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""" |
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Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch) |
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@author: tstandley |
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Adapted by cadene |
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Creates an Xception Model as defined in: |
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Francois Chollet |
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Xception: Deep Learning with Depthwise Separable Convolutions |
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https://arxiv.org/pdf/1610.02357.pdf |
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This weights ported from the Keras implementation. Achieves the following performance on the validation set: |
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Loss:0.9173 Prec@1:78.892 Prec@5:94.292 |
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REMEMBER to set your image size to 3x299x299 for both test and validation |
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normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], |
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std=[0.5, 0.5, 0.5]) |
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The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299 |
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""" |
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import torch.jit |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from .helpers import build_model_with_cfg |
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from .layers import create_classifier |
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from .registry import register_model |
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__all__ = ['Xception'] |
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default_cfgs = { |
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'xception': { |
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth', |
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'input_size': (3, 299, 299), |
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'pool_size': (10, 10), |
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'crop_pct': 0.8975, |
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'interpolation': 'bicubic', |
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'mean': (0.5, 0.5, 0.5), |
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'std': (0.5, 0.5, 0.5), |
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'num_classes': 1000, |
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'first_conv': 'conv1', |
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'classifier': 'fc' |
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} |
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} |
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class SeparableConv2d(nn.Module): |
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def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1): |
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super(SeparableConv2d, self).__init__() |
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self.conv1 = nn.Conv2d( |
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in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=False) |
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self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=False) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.pointwise(x) |
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return x |
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class Block(nn.Module): |
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def __init__(self, in_channels, out_channels, reps, strides=1, start_with_relu=True, grow_first=True): |
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super(Block, self).__init__() |
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if out_channels != in_channels or strides != 1: |
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self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False) |
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self.skipbn = nn.BatchNorm2d(out_channels) |
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else: |
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self.skip = None |
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rep = [] |
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for i in range(reps): |
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if grow_first: |
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inc = in_channels if i == 0 else out_channels |
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outc = out_channels |
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else: |
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inc = in_channels |
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outc = in_channels if i < (reps - 1) else out_channels |
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rep.append(nn.ReLU(inplace=True)) |
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rep.append(SeparableConv2d(inc, outc, 3, stride=1, padding=1)) |
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rep.append(nn.BatchNorm2d(outc)) |
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if not start_with_relu: |
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rep = rep[1:] |
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else: |
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rep[0] = nn.ReLU(inplace=False) |
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if strides != 1: |
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rep.append(nn.MaxPool2d(3, strides, 1)) |
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self.rep = nn.Sequential(*rep) |
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def forward(self, inp): |
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x = self.rep(inp) |
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if self.skip is not None: |
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skip = self.skip(inp) |
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skip = self.skipbn(skip) |
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else: |
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skip = inp |
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x += skip |
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return x |
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class Xception(nn.Module): |
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""" |
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Xception optimized for the ImageNet dataset, as specified in |
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https://arxiv.org/pdf/1610.02357.pdf |
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""" |
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def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'): |
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""" Constructor |
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Args: |
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num_classes: number of classes |
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""" |
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super(Xception, self).__init__() |
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self.drop_rate = drop_rate |
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self.global_pool = global_pool |
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self.num_classes = num_classes |
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self.num_features = 2048 |
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self.conv1 = nn.Conv2d(in_chans, 32, 3, 2, 0, bias=False) |
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self.bn1 = nn.BatchNorm2d(32) |
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self.act1 = nn.ReLU(inplace=True) |
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self.conv2 = nn.Conv2d(32, 64, 3, bias=False) |
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self.bn2 = nn.BatchNorm2d(64) |
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self.act2 = nn.ReLU(inplace=True) |
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self.block1 = Block(64, 128, 2, 2, start_with_relu=False) |
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self.block2 = Block(128, 256, 2, 2) |
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self.block3 = Block(256, 728, 2, 2) |
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self.block4 = Block(728, 728, 3, 1) |
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self.block5 = Block(728, 728, 3, 1) |
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self.block6 = Block(728, 728, 3, 1) |
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self.block7 = Block(728, 728, 3, 1) |
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self.block8 = Block(728, 728, 3, 1) |
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self.block9 = Block(728, 728, 3, 1) |
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self.block10 = Block(728, 728, 3, 1) |
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self.block11 = Block(728, 728, 3, 1) |
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self.block12 = Block(728, 1024, 2, 2, grow_first=False) |
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self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1) |
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self.bn3 = nn.BatchNorm2d(1536) |
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self.act3 = nn.ReLU(inplace=True) |
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self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1) |
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self.bn4 = nn.BatchNorm2d(self.num_features) |
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self.act4 = nn.ReLU(inplace=True) |
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self.feature_info = [ |
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dict(num_chs=64, reduction=2, module='act2'), |
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dict(num_chs=128, reduction=4, module='block2.rep.0'), |
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dict(num_chs=256, reduction=8, module='block3.rep.0'), |
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dict(num_chs=728, reduction=16, module='block12.rep.0'), |
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dict(num_chs=2048, reduction=32, module='act4'), |
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] |
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self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, nn.BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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@torch.jit.ignore |
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def group_matcher(self, coarse=False): |
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return dict( |
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stem=r'^conv[12]|bn[12]', |
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blocks=[ |
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(r'^block(\d+)', None), |
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(r'^conv[34]|bn[34]', (99,)), |
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], |
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) |
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@torch.jit.ignore |
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def set_grad_checkpointing(self, enable=True): |
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assert not enable, "gradient checkpointing not supported" |
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@torch.jit.ignore |
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def get_classifier(self): |
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return self.fc |
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def reset_classifier(self, num_classes, global_pool='avg'): |
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self.num_classes = num_classes |
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self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) |
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def forward_features(self, x): |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.act1(x) |
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x = self.conv2(x) |
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x = self.bn2(x) |
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x = self.act2(x) |
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x = self.block1(x) |
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x = self.block2(x) |
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x = self.block3(x) |
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x = self.block4(x) |
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x = self.block5(x) |
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x = self.block6(x) |
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x = self.block7(x) |
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x = self.block8(x) |
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x = self.block9(x) |
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x = self.block10(x) |
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x = self.block11(x) |
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x = self.block12(x) |
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x = self.conv3(x) |
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x = self.bn3(x) |
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x = self.act3(x) |
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x = self.conv4(x) |
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x = self.bn4(x) |
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x = self.act4(x) |
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return x |
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def forward_head(self, x, pre_logits: bool = False): |
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x = self.global_pool(x) |
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if self.drop_rate: |
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F.dropout(x, self.drop_rate, training=self.training) |
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return x if pre_logits else self.fc(x) |
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def forward(self, x): |
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x = self.forward_features(x) |
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x = self.forward_head(x) |
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return x |
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def _xception(variant, pretrained=False, **kwargs): |
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return build_model_with_cfg( |
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Xception, variant, pretrained, |
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feature_cfg=dict(feature_cls='hook'), |
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**kwargs) |
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@register_model |
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def xception(pretrained=False, **kwargs): |
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return _xception('xception', pretrained=pretrained, **kwargs) |
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