##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

## Created by: Hang Zhang

## Email: zhanghang0704@gmail.com

## Copyright (c) 2020

##

## LICENSE file in the root directory of this source tree 

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""ResNeSt models"""

import torch

from .resnet import ResNet, Bottleneck

__all__ = ['resnest50', 'resnest101', 'resnest200', 'resnest269', 'resnest14', 'resnest26']

_url_format = 'https://hangzh.s3.amazonaws.com/encoding/models/{}-{}.pth'

_model_sha256 = {name: checksum for checksum, name in [

    ('528c19ca', 'resnest50'),

    ('22405ba7', 'resnest101'),

    ('75117900', 'resnest200'),

    ('0cc87c48', 'resnest269'),

    ]}

def short_hash(name):

    if name not in _model_sha256:

        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))

    return _model_sha256[name][:8]

resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for

    name in _model_sha256.keys()

}

def resnest14(pretrained=False, root='~/.encoding/models', **kwargs):

    model = ResNet(Bottleneck, [1, 1, 1, 1],

                   radix=2, groups=1, bottleneck_width=64,

                   deep_stem=True, stem_width=32, avg_down=True,

                   avd=True, avd_first=False, **kwargs)

    if pretrained:

        model.load_state_dict(torch.hub.load_state_dict_from_url(

            resnest_model_urls['resnest14'], progress=True, check_hash=True))

    return model

def resnest26(pretrained=False, root='~/.encoding/models', **kwargs):

    model = ResNet(Bottleneck, [2, 2, 2, 2],

                   radix=2, groups=1, bottleneck_width=64,

                   deep_stem=True, stem_width=32, avg_down=True,

                   avd=True, avd_first=False, **kwargs)

    if pretrained:

        model.load_state_dict(torch.hub.load_state_dict_from_url(

            resnest_model_urls['resnest26'], progress=True, check_hash=True))

    return model

def resnest50(pretrained=False, root='~/.encoding/models', **kwargs):

    model = ResNet(Bottleneck, [3, 4, 6, 3],

                   radix=2, groups=1, bottleneck_width=64,

                   deep_stem=True, stem_width=32, avg_down=True,

                   avd=True, avd_first=False, **kwargs)

    if pretrained:

        model.load_state_dict(torch.hub.load_state_dict_from_url(

            resnest_model_urls['resnest50'], progress=True, check_hash=True))

    return model

def resnest101(pretrained=False, root='~/.encoding/models', **kwargs):

    model = ResNet(Bottleneck, [3, 4, 23, 3],

                   radix=2, groups=1, bottleneck_width=64,

                   deep_stem=True, stem_width=64, avg_down=True,

                   avd=True, avd_first=False, **kwargs)

    if pretrained:

        model.load_state_dict(torch.hub.load_state_dict_from_url(

            resnest_model_urls['resnest101'], progress=True, check_hash=True))

    return model

def resnest200(pretrained=False, root='~/.encoding/models', **kwargs):

    model = ResNet(Bottleneck, [3, 24, 36, 3],

                   radix=2, groups=1, bottleneck_width=64,

                   deep_stem=True, stem_width=64, avg_down=True,

                   avd=True, avd_first=False, **kwargs)

    if pretrained:

        model.load_state_dict(torch.hub.load_state_dict_from_url(

            resnest_model_urls['resnest200'], progress=True, check_hash=True))

    return model

def resnest269(pretrained=False, root='~/.encoding/models', **kwargs):

    model = ResNet(Bottleneck, [3, 30, 48, 8],

                   radix=2, groups=1, bottleneck_width=64,

                   deep_stem=True, stem_width=64, avg_down=True,

                   avd=True, avd_first=False, **kwargs)

    if pretrained:

        model.load_state_dict(torch.hub.load_state_dict_from_url(

            resnest_model_urls['resnest269'], progress=True, check_hash=True))

    return model