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https://github.com/huggingface/pytorch-image-models.git
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Adding InceptionNeXt
This commit is contained in:
Ross Wightman
Ross Wightman
parent
d2e3c09ce1
commit
f4cf9775c3
@ -26,6 +26,7 @@ from .gcvit import *
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from .ghostnet import *
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from .hardcorenas import *
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from .hrnet import *
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from .inception_next import *
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from .inception_resnet_v2 import *
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from .inception_v3 import *
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from .inception_v4 import *
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374
timm/models/inception_next.py
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374
timm/models/inception_next.py
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@ -0,0 +1,374 @@
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"""
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InceptionNeXt implementation, paper: https://arxiv.org/abs/2303.16900
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Some code is borrowed from timm: https://github.com/huggingface/pytorch-image-models
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"""
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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_, DropPath, to_2tuple
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from ._manipulate import checkpoint_seq
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from ._registry import register_model
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class InceptionDWConv2d(nn.Module):
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""" Inception depthweise convolution
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"""
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def __init__(self, in_channels, square_kernel_size=3, band_kernel_size=11, branch_ratio=0.125):
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super().__init__()
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gc = int(in_channels * branch_ratio) # channel numbers of a convolution branch
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self.dwconv_hw = nn.Conv2d(gc, gc, square_kernel_size, padding=square_kernel_size // 2, groups=gc)
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self.dwconv_w = nn.Conv2d(
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gc, gc, kernel_size=(1, band_kernel_size), padding=(0, band_kernel_size // 2), groups=gc)
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self.dwconv_h = nn.Conv2d(
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gc, gc, kernel_size=(band_kernel_size, 1), padding=(band_kernel_size // 2, 0), groups=gc)
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self.split_indexes = (in_channels - 3 * gc, gc, gc, gc)
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def forward(self, x):
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x_id, x_hw, x_w, x_h = torch.split(x, self.split_indexes, dim=1)
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return torch.cat((
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x_id,
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self.dwconv_hw(x_hw),
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self.dwconv_w(x_w),
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self.dwconv_h(x_h)
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), dim=1,
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)
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class ConvMlp(nn.Module):
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""" MLP using 1x1 convs that keeps spatial dims
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copied from timm: https://github.com/huggingface/pytorch-image-models/blob/v0.6.11/timm/models/layers/mlp.py
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"""
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def __init__(
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self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU,
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norm_layer=None, bias=True, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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bias = to_2tuple(bias)
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self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
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self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
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self.act = act_layer()
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self.drop = nn.Dropout(drop)
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self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])
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def forward(self, x):
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x = self.fc1(x)
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x = self.norm(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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return x
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class MlpHead(nn.Module):
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""" MLP classification head
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"""
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def __init__(
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self, dim, num_classes=1000, mlp_ratio=3, act_layer=nn.GELU,
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norm_layer=partial(nn.LayerNorm, eps=1e-6), drop=0., bias=True):
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super().__init__()
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hidden_features = int(mlp_ratio * dim)
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self.fc1 = nn.Linear(dim, hidden_features, bias=bias)
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self.act = act_layer()
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self.norm = norm_layer(hidden_features)
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self.fc2 = nn.Linear(hidden_features, num_classes, bias=bias)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = x.mean((2, 3)) # global average pooling
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x = self.fc1(x)
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x = self.act(x)
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x = self.norm(x)
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x = self.drop(x)
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x = self.fc2(x)
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return x
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class MetaNeXtBlock(nn.Module):
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""" MetaNeXtBlock Block
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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__(
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self,
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dim,
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token_mixer=nn.Identity,
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norm_layer=nn.BatchNorm2d,
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mlp_layer=ConvMlp,
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mlp_ratio=4,
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act_layer=nn.GELU,
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ls_init_value=1e-6,
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drop_path=0.,
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):
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super().__init__()
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self.token_mixer = token_mixer(dim)
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self.norm = norm_layer(dim)
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self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=act_layer)
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self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value 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.token_mixer(x)
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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 = 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 MetaNeXtStage(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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ds_stride=2,
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depth=2,
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drop_path_rates=None,
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ls_init_value=1.0,
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token_mixer=nn.Identity,
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act_layer=nn.GELU,
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norm_layer=None,
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mlp_ratio=4,
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):
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super().__init__()
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self.grad_checkpointing = False
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if ds_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=ds_stride, stride=ds_stride),
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)
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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(MetaNeXtBlock(
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dim=out_chs,
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drop_path=drop_path_rates[i],
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ls_init_value=ls_init_value,
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token_mixer=token_mixer,
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act_layer=act_layer,
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norm_layer=norm_layer,
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mlp_ratio=mlp_ratio,
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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 MetaNeXt(nn.Module):
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r""" MetaNeXt
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A PyTorch impl of : `InceptionNeXt: When Inception Meets ConvNeXt` - https://arxiv.org/pdf/2203.xxxxx.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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token_mixers: Token mixer function. Default: nn.Identity
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norm_layer: Normalziation layer. Default: nn.BatchNorm2d
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act_layer: Activation function for MLP. Default: nn.GELU
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mlp_ratios (int or tuple(int)): MLP ratios. Default: (4, 4, 4, 3)
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head_fn: classifier head
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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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"""
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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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depths=(3, 3, 9, 3),
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dims=(96, 192, 384, 768),
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token_mixers=nn.Identity,
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norm_layer=nn.BatchNorm2d,
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act_layer=nn.GELU,
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mlp_ratios=(4, 4, 4, 3),
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head_fn=MlpHead,
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drop_rate=0.,
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drop_path_rate=0.,
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ls_init_value=1e-6,
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**kwargs,
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):
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super().__init__()
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num_stage = len(depths)
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if not isinstance(token_mixers, (list, tuple)):
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token_mixers = [token_mixers] * num_stage
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if not isinstance(mlp_ratios, (list, tuple)):
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mlp_ratios = [mlp_ratios] * num_stage
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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self.stem = nn.Sequential(
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nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
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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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stages = []
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prev_chs = dims[0]
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# feature resolution stages, each consisting of multiple residual blocks
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for i in range(num_stage):
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out_chs = dims[i]
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stages.append(MetaNeXtStage(
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prev_chs,
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out_chs,
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ds_stride=2 if i > 0 else 1,
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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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act_layer=act_layer,
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token_mixer=token_mixers[i],
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norm_layer=norm_layer,
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mlp_ratio=mlp_ratios[i],
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))
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prev_chs = out_chs
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self.stages = nn.Sequential(*stages)
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self.num_features = prev_chs
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self.head = head_fn(self.num_features, num_classes, drop=drop_rate)
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self.apply(self._init_weights)
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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 no_weight_decay(self):
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return {'norm'}
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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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return x
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def forward_head(self, x):
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x = self.head(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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x = self.forward_head(x)
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return x
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2d, nn.Linear)):
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trunc_normal_(m.weight, std=.02)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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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 = dict(
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inception_next_tiny=_cfg(
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url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_tiny.pth',
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),
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inception_next_small=_cfg(
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url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_small.pth',
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),
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inception_next_base=_cfg(
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url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
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),
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inception_next_base_384=_cfg(
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url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base_384.pth',
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input_size=(3, 384, 384), crop_pct=1.0,
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),
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)
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@register_model
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def inception_next_tiny(pretrained=False, **kwargs):
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model = MetaNeXt(
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depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),
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token_mixers=InceptionDWConv2d,
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**kwargs
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)
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model.default_cfg = default_cfgs['inception_next_tiny']
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if pretrained:
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state_dict = torch.hub.load_state_dict_from_url(
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url=model.default_cfg['url'], map_location="cpu", check_hash=True)
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model.load_state_dict(state_dict)
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return model
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@register_model
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def inception_next_small(pretrained=False, **kwargs):
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model = MetaNeXt(
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depths=(3, 3, 27, 3), dims=(96, 192, 384, 768),
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token_mixers=InceptionDWConv2d,
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**kwargs
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)
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model.default_cfg = default_cfgs['inception_next_small']
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if pretrained:
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state_dict = torch.hub.load_state_dict_from_url(
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url=model.default_cfg['url'], map_location="cpu", check_hash=True)
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model.load_state_dict(state_dict)
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return model
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@register_model
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def inception_next_base(pretrained=False, **kwargs):
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model = MetaNeXt(
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depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024),
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token_mixers=InceptionDWConv2d,
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**kwargs
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)
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model.default_cfg = default_cfgs['inception_next_base']
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if pretrained:
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state_dict = torch.hub.load_state_dict_from_url(
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url=model.default_cfg['url'], map_location="cpu", check_hash=True)
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model.load_state_dict(state_dict)
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return model
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@register_model
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def inception_next_base_384(pretrained=False, **kwargs):
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model = MetaNeXt(
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depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024],
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mlp_ratios=[4, 4, 4, 3],
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token_mixers=InceptionDWConv2d,
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**kwargs
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)
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model.default_cfg = default_cfgs['inception_next_base_384']
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if pretrained:
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state_dict = torch.hub.load_state_dict_from_url(
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url=model.default_cfg['url'], map_location="cpu", check_hash=True)
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model.load_state_dict(state_dict)
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return model
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