Spaces:
Runtime error
Runtime error
| """ ResNeSt Models | |
| Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955 | |
| Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang | |
| Modified for torchscript compat, and consistency with timm by Ross Wightman | |
| """ | |
| import torch | |
| from torch import nn | |
| from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD | |
| from .helpers import build_model_with_cfg | |
| from .layers import SplitAttn | |
| from .registry import register_model | |
| from .resnet import ResNet | |
| 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': 'conv1.0', 'classifier': 'fc', | |
| **kwargs | |
| } | |
| default_cfgs = { | |
| 'resnest14d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'), | |
| 'resnest26d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'), | |
| 'resnest50d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50-528c19ca.pth'), | |
| 'resnest101e': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth', | |
| input_size=(3, 256, 256), pool_size=(8, 8)), | |
| 'resnest200e': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest200-75117900.pth', | |
| input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=0.909, interpolation='bicubic'), | |
| 'resnest269e': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest269-0cc87c48.pth', | |
| input_size=(3, 416, 416), pool_size=(13, 13), crop_pct=0.928, interpolation='bicubic'), | |
| 'resnest50d_4s2x40d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_4s2x40d-41d14ed0.pth', | |
| interpolation='bicubic'), | |
| 'resnest50d_1s4x24d': _cfg( | |
| url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_1s4x24d-d4a4f76f.pth', | |
| interpolation='bicubic') | |
| } | |
| class ResNestBottleneck(nn.Module): | |
| """ResNet Bottleneck | |
| """ | |
| # pylint: disable=unused-argument | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, | |
| radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False, | |
| reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, | |
| attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): | |
| super(ResNestBottleneck, self).__init__() | |
| assert reduce_first == 1 # not supported | |
| assert attn_layer is None # not supported | |
| assert aa_layer is None # TODO not yet supported | |
| assert drop_path is None # TODO not yet supported | |
| group_width = int(planes * (base_width / 64.)) * cardinality | |
| first_dilation = first_dilation or dilation | |
| if avd and (stride > 1 or is_first): | |
| avd_stride = stride | |
| stride = 1 | |
| else: | |
| avd_stride = 0 | |
| self.radix = radix | |
| self.drop_block = drop_block | |
| self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False) | |
| self.bn1 = norm_layer(group_width) | |
| self.act1 = act_layer(inplace=True) | |
| self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None | |
| if self.radix >= 1: | |
| self.conv2 = SplitAttn( | |
| group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation, | |
| dilation=first_dilation, groups=cardinality, radix=radix, norm_layer=norm_layer, drop_block=drop_block) | |
| self.bn2 = nn.Identity() | |
| self.act2 = nn.Identity() | |
| else: | |
| self.conv2 = nn.Conv2d( | |
| group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation, | |
| dilation=first_dilation, groups=cardinality, bias=False) | |
| self.bn2 = norm_layer(group_width) | |
| self.act2 = act_layer(inplace=True) | |
| self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None | |
| self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = norm_layer(planes*4) | |
| self.act3 = act_layer(inplace=True) | |
| self.downsample = downsample | |
| def zero_init_last_bn(self): | |
| nn.init.zeros_(self.bn3.weight) | |
| def forward(self, x): | |
| shortcut = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| if self.drop_block is not None: | |
| out = self.drop_block(out) | |
| out = self.act1(out) | |
| if self.avd_first is not None: | |
| out = self.avd_first(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.drop_block is not None: | |
| out = self.drop_block(out) | |
| out = self.act2(out) | |
| if self.avd_last is not None: | |
| out = self.avd_last(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.drop_block is not None: | |
| out = self.drop_block(out) | |
| if self.downsample is not None: | |
| shortcut = self.downsample(x) | |
| out += shortcut | |
| out = self.act3(out) | |
| return out | |
| def _create_resnest(variant, pretrained=False, **kwargs): | |
| return build_model_with_cfg( | |
| ResNet, variant, pretrained, | |
| default_cfg=default_cfgs[variant], | |
| **kwargs) | |
| def resnest14d(pretrained=False, **kwargs): | |
| """ ResNeSt-14d model. Weights ported from GluonCV. | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[1, 1, 1, 1], | |
| stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1, | |
| block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) | |
| return _create_resnest('resnest14d', pretrained=pretrained, **model_kwargs) | |
| def resnest26d(pretrained=False, **kwargs): | |
| """ ResNeSt-26d model. Weights ported from GluonCV. | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[2, 2, 2, 2], | |
| stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1, | |
| block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) | |
| return _create_resnest('resnest26d', pretrained=pretrained, **model_kwargs) | |
| def resnest50d(pretrained=False, **kwargs): | |
| """ ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955 | |
| Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample. | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[3, 4, 6, 3], | |
| stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1, | |
| block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) | |
| return _create_resnest('resnest50d', pretrained=pretrained, **model_kwargs) | |
| def resnest101e(pretrained=False, **kwargs): | |
| """ ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955 | |
| Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample. | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[3, 4, 23, 3], | |
| stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1, | |
| block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) | |
| return _create_resnest('resnest101e', pretrained=pretrained, **model_kwargs) | |
| def resnest200e(pretrained=False, **kwargs): | |
| """ ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955 | |
| Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample. | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[3, 24, 36, 3], | |
| stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1, | |
| block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) | |
| return _create_resnest('resnest200e', pretrained=pretrained, **model_kwargs) | |
| def resnest269e(pretrained=False, **kwargs): | |
| """ ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955 | |
| Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample. | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[3, 30, 48, 8], | |
| stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1, | |
| block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) | |
| return _create_resnest('resnest269e', pretrained=pretrained, **model_kwargs) | |
| def resnest50d_4s2x40d(pretrained=False, **kwargs): | |
| """ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[3, 4, 6, 3], | |
| stem_type='deep', stem_width=32, avg_down=True, base_width=40, cardinality=2, | |
| block_args=dict(radix=4, avd=True, avd_first=True), **kwargs) | |
| return _create_resnest('resnest50d_4s2x40d', pretrained=pretrained, **model_kwargs) | |
| def resnest50d_1s4x24d(pretrained=False, **kwargs): | |
| """ResNeSt-50 1s4x24d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md | |
| """ | |
| model_kwargs = dict( | |
| block=ResNestBottleneck, layers=[3, 4, 6, 3], | |
| stem_type='deep', stem_width=32, avg_down=True, base_width=24, cardinality=4, | |
| block_args=dict(radix=1, avd=True, avd_first=True), **kwargs) | |
| return _create_resnest('resnest50d_1s4x24d', pretrained=pretrained, **model_kwargs) | |