TensorBoard 可视化
github
import sys
sys.path.append('..')
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.datasets import CIFAR10
from utils import resnet
from torchvision import transforms as tfs
from datetime import datetime
from tensorboardX import SummaryWriter
def train_tf(x):
im_aug = tfs.Compose([
tfs.Resize(120),
tfs.RandomHorizontalFlip(),
tfs.RandomCrop(96),
tfs.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
tfs.ToTensor(),
tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
x = im_aug(x)
return x
def test_tf(x):
im_aug = tfs.Compose([
tfs.Resize(96),
tfs.ToTensor(),
tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
x = im_aug(x)
return x
train_set = CIFAR10('./data', train=True, transform=train_tf)
train_data = torch.utils.data.DataLoader(train_set, batch_size=256, shuffle=True, num_workers=4)
valid_set = CIFAR10('./data', train=False, transform=test_tf)
valid_data = torch.utils.data.DataLoader(valid_set, batch_size=256, shuffle=False, num_workers=4)
net = resnet(3, 10)
optimizer = torch.optim.SGD(net.parameters(), lr=0.1, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss()
writer = SummaryWriter()
def get_acc(output, label):
total = output.shape[0]
_, pred_label = output.max(1)
num_correct = (pred_label == label).sum().data[0]
return num_correct / total
if torch.cuda.is_available():
net = net.cuda()
prev_time = datetime.now()
for epoch in range(30):
train_loss = 0
train_acc = 0
net = net.train()
for im, label in train_data:
if torch.cuda.is_available():
im = Variable(im.cuda())
label = Variable(label.cuda())
else:
im = Variable(im)
label = Variable(label)
output = net(im)
loss = criterion(output, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.data[0]
train_acc += get_acc(output, label)
cur_time = datetime.now()
h, remainder = divmod((cur_time - prev_time).seconds, 3600)
m, s = divmod(remainder, 60)
time_str = "Time %02d:%02d:%02d" % (h, m, s)
valid_loss = 0
valid_acc = 0
net = net.eval()
for im, label in valid_data:
if torch.cuda.is_available():
im = Variable(im.cuda(), volatile=True)
label = Variable(label.cuda(), volatile=True)
else:
im = Variable(im, volatile=True)
label = Variable(label, volatile=True)
output = net(im)
loss = criterion(output, label)
valid_loss += loss.data[0]
valid_acc += get_acc(output, label)
epoch_str = (
"Epoch %d. Train Loss: %f, Train Acc: %f, Valid Loss: %f, Valid Acc: %f, "
% (epoch, train_loss / len(train_data),
train_acc / len(train_data), valid_loss / len(valid_data),
valid_acc / len(valid_data)))
prev_time = cur_time
writer.add_scalars('Loss', {'train': train_loss / len(train_data),
'valid': valid_loss / len(valid_data)}, epoch)
writer.add_scalars('Acc', {'train': train_acc / len(train_data),
'valid': valid_acc / len(valid_data)}, epoch)
print(epoch_str + time_str)
Epoch 0. Train Loss: 1.877906, Train Acc: 0.315410, Valid Loss: 2.198587, Valid Acc: 0.293164, Time 00:00:26
Epoch 1. Train Loss: 1.398501, Train Acc: 0.498657, Valid Loss: 1.877540, Valid Acc: 0.400098, Time 00:00:27
Epoch 2. Train Loss: 1.141419, Train Acc: 0.597628, Valid Loss: 1.872355, Valid Acc: 0.446777, Time 00:00:27
Epoch 3. Train Loss: 0.980048, Train Acc: 0.658367, Valid Loss: 1.672951, Valid Acc: 0.475391, Time 00:00:27
Epoch 4. Train Loss: 0.871448, Train Acc: 0.695073, Valid Loss: 1.263234, Valid Acc: 0.578613, Time 00:00:28
Epoch 5. Train Loss: 0.794649, Train Acc: 0.723992, Valid Loss: 2.142715, Valid Acc: 0.466699, Time 00:00:27
Epoch 6. Train Loss: 0.736611, Train Acc: 0.741554, Valid Loss: 1.701331, Valid Acc: 0.500391, Time 00:00:27
Epoch 7. Train Loss: 0.695095, Train Acc: 0.756816, Valid Loss: 1.385478, Valid Acc: 0.597656, Time 00:00:28
Epoch 8. Train Loss: 0.652659, Train Acc: 0.773796, Valid Loss: 1.029726, Valid Acc: 0.676465, Time 00:00:27
Epoch 9. Train Loss: 0.623829, Train Acc: 0.784144, Valid Loss: 0.933388, Valid Acc: 0.682520, Time 00:00:27
Epoch 10. Train Loss: 0.581615, Train Acc: 0.798792, Valid Loss: 1.291557, Valid Acc: 0.635938, Time 00:00:27
Epoch 11. Train Loss: 0.559358, Train Acc: 0.805708, Valid Loss: 1.430408, Valid Acc: 0.586426, Time 00:00:28
Epoch 12. Train Loss: 0.534197, Train Acc: 0.816853, Valid Loss: 0.960802, Valid Acc: 0.704785, Time 00:00:27
Epoch 13. Train Loss: 0.512111, Train Acc: 0.822389, Valid Loss: 0.923353, Valid Acc: 0.716602, Time 00:00:27
Epoch 14. Train Loss: 0.494577, Train Acc: 0.828225, Valid Loss: 1.023517, Valid Acc: 0.687207, Time 00:00:27
Epoch 15. Train Loss: 0.473396, Train Acc: 0.835212, Valid Loss: 0.842679, Valid Acc: 0.727930, Time 00:00:27
Epoch 16. Train Loss: 0.459708, Train Acc: 0.840290, Valid Loss: 0.826854, Valid Acc: 0.726953, Time 00:00:28
Epoch 17. Train Loss: 0.433836, Train Acc: 0.847931, Valid Loss: 0.730658, Valid Acc: 0.764258, Time 00:00:27
Epoch 18. Train Loss: 0.422375, Train Acc: 0.854401, Valid Loss: 0.677953, Valid Acc: 0.778125, Time 00:00:27
Epoch 19. Train Loss: 0.410208, Train Acc: 0.857370, Valid Loss: 0.787286, Valid Acc: 0.754102, Time 00:00:27
Epoch 20. Train Loss: 0.395556, Train Acc: 0.862923, Valid Loss: 0.859754, Valid Acc: 0.738965, Time 00:00:27
Epoch 21. Train Loss: 0.382050, Train Acc: 0.866554, Valid Loss: 1.266704, Valid Acc: 0.651660, Time 00:00:27
Epoch 22. Train Loss: 0.368614, Train Acc: 0.871213, Valid Loss: 0.912465, Valid Acc: 0.738672, Time 00:00:27
Epoch 23. Train Loss: 0.358302, Train Acc: 0.873964, Valid Loss: 0.963238, Valid Acc: 0.706055, Time 00:00:27
Epoch 24. Train Loss: 0.347568, Train Acc: 0.879620, Valid Loss: 0.777171, Valid Acc: 0.751855, Time 00:00:27
Epoch 25. Train Loss: 0.339247, Train Acc: 0.882215, Valid Loss: 0.707863, Valid Acc: 0.777734, Time 00:00:27
Epoch 26. Train Loss: 0.329292, Train Acc: 0.885830, Valid Loss: 0.682976, Valid Acc: 0.790527, Time 00:00:27
Epoch 27. Train Loss: 0.313049, Train Acc: 0.890761, Valid Loss: 0.665912, Valid Acc: 0.795410, Time 00:00:27
Epoch 28. Train Loss: 0.305482, Train Acc: 0.891944, Valid Loss: 0.880263, Valid Acc: 0.743848, Time 00:00:27
Epoch 29. Train Loss: 0.301507, Train Acc: 0.895289, Valid Loss: 1.062325, Valid Acc: 0.708398, Time 00:00:27
