import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
Inception Blocks
class Inception(nn.Module):
def __init__(self, c1, c2, c3, c4, **kwargs):
super(Inception, self).__init__(**kwargs)
self.b1_1 = nn.LazyConv2d(c1, kernel_size=1)
self.b2_1 = nn.LazyConv2d(c2[0], kernel_size=1)
self.b2_2 = nn.LazyConv2d(c2[1], kernel_size=3, padding=1)
self.b3_1 = nn.LazyConv2d(c3[0], kernel_size=1)
self.b3_2 = nn.LazyConv2d(c3[1], kernel_size=5, padding=2)
self.b4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.b4_2 = nn.LazyConv2d(c4, kernel_size=1)
def forward(self, x):
b1 = F.relu(self.b1_1(x))
b2 = F.relu(self.b2_2(F.relu(self.b2_1(x))))
b3 = F.relu(self.b3_2(F.relu(self.b3_1(x))))
b4 = F.relu(self.b4_2(self.b4_1(x)))
return torch.cat((b1, b2, b3, b4), dim=1)
GoogLeNet Model
class GoogleNet(d2l.Classifier):
def b1(self):
return nn.Sequential(
nn.LazyConv2d(64, kernel_size=7, stride=2, padding=3),
nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
@d2l.add_to_class(GoogleNet)
def b2(self):
return nn.Sequential(
nn.LazyConv2d(64, kernel_size=1), nn.ReLU(),
nn.LazyConv2d(192, kernel_size=3, padding=1), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
@d2l.add_to_class(GoogleNet)
def b3(self):
return nn.Sequential(Inception(64, (96, 128), (16, 32), 32),
Inception(128, (128, 192), (32, 96), 64),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
@d2l.add_to_class(GoogleNet)
def b4(self):
return nn.Sequential(Inception(192, (96, 208), (16, 48), 64),
Inception(160, (112, 224), (24, 64), 64),
Inception(128, (128, 256), (24, 64), 64),
Inception(112, (144, 288), (32, 64), 64),
Inception(256, (160, 320), (32, 128), 128),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
@d2l.add_to_class(GoogleNet)
def b5(self):
return nn.Sequential(Inception(256, (160, 320), (32, 128), 128),
Inception(384, (192, 384), (48, 128), 128),
nn.AdaptiveAvgPool2d((1,1)), nn.Flatten())
@d2l.add_to_class(GoogleNet)
def __init__(self, lr=0.1, num_classes=10):
super(GoogleNet, self).__init__()
self.save_hyperparameters()
self.net = nn.Sequential(self.b1(), self.b2(), self.b3(), self.b4(),
self.b5(), nn.LazyLinear(num_classes))
self.net.apply(d2l.init_cnn)
Reduce the input height and width from 224 to 96 to have a reasonable training time on Fashion-MNIST
model = GoogleNet().layer_summary((1, 1, 96, 96))
Sequential output shape: torch.Size([1, 64, 24, 24]) Sequential output shape: torch.Size([1, 192, 12, 12]) Sequential output shape: torch.Size([1, 480, 6, 6]) Sequential output shape: torch.Size([1, 832, 3, 3]) Sequential output shape: torch.Size([1, 1024]) Linear output shape: torch.Size([1, 10])
Training
model = GoogleNet(lr=0.01)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))
model.apply_init([next(iter(data.get_dataloader(True)))[0]], d2l.init_cnn)
trainer.fit(model, data)