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475 lines
18 KiB
475 lines
18 KiB
""" 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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from typing import List, Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import create_classifier
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from ._builder import build_model_with_cfg
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from ._registry import register_model
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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='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla34-2b83ff04.pth'),
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'dla46_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla46_c-9b68d685.pth'),
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'dla46x_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla46x_c-6bc5b5c8.pth'),
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'dla60x_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla60x_c-a38e054a.pth'),
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'dla60': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla60-9e91bd4d.pth'),
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'dla60x': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla60x-6818f6bb.pth'),
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'dla102': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla102-21f57b54.pth'),
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'dla102x': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla102x-7ec0aa2a.pth'),
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'dla102x2': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla102x2-ac4239c4.pth'),
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'dla169': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla169-7c767967.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, shortcut=None, children: Optional[List[torch.Tensor]] = None):
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if shortcut is None:
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shortcut = 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 += shortcut
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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, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
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if shortcut is None:
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shortcut = 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 += shortcut
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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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self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1) if self.is_first else None
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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, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
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if shortcut is None:
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shortcut = 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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sp = spx[0] # redundant, for torchscript
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for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
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if i == 0 or self.is_first:
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sp = spx[i]
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else:
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sp = 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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if self.pool is not None: # self.is_first == True, None check for torchscript
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spo.append(self.pool(spx[-1]))
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else:
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spo.append(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 += shortcut
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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, shortcut):
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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.shortcut = shortcut
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def forward(self, x_children: List[torch.Tensor]):
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x = self.conv(torch.cat(x_children, 1))
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x = self.bn(x)
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if self.shortcut:
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x += 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__(
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self, levels, block, in_channels, out_channels, stride=1, dilation=1, cardinality=1,
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base_width=64, level_root=False, root_dim=0, root_kernel_size=1, root_shortcut=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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self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else nn.Identity()
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self.project = nn.Identity()
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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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if in_channels != out_channels:
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# NOTE the official impl/weights have project layers in levels > 1 case that are never
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# used, I've moved the project layer here to avoid wasted params but old checkpoints will
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# need strict=False while loading.
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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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self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_shortcut)
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else:
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cargs.update(dict(root_kernel_size=root_kernel_size, root_shortcut=root_shortcut))
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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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self.root = None
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self.level_root = level_root
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self.root_dim = root_dim
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self.levels = levels
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def forward(self, x, shortcut: Optional[torch.Tensor] = None, children: Optional[List[torch.Tensor]] = None):
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if children is None:
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children = []
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bottom = self.downsample(x)
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shortcut = self.project(bottom)
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if self.level_root:
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children.append(bottom)
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x1 = self.tree1(x, shortcut)
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if self.root is not None: # 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, None, children)
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return x
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class DLA(nn.Module):
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def __init__(
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self, levels, channels, output_stride=32, num_classes=1000, in_chans=3, global_pool='avg',
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cardinality=1, base_width=64, block=DlaBottle2neck, shortcut_root=False, drop_rate=0.0):
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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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assert output_stride == 32 # FIXME support dilation
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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_shortcut=shortcut_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.feature_info = [
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dict(num_chs=channels[0], reduction=1, module='level0'), # rare to have a meaningful stride 1 level
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dict(num_chs=channels[1], reduction=2, module='level1'),
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dict(num_chs=channels[2], reduction=4, module='level2'),
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dict(num_chs=channels[3], reduction=8, module='level3'),
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dict(num_chs=channels[4], reduction=16, module='level4'),
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dict(num_chs=channels[5], reduction=32, module='level5'),
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]
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self.num_features = channels[-1]
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self.global_pool, self.fc = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
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self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
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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(
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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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@torch.jit.ignore
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def group_matcher(self, coarse=False):
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matcher = dict(
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stem=r'^base_layer',
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blocks=r'^level(\d+)' if coarse else [
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# an unusual arch, this achieves somewhat more granularity without getting super messy
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(r'^level(\d+)\.tree(\d+)', None),
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(r'^level(\d+)\.root', (2,)),
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(r'^level(\d+)', (1,))
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]
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)
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return matcher
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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assert not enable, 'gradient checkpointing not supported'
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@torch.jit.ignore
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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.num_classes = num_classes
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self.global_pool, self.fc = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
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self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
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def forward_features(self, x):
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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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return x
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def forward_head(self, x, pre_logits: bool = False):
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x = self.global_pool(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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if pre_logits:
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return x.flatten(1)
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else:
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x = self.fc(x)
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return self.flatten(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _create_dla(variant, pretrained=False, **kwargs):
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return build_model_with_cfg(
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DLA, variant, pretrained,
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pretrained_strict=False,
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feature_cfg=dict(out_indices=(1, 2, 3, 4, 5)),
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**kwargs)
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@register_model
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def dla60_res2net(pretrained=False, **kwargs):
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model_kwargs = dict(
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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, **kwargs)
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return _create_dla('dla60_res2net', pretrained, **model_kwargs)
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@register_model
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def dla60_res2next(pretrained=False,**kwargs):
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model_kwargs = dict(
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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, **kwargs)
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return _create_dla('dla60_res2next', pretrained, **model_kwargs)
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@register_model
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def dla34(pretrained=False, **kwargs): # DLA-34
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model_kwargs = dict(
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levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512],
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block=DlaBasic, **kwargs)
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return _create_dla('dla34', pretrained, **model_kwargs)
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@register_model
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def dla46_c(pretrained=False, **kwargs): # DLA-46-C
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model_kwargs = dict(
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levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
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block=DlaBottleneck, **kwargs)
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return _create_dla('dla46_c', pretrained, **model_kwargs)
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@register_model
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def dla46x_c(pretrained=False, **kwargs): # DLA-X-46-C
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model_kwargs = dict(
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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, **kwargs)
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|
return _create_dla('dla46x_c', pretrained, **model_kwargs)
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|
|
|
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|
@register_model
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|
def dla60x_c(pretrained=False, **kwargs): # DLA-X-60-C
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|
model_kwargs = dict(
|
|
levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 64, 64, 128, 256],
|
|
block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
|
|
return _create_dla('dla60x_c', pretrained, **model_kwargs)
|
|
|
|
|
|
@register_model
|
|
def dla60(pretrained=False, **kwargs): # DLA-60
|
|
model_kwargs = dict(
|
|
levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
|
|
block=DlaBottleneck, **kwargs)
|
|
return _create_dla('dla60', pretrained, **model_kwargs)
|
|
|
|
|
|
@register_model
|
|
def dla60x(pretrained=False, **kwargs): # DLA-X-60
|
|
model_kwargs = dict(
|
|
levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
|
|
block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
|
|
return _create_dla('dla60x', pretrained, **model_kwargs)
|
|
|
|
|
|
@register_model
|
|
def dla102(pretrained=False, **kwargs): # DLA-102
|
|
model_kwargs = dict(
|
|
levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
|
|
block=DlaBottleneck, shortcut_root=True, **kwargs)
|
|
return _create_dla('dla102', pretrained, **model_kwargs)
|
|
|
|
|
|
@register_model
|
|
def dla102x(pretrained=False, **kwargs): # DLA-X-102
|
|
model_kwargs = dict(
|
|
levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
|
|
block=DlaBottleneck, cardinality=32, base_width=4, shortcut_root=True, **kwargs)
|
|
return _create_dla('dla102x', pretrained, **model_kwargs)
|
|
|
|
|
|
@register_model
|
|
def dla102x2(pretrained=False, **kwargs): # DLA-X-102 64
|
|
model_kwargs = dict(
|
|
levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
|
|
block=DlaBottleneck, cardinality=64, base_width=4, shortcut_root=True, **kwargs)
|
|
return _create_dla('dla102x2', pretrained, **model_kwargs)
|
|
|
|
|
|
@register_model
|
|
def dla169(pretrained=False, **kwargs): # DLA-169
|
|
model_kwargs = dict(
|
|
levels=[1, 1, 2, 3, 5, 1], channels=[16, 32, 128, 256, 512, 1024],
|
|
block=DlaBottleneck, shortcut_root=True, **kwargs)
|
|
return _create_dla('dla169', pretrained, **model_kwargs)
|