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T_ResNet.py
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T_ResNet.py
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#coding:utf8
from config import opt
import os
import torch as t
import models
from data.CAG import CAG
from torch.utils.data import DataLoader
from torch.autograd import Variable
from utils.logger import Logger
import torch
import torchvision.models as Pre_models
logger = Logger(opt.log_path)
def test(**kwargs):
opt.parse(kwargs)
# configure model
model = Pre_models.resnet152(pretrained=True, num_classes=2)
# data
train_data = CAG(opt.test_data_root,test=True)
test_dataloader = DataLoader(train_data,batch_size=opt.batch_size,shuffle=False,num_workers=opt.num_workers)
results = []
for ii,(data,path) in enumerate(test_dataloader):
input = t.autograd.Variable(data,volatile = True)
if opt.use_gpu: input = input.cuda()
score = model(input)
probability = t.nn.functional.softmax(score)[:,0].data.tolist()
# label = score.max(dim = 1)[1].data.tolist()
batch_results = [(path_,probability_) for path_,probability_ in zip(path,probability) ]
results += batch_results
write_csv(results,opt.result_file)
return results
def write_csv(results,file_name):
import csv
with open(file_name,'w') as f:
writer = csv.writer(f)
writer.writerow(['id','label'])
writer.writerows(results)
def train(**kwargs):
opt.parse(kwargs)
# step1: configure model
#model = getattr(models, opt.model)()
model = Pre_models.resnet152(pretrained=True);
model.classifier = torch.nn.Linear(2208, 2);
#print(model)
model = model.cuda()
model = torch.nn.DataParallel(model)
#if opt.load_model_path:
# model.load(opt.load_model_path)
#if opt.use_gpu: model.cuda()
# step2: data
train_data = CAG(opt.train_data_root,train=True)
val_data = CAG(opt.train_data_root,train=False)
train_dataloader = DataLoader(train_data,opt.batch_size,
shuffle=True,num_workers=opt.num_workers)
val_dataloader = DataLoader(val_data,opt.batch_size,
shuffle=False,num_workers=opt.num_workers)
# step3: criterion and optimizer
loss_func = t.nn.CrossEntropyLoss()
lr = opt.lr
optimizer = t.optim.Adam(model.parameters(),lr = lr,weight_decay = opt.weight_decay)
previous_loss = 1.0
# train
for epoch in range(opt.max_epoch):
print('epoch {}'.format(epoch + 1))
train_num = 1
train_acc = 0
sum_loss = 0
batch_num = 0
for ii,(data,label) in enumerate(train_dataloader):
# train model
input = Variable(data)
target = Variable(label)
if opt.use_gpu:
input = input.cuda()
target = target.cuda()
optimizer.zero_grad()
score = model(input)
probability = t.nn.functional.softmax(score,dim=1)
_, result = torch.max(probability, 1)
train_correct = (result == target.squeeze(0)).sum()
train_acc += train_correct.item()
train_num += target.size(0)
loss = loss_func(score,target)
sum_loss += loss.item()
loss.backward()
optimizer.step()
batch_num += 1
print("当前loss:", sum_loss / batch_num)
logger.scalar_summary('train_loss',sum_loss / batch_num, epoch)
accuracy = train_acc / train_num
logger.scalar_summary('train_accurancy', accuracy, epoch)
if (epoch+1)%100==0:
#if loss.item() < previous_loss:
#model.save()
lr = lr * opt.lr_decay
print("当前学习率",lr)
logger.scalar_summary('lr', lr, epoch)
for param_group in optimizer.param_groups:#optimizer通过param_group来管理参数组. 通过更改param_group[‘lr’]的值来更改对应参数组的学习率。
param_group['lr'] = lr
#previous_loss = loss.item()
# validate and visualize
if (epoch+1)%5 == 0:
val_accuracy,val_loss = val(model, val_dataloader)
print("验证集上准确率为：", val_accuracy)
logger.scalar_summary('val_accurancy', val_accuracy, epoch)
print("val_loss:", val_loss)
logger.scalar_summary('val_loss', val_loss, epoch)
def val(model,dataloader):
'''
计算模型在验证集上的准确率等信息
'''
# 把模型设为验证模式
model.eval()
train_acc = 0
val_num = 1
loss_func = t.nn.CrossEntropyLoss()
sum_loss = 0
batch_num = 0
for ii, data in enumerate(dataloader):
input, label = data
val_input = Variable(input)
val_label = Variable(label.type(t.LongTensor))
if opt.use_gpu:
val_input = val_input.cuda()
val_label = val_label.cuda()
score = model(val_input)
probability = t.nn.functional.softmax(score,dim=1)
_, predicted = torch.max(probability, 1)
loss = loss_func(probability, val_label)
sum_loss += loss.item()
train_correct = (predicted == val_label.squeeze(0)).sum()
train_acc += train_correct.item()
val_num += val_label.size(0)
batch_num += 1
# 把模型恢复为训练模式
loss = sum_loss / batch_num
model.train()
accuracy = train_acc/val_num
return accuracy,loss
def help():
'''
打印帮助的信息： python file.py help
'''
print('''
usage : python file.py <function> [--args=value]
<function> := train | test | help
example:
python {0} train --env='env0701' --lr=0.01
python {0} test --dataset='path/to/dataset/root/'
python {0} help
avaiable args:'''.format(__file__))
from inspect import getsource
source = (getsource(opt.__class__))
print(source)
if __name__=='__main__':
train();
# import fire
#train()