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GoogLeNet.py
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import os
import sys
sys.path.append('/mnt/hdd1/wearly/kaggle/shopee/pytorch-image-models')
import timm
import torch
import torch.nn as nn
from config import CFG
import math
class GoogLeNet(nn.Module):
def __init__(self, aux_logits=True, num_classes=CFG.n_classes, init_weights=True):
super(GoogLeNet, self).__init__()
assert aux_logits == True or aux_logits == False
self.aux_logits = aux_logits
self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(3, 2, 1)
self.conv2 = conv_block(64, 192, kernel_size=3, stride=1, padding=1)
self.maxpool2 = nn.MaxPool2d(3, 2, 1)
self.inception3a = Inception_block(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception_block(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(3, 2, 1)
self.inception4a = Inception_block(480, 192, 96, 208, 16, 48, 64)
# auxiliary classifier
self.inception4b = Inception_block(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception_block(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception_block(512, 112, 144, 288, 32, 64, 64)
# auxiliary classifier
self.inception4e = Inception_block(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(3, 2, 1)
self.inception5a = Inception_block(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception_block(832, 384, 192, 384, 48, 128, 128)
self.avgpool = nn.AvgPool2d(7, 1)
self.dropout = nn.Dropout(p=0.4)
self.fc1 = nn.Linear(1024, num_classes)
if self.aux_logits:
self.aux1 = InceptionAux(512, num_classes)
self.aux2 = InceptionAux(528, num_classes)
else:
self.aux1 = self.aux2 = None
# weight initialization
if init_weights:
self._initialize_weights()
def forward(self, x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.maxpool2(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.maxpool3(x)
x = self.inception4a(x)
# auxiliary classifier 1
if self.aux_logits and self.training:
aux1 = self.aux1(x)
x = self.inception4b(x)
x = self.inception4c(x)
x = self.inception4d(x)
# auxiliary classifier 2
if self.aux_logits and self.training:
aux2 = self.aux2(x)
x = self.inception4e(x)
x = self.maxpool4(x)
x = self.inception5a(x)
x = self.inception5b(x)
x = self.avgpool(x)
x = x.view(x.shape[0], -1)
x = self.dropout(x)
x = self.fc1(x)
if self.aux_logits and self.training:
return x, aux1, aux2
else:
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)
class conv_block(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(conv_block, self).__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(in_channels, out_channels, **kwargs),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
)
def forward(self, x):
return self.conv_layer(x)
class Inception_block(nn.Module):
def __init__(self, in_channels, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool):
super(Inception_block, self).__init__()
self.branch1 = conv_block(in_channels, out_1x1, kernel_size=1)
self.branch2 = nn.Sequential(
conv_block(in_channels, red_3x3, kernel_size=1),
conv_block(red_3x3, out_3x3, kernel_size=3, padding=1),
)
self.branch3 = nn.Sequential(
conv_block(in_channels, red_5x5, kernel_size=1),
conv_block(red_5x5, out_5x5, kernel_size=5, padding=2),
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
conv_block(in_channels, out_1x1pool, kernel_size=1)
)
def forward(self, x):
x = torch.cat([self.branch1(x), self.branch2(x), self.branch3(x), self.branch4(x)], 1)
return x
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes):
super(InceptionAux, self).__init__()
self.conv = nn.Sequential(
nn.AvgPool2d(kernel_size=5, stride=3),
conv_block(in_channels, 128, kernel_size=1),
)
self.fc = nn.Sequential(
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Dropout(),
nn.Linear(1024, num_classes),
)
def forward(self,x):
x = self.conv(x)
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x