"""
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)