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250 lines
7.7 KiB
250 lines
7.7 KiB
"""
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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 timm.layers import create_classifier
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from ._builder import build_model_with_cfg
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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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# 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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}
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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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# #------- init weights --------
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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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