AI艺术鉴赏挑战赛 - 看画猜作者 代码方案
AI研习社 AI艺术鉴赏挑战赛 - 看画猜作者
亚军方案：
- 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。
'''
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 efficientnet_pytorch import EfficientNet
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(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
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])
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)
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)
'''
季军方案：
- 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。
'''
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
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
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
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':
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)
with open(os.path.join(savepath, 'log.txt'), 'a+')as f:
f.write('# best_acc:{:.4f}, best_epoch:{}'.format(best_acc, best_epoch))
'''
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