diff --git a/README.md b/README.md index e78dd241..ce0d690a 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,11 @@ For a few months now, `timm` has been part of the Hugging Face ecosystem. Yearly If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: [**hf.co/oss-survey**](https://hf.co/oss-survey) 🙏 +### Jan 5, 2023 +* ConvNeXt-V2 models and weights added to existing `convnext.py` + * Paper: [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](http://arxiv.org/abs/2301.00808) + * Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC) + ### Dec 23, 2022 🎄☃ * Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013) * NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP @@ -396,6 +401,7 @@ A full version of the list below with source links can be found in the [document * CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399 * CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803 * ConvNeXt - https://arxiv.org/abs/2201.03545 +* ConvNeXt-V2 - http://arxiv.org/abs/2301.00808 * ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697 * CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929 * DeiT - https://arxiv.org/abs/2012.12877 @@ -418,6 +424,7 @@ A full version of the list below with source links can be found in the [document * Single-Path NAS - https://arxiv.org/abs/1904.02877 * TinyNet - https://arxiv.org/abs/2010.14819 * EVA - https://arxiv.org/abs/2211.07636 +* FlexiViT - https://arxiv.org/abs/2212.08013 * GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959 * GhostNet - https://arxiv.org/abs/1911.11907 * gMLP - https://arxiv.org/abs/2105.08050 diff --git a/tests/test_models.py b/tests/test_models.py index b6a61727..3e91d9a8 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -38,7 +38,7 @@ if 'GITHUB_ACTIONS' in os.environ: '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm', '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*', '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*', 'swin*huge*', - 'swin*giant*'] + 'swin*giant*', 'convnextv2_huge*'] NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*', 'eva_giant*'] else: EXCLUDE_FILTERS = [] @@ -129,7 +129,7 @@ def test_model_backward(model_name, batch_size): @pytest.mark.timeout(300) -@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS)) +@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS, include_tags=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_default_cfgs(model_name, batch_size): """Run a single forward pass with each model""" @@ -191,7 +191,7 @@ def test_model_default_cfgs(model_name, batch_size): @pytest.mark.timeout(300) -@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS)) +@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS, include_tags=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_default_cfgs_non_std(model_name, batch_size): """Run a single forward pass with each model""" @@ -304,7 +304,7 @@ if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system(): @pytest.mark.timeout(120) -@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS)) +@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS, include_tags=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_forward_features(model_name, batch_size): """Run a single forward pass with each model in feature extraction mode""" diff --git a/timm/layers/__init__.py b/timm/layers/__init__.py index 03c4d8eb..625b4826 100644 --- a/timm/layers/__init__.py +++ b/timm/layers/__init__.py @@ -26,7 +26,7 @@ from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, from .inplace_abn import InplaceAbn from .linear import Linear from .mixed_conv2d import MixedConv2d -from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp +from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp, GlobalResponseNormMlp from .non_local_attn import NonLocalAttn, BatNonLocalAttn from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm diff --git a/timm/layers/grn.py b/timm/layers/grn.py new file mode 100644 index 00000000..ae71e013 --- /dev/null +++ b/timm/layers/grn.py @@ -0,0 +1,39 @@ +""" Global Response Normalization Module + +Based on the GRN layer presented in +`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808 + +This implementation +* works for both NCHW and NHWC tensor layouts +* uses affine param names matching existing torch norm layers +* slightly improves eager mode performance via fused addcmul + +Hacked together by / Copyright 2023 Ross Wightman +""" + +import torch +from torch import nn as nn + + +class GlobalResponseNorm(nn.Module): + """ Global Response Normalization layer + """ + def __init__(self, dim, eps=1e-6, channels_last=True): + super().__init__() + self.eps = eps + if channels_last: + self.spatial_dim = (1, 2) + self.channel_dim = -1 + self.wb_shape = (1, 1, 1, -1) + else: + self.spatial_dim = (2, 3) + self.channel_dim = 1 + self.wb_shape = (1, -1, 1, 1) + + self.weight = nn.Parameter(torch.zeros(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x): + x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True) + x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps) + return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n) diff --git a/timm/layers/mlp.py b/timm/layers/mlp.py index 91e80a84..d0188291 100644 --- a/timm/layers/mlp.py +++ b/timm/layers/mlp.py @@ -2,25 +2,38 @@ Hacked together by / Copyright 2020 Ross Wightman """ +from functools import partial + from torch import nn as nn +from .grn import GlobalResponseNorm from .helpers import to_2tuple class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + bias=True, + drop=0., + use_conv=False, + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features bias = to_2tuple(bias) drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear - self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1]) + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x): @@ -36,18 +49,29 @@ class GluMlp(nn.Module): """ MLP w/ GLU style gating See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202 """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, bias=True, drop=0.): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.Sigmoid, + bias=True, + drop=0., + use_conv=False, + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features assert hidden_features % 2 == 0 bias = to_2tuple(bias) drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + self.chunk_dim = 1 if use_conv else -1 - self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.fc2 = nn.Linear(hidden_features // 2, out_features, bias=bias[1]) + self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def init_weights(self): @@ -58,7 +82,7 @@ class GluMlp(nn.Module): def forward(self, x): x = self.fc1(x) - x, gates = x.chunk(2, dim=-1) + x, gates = x.chunk(2, dim=self.chunk_dim) x = x * self.act(gates) x = self.drop1(x) x = self.fc2(x) @@ -70,8 +94,15 @@ class GatedMlp(nn.Module): """ MLP as used in gMLP """ def __init__( - self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, - gate_layer=None, bias=True, drop=0.): + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + gate_layer=None, + bias=True, + drop=0., + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features @@ -104,8 +135,15 @@ class ConvMlp(nn.Module): """ MLP using 1x1 convs that keeps spatial dims """ def __init__( - self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, - norm_layer=None, bias=True, drop=0.): + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.ReLU, + norm_layer=None, + bias=True, + drop=0., + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features @@ -124,3 +162,40 @@ class ConvMlp(nn.Module): x = self.drop(x) x = self.fc2(x) return x + + +class GlobalResponseNormMlp(nn.Module): + """ MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + bias=True, + drop=0., + use_conv=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + bias = to_2tuple(bias) + drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv) + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.grn(x) + x = self.fc2(x) + x = self.drop2(x) + return x diff --git a/timm/models/convnext.py b/timm/models/convnext.py index d30e4137..e9214429 100644 --- a/timm/models/convnext.py +++ b/timm/models/convnext.py @@ -1,16 +1,42 @@ """ 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. +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 @@ -18,8 +44,8 @@ import torch import torch.nn as nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \ - create_conv2d, get_act_layer, make_divisible, to_ntuple +from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, Mlp, GlobalResponseNormMlp, \ + LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple from ._builder import build_model_with_cfg from ._manipulate import named_apply, checkpoint_seq from ._pretrained import generate_default_cfgs @@ -54,6 +80,7 @@ class ConvNeXtBlock(nn.Module): mlp_ratio=4, conv_mlp=False, conv_bias=True, + use_grn=False, ls_init_value=1e-6, act_layer='gelu', norm_layer=None, @@ -64,14 +91,13 @@ class ConvNeXtBlock(nn.Module): 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 + 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, 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.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None else None self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x): @@ -106,6 +132,7 @@ class ConvNeXtStage(nn.Module): ls_init_value=1.0, conv_mlp=False, conv_bias=True, + use_grn=False, act_layer='gelu', norm_layer=None, norm_layer_cl=None @@ -138,6 +165,7 @@ class ConvNeXtStage(nn.Module): 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 )) @@ -184,6 +212,7 @@ class ConvNeXt(nn.Module): head_norm_first=False, conv_mlp=False, conv_bias=True, + use_grn=False, act_layer='gelu', norm_layer=None, drop_rate=0., @@ -247,6 +276,7 @@ class ConvNeXt(nn.Module): 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 @@ -259,10 +289,11 @@ class ConvNeXt(nn.Module): # 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.head_norm_first = head_norm_first 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)), + ('norm', nn.Identity() if head_norm_first or num_classes == 0 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())])) @@ -293,7 +324,14 @@ class ConvNeXt(nn.Module): 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() + if num_classes == 0: + self.head.norm = nn.Identity() + self.head.fc = nn.Identity() + else: + if not self.head_norm_first: + norm_layer = type(self.stem[-1]) # obtain type from stem norm + self.head.norm = norm_layer(self.num_features) + self.head.fc = nn.Linear(self.num_features, num_classes) def forward_features(self, x): x = self.stem(x) @@ -342,6 +380,10 @@ def checkpoint_filter_fn(state_dict, model): 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') @@ -372,6 +414,20 @@ def _cfg(url='', **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({ # timm specific variants 'convnext_atto.d2_in1k': _cfg( @@ -499,6 +555,115 @@ default_cfgs = generate_default_cfgs({ url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", hf_hub_id='timm/', num_classes=21841), + + 'convnextv2_nano.fcmae_ft_in22k_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_nano.fcmae_ft_in22k_in1k_384': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt', + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_tiny.fcmae_ft_in22k_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_tiny.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_base.fcmae_ft_in22k_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_base.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_large.fcmae_ft_in22k_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_large.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_huge.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_huge.fcmae_ft_in22k_in1k_512': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt", + hf_hub_id='timm/', + input_size=(3, 512, 512), pool_size=(15, 15), crop_pct=1.0, crop_mode='squash'), + + 'convnextv2_atto.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=0.95), + 'convnextv2_femto.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=0.95), + 'convnextv2_pico.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=0.95), + 'convnextv2_nano.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_tiny.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_base.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_large.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_huge.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + + 'convnextv2_atto.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_atto_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_femto.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_femto_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_pico.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_pico_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_nano.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_nano_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_tiny.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_tiny_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_base.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_base_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_large.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_large_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_huge.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_huge_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + + 'convnextv2_small.untrained': _cfg(), }) @@ -623,3 +788,75 @@ def convnext_xxlarge(pretrained=False, **kwargs): model_args = dict(depths=[3, 4, 30, 3], dims=[384, 768, 1536, 3072], **kwargs) model = _create_convnext('convnext_xxlarge', pretrained=pretrained, **model_args) return model + + +@register_model +def convnextv2_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), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_atto', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_femto(pretrained=False, **kwargs): + # timm femto variant + model_args = dict( + depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_femto', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_pico(pretrained=False, **kwargs): + # timm pico variant + model_args = dict( + depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_pico', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_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), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_nano', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_tiny(pretrained=False, **kwargs): + model_args = dict( + depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_tiny', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_small(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_small', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_base(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_base', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_large(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_large', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_huge(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[352, 704, 1408, 2816], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_huge', pretrained=pretrained, **model_args) + return model \ No newline at end of file