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Forked from mjdietzx/ResNeXt_pytorch.py
Created October 21, 2017 07:27
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pyt🔥rch implementation of ResNeXt
import torch
from torch.autograd import Variable
import torch.nn as nn
class Bottleneck(nn.Module):
cardinality = 32 # the size of the set of transformations
def __init__(self, nb_channels_in, nb_channels, nb_channels_out, stride=1):
super().__init__()
self.conv1 = nn.Conv2d(nb_channels_in, nb_channels, kernel_size=1)
self.bn1 = nn.BatchNorm2d(nb_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, stride=stride, padding=1, groups=self.cardinality)
self.bn2 = nn.BatchNorm2d(nb_channels)
self.conv3 = nn.Conv2d(nb_channels, nb_channels_out, kernel_size=1)
self.bn3 = nn.BatchNorm2d(nb_channels_out)
if nb_channels_in != nb_channels_out or stride != 1:
self.project = nn.Conv2d(nb_channels_in, nb_channels_out, kernel_size=1, stride=stride)
else:
self.project = None
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if callable(self.project):
residual = self.project(residual)
out += residual
out = self.relu(out)
return out
class ResNeXt(nn.Module):
def __init__(self):
super().__init__()
# conv1
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
# conv2
self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
conv2 = []
for i in range(2):
nb_channels_in = 64 if i == 0 else 256
conv2.append(Bottleneck(nb_channels_in, 128, 256))
self.conv2 = nn.Sequential(*conv2)
# conv3
conv3 = []
for i in range(2):
if i == 0:
nb_channels_in = 256
stride = 2
else:
nb_channels_in = 512
stride = 1
conv3.append(Bottleneck(nb_channels_in, 256, 512, stride=stride))
self.conv3 = nn.Sequential(*conv3)
# conv4
conv4 = []
for i in range(2):
if i == 0:
nb_channels_in = 512
stride = 2
else:
nb_channels_in = 1024
stride = 1
conv4.append(Bottleneck(nb_channels_in, 512, 1024, stride=stride))
self.conv4 = nn.Sequential(*conv4)
# conv5
conv5 = []
for i in range(2):
if i == 0:
nb_channels_in = 1024
stride = 2
else:
nb_channels_in = 2048
stride = 1
conv5.append(Bottleneck(nb_channels_in, 1024, 2048, stride=stride))
self.conv5 = nn.Sequential(*conv5)
self.avg_pool = nn.AvgPool2d(7)
self.fc = nn.Linear(2048, 10)
def forward(self, x):
# conv1
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
# conv2
x = self.max_pool(x)
for block in self.conv2:
x = block(x)
# conv3
for block in self.conv3:
x = block(x)
# conv4
for block in self.conv4:
x = block(x)
# conv5
for block in self.conv5:
x = block(x)
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def main():
model = ResNeXt()
print(model)
inputs = torch.randn(1, 3, 224, 224)
y = model.forward(Variable(inputs))
print(y)
if __name__ == '__main__':
main()
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