image-match/python/py/Lib/site-packages/timm/models/res2net.py

""" Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
from typing import Optional, Type

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
from .resnet import ResNet

__all__ = []


class Bottle2neck(nn.Module):
    """ Res2Net/Res2NeXT Bottleneck
    Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
    """
    expansion = 4

    def __init__(
            self,
            inplanes: int,
            planes: int,
            stride: int = 1,
            downsample: Optional[nn.Module] = None,
            cardinality: int = 1,
            base_width: int = 26,
            scale: int = 4,
            dilation: int = 1,
            first_dilation: Optional[int] = None,
            act_layer: Type[nn.Module] = nn.ReLU,
            norm_layer: Optional[Type[nn.Module]] = None,
            attn_layer: Optional[Type[nn.Module]] = None,
            device=None,
            dtype=None,
            **_,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        self.scale = scale
        self.is_first = stride > 1 or downsample is not None
        self.num_scales = max(1, scale - 1)
        width = int(math.floor(planes * (base_width / 64.0))) * cardinality
        self.width = width
        outplanes = planes * self.expansion
        first_dilation = first_dilation or dilation

        self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False, **dd)
        self.bn1 = norm_layer(width * scale, **dd)

        convs = []
        bns = []
        for i in range(self.num_scales):
            convs.append(nn.Conv2d(
                width,
                width,
                kernel_size=3,
                stride=stride,
                padding=first_dilation,
                dilation=first_dilation,
                groups=cardinality,
                bias=False,
                **dd,
            ))
            bns.append(norm_layer(width, **dd))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        if self.is_first:
            # FIXME this should probably have count_include_pad=False, but hurts original weights
            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
        else:
            self.pool = None

        self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False, **dd)
        self.bn3 = norm_layer(outplanes, **dd)
        self.se = attn_layer(outplanes, **dd) if attn_layer is not None else None

        self.relu = act_layer(inplace=True)
        self.downsample = downsample

    def zero_init_last(self):
        if getattr(self.bn3, 'weight', None) is not None:
            nn.init.zeros_(self.bn3.weight)

    def forward(self, x):
        shortcut = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        spx = torch.split(out, self.width, 1)
        spo = []
        sp = spx[0]  # redundant, for torchscript
        for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
            if i == 0 or self.is_first:
                sp = spx[i]
            else:
                sp = sp + spx[i]
            sp = conv(sp)
            sp = bn(sp)
            sp = self.relu(sp)
            spo.append(sp)
        if self.scale > 1:
            if self.pool is not None:  # self.is_first == True, None check for torchscript
                spo.append(self.pool(spx[-1]))
            else:
                spo.append(spx[-1])
        out = torch.cat(spo, 1)

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        out += shortcut
        out = self.relu(out)

        return out


def _create_res2net(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)


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',
        'license': 'unknown',
        **kwargs
    }


default_cfgs = generate_default_cfgs({
    'res2net50_26w_4s.in1k': _cfg(hf_hub_id='timm/'),
    'res2net50_48w_2s.in1k': _cfg(hf_hub_id='timm/'),
    'res2net50_14w_8s.in1k': _cfg(hf_hub_id='timm/'),
    'res2net50_26w_6s.in1k': _cfg(hf_hub_id='timm/'),
    'res2net50_26w_8s.in1k': _cfg(hf_hub_id='timm/'),
    'res2net101_26w_4s.in1k': _cfg(hf_hub_id='timm/'),
    'res2next50.in1k': _cfg(hf_hub_id='timm/'),
    'res2net50d.in1k': _cfg(hf_hub_id='timm/', first_conv='conv1.0'),
    'res2net101d.in1k': _cfg(hf_hub_id='timm/', first_conv='conv1.0'),
})


@register_model
def res2net50_26w_4s(pretrained=False, **kwargs) -> ResNet:
    """Constructs a Res2Net-50 26w4s model.
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4))
    return _create_res2net('res2net50_26w_4s', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net101_26w_4s(pretrained=False, **kwargs) -> ResNet:
    """Constructs a Res2Net-101 26w4s model.
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4))
    return _create_res2net('res2net101_26w_4s', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net50_26w_6s(pretrained=False, **kwargs) -> ResNet:
    """Constructs a Res2Net-50 26w6s model.
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6))
    return _create_res2net('res2net50_26w_6s', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net50_26w_8s(pretrained=False, **kwargs) -> ResNet:
    """Constructs a Res2Net-50 26w8s model.
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8))
    return _create_res2net('res2net50_26w_8s', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net50_48w_2s(pretrained=False, **kwargs) -> ResNet:
    """Constructs a Res2Net-50 48w2s model.
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2))
    return _create_res2net('res2net50_48w_2s', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net50_14w_8s(pretrained=False, **kwargs) -> ResNet:
    """Constructs a Res2Net-50 14w8s model.
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8))
    return _create_res2net('res2net50_14w_8s', pretrained, **dict(model_args, **kwargs))


@register_model
def res2next50(pretrained=False, **kwargs) -> ResNet:
    """Construct Res2NeXt-50 4s
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4))
    return _create_res2net('res2next50', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net50d(pretrained=False, **kwargs) -> ResNet:
    """Construct Res2Net-50
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, stem_type='deep',
        avg_down=True, stem_width=32, block_args=dict(scale=4))
    return _create_res2net('res2net50d', pretrained, **dict(model_args, **kwargs))


@register_model
def res2net101d(pretrained=False, **kwargs) -> ResNet:
    """Construct Res2Net-50
    """
    model_args = dict(
        block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, stem_type='deep',
        avg_down=True, stem_width=32, block_args=dict(scale=4))
    return _create_res2net('res2net101d', pretrained, **dict(model_args, **kwargs))