""" Xception is adapted from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py 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 """ from __future__ import print_function, division, absolute_import import math import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as model_zoo from torch.nn import init __all__ = ['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, dilation=1): 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=dilation, dilation=dilation, 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=dilation,dilation=dilation,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=dilation,dilation=dilation,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 class Xception(nn.Module): """ Xception optimized for the ImageNet dataset, as specified in https://arxiv.org/pdf/1610.02357.pdf """ def __init__(self, num_classes=1000, replace_stride_with_dilation=None): """ Constructor Args: num_classes: number of classes """ super(Xception, self).__init__() self.num_classes = num_classes self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False, False] if len(replace_stride_with_dilation) != 4: raise ValueError("replace_stride_with_dilation should be None " "or a 4-element tuple, got {}".format(replace_stride_with_dilation)) self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False) # 1 / 2 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=self._make_block(64,128,2,2,start_with_relu=False,grow_first=True, dilate=replace_stride_with_dilation[0]) # 1 / 4 self.block2=self._make_block(128,256,2,2,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[1]) # 1 / 8 self.block3=self._make_block(256,728,2,2,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) # 1 / 16 self.block4=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block5=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block6=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block7=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block8=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block9=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block10=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block11=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) self.block12=self._make_block(728,1024,2,2,start_with_relu=True,grow_first=False, dilate=replace_stride_with_dilation[3]) # 1 / 32 self.conv3 = SeparableConv2d(1024,1536,3,1,1, dilation=self.dilation) self.bn3 = nn.BatchNorm2d(1536) self.relu3 = nn.ReLU(inplace=True) #do relu here self.conv4 = SeparableConv2d(1536,2048,3,1,1, dilation=self.dilation) 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 _make_block(self, in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True, dilate=False): if dilate: self.dilation *= strides strides = 1 return Block(in_filters,out_filters,reps,strides,start_with_relu=start_with_relu,grow_first=grow_first, dilation=self.dilation) 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 def xception(num_classes=1000, pretrained='imagenet', replace_stride_with_dilation=None): model = Xception(num_classes=num_classes, replace_stride_with_dilation=replace_stride_with_dilation) if pretrained: settings = pretrained_settings['xception'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model = Xception(num_classes=num_classes, replace_stride_with_dilation=replace_stride_with_dilation) model.load_state_dict(model_zoo.load_url(settings['url'])) # TODO: ugly model.last_linear = model.fc del model.fc return model