Extra vit_huge model likely to cause test issue (non in21k variant), adding to filters

README.md

@@ -23,11 +23,14 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
-### Jan 6, 2022
-* Version 0.5.2 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
-* Tried training a few small / mobile optimized models, a few are good so far, more on the way...
+### Jan 14, 2022
+* Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon....
+* Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features
+* Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way...
   * `mnasnet_small` - 65.6 top-1
-  * `lcnet_100` - 72.1 top-1
+  * `mobilenetv2_050` - 65.9
+  * `lcnet_100/075/050` - 72.1 / 68.8 / 63.1
+  * `semnasnet_075` - 73
   * `fbnetv3_b/d/g` - 79.1 / 79.7 / 82.0
 * TinyNet models added by [rsomani95](https://github.com/rsomani95)
 * LCNet added via MobileNetV3 architecture

tests/test_models.py

@@ -28,12 +28,12 @@ NON_STD_FILTERS = [
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures
-if 'GITHUB_ACTIONS' in os.environ:  # and 'Linux' in platform.system():
+if 'GITHUB_ACTIONS' in os.environ:
     # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
     EXCLUDE_FILTERS = [
         '*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*']
+        '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*']
 else:
     EXCLUDE_FILTERS = []
 
@@ -255,7 +255,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
 EXCLUDE_JIT_FILTERS = [
     '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable
     'dla*', 'hrnet*', 'ghostnet*',  # hopefully fix at some point
-    'vit_large_*', 'vit_huge_*',
+    'vit_large_*', 'vit_huge_*', 'vit_gi*',
 ]
 
 
@@ -334,7 +334,7 @@ def _create_fx_model(model, train=False):
     return fx_model
 
 
-EXCLUDE_FX_FILTERS = []
+EXCLUDE_FX_FILTERS = ['vit_gi*']
 # not enough memory to run fx on more models than other tests
 if 'GITHUB_ACTIONS' in os.environ:
     EXCLUDE_FX_FILTERS += [

timm/data/auto_augment.py

@@ -15,7 +15,7 @@ Papers:
     RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719
     AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 import random
 import math

timm/data/dataset.py

@@ -1,6 +1,6 @@
 """ Quick n Simple Image Folder, Tarfile based DataSet
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 import torch.utils.data as data
 import os

timm/data/dataset_factory.py

@@ -1,3 +1,7 @@
+""" Dataset Factory
+
+Hacked together by / Copyright 2021, Ross Wightman
+"""
 import os
 
 from torchvision.datasets import CIFAR100, CIFAR10, MNIST, QMNIST, KMNIST, FashionMNIST, ImageNet, ImageFolder

timm/data/loader.py

@@ -3,7 +3,7 @@
 Prefetcher and Fast Collate inspired by NVIDIA APEX example at
 https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
 
-Hacked together by / Copyright 2021 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 import random
 from functools import partial

timm/data/mixup.py

@@ -8,7 +8,7 @@ CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Fea
 Code Reference:
 CutMix: https://github.com/clovaai/CutMix-PyTorch
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 import numpy as np
 import torch

timm/data/random_erasing.py

@@ -3,7 +3,7 @@
 Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
 Copyright Zhun Zhong & Liang Zheng
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 import random
 import math

timm/data/transforms_factory.py

@@ -1,7 +1,7 @@
 """ Transforms Factory
 Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 import math
 

timm/models/__init__.py

@@ -5,6 +5,7 @@ from .cait import *
 from .coat import *
 from .convit import *
 from .convmixer import *
+from .convnext import *
 from .crossvit import *
 from .cspnet import *
 from .densenet import *

timm/models/cait.py

@@ -4,6 +4,7 @@ Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
 
 Original code and weights from https://github.com/facebookresearch/deit, copyright below
 
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 # Copyright (c) 2015-present, Facebook, Inc.
 # All rights reserved.

timm/models/convit.py

@@ -9,6 +9,8 @@
 
 Paper link: https://arxiv.org/abs/2103.10697
 Original code: https://github.com/facebookresearch/convit, original copyright below
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 # Copyright (c) 2015-present, Facebook, Inc.
 # All rights reserved.

timm/models/convnext.py

@@ -0,0 +1,368 @@
+""" 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
+
+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
+import torch.nn.functional as F
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .fx_features import register_notrace_module
+from .helpers import named_apply, build_model_with_cfg
+from .layers import trunc_normal_, ClassifierHead, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp
+from .registry import register_model
+
+
+__all__ = ['ConvNeXt']  # model_registry will add each entrypoint fn to this
+
+
+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 = dict(
+    convnext_tiny=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth"),
+    convnext_small=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth"),
+    convnext_base=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"),
+    convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
+
+    convnext_tiny_hnf=_cfg(url=''),
+
+    convnext_base_in22k=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841),
+    convnext_large_in22k=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841),
+    convnext_xlarge_in22k=_cfg(
+        url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841),
+)
+
+
+def _is_contiguous(tensor: torch.Tensor) -> bool:
+    # jit is oh so lovely :/
+    # if torch.jit.is_tracing():
+    #     return True
+    if torch.jit.is_scripting():
+        return tensor.is_contiguous()
+    else:
+        return tensor.is_contiguous(memory_format=torch.contiguous_format)
+
+
+@register_notrace_module
+class LayerNorm2d(nn.LayerNorm):
+    r""" LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
+    """
+
+    def __init__(self, normalized_shape, eps=1e-6):
+        super().__init__(normalized_shape, eps=eps)
+
+    def forward(self, x) -> torch.Tensor:
+        if _is_contiguous(x):
+            return F.layer_norm(
+                x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
+        else:
+            s, u = torch.var_mean(x, dim=1, keepdim=True)
+            x = (x - u) * torch.rsqrt(s + self.eps)
+            x = x * self.weight[:, None, None] + self.bias[:, None, None]
+            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.
+
+    Args:
+        dim (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, dim, drop_path=0., ls_init_value=1e-6, conv_mlp=True, mlp_ratio=4, norm_layer=None):
+        super().__init__()
+        if not norm_layer:
+            norm_layer = partial(LayerNorm2d, eps=1e-6) if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
+        mlp_layer = ConvMlp if conv_mlp else Mlp
+        self.use_conv_mlp = conv_mlp
+        self.conv_dw = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
+        self.norm = norm_layer(dim)
+        self.mlp = mlp_layer(dim, int(mlp_ratio * dim), act_layer=nn.GELU)
+        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim)) 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, stride=2, depth=2, dp_rates=None, ls_init_value=1.0, conv_mlp=True,
+            norm_layer=None, cl_norm_layer=None, cross_stage=False):
+        super().__init__()
+
+        if in_chs != out_chs or stride > 1:
+            self.downsample = nn.Sequential(
+                norm_layer(in_chs),
+                nn.Conv2d(in_chs, out_chs, kernel_size=stride, stride=stride),
+            )
+        else:
+            self.downsample = nn.Identity()
+
+        dp_rates = dp_rates or [0.] * depth
+        self.blocks = nn.Sequential(*[ConvNeXtBlock(
+            dim=out_chs, drop_path=dp_rates[j], ls_init_value=ls_init_value, conv_mlp=conv_mlp,
+            norm_layer=norm_layer if conv_mlp else cl_norm_layer)
+            for j in range(depth)]
+        )
+
+    def forward(self, x):
+        x = self.downsample(x)
+        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, patch_size=4,
+            depths=(3, 3, 9, 3), dims=(96, 192, 384, 768),  ls_init_value=1e-6, conv_mlp=True,
+            head_init_scale=1., head_norm_first=False, norm_layer=None, drop_rate=0., drop_path_rate=0.,
+    ):
+        super().__init__()
+        assert output_stride == 32
+        if norm_layer is None:
+            norm_layer = partial(LayerNorm2d, eps=1e-6)
+            cl_norm_layer = norm_layer if conv_mlp else partial(nn.LayerNorm, eps=1e-6)
+        else:
+            assert conv_mlp,\
+                'If a norm_layer is specified, conv MLP must be used so all norm expect rank-4, channels-first input'
+            cl_norm_layer = norm_layer
+
+        self.num_classes = num_classes
+        self.drop_rate = drop_rate
+        self.feature_info = []
+
+        # 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),
+            norm_layer(dims[0])
+        )
+
+        self.stages = nn.Sequential()
+        dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
+        curr_stride = patch_size
+        prev_chs = dims[0]
+        stages = []
+        # 4 feature resolution stages, each consisting of multiple residual blocks
+        for i in range(4):
+            stride = 2 if i > 0 else 1
+            # FIXME support dilation / output_stride
+            curr_stride *= stride
+            out_chs = dims[i]
+            stages.append(ConvNeXtStage(
+                prev_chs, out_chs, stride=stride,
+                depth=depths[i], dp_rates=dp_rates[i], ls_init_value=ls_init_value, conv_mlp=conv_mlp,
+                norm_layer=norm_layer, cl_norm_layer=cl_norm_layer)
+            )
+            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:
+            # norm -> global pool -> fc ordering, like most other nets (not compat with FB weights)
+            self.norm_pre = norm_layer(self.num_features)  # final norm layer, before pooling
+            self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
+        else:
+            # pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
+            self.norm_pre = nn.Identity()
+            self.head = nn.Sequential(OrderedDict([
+                ('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
+                ('norm', 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)
+
+    def get_classifier(self):
+        return self.head.fc
+
+    def reset_classifier(self, num_classes=0, global_pool='avg'):
+        if isinstance(self.head, ClassifierHead):
+            # norm -> global pool -> fc
+            self.head = ClassifierHead(
+                self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
+        else:
+            # pool -> norm -> fc
+            self.head = nn.Sequential(OrderedDict([
+                ('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
+                ('norm', self.head.norm),
+                ('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())
+            ]))
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        x = self.stages(x)
+        x = self.norm_pre(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.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)
+        nn.init.constant_(module.bias, 0)
+    elif isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=.02)
+        nn.init.constant_(module.bias, 0)
+        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 '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,
+        default_cfg=default_cfgs[variant],
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
+        **kwargs)
+    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_tiny_hnf(pretrained=False, **kwargs):
+    model_args = dict(depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, **kwargs)
+    model = _create_convnext('convnext_tiny_hnf', 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_base_in22k(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
+    model = _create_convnext('convnext_base_in22k', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def convnext_large_in22k(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
+    model = _create_convnext('convnext_large_in22k', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def convnext_xlarge_in22k(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], conv_mlp=False, **kwargs)
+    model = _create_convnext('convnext_xlarge_in22k', pretrained=pretrained, **model_args)
+    return model
+
+
+

timm/models/crossvit.py

@@ -12,6 +12,8 @@ Paper link: https://arxiv.org/abs/2103.14899
 Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
 
 NOTE: model names have been renamed from originals to represent actual input res all *_224 -> *_240 and *_384 -> *_408
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 
 # Copyright IBM All Rights Reserved.

timm/models/cspnet.py

@@ -409,7 +409,7 @@ class CspNet(nn.Module):
 def _create_cspnet(variant, pretrained=False, **kwargs):
     cfg_variant = variant.split('_')[0]
     # NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
-    out_indices = kwargs.get('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4))
+    out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4))
     return build_model_with_cfg(
         CspNet, variant, pretrained,
         default_cfg=default_cfgs[variant],

timm/models/efficientnet.py

@@ -33,7 +33,7 @@ The majority of the above models (EfficientNet*, MixNet, MnasNet) and original w
 by Mingxing Tan, Quoc Le, and other members of their Google Brain team. Thanks for consistently releasing
 the models and weights open source!
 
-Hacked together by / Copyright 2021 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 from functools import partial
 from typing import List
@@ -73,7 +73,8 @@ default_cfgs = {
     'mnasnet_140': _cfg(url=''),
 
     'semnasnet_050': _cfg(url=''),
-    'semnasnet_075': _cfg(url=''),
+    'semnasnet_075': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/semnasnet_075-18710866.pth'),
     'semnasnet_100': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'),
     'semnasnet_140': _cfg(url=''),
@@ -83,7 +84,9 @@ default_cfgs = {
     'mobilenetv2_035': _cfg(
         url=''),
     'mobilenetv2_050': _cfg(
-        url=''),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth',
+        interpolation='bicubic',
+    ),
     'mobilenetv2_075': _cfg(
         url=''),
     'mobilenetv2_100': _cfg(
@@ -718,7 +721,7 @@ def _gen_mobilenet_v2(
     round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
     model_kwargs = dict(
         block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
-        num_features=1280 if fix_stem_head else round_chs_fn(1280),
+        num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)),
         stem_size=32,
         fix_stem=fix_stem_head,
         round_chs_fn=round_chs_fn,

timm/models/efficientnet_blocks.py

@@ -1,6 +1,6 @@
 """ EfficientNet, MobileNetV3, etc Blocks
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 
 import torch

timm/models/efficientnet_builder.py

@@ -3,7 +3,7 @@
 Assembles EfficieNet and related network feature blocks from string definitions.
 Handles stride, dilation calculations, and selects feature extraction points.
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 
 import logging

timm/models/layers/__init__.py

@@ -21,7 +21,7 @@ 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
+from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
 from .non_local_attn import NonLocalAttn, BatNonLocalAttn
 from .norm import GroupNorm, LayerNorm2d
 from .norm_act import BatchNormAct2d, GroupNormAct

timm/models/levit.py

@@ -14,7 +14,7 @@ Adapted from official impl at https://github.com/facebookresearch/LeViT, origina
 
 This version combines both conv/linear models and fixes torchscript compatibility.
 
-Modifications by/coyright Copyright 2021 Ross Wightman
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 
 # Copyright (c) 2015-present, Facebook, Inc.

timm/models/mobilenetv3.py

@@ -1,11 +1,10 @@
-
 """ MobileNet V3
 
 A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
 
 Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
 
-Hacked together by / Copyright 2021 Ross Wightman
+Hacked together by / Copyright 2019, Ross Wightman
 """
 from functools import partial
 from typing import List
@@ -86,8 +85,14 @@ default_cfgs = {
         input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95),
 
     "lcnet_035": _cfg(),
-    "lcnet_050": _cfg(),
-    "lcnet_075": _cfg(),
+    "lcnet_050": _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth',
+        interpolation='bicubic',
+    ),
+    "lcnet_075": _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth',
+        interpolation='bicubic',
+    ),
     "lcnet_100": _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth',
         interpolation='bicubic',
@@ -111,8 +116,9 @@ class MobileNetV3(nn.Module):
       * LCNet - https://arxiv.org/abs/2109.15099
     """
 
-    def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
-                 pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True,
+    def __init__(
+            self, block_args, num_classes=1000, in_chans=3, stem_size=16, fix_stem=False, num_features=1280,
+            head_bias=True, pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True,
             round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'):
         super(MobileNetV3, self).__init__()
         act_layer = act_layer or nn.ReLU
@@ -123,6 +129,7 @@ class MobileNetV3(nn.Module):
         self.drop_rate = drop_rate
 
         # Stem
+        if not fix_stem:
             stem_size = round_chs_fn(stem_size)
         self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
         self.bn1 = norm_layer(stem_size)
@@ -189,8 +196,8 @@ class MobileNetV3Features(nn.Module):
     """
 
     def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3,
-                 stem_size=16, output_stride=32, pad_type='', round_chs_fn=round_channels, se_from_exp=True,
-                 act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
+                 stem_size=16, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels,
+                 se_from_exp=True, act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
         super(MobileNetV3Features, self).__init__()
         act_layer = act_layer or nn.ReLU
         norm_layer = norm_layer or nn.BatchNorm2d
@@ -198,6 +205,7 @@ class MobileNetV3Features(nn.Module):
         self.drop_rate = drop_rate
 
         # Stem
+        if not fix_stem:
             stem_size = round_chs_fn(stem_size)
         self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
         self.bn1 = norm_layer(stem_size)
@@ -382,6 +390,7 @@ def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwarg
         block_args=decode_arch_def(arch_def),
         num_features=num_features,
         stem_size=16,
+        fix_stem=channel_multiplier < 0.75,
         round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
         norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
         act_layer=act_layer,

timm/models/resnet.py

@@ -4,7 +4,8 @@ This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-C
 additional dropout and dynamic global avg/max pool.
 
 ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
-Copyright 2020 Ross Wightman
+
+Copyright 2019, Ross Wightman
 """
 import math
 from functools import partial

timm/models/swin_transformer.py

@@ -4,6 +4,7 @@ A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shi
 
 Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
 
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 # --------------------------------------------------------
 # Swin Transformer

timm/models/vgg.py

@@ -180,7 +180,7 @@ def _filter_fn(state_dict):
 def _create_vgg(variant: str, pretrained: bool, **kwargs: Any) -> VGG:
     cfg = variant.split('_')[0]
     # NOTE: VGG is one of few models with stride==1 features w/ 6 out_indices [0..5]
-    out_indices = kwargs.get('out_indices', (0, 1, 2, 3, 4, 5))
+    out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5))
     model = build_model_with_cfg(
         VGG, variant, pretrained,
         default_cfg=default_cfgs[variant],

timm/models/visformer.py

@@ -4,6 +4,7 @@ Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.1
 
 From original at https://github.com/danczs/Visformer
 
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 from copy import deepcopy
 

timm/models/vision_transformer.py

@@ -20,7 +20,7 @@ for some einops/einsum fun
 * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
 * Bert reference code checks against Huggingface Transformers and Tensorflow Bert
 
-Hacked together by / Copyright 2021 Ross Wightman
+Hacked together by / Copyright 2020, Ross Wightman
 """
 import math
 import logging
@@ -105,6 +105,10 @@ default_cfgs = {
             'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
         input_size=(3, 384, 384), crop_pct=1.0),
 
+    'vit_huge_patch14_224': _cfg(url=''),
+    'vit_giant_patch14_224': _cfg(url=''),
+    'vit_gigantic_patch14_224': _cfg(url=''),
+
     # patch models, imagenet21k (weights from official Google JAX impl)
     'vit_tiny_patch16_224_in21k': _cfg(
         url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
@@ -715,6 +719,33 @@ def vit_base_patch32_sam_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_huge_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_giant_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Giant model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_gigantic_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Gigantic model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
     """ ViT-Tiny (Vit-Ti/16).

timm/models/vision_transformer_hybrid.py

@@ -11,7 +11,7 @@ A PyTorch implement of the Hybrid Vision Transformers as described in:
 NOTE These hybrid model definitions depend on code in vision_transformer.py.
 They were moved here to keep file sizes sane.
 
-Hacked together by / Copyright 2021 Ross Wightman
+Hacked together by / Copyright 2020, Ross Wightman
 """
 from copy import deepcopy
 from functools import partial

timm/models/xcit.py

@@ -1,10 +1,12 @@
 """ Cross-Covariance Image Transformer (XCiT) in PyTorch
 
-Same as the official implementation, with some minor adaptations.
-    - https://github.com/facebookresearch/xcit/blob/master/xcit.py
-
 Paper:
     - https://arxiv.org/abs/2106.09681
+
+Same as the official implementation, with some minor adaptations, original copyright below
+    - https://github.com/facebookresearch/xcit/blob/master/xcit.py
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
 """
 # Copyright (c) 2015-present, Facebook, Inc.
 # All rights reserved.

timm/version.py

@@ -1 +1 @@
-__version__ = '0.5.2'
+__version__ = '0.5.4'