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