""" ConvNeXt Papers: * `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf @Article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } * `ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808 @article{Woo2023ConvNeXtV2, title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie}, year={2023}, journal={arXiv preprint arXiv:2301.00808}, } Original code and weights from: * https://github.com/facebookresearch/ConvNeXt, original copyright below * https://github.com/facebookresearch/ConvNeXt-V2, original copyright below Model defs atto, femto, pico, nano and _ols / _hnf variants are timm originals. Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman """ # ConvNeXt # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the MIT license # ConvNeXt-V2 # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)) # No code was used directly from ConvNeXt-V2, however the weights are CC BY-NC 4.0 so beware if using commercially. from collections import OrderedDict from functools import partial from typing import Callable, Optional, Tuple, Union import torch import torch.nn as nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD from timm.layers import trunc_normal_, AvgPool2dSame, DropPath, Mlp, GlobalResponseNormMlp, \ LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple from timm.layers import NormMlpClassifierHead, ClassifierHead from timm.models._builder import build_model_with_cfg from timm.models._manipulate import named_apply, checkpoint_seq from timm.models._registry import generate_default_cfgs, register_model, register_model_deprecations __all__ = ['ConvNeXt'] # model_registry will add each entrypoint fn to this class Downsample(nn.Module): def __init__(self, in_chs, out_chs, stride=1, dilation=1): super().__init__() avg_stride = stride if dilation == 1 else 1 if stride > 1 or dilation > 1: avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False) else: self.pool = nn.Identity() if in_chs != out_chs: self.conv = create_conv2d(in_chs, out_chs, 1, stride=1) else: self.conv = nn.Identity() def forward(self, x): x = self.pool(x) x = self.conv(x) return x 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. """ def __init__( self, in_chs: int, out_chs: Optional[int] = None, kernel_size: int = 7, stride: int = 1, dilation: Union[int, Tuple[int, int]] = (1, 1), mlp_ratio: float = 4, conv_mlp: bool = False, conv_bias: bool = True, use_grn: bool = False, ls_init_value: Optional[float] = 1e-6, act_layer: Union[str, Callable] = 'gelu', norm_layer: Optional[Callable] = None, drop_path: float = 0., ): """ Args: in_chs: Block input channels. out_chs: Block output channels (same as in_chs if None). kernel_size: Depthwise convolution kernel size. stride: Stride of depthwise convolution. dilation: Tuple specifying input and output dilation of block. mlp_ratio: MLP expansion ratio. conv_mlp: Use 1x1 convolutions for MLP and a NCHW compatible norm layer if True. conv_bias: Apply bias for all convolution (linear) layers. use_grn: Use GlobalResponseNorm in MLP (from ConvNeXt-V2) ls_init_value: Layer-scale init values, layer-scale applied if not None. act_layer: Activation layer. norm_layer: Normalization layer (defaults to LN if not specified). drop_path: Stochastic depth probability. """ super().__init__() out_chs = out_chs or in_chs dilation = to_ntuple(2)(dilation) act_layer = get_act_layer(act_layer) if not norm_layer: norm_layer = LayerNorm2d if conv_mlp else LayerNorm mlp_layer = partial(GlobalResponseNormMlp if use_grn else Mlp, use_conv=conv_mlp) self.use_conv_mlp = conv_mlp self.conv_dw = create_conv2d( in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation[0], 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.weight = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None else None if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]: self.shortcut = Downsample(in_chs, out_chs, stride=stride, dilation=dilation[0]) else: self.shortcut = nn.Identity() 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.weight is not None: x = x.mul(self.weight.reshape(1, -1, 1, 1)) x = self.drop_path(x) + self.shortcut(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, use_grn=False, 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, use_grn=use_grn, 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 """ def __init__( self, in_chans: int = 3, num_classes: int = 1000, global_pool: str = 'avg', output_stride: int = 32, depths: Tuple[int, ...] = (3, 3, 9, 3), dims: Tuple[int, ...] = (96, 192, 384, 768), kernel_sizes: Union[int, Tuple[int, ...]] = 7, ls_init_value: Optional[float] = 1e-6, stem_type: str = 'patch', patch_size: int = 4, head_init_scale: float = 1., head_norm_first: bool = False, head_hidden_size: Optional[int] = None, conv_mlp: bool = False, conv_bias: bool = True, use_grn: bool = False, act_layer: Union[str, Callable] = 'gelu', norm_layer: Optional[Union[str, Callable]] = None, norm_eps: Optional[float] = None, drop_rate: float = 0., drop_path_rate: float = 0., ): """ Args: in_chans: Number of input image channels. num_classes: Number of classes for classification head. global_pool: Global pooling type. output_stride: Output stride of network, one of (8, 16, 32). depths: Number of blocks at each stage. dims: Feature dimension at each stage. kernel_sizes: Depthwise convolution kernel-sizes for each stage. ls_init_value: Init value for Layer Scale, disabled if None. stem_type: Type of stem. patch_size: Stem patch size for patch stem. head_init_scale: Init scaling value for classifier weights and biases. head_norm_first: Apply normalization before global pool + head. head_hidden_size: Size of MLP hidden layer in head if not None and head_norm_first == False. conv_mlp: Use 1x1 conv in MLP, improves speed for small networks w/ chan last. conv_bias: Use bias layers w/ all convolutions. use_grn: Use Global Response Norm (ConvNeXt-V2) in MLP. act_layer: Activation layer type. norm_layer: Normalization layer type. drop_rate: Head pre-classifier dropout rate. drop_path_rate: Stochastic depth drop rate. """ 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 if norm_eps is not None: norm_layer = partial(norm_layer, eps=norm_eps) norm_layer_cl = partial(norm_layer_cl, eps=norm_eps) 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 if norm_eps is not None: norm_layer_cl = partial(norm_layer_cl, eps=norm_eps) 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, use_grn=use_grn, 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) if head_norm_first: assert not head_hidden_size self.norm_pre = norm_layer(self.num_features) self.head = ClassifierHead( self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, ) else: self.norm_pre = nn.Identity() self.head = NormMlpClassifierHead( self.num_features, num_classes, hidden_size=head_hidden_size, pool_type=global_pool, drop_rate=self.drop_rate, norm_layer=norm_layer, act_layer='gelu', ) 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): self.head.reset(num_classes, global_pool) 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): return self.head(x, pre_logits=True) if pre_logits else self.head(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: out_dict={} out_dict = {k.replace('gamma', 'weight'): v for k, v in state_dict.items()} return out_dict # non-FB checkpoint if 'model' in state_dict: state_dict = state_dict['model'] out_dict = {} if 'visual.trunk.stem.0.weight' in state_dict: out_dict = {k.replace('visual.trunk.', '').replace('gamma', 'weight'): v for k, v in state_dict.items() if k.startswith('visual.trunk.')} if 'visual.head.proj.weight' in state_dict: out_dict['head.fc.weight'] = state_dict['visual.head.proj.weight'] out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.proj.weight'].shape[0]) elif 'visual.head.mlp.fc1.weight' in state_dict: out_dict['head.pre_logits.fc.weight'] = state_dict['visual.head.mlp.fc1.weight'] out_dict['head.pre_logits.fc.bias'] = state_dict['visual.head.mlp.fc1.bias'] out_dict['head.fc.weight'] = state_dict['visual.head.mlp.fc2.weight'] out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.mlp.fc2.weight'].shape[0]) return 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') if 'grn' in k: k = k.replace('grn.beta', 'mlp.grn.bias') k = k.replace('grn.gamma', 'mlp.grn.weight') v = v.reshape(v.shape[-1]) 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) k=k.replace('gamma','weight') out_dict[k] = v return out_dict def _create_convnext(variant, pretrained=False, **kwargs): if kwargs.get('pretrained_cfg', '') == 'fcmae': # NOTE fcmae pretrained weights have no classifier or final norm-layer (`head.norm`) # This is workaround loading with num_classes=0 w/o removing norm-layer. kwargs.setdefault('pretrained_strict', False) 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 } def _cfgv2(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', 'license': 'cc-by-nc-4.0', 'paper_ids': 'arXiv:2301.00808', 'paper_name': 'ConvNeXt-V2: Co-designing and Scaling ConvNets with Masked Autoencoders', 'origin_url': 'https://github.com/facebookresearch/ConvNeXt-V2', **kwargs } default_cfgs = generate_default_cfgs({ 'convnext_xxlarge.clip_laion2b_soup_ft_in1k': _cfg( hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0), 'convnext_xxlarge.clip_laion2b_soup_ft_in12k': _cfg( hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0), 'convnext_xxlarge.clip_laion2b_soup': _cfg( hf_hub_id='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup', hf_hub_filename='open_clip_pytorch_model.bin', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=1024), 'convnext_xxlarge.clip_laion2b_rewind': _cfg( hf_hub_id='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind', hf_hub_filename='open_clip_pytorch_model.bin', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, num_classes=1024), }) @register_model def convnext_xxlarge(pretrained=False, **kwargs) -> ConvNeXt: model_args = dict(depths=[3, 4, 30, 3], dims=[384, 768, 1536, 3072], norm_eps=kwargs.pop('norm_eps', 1e-5)) model = _create_convnext('convnext_xxlarge', pretrained=pretrained, **dict(model_args, **kwargs)) return model # register_model_deprecations(__name__, { # 'convnext_tiny_in22ft1k': 'convnext_tiny.fb_in22k_ft_in1k', # 'convnext_small_in22ft1k': 'convnext_small.fb_in22k_ft_in1k', # 'convnext_base_in22ft1k': 'convnext_base.fb_in22k_ft_in1k', # 'convnext_large_in22ft1k': 'convnext_large.fb_in22k_ft_in1k', # 'convnext_xlarge_in22ft1k': 'convnext_xlarge.fb_in22k_ft_in1k', # 'convnext_tiny_384_in22ft1k': 'convnext_tiny.fb_in22k_ft_in1k_384', # 'convnext_small_384_in22ft1k': 'convnext_small.fb_in22k_ft_in1k_384', # 'convnext_base_384_in22ft1k': 'convnext_base.fb_in22k_ft_in1k_384', # 'convnext_large_384_in22ft1k': 'convnext_large.fb_in22k_ft_in1k_384', # 'convnext_xlarge_384_in22ft1k': 'convnext_xlarge.fb_in22k_ft_in1k_384', # 'convnext_tiny_in22k': 'convnext_tiny.fb_in22k', # 'convnext_small_in22k': 'convnext_small.fb_in22k', # 'convnext_base_in22k': 'convnext_base.fb_in22k', # 'convnext_large_in22k': 'convnext_large.fb_in22k', # 'convnext_xlarge_in22k': 'convnext_xlarge.fb_in22k', # })