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@ -12,7 +12,7 @@ import torch.nn.functional as F
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from .registry import register_model
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from .registry import register_model
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from .helpers import load_pretrained, adapt_model_from_file
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from .helpers import load_pretrained, adapt_model_from_file
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from .layers import SelectAdaptivePool2d, DropBlock2d, DropPath, AvgPool2dSame, create_attn
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from .layers import SelectAdaptivePool2d, DropBlock2d, DropPath, AvgPool2dSame, create_attn, BlurPool2d
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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__all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this
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__all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this
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@ -118,6 +118,8 @@ default_cfgs = {
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'ecaresnet101d_pruned': _cfg(
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'ecaresnet101d_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
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interpolation='bicubic'),
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interpolation='bicubic'),
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'resnetblur18': _cfg(),
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'resnetblur50': _cfg()
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}
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}
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@ -131,7 +133,7 @@ class BasicBlock(nn.Module):
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def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
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def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
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reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
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reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
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attn_layer=None, drop_block=None, drop_path=None):
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attn_layer=None, drop_block=None, drop_path=None, blur=False):
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super(BasicBlock, self).__init__()
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super(BasicBlock, self).__init__()
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assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
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assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
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@ -141,10 +143,12 @@ class BasicBlock(nn.Module):
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first_dilation = first_dilation or dilation
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first_dilation = first_dilation or dilation
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self.conv1 = nn.Conv2d(
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self.conv1 = nn.Conv2d(
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inplanes, first_planes, kernel_size=3, stride=stride, padding=first_dilation,
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inplanes, first_planes, kernel_size=3, stride=1 if blur else stride, padding=first_dilation,
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dilation=first_dilation, bias=False)
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dilation=first_dilation, bias=False)
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self.bn1 = norm_layer(first_planes)
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self.bn1 = norm_layer(first_planes)
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self.act1 = act_layer(inplace=True)
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self.act1 = act_layer(inplace=True)
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self.blurpool = BlurPool2d(channels=first_planes) if stride == 2 and blur else None
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self.conv2 = nn.Conv2d(
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self.conv2 = nn.Conv2d(
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first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
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first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
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self.bn2 = norm_layer(outplanes)
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self.bn2 = norm_layer(outplanes)
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@ -169,6 +173,8 @@ class BasicBlock(nn.Module):
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if self.drop_block is not None:
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if self.drop_block is not None:
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x = self.drop_block(x)
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x = self.drop_block(x)
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x = self.act1(x)
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x = self.act1(x)
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if self.blurpool is not None:
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x = self.blurpool(x)
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x = self.conv2(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.bn2(x)
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@ -195,22 +201,26 @@ class Bottleneck(nn.Module):
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def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
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def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64,
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reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
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reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
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attn_layer=None, drop_block=None, drop_path=None):
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attn_layer=None, drop_block=None, drop_path=None, blur=False):
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super(Bottleneck, self).__init__()
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super(Bottleneck, self).__init__()
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width = int(math.floor(planes * (base_width / 64)) * cardinality)
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width = int(math.floor(planes * (base_width / 64)) * cardinality)
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first_planes = width // reduce_first
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first_planes = width // reduce_first
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outplanes = planes * self.expansion
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outplanes = planes * self.expansion
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first_dilation = first_dilation or dilation
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first_dilation = first_dilation or dilation
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self.blur = blur
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self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
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self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
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self.bn1 = norm_layer(first_planes)
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self.bn1 = norm_layer(first_planes)
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self.act1 = act_layer(inplace=True)
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self.act1 = act_layer(inplace=True)
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self.conv2 = nn.Conv2d(
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self.conv2 = nn.Conv2d(
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first_planes, width, kernel_size=3, stride=stride,
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first_planes, width, kernel_size=3, stride=1 if blur else stride,
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padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
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padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
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self.bn2 = norm_layer(width)
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self.bn2 = norm_layer(width)
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self.act2 = act_layer(inplace=True)
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self.act2 = act_layer(inplace=True)
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self.blurpool = BlurPool2d(channels=width) if stride == 2 and blur else None
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self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
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self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
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self.bn3 = norm_layer(outplanes)
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self.bn3 = norm_layer(outplanes)
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@ -240,6 +250,8 @@ class Bottleneck(nn.Module):
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if self.drop_block is not None:
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if self.drop_block is not None:
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x = self.drop_block(x)
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x = self.drop_block(x)
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x = self.act2(x)
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x = self.act2(x)
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if self.blurpool is not None:
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x = self.blurpool(x)
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x = self.conv3(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x = self.bn3(x)
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@ -359,12 +371,19 @@ class ResNet(nn.Module):
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Dropout probability before classifier, for training
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Dropout probability before classifier, for training
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global_pool : str, default 'avg'
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global_pool : str, default 'avg'
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Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
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Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
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blur : str, default ''
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Location of Blurring:
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* '', default - Not applied
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* 'max' - only stem layer MaxPool will be blurred
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* 'strided' - only strided convolutions in the downsampling blocks (assembled-cnn style)
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* 'max_strided' - on both stem MaxPool and strided convolutions (zhang2019shiftinvar style for ResNets)
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"""
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"""
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def __init__(self, block, layers, num_classes=1000, in_chans=3,
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def __init__(self, block, layers, num_classes=1000, in_chans=3,
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cardinality=1, base_width=64, stem_width=64, stem_type='',
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cardinality=1, base_width=64, stem_width=64, stem_type='',
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block_reduce_first=1, down_kernel_size=1, avg_down=False, output_stride=32,
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block_reduce_first=1, down_kernel_size=1, avg_down=False, output_stride=32,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0.0, drop_path_rate=0.,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0.0, drop_path_rate=0.,
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drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None):
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drop_block_rate=0., global_pool='avg', blur='', zero_init_last_bn=True, block_args=None):
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block_args = block_args or dict()
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block_args = block_args or dict()
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self.num_classes = num_classes
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self.num_classes = num_classes
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deep_stem = 'deep' in stem_type
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deep_stem = 'deep' in stem_type
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@ -373,6 +392,7 @@ class ResNet(nn.Module):
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self.base_width = base_width
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self.base_width = base_width
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self.drop_rate = drop_rate
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self.drop_rate = drop_rate
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self.expansion = block.expansion
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self.expansion = block.expansion
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self.blur = 'strided' in blur
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super(ResNet, self).__init__()
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super(ResNet, self).__init__()
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# Stem
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# Stem
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@ -393,7 +413,14 @@ class ResNet(nn.Module):
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self.conv1 = nn.Conv2d(in_chans, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
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self.conv1 = nn.Conv2d(in_chans, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = norm_layer(self.inplanes)
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self.bn1 = norm_layer(self.inplanes)
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self.act1 = act_layer(inplace=True)
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self.act1 = act_layer(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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# Stem Pooling
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if 'max' in blur :
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self.maxpool = nn.Sequential(*[
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
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BlurPool2d(channels=self.inplanes, stride=2)
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])
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else :
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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# Feature Blocks
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# Feature Blocks
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dp = DropPath(drop_path_rate) if drop_path_rate else None
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dp = DropPath(drop_path_rate) if drop_path_rate else None
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@ -445,7 +472,7 @@ class ResNet(nn.Module):
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block_kwargs = dict(
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block_kwargs = dict(
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cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
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cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
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dilation=dilation, **kwargs)
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dilation=dilation, blur=self.blur, **kwargs)
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layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, **block_kwargs)]
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layers = [block(self.inplanes, planes, stride, downsample, first_dilation=first_dilation, **block_kwargs)]
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self.inplanes = planes * block.expansion
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self.inplanes = planes * block.expansion
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layers += [block(self.inplanes, planes, **block_kwargs) for _ in range(1, blocks)]
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layers += [block(self.inplanes, planes, **block_kwargs) for _ in range(1, blocks)]
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@ -1114,3 +1141,26 @@ def ecaresnet101d_pruned(pretrained=False, num_classes=1000, in_chans=3, **kwarg
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if pretrained:
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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return model
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@register_model
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def resnetblur18(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-18 model with blur anti-aliasing
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"""
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default_cfg = default_cfgs['resnetblur18']
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model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans, blur='max_strided',**kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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@register_model
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def resnetblur50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""Constructs a ResNet-50 model with blur anti-aliasing
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"""
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default_cfg = default_cfgs['resnetblur50']
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model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans, blur='max_strided', **kwargs)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans)
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return model
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