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pytorch-image-models/timm/models/resnetv2.py

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"""Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfoer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their original .NPZ checkpoints.
Additionally, supports non pre-activation bottleneck for use as a backbone for Vision Transfomers (ViT) and
extra padding support to allow porting of official Hybrid ResNet pretrained weights from
https://github.com/google-research/vision_transformer
Thanks to the Google team for the above two repositories and associated papers:
* Big Transfer (BiT): General Visual Representation Learning - https://arxiv.org/abs/1912.11370
* An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - https://arxiv.org/abs/2010.11929
* Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
Original copyright of Google code below, modifications by Ross Wightman, Copyright 2020.
"""
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict # pylint: disable=g-importing-member
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dB0, EvoNorm2dS0, FilterResponseNormTlu2d, \
ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv
from ._registry import register_model
__all__ = ['ResNetV2'] # model_registry will add each entrypoint fn to this
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_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = {
# pretrained on imagenet21k, finetuned on imagenet1k
'resnetv2_50x1_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_50x3_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_101x1_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_101x3_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_152x2_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, custom_load=True),
'resnetv2_152x4_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz',
input_size=(3, 480, 480), pool_size=(15, 15), crop_pct=1.0, custom_load=True), # only one at 480x480?
# trained on imagenet-21k
'resnetv2_50x1_bitm_in21k': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R50x1.npz',
num_classes=21843, custom_load=True),
'resnetv2_50x3_bitm_in21k': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R50x3.npz',
num_classes=21843, custom_load=True),
'resnetv2_101x1_bitm_in21k': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R101x1.npz',
num_classes=21843, custom_load=True),
'resnetv2_101x3_bitm_in21k': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R101x3.npz',
num_classes=21843, custom_load=True),
'resnetv2_152x2_bitm_in21k': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R152x2.npz',
num_classes=21843, custom_load=True),
'resnetv2_152x4_bitm_in21k': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R152x4.npz',
num_classes=21843, custom_load=True),
'resnetv2_50x1_bit_distilled': _cfg(
url='https://storage.googleapis.com/bit_models/distill/R50x1_224.npz',
interpolation='bicubic', custom_load=True),
'resnetv2_152x2_bit_teacher': _cfg(
url='https://storage.googleapis.com/bit_models/distill/R152x2_T_224.npz',
interpolation='bicubic', custom_load=True),
'resnetv2_152x2_bit_teacher_384': _cfg(
url='https://storage.googleapis.com/bit_models/distill/R152x2_T_384.npz',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, interpolation='bicubic', custom_load=True),
'resnetv2_50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetv2_50_a1h-000cdf49.pth',
interpolation='bicubic', crop_pct=0.95),
'resnetv2_50d': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_50t': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_101': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetv2_101_a1h-5d01f016.pth',
interpolation='bicubic', crop_pct=0.95),
'resnetv2_101d': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_152': _cfg(
interpolation='bicubic'),
'resnetv2_152d': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_50d_gn': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnetv2_50d_gn_ah-c415c11a.pth',
interpolation='bicubic', first_conv='stem.conv1', test_input_size=(3, 288, 288), crop_pct=0.95),
'resnetv2_50d_evob': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
'resnetv2_50d_evos': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnetv2_50d_evos_ah-7c4dd548.pth',
interpolation='bicubic', first_conv='stem.conv1', test_input_size=(3, 288, 288), crop_pct=0.95),
'resnetv2_50d_frn': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
}
def make_div(v, divisor=8):
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
class PreActBottleneck(nn.Module):
"""Pre-activation (v2) bottleneck block.
Follows the implementation of "Identity Mappings in Deep Residual Networks":
https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
Except it puts the stride on 3x3 conv when available.
"""
def __init__(
self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1,
act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.):
super().__init__()
first_dilation = first_dilation or dilation
conv_layer = conv_layer or StdConv2d
norm_layer = norm_layer or partial(GroupNormAct, num_groups=32)
out_chs = out_chs or in_chs
mid_chs = make_div(out_chs * bottle_ratio)
if proj_layer is not None:
self.downsample = proj_layer(
in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, preact=True,
conv_layer=conv_layer, norm_layer=norm_layer)
else:
self.downsample = None
self.norm1 = norm_layer(in_chs)
self.conv1 = conv_layer(in_chs, mid_chs, 1)
self.norm2 = norm_layer(mid_chs)
self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
self.norm3 = norm_layer(mid_chs)
self.conv3 = conv_layer(mid_chs, out_chs, 1)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last(self):
nn.init.zeros_(self.conv3.weight)
def forward(self, x):
x_preact = self.norm1(x)
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(x_preact)
# residual branch
x = self.conv1(x_preact)
x = self.conv2(self.norm2(x))
x = self.conv3(self.norm3(x))
x = self.drop_path(x)
return x + shortcut
class Bottleneck(nn.Module):
"""Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT.
"""
def __init__(
self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1,
act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.):
super().__init__()
first_dilation = first_dilation or dilation
act_layer = act_layer or nn.ReLU
conv_layer = conv_layer or StdConv2d
norm_layer = norm_layer or partial(GroupNormAct, num_groups=32)
out_chs = out_chs or in_chs
mid_chs = make_div(out_chs * bottle_ratio)
if proj_layer is not None:
self.downsample = proj_layer(
in_chs, out_chs, stride=stride, dilation=dilation, preact=False,
conv_layer=conv_layer, norm_layer=norm_layer)
else:
self.downsample = None
self.conv1 = conv_layer(in_chs, mid_chs, 1)
self.norm1 = norm_layer(mid_chs)
self.conv2 = conv_layer(mid_chs, mid_chs, 3, stride=stride, dilation=first_dilation, groups=groups)
self.norm2 = norm_layer(mid_chs)
self.conv3 = conv_layer(mid_chs, out_chs, 1)
self.norm3 = norm_layer(out_chs, apply_act=False)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
self.act3 = act_layer(inplace=True)
def zero_init_last(self):
nn.init.zeros_(self.norm3.weight)
def forward(self, x):
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(x)
# residual
x = self.conv1(x)
x = self.norm1(x)
x = self.conv2(x)
x = self.norm2(x)
x = self.conv3(x)
x = self.norm3(x)
x = self.drop_path(x)
x = self.act3(x + shortcut)
return x
class DownsampleConv(nn.Module):
def __init__(
self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, preact=True,
conv_layer=None, norm_layer=None):
super(DownsampleConv, self).__init__()
self.conv = conv_layer(in_chs, out_chs, 1, stride=stride)
self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)
def forward(self, x):
return self.norm(self.conv(x))
class DownsampleAvg(nn.Module):
def __init__(
self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None,
preact=True, conv_layer=None, norm_layer=None):
""" AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
super(DownsampleAvg, self).__init__()
avg_stride = stride if dilation == 1 else 1
if stride > 1 or dilation > 1:
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
else:
self.pool = nn.Identity()
self.conv = conv_layer(in_chs, out_chs, 1, stride=1)
self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False)
def forward(self, x):
return self.norm(self.conv(self.pool(x)))
class ResNetStage(nn.Module):
"""ResNet Stage."""
def __init__(
self, in_chs, out_chs, stride, dilation, depth, bottle_ratio=0.25, groups=1,
avg_down=False, block_dpr=None, block_fn=PreActBottleneck,
act_layer=None, conv_layer=None, norm_layer=None, **block_kwargs):
super(ResNetStage, self).__init__()
first_dilation = 1 if dilation in (1, 2) else 2
layer_kwargs = dict(act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer)
proj_layer = DownsampleAvg if avg_down else DownsampleConv
prev_chs = in_chs
self.blocks = nn.Sequential()
for block_idx in range(depth):
drop_path_rate = block_dpr[block_idx] if block_dpr else 0.
stride = stride if block_idx == 0 else 1
self.blocks.add_module(str(block_idx), block_fn(
prev_chs, out_chs, stride=stride, dilation=dilation, bottle_ratio=bottle_ratio, groups=groups,
first_dilation=first_dilation, proj_layer=proj_layer, drop_path_rate=drop_path_rate,
**layer_kwargs, **block_kwargs))
prev_chs = out_chs
first_dilation = dilation
proj_layer = None
def forward(self, x):
x = self.blocks(x)
return x
def is_stem_deep(stem_type):
return any([s in stem_type for s in ('deep', 'tiered')])
def create_resnetv2_stem(
in_chs, out_chs=64, stem_type='', preact=True,
conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32)):
stem = OrderedDict()
assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same', 'tiered')
# NOTE conv padding mode can be changed by overriding the conv_layer def
if is_stem_deep(stem_type):
# A 3 deep 3x3 conv stack as in ResNet V1D models
if 'tiered' in stem_type:
stem_chs = (3 * out_chs // 8, out_chs // 2) # 'T' resnets in resnet.py
else:
stem_chs = (out_chs // 2, out_chs // 2) # 'D' ResNets
stem['conv1'] = conv_layer(in_chs, stem_chs[0], kernel_size=3, stride=2)
stem['norm1'] = norm_layer(stem_chs[0])
stem['conv2'] = conv_layer(stem_chs[0], stem_chs[1], kernel_size=3, stride=1)
stem['norm2'] = norm_layer(stem_chs[1])
stem['conv3'] = conv_layer(stem_chs[1], out_chs, kernel_size=3, stride=1)
if not preact:
stem['norm3'] = norm_layer(out_chs)
else:
# The usual 7x7 stem conv
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2)
if not preact:
stem['norm'] = norm_layer(out_chs)
if 'fixed' in stem_type:
# 'fixed' SAME padding approximation that is used in BiT models
stem['pad'] = nn.ConstantPad2d(1, 0.)
stem['pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
elif 'same' in stem_type:
# full, input size based 'SAME' padding, used in ViT Hybrid model
stem['pool'] = create_pool2d('max', kernel_size=3, stride=2, padding='same')
else:
# the usual PyTorch symmetric padding
stem['pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
return nn.Sequential(stem)
class ResNetV2(nn.Module):
"""Implementation of Pre-activation (v2) ResNet mode.
"""
def __init__(
self, layers, channels=(256, 512, 1024, 2048),
num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
width_factor=1, stem_chs=64, stem_type='', avg_down=False, preact=True,
act_layer=nn.ReLU, conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32),
drop_rate=0., drop_path_rate=0., zero_init_last=False):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
wf = width_factor
self.feature_info = []
stem_chs = make_div(stem_chs * wf)
self.stem = create_resnetv2_stem(
in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer)
stem_feat = ('stem.conv3' if is_stem_deep(stem_type) else 'stem.conv') if preact else 'stem.norm'
self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=stem_feat))
prev_chs = stem_chs
curr_stride = 4
dilation = 1
block_dprs = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(layers)).split(layers)]
block_fn = PreActBottleneck if preact else Bottleneck
self.stages = nn.Sequential()
for stage_idx, (d, c, bdpr) in enumerate(zip(layers, channels, block_dprs)):
out_chs = make_div(c * wf)
stride = 1 if stage_idx == 0 else 2
if curr_stride >= output_stride:
dilation *= stride
stride = 1
stage = ResNetStage(
prev_chs, out_chs, stride=stride, dilation=dilation, depth=d, avg_down=avg_down,
act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer, block_dpr=bdpr, block_fn=block_fn)
prev_chs = out_chs
curr_stride *= stride
self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{stage_idx}')]
self.stages.add_module(str(stage_idx), stage)
self.num_features = prev_chs
self.norm = norm_layer(self.num_features) if preact else nn.Identity()
self.head = ClassifierHead(
self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, use_conv=True)
self.init_weights(zero_init_last=zero_init_last)
self.grad_checkpointing = False
@torch.jit.ignore
def init_weights(self, zero_init_last=True):
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix='resnet/'):
_load_weights(self, checkpoint_path, prefix)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^norm', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.head = ClassifierHead(
self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, use_conv=True)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x, flatten=True)
else:
x = self.stages(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module: nn.Module, name: str = '', zero_init_last=True):
if isinstance(module, nn.Linear) or ('head.fc' in name and isinstance(module, nn.Conv2d)):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.BatchNorm2d, nn.LayerNorm, nn.GroupNorm)):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif zero_init_last and hasattr(module, 'zero_init_last'):
module.zero_init_last()
@torch.no_grad()
def _load_weights(model: nn.Module, checkpoint_path: str, prefix: str = 'resnet/'):
import numpy as np
def t2p(conv_weights):
"""Possibly convert HWIO to OIHW."""
if conv_weights.ndim == 4:
conv_weights = conv_weights.transpose([3, 2, 0, 1])
return torch.from_numpy(conv_weights)
weights = np.load(checkpoint_path)
stem_conv_w = adapt_input_conv(
model.stem.conv.weight.shape[1], t2p(weights[f'{prefix}root_block/standardized_conv2d/kernel']))
model.stem.conv.weight.copy_(stem_conv_w)
model.norm.weight.copy_(t2p(weights[f'{prefix}group_norm/gamma']))
model.norm.bias.copy_(t2p(weights[f'{prefix}group_norm/beta']))
if isinstance(getattr(model.head, 'fc', None), nn.Conv2d) and \
model.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
model.head.fc.weight.copy_(t2p(weights[f'{prefix}head/conv2d/kernel']))
model.head.fc.bias.copy_(t2p(weights[f'{prefix}head/conv2d/bias']))
for i, (sname, stage) in enumerate(model.stages.named_children()):
for j, (bname, block) in enumerate(stage.blocks.named_children()):
cname = 'standardized_conv2d'
block_prefix = f'{prefix}block{i + 1}/unit{j + 1:02d}/'
block.conv1.weight.copy_(t2p(weights[f'{block_prefix}a/{cname}/kernel']))
block.conv2.weight.copy_(t2p(weights[f'{block_prefix}b/{cname}/kernel']))
block.conv3.weight.copy_(t2p(weights[f'{block_prefix}c/{cname}/kernel']))
block.norm1.weight.copy_(t2p(weights[f'{block_prefix}a/group_norm/gamma']))
block.norm2.weight.copy_(t2p(weights[f'{block_prefix}b/group_norm/gamma']))
block.norm3.weight.copy_(t2p(weights[f'{block_prefix}c/group_norm/gamma']))
block.norm1.bias.copy_(t2p(weights[f'{block_prefix}a/group_norm/beta']))
block.norm2.bias.copy_(t2p(weights[f'{block_prefix}b/group_norm/beta']))
block.norm3.bias.copy_(t2p(weights[f'{block_prefix}c/group_norm/beta']))
if block.downsample is not None:
w = weights[f'{block_prefix}a/proj/{cname}/kernel']
block.downsample.conv.weight.copy_(t2p(w))
def _create_resnetv2(variant, pretrained=False, **kwargs):
feature_cfg = dict(flatten_sequential=True)
return build_model_with_cfg(
ResNetV2, variant, pretrained,
feature_cfg=feature_cfg,
**kwargs,
)
def _create_resnetv2_bit(variant, pretrained=False, **kwargs):
return _create_resnetv2(
variant, pretrained=pretrained, stem_type='fixed', conv_layer=partial(StdConv2d, eps=1e-8), **kwargs)
@register_model
def resnetv2_50x1_bitm(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_50x1_bitm', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_50x3_bitm(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_50x3_bitm', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_101x1_bitm(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_101x1_bitm', pretrained=pretrained, layers=[3, 4, 23, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_101x3_bitm(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_101x3_bitm', pretrained=pretrained, layers=[3, 4, 23, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_152x2_bitm(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_152x2_bitm', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x4_bitm(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_152x4_bitm', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=4, **kwargs)
@register_model
def resnetv2_50x1_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_50x1_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 6, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_50x3_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_50x3_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 6, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_101x1_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_101x1_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 23, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_101x3_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_101x3_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 23, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_152x2_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_152x2_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x4_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2_bit(
'resnetv2_152x4_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 8, 36, 3], width_factor=4, **kwargs)
@register_model
def resnetv2_50x1_bit_distilled(pretrained=False, **kwargs):
""" ResNetV2-50x1-BiT Distilled
Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
"""
return _create_resnetv2_bit(
'resnetv2_50x1_bit_distilled', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_152x2_bit_teacher(pretrained=False, **kwargs):
""" ResNetV2-152x2-BiT Teacher
Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
"""
return _create_resnetv2_bit(
'resnetv2_152x2_bit_teacher', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x2_bit_teacher_384(pretrained=False, **kwargs):
""" ResNetV2-152xx-BiT Teacher @ 384x384
Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
"""
return _create_resnetv2_bit(
'resnetv2_152x2_bit_teacher_384', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_50(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, **kwargs)
@register_model
def resnetv2_50d(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50d', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='deep', avg_down=True, **kwargs)
@register_model
def resnetv2_50t(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50t', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='tiered', avg_down=True, **kwargs)
@register_model
def resnetv2_101(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_101', pretrained=pretrained,
layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, **kwargs)
@register_model
def resnetv2_101d(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_101d', pretrained=pretrained,
layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='deep', avg_down=True, **kwargs)
@register_model
def resnetv2_152(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_152', pretrained=pretrained,
layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, **kwargs)
@register_model
def resnetv2_152d(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_152d', pretrained=pretrained,
layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d,
stem_type='deep', avg_down=True, **kwargs)
# Experimental configs (may change / be removed)
@register_model
def resnetv2_50d_gn(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50d_gn', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=GroupNormAct,
stem_type='deep', avg_down=True, **kwargs)
@register_model
def resnetv2_50d_evob(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50d_evob', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=EvoNorm2dB0,
stem_type='deep', avg_down=True, zero_init_last=True, **kwargs)
@register_model
def resnetv2_50d_evos(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50d_evos', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=EvoNorm2dS0,
stem_type='deep', avg_down=True, **kwargs)
@register_model
def resnetv2_50d_frn(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50d_frn', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=FilterResponseNormTlu2d,
stem_type='deep', avg_down=True, **kwargs)
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