pytorch06
CIFAR10数据集
Lenet5
import torch
from torch import nn
from torch.nn import functional as F
class Lenet5(nn.Module):
"""
for cifar10 dataset.
"""
def __init__(self):
super(Lenet5, self).__init__()
self.conv_unit = nn.Sequential(
# x: [b, 3, 32, 32] => [b, 16, ]
nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=0),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
#
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=0),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
#
)
# flatten
# fc unit
self.fc_unit = nn.Sequential(
nn.Linear(32*5*5, 32),
nn.ReLU(),
# nn.Linear(120, 84),
# nn.ReLU(),
nn.Linear(32, 10)
)
# [b, 3, 32, 32]
tmp = torch.randn(2, 3, 32, 32)
out = self.conv_unit(tmp)
# [b, 16, 5, 5]
print('conv out:', out.shape)
# # use Cross Entropy Loss
# self.criteon = nn.CrossEntropyLoss()
def forward(self, x):
"""
:param x: [b, 3, 32, 32]
:return:
"""
batchsz = x.size(0)
# [b, 3, 32, 32] => [b, 16, 5, 5]
x = self.conv_unit(x)
# [b, 16, 5, 5] => [b, 16*5*5]
x = x.view(batchsz, 32*5*5)
# [b, 16*5*5] => [b, 10]
logits = self.fc_unit(x)
# # [b, 10]
# pred = F.softmax(logits, dim=1)
# loss = self.criteon(logits, y)
return logits
def main():
net = Lenet5()
tmp = torch.randn(2, 3, 32, 32)
out = net(tmp)
print('lenet out:', out.shape)
if __name__ == '__main__':
main()resnet
import torch
from torch import nn
from torch.nn import functional as F
class ResBlk(nn.Module):
"""
resnet block
"""
def __init__(self, ch_in, ch_out, stride=1):
"""
:param ch_in:
:param ch_out:
"""
super(ResBlk, self).__init__()
# we add stride support for resbok, which is distinct from tutorials.
self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(ch_out)
self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(ch_out)
self.extra = nn.Sequential()
if ch_out != ch_in:
# [b, ch_in, h, w] => [b, ch_out, h, w]
self.extra = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
nn.BatchNorm2d(ch_out)
)
def forward(self, x):
"""
:param x: [b, ch, h, w]
:return:
"""
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
# short cut.
# extra module: [b, ch_in, h, w] => [b, ch_out, h, w]
# element-wise add:
out = self.extra(x) + out
out = F.relu(out)
return out
class ResNet18(nn.Module):
def __init__(self):
super(ResNet18, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
nn.BatchNorm2d(64)
)
# followed 4 blocks
# [b, 64, h, w] => [b, 128, h ,w]
self.blk1 = ResBlk(64, 128, stride=2)
# [b, 128, h, w] => [b, 256, h, w]
self.blk2 = ResBlk(128, 256, stride=2)
# # [b, 256, h, w] => [b, 512, h, w]
self.blk3 = ResBlk(256, 512, stride=2)
# # [b, 512, h, w] => [b, 1024, h, w]
self.blk4 = ResBlk(512, 512, stride=2)
self.outlayer = nn.Linear(512*1*1, 10)
def forward(self, x):
"""
:param x:
:return:
"""
x = F.relu(self.conv1(x))
# [b, 64, h, w] => [b, 1024, h, w]
x = self.blk1(x)
x = self.blk2(x)
x = self.blk3(x)
x = self.blk4(x)
# print('after conv:', x.shape) #[b, 512, 2, 2]
# [b, 512, h, w] => [b, 512, 1, 1]
x = F.adaptive_avg_pool2d(x, [1, 1])
# print('after pool:', x.shape)
x = x.view(x.size(0), -1)
x = self.outlayer(x)
return x
def main():
blk = ResBlk(64, 128, stride=4)
tmp = torch.randn(2, 64, 32, 32)
out = blk(tmp)
print('block:', out.shape)
x = torch.randn(2, 3, 32, 32)
model = ResNet18()
out = model(x)
print('resnet:', out.shape)
if __name__ == '__main__':
main()main
import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from torch import nn, optim
from lenet5 import Lenet5
from resnet import ResNet18
def main():
batchsz = 128
cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]), download=True)
cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)
cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]), download=True)
cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)
x, label = iter(cifar_train).next()
print('x:', x.shape, 'label:', label.shape)
device = torch.device('cuda')
# model = Lenet5().to(device)
model = ResNet18().to(device)
criteon = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
print(model)
for epoch in range(1000):
model.train()
for batchidx, (x, label) in enumerate(cifar_train):
# [b, 3, 32, 32]
# [b]
x, label = x.to(device), label.to(device)
logits = model(x)
# logits: [b, 10]
# label: [b]
# loss: tensor scalar
loss = criteon(logits, label)
# backprop
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(epoch, 'loss:', loss.item())
model.eval()
with torch.no_grad():
# test
total_correct = 0
total_num = 0
for x, label in cifar_test:
# [b, 3, 32, 32]
# [b]
x, label = x.to(device), label.to(device)
# [b, 10]
logits = model(x)
# [b]
pred = logits.argmax(dim=1)
# [b] vs [b] => scalar tensor
correct = torch.eq(pred, label).float().sum().item()
total_correct += correct
total_num += x.size(0)
# print(correct)
acc = total_correct / total_num
print(epoch, 'test acc:', acc)
if __name__ == '__main__':
main()
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