""" 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.jit import torch.nn as nn import torch.nn.functional as F from timm.layers import create_classifier from ._builder import build_model_with_cfg from ._registry import register_model __all__ = ['Xception'] default_cfgs = { 'xception': { 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth', 'input_size': (3, 299, 299), 'pool_size': (10, 10), 'crop_pct': 0.8975, 'interpolation': 'bicubic', 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), 'num_classes': 1000, 'first_conv': 'conv1', 'classifier': 'fc' # 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): super(SeparableConv2d, self).__init__() self.conv1 = nn.Conv2d( in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=False) self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=False) def forward(self, x): x = self.conv1(x) x = self.pointwise(x) return x class Block(nn.Module): def __init__(self, in_channels, out_channels, reps, strides=1, start_with_relu=True, grow_first=True): super(Block, self).__init__() if out_channels != in_channels or strides != 1: self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False) self.skipbn = nn.BatchNorm2d(out_channels) else: self.skip = None rep = [] for i in range(reps): if grow_first: inc = in_channels if i == 0 else out_channels outc = out_channels else: inc = in_channels outc = in_channels if i < (reps - 1) else out_channels rep.append(nn.ReLU(inplace=True)) rep.append(SeparableConv2d(inc, outc, 3, stride=1, padding=1)) rep.append(nn.BatchNorm2d(outc)) 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, in_chans=3, drop_rate=0., global_pool='avg'): """ Constructor Args: num_classes: number of classes """ super(Xception, self).__init__() self.drop_rate = drop_rate self.global_pool = global_pool self.num_classes = num_classes self.num_features = 2048 self.conv1 = nn.Conv2d(in_chans, 32, 3, 2, 0, bias=False) self.bn1 = nn.BatchNorm2d(32) self.act1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(32, 64, 3, bias=False) self.bn2 = nn.BatchNorm2d(64) self.act2 = nn.ReLU(inplace=True) self.block1 = Block(64, 128, 2, 2, start_with_relu=False) self.block2 = Block(128, 256, 2, 2) self.block3 = Block(256, 728, 2, 2) self.block4 = Block(728, 728, 3, 1) self.block5 = Block(728, 728, 3, 1) self.block6 = Block(728, 728, 3, 1) self.block7 = Block(728, 728, 3, 1) self.block8 = Block(728, 728, 3, 1) self.block9 = Block(728, 728, 3, 1) self.block10 = Block(728, 728, 3, 1) self.block11 = Block(728, 728, 3, 1) self.block12 = Block(728, 1024, 2, 2, grow_first=False) self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1) self.bn3 = nn.BatchNorm2d(1536) self.act3 = nn.ReLU(inplace=True) self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1) self.bn4 = nn.BatchNorm2d(self.num_features) self.act4 = nn.ReLU(inplace=True) self.feature_info = [ dict(num_chs=64, reduction=2, module='act2'), dict(num_chs=128, reduction=4, module='block2.rep.0'), dict(num_chs=256, reduction=8, module='block3.rep.0'), dict(num_chs=728, reduction=16, module='block12.rep.0'), dict(num_chs=2048, reduction=32, module='act4'), ] self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) # #------- init weights -------- for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() @torch.jit.ignore def group_matcher(self, coarse=False): return dict( stem=r'^conv[12]|bn[12]', blocks=[ (r'^block(\d+)', None), (r'^conv[34]|bn[34]', (99,)), ], ) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): assert not enable, "gradient checkpointing not supported" @torch.jit.ignore def get_classifier(self): return self.fc def reset_classifier(self, num_classes, global_pool='avg'): self.num_classes = num_classes self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) def forward_features(self, x): x = self.conv1(x) x = self.bn1(x) x = self.act1(x) x = self.conv2(x) x = self.bn2(x) x = self.act2(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.act3(x) x = self.conv4(x) x = self.bn4(x) x = self.act4(x) return x def forward_head(self, x, pre_logits: bool = False): x = self.global_pool(x) if self.drop_rate: F.dropout(x, self.drop_rate, training=self.training) return x if pre_logits else self.fc(x) def forward(self, x): x = self.forward_features(x) x = self.forward_head(x) return x def _xception(variant, pretrained=False, **kwargs): return build_model_with_cfg( Xception, variant, pretrained, feature_cfg=dict(feature_cls='hook'), **kwargs) @register_model def xception(pretrained=False, **kwargs): return _xception('xception', pretrained=pretrained, **kwargs)