In [25]:
dataiter = iter(testloader)
images, labels = next(dataiter)
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
GroundTruth: cat ship ship plane
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import visdom
vis = visdom.Visdom()
vis.close(env="main")
Setting up a new session...
''
def loss_tracker(loss_plot, loss_value, num):
'''num, loss_value, are Tensor'''
vis.line(X=num,
Y=loss_value,
win = loss_plot,
update='append'
)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.manual_seed(777)
if device =='cuda':
torch.cuda.manual_seed_all(777)
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./cifar10', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=512,
shuffle=True, num_workers=0)
testset = torchvision.datasets.CIFAR10(root='./cifar10', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=0)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
Files already downloaded and verified Files already downloaded and verified
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# get some random training images
dataiter = iter(trainloader)
images, labels = next(dataiter)
vis.images(images/2 + 0.5)
# show images
#imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
truck car car cat
import torchvision.models.vgg as vgg
cfg = [32,32,'M', 64,64,128,128,128,'M',256,256,256,512,512,512,'M'] #13 + 3 =vgg16
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, init_weights=True):
super(VGG, self).__init__()
self.features = features
#self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Linear(512 * 4 * 4, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
if init_weights:
self._initialize_weights()
def forward(self, x):
x = self.features(x)
#x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
vgg16= VGG(vgg.make_layers(cfg),10,True).to(device)
a=torch.Tensor(1,3,32,32).to(device)
out = vgg16(a)
print(out)
tensor([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward0>)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(vgg16.parameters(), lr = 0.005,momentum=0.9)
lr_sche = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)
loss_plt = vis.line(Y=torch.Tensor(1).zero_(),opts=dict(title='loss_tracker', legend=['loss'], showlegend=True))
print(len(trainloader))
epochs = 50
for epoch in range(epochs): # loop over the dataset multiple times
running_loss = 0.0
lr_sche.step()
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = vgg16(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 30 == 29: # print every 30 mini-batches
loss_tracker(loss_plt, torch.Tensor([running_loss/30]), torch.Tensor([i + epoch*len(trainloader) ]))
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 30))
running_loss = 0.0
print('Finished Training')
98
/opt/conda/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
[1, 30] loss: 2.302 [1, 60] loss: 2.299 [1, 90] loss: 2.293 [2, 30] loss: 2.245 [2, 60] loss: 2.137 [2, 90] loss: 2.018 [3, 30] loss: 1.894 [3, 60] loss: 1.815 [3, 90] loss: 1.739 [4, 30] loss: 1.680 [4, 60] loss: 1.628 [4, 90] loss: 1.605 [5, 30] loss: 1.565 [5, 60] loss: 1.510 [5, 90] loss: 1.476 [6, 30] loss: 1.426 [6, 60] loss: 1.398 [6, 90] loss: 1.394 [7, 30] loss: 1.331 [7, 60] loss: 1.287 [7, 90] loss: 1.269 [8, 30] loss: 1.249 [8, 60] loss: 1.213 [8, 90] loss: 1.184 [9, 30] loss: 1.159 [9, 60] loss: 1.124 [9, 90] loss: 1.129 [10, 30] loss: 1.074 [10, 60] loss: 1.047 [10, 90] loss: 1.057 [11, 30] loss: 1.008 [11, 60] loss: 0.997 [11, 90] loss: 0.986 [12, 30] loss: 0.933 [12, 60] loss: 0.965 [12, 90] loss: 0.923 [13, 30] loss: 0.913 [13, 60] loss: 0.866 [13, 90] loss: 0.899 [14, 30] loss: 0.865 [14, 60] loss: 0.843 [14, 90] loss: 0.853 [15, 30] loss: 0.790 [15, 60] loss: 0.792 [15, 90] loss: 0.825 [16, 30] loss: 0.762 [16, 60] loss: 0.758 [16, 90] loss: 0.742 [17, 30] loss: 0.727 [17, 60] loss: 0.706 [17, 90] loss: 0.726 [18, 30] loss: 0.709 [18, 60] loss: 0.692 [18, 90] loss: 0.696 [19, 30] loss: 0.679 [19, 60] loss: 0.657 [19, 90] loss: 0.665 [20, 30] loss: 0.601 [20, 60] loss: 0.621 [20, 90] loss: 0.635 [21, 30] loss: 0.569 [21, 60] loss: 0.579 [21, 90] loss: 0.561 [22, 30] loss: 0.533 [22, 60] loss: 0.533 [22, 90] loss: 0.559 [23, 30] loss: 0.515 [23, 60] loss: 0.520 [23, 90] loss: 0.521 [24, 30] loss: 0.462 [24, 60] loss: 0.483 [24, 90] loss: 0.469 [25, 30] loss: 0.420 [25, 60] loss: 0.426 [25, 90] loss: 0.456 [26, 30] loss: 0.397 [26, 60] loss: 0.391 [26, 90] loss: 0.401 [27, 30] loss: 0.365 [27, 60] loss: 0.356 [27, 90] loss: 0.373 [28, 30] loss: 0.330 [28, 60] loss: 0.350 [28, 90] loss: 0.348 [29, 30] loss: 0.309 [29, 60] loss: 0.296 [29, 90] loss: 0.322 [30, 30] loss: 0.268 [30, 60] loss: 0.255 [30, 90] loss: 0.276 [31, 30] loss: 0.223 [31, 60] loss: 0.224 [31, 90] loss: 0.239 [32, 30] loss: 0.194 [32, 60] loss: 0.213 [32, 90] loss: 0.218 [33, 30] loss: 0.179 [33, 60] loss: 0.180 [33, 90] loss: 0.202 [34, 30] loss: 0.166 [34, 60] loss: 0.169 [34, 90] loss: 0.172 [35, 30] loss: 0.147 [35, 60] loss: 0.139 [35, 90] loss: 0.149 [36, 30] loss: 0.117 [36, 60] loss: 0.129 [36, 90] loss: 0.124 [37, 30] loss: 0.105 [37, 60] loss: 0.110 [37, 90] loss: 0.110 [38, 30] loss: 0.104 [38, 60] loss: 0.094 [38, 90] loss: 0.108 [39, 30] loss: 0.092 [39, 60] loss: 0.086 [39, 90] loss: 0.091 [40, 30] loss: 0.076 [40, 60] loss: 0.076 [40, 90] loss: 0.065 [41, 30] loss: 0.062 [41, 60] loss: 0.065 [41, 90] loss: 0.069 [42, 30] loss: 0.065 [42, 60] loss: 0.057 [42, 90] loss: 0.067 [43, 30] loss: 0.055 [43, 60] loss: 0.056 [43, 90] loss: 0.051 [44, 30] loss: 0.048 [44, 60] loss: 0.048 [44, 90] loss: 0.053 [45, 30] loss: 0.042 [45, 60] loss: 0.037 [45, 90] loss: 0.044 [46, 30] loss: 0.033 [46, 60] loss: 0.039 [46, 90] loss: 0.042 [47, 30] loss: 0.031 [47, 60] loss: 0.034 [47, 90] loss: 0.040 [48, 30] loss: 0.034 [48, 60] loss: 0.030 [48, 90] loss: 0.030 [49, 30] loss: 0.025 [49, 60] loss: 0.030 [49, 90] loss: 0.029 [50, 30] loss: 0.026 [50, 60] loss: 0.025 [50, 90] loss: 0.029 Finished Training
dataiter = iter(testloader)
images, labels = next(dataiter)
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
GroundTruth: cat ship ship plane
outputs = vgg16(images.to(device))
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(4)))
Predicted: cat ship ship plane
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images = images.to(device)
labels = labels.to(device)
outputs = vgg16(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
Accuracy of the network on the 10000 test images: 76 %