|
|
| """ |
| 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 |
| } |
| } |
| } |
|
|
|
|
| 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: |
| |
| |
| 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) |
| 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) |
| |
|
|
| self.block1=self._make_block(64,128,2,2,start_with_relu=False,grow_first=True, dilate=replace_stride_with_dilation[0]) |
| self.block2=self._make_block(128,256,2,2,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[1]) |
| self.block3=self._make_block(256,728,2,2,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) |
|
|
| 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]) |
|
|
| self.conv3 = SeparableConv2d(1024,1536,3,1,1, dilation=self.dilation) |
| self.bn3 = nn.BatchNorm2d(1536) |
| self.relu3 = nn.ReLU(inplace=True) |
|
|
| |
| self.conv4 = SeparableConv2d(1536,2048,3,1,1, dilation=self.dilation) |
| self.bn4 = nn.BatchNorm2d(2048) |
|
|
| self.fc = nn.Linear(2048, num_classes) |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| 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'])) |
|
|
| |
| model.last_linear = model.fc |
| del model.fc |
| return model |
