pytorch-image-models/timm/models/resnest.py

""" 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 math
import torch
import torch.nn.functional as F
from torch import nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropBlock2d
from .helpers import load_pretrained
from .layers import SelectiveKernelConv, ConvBnAct, create_attn
from .layers.split_attn import SplitAttnConv2d
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', '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://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
    'resnest101e': _cfg(
        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth', input_size=(3, 256, 256)),
    'resnest200e': _cfg(
        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth', input_size=(3, 320, 320)),
    'resnest269e': _cfg(
        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth', input_size=(3, 416, 416)),
    'resnest50d_4s2x40d': _cfg(
        url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_4s2x40d-41d14ed0.pth',
        interpolation='bicubic'),
    'resnest50d_1s4x24d': _cfg(
        url='https://hangzh.s3.amazonaws.com/encoding/models/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.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
        self.bn1 = norm_layer(group_width)
        self.drop_block1 = drop_block if drop_block is not None else None
        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 = SplitAttnConv2d(
                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 = None  # FIXME revisit, here to satisfy current torchscript fussyness
            self.drop_block2 = None
            self.act2 = None
        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.drop_block2 = drop_block if drop_block is not None else None
            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.drop_block3 = drop_block if drop_block is not None else None
        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):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        if self.drop_block1 is not None:
            out = self.drop_block1(out)
        out = self.act1(out)

        if self.avd_first is not None:
            out = self.avd_first(out)

        out = self.conv2(out)
        if self.bn2 is not None:
            out = self.bn2(out)
            if self.drop_block2 is not None:
                out = self.drop_block2(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_block3 is not None:
            out = self.drop_block3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.act3(out)
        return out


@register_model
def resnest14d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ ResNeSt-14d model. Weights ported from GluonCV.
    """
    default_cfg = default_cfgs['resnest14d']
    model = ResNet(
        ResNestBottleneck, [1, 1, 1, 1], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def resnest26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """ ResNeSt-26d model. Weights ported from GluonCV.
    """
    default_cfg = default_cfgs['resnest26d']
    model = ResNet(
        ResNestBottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def resnest50d(pretrained=False, num_classes=1000, in_chans=3, **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.
    """
    default_cfg = default_cfgs['resnest50d']
    model = ResNet(
        ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def resnest101e(pretrained=False, num_classes=1000, in_chans=3, **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.
    """
    default_cfg = default_cfgs['resnest101e']
    model = ResNet(
        ResNestBottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def resnest200e(pretrained=False, num_classes=1000, in_chans=3, **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.
    """
    default_cfg = default_cfgs['resnest200e']
    model = ResNet(
        ResNestBottleneck, [3, 24, 36, 3], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def resnest269e(pretrained=False, num_classes=1000, in_chans=3, **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.
    """
    default_cfg = default_cfgs['resnest269e']
    model = ResNet(
        ResNestBottleneck, [3, 30, 48, 8], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def resnest50d_1s4x24d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    default_cfg = default_cfgs['resnest50d_1s4x24d']
    model = ResNet(
        ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
        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)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model