Ross Wightman
5 years ago
committed by
GitHub
commit
c4ca016656
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""" Split Attention Conv2d (for ResNeSt Models)
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Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
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Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
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Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
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"""
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import torch
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import torch.nn.functional as F
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from torch import nn
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class RadixSoftmax(nn.Module):
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def __init__(self, radix, cardinality):
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super(RadixSoftmax, self).__init__()
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self.radix = radix
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self.cardinality = cardinality
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def forward(self, x):
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batch = x.size(0)
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if self.radix > 1:
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x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
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x = F.softmax(x, dim=1)
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x = x.reshape(batch, -1)
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else:
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x = torch.sigmoid(x)
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return x
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class SplitAttnConv2d(nn.Module):
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"""Split-Attention Conv2d
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"""
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def __init__(self, in_channels, channels, kernel_size, stride=1, padding=0,
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dilation=1, groups=1, bias=False, radix=2, reduction_factor=4,
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act_layer=nn.ReLU, norm_layer=None, drop_block=None, **kwargs):
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super(SplitAttnConv2d, self).__init__()
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self.radix = radix
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self.cardinality = groups
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self.channels = channels
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mid_chs = channels * radix
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attn_chs = max(in_channels * radix // reduction_factor, 32)
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self.conv = nn.Conv2d(
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in_channels, mid_chs, kernel_size, stride, padding, dilation,
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groups=groups * radix, bias=bias, **kwargs)
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self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None
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self.act0 = act_layer(inplace=True)
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self.fc1 = nn.Conv2d(channels, attn_chs, 1, groups=self.cardinality)
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self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None
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self.act1 = act_layer(inplace=True)
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self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=self.cardinality)
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self.drop_block = drop_block
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self.rsoftmax = RadixSoftmax(radix, groups)
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def forward(self, x):
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x = self.conv(x)
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if self.bn0 is not None:
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x = self.bn0(x)
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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.act0(x)
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B, RC, H, W = x.shape
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if self.radix > 1:
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x = x.reshape((B, self.radix, RC // self.radix, H, W))
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x_gap = torch.sum(x, dim=1)
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else:
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x_gap = x
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x_gap = F.adaptive_avg_pool2d(x_gap, 1)
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x_gap = self.fc1(x_gap)
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if self.bn1 is not None:
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x_gap = self.bn1(x_gap)
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x_gap = self.act1(x_gap)
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x_attn = self.fc2(x_gap)
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x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
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if self.radix > 1:
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out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))).sum(dim=1)
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else:
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out = x * x_attn
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return out.contiguous()
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""" ResNeSt Models
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Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
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Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
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Modified for torchscript compat, and consistency with timm by Ross Wightman
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"""
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import math
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import torch
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import torch.nn.functional as F
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from torch import nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.models.layers import DropBlock2d
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from .helpers import load_pretrained
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from .layers import SelectiveKernelConv, ConvBnAct, create_attn
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from .layers.split_attn import SplitAttnConv2d
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from .registry import register_model
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from .resnet import ResNet
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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'resnest14d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'),
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'resnest26d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'),
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'resnest50d': _cfg(
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url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
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'resnest101e': _cfg(
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url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth', input_size=(3, 256, 256)),
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'resnest200e': _cfg(
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url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth', input_size=(3, 320, 320)),
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'resnest269e': _cfg(
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url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth', input_size=(3, 416, 416)),
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'resnest50d_4s2x40d': _cfg(
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url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_4s2x40d-41d14ed0.pth',
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interpolation='bicubic'),
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'resnest50d_1s4x24d': _cfg(
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url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_1s4x24d-d4a4f76f.pth',
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interpolation='bicubic')
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}
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class ResNestBottleneck(nn.Module):
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"""ResNet Bottleneck
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"""
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# pylint: disable=unused-argument
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False,
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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, aa_layer=None, drop_block=None, drop_path=None):
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super(ResNestBottleneck, self).__init__()
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assert reduce_first == 1 # not supported
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assert attn_layer is None # not supported
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assert aa_layer is None # TODO not yet supported
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assert drop_path is None # TODO not yet supported
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group_width = int(planes * (base_width / 64.)) * cardinality
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first_dilation = first_dilation or dilation
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if avd and (stride > 1 or is_first):
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avd_stride = stride
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stride = 1
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else:
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avd_stride = 0
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self.radix = radix
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self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
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self.bn1 = norm_layer(group_width)
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self.drop_block1 = drop_block if drop_block is not None else None
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self.act1 = act_layer(inplace=True)
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self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None
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if self.radix >= 1:
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self.conv2 = SplitAttnConv2d(
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group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
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dilation=first_dilation, groups=cardinality, radix=radix, norm_layer=norm_layer, drop_block=drop_block)
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self.bn2 = None # FIXME revisit, here to satisfy current torchscript fussyness
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self.drop_block2 = None
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self.act2 = None
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else:
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self.conv2 = nn.Conv2d(
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group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
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dilation=first_dilation, groups=cardinality, bias=False)
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self.bn2 = norm_layer(group_width)
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self.drop_block2 = drop_block if drop_block is not None else None
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self.act2 = act_layer(inplace=True)
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self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None
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self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False)
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self.bn3 = norm_layer(planes*4)
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self.drop_block3 = drop_block if drop_block is not None else None
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self.act3 = act_layer(inplace=True)
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self.downsample = downsample
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def zero_init_last_bn(self):
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nn.init.zeros_(self.bn3.weight)
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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if self.drop_block1 is not None:
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out = self.drop_block1(out)
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out = self.act1(out)
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if self.avd_first is not None:
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out = self.avd_first(out)
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out = self.conv2(out)
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if self.bn2 is not None:
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out = self.bn2(out)
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if self.drop_block2 is not None:
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out = self.drop_block2(out)
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out = self.act2(out)
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if self.avd_last is not None:
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out = self.avd_last(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.drop_block3 is not None:
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out = self.drop_block3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.act3(out)
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return out
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@register_model
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def resnest14d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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""" ResNeSt-14d model. Weights ported from GluonCV.
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"""
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default_cfg = default_cfgs['resnest14d']
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model = ResNet(
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ResNestBottleneck, [1, 1, 1, 1], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
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block_args=dict(radix=2, avd=True, avd_first=False), **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 resnest26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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""" ResNeSt-26d model. Weights ported from GluonCV.
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"""
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default_cfg = default_cfgs['resnest26d']
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model = ResNet(
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ResNestBottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
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block_args=dict(radix=2, avd=True, avd_first=False), **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 resnest50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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""" ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955
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Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample.
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"""
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default_cfg = default_cfgs['resnest50d']
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model = ResNet(
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ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
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block_args=dict(radix=2, avd=True, avd_first=False), **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 resnest101e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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""" ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955
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Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
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"""
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default_cfg = default_cfgs['resnest101e']
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model = ResNet(
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ResNestBottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
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block_args=dict(radix=2, avd=True, avd_first=False), **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 resnest200e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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""" ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955
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Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
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"""
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default_cfg = default_cfgs['resnest200e']
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model = ResNet(
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ResNestBottleneck, [3, 24, 36, 3], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
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block_args=dict(radix=2, avd=True, avd_first=False), **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 resnest269e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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""" ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955
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Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
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"""
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default_cfg = default_cfgs['resnest269e']
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model = ResNet(
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ResNestBottleneck, [3, 30, 48, 8], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
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block_args=dict(radix=2, avd=True, avd_first=False), **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 resnest50d_4s2x40d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
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"""
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default_cfg = default_cfgs['resnest50d_4s2x40d']
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model = ResNet(
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ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=32, avg_down=True, base_width=40, cardinality=2,
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block_args=dict(radix=4, avd=True, avd_first=True), **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 resnest50d_1s4x24d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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"""ResNeSt-50 1s4x24d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
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"""
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default_cfg = default_cfgs['resnest50d_1s4x24d']
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model = ResNet(
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ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
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stem_type='deep', stem_width=32, avg_down=True, base_width=24, cardinality=4,
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block_args=dict(radix=1, avd=True, avd_first=True), **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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