Merge pull request #35 from rwightman/res2net_dla
Add Res2net and DLA models. Update sotabench.pull/65/head
Ross Wightman
5 years ago
committed by
GitHub
commit
96364fc336
@ -0,0 +1,471 @@
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""" Deep Layer Aggregation and DLA w/ Res2Net
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DLA original adapted from Official Pytorch impl at:
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DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
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Res2Net additions from: https://github.com/gasvn/Res2Net/
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Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
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"""
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import math
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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 .registry import register_model
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from .helpers import load_pretrained
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from .adaptive_avgmax_pool import SelectAdaptivePool2d
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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__all__ = ['DLA']
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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': 'base_layer.0', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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'dla34': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth'),
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'dla46_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth'),
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'dla46x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth'),
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'dla60x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth'),
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'dla60': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth'),
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'dla60x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth'),
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'dla102': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth'),
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'dla102x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth'),
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'dla102x2': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth'),
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'dla169': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth'),
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'dla60_res2net': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth'),
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'dla60_res2next': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next_dla60_4s-d327927b.pth'),
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}
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class DlaBasic(nn.Module):
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"""DLA Basic"""
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def __init__(self, inplanes, planes, stride=1, dilation=1, **_):
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super(DlaBasic, self).__init__()
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self.conv1 = nn.Conv2d(
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inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation)
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self.bn1 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(
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planes, planes, kernel_size=3, stride=1, padding=dilation, bias=False, dilation=dilation)
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self.bn2 = nn.BatchNorm2d(planes)
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self.stride = stride
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def forward(self, x, residual=None):
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if residual is None:
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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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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out += residual
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out = self.relu(out)
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return out
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class DlaBottleneck(nn.Module):
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"""DLA/DLA-X Bottleneck"""
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expansion = 2
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def __init__(self, inplanes, outplanes, stride=1, dilation=1, cardinality=1, base_width=64):
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super(DlaBottleneck, self).__init__()
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self.stride = stride
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mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
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mid_planes = mid_planes // self.expansion
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self.conv1 = nn.Conv2d(inplanes, mid_planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(mid_planes)
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self.conv2 = nn.Conv2d(
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mid_planes, mid_planes, kernel_size=3, stride=stride, padding=dilation,
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bias=False, dilation=dilation, groups=cardinality)
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self.bn2 = nn.BatchNorm2d(mid_planes)
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self.conv3 = nn.Conv2d(mid_planes, outplanes, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(outplanes)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x, residual=None):
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if residual is None:
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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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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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out += residual
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out = self.relu(out)
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return out
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class DlaBottle2neck(nn.Module):
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""" Res2Net/Res2NeXT DLA Bottleneck
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Adapted from https://github.com/gasvn/Res2Net/blob/master/dla.py
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"""
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expansion = 2
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def __init__(self, inplanes, outplanes, stride=1, dilation=1, scale=4, cardinality=8, base_width=4):
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super(DlaBottle2neck, self).__init__()
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self.is_first = stride > 1
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self.scale = scale
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mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
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mid_planes = mid_planes // self.expansion
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self.width = mid_planes
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self.conv1 = nn.Conv2d(inplanes, mid_planes * scale, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(mid_planes * scale)
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num_scale_convs = max(1, scale - 1)
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convs = []
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bns = []
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for _ in range(num_scale_convs):
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convs.append(nn.Conv2d(
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mid_planes, mid_planes, kernel_size=3, stride=stride,
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padding=dilation, dilation=dilation, groups=cardinality, bias=False))
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bns.append(nn.BatchNorm2d(mid_planes))
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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if self.is_first:
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self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
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self.conv3 = nn.Conv2d(mid_planes * scale, outplanes, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(outplanes)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x, residual=None):
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if residual is None:
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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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out = self.relu(out)
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spx = torch.split(out, self.width, 1)
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spo = []
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for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
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sp = spx[i] if i == 0 or self.is_first else sp + spx[i]
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sp = conv(sp)
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sp = bn(sp)
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sp = self.relu(sp)
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spo.append(sp)
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if self.scale > 1 :
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spo.append(self.pool(spx[-1]) if self.is_first else spx[-1])
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out = torch.cat(spo, 1)
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out = self.conv3(out)
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out = self.bn3(out)
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out += residual
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out = self.relu(out)
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return out
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class DlaRoot(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, residual):
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super(DlaRoot, self).__init__()
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self.conv = nn.Conv2d(
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in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2)
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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self.residual = residual
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def forward(self, *x):
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children = x
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x = self.conv(torch.cat(x, 1))
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x = self.bn(x)
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if self.residual:
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x += children[0]
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x = self.relu(x)
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return x
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class DlaTree(nn.Module):
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def __init__(self, levels, block, in_channels, out_channels, stride=1,
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dilation=1, cardinality=1, base_width=64,
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level_root=False, root_dim=0, root_kernel_size=1, root_residual=False):
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super(DlaTree, self).__init__()
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if root_dim == 0:
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root_dim = 2 * out_channels
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if level_root:
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root_dim += in_channels
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cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width)
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if levels == 1:
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self.tree1 = block(in_channels, out_channels, stride, **cargs)
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self.tree2 = block(out_channels, out_channels, 1, **cargs)
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else:
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cargs.update(dict(root_kernel_size=root_kernel_size, root_residual=root_residual))
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self.tree1 = DlaTree(
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levels - 1, block, in_channels, out_channels, stride, root_dim=0, **cargs)
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self.tree2 = DlaTree(
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levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, **cargs)
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if levels == 1:
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self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_residual)
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self.level_root = level_root
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self.root_dim = root_dim
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self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else None
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self.project = None
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if in_channels != out_channels:
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self.project = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(out_channels)
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)
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self.levels = levels
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def forward(self, x, residual=None, children=None):
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children = [] if children is None else children
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bottom = self.downsample(x) if self.downsample else x
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residual = self.project(bottom) if self.project else bottom
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if self.level_root:
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children.append(bottom)
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x1 = self.tree1(x, residual)
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if self.levels == 1:
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x2 = self.tree2(x1)
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x = self.root(x2, x1, *children)
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else:
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children.append(x1)
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x = self.tree2(x1, children=children)
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return x
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class DLA(nn.Module):
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def __init__(self, levels, channels, num_classes=1000, in_chans=3, cardinality=1, base_width=64,
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block=DlaBottle2neck, residual_root=False, linear_root=False,
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drop_rate=0.0, global_pool='avg'):
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super(DLA, self).__init__()
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self.channels = channels
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self.num_classes = num_classes
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self.cardinality = cardinality
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self.base_width = base_width
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self.drop_rate = drop_rate
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self.base_layer = nn.Sequential(
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nn.Conv2d(in_chans, channels[0], kernel_size=7, stride=1, padding=3, bias=False),
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nn.BatchNorm2d(channels[0]),
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nn.ReLU(inplace=True))
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self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
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self.level1 = self._make_conv_level(channels[0], channels[1], levels[1], stride=2)
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cargs = dict(cardinality=cardinality, base_width=base_width, root_residual=residual_root)
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self.level2 = DlaTree(levels[2], block, channels[1], channels[2], 2, level_root=False, **cargs)
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self.level3 = DlaTree(levels[3], block, channels[2], channels[3], 2, level_root=True, **cargs)
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self.level4 = DlaTree(levels[4], block, channels[3], channels[4], 2, level_root=True, **cargs)
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self.level5 = DlaTree(levels[5], block, channels[4], channels[5], 2, level_root=True, **cargs)
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self.num_features = channels[-1]
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.fc = nn.Conv2d(self.num_features * self.global_pool.feat_mult(), num_classes,
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kernel_size=1, stride=1, padding=0, bias=True)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
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modules = []
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for i in range(convs):
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modules.extend([
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nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride if i == 0 else 1,
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padding=dilation, bias=False, dilation=dilation),
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nn.BatchNorm2d(planes),
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nn.ReLU(inplace=True)])
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inplanes = planes
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return nn.Sequential(*modules)
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def get_classifier(self):
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return self.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.num_classes = num_classes
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del self.fc
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if num_classes:
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self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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else:
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self.fc = None
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def forward_features(self, x, pool=True):
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x = self.base_layer(x)
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x = self.level0(x)
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x = self.level1(x)
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x = self.level2(x)
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x = self.level3(x)
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x = self.level4(x)
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x = self.level5(x)
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if pool:
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x = self.global_pool(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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x = self.fc(x)
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x = x.flatten(1)
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return x
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@register_model
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def dla60_res2net(pretrained=None, num_classes=1000, in_chans=3, **kwargs):
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default_cfg = default_cfgs['dla60_res2net']
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model = DLA(levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottle2neck, cardinality=1, base_width=28,
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num_classes=num_classes, in_chans=in_chans, **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 dla60_res2next(pretrained=None, num_classes=1000, in_chans=3, **kwargs):
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default_cfg = default_cfgs['dla60_res2next']
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model = DLA(levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
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block=DlaBottle2neck, cardinality=8, base_width=4,
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num_classes=num_classes, in_chans=in_chans, **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 dla34(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-34
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default_cfg = default_cfgs['dla34']
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model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=DlaBasic, **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 dla46_c(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-46-C
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default_cfg = default_cfgs['dla46_c']
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model = DLA(levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
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block=DlaBottleneck, num_classes=num_classes, in_chans=in_chans, **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 dla46x_c(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-X-46-C
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default_cfg = default_cfgs['dla46x_c']
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model = DLA(levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
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block=DlaBottleneck, cardinality=32, base_width=4,
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num_classes=num_classes, in_chans=in_chans, **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 dla60x_c(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-X-60-C
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default_cfg = default_cfgs['dla60x_c']
|
||||
model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 64, 64, 128, 256],
|
||||
block=DlaBottleneck, cardinality=32, base_width=4,
|
||||
num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def dla60(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-60
|
||||
default_cfg = default_cfgs['dla60']
|
||||
model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=DlaBottleneck, num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def dla60x(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-X-60
|
||||
default_cfg = default_cfgs['dla60x']
|
||||
model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=DlaBottleneck, cardinality=32, base_width=4,
|
||||
num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def dla102(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-102
|
||||
default_cfg = default_cfgs['dla102']
|
||||
model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=DlaBottleneck, residual_root=True,
|
||||
num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def dla102x(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-X-102
|
||||
default_cfg = default_cfgs['dla102x']
|
||||
model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=DlaBottleneck, cardinality=32, base_width=4, residual_root=True,
|
||||
num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def dla102x2(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-X-102 64
|
||||
default_cfg = default_cfgs['dla102x2']
|
||||
model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=DlaBottleneck, cardinality=64, base_width=4, residual_root=True,
|
||||
num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def dla169(pretrained=None, num_classes=1000, in_chans=3, **kwargs): # DLA-169
|
||||
default_cfg = default_cfgs['dla169']
|
||||
model = DLA([1, 1, 2, 3, 5, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=DlaBottleneck, residual_root=True,
|
||||
num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
@ -0,0 +1,231 @@
|
||||
""" Res2Net and Res2NeXt
|
||||
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
|
||||
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
|
||||
"""
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .resnet import ResNet, SEModule
|
||||
from .registry import register_model
|
||||
from .helpers import load_pretrained
|
||||
from .adaptive_avgmax_pool import SelectAdaptivePool2d
|
||||
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
||||
|
||||
__all__ = []
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
||||
'crop_pct': 0.875, 'interpolation': 'bilinear',
|
||||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'conv1', 'classifier': 'fc',
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = {
|
||||
'res2net50_26w_4s': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth'),
|
||||
'res2net50_48w_2s': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth'),
|
||||
'res2net50_14w_8s': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth'),
|
||||
'res2net50_26w_6s': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth'),
|
||||
'res2net50_26w_8s': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth'),
|
||||
'res2net101_26w_4s': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth'),
|
||||
'res2next50': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth'),
|
||||
}
|
||||
|
||||
|
||||
class Bottle2neck(nn.Module):
|
||||
""" Res2Net/Res2NeXT Bottleneck
|
||||
Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
|
||||
"""
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None,
|
||||
cardinality=1, base_width=26, scale=4, use_se=False,
|
||||
norm_layer=None, dilation=1, previous_dilation=1, **_):
|
||||
super(Bottle2neck, self).__init__()
|
||||
assert dilation == 1 and previous_dilation == 1 # FIXME support dilation
|
||||
self.scale = scale
|
||||
self.is_first = True if stride > 1 or downsample is not None else False
|
||||
self.num_scales = max(1, scale - 1)
|
||||
width = int(math.floor(planes * (base_width / 64.0))) * cardinality
|
||||
outplanes = planes * self.expansion
|
||||
self.width = width
|
||||
|
||||
self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
|
||||
self.bn1 = norm_layer(width * scale)
|
||||
|
||||
convs = []
|
||||
bns = []
|
||||
for i in range(self.num_scales):
|
||||
convs.append(nn.Conv2d(
|
||||
width, width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False))
|
||||
bns.append(norm_layer(width))
|
||||
self.convs = nn.ModuleList(convs)
|
||||
self.bns = nn.ModuleList(bns)
|
||||
if self.is_first:
|
||||
self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
|
||||
|
||||
self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False)
|
||||
self.bn3 = norm_layer(outplanes)
|
||||
self.se = SEModule(outplanes, planes // 4) if use_se else None
|
||||
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = downsample
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
spx = torch.split(out, self.width, 1)
|
||||
spo = []
|
||||
for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
|
||||
sp = spx[i] if i == 0 or self.is_first else sp + spx[i]
|
||||
sp = conv(sp)
|
||||
sp = bn(sp)
|
||||
sp = self.relu(sp)
|
||||
spo.append(sp)
|
||||
if self.scale > 1 :
|
||||
spo.append(self.pool(spx[-1]) if self.is_first else spx[-1])
|
||||
out = torch.cat(spo, 1)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
if self.se is not None:
|
||||
out = self.se(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
residual = self.downsample(x)
|
||||
|
||||
out += residual
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
@register_model
|
||||
def res2net50_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Constructs a Res2Net-50_26w_4s model.
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2net50_26w_4s']
|
||||
res2net_block_args = dict(scale=4)
|
||||
model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
|
||||
num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def res2net101_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Constructs a Res2Net-50_26w_4s model.
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2net101_26w_4s']
|
||||
res2net_block_args = dict(scale=4)
|
||||
model = ResNet(Bottle2neck, [3, 4, 23, 3], base_width=26,
|
||||
num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def res2net50_26w_6s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Constructs a Res2Net-50_26w_4s model.
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2net50_26w_6s']
|
||||
res2net_block_args = dict(scale=6)
|
||||
model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
|
||||
num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def res2net50_26w_8s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Constructs a Res2Net-50_26w_4s model.
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2net50_26w_8s']
|
||||
res2net_block_args = dict(scale=8)
|
||||
model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
|
||||
num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def res2net50_48w_2s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Constructs a Res2Net-50_48w_2s model.
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2net50_48w_2s']
|
||||
res2net_block_args = dict(scale=2)
|
||||
model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=48,
|
||||
num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def res2net50_14w_8s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Constructs a Res2Net-50_14w_8s model.
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2net50_14w_8s']
|
||||
res2net_block_args = dict(scale=8)
|
||||
model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=14, num_classes=num_classes, in_chans=in_chans,
|
||||
block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def res2next50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
"""Construct Res2NeXt-50 4s
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
default_cfg = default_cfgs['res2next50']
|
||||
res2net_block_args = dict(scale=4)
|
||||
model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=4, cardinality=8,
|
||||
num_classes=1000, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
Loading…
Reference in new issue