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

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""" ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channel variant hacked together by Ross Wightman
Copyright 2020 Ross Wightman
"""
3 years ago
import torch
import torch.nn as nn
from functools import partial
from math import ceil
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import ClassifierHead, create_act_layer, ConvNormAct, DropPath, make_divisible, SEModule
from .registry import register_model
from .efficientnet_builder import efficientnet_init_weights
def _cfg(url=''):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
}
default_cfgs = dict(
rexnet_100=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth'),
rexnet_130=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth'),
rexnet_150=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth'),
rexnet_200=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth'),
rexnetr_100=_cfg(
url=''),
rexnetr_130=_cfg(
url=''),
rexnetr_150=_cfg(
url=''),
rexnetr_200=_cfg(
url=''),
)
SEWithNorm = partial(SEModule, norm_layer=nn.BatchNorm2d)
class LinearBottleneck(nn.Module):
def __init__(self, in_chs, out_chs, stride, exp_ratio=1.0, se_ratio=0., ch_div=1,
act_layer='swish', dw_act_layer='relu6', drop_path=None):
super(LinearBottleneck, self).__init__()
self.use_shortcut = stride == 1 and in_chs <= out_chs
self.in_channels = in_chs
self.out_channels = out_chs
if exp_ratio != 1.:
dw_chs = make_divisible(round(in_chs * exp_ratio), divisor=ch_div)
self.conv_exp = ConvNormAct(in_chs, dw_chs, act_layer=act_layer)
else:
dw_chs = in_chs
self.conv_exp = None
self.conv_dw = ConvNormAct(dw_chs, dw_chs, 3, stride=stride, groups=dw_chs, apply_act=False)
if se_ratio > 0:
self.se = SEWithNorm(dw_chs, rd_channels=make_divisible(int(dw_chs * se_ratio), ch_div))
else:
self.se = None
self.act_dw = create_act_layer(dw_act_layer)
self.conv_pwl = ConvNormAct(dw_chs, out_chs, 1, apply_act=False)
self.drop_path = drop_path
def feat_channels(self, exp=False):
return self.conv_dw.out_channels if exp else self.out_channels
def forward(self, x):
shortcut = x
if self.conv_exp is not None:
x = self.conv_exp(x)
x = self.conv_dw(x)
if self.se is not None:
x = self.se(x)
x = self.act_dw(x)
x = self.conv_pwl(x)
if self.use_shortcut:
if self.drop_path is not None:
x = self.drop_path(x)
3 years ago
x = torch.cat([x[:, 0:self.in_channels] + shortcut, x[:, self.in_channels:]], dim=1)
return x
def _block_cfg(width_mult=1.0, depth_mult=1.0, initial_chs=16, final_chs=180, se_ratio=0., ch_div=1):
layers = [1, 2, 2, 3, 3, 5]
strides = [1, 2, 2, 2, 1, 2]
layers = [ceil(element * depth_mult) for element in layers]
strides = sum([[element] + [1] * (layers[idx] - 1) for idx, element in enumerate(strides)], [])
exp_ratios = [1] * layers[0] + [6] * sum(layers[1:])
depth = sum(layers[:]) * 3
base_chs = initial_chs / width_mult if width_mult < 1.0 else initial_chs
# The following channel configuration is a simple instance to make each layer become an expand layer.
out_chs_list = []
for i in range(depth // 3):
out_chs_list.append(make_divisible(round(base_chs * width_mult), divisor=ch_div))
base_chs += final_chs / (depth // 3 * 1.0)
se_ratios = [0.] * (layers[0] + layers[1]) + [se_ratio] * sum(layers[2:])
return list(zip(out_chs_list, exp_ratios, strides, se_ratios))
def _build_blocks(
block_cfg, prev_chs, width_mult, ch_div=1, act_layer='swish', dw_act_layer='relu6', drop_path_rate=0.):
feat_chs = [prev_chs]
feature_info = []
curr_stride = 2
features = []
num_blocks = len(block_cfg)
for block_idx, (chs, exp_ratio, stride, se_ratio) in enumerate(block_cfg):
if stride > 1:
fname = 'stem' if block_idx == 0 else f'features.{block_idx - 1}'
feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=fname)]
curr_stride *= stride
block_dpr = drop_path_rate * block_idx / (num_blocks - 1) # stochastic depth linear decay rule
drop_path = DropPath(block_dpr) if block_dpr > 0. else None
features.append(LinearBottleneck(
in_chs=prev_chs, out_chs=chs, exp_ratio=exp_ratio, stride=stride, se_ratio=se_ratio,
ch_div=ch_div, act_layer=act_layer, dw_act_layer=dw_act_layer, drop_path=drop_path))
prev_chs = chs
feat_chs += [features[-1].feat_channels()]
pen_chs = make_divisible(1280 * width_mult, divisor=ch_div)
feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=f'features.{len(features) - 1}')]
features.append(ConvNormAct(prev_chs, pen_chs, act_layer=act_layer))
return features, feature_info
class ReXNetV1(nn.Module):
def __init__(self, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32,
initial_chs=16, final_chs=180, width_mult=1.0, depth_mult=1.0, se_ratio=1/12.,
ch_div=1, act_layer='swish', dw_act_layer='relu6', drop_rate=0.2, drop_path_rate=0.):
super(ReXNetV1, self).__init__()
self.drop_rate = drop_rate
self.num_classes = num_classes
assert output_stride == 32 # FIXME support dilation
stem_base_chs = 32 / width_mult if width_mult < 1.0 else 32
stem_chs = make_divisible(round(stem_base_chs * width_mult), divisor=ch_div)
self.stem = ConvNormAct(in_chans, stem_chs, 3, stride=2, act_layer=act_layer)
block_cfg = _block_cfg(width_mult, depth_mult, initial_chs, final_chs, se_ratio, ch_div)
features, self.feature_info = _build_blocks(
block_cfg, stem_chs, width_mult, ch_div, act_layer, dw_act_layer, drop_path_rate)
self.num_features = features[-1].out_channels
self.features = nn.Sequential(*features)
self.head = ClassifierHead(self.num_features, num_classes, global_pool, drop_rate)
efficientnet_init_weights(self)
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.features(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _create_rexnet(variant, pretrained, **kwargs):
feature_cfg = dict(flatten_sequential=True)
return build_model_with_cfg(
ReXNetV1, variant, pretrained,
default_cfg=default_cfgs[variant],
feature_cfg=feature_cfg,
**kwargs)
@register_model
def rexnet_100(pretrained=False, **kwargs):
"""ReXNet V1 1.0x"""
return _create_rexnet('rexnet_100', pretrained, **kwargs)
@register_model
def rexnet_130(pretrained=False, **kwargs):
"""ReXNet V1 1.3x"""
return _create_rexnet('rexnet_130', pretrained, width_mult=1.3, **kwargs)
@register_model
def rexnet_150(pretrained=False, **kwargs):
"""ReXNet V1 1.5x"""
return _create_rexnet('rexnet_150', pretrained, width_mult=1.5, **kwargs)
@register_model
def rexnet_200(pretrained=False, **kwargs):
"""ReXNet V1 2.0x"""
return _create_rexnet('rexnet_200', pretrained, width_mult=2.0, **kwargs)
@register_model
def rexnetr_100(pretrained=False, **kwargs):
"""ReXNet V1 1.0x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_100', pretrained, ch_div=8, **kwargs)
@register_model
def rexnetr_130(pretrained=False, **kwargs):
"""ReXNet V1 1.3x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_130', pretrained, width_mult=1.3, ch_div=8, **kwargs)
@register_model
def rexnetr_150(pretrained=False, **kwargs):
"""ReXNet V1 1.5x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_150', pretrained, width_mult=1.5, ch_div=8, **kwargs)
@register_model
def rexnetr_200(pretrained=False, **kwargs):
"""ReXNet V1 2.0x w/ rounded (mod 8) channels"""
return _create_rexnet('rexnetr_200', pretrained, width_mult=2.0, ch_div=8, **kwargs)