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Add ConvNeXt-V2 support (model additions and weights) (#1614)
* Add ConvNeXt-V2 support (model additions and weights) * ConvNeXt-V2 weights on HF Hub, tweaking some tests * Update README, fixing convnextv2 tests
This commit is contained in:
Ross Wightman
GitHub
parent
3698e79ac5
commit
6e5553da5f
@ -28,6 +28,11 @@ For a few months now, `timm` has been part of the Hugging Face ecosystem. Yearly
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If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts:
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[**hf.co/oss-survey**](https://hf.co/oss-survey) 🙏
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### Jan 5, 2023
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* ConvNeXt-V2 models and weights added to existing `convnext.py`
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* Paper: [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](http://arxiv.org/abs/2301.00808)
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* Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC)
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### Dec 23, 2022 🎄☃
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* Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013)
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* NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP
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@ -396,6 +401,7 @@ A full version of the list below with source links can be found in the [document
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* CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399
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* CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803
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* ConvNeXt - https://arxiv.org/abs/2201.03545
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* ConvNeXt-V2 - http://arxiv.org/abs/2301.00808
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* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697
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* CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
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* DeiT - https://arxiv.org/abs/2012.12877
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@ -418,6 +424,7 @@ A full version of the list below with source links can be found in the [document
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* Single-Path NAS - https://arxiv.org/abs/1904.02877
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* TinyNet - https://arxiv.org/abs/2010.14819
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* EVA - https://arxiv.org/abs/2211.07636
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* FlexiViT - https://arxiv.org/abs/2212.08013
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* GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959
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* GhostNet - https://arxiv.org/abs/1911.11907
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* gMLP - https://arxiv.org/abs/2105.08050
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@ -38,7 +38,7 @@ if 'GITHUB_ACTIONS' in os.environ:
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'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
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'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
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'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*', 'swin*huge*',
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'swin*giant*']
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'swin*giant*', 'convnextv2_huge*']
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NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*', 'eva_giant*']
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else:
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EXCLUDE_FILTERS = []
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@ -129,7 +129,7 @@ def test_model_backward(model_name, batch_size):
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@pytest.mark.timeout(300)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS))
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS, include_tags=True))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_default_cfgs(model_name, batch_size):
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"""Run a single forward pass with each model"""
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@ -191,7 +191,7 @@ def test_model_default_cfgs(model_name, batch_size):
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@pytest.mark.timeout(300)
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@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS))
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@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS, include_tags=True))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_default_cfgs_non_std(model_name, batch_size):
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"""Run a single forward pass with each model"""
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@ -304,7 +304,7 @@ if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS, include_tags=True))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_forward_features(model_name, batch_size):
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"""Run a single forward pass with each model in feature extraction mode"""
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@ -26,7 +26,7 @@ from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible,
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from .inplace_abn import InplaceAbn
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from .linear import Linear
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from .mixed_conv2d import MixedConv2d
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from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
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from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp, GlobalResponseNormMlp
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from .non_local_attn import NonLocalAttn, BatNonLocalAttn
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from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
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from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm
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39
timm/layers/grn.py
Normal file
39
timm/layers/grn.py
Normal file
@ -0,0 +1,39 @@
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""" Global Response Normalization Module
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Based on the GRN layer presented in
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`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
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This implementation
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* works for both NCHW and NHWC tensor layouts
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* uses affine param names matching existing torch norm layers
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* slightly improves eager mode performance via fused addcmul
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Hacked together by / Copyright 2023 Ross Wightman
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"""
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import torch
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from torch import nn as nn
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class GlobalResponseNorm(nn.Module):
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""" Global Response Normalization layer
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"""
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def __init__(self, dim, eps=1e-6, channels_last=True):
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super().__init__()
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self.eps = eps
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if channels_last:
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self.spatial_dim = (1, 2)
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self.channel_dim = -1
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self.wb_shape = (1, 1, 1, -1)
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else:
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self.spatial_dim = (2, 3)
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self.channel_dim = 1
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self.wb_shape = (1, -1, 1, 1)
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self.weight = nn.Parameter(torch.zeros(dim))
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self.bias = nn.Parameter(torch.zeros(dim))
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def forward(self, x):
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x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True)
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x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps)
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return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n)
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@ -2,25 +2,38 @@
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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from functools import partial
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from torch import nn as nn
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from .grn import GlobalResponseNorm
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from .helpers import to_2tuple
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class Mlp(nn.Module):
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""" MLP as used in Vision Transformer, MLP-Mixer and related networks
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"""
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.):
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def __init__(
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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.GELU,
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bias=True,
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drop=0.,
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use_conv=False,
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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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bias = to_2tuple(bias)
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drop_probs = to_2tuple(drop)
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linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
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self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
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self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
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self.act = act_layer()
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self.drop1 = nn.Dropout(drop_probs[0])
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self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
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self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
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self.drop2 = nn.Dropout(drop_probs[1])
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def forward(self, x):
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@ -36,18 +49,29 @@ class GluMlp(nn.Module):
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""" MLP w/ GLU style gating
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See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
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"""
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, bias=True, drop=0.):
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def __init__(
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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.Sigmoid,
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bias=True,
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drop=0.,
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use_conv=False,
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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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assert hidden_features % 2 == 0
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bias = to_2tuple(bias)
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drop_probs = to_2tuple(drop)
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linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
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self.chunk_dim = 1 if use_conv else -1
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self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
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self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
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self.act = act_layer()
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self.drop1 = nn.Dropout(drop_probs[0])
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self.fc2 = nn.Linear(hidden_features // 2, out_features, bias=bias[1])
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self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1])
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self.drop2 = nn.Dropout(drop_probs[1])
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def init_weights(self):
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@ -58,7 +82,7 @@ class GluMlp(nn.Module):
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def forward(self, x):
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x = self.fc1(x)
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x, gates = x.chunk(2, dim=-1)
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x, gates = x.chunk(2, dim=self.chunk_dim)
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x = x * self.act(gates)
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x = self.drop1(x)
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x = self.fc2(x)
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@ -70,8 +94,15 @@ class GatedMlp(nn.Module):
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""" MLP as used in gMLP
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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.GELU,
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gate_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.GELU,
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gate_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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@ -104,8 +135,15 @@ class ConvMlp(nn.Module):
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""" MLP using 1x1 convs that keeps spatial dims
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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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@ -124,3 +162,40 @@ class ConvMlp(nn.Module):
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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 GlobalResponseNormMlp(nn.Module):
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""" MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d
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"""
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def __init__(
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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.GELU,
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bias=True,
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drop=0.,
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use_conv=False,
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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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bias = to_2tuple(bias)
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drop_probs = to_2tuple(drop)
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linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
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self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
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self.act = act_layer()
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self.drop1 = nn.Dropout(drop_probs[0])
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self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv)
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self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
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self.drop2 = nn.Dropout(drop_probs[1])
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop1(x)
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x = self.grn(x)
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x = self.fc2(x)
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x = self.drop2(x)
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return x
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@ -1,16 +1,42 @@
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""" ConvNeXt
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Paper: `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
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Papers:
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* `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
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@Article{liu2022convnet,
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author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
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title = {A ConvNet for the 2020s},
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journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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year = {2022},
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}
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Original code and weights from https://github.com/facebookresearch/ConvNeXt, original copyright below
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* `ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
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@article{Woo2023ConvNeXtV2,
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title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
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author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
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year={2023},
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journal={arXiv preprint arXiv:2301.00808},
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}
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Model defs atto, femto, pico, nano and _ols / _hnf variants are timm specific.
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Original code and weights from:
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* https://github.com/facebookresearch/ConvNeXt, original copyright below
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* https://github.com/facebookresearch/ConvNeXt-V2, original copyright below
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Model defs atto, femto, pico, nano and _ols / _hnf variants are timm originals.
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Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
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"""
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# ConvNeXt
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the MIT license
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# ConvNeXt-V2
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0))
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# No code was used directly from ConvNeXt-V2, however the weights are CC BY-NC 4.0 so beware if using commercially.
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from collections import OrderedDict
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from functools import partial
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@ -18,8 +44,8 @@ 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_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \
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create_conv2d, get_act_layer, make_divisible, to_ntuple
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from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, Mlp, GlobalResponseNormMlp, \
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LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple
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from ._builder import build_model_with_cfg
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from ._manipulate import named_apply, checkpoint_seq
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from ._pretrained import generate_default_cfgs
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@ -54,6 +80,7 @@ class ConvNeXtBlock(nn.Module):
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mlp_ratio=4,
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conv_mlp=False,
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conv_bias=True,
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use_grn=False,
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ls_init_value=1e-6,
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act_layer='gelu',
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norm_layer=None,
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@ -64,14 +91,13 @@ class ConvNeXtBlock(nn.Module):
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act_layer = get_act_layer(act_layer)
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if not norm_layer:
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norm_layer = LayerNorm2d if conv_mlp else LayerNorm
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mlp_layer = ConvMlp if conv_mlp else Mlp
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mlp_layer = partial(GlobalResponseNormMlp if use_grn else Mlp, use_conv=conv_mlp)
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self.use_conv_mlp = conv_mlp
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self.conv_dw = create_conv2d(
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in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias)
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self.norm = norm_layer(out_chs)
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self.mlp = mlp_layer(out_chs, int(mlp_ratio * out_chs), act_layer=act_layer)
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self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value > 0 else None
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self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None 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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@ -106,6 +132,7 @@ class ConvNeXtStage(nn.Module):
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ls_init_value=1.0,
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conv_mlp=False,
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conv_bias=True,
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use_grn=False,
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act_layer='gelu',
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norm_layer=None,
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norm_layer_cl=None
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@ -138,6 +165,7 @@ class ConvNeXtStage(nn.Module):
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ls_init_value=ls_init_value,
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conv_mlp=conv_mlp,
|
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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
|
||||
Loading…
x
Reference in New Issue
Block a user