""" 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 """ #------------------------------------------------------------------------------ # Libraries #------------------------------------------------------------------------------ import torch, math import torch.nn as nn from torch.nn import init import torch.nn.functional as F import torch.utils.model_zoo as model_zoo model_urls = { 'xception':'https://www.dropbox.com/s/1hplpzet9d7dv29/xception-c0a72b38.pth.tar?dl=1' } #------------------------------------------------------------------------------ # Depthwise Separable Convolution #------------------------------------------------------------------------------ class DWSConv2d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False): super(DWSConv2d, 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 #------------------------------------------------------------------------------ # Xception block #------------------------------------------------------------------------------ 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 self.relu = nn.ReLU(inplace=True) rep=[] filters=in_filters if grow_first: rep.append(self.relu) rep.append(DWSConv2d(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(self.relu) rep.append(DWSConv2d(filters,filters,3,stride=1,padding=1,bias=False)) rep.append(nn.BatchNorm2d(filters)) if not grow_first: rep.append(self.relu) rep.append(DWSConv2d(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 #------------------------------------------------------------------------------ # Xception #------------------------------------------------------------------------------ class Xception(nn.Module): def __init__(self, num_classes=1000): 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.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(32,64,3,bias=False) self.bn2 = nn.BatchNorm2d(64) #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 = DWSConv2d(1024,1536,3,1,1) self.bn3 = nn.BatchNorm2d(1536) #do relu here self.conv4 = DWSConv2d(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 forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(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.relu(x) x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = F.adaptive_avg_pool2d(x, (1, 1)) x = x.view(x.size(0), -1) x = self.fc(x) return x #------------------------------------------------------------------------------ # Instance #------------------------------------------------------------------------------ def xception(pretrained=False,**kwargs): model = Xception(**kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xception'])) return model