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579 lines
24 KiB
579 lines
24 KiB
""" ConvNeXt
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Paper: `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
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Original code and weights from https://github.com/facebookresearch/ConvNeXt, original copyright below
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Model defs atto, femto, pico, nano and _ols / _hnf variants are timm specific.
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Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
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"""
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the MIT license
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from collections import OrderedDict
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from functools import partial
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \
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create_conv2d, get_act_layer, make_divisible, to_ntuple
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from ._builder import build_model_with_cfg
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from ._manipulate import named_apply, checkpoint_seq
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from ._pretrained import generate_default_cfgs
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from ._registry import register_model
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__all__ = ['ConvNeXt'] # model_registry will add each entrypoint fn to this
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class ConvNeXtBlock(nn.Module):
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""" ConvNeXt Block
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There are two equivalent implementations:
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(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
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(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
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Unlike the official impl, this one allows choice of 1 or 2, 1x1 conv can be faster with appropriate
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choice of LayerNorm impl, however as model size increases the tradeoffs appear to change and nn.Linear
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is a better choice. This was observed with PyTorch 1.10 on 3090 GPU, it could change over time & w/ different HW.
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Args:
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in_chs (int): Number of input channels.
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drop_path (float): Stochastic depth rate. Default: 0.0
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ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
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"""
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def __init__(
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self,
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in_chs,
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out_chs=None,
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kernel_size=7,
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stride=1,
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dilation=1,
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mlp_ratio=4,
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conv_mlp=False,
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conv_bias=True,
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ls_init_value=1e-6,
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act_layer='gelu',
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norm_layer=None,
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drop_path=0.,
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):
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super().__init__()
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out_chs = out_chs or in_chs
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act_layer = get_act_layer(act_layer)
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if not norm_layer:
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norm_layer = LayerNorm2d if conv_mlp else LayerNorm
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mlp_layer = ConvMlp if conv_mlp else Mlp
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self.use_conv_mlp = conv_mlp
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self.conv_dw = create_conv2d(
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in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias)
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self.norm = norm_layer(out_chs)
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self.mlp = mlp_layer(out_chs, int(mlp_ratio * out_chs), act_layer=act_layer)
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self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value > 0 else None
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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shortcut = x
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x = self.conv_dw(x)
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if self.use_conv_mlp:
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x = self.norm(x)
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x = self.mlp(x)
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else:
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x = x.permute(0, 2, 3, 1)
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x = self.norm(x)
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x = self.mlp(x)
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x = x.permute(0, 3, 1, 2)
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if self.gamma is not None:
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x = x.mul(self.gamma.reshape(1, -1, 1, 1))
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x = self.drop_path(x) + shortcut
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return x
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class ConvNeXtStage(nn.Module):
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def __init__(
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self,
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in_chs,
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out_chs,
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kernel_size=7,
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stride=2,
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depth=2,
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dilation=(1, 1),
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drop_path_rates=None,
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ls_init_value=1.0,
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conv_mlp=False,
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conv_bias=True,
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act_layer='gelu',
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norm_layer=None,
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norm_layer_cl=None
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):
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super().__init__()
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self.grad_checkpointing = False
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if in_chs != out_chs or stride > 1 or dilation[0] != dilation[1]:
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ds_ks = 2 if stride > 1 or dilation[0] != dilation[1] else 1
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pad = 'same' if dilation[1] > 1 else 0 # same padding needed if dilation used
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self.downsample = nn.Sequential(
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norm_layer(in_chs),
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create_conv2d(
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in_chs, out_chs, kernel_size=ds_ks, stride=stride,
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dilation=dilation[0], padding=pad, bias=conv_bias),
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)
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in_chs = out_chs
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else:
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self.downsample = nn.Identity()
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drop_path_rates = drop_path_rates or [0.] * depth
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stage_blocks = []
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for i in range(depth):
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stage_blocks.append(ConvNeXtBlock(
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in_chs=in_chs,
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out_chs=out_chs,
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kernel_size=kernel_size,
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dilation=dilation[1],
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drop_path=drop_path_rates[i],
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ls_init_value=ls_init_value,
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conv_mlp=conv_mlp,
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conv_bias=conv_bias,
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act_layer=act_layer,
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norm_layer=norm_layer if conv_mlp else norm_layer_cl
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))
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in_chs = out_chs
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self.blocks = nn.Sequential(*stage_blocks)
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def forward(self, x):
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x = self.downsample(x)
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if self.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint_seq(self.blocks, x)
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else:
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x = self.blocks(x)
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return x
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class ConvNeXt(nn.Module):
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r""" ConvNeXt
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A PyTorch impl of : `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
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Args:
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in_chans (int): Number of input image channels. Default: 3
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (tuple(int)): Feature dimension at each stage. Default: [96, 192, 384, 768]
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drop_rate (float): Head dropout rate
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(
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self,
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in_chans=3,
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num_classes=1000,
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global_pool='avg',
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output_stride=32,
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depths=(3, 3, 9, 3),
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dims=(96, 192, 384, 768),
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kernel_sizes=7,
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ls_init_value=1e-6,
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stem_type='patch',
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patch_size=4,
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head_init_scale=1.,
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head_norm_first=False,
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conv_mlp=False,
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conv_bias=True,
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act_layer='gelu',
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norm_layer=None,
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drop_rate=0.,
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drop_path_rate=0.,
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):
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super().__init__()
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assert output_stride in (8, 16, 32)
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kernel_sizes = to_ntuple(4)(kernel_sizes)
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if norm_layer is None:
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norm_layer = LayerNorm2d
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norm_layer_cl = norm_layer if conv_mlp else LayerNorm
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else:
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assert conv_mlp,\
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'If a norm_layer is specified, conv MLP must be used so all norm expect rank-4, channels-first input'
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norm_layer_cl = norm_layer
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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self.feature_info = []
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assert stem_type in ('patch', 'overlap', 'overlap_tiered')
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if stem_type == 'patch':
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# NOTE: this stem is a minimal form of ViT PatchEmbed, as used in SwinTransformer w/ patch_size = 4
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self.stem = nn.Sequential(
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nn.Conv2d(in_chans, dims[0], kernel_size=patch_size, stride=patch_size, bias=conv_bias),
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norm_layer(dims[0])
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)
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stem_stride = patch_size
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else:
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mid_chs = make_divisible(dims[0] // 2) if 'tiered' in stem_type else dims[0]
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self.stem = nn.Sequential(
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nn.Conv2d(in_chans, mid_chs, kernel_size=3, stride=2, padding=1, bias=conv_bias),
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nn.Conv2d(mid_chs, dims[0], kernel_size=3, stride=2, padding=1, bias=conv_bias),
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norm_layer(dims[0]),
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)
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stem_stride = 4
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self.stages = nn.Sequential()
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dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
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stages = []
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prev_chs = dims[0]
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curr_stride = stem_stride
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dilation = 1
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# 4 feature resolution stages, each consisting of multiple residual blocks
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for i in range(4):
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stride = 2 if curr_stride == 2 or i > 0 else 1
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if curr_stride >= output_stride and stride > 1:
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dilation *= stride
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stride = 1
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curr_stride *= stride
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first_dilation = 1 if dilation in (1, 2) else 2
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out_chs = dims[i]
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stages.append(ConvNeXtStage(
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prev_chs,
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out_chs,
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kernel_size=kernel_sizes[i],
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stride=stride,
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dilation=(first_dilation, dilation),
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depth=depths[i],
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drop_path_rates=dp_rates[i],
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ls_init_value=ls_init_value,
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conv_mlp=conv_mlp,
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conv_bias=conv_bias,
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act_layer=act_layer,
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norm_layer=norm_layer,
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norm_layer_cl=norm_layer_cl
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))
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prev_chs = out_chs
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# NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
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self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
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self.stages = nn.Sequential(*stages)
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self.num_features = prev_chs
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# if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
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# otherwise pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
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self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity()
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self.head = nn.Sequential(OrderedDict([
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('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
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('norm', nn.Identity() if head_norm_first else norm_layer(self.num_features)),
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('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
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('drop', nn.Dropout(self.drop_rate)),
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('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())]))
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named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
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@torch.jit.ignore
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def group_matcher(self, coarse=False):
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return dict(
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stem=r'^stem',
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blocks=r'^stages\.(\d+)' if coarse else [
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(r'^stages\.(\d+)\.downsample', (0,)), # blocks
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(r'^stages\.(\d+)\.blocks\.(\d+)', None),
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(r'^norm_pre', (99999,))
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]
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)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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for s in self.stages:
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s.grad_checkpointing = enable
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@torch.jit.ignore
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def get_classifier(self):
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return self.head.fc
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def reset_classifier(self, num_classes=0, global_pool=None):
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if global_pool is not None:
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self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity()
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self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x):
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x = self.stem(x)
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x = self.stages(x)
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x = self.norm_pre(x)
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return x
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def forward_head(self, x, pre_logits: bool = False):
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# NOTE nn.Sequential in head broken down since can't call head[:-1](x) in torchscript :(
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x = self.head.global_pool(x)
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x = self.head.norm(x)
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x = self.head.flatten(x)
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x = self.head.drop(x)
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return x if pre_logits else self.head.fc(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _init_weights(module, name=None, head_init_scale=1.0):
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if isinstance(module, nn.Conv2d):
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trunc_normal_(module.weight, std=.02)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Linear):
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trunc_normal_(module.weight, std=.02)
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nn.init.zeros_(module.bias)
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if name and 'head.' in name:
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module.weight.data.mul_(head_init_scale)
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module.bias.data.mul_(head_init_scale)
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def checkpoint_filter_fn(state_dict, model):
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""" Remap FB checkpoints -> timm """
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if 'head.norm.weight' in state_dict or 'norm_pre.weight' in state_dict:
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return state_dict # non-FB checkpoint
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if 'model' in state_dict:
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state_dict = state_dict['model']
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out_dict = {}
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import re
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for k, v in state_dict.items():
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k = k.replace('downsample_layers.0.', 'stem.')
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k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
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k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k)
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k = k.replace('dwconv', 'conv_dw')
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k = k.replace('pwconv', 'mlp.fc')
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k = k.replace('head.', 'head.fc.')
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if k.startswith('norm.'):
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k = k.replace('norm', 'head.norm')
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if v.ndim == 2 and 'head' not in k:
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model_shape = model.state_dict()[k].shape
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v = v.reshape(model_shape)
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out_dict[k] = v
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return out_dict
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def _create_convnext(variant, pretrained=False, **kwargs):
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model = build_model_with_cfg(
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ConvNeXt, variant, pretrained,
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pretrained_filter_fn=checkpoint_filter_fn,
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feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
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**kwargs)
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return model
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.0', 'classifier': 'head.fc',
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**kwargs
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}
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default_cfgs = generate_default_cfgs({
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# timm specific variants
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'convnext_atto.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_d2-01bb0f51.pth',
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test_input_size=(3, 288, 288), test_crop_pct=0.95),
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'convnext_atto_ols.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_ols_a2-78d1c8f3.pth',
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test_input_size=(3, 288, 288), test_crop_pct=0.95),
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'convnext_femto.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_d1-d71d5b4c.pth',
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test_input_size=(3, 288, 288), test_crop_pct=0.95),
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'convnext_femto_ols.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_ols_d1-246bf2ed.pth',
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test_input_size=(3, 288, 288), test_crop_pct=0.95),
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'convnext_pico.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_d1-10ad7f0d.pth',
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test_input_size=(3, 288, 288), test_crop_pct=0.95),
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'convnext_pico_ols.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_ols_d1-611f0ca7.pth',
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crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
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'convnext_nano.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_d1h-7eb4bdea.pth',
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crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
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'convnext_nano_ols.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_ols_d1h-ae424a9a.pth',
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crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
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'convnext_tiny_hnf.timm_in1k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
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crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
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'convnext_tiny.fb_in1k': _cfg(
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url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
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test_input_size=(3, 288, 288), test_crop_pct=1.0),
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'convnext_small.fb_in1k': _cfg(
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url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
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test_input_size=(3, 288, 288), test_crop_pct=1.0),
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'convnext_base.fb_in1k': _cfg(
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url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
'convnext_large.fb_in1k': _cfg(
|
|
url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
'convnext_xlarge.untrained': _cfg(),
|
|
|
|
'convnext_tiny.fb_in22k_ft_in1k': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth',
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
'convnext_small.fb_in22k_ft_in1k': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth',
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
'convnext_base.fb_in22k_ft_in1k': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth',
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
'convnext_large.fb_in22k_ft_in1k': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth',
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
'convnext_xlarge.fb_in22k_ft_in1k': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth',
|
|
test_input_size=(3, 288, 288), test_crop_pct=1.0),
|
|
|
|
'convnext_tiny.fb_in22k_ft_in1k_384': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
|
|
'convnext_small..fb_in22k_ft_in1k_384': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_384.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
|
|
'convnext_base.fb_in22k_ft_in1k_384': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
|
|
'convnext_large.fb_in22k_ft_in1k_384': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
|
|
'convnext_xlarge.fb_in22k_ft_in1k_384': _cfg(
|
|
url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'),
|
|
|
|
'convnext_tiny_in22k.fb_in22k': _cfg(
|
|
url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth", num_classes=21841),
|
|
'convnext_small_in22k.fb_in22k': _cfg(
|
|
url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth", num_classes=21841),
|
|
'convnext_base_in22k.fb_in22k': _cfg(
|
|
url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841),
|
|
'convnext_large_in22k.fb_in22k': _cfg(
|
|
url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841),
|
|
'convnext_xlarge_in22k.fb_in22k': _cfg(
|
|
url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841),
|
|
})
|
|
|
|
|
|
@register_model
|
|
def convnext_atto(pretrained=False, **kwargs):
|
|
# timm femto variant (NOTE: still tweaking depths, will vary between 3-4M param, current is 3.7M
|
|
model_args = dict(
|
|
depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), conv_mlp=True, **kwargs)
|
|
model = _create_convnext('convnext_atto', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_atto_ols(pretrained=False, **kwargs):
|
|
# timm femto variant with overlapping 3x3 conv stem, wider than non-ols femto above, current param count 3.7M
|
|
model_args = dict(
|
|
depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), conv_mlp=True, stem_type='overlap_tiered', **kwargs)
|
|
model = _create_convnext('convnext_atto_ols', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_femto(pretrained=False, **kwargs):
|
|
# timm femto variant
|
|
model_args = dict(
|
|
depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), conv_mlp=True, **kwargs)
|
|
model = _create_convnext('convnext_femto', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_femto_ols(pretrained=False, **kwargs):
|
|
# timm femto variant
|
|
model_args = dict(
|
|
depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), conv_mlp=True, stem_type='overlap_tiered', **kwargs)
|
|
model = _create_convnext('convnext_femto_ols', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_pico(pretrained=False, **kwargs):
|
|
# timm pico variant
|
|
model_args = dict(
|
|
depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), conv_mlp=True, **kwargs)
|
|
model = _create_convnext('convnext_pico', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_pico_ols(pretrained=False, **kwargs):
|
|
# timm nano variant with overlapping 3x3 conv stem
|
|
model_args = dict(
|
|
depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), conv_mlp=True, stem_type='overlap_tiered', **kwargs)
|
|
model = _create_convnext('convnext_pico_ols', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_nano(pretrained=False, **kwargs):
|
|
# timm nano variant with standard stem and head
|
|
model_args = dict(
|
|
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), conv_mlp=True, **kwargs)
|
|
model = _create_convnext('convnext_nano', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_nano_ols(pretrained=False, **kwargs):
|
|
# experimental nano variant with overlapping conv stem
|
|
model_args = dict(
|
|
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), conv_mlp=True, stem_type='overlap', **kwargs)
|
|
model = _create_convnext('convnext_nano_ols', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_tiny_hnf(pretrained=False, **kwargs):
|
|
# experimental tiny variant with norm before pooling in head (head norm first)
|
|
model_args = dict(
|
|
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
|
|
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_tiny(pretrained=False, **kwargs):
|
|
model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
|
|
model = _create_convnext('convnext_tiny', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_small(pretrained=False, **kwargs):
|
|
model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
|
|
model = _create_convnext('convnext_small', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_base(pretrained=False, **kwargs):
|
|
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
|
|
model = _create_convnext('convnext_base', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_large(pretrained=False, **kwargs):
|
|
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
|
|
model = _create_convnext('convnext_large', pretrained=pretrained, **model_args)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def convnext_xlarge(pretrained=False, **kwargs):
|
|
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
|
|
model = _create_convnext('convnext_xlarge', pretrained=pretrained, **model_args)
|
|
return model
|