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train_3epoch.py
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import torch
from torch.autograd import Variable
import argparse
import copy
import pickle
from Datahelper2 import *
from Model import *
from gloable_parameter import *
from hyperboard import Agent
# train 36431 32384
# validate 4048 8095
# test 61191
torch.backends.cudnn.benchmark=False
def train_3epoch(model_path,lr,train_batch_size,validate_batch_size,validate_batch_num,resize,train_gpu,validate_gpu=-1):
# train_gpu = 0
# validate_gpu = 1
# model_path = '../amazon2/alexnet'
# train_batch_size = 256
# validate_batch_size = 128
# validate_batch_num = 8
# parameters
k=5
epochs = 3
# lr = 1e-4
weight_decay = 0
momentum = 0
criteria2metric = {
'train loss': 'loss',
'valid loss': 'loss'
}
hyperparameters_train = {
'name':'train',
'learning rate': '%f,\n%f'%(lr,lr/50),
'batch size': train_batch_size,
'optimizer': 'Adam',
'momentum': 0,
'net':model_path.split('/')[-1],
'epoch':'No.1-2',
}
hyperparameters_validate = {
'name':'validate',
'learning rate': '%f,\n%f'%(lr,lr/50),
'batch size': train_batch_size,
'optimizer': 'Adam',
'momentum': 0,
'net':model_path.split('/')[-1],
'epoch': 'No.1-2',
}
agent = Agent(username='jlb',password='1993610')
train_loss_show = agent.register(hyperparameters_train, criteria2metric['train loss'])
validate_loss_show = agent.register(hyperparameters_validate, criteria2metric['valid loss'])
global_step = 0
with open('kdf.pkl', 'rb') as f:
kfold = pickle.load(f,encoding='latin1')
with open(os.path.join(model_path,'test'),'w') as f:
f.write('test')
loss_info = [] # 第i个记录了 fold i 的最小(train_loss,validate_loss)
lr = lr*train_batch_size/32
for fold in range(k):
train_index = kfold[fold][0]
validate_index = kfold[fold][1]
# model = AM_resnet101()
# model = PyNet_10((3, 256, 256), 17)
# model = AM_densenet161()
# model = AM_resnet152()
# model = AM_squeezenet11()
# model = AM_vgg13()
# model = AM_densenet121()
# model = AM_resnet50()
# model = AM_densenet161()
# model = AM_resnet18()
# model = AM_densenet201_new()
# model = AM_resnet34_new()
# model = AM_resnet50_new()
model = AM_resnet101_new()
if model.getname()!=model_path.split('/')[-1]:
print('Wrong Model!')
return
model.cuda(device_id=train_gpu)
min_loss = [0.9,0.9]
for epoch in range(epochs):
print('--------------Epoch %d: train-----------' % epoch)
if epoch==0:
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
elif epoch ==1:
optimizer = torch.optim.Adam(model.parameters(), lr=lr/50, weight_decay=weight_decay)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=lr/500, weight_decay=weight_decay)
dset_train = AmazonDateset_train(train_index, IMG_TRAIN_PATH, IMG_EXT, LABEL_PATH, resize=resize)
train_loader = DataLoader(dset_train, batch_size=train_batch_size, shuffle=True, num_workers=3)
model.train()
for step, (data, target) in enumerate(train_loader):
data, target = Variable(data), Variable(target)
data = data.cuda(device_id=train_gpu)
target = target.cuda(device_id=train_gpu)
optimizer.zero_grad()
output = model(data)
loss = F.binary_cross_entropy(output, target)
loss.backward()
optimizer.step()
agent.append(train_loss_show, global_step, loss.data[0])
global_step += 1
if step % 10 == 0:
model.eval()
if validate_gpu != -1:
model.cuda(validate_gpu)
dset_validate = AmazonDateset_validate(validate_index, IMG_TRAIN_PATH, IMG_EXT, LABEL_PATH,random_transform=True,resize=resize)
validate_loader = DataLoader(dset_validate, batch_size=validate_batch_size, shuffle=True, num_workers=4)
total_vloss = 0
for vstep, (vdata, vtarget) in enumerate(validate_loader):
vdata, vtarget = Variable(vdata,volatile=True), Variable(vtarget,volatile=True)
if validate_gpu != -1:
vdata = vdata.cuda(validate_gpu)
vtarget = vtarget.cuda(validate_gpu)
else:
vdata = vdata.cuda(train_gpu)
vtarget = vtarget.cuda(train_gpu)
voutput = model(vdata)
vloss = F.binary_cross_entropy(voutput, vtarget)
total_vloss += vloss.data[0]
if vstep == (validate_batch_num-1):
break
vloss = total_vloss / validate_batch_num
model.train()
if validate_gpu != -1:
model.cuda(train_gpu)
agent.append(validate_loss_show, global_step, vloss)
# vloss = 0
print('{} Fold{} Epoch{} Step{}: [{}/{} ({:.0f}%)]\tTrain Loss: {:.6f}\tValidate Loss: {:.6f}'.format(model_path.split('/')[-1],fold, epoch,global_step-1, step * train_batch_size,
len(train_loader.dataset),
100. * step / len(train_loader),
loss.data[0],vloss))
if vloss<min_loss[1]:
min_loss[1] = vloss
min_loss[0] = loss.data[0]
model_save = copy.deepcopy(model)
torch.save(model_save.cpu(), os.path.join(model_path, 'fold%d.mod' % (fold)))
loss_info.append(min_loss)
print('-----------------------------------------')
print(model_path.split('/')[-1]+':')
for i,l in enumerate(loss_info):
print('Fold%d: Train loss:%f\tValidate loss:%f'%(i,l[0],l[1]))
with open(os.path.join(model_path,'train_loss_info.pkl'),'wb') as f:
pickle.dump(loss_info,f)