.225])
])
dataset = datasets.ImageFolder(root=TRAIN_ROOT,
transform=data_transform)
train_data_loader = torch.utils.data.DataLoader(
dataset, batch_size=25, shuffle=True, num_workers=10)
dataset = datasets.ImageFolder(root=VAL_ROOT,
transform=data_transform)
val_data_loader = torch.utils.data.DataLoader(
dataset, batch_size=50, shuffle=True, num_workers=10)
model = Model().cuda()
optimizer = optim.SGD(
filter(lambda p: p.requires_grad, model.parameters()), lr=1e-2)
model.train()
for i in range(EPOCH_NUM):
loss_list = []
for batch in tqdm(train_data_loader):
x = Variable(batch[0].cuda())
img = transforms.ToPILImage()(batch[0][0])
label = Variable(batch[1].cuda())
output = model(x)
output = F.log_softmax(output)
optimizer.zero_grad()
loss = F.nll_loss(output, label)
loss.backward()
optimizer.step()
loss_list.append(loss.data[0])
total_list = []
correct_list = []
for batch in (val_data_loader):
x = Variable(batch[0].cuda())
label = batch[1].cuda()
output = model(x)
_, predicted = torch.max(output.data, 1)
total = label.shape[0]
correct = (predicted == label).sum()
total_list.append(total)
correct_list.append(correct)
print 'epoch:%d avg_loss=%lf acc=%lf' % (i, sum(loss_list) / len(loss_list), sum(correct_list) * 1.0 / sum(total_list))
if i % MODEL_SAVE_INTERVAL == 0:
torch.save(model, '%s/model_%d' % ('./output', i))
class TestImageFolder(torch.utils.data.Dataset):
def __init__(self, root, transform=None):
self.imgs = []
for filename in os.listdir('./test'):
if filename.endswith('jpg'):
self.imgs.append('{}'.format(filename))
self.root = root
self.transform = transform
def __getitem__(self, index):
filename = self.imgs[index]
img = Image.open(os.path.join(self.root, filename))
if self.transform is not None:
img = self.transform(img)
return img, filename
def __len__(self):
return len(self.imgs)
def predict():
data_transform = transforms.Compose([
transforms.Scale((224, 224), interpolation=2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
0.229, 0.224, 0.225])
])
dataset = TestImageFolder(TEST_ROOT, data_transform)
test_data_loader = torch.utils.data.DataLoader(
dataset, batch_size=50, shuffle=True, num_workers=10)
model = torch.load(MODEL_PATH)
model.eval()
f_test_predict = open(RESULT_PATH, 'w')
f_test_predict.write('id,label\n')
for batch in tqdm(test_data_loader):
x = Variable(batch[0].cuda())
output = model(x)
for i in range(output.data.shape[0]):
filepath = batch[1][i]
filepath = os.path.splitext(os.path.basename(filepath))[0]
filepath = int(filepath)
f_test_predict.write(str(filepath) + ',' +
str(math.exp(output.data[i][1])) + '\n')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='')
args = parser.parse_args()
if not PREDICT:
train()
else:
predict()