# Source: https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py (License: BSD-3-Clause)
# Pretrained: Yes
"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
`Xception: Deep Learning with Depthwise Separable Convolutions <https://arxiv.org/pdf/1610.02357.pdf>`_.
"""
# This weights ported from the Keras implementation. Achieves the following performance on the validation set:
#
# Loss:0.9173 Prec@1:78.892 Prec@5:94.292
#
# REMEMBER to set your image size to 3x299x299 for both test and validation
#
# normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
# std=[0.5, 0.5, 0.5])
#
# The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['Xception', 'xception']
pretrained_settings = {
'xception': {
'imagenet': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
'input_space': 'RGB',
'input_size': [3, 299, 299],
'input_range': [0, 1],
'mean': [0.5, 0.5, 0.5],
'std': [0.5, 0.5, 0.5],
'num_classes': 1000,
'scale': 0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
}
}
}
class SeparableConv2d(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
super(SeparableConv2d,self).__init__()
self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)
self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)
def forward(self,x):
x = self.conv1(x)
x = self.pointwise(x)
return x
class Block(nn.Module):
def __init__(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True):
super(Block, self).__init__()
if out_filters != in_filters or strides!=1:
self.skip = nn.Conv2d(in_filters,out_filters,1,stride=strides, bias=False)
self.skipbn = nn.BatchNorm2d(out_filters)
else:
self.skip=None
rep=[]
filters=in_filters
if grow_first:
rep.append(nn.ReLU(inplace=True))
rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))
rep.append(nn.BatchNorm2d(out_filters))
filters = out_filters
for i in range(reps-1):
rep.append(nn.ReLU(inplace=True))
rep.append(SeparableConv2d(filters,filters,3,stride=1,padding=1,bias=False))
rep.append(nn.BatchNorm2d(filters))
if not grow_first:
rep.append(nn.ReLU(inplace=True))
rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))
rep.append(nn.BatchNorm2d(out_filters))
if not start_with_relu:
rep = rep[1:]
else:
rep[0] = nn.ReLU(inplace=False)
if strides != 1:
rep.append(nn.MaxPool2d(3,strides,1))
self.rep = nn.Sequential(*rep)
def forward(self,inp):
x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x+=skip
return x
[docs]class Xception(nn.Module):
def __init__(self, num_classes=1000):
""" Constructor
Args:
num_classes: number of classes
"""
super(Xception, self).__init__()
self.num_classes = num_classes
self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(32,64,3,bias=False)
self.bn2 = nn.BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=True)
#do relu here
self.block1=Block(64,128,2,2,start_with_relu=False,grow_first=True)
self.block2=Block(128,256,2,2,start_with_relu=True,grow_first=True)
self.block3=Block(256,728,2,2,start_with_relu=True,grow_first=True)
self.block4=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block5=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block6=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block7=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block8=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block9=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block10=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block11=Block(728,728,3,1,start_with_relu=True,grow_first=True)
self.block12=Block(728,1024,2,2,start_with_relu=True,grow_first=False)
self.conv3 = SeparableConv2d(1024,1536,3,1,1)
self.bn3 = nn.BatchNorm2d(1536)
self.relu3 = nn.ReLU(inplace=True)
#do relu here
self.conv4 = SeparableConv2d(1536,2048,3,1,1)
self.bn4 = nn.BatchNorm2d(2048)
self.fc = nn.Linear(2048, num_classes)
# #------- init weights --------
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
# elif isinstance(m, nn.BatchNorm2d):
# m.weight.data.fill_(1)
# m.bias.data.zero_()
# #-----------------------------
def features(self, input_):
x = self.conv1(input_)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
x = self.block7(x)
x = self.block8(x)
x = self.block9(x)
x = self.block10(x)
x = self.block11(x)
x = self.block12(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.bn4(x)
return x
def logits(self, features):
x = nn.ReLU(inplace=True)(features)
x = F.adaptive_avg_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input_):
x = self.features(input_)
x = self.logits(x)
return x
[docs]def xception(pretrained='imagenet'):
"""Pretrained Xception model."""
model = Xception(num_classes=1000)
if pretrained:
settings = pretrained_settings['xception'][pretrained]
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
# TODO: ugly
model.last_linear = model.fc
del model.fc
return model