赛题链接

https://www.kaggle.com/competitions/quickdraw-doodle-recognition/overview/evaluation
数据集从上述链接中找

赛题背景

'Quick，Draw！'作为实验性游戏发布，以有趣的方式向公众宣传 AI 的工作原理。游戏提示用户绘制描绘特定类别的图像，例如“香蕉”、“桌子”等。游戏生成了超过 1B 幅图画，其中的一个子集被公开发布，作为本次比赛训练集的基础。该子集包含 5000 万张图纸，涵盖 340 个标签类别。

听起来很有趣，对吧？挑战在于：由于训练数据来自游戏本身，绘图可能不完整或可能与标签不匹配。您需要构建一个识别器，它可以有效地从这些嘈杂的数据中学习，并在来自不同分布的手动标记的测试集上表现良好。

您的任务是为现有的 Quick, Draw! 构建一个更好的分类器。数据集。通过在此数据集上推进模型，Kagglers 可以更广泛地改进模式识别解决方案。这将对手写识别及其在 OCR（光学字符识别）、ASR（自动语音识别）和 NLP（自然语言处理）等领域的稳健应用产生直接影响。

属于多分类问题

数据集探索

字段解释：

Key	Type	Description
key_id	64 位无符号整数	所有图纸的唯一标识符。
word	string	玩家绘制的类别。
recognized	boolean	该词是否被游戏识别。
timestamp	datetime	创建绘图时间
countrycode	string	玩家所在位置的两个字母国家代码
drawing	string	表示矢量绘图的 JSON 数组

example:

根据矢量绘图的JSON数组画图

def show_imale(n,owls,drawing):fig,axs = plt.subplots(nrows=n,ncols=n,sharex=True,sharey=True,figsize = (16.10))for i , drawing in enumerate(owls,drawing):ax = axs[i//n,i%n]for x,y in drawing:ax.plot(x,-np.array(y),lw=3)fig.savefig('owls.png',dpi=200)plt.show();

赛题建模思路：

1. 读取数据并转化为图像
2. 构建分类模型
3. 确定训练细节和数据扩增方法；
4. 对测试集完成预测并完成模型集成

数据集的文件结构：

每一种类型的数据图片，都放在一个单独的csv中，下面要对整个数据集进行处理。

合并多个类别CSV数据集

import os, sys, codecs, glob
import numpy as np
import pandas as pd
import cv2
import timmfrom sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split# 读取单个csv文件
def read_df(path, nrows):print('Reading...', path)if nrows.isdigit():return pd.read_csv(path, nrows=int(nrows), parse_dates=['timestamp'])else:return pd.read_csv(path, parse_dates=['timestamp'])# 读取多个csv文件
def contcat_df(paths, nrows):dfs = []for path in paths:dfs.append(read_df(path, nrows))return pd.concat(dfs, axis=0, ignore_index=True)def main():if not os.path.exists('./data'):os.mkdir('./data')CLASSES_CSV = glob.glob('../input/train_simplified/*.csv')CLASSES = [x.split('/')[-1][:-4] for x in CLASSES_CSV]print('Reading data...')# 读取指定行数的csv文本，并进行拼接df = contcat_df(CLASSES_CSV, number)# 数据打乱df = df.reindex(np.random.permutation(df.index))lbl = LabelEncoder().fit(df['word'])df['word'] = lbl.transform(df['word'])if df.shape[0] * 0.05 < 120000:df_train, df_val = train_test_split(df, test_size=0.05)else:df_train, df_val = df.iloc[:-500000], df.iloc[-500000:]print('Train:', df_train.shape[0], 'Val', df_val.shape[0])print('Save data...')df_train.to_pickle(os.path.join('./data', 'train_' + str(number) + '.pkl'))df_val.to_pickle(os.path.join('./data', 'val_' + str(number) + '.pkl'))# python 1_save2df.py 50000
# python 1_save2df.py all
if __name__ == "__main__":number = str(sys.argv[1])main()

其中glob的作用如下注释所示

import glob#获取指定目录下的所有图片
print (glob.glob(r"/home/qiaoyunhao/*/*.png"),"\n")#加上r让字符串不转义#获取上级目录的所有.py文件
print (glob.glob(r'../*.py')) #相对路径

得到的结果如下所示：
32300个训练集，1700个测试集
这里我们是先采用少量数据集训练，试一下数据是否拟合，若拟合

数据建模 (pytorch)

导入所需库

import os, sys, codecs, glob
from PIL import Image, ImageDrawimport numpy as np
import pandas as pd
import cv2import torch
torch.backends.cudnn.benchmark = False
import timmimport torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data.dataset import Datasetimport logging
logging.basicConfig(level=logging.DEBUG, filename='example.log',format='%(asctime)s - %(filename)s[line:%(lineno)d]: %(message)s')  

将绘图的轨迹转变为图片
这里用的是opencv，cv的处理速度大于pillow

def draw_cv2(raw_strokes, size=256, lw=6, time_color=True):BASE_SIZE = 299img = np.zeros((BASE_SIZE, BASE_SIZE), np.uint8)for t, stroke in enumerate(eval(raw_strokes)):str_len = len(stroke[0])for i in range(len(stroke[0]) - 1):# 数据集随机丢弃一些像素，属于数据集的drop out，防止过拟合if np.random.uniform() > 0.95:continuecolor = 255 - min(t, 10) * 13 if time_color else 255_ = cv2.line(img, (stroke[0][i] + 22, stroke[1][i]  + 22),(stroke[0][i + 1] + 22, stroke[1][i + 1] + 22), color, lw)if size != BASE_SIZE:return cv2.resize(img, (size, size))else:return img

计算topk准确率

def accuracy(output, target, topk=(1,)):with torch.no_grad():maxk = max(topk)batch_size = target.size(0)_, pred = output.topk(maxk, 1, True, True)pred = pred.t()correct = pred.eq(target.view(1, -1).expand_as(pred))# print(correct.shape)res = []for k in topk:# print(correct[:k].shape)correct_k = correct[:k].float().sum()res.append(correct_k.mul_(100.0 / batch_size))# print(res)return res

数据扩展

class QRDataset(Dataset):def __init__(self, img_drawing, img_label, img_size, transform=None):self.img_drawing = img_drawingself.img_label = img_labelself.img_size = img_sizeself.transform = transformdef __getitem__(self, index):img = np.zeros((self.img_size, self.img_size, 3))img[:, :, 0] = draw_cv2(self.img_drawing[index], self.img_size)img[:, :, 1] = img[:, :, 0]img[:, :, 2] = img[:, :, 0]img = Image.fromarray(np.uint8(img))if self.transform is not None:img = self.transform(img)label = torch.from_numpy(np.array([self.img_label[index]]))return img, labeldef __len__(self):return len(self.img_drawing)

载入模型

def get_resnet18():model = models.resnet18(True)model.avgpool = nn.AdaptiveAvgPool2d(1) # 匹配不固定的输入尺寸model.fc = nn.Linear(512, 340)return modeldef get_resnet34():model = models.resnet34(True)model.avgpool = nn.AdaptiveAvgPool2d(1)model.fc = nn.Linear(512, 340)return modeldef get_resnet50():model = models.resnet50(True)model.avgpool = nn.AdaptiveAvgPool2d(1)model.fc = nn.Linear(2048, 340)return modeldef get_resnet101():model = models.resnet101(True)model.avgpool = nn.AdaptiveAvgPool2d(1)model.fc = nn.Linear(2048, 340)

图片mixup操作

def mixup_data(x, y, alpha=1.0, use_cuda=True):'''Returns mixed inputs, pairs of targets, and lambda'''if alpha > 0:lam = np.random.beta(alpha, alpha)else:lam = 1batch_size = x.size()[0]if use_cuda:index = torch.randperm(batch_size).cuda()else:index = torch.randperm(batch_size)# x 是一个batch 一批的输入mixed_x = lam * x + (1 - lam) * x[index, :]y_a, y_b = y, y[index]return mixed_x, y_a, y_b, lamdef mixup_criterion(criterion, pred, y_a, y_b, lam):return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)

主函数

1. 数据扩展

def main():df_train = pd.read_pickle(os.path.join('./data', 'train_' + dataset + '.pkl'))# df_train = df_train.reindex(np.random.permutation(df_train.index))df_val = pd.read_pickle(os.path.join('./data', 'val_' + dataset + '.pkl'))train_loader = torch.utils.data.DataLoader(QRDataset(df_train['drawing'].values, df_train['word'].values, imgsize,transforms.Compose([transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),# transforms.RandomAffine(5, scale=[0.95, 1.05]),transforms.ToTensor(),# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])),batch_size=200, shuffle=True, num_workers=5,)val_loader = torch.utils.data.DataLoader(QRDataset(df_val['drawing'].values, df_val['word'].values, imgsize,transforms.Compose([transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),transforms.ToTensor(),# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])),batch_size=200, shuffle=False, num_workers=5,)

载入模型

if modelname == 'resnet18':model = get_resnet18()elif modelname == 'resnet34':model = get_resnet34()elif modelname == 'resnet50':model = get_resnet50()elif modelname == 'resnet101':model = get_resnet101()else:model = timm.create_model(modelname, num_classes=340, pretrained=True, in_chans=3)

设置优化器等损失函数

# model = nn.DataParallel(model).cuda()# nvismodel.load_state_dict(torch.load('./resnet50_64_7_0.pt'))# model.load_state_dict(torch.load('./data/resnet18_64_16_110.pt'))model = model.cuda()loss_fn = nn.CrossEntropyLoss()optimizer = optim.Adam(model.parameters(), lr=0.01)# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[2, 3, 5, 7, 8], gamma=0.1)scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=len(train_loader) / 10, gamma=0.95)print('Train:', df_train.shape[0], 'Val', df_val.shape[0])print('Epoch/Batch\t\tTrain: loss/Top1/Top3\t\tTest: loss/Top1/Top3')

训练50次

for epoch in range(50):train_losss, train_acc1s, train_acc5s = [], [], []for i, data in enumerate(train_loader):scheduler.step()model = model.train()train_img, train_label = dataoptimizer.zero_grad()# TODO: data paraell# train_img = Variable(train_img).cuda(async=True)# train_label = Variable(train_label.view(-1)).cuda()train_img = Variable(train_img).cuda()train_label = Variable(train_label.view(-1)).cuda()# 加入mixupif np.random.randint(1, 10) >= 5:mixed_x, y_a, y_b, lam = mixup_data(train_img, train_label)output = model(mixed_x)train_loss = mixup_criterion(loss_fn, output, y_a, y_b, lam)else:output = model(train_img)train_loss = loss_fn(output, train_label)# output = model(train_img)# train_loss = loss_fn(output, train_label)train_loss.backward()optimizer.step()train_losss.append(train_loss.item())if i % 5 == 0:logging.info('{0}/{1}:\t{2}\t{3}.'.format(epoch, i, optimizer.param_groups[0]['lr'], train_losss[-1]))if i % int(10) == 0:val_losss, val_acc1s, val_acc5s = [], [], []with torch.no_grad():train_acc1, train_acc3 = accuracy(output, train_label, topk=(1, 3))train_acc1s.append(train_acc1.data.item())train_acc5s.append(train_acc3.item())for data in val_loader:val_images, val_labels = data# val_images = Variable(val_images).cuda(async=True)# val_labels = Variable(val_labels.view(-1)).cuda()val_images = Variable(val_images).cuda()val_labels = Variable(val_labels.view(-1)).cuda() output = model(val_images)val_loss = loss_fn(output, val_labels)val_acc1, val_acc3 = accuracy(output, val_labels, topk=(1, 3))val_losss.append(val_loss.item())val_acc1s.append(val_acc1.item())val_acc5s.append(val_acc3.item())logstr = '{0:2s}/{1:6s}\t\t{2:.4f}/{3:.4f}/{4:.4f}\t\t{5:.4f}/{6:.4f}/{7:.4f}'.format(str(epoch), str(i),np.mean(train_losss, 0), np.mean(train_acc1s, 0), np.mean(train_acc5s, 0),np.mean(val_losss, 0), np.mean(val_acc1s, 0), np.mean(val_acc5s, 0),)torch.save(model.state_dict(), './data/{0}_{1}_{2}_{3}.pt'.format(modelname, imgsize, epoch, i))print(logstr)

运行

# python 2_train.py 模型 数量 图片尺寸
# python 2_train.py resnet18 5000 64
if __name__ == "__main__":modelname = str(sys.argv[1]) # 模型名字dataset = str(sys.argv[2]) # 数据集规模imgsize = int(sys.argv[3]) # 图片的尺寸main()