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"""Pytorch impl of MxNet Gluon ResNet/(SE)ResNeXt variants
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This file evolved from https://github.com/pytorch/vision 'resnet.py' with (SE)-ResNeXt additions
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and ports of Gluon variations (https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnet.py)
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by Ross Wightman
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"""
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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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import math
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from models.helpers import load_pretrained
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from models.adaptive_avgmax_pool import SelectAdaptivePool2d
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from data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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_models = [
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'gluon_resnet18_v1b', 'gluon_resnet34_v1b', 'gluon_resnet50_v1b', 'gluon_resnet101_v1b', 'gluon_resnet152_v1b',
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'gluon_resnet50_v1c', 'gluon_resnet101_v1c', 'gluon_resnet152_v1c', 'gluon_resnet50_v1d', 'gluon_resnet101_v1d',
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'gluon_resnet152_v1d', 'gluon_resnet50_v1e', 'gluon_resnet101_v1e', 'gluon_resnet152_v1e', 'gluon_resnet50_v1s',
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'gluon_resnet101_v1s', 'gluon_resnet152_v1s', 'gluon_resnext50_32x4d', 'gluon_resnext101_32x4d',
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'gluon_resnext101_64x4d', 'gluon_resnext152_32x4d', 'gluon_seresnext50_32x4d', 'gluon_seresnext101_32x4d',
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'gluon_seresnext101_64x4d', 'gluon_seresnext152_32x4d', 'gluon_senet154']
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__all__ = ['GluonResNet'] + _models
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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': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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'gluon_resnet18_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
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'gluon_resnet34_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
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'gluon_resnet50_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
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'gluon_resnet101_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
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'gluon_resnet152_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
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'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth'),
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'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth'),
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'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth'),
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'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'),
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'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'),
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'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'),
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'gluon_resnet50_v1e': _cfg(url=''),
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'gluon_resnet101_v1e': _cfg(url=''),
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'gluon_resnet152_v1e': _cfg(url=''),
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'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'),
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'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'),
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'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'),
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'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
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'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
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'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
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'gluon_resnext152_32x4d': _cfg(url=''),
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'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
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'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
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'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
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'gluon_seresnext152_32x4d': _cfg(url=''),
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'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth'),
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}
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def _get_padding(kernel_size, stride, dilation=1):
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padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
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return padding
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class SEModule(nn.Module):
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def __init__(self, channels, reduction_channels):
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super(SEModule, self).__init__()
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#self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc1 = nn.Conv2d(
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channels, reduction_channels, kernel_size=1, padding=0, bias=True)
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self.relu = nn.ReLU()
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self.fc2 = nn.Conv2d(
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reduction_channels, channels, kernel_size=1, padding=0, bias=True)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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module_input = x
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#x = self.avg_pool(x)
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x = x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.sigmoid(x)
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return module_input * x
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class BasicBlockGl(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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cardinality=1, base_width=64, use_se=False,
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reduce_first=1, dilation=1, previous_dilation=1, norm_layer=nn.BatchNorm2d):
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super(BasicBlockGl, self).__init__()
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assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
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assert base_width == 64, 'BasicBlock doest not support changing base width'
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first_planes = planes // reduce_first
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outplanes = planes * self.expansion
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self.conv1 = nn.Conv2d(
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inplanes, first_planes, kernel_size=3, stride=stride, padding=dilation,
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dilation=dilation, bias=False)
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self.bn1 = norm_layer(first_planes)
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self.relu = nn.ReLU()
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self.conv2 = nn.Conv2d(
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first_planes, outplanes, kernel_size=3, padding=previous_dilation,
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dilation=previous_dilation, bias=False)
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self.bn2 = norm_layer(outplanes)
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self.se = SEModule(outplanes, planes // 4) if use_se else None
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self.downsample = downsample
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self.stride = stride
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self.dilation = dilation
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def forward(self, x):
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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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if self.se is not None:
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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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 BottleneckGl(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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cardinality=1, base_width=64, use_se=False,
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reduce_first=1, dilation=1, previous_dilation=1, norm_layer=nn.BatchNorm2d):
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super(BottleneckGl, self).__init__()
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width = int(math.floor(planes * (base_width / 64)) * cardinality)
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first_planes = width // reduce_first
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outplanes = planes * self.expansion
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self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
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self.bn1 = norm_layer(first_planes)
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self.conv2 = nn.Conv2d(
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first_planes, width, kernel_size=3, stride=stride,
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padding=dilation, dilation=dilation, groups=cardinality, bias=False)
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self.bn2 = norm_layer(width)
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self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
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self.bn3 = norm_layer(outplanes)
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self.se = SEModule(outplanes, planes // 4) if use_se else None
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self.relu = nn.ReLU()
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self.downsample = downsample
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self.stride = stride
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self.dilation = dilation
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def forward(self, x):
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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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if self.se is not None:
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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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 GluonResNet(nn.Module):
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""" Gluon ResNet (https://gluon-cv.mxnet.io/model_zoo/classification.html)
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This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet found in the gluon model zoo that
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* have stride in 3x3 conv layer of bottleneck
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* have conv-bn-act ordering
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Included ResNet variants are:
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* v1b - 7x7 stem, stem_width=64, same as torchvision ResNet (checkpoint compatible), or NVIDIA ResNet 'v1.5'
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* v1c - 3 layer deep 3x3 stem, stem_width = 32
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* v1d - 3 layer deep 3x3 stem, stem_width = 32, average pool in downsample
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* v1e - 3 layer deep 3x3 stem, stem_width = 64, average pool in downsample *no pretrained weights available
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* v1s - 3 layer deep 3x3 stem, stem_width = 64
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ResNeXt is standard and checkpoint compatible with torchvision pretrained models. 7x7 stem,
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stem_width = 64, standard cardinality and base width calcs
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SE-ResNeXt is standard. 7x7 stem, stem_width = 64,
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checkpoints are not compatible with Cadene pretrained, but could be with key mapping
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SENet-154 is standard. 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
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reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
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Original ResNet-V1, ResNet-V2 (bn-act-conv), and SE-ResNet (stride in first bottleneck conv) are NOT supported.
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They do have Gluon pretrained weights but are, at best, comparable (or inferior) to the supported models.
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Parameters
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----------
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block : Block
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Class for the residual block. Options are BasicBlockGl, BottleneckGl.
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layers : list of int
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Numbers of layers in each block
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num_classes : int, default 1000
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Number of classification classes.
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deep_stem : bool, default False
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Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
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block_reduce_first: int, default 1
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Reduction factor for first convolution output width of residual blocks,
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1 for all archs except senets, where 2
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down_kernel_size: int, default 1
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Kernel size of residual block downsampling path, 1x1 for most archs, 3x3 for senets
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avg_down : bool, default False
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Whether to use average pooling for projection skip connection between stages/downsample.
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dilated : bool, default False
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Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
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typically used in Semantic Segmentation.
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"""
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def __init__(self, block, layers, num_classes=1000, in_chans=3, use_se=False,
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cardinality=1, base_width=64, stem_width=64, deep_stem=False,
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block_reduce_first=1, down_kernel_size=1, avg_down=False, dilated=False,
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norm_layer=nn.BatchNorm2d, drop_rate=0.0, global_pool='avg'):
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self.num_classes = num_classes
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self.inplanes = stem_width * 2 if deep_stem else 64
|
|
|
|
|
self.cardinality = cardinality
|
|
|
|
|
self.base_width = base_width
|
|
|
|
|
self.drop_rate = drop_rate
|
|
|
|
|
self.expansion = block.expansion
|
|
|
|
|
self.dilated = dilated
|
|
|
|
|
super(GluonResNet, self).__init__()
|
|
|
|
|
|
|
|
|
|
if not deep_stem:
|
|
|
|
|
self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=7, stride=2, padding=3, bias=False)
|
|
|
|
|
else:
|
|
|
|
|
conv1_modules = [
|
|
|
|
|
nn.Conv2d(in_chans, stem_width, 3, stride=2, padding=1, bias=False),
|
|
|
|
|
norm_layer(stem_width),
|
|
|
|
|
nn.ReLU(),
|
|
|
|
|
nn.Conv2d(stem_width, stem_width, 3, stride=1, padding=1, bias=False),
|
|
|
|
|
norm_layer(stem_width),
|
|
|
|
|
nn.ReLU(),
|
|
|
|
|
nn.Conv2d(stem_width, self.inplanes, 3, stride=1, padding=1, bias=False),
|
|
|
|
|
]
|
|
|
|
|
self.conv1 = nn.Sequential(*conv1_modules)
|
|
|
|
|
self.bn1 = norm_layer(self.inplanes)
|
|
|
|
|
self.relu = nn.ReLU()
|
|
|
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
|
|
|
stride_3_4 = 1 if self.dilated else 2
|
|
|
|
|
dilation_3 = 2 if self.dilated else 1
|
|
|
|
|
dilation_4 = 4 if self.dilated else 1
|
|
|
|
|
self.layer1 = self._make_layer(
|
|
|
|
|
block, 64, layers[0], stride=1, reduce_first=block_reduce_first,
|
|
|
|
|
use_se=use_se, avg_down=avg_down, down_kernel_size=1, norm_layer=norm_layer)
|
|
|
|
|
self.layer2 = self._make_layer(
|
|
|
|
|
block, 128, layers[1], stride=2, reduce_first=block_reduce_first,
|
|
|
|
|
use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
|
|
|
|
|
self.layer3 = self._make_layer(
|
|
|
|
|
block, 256, layers[2], stride=stride_3_4, dilation=dilation_3, reduce_first=block_reduce_first,
|
|
|
|
|
use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
|
|
|
|
|
self.layer4 = self._make_layer(
|
|
|
|
|
block, 512, layers[3], stride=stride_3_4, dilation=dilation_4, reduce_first=block_reduce_first,
|
|
|
|
|
use_se=use_se, avg_down=avg_down, down_kernel_size=down_kernel_size, norm_layer=norm_layer)
|
|
|
|
|
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
|
|
|
|
self.num_features = 512 * block.expansion
|
|
|
|
|
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
|
|
|
|
|
|
|
|
|
|
for m in self.modules():
|
|
|
|
|
if isinstance(m, nn.Conv2d):
|
|
|
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
|
|
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
|
|
|
nn.init.constant_(m.weight, 1.)
|
|
|
|
|
nn.init.constant_(m.bias, 0.)
|
|
|
|
|
|
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, reduce_first=1,
|
|
|
|
|
use_se=False, avg_down=False, down_kernel_size=1, norm_layer=nn.BatchNorm2d):
|
|
|
|
|
downsample = None
|
|
|
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
|
|
|
downsample_padding = _get_padding(down_kernel_size, stride)
|
|
|
|
|
if avg_down:
|
|
|
|
|
avg_stride = stride if dilation == 1 else 1
|
|
|
|
|
downsample_layers = [
|
|
|
|
|
nn.AvgPool2d(avg_stride, avg_stride, ceil_mode=True, count_include_pad=False),
|
|
|
|
|
nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
|
|
|
|
|
stride=1, padding=downsample_padding, bias=False),
|
|
|
|
|
norm_layer(planes * block.expansion),
|
|
|
|
|
]
|
|
|
|
|
else:
|
|
|
|
|
downsample_layers = [
|
|
|
|
|
nn.Conv2d(self.inplanes, planes * block.expansion, down_kernel_size,
|
|
|
|
|
stride=stride, padding=downsample_padding, bias=False),
|
|
|
|
|
norm_layer(planes * block.expansion),
|
|
|
|
|
]
|
|
|
|
|
downsample = nn.Sequential(*downsample_layers)
|
|
|
|
|
|
|
|
|
|
first_dilation = 1 if dilation in (1, 2) else 2
|
|
|
|
|
layers = [block(
|
|
|
|
|
self.inplanes, planes, stride, downsample,
|
|
|
|
|
cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
|
|
|
|
|
use_se=use_se, dilation=first_dilation, previous_dilation=dilation, norm_layer=norm_layer)]
|
|
|
|
|
self.inplanes = planes * block.expansion
|
|
|
|
|
for i in range(1, blocks):
|
|
|
|
|
layers.append(block(
|
|
|
|
|
self.inplanes, planes,
|
|
|
|
|
cardinality=self.cardinality, base_width=self.base_width, reduce_first=reduce_first,
|
|
|
|
|
use_se=use_se, dilation=dilation, previous_dilation=dilation, norm_layer=norm_layer))
|
|
|
|
|
|
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
|
|
|
|
def get_classifier(self):
|
|
|
|
|
return self.fc
|
|
|
|
|
|
|
|
|
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
|
|
|
|
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
|
|
|
|
self.num_classes = num_classes
|
|
|
|
|
del self.fc
|
|
|
|
|
if num_classes:
|
|
|
|
|
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
|
|
|
|
|
else:
|
|
|
|
|
self.fc = None
|
|
|
|
|
|
|
|
|
|
def forward_features(self, x, pool=True):
|
|
|
|
|
x = self.conv1(x)
|
|
|
|
|
x = self.bn1(x)
|
|
|
|
|
x = self.relu(x)
|
|
|
|
|
x = self.maxpool(x)
|
|
|
|
|
|
|
|
|
|
x = self.layer1(x)
|
|
|
|
|
x = self.layer2(x)
|
|
|
|
|
x = self.layer3(x)
|
|
|
|
|
x = self.layer4(x)
|
|
|
|
|
|
|
|
|
|
if pool:
|
|
|
|
|
x = self.global_pool(x)
|
|
|
|
|
x = x.view(x.size(0), -1)
|
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
x = self.forward_features(x)
|
|
|
|
|
if self.drop_rate > 0.:
|
|
|
|
|
x = F.dropout(x, p=self.drop_rate, training=self.training)
|
|
|
|
|
x = self.fc(x)
|
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet18_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-18 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet18_v1b']
|
|
|
|
|
model = GluonResNet(BasicBlockGl, [2, 2, 2, 2], 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet34_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-34 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet34_v1b']
|
|
|
|
|
model = GluonResNet(BasicBlockGl, [3, 4, 6, 3], 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet50_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet50_v1b']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet101_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-101 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet101_v1b']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet152_v1b(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-152 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet152_v1b']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet50_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet50_v1c']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=32, deep_stem=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet101_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-101 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet101_v1c']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=32, deep_stem=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet152_v1c(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-152 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet152_v1c']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=32, deep_stem=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet50_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet50_v1d']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=32, deep_stem=True, avg_down=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet101_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-101 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet101_v1d']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=32, deep_stem=True, avg_down=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet152_v1d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-152 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet152_v1d']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=32, deep_stem=True, avg_down=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet50_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-50-V1e model. No pretrained weights for any 'e' variants
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet50_v1e']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=64, deep_stem=True, avg_down=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
#if pretrained:
|
|
|
|
|
# load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet101_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-101 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet101_v1e']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
|
|
|
|
|
stem_width=64, deep_stem=True, avg_down=True, **kwargs)
|
|
|
|
|
model.default_cfg = default_cfg
|
|
|
|
|
if pretrained:
|
|
|
|
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnet152_v1e(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNet-152 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnet152_v1e']
|
|
|
|
|
model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
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stem_width=64, deep_stem=True, avg_down=True, **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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def gluon_resnet50_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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"""
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default_cfg = default_cfgs['gluon_resnet50_v1s']
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model = GluonResNet(BottleneckGl, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
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stem_width=64, deep_stem=True, **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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def gluon_resnet101_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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"""
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default_cfg = default_cfgs['gluon_resnet101_v1s']
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model = GluonResNet(BottleneckGl, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
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stem_width=64, deep_stem=True, **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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def gluon_resnet152_v1s(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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"""
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default_cfg = default_cfgs['gluon_resnet152_v1s']
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model = GluonResNet(BottleneckGl, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
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stem_width=64, deep_stem=True, **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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def gluon_resnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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"""Constructs a ResNeXt50-32x4d model.
|
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"""
|
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|
default_cfg = default_cfgs['gluon_resnext50_32x4d']
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model = GluonResNet(
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BottleneckGl, [3, 4, 6, 3], 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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def gluon_resnext101_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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|
"""Constructs a ResNeXt-101 model.
|
|
|
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|
"""
|
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|
default_cfg = default_cfgs['gluon_resnext101_32x4d']
|
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|
model = GluonResNet(
|
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|
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|
BottleneckGl, [3, 4, 23, 3], 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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|
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|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
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|
return model
|
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def gluon_resnext101_64x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt-101 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnext101_64x4d']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 4, 23, 3], cardinality=64, 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_resnext152_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a ResNeXt152-32x4d model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_resnext152_32x4d']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 8, 36, 3], 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_seresnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a SEResNeXt50-32x4d model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_seresnext50_32x4d']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 4, 6, 3], cardinality=32, base_width=4, use_se=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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_seresnext101_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a SEResNeXt-101-32x4d model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_seresnext101_32x4d']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 4, 23, 3], cardinality=32, base_width=4, use_se=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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_seresnext101_64x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a SEResNeXt-101-64x4d model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_seresnext101_64x4d']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 4, 23, 3], cardinality=64, base_width=4, use_se=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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_seresnext152_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs a SEResNeXt152-32x4d model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_seresnext152_32x4d']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 8, 36, 3], cardinality=32, base_width=4, use_se=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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def gluon_senet154(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
|
|
|
|
|
"""Constructs an SENet-154 model.
|
|
|
|
|
"""
|
|
|
|
|
default_cfg = default_cfgs['gluon_senet154']
|
|
|
|
|
model = GluonResNet(
|
|
|
|
|
BottleneckGl, [3, 8, 36, 3], cardinality=64, base_width=4, use_se=True,
|
|
|
|
|
deep_stem=True, down_kernel_size=3, block_reduce_first=2,
|
|
|
|
|
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
|
|
|
|
|
|
