Ross Wightman
3 years ago
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""" 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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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 functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .fx_features import register_notrace_module
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from .helpers import named_apply, build_model_with_cfg
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from .layers import trunc_normal_, ClassifierHead, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp
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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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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',
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**kwargs
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}
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default_cfgs = dict(
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convnext_tiny=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth"),
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convnext_small=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth"),
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convnext_base=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"),
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convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
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convnext_tiny_hnf=_cfg(url='', classifier='head.fc'),
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convnext_base_in22k=_cfg(
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url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841),
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convnext_large_in22k=_cfg(
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url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841),
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convnext_xlarge_in22k=_cfg(
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url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841),
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)
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def _is_contiguous(tensor: torch.Tensor) -> bool:
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# jit is oh so lovely :/
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# if torch.jit.is_tracing():
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# return True
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if torch.jit.is_scripting():
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return tensor.is_contiguous()
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else:
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return tensor.is_contiguous(memory_format=torch.contiguous_format)
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@register_notrace_module
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class LayerNorm2d(nn.Module):
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r""" LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
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"""
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def __init__(self, normalized_shape, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.eps = eps
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self.normalized_shape = (normalized_shape,)
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def forward(self, x) -> torch.Tensor:
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if _is_contiguous(x):
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return F.layer_norm(
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x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
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else:
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s, u = torch.var_mean(x, dim=1, keepdim=True)
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x = (x - u) * torch.rsqrt(s + self.eps)
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x = x * self.weight[:, None, None] + self.bias[:, None, None]
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return x
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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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dim (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__(self, dim, drop_path=0., ls_init_value=1e-6, conv_mlp=True, mlp_ratio=4, norm_layer=None):
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super().__init__()
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norm_layer = norm_layer or (partial(LayerNorm2d, eps=1e-6) if conv_mlp else partial(nn.LayerNorm, eps=1e-6))
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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 = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
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self.norm = norm_layer(dim)
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self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=nn.GELU)
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self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) 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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if self.gamma is not None:
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x.mul_(self.gamma.reshape(1, -1, 1, 1))
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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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if self.gamma is not None:
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x.mul_(self.gamma)
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x = x.permute(0, 3, 1, 2)
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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, in_chs, out_chs, stride=2, depth=2, dp_rates=None, ls_init_value=1.0, conv_mlp=True,
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norm_layer=None, cl_norm_layer=None, cross_stage=False):
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super().__init__()
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if in_chs != out_chs or stride > 1:
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self.downsample = nn.Sequential(
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norm_layer(in_chs),
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nn.Conv2d(in_chs, out_chs, kernel_size=stride, stride=stride),
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)
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else:
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self.downsample = nn.Identity()
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dp_rates = dp_rates or [0.] * depth
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self.blocks = nn.Sequential(*[ConvNeXtBlock(
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dim=out_chs, drop_path=dp_rates[j], ls_init_value=ls_init_value, conv_mlp=conv_mlp,
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norm_layer=norm_layer if conv_mlp else cl_norm_layer)
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for j in range(depth)]
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)
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def forward(self, x):
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x = self.downsample(x)
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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, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32, patch_size=4,
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depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), ls_init_value=1e-6, conv_mlp=True,
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head_init_scale=1., head_norm_first=False, norm_layer=None, drop_rate=0., drop_path_rate=0.,
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):
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super().__init__()
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assert output_stride == 32
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if norm_layer is None:
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norm_layer = partial(LayerNorm2d, eps=1e-6)
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cl_norm_layer = norm_layer if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
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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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cl_norm_layer = norm_layer
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partial(LayerNorm2d, eps=1e-6)
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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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# 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),
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norm_layer(dims[0])
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)
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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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curr_stride = patch_size
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prev_chs = dims[0]
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stages = []
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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 i > 0 else 1
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# FIXME support dilation / output_stride
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curr_stride *= stride
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out_chs = dims[i]
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stages.append(ConvNeXtStage(
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prev_chs, out_chs, stride=stride,
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depth=depths[i], dp_rates=dp_rates[i], ls_init_value=ls_init_value, conv_mlp=conv_mlp,
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norm_layer=norm_layer, cl_norm_layer=cl_norm_layer)
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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:
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# norm -> global pool -> fc ordering, like most other nets (not compat with FB weights)
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self.norm = norm_layer(self.num_features) # final norm layer
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self.pool = None # global pool in ClassifierHead, pool == None being used to differentiate
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self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
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else:
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# pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
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self.pool = SelectAdaptivePool2d(pool_type=global_pool)
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# NOTE when cl_norm_layer != norm_layer we could flatten here and use cl, but makes no performance diff
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self.norm = norm_layer(self.num_features)
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self.head = 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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def get_classifier(self):
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return self.head.fc if self.pool is None else self.head
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def reset_classifier(self, num_classes=0, global_pool='avg'):
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if self.pool is None:
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# norm -> global pool -> fc ordering
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self.head = ClassifierHead(
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self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
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else:
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# pool -> norm -> fc
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self.pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.head = 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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if self.pool is None:
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# standard head, norm -> spatial pool -> fc
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# ideally, last norm is within forward_features, but can only do so if norm precedes pooling
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x = self.norm(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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if self.pool is not None:
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# ConvNeXt head, spatial pool -> norm -> fc
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# FIXME clean this up
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x = self.pool(x)
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x = self.norm(x)
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if not self.pool.is_identity():
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x = x.flatten(1)
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if self.drop_rate > 0:
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x = F.dropout(x, self.drop_rate, self.training)
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x = self.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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nn.init.constant_(module.bias, 0)
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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.constant_(module.bias, 0)
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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 '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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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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default_cfg=default_cfgs[variant],
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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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@register_model
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def convnext_tiny(pretrained=False, **kwargs):
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model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), **kwargs)
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model = _create_convnext('convnext_tiny', pretrained=pretrained, **model_args)
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return model
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@register_model
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def convnext_tiny_hnf(pretrained=False, **kwargs):
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model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, **kwargs)
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model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
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return model
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||||||
|
|
||||||
|
|
||||||
|
@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_base_in22k(pretrained=False, **kwargs):
|
||||||
|
model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
|
||||||
|
model = _create_convnext('convnext_base_in22k', pretrained=pretrained, **model_args)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def convnext_large_in22k(pretrained=False, **kwargs):
|
||||||
|
model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
|
||||||
|
model = _create_convnext('convnext_large_in22k', pretrained=pretrained, **model_args)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def convnext_xlarge_in22k(pretrained=False, **kwargs):
|
||||||
|
model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], conv_mlp=False, **kwargs)
|
||||||
|
model = _create_convnext('convnext_xlarge_in22k', pretrained=pretrained, **model_args)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Loading…
Reference in new issue