""" Deep Layer Aggregation and DLA w/ Res2Net DLA original adapted from Official Pytorch impl at: DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484 Res2Net additions from: https://github.com/gasvn/Res2Net/ 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 timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import create_classifier from .registry import register_model __all__ = ['DLA'] 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': 'base_layer.0', 'classifier': 'fc', **kwargs } default_cfgs = { 'dla34': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth'), 'dla46_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth'), 'dla46x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth'), 'dla60x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth'), 'dla60': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth'), 'dla60x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth'), 'dla102': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth'), 'dla102x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth'), 'dla102x2': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth'), 'dla169': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth'), 'dla60_res2net': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth'), 'dla60_res2next': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next_dla60_4s-d327927b.pth'), } class DlaBasic(nn.Module): """DLA Basic""" def __init__(self, inplanes, planes, stride=1, dilation=1, **_): super(DlaBasic, self).__init__() self.conv1 = nn.Conv2d( inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d( planes, planes, kernel_size=3, stride=1, padding=dilation, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.stride = stride def forward(self, x, shortcut=None): if shortcut is None: shortcut = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += shortcut out = self.relu(out) return out class DlaBottleneck(nn.Module): """DLA/DLA-X Bottleneck""" expansion = 2 def __init__(self, inplanes, outplanes, stride=1, dilation=1, cardinality=1, base_width=64): super(DlaBottleneck, self).__init__() self.stride = stride mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality) mid_planes = mid_planes // self.expansion self.conv1 = nn.Conv2d(inplanes, mid_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(mid_planes) self.conv2 = nn.Conv2d( mid_planes, mid_planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation, groups=cardinality) self.bn2 = nn.BatchNorm2d(mid_planes) self.conv3 = nn.Conv2d(mid_planes, outplanes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(outplanes) self.relu = nn.ReLU(inplace=True) def forward(self, x, shortcut=None): if shortcut is None: shortcut = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += shortcut out = self.relu(out) return out class DlaBottle2neck(nn.Module): """ Res2Net/Res2NeXT DLA Bottleneck Adapted from https://github.com/gasvn/Res2Net/blob/master/dla.py """ expansion = 2 def __init__(self, inplanes, outplanes, stride=1, dilation=1, scale=4, cardinality=8, base_width=4): super(DlaBottle2neck, self).__init__() self.is_first = stride > 1 self.scale = scale mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality) mid_planes = mid_planes // self.expansion self.width = mid_planes self.conv1 = nn.Conv2d(inplanes, mid_planes * scale, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(mid_planes * scale) num_scale_convs = max(1, scale - 1) convs = [] bns = [] for _ in range(num_scale_convs): convs.append(nn.Conv2d( mid_planes, mid_planes, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, groups=cardinality, bias=False)) bns.append(nn.BatchNorm2d(mid_planes)) 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(mid_planes * scale, outplanes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(outplanes) self.relu = nn.ReLU(inplace=True) def forward(self, x, shortcut=None): if shortcut is None: shortcut = 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) out += shortcut out = self.relu(out) return out class DlaRoot(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, shortcut): super(DlaRoot, self).__init__() self.conv = nn.Conv2d( in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2) self.bn = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) self.shortcut = shortcut def forward(self, *x): children = x x = self.conv(torch.cat(x, 1)) x = self.bn(x) if self.shortcut: x += children[0] x = self.relu(x) return x class DlaTree(nn.Module): def __init__(self, levels, block, in_channels, out_channels, stride=1, dilation=1, cardinality=1, base_width=64, level_root=False, root_dim=0, root_kernel_size=1, root_shortcut=False): super(DlaTree, self).__init__() if root_dim == 0: root_dim = 2 * out_channels if level_root: root_dim += in_channels self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else nn.Identity() self.project = nn.Identity() cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width) if levels == 1: self.tree1 = block(in_channels, out_channels, stride, **cargs) self.tree2 = block(out_channels, out_channels, 1, **cargs) if in_channels != out_channels: # NOTE the official impl/weights have project layers in levels > 1 case that are never # used, I've moved the project layer here to avoid wasted params but old checkpoints will # need strict=False while loading. self.project = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(out_channels)) else: cargs.update(dict(root_kernel_size=root_kernel_size, root_shortcut=root_shortcut)) self.tree1 = DlaTree( levels - 1, block, in_channels, out_channels, stride, root_dim=0, **cargs) self.tree2 = DlaTree( levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, **cargs) if levels == 1: self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_shortcut) self.level_root = level_root self.root_dim = root_dim self.levels = levels def forward(self, x, shortcut=None, children=None): children = [] if children is None else children bottom = self.downsample(x) shortcut = self.project(bottom) if self.level_root: children.append(bottom) x1 = self.tree1(x, shortcut) if self.levels == 1: x2 = self.tree2(x1) x = self.root(x2, x1, *children) else: children.append(x1) x = self.tree2(x1, children=children) return x class DLA(nn.Module): def __init__(self, levels, channels, output_stride=32, num_classes=1000, in_chans=3, cardinality=1, base_width=64, block=DlaBottle2neck, shortcut_root=False, drop_rate=0.0, global_pool='avg'): super(DLA, self).__init__() self.channels = channels self.num_classes = num_classes self.cardinality = cardinality self.base_width = base_width self.drop_rate = drop_rate assert output_stride == 32 # FIXME support dilation self.base_layer = nn.Sequential( nn.Conv2d(in_chans, channels[0], kernel_size=7, stride=1, padding=3, bias=False), nn.BatchNorm2d(channels[0]), nn.ReLU(inplace=True)) self.level0 = self._make_conv_level(channels[0], channels[0], levels[0]) self.level1 = self._make_conv_level(channels[0], channels[1], levels[1], stride=2) cargs = dict(cardinality=cardinality, base_width=base_width, root_shortcut=shortcut_root) self.level2 = DlaTree(levels[2], block, channels[1], channels[2], 2, level_root=False, **cargs) self.level3 = DlaTree(levels[3], block, channels[2], channels[3], 2, level_root=True, **cargs) self.level4 = DlaTree(levels[4], block, channels[3], channels[4], 2, level_root=True, **cargs) self.level5 = DlaTree(levels[5], block, channels[4], channels[5], 2, level_root=True, **cargs) self.feature_info = [ dict(num_chs=channels[0], reduction=1, module='level0'), # rare to have a meaningful stride 1 level dict(num_chs=channels[1], reduction=2, module='level1'), dict(num_chs=channels[2], reduction=4, module='level2'), dict(num_chs=channels[3], reduction=8, module='level3'), dict(num_chs=channels[4], reduction=16, module='level4'), dict(num_chs=channels[5], reduction=32, module='level5'), ] self.num_features = channels[-1] self.global_pool, self.fc = create_classifier( self.num_features, self.num_classes, pool_type=global_pool, use_conv=True) self.flatten = nn.Flatten(1) if global_pool else nn.Identity() for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1): modules = [] for i in range(convs): modules.extend([ nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride if i == 0 else 1, padding=dilation, bias=False, dilation=dilation), nn.BatchNorm2d(planes), nn.ReLU(inplace=True)]) inplanes = planes return nn.Sequential(*modules) def get_classifier(self): return self.fc def reset_classifier(self, num_classes, global_pool='avg'): self.num_classes = num_classes self.global_pool, self.fc = create_classifier( self.num_features, self.num_classes, pool_type=global_pool, use_conv=True) self.flatten = nn.Flatten(1) if global_pool else nn.Identity() def forward_features(self, x): x = self.base_layer(x) x = self.level0(x) x = self.level1(x) x = self.level2(x) x = self.level3(x) x = self.level4(x) x = self.level5(x) return x def forward(self, x): x = self.forward_features(x) x = self.global_pool(x) if self.drop_rate > 0.: x = F.dropout(x, p=self.drop_rate, training=self.training) x = self.fc(x) x = self.flatten(x) return x def _create_dla(variant, pretrained=False, **kwargs): return build_model_with_cfg( DLA, variant, pretrained, default_cfg=default_cfgs[variant], pretrained_strict=False, feature_cfg=dict(out_indices=(1, 2, 3, 4, 5)), **kwargs) @register_model def dla60_res2net(pretrained=False, **kwargs): model_kwargs = dict( levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024), block=DlaBottle2neck, cardinality=1, base_width=28, **kwargs) return _create_dla('dla60_res2net', pretrained, **model_kwargs) @register_model def dla60_res2next(pretrained=False,**kwargs): model_kwargs = dict( levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024), block=DlaBottle2neck, cardinality=8, base_width=4, **kwargs) return _create_dla('dla60_res2next', pretrained, **model_kwargs) @register_model def dla34(pretrained=False, **kwargs): # DLA-34 model_kwargs = dict( levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512], block=DlaBasic, **kwargs) return _create_dla('dla34', pretrained, **model_kwargs) @register_model def dla46_c(pretrained=False, **kwargs): # DLA-46-C model_kwargs = dict( levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256], block=DlaBottleneck, **kwargs) return _create_dla('dla46_c', pretrained, **model_kwargs) @register_model def dla46x_c(pretrained=False, **kwargs): # DLA-X-46-C model_kwargs = dict( levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256], block=DlaBottleneck, cardinality=32, base_width=4, **kwargs) return _create_dla('dla46x_c', pretrained, **model_kwargs) @register_model def dla60x_c(pretrained=False, **kwargs): # DLA-X-60-C 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)