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train.py
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train.py
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# -*- coding: utf-8 -*-
"""
------------------------------------------------------------------------------
Copyright (C) 2019 Université catholique de Louvain (UCLouvain), Belgium.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
------------------------------------------------------------------------------
"train.py" - Initializing the network, optimizer and loss for training and testing.
Project: DRTP - Direct Random Target Projection
Authors: C. Frenkel and M. Lefebvre, Université catholique de Louvain (UCLouvain), 09/2019
Cite/paper: C. Frenkel, M. Lefebvre and D. Bol, "Learning without feedback: Direct random target projection
as a feedback-alignment algorithm with layerwise feedforward training," arXiv preprint arXiv:1909.01311, 2019.
------------------------------------------------------------------------------
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
import models
from tqdm import tqdm
from torch.autograd import Variable
from tensorboardX import SummaryWriter
import os
import time
writer = SummaryWriter('logs')
loss_function = nn.MSELoss()
def train(args, device, train_loader, traintest_loader, test_loader):
torch.manual_seed(42)
for trial in range(1,args.trials+1):
# Network topology
model = models.NetworkBuilder(args.topology, input_size=args.input_size, input_channels=args.input_channels, label_features=args.label_features, train_batch_size=args.batch_size, train_mode=args.train_mode, dropout=args.dropout, conv_act=args.conv_act, hidden_act=args.hidden_act, output_act=args.output_act, fc_zero_init=args.fc_zero_init, spike_window=args.spike_window, device=device, thresh=args.thresh, randKill=args.randKill, lens=args.lens, decay=args.decay)
if args.cuda:
model.cuda()
if args.optimizer == 'SGD':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, nesterov=False)
elif args.optimizer == 'NAG':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, nesterov=True)
elif args.optimizer == 'Adam':
optimizer = optim.Adam(model.parameters(), lr=args.lr)
elif args.optimizer == 'RMSprop':
optimizer = optim.RMSprop(model.parameters(), lr=args.lr)
else:
raise NameError("=== ERROR: optimizer " + str(args.optimizer) + " not supported")
# Loss function
if args.loss == 'MSE':
loss = (F.mse_loss, (lambda l : l))
elif args.loss == 'BCE':
loss = (F.binary_cross_entropy, (lambda l : l))
elif args.loss == 'CE':
loss = (F.cross_entropy, (lambda l : torch.max(l, 1)[1]))
else:
raise NameError("=== ERROR: loss " + str(args.loss) + " not supported")
print("\n\n=== Starting model training with %d epochs:\n" % (args.epochs,))
if os.path.exists('./model.pth') and args.cont==True:
checkpoint = torch.load('./model.pth')
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']
print('加载 epoch {} 成功！'.format(start_epoch))
else:
start_epoch = 1
print('无保存模型，将从头开始训练！')
for epoch in range(start_epoch, args.epochs + 1):
# Training
# print(epoch)
train_epoch(args, model, device, train_loader, optimizer, loss, epoch)
# now_time[0] = int(round(time.time() * 1000))
# if now_time[0] - start_time[0] > 2000:
# print('FINIST')
# break
# Compute accuracy on training and testing set
# print("\nSummary of epoch %d:" % (epoch))
# test_epoch(args, model, device, traintest_loader, loss, 'Train',epoch)
# test_epoch(args, model, device, test_loader, loss, 'Test',epoch)
# if args.cont==True:
# state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'epoch': epoch}
# torch.save(state, './model.pth')
def train_epoch(args, model, device, train_loader, optimizer, loss, epoch):
model.train()
if args.freeze_conv_layers:
for i in range(model.conv_to_fc):
for param in model.layers[i].conv.parameters():
param.requires_grad = False
for batch_idx, (data, label) in enumerate(tqdm(train_loader)):
data, label = data.to(device), label.to(device)
if args.regression:
targets = label
else:
targets = torch.zeros(label.shape[0], args.label_features, device=device).scatter_(1, label.unsqueeze(1).long(), 1.0)
optimizer.zero_grad()
output = model(data, targets, epoch)
# loss_val = loss_function(output, targets)
loss_val = loss[0](output, loss[1](targets))
loss_val.backward(retain_graph = True)
optimizer.step()
def writefile(args, file):
filepath = 'output/'+args.codename.split('-')[0]+'/'+args.codename
filetestloss = open(filepath + file, 'a')
return filetestloss
def test_epoch(args, model, device, test_loader, loss, phase,epoch):
model.eval()
test_loss, correct = 0, 0
# if args.dataset != 'tidigits':
len_dataset = len(test_loader.dataset)
# else:
# len_dataset = test_loader[1].shape[0]*test_loader[1].shape[1]
with torch.no_grad():
for data, label in test_loader:
data, label = data.to(device), label.to(device)
if args.regression:
targets = label
else:
targets = torch.zeros(label.shape[0], args.label_features, device=device).scatter_(1,label.unsqueeze(1).long(), 1.0)
output = model(data, None)
test_loss += loss[0](output, loss[1](targets), reduction='sum').item()
pred = output.max(1, keepdim=True)[1]
if not args.regression:
correct += pred.eq(label.view_as(pred).long()).sum().item()
loss = test_loss / len_dataset
if not args.regression:
acc = 100. * correct / len_dataset
print("\t[%5sing set] Loss: %6f, Accuracy: %6.2f%%" % (phase, loss, acc))
filetestloss = writefile(args, '/testloss.txt')
filetestacc = writefile(args, '/testacc.txt')
filetrainloss = writefile(args, '/trainloss.txt')
filetrainacc = writefile(args, '/trainacc.txt')
if phase == 'Train':
writer.add_scalar('train_loss', loss, epoch)
writer.add_scalar('train_acc', acc, epoch)
filetrainloss.write(str(epoch) + ' ' + str(loss) + '\n')
filetrainacc.write(str(epoch) + ' ' + str(acc) + '\n')
if phase == 'Test':
writer.add_scalar('test_loss', loss, epoch)
writer.add_scalar('test_acc', acc, epoch)
filetestloss.write(str(epoch) + ' ' + str(loss) + '\n')
filetestacc.write(str(epoch) + ' ' + str(acc) + '\n')
else:
print("\t[%5sing set] Loss: %6f" % (phase, loss))