##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## 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
from ..model_store import get_model_file

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

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


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.load(
            get_model_file('resnest50', root=root)), strict=False)
    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.load(
            get_model_file('resnest101', root=root)), strict=False)
    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.load(
            get_model_file('resnest200', root=root)), strict=False)
    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.load(
            get_model_file('resnest269', root=root)), strict=False)
    return model

def resnest50_fast(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=True, **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(
            get_model_file('resnest50fast', root=root)), strict=False)
    return model

def resnest101_fast(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=True, **kwargs)
    if pretrained:
        model.load_state_dict(torch.load(
            get_model_file('resnest101fast', root=root)), strict=False)
    return model