AI艺术鉴赏挑战赛 - 看画猜作者代码解读
AI艺术鉴赏挑战赛
AI艺术鉴赏-3rd-solution form 今天没吃饭
基本框架：基于Resnext50，eff-b3训练图像尺寸448，512，600的模型，取得分最高的4组结果进行投票
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data_count.py
划分数据集，30张以下的数据没有被划分验证集
class_cnt:lable idx idx with open() as读写文件 -
dataload.py
size:448/512/600torchvision是pytorch的一个图形库，它服务于PyTorch深度学习框架的，主要用来构建计算机视觉模型。torchvision.transforms主要是用于常见的一些图形变换。以下是torchvision的构成：torchvision.datasets: 一些加载数据的函数及常用的数据集接口；torchvision.models: 包含常用的模型结构（含预训练模型），例如AlexNet、VGG、ResNet等；torchvision.transforms: 常用的图片变换，例如裁剪、旋转等；torchvision.utils: 其他的一些有用的方法。
torchvision.transforms.Compose()的主要作用是串联多个图片变换的操作，进行数据增强trans = { 'train': transforms.Compose([ #以0.5的概率水平翻转给定的PIL图像 transforms.RandomHorizontalFlip(), transforms.Resize((int(size / 0.875), int(size / 0.875))), #在一个随机的位置进行裁剪 transforms.RandomCrop((size, size)), #Convert a PIL Image or numpy.ndarray to tensor transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), #随机选择图像中的矩形区域并删除其像素 transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3)) ]), 'val': transforms.Compose([ transforms.Resize((int(size / 0.875), int(size / 0.875))), transforms.CenterCrop((size, size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) } -
train.py
数据导入，进行训练，记录最好结果。
（代码跑到这里colab报错untimeError: CUDA out of memory. ）
from torch.utils.data import DataLoader from ArtModel import BaseModel import time import numpy as np import random from torch.optim import lr_scheduler from torch.backends import cudnn import argparse import os import torch import torch.nn as nn from dataload import Dataset #参数 parser = argparse.ArgumentParser() parser.add_argument('--model_name', default='resnext50', type=str) parser.add_argument('--savepath', default='./Art/', type=str) parser.add_argument('--loss', default='ce', type=str) parser.add_argument('--num_classes', default=49, type=int) parser.add_argument('--pool_type', default='avg', type=str) parser.add_argument('--metric', default='linear', type=str) parser.add_argument('--down', default=0, type=int) parser.add_argument('--lr', default=0.01, type=float) parser.add_argument('--weight_decay', default=5e-4, type=float) parser.add_argument('--momentum', default=0.9, type=float) parser.add_argument('--scheduler', default='cos', type=str) parser.add_argument('--resume', default=None, type=str) parser.add_argument('--lr_step', default=25, type=int) parser.add_argument('--lr_gamma', default=0.1, type=float) parser.add_argument('--total_epoch', default=60, type=int) parser.add_argument('--batch_size', default=32, type=int) parser.add_argument('--num_workers', default=8, type=int) parser.add_argument('--multi-gpus', default=0, type=int) parser.add_argument('--gpu', default=0, type=int) parser.add_argument('--seed', default=2020, type=int) parser.add_argument('--pretrained', default=1, type=int) parser.add_argument('--gray', default=0, type=int) args = parser.parse_args() def train(): model.train() epoch_loss = 0 correct = 0. total = 0. t1 = time.time() for idx, (data, labels) in enumerate(trainloader): data, labels = data.to(device), labels.long().to(device) out, se, feat_flat = model(data) loss = criterion(out, labels) optimizer.zero_grad() loss.backward() optimizer.step() epoch_loss += loss.item() * data.size(0) total += data.size(0) _, pred = torch.max(out, 1) correct += pred.eq(labels).sum().item() acc = correct / total loss = epoch_loss / total print(f'loss:{loss:.4f} acc@1:{acc:.4f} time:{time.time() - t1:.2f}s', end=' --> ') with open(os.path.join(savepath, 'log.txt'), 'a+')as f: f.write('loss:{:.4f}, acc:{:.4f} ->'.format(loss, acc)) return {'loss': loss, 'acc': acc} def test(epoch): model.eval() epoch_loss = 0 correct = 0. total = 0. with torch.no_grad(): for idx, (data, labels) in enumerate(valloader): data, labels = data.to(device), labels.long().to(device) out = model(data) loss = criterion(out, labels) epoch_loss += loss.item() * data.size(0) total += data.size(0) _, pred = torch.max(out, 1) correct += pred.eq(labels).sum().item() acc = correct / total loss = epoch_loss / total print(f'test loss:{loss:.4f} acc@1:{acc:.4f}', end=' ') global best_acc, best_epoch state = { 'net': model.state_dict(), 'acc': acc, 'epoch': epoch } if acc > best_acc: best_acc = acc best_epoch = epoch torch.save(state, os.path.join(savepath, 'best.pth')) print('*') else: print() torch.save(state, os.path.join(savepath, 'last.pth')) with open(os.path.join(savepath, 'log.txt'), 'a+')as f: f.write('epoch:{}, loss:{:.4f}, acc:{:.4f}\n'.format(epoch, loss, acc)) return {'loss': loss, 'acc': acc} def plot(d, mode='train', best_acc_=None): import matplotlib.pyplot as plt plt.figure(figsize=(10, 4)) plt.suptitle('%s_curve' % mode) plt.subplots_adjust(wspace=0.2, hspace=0.2) epochs = len(d['acc']) plt.subplot(1, 2, 1) plt.plot(np.arange(epochs), d['loss'], label='loss') plt.xlabel('epoch') plt.ylabel('loss') plt.legend(loc='upper left') plt.subplot(1, 2, 2) plt.plot(np.arange(epochs), d['acc'], label='acc') if best_acc_ is not None: plt.scatter(best_acc_[0], best_acc_[1], c='r') plt.xlabel('epoch') plt.ylabel('acc') plt.legend(loc='upper left') plt.savefig(os.path.join(savepath, '%s.jpg' % mode), bbox_inches='tight') plt.close() if __name__ == '__main__': best_epoch = 0 best_acc = 0. use_gpu = False if args.seed is not None: print('use random seed:', args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) np.random.seed(args.seed) random.seed(args.seed) cudnn.deterministic = False if torch.cuda.is_available(): use_gpu = True cudnn.benchmark = True # loss交叉熵损失 criterion = nn.CrossEntropyLoss() # dataloader trainset = Dataset(mode='train') valset = Dataset(mode='val') trainloader = DataLoader(dataset=trainset, batch_size=args.batch_size, shuffle=True, \ num_workers=args.num_workers, pin_memory=True, drop_last=True) valloader = DataLoader(dataset=valset, batch_size=128, shuffle=False, num_workers=args.num_workers, \ pin_memory=True) # model model = BaseModel(model_name=args.model_name, num_classes=args.num_classes, pretrained=args.pretrained, pool_type=args.pool_type, down=args.down, metric=args.metric) if args.resume: state = torch.load(args.resume) print('best_epoch:{}, best_acc:{}'.format(state['epoch'], state['acc'])) model.load_state_dict(state['net']) if torch.cuda.device_count() > 1 and args.multi_gpus: print('use multi-gpus...') os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') torch.distributed.init_process_group(backend="nccl", init_method='tcp://localhost:23456', rank=0, world_size=1) model = model.to(device) model = nn.parallel.DistributedDataParallel(model) else: device = ('cuda:%d'%args.gpu if torch.cuda.is_available() else 'cpu') model = model.to(device) print('device:', device) # optim优化 optimizer = torch.optim.SGD( [{'params': filter(lambda p: p.requires_grad, model.parameters()), 'lr': args.lr}], weight_decay=args.weight_decay, momentum=args.momentum) print('init_lr={}, weight_decay={}, momentum={}'.format(args.lr, args.weight_decay, args.momentum)) if args.scheduler == 'step': #动态调整学习率 scheduler = lr_scheduler.StepLR(optimizer, step_size=args.lr_step, gamma=args.lr_gamma, last_epoch=-1) elif args.scheduler == 'multi': scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[150, 225], gamma=args.lr_gamma, last_epoch=-1) elif args.scheduler == 'cos': warm_up_step = 10 lambda_ = lambda epoch: (epoch + 1) / warm_up_step if epoch < warm_up_step else 0.5 * ( np.cos((epoch - warm_up_step) / (args.total_epoch - warm_up_step) * np.pi) + 1) scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda_) # savepath savepath = os.path.join(args.savepath, args.model_name+args.pool_type+args.metric+'_'+str(args.down)) print('savepath:', savepath) if not os.path.exists(savepath): os.makedirs(savepath) with open(os.path.join(savepath, 'setting.txt'), 'w')as f: for k, v in vars(args).items(): f.write('{}:{}\n'.format(k, v)) f = open(os.path.join(savepath, 'log.txt'), 'w') f.close() total = args.total_epoch start = time.time() train_info = {'loss': [], 'acc': []} test_info = {'loss': [], 'acc': []} for epoch in range(total): print('epoch[{:>3}/{:>3}]'.format(epoch, total), end=' ') d_train = train() scheduler.step() d_test = test(epoch) for k in train_info.keys(): train_info[k].append(d_train[k]) test_info[k].append(d_test[k]) plot(train_info, mode='train') plot(test_info, mode='test', best_acc_=[best_epoch, best_acc]) end = time.time() print('total time:{}m{:.2f}s'.format((end - start) // 60, (end - start) % 60)) print('best_epoch:', best_epoch) print('best_acc:', best_acc) #记录最好结果 with open(os.path.join(savepath, 'log.txt'), 'a+')as f: f.write('# best_acc:{:.4f}, best_epoch:{}'.format(best_acc, best_epoch)) -
test.py
进行预测并记录预测结果
import torch from ArtModel import BaseModel import os import pandas as pd from PIL import Image from torchvision import transforms import numpy as np import argparse def get_setting(path): args = {} with open(os.path.join(path, 'setting.txt'), 'r')as f: for i in f.readlines(): k, v = i.strip().split(':') args[k] = v return args def load_pretrained_model(path, model, mode='best'): print('load pretrained model...') state = torch.load(os.path.join(path, '%s.pth' % mode)) print('best_epoch:{}, best_acc:{}'.format(state['epoch'], state['acc'])) model.load_state_dict(state['net']) if __name__ == '__main__': mode = 'best' parser = argparse.ArgumentParser() parser.add_argument('--savepath', default='./Base224L2/eff-b3', type=str) parser.add_argument('--last', action='store_true') args = parser.parse_args() path = args.savepath if args.last: mode = 'last' args = get_setting(path) # print(args) # model model = BaseModel(model_name=args['model_name'], num_classes=int(args['num_classes']), \ pretrained=int(args['pretrained']), pool_type=args['pool_type'], down=int(args['down']), metric=args['metric']) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # device = torch.device('cpu') model = model.to(device) #加载预训练模型 load_pretrained_model(path, model, mode=mode) size = 255 trans = transforms.Compose([ transforms.Resize((int(size / 0.875), int(size / 0.875))), transforms.RandomHorizontalFlip(), transforms.RandomCrop((size, size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) submit = {'uuid': [], 'label': []} TTA_times = 7 model.eval() with torch.no_grad(): for i in range(0, 800): img_path = 'test/%d.jpg' % i raw_img = Image.open(img_path).convert('RGB') results = np.zeros(49) for j in range(TTA_times): img = trans(raw_img) img = img.unsqueeze(0).to(device) out = model(img) out = torch.softmax(out, dim=1) _, pred = torch.max(out.cpu(), dim=1) results[pred] += 1 pred = np.argmax(results) print(i, ',', pred) submit['uuid'].append(i) submit['label'].append(pred) #保存结果 df = pd.DataFrame(submit) df.to_csv(os.path.join(path, 'result.csv'), encoding='utf-8', index=False, header=False) -
ArtModel.py
分类模型：resnext50/eff-b3
搭建模型框架
import torch import torch.nn as nn import torch.nn.functional as F from torchvision import models import math import numpy as np from efficientnet_pytorch import EfficientNet import random class SELayer(nn.Module): def __init__(self, channel, reduction=16): super(SELayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, channel // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(channel // reduction, channel, bias=False), nn.Sigmoid() ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return y class AdaptiveConcatPool2d(nn.Module): def __init__(self, sz=(1,1)): super().__init__() self.ap = nn.AdaptiveAvgPool2d(sz) self.mp = nn.AdaptiveMaxPool2d(sz) def forward(self, x): return torch.cat([self.mp(x), self.ap(x)], 1) class GeneralizedMeanPooling(nn.Module): def __init__(self, norm=3, output_size=1, eps=1e-6): super().__init__() assert norm > 0 self.p = float(norm) self.output_size = output_size self.eps = eps def forward(self, x): x = x.clamp(min=self.eps).pow(self.p) return torch.nn.functional.adaptive_avg_pool2d(x, self.output_size).pow(1. / self.p) def __repr__(self): return self.__class__.__name__ + '(' \ + str(self.p) + ', ' \ + 'output_size=' + str(self.output_size) + ')' #模型框架 class BaseModel(nn.Module): def __init__(self, model_name, num_classes=2, pretrained=True, pool_type='max', down=True, metric='linear'): super().__init__() self.model_name = model_name #eff-b3/resnext50 if model_name == 'eff-b3': backbone = EfficientNet.from_pretrained('efficientnet-b3') plane = 1536 elif model_name == 'resnext50': backbone = nn.Sequential(*list(models.resnext50_32x4d(pretrained=pretrained).children())[:-2]) plane = 2048 else: backbone = None plane = None self.backbone = backbone #pool if pool_type == 'avg': self.pool = nn.AdaptiveAvgPool2d((1, 1)) elif pool_type == 'cat': self.pool = AdaptiveConcatPool2d() down = 1 elif pool_type == 'max': self.pool = nn.AdaptiveMaxPool2d((1, 1)) elif pool_type == 'gem': self.pool = GeneralizedMeanPooling() else: self.pool = None if down: if pool_type == 'cat': self.down = nn.Sequential( nn.Linear(plane * 2, plane), nn.BatchNorm1d(plane), nn.Dropout(0.2), nn.ReLU(True) ) else: self.down = nn.Sequential( nn.Linear(plane, plane), nn.BatchNorm1d(plane), nn.Dropout(0.2), nn.ReLU(True) ) else: self.down = nn.Identity() self.se = SELayer(plane) self.hidden = nn.Linear(plane, plane) self.relu = nn.ReLU(True) if metric == 'linear': self.metric = nn.Linear(plane, num_classes) elif metric == 'am': self.metric = AddMarginProduct(plane, num_classes) else: self.metric = None def forward(self, x): if self.model_name == 'eff-b3': feat = self.backbone.extract_features(x) else: feat = self.backbone(x) feat = self.pool(feat) se = self.se(feat).view(feat.size(0), -1) feat_flat = feat.view(feat.size(0), -1) feat_flat = self.relu(self.hidden(feat_flat) * se) out = self.metric(feat_flat) return out if __name__ == '__main__': model = BaseModel(model_name='eff-b3').eval() x = torch.randn((1, 3, 224, 224)) out = model(x) print(out.size()) print(model) -
vote.py
投出最好的结果
classification——art form 000wangbo
主干网络resnest200，输入448尺寸，在不同loss下取得5组最好效果，最后进行投票，得到最后分数。单模最高93.75。
这个代码好像不全orz没有跑成功
import os
import math
import copy
import shutil
import time
import random
import pickle
import pandas as pd
import numpy as np
from PIL import Image
from tqdm import tqdm
from collections import OrderedDict, namedtuple
from sklearn.metrics import roc_auc_score, average_precision_score
#import se_resnext101_32x4d
from pretrainedmodels import se_resnext101_32x4d
from efficientnet_pytorch import EfficientNet
#没有找到这个包
from data_augmentation import FixedRotation
from inceptionv4 import inceptionv4
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.models as models
from torchvision.models import resnet101,resnet50,resnet152,resnet34
import torchvision.transforms as transforms
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
from resnest.torch import resnest200,resnest269,resnest101
from torch.utils.data import Dataset, DataLoader
import warnings
warnings.filterwarnings("ignore")
#def main():https://www.cnblogs.com/chentianwei/p/11912463.html
def main(index):
np.random.seed(359)
torch.manual_seed(359)
torch.cuda.manual_seed_all(359)
random.seed(359)
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2,3,4,5,6,7'
batch_size = 48
workers = 16
# stage_epochs = [8, 8, 8, 6, 5, 4, 3, 2]
# stage_epochs = [12, 6, 5, 3, 4]
lr = 5e-4
lr_decay = 10
weight_decay = 1e-4
#参数
stage = 0
start_epoch = 0
# total_epochs = sum(stage_epochs)
total_epochs = 200
patience = 4
no_improved_times = 0
total_stages = 3
best_score = 0
samples_num = 54
print_freq = 20
train_ratio = 0.9 # others for validation
momentum = 0.9
pre_model = 'senet'
pre_trained = True
evaluate = False
use_pre_model = False
# file_name = os.path.basename(__file__).split('.')[0]
file_name = "resnest200_448_all_{}".format(index)
img_size = 448
resumeflg = False
resume = ''
# 创建保存模型和结果的文件夹
if not os.path.exists('./model/%s' % file_name):
os.makedirs('./model/%s' % file_name)
if not os.path.exists('./result/%s' % file_name):
os.makedirs('./result/%s' % file_name)
if not os.path.exists('./result/%s.txt' % file_name):
txt_mode = 'w'
else:
txt_mode = 'a'
with open('./result/%s.txt' % file_name, txt_mode) as acc_file:
acc_file.write('\n%s %s\n' % (time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())), file_name))
# build a model
model =resnest200(pretrained=True)
model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
model.fc = torch.nn.Linear(model.fc.in_features,49)
# model = se_resnext101_32x4d.se_resnext101(num_classes=3)
# model = EfficientNet.from_pretrained('efficientnet-b4',num_classes=2)
# model = inceptionv4(pretrained='imagenet')
# model.last_linear = torch.nn.Linear(model.last_linear.in_features,2)
model = torch.nn.DataParallel(model).cuda()
def load_pre_cloth_model_dict(self, state_dict):
own_state = self.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
if 'fc' in name:
continue
if isinstance(param, nn.Parameter):
# backwards compatibility for serialized parameters
param = param.data
own_state[name].copy_(param)
if use_pre_model:
print('using pre model')
pre_model_path = ''
load_pre_cloth_model_dict(model, torch.load(pre_model_path)['state_dict'])
# optionally resume from a checkpoint
if resume:
if os.path.isfile(resume):
print("=> loading checkpoint '{}'".format(resume))
checkpoint = torch.load(resume)
start_epoch = checkpoint['epoch']
best_score = checkpoint['best_score']
stage = checkpoint['stage']
lr = checkpoint['lr']
model.load_state_dict(checkpoint['state_dict'])
no_improved_times = checkpoint['no_improved_times']
if no_improved_times == 0:
model.load_state_dict(torch.load('./model/%s/model_best.pth.tar' % file_name)['state_dict'])
print("=> loaded checkpoint (epoch {})".format(checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(resume))
def default_loader(root_dir,path):
final_path = os.path.join(root_dir,str(path))
return Image.open(final_path+".jpg").convert('RGB')
# return Image.open(path)
class TrainDataset(Dataset):
def __init__(self, label_list, transform=None, target_transform=None, loader=default_loader):
imgs = []
for index, row in label_list.iterrows():
imgs.append((row['filename'], row['label']))
self.imgs = imgs
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self, index):
filename, label= self.imgs[index]
label = label
img = self.loader('../train/',filename)
if self.transform is not None:
img = self.transform(img)
return img, label
def __len__(self):
return len(self.imgs)
class ValDataset(Dataset):
def __init__(self, label_list, transform=None, target_transform=None, loader=default_loader):
imgs = []
for index, row in label_list.iterrows():
imgs.append((row['filename'], row['label']))
self.imgs = imgs
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self, index):
filename, label= self.imgs[index]
label = label
img = self.loader('../train/',filename)
if self.transform is not None:
img = self.transform(img)
return img, label, filename
def __len__(self):
return len(self.imgs)
class TestDataset(Dataset):
def __init__(self, label_list, transform=None, target_transform=None, loader=default_loader):
imgs = []
for index, row in label_list.iterrows():
imgs.append((row['filename'], row['label']))
self.imgs = imgs
self.transform = transform
self.target_transform = target_transform
self.loader = loader
def __getitem__(self, index):
filename,label = self.imgs[index]
img = self.loader('../test/',filename)
if self.transform is not None:
img = self.transform(img)
return img, filename
def __len__(self):
return len(self.imgs)
train_data_list = pd.read_csv("data/train_{}.csv".format(index), sep=",")
val_data_list = pd.read_csv("data/test_{}.csv".format(index), sep=",")
test_data_list = pd.read_csv("../test.csv",sep=",")
train_data_list = train_data_list.fillna(0)
# 训练集正常样本尺寸
random_crop = [transforms.RandomCrop(640), transforms.RandomCrop(768), transforms.RandomCrop(896)]
smax = nn.Softmax()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
#数据处理
train_data = TrainDataset(train_data_list,
transform=transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ColorJitter(0.3, 0.3, 0.3, 0.15),
# transforms.RandomRotation(30),
transforms.RandomHorizontalFlip(),
# transforms.RandomVerticalFlip(),
# transforms.RandomGrayscale(),
FixedRotation([-16,-14,-12,-10,-8,-6,-4,-2,0,2,4,6,8,10,12,14,16]),
transforms.ToTensor(),
normalize,
]))
val_data = ValDataset(val_data_list,
transform=transforms.Compose([
transforms.Resize((img_size, img_size)),
# transforms.CenterCrop((500, 500)),
transforms.ToTensor(),
normalize,
]))
test_data = TestDataset(test_data_list,
transform=transforms.Compose([
transforms.Resize((img_size, img_size)),
# transforms.CenterCrop((500, 500)),
transforms.ToTensor(),
normalize,
# transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
# transforms.Lambda(lambda crops: torch.stack([normalize(crop) for crop in crops])),
]))
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=workers,drop_last=True)
val_loader = DataLoader(val_data, batch_size=batch_size * 2, shuffle=False, pin_memory=False, num_workers=workers,drop_last=True)
test_loader = DataLoader(test_data, batch_size=batch_size * 2, shuffle=False, pin_memory=False, num_workers=workers)
test_data_hflip = TestDataset(test_data_list,
transform=transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.RandomHorizontalFlip(p=2),
# transforms.CenterCrop((500, 500)),
transforms.ToTensor(),
normalize,
]))
test_loader_hflip = DataLoader(test_data_hflip, batch_size=batch_size * 2, shuffle=False, pin_memory=False, num_workers=workers)
test_data_vflip = TestDataset(test_data_list,
transform=transforms.Compose([
transforms.Resize((336, 336)),
transforms.RandomVerticalFlip(p=2),
# transforms.CenterCrop((500, 500)),
transforms.ToTensor(),
normalize,
]))
test_loader_vflip = DataLoader(test_data_vflip, batch_size=batch_size * 2, shuffle=False, pin_memory=False,
num_workers=workers)
test_data_vhflip = TestDataset(test_data_list,
transform=transforms.Compose([
transforms.Resize((336, 336)),
transforms.RandomHorizontalFlip(p=2),
transforms.RandomVerticalFlip(p=2),
# transforms.CenterCrop((500, 500)),
transforms.ToTensor(),
normalize,
]))
test_loader_vhflip = DataLoader(test_data_vhflip, batch_size=batch_size * 2, shuffle=False, pin_memory=False,
num_workers=workers)
#train
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading
# if len(target) % workers == 1:
# images = images[:-1]
# target = target[:-1]
data_time.update(time.time() - end)
image_var = torch.tensor(images, requires_grad=False).cuda(non_blocking=True)
# print(image_var)
label = torch.tensor(target).cuda(non_blocking=True)
# compute y_pred
y_pred = model(image_var)
loss = criterion(y_pred, label)
# measure accuracy and record loss
prec, PRED_COUNT = accuracy(y_pred.data, target, topk=(1, 1))
losses.update(loss.item(), images.size(0))
acc.update(prec, PRED_COUNT)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accuray {acc.val:.3f} ({acc.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, loss=losses, acc=acc))
def validate(val_loader, model, criterion):
batch_time = AverageMeter()
# losses = AverageMeter()
# acc = AverageMeter()
# switch to evaluate mode
model.eval()
# 保存概率，用于评测
val_imgs, val_preds, val_labels, = [], [], []
end = time.time()
for i, (images, labels, img_path) in enumerate(val_loader):
# if len(labels) % workers == 1:
# images = images[:-1]
# labels = labels[:-1]
image_var = torch.tensor(images, requires_grad=False).cuda(non_blocking=True) # for pytorch 0.4
# label_var = torch.tensor(labels, requires_grad=False).cuda(async=True) # for pytorch 0.4
target = torch.tensor(labels).cuda(non_blocking=True)
# compute y_pred
with torch.no_grad():
y_pred = model(image_var)
loss = criterion(y_pred, target)
# measure accuracy and record loss
# prec, PRED_COUNT = accuracy(y_pred.data, labels, topk=(1, 1))
# losses.update(loss.item(), images.size(0))
# acc.update(prec, PRED_COUNT)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % (print_freq * 5) == 0:
print('TrainVal: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'.format(i, len(val_loader),
batch_time=batch_time))
# 保存概率，用于评测
smax_out = smax(y_pred)
val_imgs.extend(img_path)
val_preds.extend([i.tolist() for i in smax_out])
val_labels.extend([i.item() for i in labels])
val_preds = [';'.join([str(j) for j in i]) for i in val_preds]
val_score = pd.DataFrame({'img_path': val_imgs, 'preds': val_preds, 'label': val_labels,})
val_score.to_csv('./result/%s/val_score.csv' % file_name, index=False)
acc, f1 = score(val_score)
print('acc: %.4f, f1: %.4f' % (acc, f1))
print(' * Score {final_score:.4f}'.format(final_score=f1), '(Previous Best Score: %.4f)' % best_score)
return acc, f1
def test(test_loader, model):
csv_map = OrderedDict({'FileName': [], 'type': [], 'probability': []})
# switch to evaluate mode
model.eval()
for i, (images, filepath) in enumerate(tqdm(test_loader)):
# bs, ncrops, c, h, w = images.size()
filepath = [str(i) for i in filepath]
image_var = torch.tensor(images, requires_grad=False) # for pytorch 0.4
with torch.no_grad():
y_pred = model(image_var) # fuse batch size and ncrops
# y_pred = y_pred.view(bs, ncrops, -1).mean(1) # avg over crops
# get the index of the max log-probability
smax = nn.Softmax()
smax_out = smax(y_pred)
csv_map['FileName'].extend(filepath)
for output in smax_out:
prob = ';'.join([str(i) for i in output.data.tolist()])
csv_map['probability'].append(prob)
csv_map['type'].append(np.argmax(output.data.tolist()))
# print(len(csv_map['filename']), len(csv_map['probability']))
result = pd.DataFrame(csv_map)
result.to_csv('./result/%s/submission.csv' % file_name, index=False)
result[['FileName','type']].to_csv('./result/%s/final_submission.csv' % file_name, index=False)
return
def save_checkpoint(state, is_best, filename='./model/%s/checkpoint.pth.tar' % file_name):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, './model/%s/model_best.pth.tar' % file_name)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate():
nonlocal lr
lr = lr / lr_decay
return optim.Adam(model.parameters(), lr, weight_decay=weight_decay, amsgrad=True)
def accuracy(y_pred, y_actual, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
final_acc = 0
maxk = max(topk)
# for prob_threshold in np.arange(0, 1, 0.01):
PRED_COUNT = y_actual.size(0)
PRED_CORRECT_COUNT = 0
prob, pred = y_pred.topk(maxk, 1, True, True)
# prob = np.where(prob > prob_threshold, prob, 0)
for j in range(pred.size(0)):
if int(y_actual[j]) == int(pred[j]):
PRED_CORRECT_COUNT += 1
if PRED_COUNT == 0:
final_acc = 0
else:
final_acc = PRED_CORRECT_COUNT / PRED_COUNT
return final_acc * 100, PRED_COUNT
def softmax(x):
return np.exp(x) / np.sum(np.exp(x), axis=0)
def doitf(tp, fp, fn):
if (tp + fp == 0):
return 0
if (tp + fn == 0):
return 0
pre = float(1.0 * float(tp) / float(tp + fp))
rec = float(1.0 * float(tp) / float(tp + fn))
if (pre + rec == 0):
return 0
return (2 * pre * rec) / (pre + rec)
# 参数 samples_num 表示选取多少个样本来取平均
def score(val_score):
val_score['preds'] = val_score['preds'].map(lambda x: [float(i) for i in x.split(';')])
acc = 0
tp = np.zeros(49)
fp = np.zeros(49)
fn = np.zeros(49)
f1 = np.zeros(49)
f1_tot = 0
print(val_score.head(10))
val_score['preds_label'] = val_score['preds'].apply(lambda x: np.argmax(x))
for i in range(val_score.shape[0]):
preds = val_score['preds_label'].iloc[i]
label = val_score['label'].iloc[i]
if (preds == label):
acc = acc + 1
tp[label] = tp[label] + 1
else:
fp[preds] = fp[preds] + 1
fn[label] = fn[label] + 1
for classes in range(49):
f1[classes] = doitf(tp[classes], fp[classes], fn[classes])
f1_tot = f1_tot + f1[classes]
acc = acc / val_score.shape[0]
f1_tot = f1_tot / 49
return acc, f1_tot
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss().cuda()
# optimizer = optim.Adam(model.module.last_linear.parameters(), lr, weight_decay=weight_decay, amsgrad=True)
optimizer = optim.Adam(model.parameters(), lr, weight_decay=weight_decay, amsgrad=True)
if evaluate:
validate(val_loader, model, criterion)
else:
for epoch in range(start_epoch, total_epochs):
if stage >= total_stages - 1:
break
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if epoch >= 0:
acc , f1 = validate(val_loader, model, criterion)
with open('./result/%s.txt' % file_name, 'a') as acc_file:
acc_file.write('Epoch: %2d, acc: %.8f, f1: %.8f\n' % (epoch, acc, f1))
# remember best Accuracy and save checkpoint
is_best = acc > best_score
best_score = max(acc, best_score)
# if (epoch + 1) in np.cumsum(stage_epochs)[:-1]:
# stage += 1
# optimizer = adjust_learning_rate()
if is_best:
no_improved_times = 0
else:
no_improved_times += 1
print('stage: %d, no_improved_times: %d' % (stage, no_improved_times))
if no_improved_times >= patience:
stage += 1
optimizer = adjust_learning_rate()
state = {
'epoch': epoch + 1,
'arch': pre_model,
'state_dict': model.state_dict(),
'best_score': best_score,
'no_improved_times': no_improved_times,
'stage': stage,
'lr': lr,
}
save_checkpoint(state, is_best)
# if (epoch + 1) in np.cumsum(stage_epochs)[:-1]:
if no_improved_times >= patience:
no_improved_times = 0
model.load_state_dict(torch.load('./model/%s/model_best.pth.tar' % file_name)['state_dict'])
print('Step into next stage')
with open('./result/%s.txt' % file_name, 'a') as acc_file:
acc_file.write('---------------------Step into next stage---------------------\n')
with open('./result/%s.txt' % file_name, 'a') as acc_file:
acc_file.write('* best acc: %.8f %s\n' % (best_score, os.path.basename(__file__)))
with open('./result/best_acc.txt', 'a') as acc_file:
acc_file.write('%s * best acc: %.8f %s\n' % (
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())), best_score, os.path.basename(__file__)))
# test
best_model = torch.load('model/{}/model_best.pth.tar'.format(file_name))
model.load_state_dict(best_model['state_dict'])
test(test_loader=test_loader, model=model)
torch.cuda.empty_cache()
# resume = False
if __name__ == '__main__':
for index in range(1,6):
main(index)
