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| """ 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |