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pytorch-image-models/timm/models/mobilenetv3.py

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""" 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 2019, Ross Wightman
"""
from functools import partial
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import SelectAdaptivePool2d, Linear, create_conv2d, get_norm_act_layer
from ._builder import build_model_with_cfg, pretrained_cfg_for_features
from ._efficientnet_blocks import SqueezeExcite
from ._efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, \
round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from ._features import FeatureInfo, FeatureHooks
from ._manipulate import checkpoint_seq
from ._registry import register_model
__all__ = ['MobileNetV3', 'MobileNetV3Features']
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'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_large_100_miil': _cfg(
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_1k_miil_78_0-66471c13.pth'),
'mobilenetv3_large_100_miil_in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_in21k_miil-d71cc17b.pth',
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221),
'mobilenetv3_small_050': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth',
interpolation='bicubic'),
'mobilenetv3_small_075': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_075_lambc-384766db.pth',
interpolation='bicubic'),
'mobilenetv3_small_100': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_100_lamb-266a294c.pth',
interpolation='bicubic'),
'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),
'fbnetv3_b': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_b_224-ead5d2a1.pth',
test_input_size=(3, 256, 256), crop_pct=0.95),
'fbnetv3_d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_d_224-c98bce42.pth',
test_input_size=(3, 256, 256), crop_pct=0.95),
'fbnetv3_g': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_g_240-0b1df83b.pth',
input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95, pool_size=(8, 8)),
"lcnet_035": _cfg(),
"lcnet_050": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth',
interpolation='bicubic',
),
"lcnet_075": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth',
interpolation='bicubic',
),
"lcnet_100": _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth',
interpolation='bicubic',
),
"lcnet_150": _cfg(),
}
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: `Searching for MobileNetV3` - https://arxiv.org/abs/1905.02244
Other architectures utilizing MobileNet-V3 efficient head that are supported by this impl include:
* HardCoRe-NAS - https://arxiv.org/abs/2102.11646 (defn in hardcorenas.py uses this class)
* FBNet-V3 - https://arxiv.org/abs/2006.02049
* LCNet - https://arxiv.org/abs/2109.15099
"""
def __init__(
self, block_args, num_classes=1000, in_chans=3, stem_size=16, fix_stem=False, num_features=1280,
head_bias=True, pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True,
round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'):
super(MobileNetV3, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
se_layer = se_layer or SqueezeExcite
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
self.grad_checkpointing = False
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_act_layer(stem_size, inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=32, pad_type=pad_type, round_chs_fn=round_chs_fn, se_from_exp=se_from_exp,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = builder.features
head_chs = builder.in_chs
# Head + Pooling
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
num_pooled_chs = head_chs * self.global_pool.feat_mult()
self.conv_head = create_conv2d(num_pooled_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
self.act2 = act_layer(inplace=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
efficientnet_init_weights(self)
def as_sequential(self):
layers = [self.conv_stem, self.bn1]
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)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^conv_stem|bn1',
blocks=r'^blocks\.(\d+)' if coarse else r'^blocks\.(\d+)\.(\d+)'
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
# cannot meaningfully change pooling of efficient head after creation
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.conv_stem(x)
x = self.bn1(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x, flatten=True)
else:
x = self.blocks(x)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.conv_head(x)
x = self.act2(x)
if pre_logits:
return x.flatten(1)
else:
x = self.flatten(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return 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, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels,
se_from_exp=True, act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
super(MobileNetV3Features, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn, se_from_exp=se_from_exp,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer,
drop_path_rate=drop_path_rate, feature_location=feature_location)
self.blocks = nn.Sequential(*builder(stem_size, 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}
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(variant, pretrained=False, **kwargs):
features_only = False
model_cls = MobileNetV3
kwargs_filter = None
if kwargs.pop('features_only', False):
features_only = True
kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'head_bias', 'global_pool')
model_cls = MobileNetV3Features
model = build_model_with_cfg(
model_cls, variant, pretrained,
pretrained_strict=not features_only,
kwargs_filter=kwargs_filter,
**kwargs)
if features_only:
model.default_cfg = pretrained_cfg_for_features(model.default_cfg)
return model
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,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid'),
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
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
]
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=16,
fix_stem=channel_multiplier < 0.75,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
se_layer=se_layer,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_fbnetv3(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
""" FBNetV3
Paper: `FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining`
- https://arxiv.org/abs/2006.02049
FIXME untested, this is a preliminary impl of some FBNet-V3 variants.
"""
vl = variant.split('_')[-1]
if vl in ('a', 'b'):
stem_size = 16
arch_def = [
['ds_r2_k3_s1_e1_c16'],
['ir_r1_k5_s2_e4_c24', 'ir_r3_k5_s1_e2_c24'],
['ir_r1_k5_s2_e5_c40_se0.25', 'ir_r4_k5_s1_e3_c40_se0.25'],
['ir_r1_k5_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
['ir_r1_k3_s1_e5_c120_se0.25', 'ir_r5_k5_s1_e3_c120_se0.25'],
['ir_r1_k3_s2_e6_c184_se0.25', 'ir_r5_k5_s1_e4_c184_se0.25', 'ir_r1_k5_s1_e6_c224_se0.25'],
['cn_r1_k1_s1_c1344'],
]
elif vl == 'd':
stem_size = 24
arch_def = [
['ds_r2_k3_s1_e1_c16'],
['ir_r1_k3_s2_e5_c24', 'ir_r5_k3_s1_e2_c24'],
['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r4_k3_s1_e3_c40_se0.25'],
['ir_r1_k3_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
['ir_r1_k3_s1_e5_c128_se0.25', 'ir_r6_k5_s1_e3_c128_se0.25'],
['ir_r1_k3_s2_e6_c208_se0.25', 'ir_r5_k5_s1_e5_c208_se0.25', 'ir_r1_k5_s1_e6_c240_se0.25'],
['cn_r1_k1_s1_c1440'],
]
elif vl == 'g':
stem_size = 32
arch_def = [
['ds_r3_k3_s1_e1_c24'],
['ir_r1_k5_s2_e4_c40', 'ir_r4_k5_s1_e2_c40'],
['ir_r1_k5_s2_e4_c56_se0.25', 'ir_r4_k5_s1_e3_c56_se0.25'],
['ir_r1_k5_s2_e5_c104', 'ir_r4_k3_s1_e3_c104'],
['ir_r1_k3_s1_e5_c160_se0.25', 'ir_r8_k5_s1_e3_c160_se0.25'],
['ir_r1_k3_s2_e6_c264_se0.25', 'ir_r6_k5_s1_e5_c264_se0.25', 'ir_r2_k5_s1_e6_c288_se0.25'],
['cn_r1_k1_s1_c1728'],
]
else:
raise NotImplemented
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.95)
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', rd_round_fn=round_chs_fn)
act_layer = resolve_act_layer(kwargs, 'hard_swish')
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=1984,
head_bias=False,
stem_size=stem_size,
round_chs_fn=round_chs_fn,
se_from_exp=False,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
se_layer=se_layer,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
""" LCNet
Essentially a MobileNet-V3 crossed with a MobileNet-V1
Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arxiv.org/abs/2109.15099
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['dsa_r1_k3_s1_c32'],
# stage 1, 112x112 in
['dsa_r2_k3_s2_c64'],
# stage 2, 56x56 in
['dsa_r2_k3_s2_c128'],
# stage 3, 28x28 in
['dsa_r1_k3_s2_c256', 'dsa_r1_k5_s1_c256'],
# stage 4, 14x14in
['dsa_r4_k5_s1_c256'],
# stage 5, 14x14in
['dsa_r2_k5_s2_c512_se0.25'],
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU),
num_features=1280,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
return model
def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
""" LCNet
Essentially a MobileNet-V3 crossed with a MobileNet-V1
Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arxiv.org/abs/2109.15099
Args:
channel_multiplier: multiplier to number of channels per layer.
"""
arch_def = [
# stage 0, 112x112 in
['dsa_r1_k3_s1_c32'],
# stage 1, 112x112 in
['dsa_r2_k3_s2_c64'],
# stage 2, 56x56 in
['dsa_r2_k3_s2_c128'],
# stage 3, 28x28 in
['dsa_r1_k3_s2_c256', 'dsa_r1_k5_s1_c256'],
# stage 4, 14x14in
['dsa_r4_k5_s1_c256'],
# stage 5, 14x14in
['dsa_r2_k5_s2_c512_se0.25'],
# 7x7
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU),
num_features=1280,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
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_large_100_miil(pretrained=False, **kwargs):
""" MobileNet V3
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
"""
model = _gen_mobilenet_v3('mobilenetv3_large_100_miil', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_large_100_miil_in21k(pretrained=False, **kwargs):
""" MobileNet V3, 21k pretraining
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
"""
model = _gen_mobilenet_v3('mobilenetv3_large_100_miil_in21k', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv3_small_050(pretrained=False, **kwargs):
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_small_050', 0.50, 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
@register_model
def fbnetv3_b(pretrained=False, **kwargs):
""" FBNetV3-B """
model = _gen_fbnetv3('fbnetv3_b', pretrained=pretrained, **kwargs)
return model
@register_model
def fbnetv3_d(pretrained=False, **kwargs):
""" FBNetV3-D """
model = _gen_fbnetv3('fbnetv3_d', pretrained=pretrained, **kwargs)
return model
@register_model
def fbnetv3_g(pretrained=False, **kwargs):
""" FBNetV3-G """
model = _gen_fbnetv3('fbnetv3_g', pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_035(pretrained=False, **kwargs):
""" PP-LCNet 0.35"""
model = _gen_lcnet('lcnet_035', 0.35, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_050(pretrained=False, **kwargs):
""" PP-LCNet 0.5"""
model = _gen_lcnet('lcnet_050', 0.5, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_075(pretrained=False, **kwargs):
""" PP-LCNet 1.0"""
model = _gen_lcnet('lcnet_075', 0.75, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_100(pretrained=False, **kwargs):
""" PP-LCNet 1.0"""
model = _gen_lcnet('lcnet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def lcnet_150(pretrained=False, **kwargs):
""" PP-LCNet 1.5"""
model = _gen_lcnet('lcnet_150', 1.5, pretrained=pretrained, **kwargs)
return model
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