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https://github.com/huggingface/pytorch-image-models.git
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InceptionNeXt using timm builder, more cleanup
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Ross Wightman
Ross Wightman
parent
f4cf9775c3
commit
3d8d7450ad
@ -1,7 +1,5 @@
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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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@ -11,24 +9,31 @@ 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 ._builder import build_model_with_cfg
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from ._manipulate import checkpoint_seq
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from ._registry import register_model
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from ._registry import register_model, generate_default_cfgs
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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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def __init__(
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self,
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in_chs,
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square_kernel_size=3,
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band_kernel_size=11,
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branch_ratio=0.125
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):
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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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gc = int(in_chs * 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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self.split_indexes = (in_chs - 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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@ -47,8 +52,15 @@ class ConvMlp(nn.Module):
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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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self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.ReLU,
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norm_layer=None,
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bias=True,
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drop=0.,
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):
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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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@ -69,13 +81,20 @@ class ConvMlp(nn.Module):
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return x
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class MlpHead(nn.Module):
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class MlpClassifierHead(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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self,
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dim,
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num_classes=1000,
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mlp_ratio=3,
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act_layer=nn.GELU,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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drop=0.,
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bias=True
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):
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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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@ -168,7 +187,6 @@ class MetaNeXtStage(nn.Module):
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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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@ -209,11 +227,10 @@ class MetaNeXt(nn.Module):
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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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head_fn=MlpClassifierHead,
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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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@ -255,6 +272,30 @@ class MetaNeXt(nn.Module):
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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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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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@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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]
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)
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@torch.jit.ignore
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def get_classifier(self):
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return self.head.fc2
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def reset_classifier(self, num_classes=0, global_pool=None):
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# FIXME
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self.head.reset(num_classes, global_pool)
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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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@ -262,7 +303,7 @@ class MetaNeXt(nn.Module):
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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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return set()
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def forward_features(self, x):
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x = self.stem(x)
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@ -278,12 +319,6 @@ class MetaNeXt(nn.Module):
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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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@ -291,84 +326,59 @@ def _cfg(url='', **kwargs):
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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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'first_conv': 'stem.0', 'classifier': 'head.fc2',
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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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default_cfgs = generate_default_cfgs({
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'inception_next_tiny.sail_in1k': _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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'inception_next_small.sail_in1k': _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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'inception_next_base.sail_in1k': _cfg(
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url='https://github.com/sail-sg/inceptionnext/releases/download/model/inceptionnext_base.pth',
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crop_pct=0.95,
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),
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inception_next_base_384=_cfg(
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'inception_next_base.sail_in1k_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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})
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def _create_inception_next(variant, pretrained=False, **kwargs):
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model = build_model_with_cfg(
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MetaNeXt, variant, pretrained,
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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 inception_next_tiny(pretrained=False, **kwargs):
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model = MetaNeXt(
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model_args = dict(
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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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return _create_inception_next('inception_next_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
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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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model_args = dict(
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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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return _create_inception_next('inception_next_small', pretrained=pretrained, **dict(model_args, **kwargs))
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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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model_args = dict(
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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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return _create_inception_next('inception_next_base', pretrained=pretrained, **dict(model_args, **kwargs))
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