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Merge f8ce54893c into c16d65a8a7

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pull/603/merge
Rahul Somani 4 years ago committed by GitHub
parent c16d65a8a7 f8ce54893c
commit 49956c7674
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 42 additions and 8 deletions
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8
timm/models/efficientnet_blocks.py
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@ -218,12 +218,13 @@ class InvertedResidual(nn.Module):
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
conv_kwargs=None, drop_path_rate=0.):
conv_kwargs=None, drop_path_rate=0., aa_layer=None):
super(InvertedResidual, self).__init__()
norm_kwargs = norm_kwargs or {}
conv_kwargs = conv_kwargs or {}
mid_chs = make_divisible(in_chs * exp_ratio)
has_se = se_ratio is not None and se_ratio > 0.
use_aa = aa_layer is not None and stride == 2
self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
self.drop_path_rate = drop_path_rate
@ -234,10 +235,11 @@ class InvertedResidual(nn.Module):
# Depth-wise convolution
self.conv_dw = create_conv2d(
mid_chs, mid_chs, dw_kernel_size, stride=stride, dilation=dilation,
mid_chs, mid_chs, dw_kernel_size, stride=1 if use_aa else stride, dilation=dilation,
padding=pad_type, depthwise=True, **conv_kwargs)
self.bn2 = norm_layer(mid_chs, **norm_kwargs)
self.act2 = act_layer(inplace=True)
self.aa = aa_layer(mid_chs, stride=stride) if use_aa else None
# Squeeze-and-excitation
if has_se:
@ -269,6 +271,8 @@ class InvertedResidual(nn.Module):
x = self.conv_dw(x)
x = self.bn2(x)
x = self.act2(x)
if self.aa is not None:
x = self.aa(x)
# Squeeze-and-excitation
if self.se is not None:

5
timm/models/efficientnet_builder.py
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@ -221,7 +221,7 @@ class EfficientNetBuilder:
"""
def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
output_stride=32, pad_type='', act_layer=None, se_kwargs=None,
norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0., feature_location='',
norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0., aa_layer=None, feature_location='',
verbose=False):
self.channel_multiplier = channel_multiplier
self.channel_divisor = channel_divisor
@ -233,6 +233,7 @@ class EfficientNetBuilder:
self.norm_layer = norm_layer
self.norm_kwargs = norm_kwargs
self.drop_path_rate = drop_path_rate
self.aa_layer = aa_layer
if feature_location == 'depthwise':
# old 'depthwise' mode renamed 'expansion' to match TF impl, old expansion mode didn't make sense
_logger.warning("feature_location=='depthwise' is deprecated, using 'expansion'")
@ -269,6 +270,8 @@ class EfficientNetBuilder:
if ba.get('num_experts', 0) > 0:
block = CondConvResidual(**ba)
else:
# FIXME: `aa_layer` only impl for `InvertedResidual`. Add `CondConvResidual`?
ba['aa_layer'] = self.aa_layer
block = InvertedResidual(**ba)
elif bt == 'ds' or bt == 'dsa':
ba['drop_path_rate'] = drop_path_rate

37
timm/models/mobilenetv3.py
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@ -18,7 +18,7 @@ from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_la
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
from .features import FeatureInfo, FeatureHooks
from .helpers import build_model_with_cfg, default_cfg_for_features
from .layers import SelectAdaptivePool2d, Linear, create_conv2d, get_act_fn, hard_sigmoid
from .layers import SelectAdaptivePool2d, Linear, BlurPool2d, create_conv2d, get_act_fn, hard_sigmoid
from .registry import register_model
__all__ = ['MobileNetV3']
@ -39,6 +39,9 @@ default_cfgs = {
'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_075_aa': _cfg(url=''),
'mobilenetv3_large_100_aa': _cfg(url='https://storage.googleapis.com/cinemanet-models/pretrained/mobilenetv3_large_100_aa_224x224_ema.pth'),
'mobilenetv3_large_100_aa_stem': _cfg(url=''),
'mobilenetv3_large_100_miil': _cfg(
interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_1k_miil_78_0.pth'),
@ -85,7 +88,7 @@ class MobileNetV3(nn.Module):
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'):
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg', aa_layer=None, aa_stem=None):
super(MobileNetV3, self).__init__()
self.num_classes = num_classes
@ -95,13 +98,14 @@ class MobileNetV3(nn.Module):
# Stem
stem_size = round_channels(stem_size, channel_multiplier)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.conv_stem_aa = aa_stem(in_chans) if aa_stem else None
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.act1 = act_layer(inplace=True)
# 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)
norm_layer, norm_kwargs, drop_path_rate, aa_layer, verbose=_DEBUG)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = builder.features
head_chs = builder.in_chs
@ -133,6 +137,8 @@ class MobileNetV3(nn.Module):
def forward_features(self, x):
x = self.conv_stem(x)
if self.conv_stem_aa is not None:
x = self.conv_stem_aa(x)
x = self.bn1(x)
x = self.act1(x)
x = self.blocks(x)
@ -160,7 +166,7 @@ class MobileNetV3Features(nn.Module):
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):
norm_layer=nn.BatchNorm2d, norm_kwargs=None, aa_layer=None,):
super(MobileNetV3Features, self).__init__()
norm_kwargs = norm_kwargs or {}
self.drop_rate = drop_rate
@ -174,7 +180,7 @@ class MobileNetV3Features(nn.Module):
# 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)
norm_layer, norm_kwargs, drop_path_rate, aa_layer, feature_location=feature_location, verbose=_DEBUG)
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}
@ -405,6 +411,27 @@ def mobilenetv3_small_100(pretrained=False, **kwargs):
return model
@register_model
def mobilenetv3_large_075_aa(pretrained=False, aa_layer=BlurPool2d, **kwargs):
""" MobileNet V3 """
model = _gen_mobilenet_v3('mobilenetv3_large_075', 1.0, pretrained=pretrained, aa_layer=aa_layer, **kwargs)
return model
@register_model
def mobilenetv3_large_100_aa(pretrained=False, aa_layer=BlurPool2d, **kwargs):
""" MobileNet V3 w/ Blur Pooling of IR Blocks """
model = _gen_mobilenet_v3('mobilenetv3_large_100_aa', 1.0, pretrained=pretrained, aa_layer=aa_layer, **kwargs)
return model
@register_model
def mobilenetv3_large_100_aa_stem(pretrained=False, aa_layer=BlurPool2d, aa_stem=BlurPool2d, **kwargs):
""" MobileNet V3 w/ Blur Pooling of IR Blocks & Conv Stem """
model = _gen_mobilenet_v3('mobilenetv3_large_100_aa_stem', 1.0, pretrained=pretrained,
aa_layer=aa_layer, aa_stem=aa_stem, **kwargs)
return model
@register_model
def mobilenetv3_rw(pretrained=False, **kwargs):
""" MobileNet V3 """

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