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pytorch-image-models/timm/models/xception_aligned.py

238 lines
8.6 KiB

"""Pytorch impl of Aligned Xception
This is a correct impl of Aligned Xception (Deeplab) models compatible with TF definition.
Hacked together by Ross Wightman
"""
from collections import OrderedDict
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .helpers import build_model_with_cfg
from .layers import ClassifierHead, ConvBnAct, create_conv2d
from .layers.helpers import tup_triple
from .registry import register_model
__all__ = ['XceptionAligned']
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (10, 10),
'crop_pct': 0.903, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'stem.0', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = dict(
xception41=_cfg(url=''),
xception65=_cfg(url=''),
xception71=_cfg(url=''),
)
class SeparableConv2d(nn.Module):
def __init__(
self, inplanes, planes, kernel_size=3, stride=1, dilation=1, padding='',
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
super(SeparableConv2d, self).__init__()
norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
self.kernel_size = kernel_size
self.dilation = dilation
# depthwise convolution
self.conv_dw = create_conv2d(
inplanes, inplanes, kernel_size, stride=stride,
padding=padding, dilation=dilation, depthwise=True)
self.bn_dw = norm_layer(inplanes, **norm_kwargs)
if act_layer is not None:
self.act_dw = act_layer(inplace=True)
else:
self.act_dw = None
# pointwise convolution
self.conv_pw = create_conv2d(inplanes, planes, kernel_size=1)
self.bn_pw = norm_layer(planes, **norm_kwargs)
if act_layer is not None:
self.act_pw = act_layer(inplace=True)
else:
self.act_pw = None
def forward(self, x):
x = self.conv_dw(x)
x = self.bn_dw(x)
if self.act_dw is not None:
x = self.act_dw(x)
x = self.conv_pw(x)
x = self.bn_pw(x)
if self.act_pw is not None:
x = self.act_pw(x)
return x
class XceptionModule(nn.Module):
def __init__(
self, in_chs, out_chs, stride=1, dilation=1, pad_type='',
start_with_relu=True, no_skip=False, act_layer=nn.ReLU, norm_layer=None, norm_kwargs=None):
super(XceptionModule, self).__init__()
norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
out_chs = tup_triple(out_chs)
self.in_channels = in_chs
self.out_channels = out_chs[-1]
self.no_skip = no_skip
if not no_skip and (self.out_channels != self.in_channels or stride != 1):
self.shortcut = ConvBnAct(
in_chs, self.out_channels, 1, stride=stride,
norm_layer=norm_layer, norm_kwargs=norm_kwargs, act_layer=None)
else:
self.shortcut = None
separable_act_layer = None if start_with_relu else act_layer
self.stack = nn.Sequential()
for i in range(3):
if start_with_relu:
self.stack.add_module(f'act{i + 1}', nn.ReLU(inplace=i > 0))
self.stack.add_module(f'conv{i + 1}', SeparableConv2d(
in_chs, out_chs[i], 3, stride=stride if i == 2 else 1, dilation=dilation, padding=pad_type,
act_layer=separable_act_layer, norm_layer=norm_layer, norm_kwargs=norm_kwargs))
in_chs = out_chs[i]
def forward(self, x):
skip = x
x = self.stack(x)
if self.shortcut is not None:
skip = self.shortcut(skip)
if not self.no_skip:
x = x + skip
return x
class XceptionAligned(nn.Module):
"""Modified Aligned Xception
"""
def __init__(self, block_cfg, num_classes=1000, in_chans=3, output_stride=32,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_rate=0., global_pool='avg'):
super(XceptionAligned, self).__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
assert output_stride in (8, 16, 32)
norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
layer_args = dict(act_layer=act_layer, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
self.stem = nn.Sequential(*[
ConvBnAct(in_chans, 32, kernel_size=3, stride=2, **layer_args),
ConvBnAct(32, 64, kernel_size=3, stride=1, **layer_args)
])
curr_dilation = 1
curr_stride = 2
self.feature_info = []
self.blocks = nn.Sequential()
for i, b in enumerate(block_cfg):
b['dilation'] = curr_dilation
if b['stride'] > 1:
self.feature_info += [dict(
num_chs=tup_triple(b['out_chs'])[-2], reduction=curr_stride, module=f'blocks.{i}.stack.act3')]
next_stride = curr_stride * b['stride']
if next_stride > output_stride:
curr_dilation *= b['stride']
b['stride'] = 1
else:
curr_stride = next_stride
self.blocks.add_module(str(i), XceptionModule(**b, **layer_args))
self.num_features = self.blocks[-1].out_channels
self.feature_info += [dict(
num_chs=self.num_features, reduction=curr_stride, module='blocks.' + str(len(self.blocks) - 1))]
self.head = ClassifierHead(
in_chs=self.num_features, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
def forward_features(self, x):
x = self.stem(x)
x = self.blocks(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _xception(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
XceptionAligned, variant, pretrained, default_cfg=default_cfgs[variant],
feature_cfg=dict(flatten_sequential=True, use_hooks=True), **kwargs)
@register_model
def xception41(pretrained=False, **kwargs):
""" Modified Aligned Xception-41
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 8),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs)
return _xception('xception41', pretrained=pretrained, **model_args)
@register_model
def xception65(pretrained=False, **kwargs):
""" Modified Aligned Xception-65
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs)
return _xception('xception65', pretrained=pretrained, **model_args)
@register_model
def xception71(pretrained=False, **kwargs):
""" Modified Aligned Xception-71
"""
block_cfg = [
# entry flow
dict(in_chs=64, out_chs=128, stride=2),
dict(in_chs=128, out_chs=256, stride=1),
dict(in_chs=256, out_chs=256, stride=2),
dict(in_chs=256, out_chs=728, stride=1),
dict(in_chs=728, out_chs=728, stride=2),
# middle flow
*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
# exit flow
dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
]
model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs)
return _xception('xception71', pretrained=pretrained, **model_args)