Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'ea58e0bc16'
${ noResults }
pytorch-image-models/timm/models/mobilenetv3.py

451 lines
17 KiB
Raw Blame History

""" MobileNet V3
A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
from .features import FeatureInfo, FeatureHooks
from .helpers import build_model_with_cfg
from .layers import SelectAdaptivePool2d, create_conv2d, get_act_fn, hard_sigmoid
from .registry import register_model
__all__ = ['MobileNetV3']
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (1, 1),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv_stem', 'classifier': 'classifier',
**kwargs
}
default_cfgs = {
'mobilenetv3_large_075': _cfg(url=''),
'mobilenetv3_large_100': _cfg(
interpolation='bicubic',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'),
'mobilenetv3_small_075': _cfg(url=''),
'mobilenetv3_small_100': _cfg(url=''),
'mobilenetv3_rw': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
interpolation='bicubic'),
'tf_mobilenetv3_large_075': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_large_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_large_minimal_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_small_075': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_small_100': _cfg(
url= 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'tf_mobilenetv3_small_minimal_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
}
_DEBUG = False
class MobileNetV3(nn.Module):
""" MobiletNet-V3
Based on my EfficientNet implementation and building blocks, this model utilizes the MobileNet-v3 specific
'efficient head', where global pooling is done before the head convolution without a final batch-norm
layer before the classifier.
Paper: https://arxiv.org/abs/1905.02244
"""
def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
channel_multiplier=1.0, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
super(MobileNetV3, self).__init__()
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
self._in_chs = in_chans
# Stem
stem_size = round_channels(stem_size, channel_multiplier)
self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.act1 = act_layer(inplace=True)
self._in_chs = stem_size
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, 8, None, 32, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG)
self.blocks = nn.Sequential(*builder(self._in_chs, block_args))
self.feature_info = builder.features
self._in_chs = builder.in_chs
# Head + Pooling
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.conv_head = create_conv2d(self._in_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
self.act2 = act_layer(inplace=True)
# Classifier
self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes)
efficientnet_init_weights(self)
def as_sequential(self):
layers = [self.conv_stem, self.bn1, self.act1]
layers.extend(self.blocks)
layers.extend([self.global_pool, self.conv_head, self.act2])
layers.extend([nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
return nn.Sequential(*layers)
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.num_classes = num_classes
if num_classes:
num_features = self.num_features * self.global_pool.feat_mult()
self.classifier = nn.Linear(num_features, num_classes)
else:
self.classifier = nn.Identity()
def forward_features(self, x):
x = self.conv_stem(x)
x = self.bn1(x)
x = self.act1(x)
x = self.blocks(x)
x = self.global_pool(x)
x = self.conv_head(x)
x = self.act2(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = x.flatten(1)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)
class MobileNetV3Features(nn.Module):
""" MobileNetV3 Feature Extractor
A work-in-progress feature extraction module for MobileNet-V3 to use as a backbone for segmentation
and object detection models.
"""
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck',
in_chans=3, stem_size=16, channel_multiplier=1.0, output_stride=32, pad_type='',
act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0., se_kwargs=None,
norm_layer=nn.BatchNorm2d, norm_kwargs=None):
super(MobileNetV3Features, self).__init__()
norm_kwargs = norm_kwargs or {}
self.drop_rate = drop_rate
self._in_chs = in_chans
# Stem
stem_size = round_channels(stem_size, channel_multiplier)
self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.act1 = act_layer(inplace=True)
self._in_chs = stem_size
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, 8, None, output_stride, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG)
self.blocks = nn.Sequential(*builder(self._in_chs, block_args))
self.feature_info = FeatureInfo(builder.features, out_indices)
self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
self._in_chs = builder.in_chs
efficientnet_init_weights(self)
# Register feature extraction hooks with FeatureHooks helper
self.feature_hooks = None
if feature_location != 'bottleneck':
hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
self.feature_hooks = FeatureHooks(hooks, self.named_modules())
def forward(self, x) -> List[torch.Tensor]:
x = self.conv_stem(x)
x = self.bn1(x)
x = self.act1(x)
if self.feature_hooks is None:
features = []
if 0 in self._stage_out_idx:
features.append(x) # add stem out
for i, b in enumerate(self.blocks):
x = b(x)
if i + 1 in self._stage_out_idx:
features.append(x)
return features
else:
self.blocks(x)
out = self.feature_hooks.get_output(x.device)
return list(out.values())
def _create_mnv3(model_kwargs, variant, pretrained=False):
if model_kwargs.pop('features_only', False):
load_strict = False
model_kwargs.pop('num_classes', 0)
model_kwargs.pop('num_features', 0)
model_kwargs.pop('head_conv', None)
model_kwargs.pop('head_bias', None)
model_cls = MobileNetV3Features
else:
load_strict = True
model_cls = MobileNetV3
return build_model_with_cfg(
model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
pretrained_strict=load_strict, **model_kwargs)
def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a MobileNet-V3 model.
Ref impl: ?
Paper: https://arxiv.org/abs/1905.02244
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_nre_noskip'], # relu
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # relu
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25_nre'], # relu
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'], # hard-swish
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
head_bias=False,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_kwargs=dict(gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True, divisor=1),
**kwargs,
)
model = _create_mnv3(model_kwargs, variant, pretrained)
return model
def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a MobileNet-V3 model.
Ref impl: ?
Paper: https://arxiv.org/abs/1905.02244
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
if 'small' in variant:
num_features = 1024
if 'minimal' in variant:
act_layer = resolve_act_layer(kwargs, 'relu')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s2_e1_c16'],
# stage 1, 56x56 in
['ir_r1_k3_s2_e4.5_c24', 'ir_r1_k3_s1_e3.67_c24'],
# stage 2, 28x28 in
['ir_r1_k3_s2_e4_c40', 'ir_r2_k3_s1_e6_c40'],
# stage 3, 14x14 in
['ir_r2_k3_s1_e3_c48'],
# stage 4, 14x14in
['ir_r3_k3_s2_e6_c96'],
# stage 6, 7x7 in
['cn_r1_k1_s1_c576'],
]
else:
act_layer = resolve_act_layer(kwargs, 'hard_swish')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s2_e1_c16_se0.25_nre'], # relu
# stage 1, 56x56 in
['ir_r1_k3_s2_e4.5_c24_nre', 'ir_r1_k3_s1_e3.67_c24_nre'], # relu
# stage 2, 28x28 in
['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r2_k5_s1_e6_c40_se0.25'], # hard-swish
# stage 3, 14x14 in
['ir_r2_k5_s1_e3_c48_se0.25'], # hard-swish
# stage 4, 14x14in
['ir_r3_k5_s2_e6_c96_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c576'], # hard-swish
]
else:
num_features = 1280
if 'minimal' in variant:
act_layer = resolve_act_layer(kwargs, 'relu')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16'],
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24', 'ir_r1_k3_s1_e3_c24'],
# stage 2, 56x56 in
['ir_r3_k3_s2_e3_c40'],
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112'],
# stage 5, 14x14in
['ir_r3_k3_s2_e6_c160'],
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'],
]
else:
act_layer = resolve_act_layer(kwargs, 'hard_swish')
arch_def = [
# stage 0, 112x112 in
['ds_r1_k3_s1_e1_c16_nre'], # relu
# stage 1, 112x112 in
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # relu
# stage 2, 56x56 in
['ir_r3_k5_s2_e3_c40_se0.25_nre'], # relu
# stage 3, 28x28 in
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
# stage 4, 14x14in
['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
# stage 5, 14x14in
['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'], # hard-swish
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=16,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=act_layer,
se_kwargs=dict(act_layer=nn.ReLU, gate_fn=hard_sigmoid, reduce_mid=True, divisor=8),
**kwargs,
)
model = _create_mnv3(model_kwargs, variant, pretrained)
return model
@register_model
def mobilenetv3_large_075(pretrained=False, **kwargs):
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_large_100(pretrained=False, **kwargs):
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_small_075(pretrained=False, **kwargs):
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_small_100(pretrained=False, **kwargs):
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_rw(pretrained=False, **kwargs):
""" MobileNet V3 """
if pretrained:
# pretrained model trained with non-default BN epsilon
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_large_075(pretrained=False, **kwargs):
""" MobileNet V3 """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_large_100(pretrained=False, **kwargs):
""" MobileNet V3 """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_large_minimal_100(pretrained=False, **kwargs):
""" MobileNet V3 """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_small_075(pretrained=False, **kwargs):
""" MobileNet V3 """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_small_100(pretrained=False, **kwargs):
""" MobileNet V3 """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
""" MobileNet V3 """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
return model
Reference in new issue View Git Blame Copy Permalink