mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Add more experimental darknet and 'cs2' darknet variants (different cross stage setup, closer to newer YOLO backbones) for train trials.
This commit is contained in:
Ross Wightman
parent
a050fde5cd
commit
82c311d082
@ -16,6 +16,7 @@ from functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg, named_apply, MATCH_PREV_GROUP
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@ -46,11 +47,21 @@ default_cfgs = {
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnext50_ra_224-648b4713.pth',
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input_size=(3, 224, 224), pool_size=(7, 7), crop_pct=0.875 # FIXME I trained this at 224x224, not 256 like ref impl
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),
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'cspresnext50_iabn': _cfg(url=''),
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'cspdarknet53': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth'),
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'cspdarknet53_iabn': _cfg(url=''),
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'darknet17': _cfg(url=''),
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'darknet21': _cfg(url=''),
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'darknet53': _cfg(url=''),
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'cs2darknet_m': _cfg(
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url=''),
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'cs2darknet_l': _cfg(
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url=''),
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'cs2darknet_f_m': _cfg(
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url=''),
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'cs2darknet_f_l': _cfg(
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url=''),
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}
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@ -116,6 +127,37 @@ model_cfgs = dict(
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down_growth=True,
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)
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),
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darknet17=dict(
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stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
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stage=dict(
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out_chs=(64, 128, 256, 512, 1024),
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depth=(1,) * 5,
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stride=(2,) * 5,
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bottle_ratio=(0.5,) * 5,
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block_ratio=(1.,) * 5,
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)
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),
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darknet21=dict(
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stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
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stage=dict(
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out_chs=(64, 128, 256, 512, 1024),
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depth=(1, 1, 1, 2, 2),
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stride=(2,) * 5,
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bottle_ratio=(0.5,) * 5,
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block_ratio=(1.,) * 5,
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)
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),
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sedarknet21=dict(
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stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
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stage=dict(
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out_chs=(64, 128, 256, 512, 1024),
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depth=(1, 1, 1, 2, 2),
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stride=(2,) * 5,
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bottle_ratio=(0.5,) * 5,
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block_ratio=(1.,) * 5,
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attn_layer=('se',) * 5,
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)
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),
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darknet53=dict(
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stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
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stage=dict(
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@ -125,13 +167,81 @@ model_cfgs = dict(
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bottle_ratio=(0.5,) * 5,
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block_ratio=(1.,) * 5,
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)
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),
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darknetaa53=dict(
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stem=dict(out_chs=32, kernel_size=3, stride=1, pool=''),
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stage=dict(
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out_chs=(64, 128, 256, 512, 1024),
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depth=(1, 2, 8, 8, 4),
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stride=(2,) * 5,
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bottle_ratio=(0.5,) * 5,
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block_ratio=(1.,) * 5,
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avg_down=True,
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),
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),
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cs2darknet_m=dict(
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stem=dict(out_chs=(24, 48), kernel_size=3, stride=2, pool=''),
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stage=dict(
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out_chs=(96, 192, 384, 768),
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depth=(2, 4, 6, 2),
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stride=(2,) * 4,
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bottle_ratio=(1.,) * 4,
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block_ratio=(0.5,) * 4,
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avg_down=False,
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),
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),
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cs2darknet_f_m=dict(
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stem=dict(out_chs=48, kernel_size=6, stride=2, padding=2, pool=''),
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stage=dict(
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out_chs=(96, 192, 384, 768),
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depth=(2, 4, 6, 2),
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stride=(2,) * 4,
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bottle_ratio=(1.,) * 4,
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block_ratio=(0.5,) * 4,
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avg_down=False,
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),
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),
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cs2darknet_l=dict(
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stem=dict(out_chs=(32, 64), kernel_size=3, stride=2, pool=''),
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stage=dict(
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out_chs=(128, 256, 512, 1024),
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depth=(3, 6, 9, 3),
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stride=(2,) * 4,
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bottle_ratio=(1.,) * 4,
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block_ratio=(0.5,) * 4,
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avg_down=False,
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),
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),
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cs2darknet_f_l=dict(
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stem=dict(out_chs=64, kernel_size=6, stride=2, padding=2, pool=''),
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stage=dict(
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out_chs=(128, 256, 512, 1024),
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depth=(3, 6, 9, 3),
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stride=(2,) * 4,
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bottle_ratio=(1.,) * 4,
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block_ratio=(0.5,) * 4,
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avg_down=False,
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),
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)
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)
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def create_stem(
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in_chans=3, out_chs=32, kernel_size=3, stride=2, pool='',
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
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in_chans=3,
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out_chs=32,
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kernel_size=3,
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stride=2,
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pool='',
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padding='',
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act_layer=nn.ReLU,
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norm_layer=nn.BatchNorm2d,
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aa_layer=None
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):
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stem = nn.Sequential()
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if not isinstance(out_chs, (tuple, list)):
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out_chs = [out_chs]
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@ -140,8 +250,12 @@ def create_stem(
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for i, out_c in enumerate(out_chs):
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conv_name = f'conv{i + 1}'
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stem.add_module(conv_name, ConvNormAct(
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in_c, out_c, kernel_size, stride=stride if i == 0 else 1,
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act_layer=act_layer, norm_layer=norm_layer))
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in_c, out_c, kernel_size,
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stride=stride if i == 0 else 1,
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padding=padding if i == 0 else '',
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act_layer=act_layer,
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norm_layer=norm_layer
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))
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in_c = out_c
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last_conv = conv_name
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if pool:
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@ -158,9 +272,20 @@ class ResBottleneck(nn.Module):
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"""
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def __init__(
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self, in_chs, out_chs, dilation=1, bottle_ratio=0.25, groups=1,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_last=False,
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attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
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self,
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in_chs,
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out_chs,
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dilation=1,
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bottle_ratio=0.25,
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groups=1,
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act_layer=nn.ReLU,
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norm_layer=nn.BatchNorm2d,
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attn_last=False,
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attn_layer=None,
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aa_layer=None,
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drop_block=None,
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drop_path=None
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):
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super(ResBottleneck, self).__init__()
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mid_chs = int(round(out_chs * bottle_ratio))
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ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
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@ -173,7 +298,7 @@ class ResBottleneck(nn.Module):
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self.conv3 = ConvNormAct(mid_chs, out_chs, kernel_size=1, apply_act=False, **ckwargs)
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self.attn3 = create_attn(attn_layer, channels=out_chs) if attn_last else None
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self.drop_path = drop_path
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self.act3 = act_layer(inplace=True)
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self.act3 = act_layer()
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def zero_init_last(self):
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nn.init.zeros_(self.conv3.bn.weight)
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@ -201,9 +326,19 @@ class DarkBlock(nn.Module):
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"""
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def __init__(
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self, in_chs, out_chs, dilation=1, bottle_ratio=0.5, groups=1,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None,
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drop_block=None, drop_path=None):
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self,
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in_chs,
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out_chs,
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dilation=1,
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bottle_ratio=0.5,
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groups=1,
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act_layer=nn.ReLU,
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norm_layer=nn.BatchNorm2d,
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attn_layer=None,
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aa_layer=None,
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drop_block=None,
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drop_path=None
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):
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super(DarkBlock, self).__init__()
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mid_chs = int(round(out_chs * bottle_ratio))
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ckwargs = dict(act_layer=act_layer, norm_layer=norm_layer)
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@ -211,7 +346,7 @@ class DarkBlock(nn.Module):
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self.conv2 = ConvNormActAa(
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mid_chs, out_chs, kernel_size=3, dilation=dilation, groups=groups,
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aa_layer=aa_layer, drop_layer=drop_block, **ckwargs)
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self.attn = create_attn(attn_layer, channels=out_chs)
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self.attn = create_attn(attn_layer, channels=out_chs, act_layer=act_layer)
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self.drop_path = drop_path
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def zero_init_last(self):
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@ -232,23 +367,44 @@ class DarkBlock(nn.Module):
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class CrossStage(nn.Module):
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"""Cross Stage."""
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def __init__(
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self, in_chs, out_chs, stride, dilation, depth, block_ratio=1., bottle_ratio=1., exp_ratio=1.,
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groups=1, first_dilation=None, down_growth=False, cross_linear=False, block_dpr=None,
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block_fn=ResBottleneck, **block_kwargs):
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self,
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in_chs,
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out_chs,
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stride,
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dilation,
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depth,
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block_ratio=1.,
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bottle_ratio=1.,
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exp_ratio=1.,
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groups=1,
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first_dilation=None,
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avg_down=False,
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down_growth=False,
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cross_linear=False,
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block_dpr=None,
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block_fn=ResBottleneck,
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**block_kwargs
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):
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super(CrossStage, self).__init__()
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first_dilation = first_dilation or dilation
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down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
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exp_chs = int(round(out_chs * exp_ratio))
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self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
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block_out_chs = int(round(out_chs * block_ratio))
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conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
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if stride != 1 or first_dilation != dilation:
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self.conv_down = ConvNormActAa(
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in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
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aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
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if avg_down:
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self.conv_down = nn.Sequential(
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nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(), # FIXME dilation handling
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ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
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)
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else:
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self.conv_down = ConvNormActAa(
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in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
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aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
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prev_chs = down_chs
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else:
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self.conv_down = None
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self.conv_down = nn.Identity()
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prev_chs = in_chs
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# FIXME this 1x1 expansion is pushed down into the cross and block paths in the darknet cfgs. Also,
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@ -269,30 +425,115 @@ class CrossStage(nn.Module):
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self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
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def forward(self, x):
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if self.conv_down is not None:
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x = self.conv_down(x)
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x = self.conv_down(x)
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x = self.conv_exp(x)
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split = x.shape[1] // 2
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xs, xb = x[:, :split], x[:, split:]
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xs, xb = x.split(self.exp_chs // 2, dim=1)
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xb = self.blocks(xb)
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xb = self.conv_transition_b(xb).contiguous()
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out = self.conv_transition(torch.cat([xs, xb], dim=1))
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return out
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class CrossStage2(nn.Module):
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"""Cross Stage v2.
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Similar to CrossStage, but with one transition conv for the concat output.
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"""
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def __init__(
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self,
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in_chs,
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out_chs,
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stride,
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dilation,
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depth,
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block_ratio=1.,
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bottle_ratio=1.,
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exp_ratio=1.,
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groups=1,
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first_dilation=None,
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avg_down=False,
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down_growth=False,
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cross_linear=False,
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block_dpr=None,
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block_fn=ResBottleneck,
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**block_kwargs
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):
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super(CrossStage2, self).__init__()
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first_dilation = first_dilation or dilation
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down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
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self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
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block_out_chs = int(round(out_chs * block_ratio))
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conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
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if stride != 1 or first_dilation != dilation:
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if avg_down:
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self.conv_down = nn.Sequential(
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nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(), # FIXME dilation handling
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ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
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)
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else:
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self.conv_down = ConvNormActAa(
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in_chs, down_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
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aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
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prev_chs = down_chs
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else:
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self.conv_down = None
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prev_chs = in_chs
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# expansion conv
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self.conv_exp = ConvNormAct(prev_chs, exp_chs, kernel_size=1, apply_act=not cross_linear, **conv_kwargs)
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prev_chs = exp_chs // 2 # expanded output is split in 2 for blocks and cross stage
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self.blocks = nn.Sequential()
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for i in range(depth):
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drop_path = DropPath(block_dpr[i]) if block_dpr and block_dpr[i] else None
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self.blocks.add_module(str(i), block_fn(
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prev_chs, block_out_chs, dilation, bottle_ratio, groups, drop_path=drop_path, **block_kwargs))
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prev_chs = block_out_chs
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# transition convs
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self.conv_transition = ConvNormAct(exp_chs, out_chs, kernel_size=1, **conv_kwargs)
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def forward(self, x):
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x = self.conv_down(x)
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x = self.conv_exp(x)
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x1, x2 = x.split(self.exp_chs // 2, dim=1)
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x1 = self.blocks(x1)
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out = self.conv_transition(torch.cat([x1, x2], dim=1))
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return out
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class DarkStage(nn.Module):
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"""DarkNet stage."""
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def __init__(
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self, in_chs, out_chs, stride, dilation, depth, block_ratio=1., bottle_ratio=1., groups=1,
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first_dilation=None, block_fn=ResBottleneck, block_dpr=None, **block_kwargs):
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self,
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in_chs,
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out_chs,
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stride,
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dilation,
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depth,
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block_ratio=1.,
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bottle_ratio=1.,
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groups=1,
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first_dilation=None,
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avg_down=False,
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block_fn=ResBottleneck,
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block_dpr=None,
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**block_kwargs
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):
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super(DarkStage, self).__init__()
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first_dilation = first_dilation or dilation
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conv_kwargs = dict(act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'))
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self.conv_down = ConvNormActAa(
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in_chs, out_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
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act_layer=block_kwargs.get('act_layer'), norm_layer=block_kwargs.get('norm_layer'),
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aa_layer=block_kwargs.get('aa_layer', None))
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if avg_down:
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self.conv_down = nn.Sequential(
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nn.AvgPool2d(3, 2, 1) if stride == 2 else nn.Identity(), # FIXME dilation handling
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ConvNormActAa(in_chs, out_chs, kernel_size=1, stride=1, groups=groups, **conv_kwargs)
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)
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else:
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self.conv_down = ConvNormActAa(
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in_chs, out_chs, kernel_size=3, stride=stride, dilation=first_dilation, groups=groups,
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aa_layer=block_kwargs.get('aa_layer', None), **conv_kwargs)
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prev_chs = out_chs
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block_out_chs = int(round(out_chs * block_ratio))
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@ -318,6 +559,8 @@ def _cfg_to_stage_args(cfg, curr_stride=2, output_stride=32, drop_path_rate=0.):
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cfg['down_growth'] = (cfg['down_growth'],) * num_stages
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if 'cross_linear' in cfg and not isinstance(cfg['cross_linear'], (list, tuple)):
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cfg['cross_linear'] = (cfg['cross_linear'],) * num_stages
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if 'avg_down' in cfg and not isinstance(cfg['avg_down'], (list, tuple)):
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cfg['avg_down'] = (cfg['avg_down'],) * num_stages
|
||||
cfg['block_dpr'] = [None] * num_stages if not drop_path_rate else \
|
||||
[x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg['depth'])).split(cfg['depth'])]
|
||||
stage_strides = []
|
||||
@ -352,9 +595,20 @@ class CspNet(nn.Module):
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, cfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg', drop_rate=0.,
|
||||
act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_path_rate=0.,
|
||||
zero_init_last=True, stage_fn=CrossStage, block_fn=ResBottleneck):
|
||||
self,
|
||||
cfg,
|
||||
in_chans=3,
|
||||
num_classes=1000,
|
||||
output_stride=32,
|
||||
global_pool='avg',
|
||||
act_layer=nn.LeakyReLU,
|
||||
norm_layer=nn.BatchNorm2d,
|
||||
aa_layer=None,
|
||||
drop_rate=0.,
|
||||
drop_path_rate=0.,
|
||||
zero_init_last=True,
|
||||
stage_fn=CrossStage,
|
||||
block_fn=ResBottleneck):
|
||||
super().__init__()
|
||||
self.num_classes = num_classes
|
||||
self.drop_rate = drop_rate
|
||||
@ -427,23 +681,22 @@ class CspNet(nn.Module):
|
||||
def _init_weights(module, name, zero_init_last=False):
|
||||
if isinstance(module, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(module, nn.BatchNorm2d):
|
||||
nn.init.ones_(module.weight)
|
||||
nn.init.zeros_(module.bias)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
elif isinstance(module, nn.Linear):
|
||||
nn.init.normal_(module.weight, mean=0.0, std=0.01)
|
||||
nn.init.zeros_(module.bias)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
elif zero_init_last and hasattr(module, 'zero_init_last'):
|
||||
module.zero_init_last()
|
||||
|
||||
|
||||
def _create_cspnet(variant, pretrained=False, **kwargs):
|
||||
cfg_variant = variant.split('_')[0]
|
||||
# NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
|
||||
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4))
|
||||
return build_model_with_cfg(
|
||||
CspNet, variant, pretrained,
|
||||
model_cfg=model_cfgs[cfg_variant],
|
||||
model_cfg=model_cfgs[variant],
|
||||
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
|
||||
**kwargs)
|
||||
|
||||
@ -468,23 +721,56 @@ def cspresnext50(pretrained=False, **kwargs):
|
||||
return _create_cspnet('cspresnext50', pretrained=pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cspresnext50_iabn(pretrained=False, **kwargs):
|
||||
norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
|
||||
return _create_cspnet('cspresnext50_iabn', pretrained=pretrained, norm_layer=norm_layer, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cspdarknet53(pretrained=False, **kwargs):
|
||||
return _create_cspnet('cspdarknet53', pretrained=pretrained, block_fn=DarkBlock, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cspdarknet53_iabn(pretrained=False, **kwargs):
|
||||
norm_layer = get_norm_act_layer('iabn', act_layer='leaky_relu')
|
||||
return _create_cspnet('cspdarknet53_iabn', pretrained=pretrained, block_fn=DarkBlock, norm_layer=norm_layer, **kwargs)
|
||||
def darknet17(pretrained=False, **kwargs):
|
||||
return _create_cspnet('darknet17', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def darknet21(pretrained=False, **kwargs):
|
||||
return _create_cspnet('darknet21', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def sedarknet21(pretrained=False, **kwargs):
|
||||
return _create_cspnet('sedarknet21', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def darknet53(pretrained=False, **kwargs):
|
||||
return _create_cspnet('darknet53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def darknetaa53(pretrained=False, **kwargs):
|
||||
return _create_cspnet(
|
||||
'darknetaa53', pretrained=pretrained, block_fn=DarkBlock, stage_fn=DarkStage, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cs2darknet_m(pretrained=False, **kwargs):
|
||||
return _create_cspnet(
|
||||
'cs2darknet_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cs2darknet_l(pretrained=False, **kwargs):
|
||||
return _create_cspnet(
|
||||
'cs2darknet_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cs2darknet_f_m(pretrained=False, **kwargs):
|
||||
return _create_cspnet(
|
||||
'cs2darknet_f_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def cs2darknet_f_l(pretrained=False, **kwargs):
|
||||
return _create_cspnet(
|
||||
'cs2darknet_f_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs)
|
||||
@ -2,6 +2,7 @@
|
||||
|
||||
Hacked together by / Copyright 2020 Ross Wightman
|
||||
"""
|
||||
import functools
|
||||
from torch import nn as nn
|
||||
|
||||
from .create_conv2d import create_conv2d
|
||||
@ -40,12 +41,26 @@ class ConvNormAct(nn.Module):
|
||||
ConvBnAct = ConvNormAct
|
||||
|
||||
|
||||
def create_aa(aa_layer, channels, stride=2, enable=True):
|
||||
if not aa_layer or not enable:
|
||||
return nn.Identity()
|
||||
if isinstance(aa_layer, functools.partial):
|
||||
if issubclass(aa_layer.func, nn.AvgPool2d):
|
||||
return aa_layer()
|
||||
else:
|
||||
return aa_layer(channels)
|
||||
elif issubclass(aa_layer, nn.AvgPool2d):
|
||||
return aa_layer(stride)
|
||||
else:
|
||||
return aa_layer(channels=channels, stride=stride)
|
||||
|
||||
|
||||
class ConvNormActAa(nn.Module):
|
||||
def __init__(
|
||||
self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
|
||||
bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None, drop_layer=None):
|
||||
super(ConvNormActAa, self).__init__()
|
||||
use_aa = aa_layer is not None
|
||||
use_aa = aa_layer is not None and stride == 2
|
||||
|
||||
self.conv = create_conv2d(
|
||||
in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
|
||||
@ -56,7 +71,7 @@ class ConvNormActAa(nn.Module):
|
||||
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
|
||||
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
|
||||
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
|
||||
self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else nn.Identity()
|
||||
self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
|
||||
|
||||
@property
|
||||
def in_channels(self):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user