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| """ | |
| 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 | |
| from .helpers import build_model_with_cfg | |
| from .layers import create_classifier | |
| 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_() | |
| 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(self, x): | |
| x = self.forward_features(x) | |
| x = self.global_pool(x) | |
| if self.drop_rate: | |
| F.dropout(x, self.drop_rate, training=self.training) | |
| x = self.fc(x) | |
| return x | |
| def _xception(variant, pretrained=False, **kwargs): | |
| return build_model_with_cfg( | |
| Xception, variant, pretrained, default_cfg=default_cfgs[variant], | |
| feature_cfg=dict(feature_cls='hook'), **kwargs) | |
| def xception(pretrained=False, **kwargs): | |
| return _xception('xception', pretrained=pretrained, **kwargs) | |