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

"""Pytorch impl of MxNet Gluon ResNet/(SE)ResNeXt variants
This file evolved from https://github.com/pytorch/vision 'resnet.py' with (SE)-ResNeXt additions
and ports of Gluon variations (https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnet.py) 
by Ross Wightman
"""
import math

import torch
import torch.nn as nn
import torch.nn.functional as F

from .registry import register_model
from .helpers import load_pretrained
from .layers import SqueezeExcite
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD

from .resnet import ResNet, Bottleneck, BasicBlock


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'conv1', 'classifier': 'fc',
        **kwargs
    }


default_cfgs = {
    'gluon_resnet18_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
    'gluon_resnet34_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
    'gluon_resnet50_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
    'gluon_resnet101_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
    'gluon_resnet152_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
    'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth'),
    'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth'),
    'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth'),
    'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'),
    'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'),
    'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'),
    'gluon_resnet50_v1e': _cfg(url=''),
    'gluon_resnet101_v1e': _cfg(url=''),
    'gluon_resnet152_v1e': _cfg(url=''),
    'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'),
    'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'),
    'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'),
    'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
    'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
    'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
    'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
    'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
    'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
    'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth'),
}


@register_model
def gluon_resnet18_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-18 model.
    """
    default_cfg = default_cfgs['gluon_resnet18_v1b']
    model = ResNet(BasicBlock, [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


@register_model
def gluon_resnet34_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-34 model.
    """
    default_cfg = default_cfgs['gluon_resnet34_v1b']
    model = ResNet(BasicBlock, [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


@register_model
def gluon_resnet50_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-50 model.
    """
    default_cfg = default_cfgs['gluon_resnet50_v1b']
    model = ResNet(Bottleneck, [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


@register_model
def gluon_resnet101_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-101 model.
    """
    default_cfg = default_cfgs['gluon_resnet101_v1b']
    model = ResNet(Bottleneck, [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


@register_model
def gluon_resnet152_v1b(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-152 model.
    """
    default_cfg = default_cfgs['gluon_resnet152_v1b']
    model = ResNet(Bottleneck, [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


@register_model
def gluon_resnet50_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-50 model.
    """
    default_cfg = default_cfgs['gluon_resnet50_v1c']
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=32, stem_type='deep', **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet101_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-101 model.
    """
    default_cfg = default_cfgs['gluon_resnet101_v1c']
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=32, stem_type='deep', **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet152_v1c(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-152 model.
    """
    default_cfg = default_cfgs['gluon_resnet152_v1c']
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=32, stem_type='deep', **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet50_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-50 model.
    """
    default_cfg = default_cfgs['gluon_resnet50_v1d']
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=32, stem_type='deep', avg_down=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet101_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-101 model.
    """
    default_cfg = default_cfgs['gluon_resnet101_v1d']
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=32, stem_type='deep', avg_down=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet152_v1d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-152 model.
    """
    default_cfg = default_cfgs['gluon_resnet152_v1d']
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=32, stem_type='deep', avg_down=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet50_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-50-V1e model. No pretrained weights for any 'e' variants
    """
    default_cfg = default_cfgs['gluon_resnet50_v1e']
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=64, stem_type='deep', avg_down=True, **kwargs)
    model.default_cfg = default_cfg
    #if pretrained:
    #    load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet101_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-101 model.
    """
    default_cfg = default_cfgs['gluon_resnet101_v1e']
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=64, stem_type='deep', avg_down=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet152_v1e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-152 model.
    """
    default_cfg = default_cfgs['gluon_resnet152_v1e']
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=64, stem_type='deep', avg_down=True, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet50_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-50 model.
    """
    default_cfg = default_cfgs['gluon_resnet50_v1s']
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=64, stem_type='deep', **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet101_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-101 model.
    """
    default_cfg = default_cfgs['gluon_resnet101_v1s']
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=64, stem_type='deep', **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnet152_v1s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNet-152 model.
    """
    default_cfg = default_cfgs['gluon_resnet152_v1s']
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, in_chans=in_chans,
                   stem_width=64, stem_type='deep', **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNeXt50-32x4d model.
    """
    default_cfg = default_cfgs['gluon_resnext50_32x4d']
    model = ResNet(
        Bottleneck, [3, 4, 6, 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


@register_model
def gluon_resnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNeXt-101 model.
    """
    default_cfg = default_cfgs['gluon_resnext101_32x4d']
    model = ResNet(
        Bottleneck, [3, 4, 23, 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


@register_model
def gluon_resnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a ResNeXt-101 model.
    """
    default_cfg = default_cfgs['gluon_resnext101_64x4d']
    model = ResNet(
        Bottleneck, [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


@register_model
def gluon_seresnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a SEResNeXt50-32x4d model.
    """
    default_cfg = default_cfgs['gluon_seresnext50_32x4d']
    model = ResNet(
        Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SqueezeExcite), **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_seresnext101_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a SEResNeXt-101-32x4d model.
    """
    default_cfg = default_cfgs['gluon_seresnext101_32x4d']
    model = ResNet(
        Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4,
        num_classes=num_classes, in_chans=in_chans, block_args=dict(attn_layer=SqueezeExcite), **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_seresnext101_64x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a SEResNeXt-101-64x4d model.
    """
    default_cfg = default_cfgs['gluon_seresnext101_64x4d']
    block_args = dict(attn_layer=SqueezeExcite)
    model = ResNet(
        Bottleneck, [3, 4, 23, 3], cardinality=64, base_width=4,
        num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def gluon_senet154(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs an SENet-154 model.
    """
    default_cfg = default_cfgs['gluon_senet154']
    block_args = dict(attn_layer=SqueezeExcite)
    model = ResNet(
        Bottleneck, [3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', down_kernel_size=3,
        block_reduce_first=2, num_classes=num_classes, in_chans=in_chans, block_args=block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model