Merge pull request #1327 from rwightman/edgenext_csp_and_more

EdgeNeXt, additional DarkNets, and more

benchmark.py

@@ -6,24 +6,23 @@ An inference and train step benchmark script for timm models.
 Hacked together by Ross Wightman (https://github.com/rwightman)
 """
 import argparse
-import os
 import csv
 import json
-import time
 import logging
-import torch
-import torch.nn as nn
-import torch.nn.parallel
+import time
 from collections import OrderedDict
 from contextlib import suppress
 from functools import partial
 
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+
+from timm.data import resolve_data_config
 from timm.models import create_model, is_model, list_models
 from timm.optim import create_optimizer_v2
-from timm.data import resolve_data_config
 from timm.utils import setup_default_logging, set_jit_fuser
 
-
 has_apex = False
 try:
     from apex import amp
@@ -71,6 +70,8 @@ parser.add_argument('--bench', default='both', type=str,
                     help="Benchmark mode. One of 'inference', 'train', 'both'. Defaults to 'both'")
 parser.add_argument('--detail', action='store_true', default=False,
                     help='Provide train fwd/bwd/opt breakdown detail if True. Defaults to False')
+parser.add_argument('--no-retry', action='store_true', default=False,
+                    help='Do not decay batch size and retry on error.')
 parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
                     help='Output csv file for validation results (summary)')
 parser.add_argument('--num-warm-iter', default=10, type=int,
@@ -169,10 +170,9 @@ def resolve_precision(precision: str):
 
 
 def profile_deepspeed(model, input_size=(3, 224, 224), batch_size=1, detailed=False):
-    macs, _ = get_model_profile(
+    _, macs, _ = get_model_profile(
         model=model,
-        input_res=(batch_size,) + input_size,  # input shape or input to the input_constructor
-        input_constructor=None,  # if specified, a constructor taking input_res is used as input to the model
+        input_shape=(batch_size,) + input_size,  # input shape/resolution
         print_profile=detailed,  # prints the model graph with the measured profile attached to each module
         detailed=detailed,  # print the detailed profile
         warm_up=10,  # the number of warm-ups before measuring the time of each module
@@ -197,8 +197,19 @@ def profile_fvcore(model, input_size=(3, 224, 224), batch_size=1, detailed=False
 
 class BenchmarkRunner:
     def __init__(
-            self, model_name, detail=False, device='cuda', torchscript=False, aot_autograd=False, precision='float32',
-            fuser='', num_warm_iter=10, num_bench_iter=50, use_train_size=False, **kwargs):
+            self,
+            model_name,
+            detail=False,
+            device='cuda',
+            torchscript=False,
+            aot_autograd=False,
+            precision='float32',
+            fuser='',
+            num_warm_iter=10,
+            num_bench_iter=50,
+            use_train_size=False,
+            **kwargs
+    ):
         self.model_name = model_name
         self.detail = detail
         self.device = device
@@ -225,11 +236,12 @@ class BenchmarkRunner:
         self.num_classes = self.model.num_classes
         self.param_count = count_params(self.model)
         _logger.info('Model %s created, param count: %d' % (model_name, self.param_count))
+
+        data_config = resolve_data_config(kwargs, model=self.model, use_test_size=not use_train_size)
         self.scripted = False
         if torchscript:
             self.model = torch.jit.script(self.model)
             self.scripted = True
-        data_config = resolve_data_config(kwargs, model=self.model, use_test_size=not use_train_size)
         self.input_size = data_config['input_size']
         self.batch_size = kwargs.pop('batch_size', 256)
 
@@ -255,7 +267,13 @@ class BenchmarkRunner:
 
 class InferenceBenchmarkRunner(BenchmarkRunner):
 
-    def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
+    def __init__(
+            self,
+            model_name,
+            device='cuda',
+            torchscript=False,
+            **kwargs
+    ):
         super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
         self.model.eval()
 
@@ -324,7 +342,13 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
 
 class TrainBenchmarkRunner(BenchmarkRunner):
 
-    def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
+    def __init__(
+            self,
+            model_name,
+            device='cuda',
+            torchscript=False,
+            **kwargs
+    ):
         super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
         self.model.train()
 
@@ -491,7 +515,7 @@ def decay_batch_exp(batch_size, factor=0.5, divisor=16):
     return max(0, int(out_batch_size))
 
 
-def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
+def _try_run(model_name, bench_fn, bench_kwargs, initial_batch_size, no_batch_size_retry=False):
     batch_size = initial_batch_size
     results = dict()
     error_str = 'Unknown'
@@ -506,8 +530,11 @@ def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
             if 'channels_last' in error_str:
                 _logger.error(f'{model_name} not supported in channels_last, skipping.')
                 break
-            _logger.warning(f'"{error_str}" while running benchmark. Reducing batch size to {batch_size} for retry.')
+            _logger.error(f'"{error_str}" while running benchmark.')
+            if no_batch_size_retry:
+                break
         batch_size = decay_batch_exp(batch_size)
+        _logger.warning(f'Reducing batch size to {batch_size} for retry.')
     results['error'] = error_str
     return results
 
@@ -549,7 +576,13 @@ def benchmark(args):
 
     model_results = OrderedDict(model=model)
     for prefix, bench_fn in zip(prefixes, bench_fns):
-        run_results = _try_run(model, bench_fn, initial_batch_size=batch_size, bench_kwargs=bench_kwargs)
+        run_results = _try_run(
+            model,
+            bench_fn,
+            bench_kwargs=bench_kwargs,
+            initial_batch_size=batch_size,
+            no_batch_size_retry=args.no_retry,
+        )
         if prefix and 'error' not in run_results:
             run_results = {'_'.join([prefix, k]): v for k, v in run_results.items()}
         model_results.update(run_results)

timm/data/__init__.py

@@ -6,7 +6,8 @@ from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
 from .dataset_factory import create_dataset
 from .loader import create_loader
 from .mixup import Mixup, FastCollateMixup
-from .parsers import create_parser
+from .parsers import create_parser,\
+    get_img_extensions, is_img_extension, set_img_extensions, add_img_extensions, del_img_extensions
 from .real_labels import RealLabelsImagenet
 from .transforms import *
 from .transforms_factory import create_transform

timm/data/config.py

@@ -64,11 +64,15 @@ def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, v
         new_config['std'] = default_cfg['std']
 
     # resolve default crop percentage
-    new_config['crop_pct'] = DEFAULT_CROP_PCT
+    crop_pct = DEFAULT_CROP_PCT
     if 'crop_pct' in args and args['crop_pct'] is not None:
-        new_config['crop_pct'] = args['crop_pct']
+        crop_pct = args['crop_pct']
+    else:
+        if use_test_size and 'test_crop_pct' in default_cfg:
+            crop_pct = default_cfg['test_crop_pct']
         elif 'crop_pct' in default_cfg:
-        new_config['crop_pct'] = default_cfg['crop_pct']
+            crop_pct = default_cfg['crop_pct']
+    new_config['crop_pct'] = crop_pct
 
     if verbose:
         _logger.info('Data processing configuration for current model + dataset:')

timm/data/dataset_factory.py

@@ -26,8 +26,8 @@ _TORCH_BASIC_DS = dict(
     kmnist=KMNIST,
     fashion_mnist=FashionMNIST,
 )
-_TRAIN_SYNONYM = {'train', 'training'}
-_EVAL_SYNONYM = {'val', 'valid', 'validation', 'eval', 'evaluation'}
+_TRAIN_SYNONYM = dict(train=None, training=None)
+_EVAL_SYNONYM = dict(val=None, valid=None, validation=None, eval=None, evaluation=None)
 
 
 def _search_split(root, split):

timm/data/parsers/__init__.py

@@ -1 +1,2 @@
 from .parser_factory import create_parser
+from .img_extensions import *

timm/data/parsers/constants.py

@@ -1 +0,0 @@
-IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg')

timm/data/parsers/img_extensions.py

@@ -0,0 +1,50 @@
+from copy import deepcopy
+
+__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
+
+
+IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg')  # singleton, kept public for bwd compat use
+_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS)  # set version, private, kept in sync
+
+
+def _set_extensions(extensions):
+    global IMG_EXTENSIONS
+    global _IMG_EXTENSIONS_SET
+    dedupe = set()  # NOTE de-duping tuple while keeping original order
+    IMG_EXTENSIONS = tuple(x for x in extensions if x not in dedupe and not dedupe.add(x))
+    _IMG_EXTENSIONS_SET = set(extensions)
+
+
+def _valid_extension(x: str):
+    return x and isinstance(x, str) and len(x) >= 2 and x.startswith('.')
+
+
+def is_img_extension(ext):
+    return ext in _IMG_EXTENSIONS_SET
+
+
+def get_img_extensions(as_set=False):
+    return deepcopy(_IMG_EXTENSIONS_SET if as_set else IMG_EXTENSIONS)
+
+
+def set_img_extensions(extensions):
+    assert len(extensions)
+    for x in extensions:
+        assert _valid_extension(x)
+    _set_extensions(extensions)
+
+
+def add_img_extensions(ext):
+    if not isinstance(ext, (list, tuple, set)):
+        ext = (ext,)
+    for x in ext:
+        assert _valid_extension(x)
+    extensions = IMG_EXTENSIONS + tuple(ext)
+    _set_extensions(extensions)
+
+
+def del_img_extensions(ext):
+    if not isinstance(ext, (list, tuple, set)):
+        ext = (ext,)
+    extensions = tuple(x for x in IMG_EXTENSIONS if x not in ext)
+    _set_extensions(extensions)

timm/data/parsers/parser_factory.py

@@ -1,7 +1,6 @@
 import os
 
 from .parser_image_folder import ParserImageFolder
-from .parser_image_tar import ParserImageTar
 from .parser_image_in_tar import ParserImageInTar
 
 

timm/data/parsers/parser_image_folder.py

@@ -6,15 +6,35 @@ on the folder hierarchy, just leaf folders by default.
 Hacked together by / Copyright 2020 Ross Wightman
 """
 import os
+from typing import Dict, List, Optional, Set, Tuple, Union
 
 from timm.utils.misc import natural_key
 
-from .parser import Parser
 from .class_map import load_class_map
-from .constants import IMG_EXTENSIONS
+from .img_extensions import get_img_extensions
+from .parser import Parser
+
 
+def find_images_and_targets(
+        folder: str,
+        types: Optional[Union[List, Tuple, Set]] = None,
+        class_to_idx: Optional[Dict] = None,
+        leaf_name_only: bool = True,
+        sort: bool = True
+):
+    """ Walk folder recursively to discover images and map them to classes by folder names.
 
-def find_images_and_targets(folder, types=IMG_EXTENSIONS, class_to_idx=None, leaf_name_only=True, sort=True):
+    Args:
+        folder: root of folder to recrusively search
+        types: types (file extensions) to search for in path
+        class_to_idx: specify mapping for class (folder name) to class index if set
+        leaf_name_only: use only leaf-name of folder walk for class names
+        sort: re-sort found images by name (for consistent ordering)
+
+    Returns:
+        A list of image and target tuples, class_to_idx mapping
+    """
+    types = get_img_extensions(as_set=True) if not types else set(types)
     labels = []
     filenames = []
     for root, subdirs, files in os.walk(folder, topdown=False, followlinks=True):
@@ -51,7 +71,8 @@ class ParserImageFolder(Parser):
         self.samples, self.class_to_idx = find_images_and_targets(root, class_to_idx=class_to_idx)
         if len(self.samples) == 0:
             raise RuntimeError(
-                f'Found 0 images in subfolders of {root}. Supported image extensions are {", ".join(IMG_EXTENSIONS)}')
+                f'Found 0 images in subfolders of {root}. '
+                f'Supported image extensions are {", ".join(get_img_extensions())}')
 
     def __getitem__(self, index):
         path, target = self.samples[index]

timm/data/parsers/parser_image_in_tar.py

@@ -9,20 +9,20 @@ Labels are based on the combined folder and/or tar name structure.
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
+import logging
 import os
-import tarfile
 import pickle
-import logging
-import numpy as np
+import tarfile
 from glob import glob
-from typing import List, Dict
+from typing import List, Tuple, Dict, Set, Optional, Union
+
+import numpy as np
 
 from timm.utils.misc import natural_key
 
-from .parser import Parser
 from .class_map import load_class_map
-from .constants import IMG_EXTENSIONS
-
+from .img_extensions import get_img_extensions
+from .parser import Parser
 
 _logger = logging.getLogger(__name__)
 CACHE_FILENAME_SUFFIX = '_tarinfos.pickle'
@@ -39,7 +39,7 @@ class TarState:
         self.tf = None
 
 
-def _extract_tarinfo(tf: tarfile.TarFile, parent_info: Dict, extensions=IMG_EXTENSIONS):
+def _extract_tarinfo(tf: tarfile.TarFile, parent_info: Dict, extensions: Set[str]):
     sample_count = 0
     for i, ti in enumerate(tf):
         if not ti.isfile():
@@ -60,7 +60,14 @@ def _extract_tarinfo(tf: tarfile.TarFile, parent_info: Dict, extensions=IMG_EXTE
     return sample_count
 
 
-def extract_tarinfos(root, class_name_to_idx=None, cache_tarinfo=None, extensions=IMG_EXTENSIONS, sort=True):
+def extract_tarinfos(
+        root,
+        class_name_to_idx: Optional[Dict] = None,
+        cache_tarinfo: Optional[bool] = None,
+        extensions: Optional[Union[List, Tuple, Set]] = None,
+        sort: bool = True
+):
+    extensions = get_img_extensions(as_set=True) if not extensions else set(extensions)
     root_is_tar = False
     if os.path.isfile(root):
         assert os.path.splitext(root)[-1].lower() == '.tar'
@@ -176,8 +183,8 @@ class ParserImageInTar(Parser):
         self.samples, self.targets, self.class_name_to_idx, tarfiles = extract_tarinfos(
             self.root,
             class_name_to_idx=class_name_to_idx,
-            cache_tarinfo=cache_tarinfo,
-            extensions=IMG_EXTENSIONS)
+            cache_tarinfo=cache_tarinfo
+        )
         self.class_idx_to_name = {v: k for k, v in self.class_name_to_idx.items()}
         if len(tarfiles) == 1 and tarfiles[0][0] is None:
             self.root_is_tar = True

timm/data/parsers/parser_image_tar.py

@@ -8,13 +8,15 @@ Hacked together by / Copyright 2020 Ross Wightman
 import os
 import tarfile
 
-from .parser import Parser
-from .class_map import load_class_map
-from .constants import IMG_EXTENSIONS
 from timm.utils.misc import natural_key
 
+from .class_map import load_class_map
+from .img_extensions import get_img_extensions
+from .parser import Parser
+
 
 def extract_tarinfo(tarfile, class_to_idx=None, sort=True):
+    extensions = get_img_extensions(as_set=True)
     files = []
     labels = []
     for ti in tarfile.getmembers():
@@ -23,7 +25,7 @@ def extract_tarinfo(tarfile, class_to_idx=None, sort=True):
         dirname, basename = os.path.split(ti.path)
         label = os.path.basename(dirname)
         ext = os.path.splitext(basename)[1]
-        if ext.lower() in IMG_EXTENSIONS:
+        if ext.lower() in extensions:
             files.append(ti)
             labels.append(label)
     if class_to_idx is None:

timm/models/__init__.py

@@ -12,6 +12,7 @@ from .deit import *
 from .densenet import *
 from .dla import *
 from .dpn import *
+from .edgenext import *
 from .efficientnet import *
 from .ghostnet import *
 from .gluon_resnet import *

timm/models/convnext.py

@@ -17,9 +17,8 @@ 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, checkpoint_seq
-from .layers import trunc_normal_, ClassifierHead, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp
+from .layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, create_conv2d
 from .registry import register_model
 
 
@@ -44,6 +43,7 @@ default_cfgs = dict(
     convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
 
     convnext_nano_hnf=_cfg(url=''),
+    convnext_nano_ols=_cfg(url=''),
     convnext_tiny_hnf=_cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
         crop_pct=0.95),
@@ -88,35 +88,6 @@ default_cfgs = dict(
 )
 
 
-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, unbiased=False, 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:
@@ -133,16 +104,30 @@ class ConvNeXtBlock(nn.Module):
         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=False, mlp_ratio=4, norm_layer=None):
+    def __init__(
+            self,
+            dim,
+            dim_out=None,
+            stride=1,
+            mlp_ratio=4,
+            conv_mlp=False,
+            conv_bias=True,
+            ls_init_value=1e-6,
+            norm_layer=None,
+            act_layer=nn.GELU,
+            drop_path=0.,
+    ):
         super().__init__()
+        dim_out = dim_out or dim
         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.conv_dw = create_conv2d(dim, dim_out, kernel_size=7, stride=stride, depthwise=True, bias=conv_bias)
+        self.norm = norm_layer(dim_out)
+        self.mlp = mlp_layer(dim_out, int(mlp_ratio * dim_out), act_layer=act_layer)
+        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim_out)) if ls_init_value > 0 else None
         self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
 
     def forward(self, x):
@@ -158,6 +143,7 @@ class ConvNeXtBlock(nn.Module):
             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
 
@@ -165,25 +151,44 @@ class ConvNeXtBlock(nn.Module):
 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=False,
-            norm_layer=None, cl_norm_layer=None, cross_stage=False):
+            self,
+            in_chs,
+            out_chs,
+            stride=2,
+            depth=2,
+            drop_path_rates=None,
+            ls_init_value=1.0,
+            conv_mlp=False,
+            conv_bias=True,
+            norm_layer=None,
+            norm_layer_cl=None
+    ):
         super().__init__()
         self.grad_checkpointing = False
 
         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),
+                nn.Conv2d(in_chs, out_chs, kernel_size=stride, stride=stride, bias=conv_bias),
             )
+            in_chs = out_chs
         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)]
-        )
+        drop_path_rates = drop_path_rates or [0.] * depth
+        stage_blocks = []
+        for i in range(depth):
+            stage_blocks.append(ConvNeXtBlock(
+                dim=in_chs,
+                dim_out=out_chs,
+                drop_path=drop_path_rates[i],
+                ls_init_value=ls_init_value,
+                conv_mlp=conv_mlp,
+                conv_bias=conv_bias,
+                norm_layer=norm_layer if conv_mlp else norm_layer_cl
+            ))
+            in_chs = out_chs
+        self.blocks = nn.Sequential(*stage_blocks)
 
     def forward(self, x):
         x = self.downsample(x)
@@ -210,41 +215,56 @@ class ConvNeXt(nn.Module):
     """
 
     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=False, stem_type='patch',
-            head_init_scale=1., head_norm_first=False, norm_layer=None, drop_rate=0., drop_path_rate=0.,
+            self,
+            in_chans=3,
+            num_classes=1000,
+            global_pool='avg',
+            output_stride=32,
+            depths=(3, 3, 9, 3),
+            dims=(96, 192, 384, 768),
+            ls_init_value=1e-6,
+            stem_type='patch',
+            stem_kernel_size=4,
+            stem_stride=4,
+            head_init_scale=1.,
+            head_norm_first=False,
+            conv_mlp=False,
+            conv_bias=True,
+            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)
+            norm_layer_cl = 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
+            norm_layer_cl = 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
+        assert stem_type in ('patch', 'overlap')
         if stem_type == 'patch':
+            assert stem_kernel_size == stem_stride
+            # 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),
+                nn.Conv2d(in_chans, dims[0], kernel_size=stem_kernel_size, stride=stem_stride, bias=conv_bias),
                 norm_layer(dims[0])
             )
-            curr_stride = patch_size
-            prev_chs = dims[0]
         else:
             self.stem = nn.Sequential(
-                nn.Conv2d(in_chans, 32, kernel_size=3, stride=2, padding=1),
-                norm_layer(32),
-                nn.GELU(),
-                nn.Conv2d(32, 64, kernel_size=3, padding=1),
+                nn.Conv2d(
+                    in_chans, dims[0], kernel_size=stem_kernel_size, stride=stem_stride,
+                    padding=stem_kernel_size // 2, bias=conv_bias),
+                norm_layer(dims[0]),
             )
-            curr_stride = 2
-            prev_chs = 64
+        prev_chs = dims[0]
+        curr_stride = stem_stride
 
         self.stages = nn.Sequential()
         dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
@@ -256,16 +276,23 @@ class ConvNeXt(nn.Module):
             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,
+                stride=stride,
+                depth=depths[i],
+                drop_path_rates=dp_rates[i],
+                ls_init_value=ls_init_value,
+                conv_mlp=conv_mlp,
+                conv_bias=conv_bias,
+                norm_layer=norm_layer,
+                norm_layer_cl=norm_layer_cl
+            ))
             prev_chs = out_chs
             # NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
             self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{i}')]
         self.stages = nn.Sequential(*stages)
-
         self.num_features = prev_chs
+
         # if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
         # otherwise pool -> norm -> fc, the default ConvNeXt ordering (pretrained FB weights)
         self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity()
@@ -327,10 +354,11 @@ class ConvNeXt(nn.Module):
 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)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
     elif isinstance(module, nn.Linear):
         trunc_normal_(module.weight, std=.02)
-        nn.init.constant_(module.bias, 0)
+        nn.init.zeros_(module.bias)
         if name and 'head.' in name:
             module.weight.data.mul_(head_init_scale)
             module.bias.data.mul_(head_init_scale)
@@ -371,14 +399,25 @@ def _create_convnext(variant, pretrained=False, **kwargs):
 
 @register_model
 def convnext_nano_hnf(pretrained=False, **kwargs):
-    model_args = dict(depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True, **kwargs)
+    model_args = dict(
+        depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True, **kwargs)
     model = _create_convnext('convnext_nano_hnf', pretrained=pretrained, **model_args)
     return model
 
 
+@register_model
+def convnext_nano_ols(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True,
+        conv_bias=False, stem_type='overlap', stem_kernel_size=9, **kwargs)
+    model = _create_convnext('convnext_nano_ols', 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, conv_mlp=True, **kwargs)
+    model_args = dict(
+        depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
     model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
     return model
 
@@ -386,7 +425,7 @@ def convnext_tiny_hnf(pretrained=False, **kwargs):
 @register_model
 def convnext_tiny_hnfd(pretrained=False, **kwargs):
     model_args = dict(
-        depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, stem_type='dual', **kwargs)
+        depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
     model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
     return model
 

timm/models/deit.py

@@ -1,7 +1,10 @@
 """ DeiT - Data-efficient Image Transformers
 
 DeiT model defs and weights from https://github.com/facebookresearch/deit, original copyright below
-paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
+
+paper: `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
+
+paper: `DeiT III: Revenge of the ViT` - https://arxiv.org/abs/2204.07118
 
 Modifications copyright 2021, Ross Wightman
 """
@@ -53,6 +56,46 @@ default_cfgs = {
         url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
         input_size=(3, 384, 384), crop_pct=1.0,
         classifier=('head', 'head_dist')),
+
+    'deit3_small_patch16_224': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_small_224_1k.pth'),
+    'deit3_small_patch16_384': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_small_384_1k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+    'deit3_base_patch16_224': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_base_224_1k.pth'),
+    'deit3_base_patch16_384': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_base_384_1k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+    'deit3_large_patch16_224': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_large_224_1k.pth'),
+    'deit3_large_patch16_384': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_large_384_1k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+    'deit3_huge_patch14_224': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_huge_224_1k.pth'),
+
+    'deit3_small_patch16_224_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_small_224_21k.pth',
+        crop_pct=1.0),
+    'deit3_small_patch16_384_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_small_384_21k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+    'deit3_base_patch16_224_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_base_224_21k.pth',
+        crop_pct=1.0),
+    'deit3_base_patch16_384_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_base_384_21k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+    'deit3_large_patch16_224_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_large_224_21k.pth',
+        crop_pct=1.0),
+    'deit3_large_patch16_384_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_large_384_21k.pth',
+        input_size=(3, 384, 384), crop_pct=1.0),
+    'deit3_huge_patch14_224_in21ft1k': _cfg(
+        url='https://dl.fbaipublicfiles.com/deit/deit_3_huge_224_21k_v1.pth',
+        crop_pct=1.0),
 }
 
 
@@ -68,9 +111,10 @@ class VisionTransformerDistilled(VisionTransformer):
         super().__init__(*args, **kwargs, weight_init='skip')
         assert self.global_pool in ('token',)
 
-        self.num_tokens = 2
+        self.num_prefix_tokens = 2
         self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
-        self.pos_embed = nn.Parameter(torch.zeros(1, self.patch_embed.num_patches + self.num_tokens, self.embed_dim))
+        self.pos_embed = nn.Parameter(
+            torch.zeros(1, self.patch_embed.num_patches + self.num_prefix_tokens, self.embed_dim))
         self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
         self.distilled_training = False  # must set this True to train w/ distillation token
 
@@ -220,3 +264,157 @@ def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
     model = _create_deit(
         'deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
+
+
+@register_model
+def deit3_small_patch16_224(pretrained=False, **kwargs):
+    """ DeiT-3 small model @ 224x224 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_small_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_small_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-3 small model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_small_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_base_patch16_224(pretrained=False, **kwargs):
+    """ DeiT-3 base model @ 224x224 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_base_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_base_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-3 base model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_base_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_large_patch16_224(pretrained=False, **kwargs):
+    """ DeiT-3 large model @ 224x224 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_large_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_large_patch16_384(pretrained=False, **kwargs):
+    """ DeiT-3 large model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_large_patch16_384', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_huge_patch14_224(pretrained=False, **kwargs):
+    """ DeiT-3 base model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1280, depth=32, num_heads=16, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_huge_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_small_patch16_224_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 small model @ 224x224 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_small_patch16_224_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_small_patch16_384_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 small model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_small_patch16_384_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_base_patch16_224_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 base model @ 224x224 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_base_patch16_224_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_base_patch16_384_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 base model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_base_patch16_384_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_large_patch16_224_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 large model @ 224x224 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_large_patch16_224_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_large_patch16_384_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 large model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_large_patch16_384_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def deit3_huge_patch14_224_in21ft1k(pretrained=False, **kwargs):
+    """ DeiT-3 base model @ 384x384 from paper (https://arxiv.org/abs/2204.07118).
+    ImageNet-21k pretrained weights from https://github.com/facebookresearch/deit.
+    """
+    model_kwargs = dict(
+        patch_size=14, embed_dim=1280, depth=32, num_heads=16, no_embed_class=True, init_values=1e-6, **kwargs)
+    model = _create_deit('deit3_huge_patch14_224_in21ft1k', pretrained=pretrained, **model_kwargs)
+    return model

timm/models/edgenext.py

@@ -0,0 +1,557 @@
+""" EdgeNeXt
+
+Paper: `EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications`
+ - https://arxiv.org/abs/2206.10589
+
+Original code and weights from https://github.com/mmaaz60/EdgeNeXt
+
+Modifications and additions for timm by / Copyright 2022, Ross Wightman
+"""
+import math
+import torch
+from collections import OrderedDict
+from functools import partial
+from typing import Tuple
+
+from torch import 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 .layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d
+from .helpers import named_apply, build_model_with_cfg, checkpoint_seq
+from .registry import register_model
+
+
+__all__ = ['EdgeNeXt']  # model_registry will add each entrypoint fn to this
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
+        'crop_pct': 0.9, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.0', 'classifier': 'head.fc',
+        **kwargs
+    }
+
+
+default_cfgs = dict(
+    edgenext_xx_small=_cfg(
+        url="https://github.com/mmaaz60/EdgeNeXt/releases/download/v1.0/edgenext_xx_small.pth"),
+    edgenext_x_small=_cfg(
+        url="https://github.com/mmaaz60/EdgeNeXt/releases/download/v1.0/edgenext_x_small.pth"),
+    # edgenext_small=_cfg(
+    #     url="https://github.com/mmaaz60/EdgeNeXt/releases/download/v1.0/edgenext_small.pth"),
+    edgenext_small=_cfg(  # USI weights
+        url="https://github.com/mmaaz60/EdgeNeXt/releases/download/v1.1/edgenext_small_usi.pth",
+        crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0,
+    ),
+
+    edgenext_small_rw=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/edgenext_small_rw-sw-b00041bb.pth',
+        test_input_size=(3, 320, 320), test_crop_pct=1.0,
+    ),
+)
+
+
+@register_notrace_module  # reason: FX can't symbolically trace torch.arange in forward method
+class PositionalEncodingFourier(nn.Module):
+    def __init__(self, hidden_dim=32, dim=768, temperature=10000):
+        super().__init__()
+        self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
+        self.scale = 2 * math.pi
+        self.temperature = temperature
+        self.hidden_dim = hidden_dim
+        self.dim = dim
+
+    def forward(self, shape: Tuple[int, int, int]):
+        inv_mask = ~torch.zeros(shape).to(device=self.token_projection.weight.device, dtype=torch.bool)
+        y_embed = inv_mask.cumsum(1, dtype=torch.float32)
+        x_embed = inv_mask.cumsum(2, dtype=torch.float32)
+        eps = 1e-6
+        y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+        x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.hidden_dim, dtype=torch.float32, device=inv_mask.device)
+        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode='floor') / self.hidden_dim)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(),
+             pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(),
+             pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        pos = self.token_projection(pos)
+
+        return pos
+
+
+class ConvBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_out=None,
+            kernel_size=7,
+            stride=1,
+            conv_bias=True,
+            expand_ratio=4,
+            ls_init_value=1e-6,
+            norm_layer=partial(nn.LayerNorm, eps=1e-6),
+            act_layer=nn.GELU, drop_path=0.,
+    ):
+        super().__init__()
+        dim_out = dim_out or dim
+        self.shortcut_after_dw = stride > 1 or dim != dim_out
+
+        self.conv_dw = create_conv2d(
+            dim, dim_out, kernel_size=kernel_size, stride=stride, depthwise=True, bias=conv_bias)
+        self.norm = norm_layer(dim_out)
+        self.mlp = Mlp(dim_out, int(expand_ratio * dim_out), act_layer=act_layer)
+        self.gamma = nn.Parameter(ls_init_value * torch.ones(dim_out)) 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.shortcut_after_dw:
+            shortcut = x
+
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.norm(x)
+        x = self.mlp(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = shortcut + self.drop_path(x)
+        return x
+
+
+class CrossCovarianceAttn(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            attn_drop=0.,
+            proj_drop=0.
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 4, 1)
+        q, k, v = qkv.unbind(0)
+
+        # NOTE, this is NOT spatial attn, q, k, v are B, num_heads, C, L -->  C x C attn map
+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) * self.temperature
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'temperature'}
+
+
+class SplitTransposeBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_scales=1,
+            num_heads=8,
+            expand_ratio=4,
+            use_pos_emb=True,
+            conv_bias=True,
+            qkv_bias=True,
+            ls_init_value=1e-6,
+            norm_layer=partial(nn.LayerNorm, eps=1e-6),
+            act_layer=nn.GELU,
+            drop_path=0.,
+            attn_drop=0.,
+            proj_drop=0.
+    ):
+        super().__init__()
+        width = max(int(math.ceil(dim / num_scales)), int(math.floor(dim // num_scales)))
+        self.width = width
+        self.num_scales = max(1, num_scales - 1)
+
+        convs = []
+        for i in range(self.num_scales):
+            convs.append(create_conv2d(width, width, kernel_size=3, depthwise=True, bias=conv_bias))
+        self.convs = nn.ModuleList(convs)
+
+        self.pos_embd = None
+        if use_pos_emb:
+            self.pos_embd = PositionalEncodingFourier(dim=dim)
+        self.norm_xca = norm_layer(dim)
+        self.gamma_xca = nn.Parameter(ls_init_value * torch.ones(dim)) if ls_init_value > 0 else None
+        self.xca = CrossCovarianceAttn(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=proj_drop)
+
+        self.norm = norm_layer(dim, eps=1e-6)
+        self.mlp = Mlp(dim, int(expand_ratio * dim), act_layer=act_layer)
+        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
+
+        # scales code re-written for torchscript as per my res2net fixes -rw
+        spx = torch.split(x, self.width, 1)
+        spo = []
+        sp = spx[0]
+        for i, conv in enumerate(self.convs):
+            if i > 0:
+                sp = sp + spx[i]
+            sp = conv(sp)
+            spo.append(sp)
+        spo.append(spx[-1])
+        x = torch.cat(spo, 1)
+
+        # XCA
+        B, C, H, W = x.shape
+        x = x.reshape(B, C, H * W).permute(0, 2, 1)
+        if self.pos_embd is not None:
+            pos_encoding = self.pos_embd((B, H, W)).reshape(B, -1, x.shape[1]).permute(0, 2, 1)
+            x = x + pos_encoding
+        x = x + self.drop_path(self.gamma_xca * self.xca(self.norm_xca(x)))
+        x = x.reshape(B, H, W, C)
+
+        # Inverted Bottleneck
+        x = self.norm(x)
+        x = self.mlp(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = shortcut + self.drop_path(x)
+        return x
+
+
+class EdgeNeXtStage(nn.Module):
+    def __init__(
+            self,
+            in_chs,
+            out_chs,
+            stride=2,
+            depth=2,
+            num_global_blocks=1,
+            num_heads=4,
+            scales=2,
+            kernel_size=7,
+            expand_ratio=4,
+            use_pos_emb=False,
+            downsample_block=False,
+            conv_bias=True,
+            ls_init_value=1.0,
+            drop_path_rates=None,
+            norm_layer=LayerNorm2d,
+            norm_layer_cl=partial(nn.LayerNorm, eps=1e-6),
+            act_layer=nn.GELU
+    ):
+        super().__init__()
+        self.grad_checkpointing = False
+
+        if downsample_block or stride == 1:
+            self.downsample = nn.Identity()
+        else:
+            self.downsample = nn.Sequential(
+                norm_layer(in_chs),
+                nn.Conv2d(in_chs, out_chs, kernel_size=2, stride=2, bias=conv_bias)
+            )
+            in_chs = out_chs
+
+        stage_blocks = []
+        for i in range(depth):
+            if i < depth - num_global_blocks:
+                stage_blocks.append(
+                    ConvBlock(
+                        dim=in_chs,
+                        dim_out=out_chs,
+                        stride=stride if downsample_block and i == 0 else 1,
+                        conv_bias=conv_bias,
+                        kernel_size=kernel_size,
+                        expand_ratio=expand_ratio,
+                        ls_init_value=ls_init_value,
+                        drop_path=drop_path_rates[i],
+                        norm_layer=norm_layer_cl,
+                        act_layer=act_layer,
+                    )
+                )
+            else:
+                stage_blocks.append(
+                    SplitTransposeBlock(
+                        dim=in_chs,
+                        num_scales=scales,
+                        num_heads=num_heads,
+                        expand_ratio=expand_ratio,
+                        use_pos_emb=use_pos_emb,
+                        conv_bias=conv_bias,
+                        ls_init_value=ls_init_value,
+                        drop_path=drop_path_rates[i],
+                        norm_layer=norm_layer_cl,
+                        act_layer=act_layer,
+                    )
+                )
+            in_chs = out_chs
+        self.blocks = nn.Sequential(*stage_blocks)
+
+    def forward(self, x):
+        x = self.downsample(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        return x
+
+
+class EdgeNeXt(nn.Module):
+    def __init__(
+            self,
+            in_chans=3,
+            num_classes=1000,
+            global_pool='avg',
+            dims=(24, 48, 88, 168),
+            depths=(3, 3, 9, 3),
+            global_block_counts=(0, 1, 1, 1),
+            kernel_sizes=(3, 5, 7, 9),
+            heads=(8, 8, 8, 8),
+            d2_scales=(2, 2, 3, 4),
+            use_pos_emb=(False, True, False, False),
+            ls_init_value=1e-6,
+            head_init_scale=1.,
+            expand_ratio=4,
+            downsample_block=False,
+            conv_bias=True,
+            stem_type='patch',
+            head_norm_first=False,
+            act_layer=nn.GELU,
+            drop_path_rate=0.,
+            drop_rate=0.,
+    ):
+        super().__init__()
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.drop_rate = drop_rate
+        norm_layer = partial(LayerNorm2d, eps=1e-6)
+        norm_layer_cl = partial(nn.LayerNorm, eps=1e-6)
+        self.feature_info = []
+
+        assert stem_type in ('patch', 'overlap')
+        if stem_type == 'patch':
+            self.stem = nn.Sequential(
+                nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4, bias=conv_bias),
+                norm_layer(dims[0]),
+            )
+        else:
+            self.stem = nn.Sequential(
+                nn.Conv2d(in_chans, dims[0], kernel_size=9, stride=4, padding=9 // 2, bias=conv_bias),
+                norm_layer(dims[0]),
+            )
+
+        curr_stride = 4
+        stages = []
+        dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
+        in_chs = dims[0]
+        for i in range(4):
+            stride = 2 if curr_stride == 2 or i > 0 else 1
+            # FIXME support dilation / output_stride
+            curr_stride *= stride
+            stages.append(EdgeNeXtStage(
+                in_chs=in_chs,
+                out_chs=dims[i],
+                stride=stride,
+                depth=depths[i],
+                num_global_blocks=global_block_counts[i],
+                num_heads=heads[i],
+                drop_path_rates=dp_rates[i],
+                scales=d2_scales[i],
+                expand_ratio=expand_ratio,
+                kernel_size=kernel_sizes[i],
+                use_pos_emb=use_pos_emb[i],
+                ls_init_value=ls_init_value,
+                downsample_block=downsample_block,
+                conv_bias=conv_bias,
+                norm_layer=norm_layer,
+                norm_layer_cl=norm_layer_cl,
+                act_layer=act_layer,
+            ))
+            # NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
+            in_chs = dims[i]
+            self.feature_info += [dict(num_chs=in_chs, reduction=curr_stride, module=f'stages.{i}')]
+
+        self.stages = nn.Sequential(*stages)
+
+        self.num_features = dims[-1]
+        self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity()
+        self.head = nn.Sequential(OrderedDict([
+                ('global_pool', SelectAdaptivePool2d(pool_type=global_pool)),
+                ('norm', nn.Identity() if head_norm_first else norm_layer(self.num_features)),
+                ('flatten', nn.Flatten(1) if global_pool else nn.Identity()),
+                ('drop', nn.Dropout(self.drop_rate)),
+                ('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())]))
+
+        named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse=False):
+        return dict(
+            stem=r'^stem',
+            blocks=r'^stages\.(\d+)' if coarse else [
+                (r'^stages\.(\d+)\.downsample', (0,)),  # blocks
+                (r'^stages\.(\d+)\.blocks\.(\d+)', None),
+                (r'^norm_pre', (99999,))
+            ]
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        for s in self.stages:
+            s.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self):
+        return self.head.fc
+
+    def reset_classifier(self, num_classes=0, global_pool=None):
+        if global_pool is not None:
+            self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
+            self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity()
+        self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        x = self.stages(x)
+        x = self.norm_pre(x)
+        return x
+
+    def forward_head(self, x, pre_logits: bool = False):
+        # NOTE nn.Sequential in head broken down since can't call head[:-1](x) in torchscript :(
+        x = self.head.global_pool(x)
+        x = self.head.norm(x)
+        x = self.head.flatten(x)
+        x = self.head.drop(x)
+        return x if pre_logits else self.head.fc(x)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def _init_weights(module, name=None, head_init_scale=1.0):
+    if isinstance(module, nn.Conv2d):
+        trunc_normal_tf_(module.weight, std=.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif isinstance(module, nn.Linear):
+        trunc_normal_tf_(module.weight, std=.02)
+        nn.init.zeros_(module.bias)
+        if name and 'head.' in name:
+            module.weight.data.mul_(head_init_scale)
+            module.bias.data.mul_(head_init_scale)
+
+
+def checkpoint_filter_fn(state_dict, model):
+    """ Remap FB checkpoints -> timm """
+    if 'head.norm.weight' in state_dict or 'norm_pre.weight' in state_dict:
+        return state_dict  # non-FB checkpoint
+
+    # models were released as train checkpoints... :/
+    if 'model_ema' in state_dict:
+        state_dict = state_dict['model_ema']
+    elif 'model' in state_dict:
+        state_dict = state_dict['model']
+    elif 'state_dict' in state_dict:
+        state_dict = state_dict['state_dict']
+
+    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_edgenext(variant, pretrained=False, **kwargs):
+    model = build_model_with_cfg(
+        EdgeNeXt, variant, pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
+        **kwargs)
+    return model
+
+
+@register_model
+def edgenext_xx_small(pretrained=False, **kwargs):
+    # 1.33M & 260.58M @ 256 resolution
+    # 71.23% Top-1 accuracy
+    # No AA, Color Jitter=0.4, No Mixup & Cutmix, DropPath=0.0, BS=4096, lr=0.006, multi-scale-sampler
+    # Jetson FPS=51.66 versus 47.67 for MobileViT_XXS
+    # For A100: FPS @ BS=1: 212.13 & @ BS=256: 7042.06 versus FPS @ BS=1: 96.68 & @ BS=256: 4624.71 for MobileViT_XXS
+    model_kwargs = dict(depths=(2, 2, 6, 2), dims=(24, 48, 88, 168), heads=(4, 4, 4, 4), **kwargs)
+    return _create_edgenext('edgenext_xx_small', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def edgenext_x_small(pretrained=False, **kwargs):
+    # 2.34M & 538.0M @ 256 resolution
+    # 75.00% Top-1 accuracy
+    # No AA, No Mixup & Cutmix, DropPath=0.0, BS=4096, lr=0.006, multi-scale-sampler
+    # Jetson FPS=31.61 versus 28.49 for MobileViT_XS
+    # For A100: FPS @ BS=1: 179.55 & @ BS=256: 4404.95 versus FPS @ BS=1: 94.55 & @ BS=256: 2361.53 for MobileViT_XS
+    model_kwargs = dict(depths=(3, 3, 9, 3), dims=(32, 64, 100, 192), heads=(4, 4, 4, 4), **kwargs)
+    return _create_edgenext('edgenext_x_small', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def edgenext_small(pretrained=False, **kwargs):
+    # 5.59M & 1260.59M @ 256 resolution
+    # 79.43% Top-1 accuracy
+    # AA=True, No Mixup & Cutmix, DropPath=0.1, BS=4096, lr=0.006, multi-scale-sampler
+    # Jetson FPS=20.47 versus 18.86 for MobileViT_S
+    # For A100: FPS @ BS=1: 172.33 & @ BS=256: 3010.25 versus FPS @ BS=1: 93.84 & @ BS=256: 1785.92 for MobileViT_S
+    model_kwargs = dict(depths=(3, 3, 9, 3), dims=(48, 96, 160, 304),  **kwargs)
+    return _create_edgenext('edgenext_small', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def edgenext_small_rw(pretrained=False, **kwargs):
+    # 5.59M & 1260.59M @ 256 resolution
+    # 79.43% Top-1 accuracy
+    # AA=True, No Mixup & Cutmix, DropPath=0.1, BS=4096, lr=0.006, multi-scale-sampler
+    # Jetson FPS=20.47 versus 18.86 for MobileViT_S
+    # For A100: FPS @ BS=1: 172.33 & @ BS=256: 3010.25 versus FPS @ BS=1: 93.84 & @ BS=256: 1785.92 for MobileViT_S
+    model_kwargs = dict(
+        depths=(3, 3, 9, 3), dims=(48, 96, 192, 384),
+        downsample_block=True, conv_bias=False, stem_type='overlap', **kwargs)
+    return _create_edgenext('edgenext_small_rw', pretrained=pretrained, **model_kwargs)
+

timm/models/layers/__init__.py

@@ -25,7 +25,7 @@ from .linear import Linear
 from .mixed_conv2d import MixedConv2d
 from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
 from .non_local_attn import NonLocalAttn, BatNonLocalAttn
-from .norm import GroupNorm, LayerNorm2d
+from .norm import GroupNorm, GroupNorm1, LayerNorm2d
 from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm
 from .padding import get_padding, get_same_padding, pad_same
 from .patch_embed import PatchEmbed
@@ -39,4 +39,4 @@ from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
 from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
 from .test_time_pool import TestTimePoolHead, apply_test_time_pool
 from .trace_utils import _assert, _float_to_int
-from .weight_init import trunc_normal_, variance_scaling_, lecun_normal_
+from .weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_

timm/models/layers/conv_bn_act.py

@@ -2,6 +2,7 @@
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
+import functools
 from torch import nn as nn
 
 from .create_conv2d import create_conv2d
@@ -40,12 +41,26 @@ class ConvNormAct(nn.Module):
 ConvBnAct = ConvNormAct
 
 
+def create_aa(aa_layer, channels, stride=2, enable=True):
+    if not aa_layer or not enable:
+        return nn.Identity()
+    if isinstance(aa_layer, functools.partial):
+        if issubclass(aa_layer.func, nn.AvgPool2d):
+            return aa_layer()
+        else:
+            return aa_layer(channels)
+    elif issubclass(aa_layer, nn.AvgPool2d):
+        return aa_layer(stride)
+    else:
+        return aa_layer(channels=channels, stride=stride)
+
+
 class ConvNormActAa(nn.Module):
     def __init__(
             self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
             bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None, drop_layer=None):
         super(ConvNormActAa, self).__init__()
-        use_aa = aa_layer is not None
+        use_aa = aa_layer is not None and stride == 2
 
         self.conv = create_conv2d(
             in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
@@ -56,7 +71,7 @@ class ConvNormActAa(nn.Module):
         # NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
         norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
         self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
-        self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else nn.Identity()
+        self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
 
     @property
     def in_channels(self):

timm/models/layers/create_attn.py

@@ -22,7 +22,7 @@ def get_attn(attn_type):
     if isinstance(attn_type, torch.nn.Module):
         return attn_type
     module_cls = None
-    if attn_type is not None:
+    if attn_type:
         if isinstance(attn_type, str):
             attn_type = attn_type.lower()
             # Lightweight attention modules (channel and/or coarse spatial).

timm/models/layers/norm.py

@@ -14,11 +14,59 @@ class GroupNorm(nn.GroupNorm):
         return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
 
 
+class GroupNorm1(nn.GroupNorm):
+    """ Group Normalization with 1 group.
+    Input: tensor in shape [B, C, *]
+    """
+
+    def __init__(self, num_channels, **kwargs):
+        super().__init__(1, num_channels, **kwargs)
+
+
 class LayerNorm2d(nn.LayerNorm):
-    """ LayerNorm for channels of '2D' spatial BCHW tensors """
-    def __init__(self, num_channels):
-        super().__init__(num_channels)
+    """ LayerNorm for channels of '2D' spatial NCHW tensors """
+    def __init__(self, num_channels, eps=1e-6, affine=True):
+        super().__init__(num_channels, eps=eps, elementwise_affine=affine)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return F.layer_norm(
             x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
+
+
+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)
+
+
+@torch.jit.script
+def _layer_norm_cf(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
+    s, u = torch.var_mean(x, dim=1, unbiased=False, keepdim=True)
+    x = (x - u) * torch.rsqrt(s + eps)
+    x = x * weight[:, None, None] + bias[:, None, None]
+    return x
+
+
+class LayerNormExp2d(nn.LayerNorm):
+    """ LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
+
+    Experimental implementation w/ manual norm for tensors non-contiguous tensors.
+
+    This improves throughput in some scenarios (tested on Ampere GPU), esp w/ channels_last
+    layout. However, benefits are not always clear and can perform worse on other GPUs.
+    """
+
+    def __init__(self, num_channels, eps=1e-6):
+        super().__init__(num_channels, eps=eps)
+
+    def forward(self, x) -> torch.Tensor:
+        if _is_contiguous(x):
+            x = F.layer_norm(
+                x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
+        else:
+            x = _layer_norm_cf(x, self.weight, self.bias, self.eps)
+        return x

timm/models/layers/test_time_pool.py

@@ -36,7 +36,7 @@ class TestTimePoolHead(nn.Module):
         return x.view(x.size(0), -1)
 
 
-def apply_test_time_pool(model, config, use_test_size=True):
+def apply_test_time_pool(model, config, use_test_size=False):
     test_time_pool = False
     if not hasattr(model, 'default_cfg') or not model.default_cfg:
         return model, False

timm/models/layers/weight_init.py

@@ -49,6 +49,11 @@ def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
     with values outside :math:`[a, b]` redrawn until they are within
     the bounds. The method used for generating the random values works
     best when :math:`a \leq \text{mean} \leq b`.
+
+    NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
+    applied while sampling the normal with mean/std applied, therefore a, b args
+    should be adjusted to match the range of mean, std args.
+
     Args:
         tensor: an n-dimensional `torch.Tensor`
         mean: the mean of the normal distribution
@@ -62,6 +67,35 @@ def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
     return _no_grad_trunc_normal_(tensor, mean, std, a, b)
 
 
+def trunc_normal_tf_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+
+    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
+    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
+    and the result is subsquently scaled and shifted by the mean and std args.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    _no_grad_trunc_normal_(tensor, 0, 1.0, a, b)
+    with torch.no_grad():
+        tensor.mul_(std).add_(mean)
+    return tensor
+
+
 def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
     fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
     if mode == 'fan_in':
@@ -75,7 +109,7 @@ def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
 
     if distribution == "truncated_normal":
         # constant is stddev of standard normal truncated to (-2, 2)
-        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
+        trunc_normal_tf_(tensor, std=math.sqrt(variance) / .87962566103423978)
     elif distribution == "normal":
         tensor.normal_(std=math.sqrt(variance))
     elif distribution == "uniform":

timm/models/mobilevit.py

@@ -1,7 +1,8 @@
 """ MobileViT
 
 Paper:
-`MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
+V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
+V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680
 
 MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets (original copyright below)
 License: https://github.com/apple/ml-cvnets/blob/main/LICENSE (Apple open source)
@@ -13,7 +14,7 @@ Rest of code, ByobNet, and Transformer block hacked together by / Copyright 2022
 # Copyright (C) 2020 Apple Inc. All Rights Reserved.
 #
 import math
-from typing import Union, Callable, Dict, Tuple, Optional
+from typing import Union, Callable, Dict, Tuple, Optional, Sequence
 
 import torch
 from torch import nn
@@ -21,7 +22,7 @@ import torch.nn.functional as F
 
 from .byobnet import register_block, ByoBlockCfg, ByoModelCfg, ByobNet, LayerFn, num_groups
 from .fx_features import register_notrace_module
-from .layers import to_2tuple, make_divisible
+from .layers import to_2tuple, make_divisible, LayerNorm2d, GroupNorm1, ConvMlp, DropPath
 from .vision_transformer import Block as TransformerBlock
 from .helpers import build_model_with_cfg
 from .registry import register_model
@@ -48,6 +49,48 @@ default_cfgs = {
     'mobilevit_s': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevit_s-38a5a959.pth'),
     'semobilevit_s': _cfg(),
+
+    'mobilevitv2_050': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_050-49951ee2.pth',
+        crop_pct=0.888),
+    'mobilevitv2_075': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_075-b5556ef6.pth',
+        crop_pct=0.888),
+    'mobilevitv2_100': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_100-e464ef3b.pth',
+        crop_pct=0.888),
+    'mobilevitv2_125': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_125-0ae35027.pth',
+        crop_pct=0.888),
+    'mobilevitv2_150': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_150-737c5019.pth',
+        crop_pct=0.888),
+    'mobilevitv2_175': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_175-16462ee2.pth',
+        crop_pct=0.888),
+    'mobilevitv2_200': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_200-b3422f67.pth',
+        crop_pct=0.888),
+
+    'mobilevitv2_150_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_150_in22ft1k-0b555d7b.pth',
+        crop_pct=0.888),
+    'mobilevitv2_175_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_175_in22ft1k-4117fa1f.pth',
+        crop_pct=0.888),
+    'mobilevitv2_200_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_200_in22ft1k-1d7c8927.pth',
+        crop_pct=0.888),
+
+    'mobilevitv2_150_384_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_150_384_in22ft1k-9e142854.pth',
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
+    'mobilevitv2_175_384_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_175_384_in22ft1k-059cbe56.pth',
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
+    'mobilevitv2_200_384_in22ft1k': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-mvit-weights/mobilevitv2_200_384_in22ft1k-32c87503.pth',
+        input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
 }
 
 
@@ -72,6 +115,40 @@ def _mobilevit_block(d, c, s, transformer_dim, transformer_depth, patch_size=4,
     )
 
 
+def _mobilevitv2_block(d, c, s, transformer_depth, patch_size=2, br=2.0, transformer_br=0.5):
+    # inverted residual + mobilevit blocks as per MobileViT network
+    return (
+        _inverted_residual_block(d=d, c=c, s=s, br=br),
+        ByoBlockCfg(
+            type='mobilevit2', d=1, c=c, s=1, br=transformer_br, gs=1,
+            block_kwargs=dict(
+                transformer_depth=transformer_depth,
+                patch_size=patch_size)
+        )
+    )
+
+
+def _mobilevitv2_cfg(multiplier=1.0):
+    chs = (64, 128, 256, 384, 512)
+    if multiplier != 1.0:
+        chs = tuple([int(c * multiplier) for c in chs])
+    cfg = ByoModelCfg(
+        blocks=(
+            _inverted_residual_block(d=1, c=chs[0], s=1, br=2.0),
+            _inverted_residual_block(d=2, c=chs[1], s=2, br=2.0),
+            _mobilevitv2_block(d=1, c=chs[2], s=2, transformer_depth=2),
+            _mobilevitv2_block(d=1, c=chs[3], s=2, transformer_depth=4),
+            _mobilevitv2_block(d=1, c=chs[4], s=2, transformer_depth=3),
+        ),
+        stem_chs=int(32 * multiplier),
+        stem_type='3x3',
+        stem_pool='',
+        downsample='',
+        act_layer='silu',
+    )
+    return cfg
+
+
 model_cfgs = dict(
     mobilevit_xxs=ByoModelCfg(
         blocks=(
@@ -137,11 +214,19 @@ model_cfgs = dict(
         attn_kwargs=dict(rd_ratio=1/8),
         num_features=640,
     ),
+
+    mobilevitv2_050=_mobilevitv2_cfg(.50),
+    mobilevitv2_075=_mobilevitv2_cfg(.75),
+    mobilevitv2_125=_mobilevitv2_cfg(1.25),
+    mobilevitv2_100=_mobilevitv2_cfg(1.0),
+    mobilevitv2_150=_mobilevitv2_cfg(1.5),
+    mobilevitv2_175=_mobilevitv2_cfg(1.75),
+    mobilevitv2_200=_mobilevitv2_cfg(2.0),
 )
 
 
 @register_notrace_module
-class MobileViTBlock(nn.Module):
+class MobileVitBlock(nn.Module):
     """ MobileViT block
         Paper: https://arxiv.org/abs/2110.02178?context=cs.LG
     """
@@ -165,9 +250,9 @@ class MobileViTBlock(nn.Module):
             drop_path_rate: float = 0.,
             layers: LayerFn = None,
             transformer_norm_layer: Callable = nn.LayerNorm,
-            downsample: str = ''
+            **kwargs,  # eat unused args
     ):
-        super(MobileViTBlock, self).__init__()
+        super(MobileVitBlock, self).__init__()
 
         layers = layers or LayerFn()
         groups = num_groups(group_size, in_chs)
@@ -241,7 +326,270 @@ class MobileViTBlock(nn.Module):
         return x
 
 
-register_block('mobilevit', MobileViTBlock)
+class LinearSelfAttention(nn.Module):
+    """
+    This layer applies a self-attention with linear complexity, as described in `https://arxiv.org/abs/2206.02680`
+    This layer can be used for self- as well as cross-attention.
+    Args:
+        embed_dim (int): :math:`C` from an expected input of size :math:`(N, C, H, W)`
+        attn_drop (float): Dropout value for context scores. Default: 0.0
+        bias (bool): Use bias in learnable layers. Default: True
+    Shape:
+        - Input: :math:`(N, C, P, N)` where :math:`N` is the batch size, :math:`C` is the input channels,
+        :math:`P` is the number of pixels in the patch, and :math:`N` is the number of patches
+        - Output: same as the input
+    .. note::
+        For MobileViTv2, we unfold the feature map [B, C, H, W] into [B, C, P, N] where P is the number of pixels
+        in a patch and N is the number of patches. Because channel is the first dimension in this unfolded tensor,
+        we use point-wise convolution (instead of a linear layer). This avoids a transpose operation (which may be
+        expensive on resource-constrained devices) that may be required to convert the unfolded tensor from
+        channel-first to channel-last format in case of a linear layer.
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+
+        self.qkv_proj = nn.Conv2d(
+            in_channels=embed_dim,
+            out_channels=1 + (2 * embed_dim),
+            bias=bias,
+            kernel_size=1,
+        )
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = nn.Conv2d(
+            in_channels=embed_dim,
+            out_channels=embed_dim,
+            bias=bias,
+            kernel_size=1,
+        )
+        self.out_drop = nn.Dropout(proj_drop)
+
+    def _forward_self_attn(self, x: torch.Tensor) -> torch.Tensor:
+        # [B, C, P, N] --> [B, h + 2d, P, N]
+        qkv = self.qkv_proj(x)
+
+        # Project x into query, key and value
+        # Query --> [B, 1, P, N]
+        # value, key --> [B, d, P, N]
+        query, key, value = qkv.split([1, self.embed_dim, self.embed_dim], dim=1)
+
+        # apply softmax along N dimension
+        context_scores = F.softmax(query, dim=-1)
+        context_scores = self.attn_drop(context_scores)
+
+        # Compute context vector
+        # [B, d, P, N] x [B, 1, P, N] -> [B, d, P, N] --> [B, d, P, 1]
+        context_vector = (key * context_scores).sum(dim=-1, keepdim=True)
+
+        # combine context vector with values
+        # [B, d, P, N] * [B, d, P, 1] --> [B, d, P, N]
+        out = F.relu(value) * context_vector.expand_as(value)
+        out = self.out_proj(out)
+        out = self.out_drop(out)
+        return out
+
+    @torch.jit.ignore()
+    def _forward_cross_attn(self, x: torch.Tensor, x_prev: Optional[torch.Tensor] = None) -> torch.Tensor:
+        # x --> [B, C, P, N]
+        # x_prev = [B, C, P, M]
+        batch_size, in_dim, kv_patch_area, kv_num_patches = x.shape
+        q_patch_area, q_num_patches = x.shape[-2:]
+
+        assert (
+            kv_patch_area == q_patch_area
+        ), "The number of pixels in a patch for query and key_value should be the same"
+
+        # compute query, key, and value
+        # [B, C, P, M] --> [B, 1 + d, P, M]
+        qk = F.conv2d(
+            x_prev,
+            weight=self.qkv_proj.weight[:self.embed_dim + 1],
+            bias=self.qkv_proj.bias[:self.embed_dim + 1],
+        )
+
+        # [B, 1 + d, P, M] --> [B, 1, P, M], [B, d, P, M]
+        query, key = qk.split([1, self.embed_dim], dim=1)
+        # [B, C, P, N] --> [B, d, P, N]
+        value = F.conv2d(
+            x,
+            weight=self.qkv_proj.weight[self.embed_dim + 1],
+            bias=self.qkv_proj.bias[self.embed_dim + 1] if self.qkv_proj.bias is not None else None,
+        )
+
+        # apply softmax along M dimension
+        context_scores = F.softmax(query, dim=-1)
+        context_scores = self.attn_drop(context_scores)
+
+        # compute context vector
+        # [B, d, P, M] * [B, 1, P, M] -> [B, d, P, M] --> [B, d, P, 1]
+        context_vector = (key * context_scores).sum(dim=-1, keepdim=True)
+
+        # combine context vector with values
+        # [B, d, P, N] * [B, d, P, 1] --> [B, d, P, N]
+        out = F.relu(value) * context_vector.expand_as(value)
+        out = self.out_proj(out)
+        out = self.out_drop(out)
+        return out
+
+    def forward(self, x: torch.Tensor, x_prev: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if x_prev is None:
+            return self._forward_self_attn(x)
+        else:
+            return self._forward_cross_attn(x, x_prev=x_prev)
+
+
+class LinearTransformerBlock(nn.Module):
+    """
+    This class defines the pre-norm transformer encoder with linear self-attention in `MobileViTv2 paper <>`_
+    Args:
+        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, P, N)`
+        mlp_ratio (float): Inner dimension ratio of the FFN relative to embed_dim
+        drop (float): Dropout rate. Default: 0.0
+        attn_drop (float): Dropout rate for attention in multi-head attention. Default: 0.0
+        drop_path (float): Stochastic depth rate Default: 0.0
+        norm_layer (Callable): Normalization layer. Default: layer_norm_2d
+    Shape:
+        - Input: :math:`(B, C_{in}, P, N)` where :math:`B` is batch size, :math:`C_{in}` is input embedding dim,
+            :math:`P` is number of pixels in a patch, and :math:`N` is number of patches,
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        mlp_ratio: float = 2.0,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        drop_path: float = 0.0,
+        act_layer=None,
+        norm_layer=None,
+    ) -> None:
+        super().__init__()
+        act_layer = act_layer or nn.SiLU
+        norm_layer = norm_layer or GroupNorm1
+
+        self.norm1 = norm_layer(embed_dim)
+        self.attn = LinearSelfAttention(embed_dim=embed_dim, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path1 = DropPath(drop_path)
+
+        self.norm2 = norm_layer(embed_dim)
+        self.mlp = ConvMlp(
+            in_features=embed_dim,
+            hidden_features=int(embed_dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=drop)
+        self.drop_path2 = DropPath(drop_path)
+
+    def forward(self, x: torch.Tensor, x_prev: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if x_prev is None:
+            # self-attention
+            x = x + self.drop_path1(self.attn(self.norm1(x)))
+        else:
+            # cross-attention
+            res = x
+            x = self.norm1(x)  # norm
+            x = self.attn(x, x_prev)  # attn
+            x = self.drop_path1(x) + res  # residual
+
+        # Feed forward network
+        x = x + self.drop_path2(self.mlp(self.norm2(x)))
+        return x
+
+
+@register_notrace_module
+class MobileVitV2Block(nn.Module):
+    """
+    This class defines the `MobileViTv2 block <>`_
+    """
+
+    def __init__(
+        self,
+        in_chs: int,
+        out_chs: Optional[int] = None,
+        kernel_size: int = 3,
+        bottle_ratio: float = 1.0,
+        group_size: Optional[int] = 1,
+        dilation: Tuple[int, int] = (1, 1),
+        mlp_ratio: float = 2.0,
+        transformer_dim: Optional[int] = None,
+        transformer_depth: int = 2,
+        patch_size: int = 8,
+        attn_drop: float = 0.,
+        drop: int = 0.,
+        drop_path_rate: float = 0.,
+        layers: LayerFn = None,
+        transformer_norm_layer: Callable = GroupNorm1,
+        **kwargs,  # eat unused args
+    ):
+        super(MobileVitV2Block, self).__init__()
+        layers = layers or LayerFn()
+        groups = num_groups(group_size, in_chs)
+        out_chs = out_chs or in_chs
+        transformer_dim = transformer_dim or make_divisible(bottle_ratio * in_chs)
+
+        self.conv_kxk = layers.conv_norm_act(
+            in_chs, in_chs, kernel_size=kernel_size,
+            stride=1, groups=groups, dilation=dilation[0])
+        self.conv_1x1 = nn.Conv2d(in_chs, transformer_dim, kernel_size=1, bias=False)
+
+        self.transformer = nn.Sequential(*[
+            LinearTransformerBlock(
+                transformer_dim,
+                mlp_ratio=mlp_ratio,
+                attn_drop=attn_drop,
+                drop=drop,
+                drop_path=drop_path_rate,
+                act_layer=layers.act,
+                norm_layer=transformer_norm_layer
+            )
+            for _ in range(transformer_depth)
+        ])
+        self.norm = transformer_norm_layer(transformer_dim)
+
+        self.conv_proj = layers.conv_norm_act(transformer_dim, out_chs, kernel_size=1, stride=1, apply_act=False)
+
+        self.patch_size = to_2tuple(patch_size)
+        self.patch_area = self.patch_size[0] * self.patch_size[1]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, C, H, W = x.shape
+        patch_h, patch_w = self.patch_size
+        new_h, new_w = math.ceil(H / patch_h) * patch_h, math.ceil(W / patch_w) * patch_w
+        num_patch_h, num_patch_w = new_h // patch_h, new_w // patch_w  # n_h, n_w
+        num_patches = num_patch_h * num_patch_w  # N
+        if new_h != H or new_w != W:
+            x = F.interpolate(x, size=(new_h, new_w), mode="bilinear", align_corners=True)
+
+        # Local representation
+        x = self.conv_kxk(x)
+        x = self.conv_1x1(x)
+
+        # Unfold (feature map -> patches), [B, C, H, W] -> [B, C, P, N]
+        C = x.shape[1]
+        x = x.reshape(B, C, num_patch_h, patch_h, num_patch_w, patch_w).permute(0, 1, 3, 5, 2, 4)
+        x = x.reshape(B, C, -1, num_patches)
+
+        # Global representations
+        x = self.transformer(x)
+        x = self.norm(x)
+
+        # Fold (patches -> feature map), [B, C, P, N] --> [B, C, H, W]
+        x = x.reshape(B, C, patch_h, patch_w, num_patch_h, num_patch_w).permute(0, 1, 4, 2, 5, 3)
+        x = x.reshape(B, C, num_patch_h * patch_h, num_patch_w * patch_w)
+
+        x = self.conv_proj(x)
+        return x
+
+
+register_block('mobilevit', MobileVitBlock)
+register_block('mobilevit2', MobileVitV2Block)
 
 
 def _create_mobilevit(variant, cfg_variant=None, pretrained=False, **kwargs):
@@ -252,6 +600,14 @@ def _create_mobilevit(variant, cfg_variant=None, pretrained=False, **kwargs):
         **kwargs)
 
 
+def _create_mobilevit2(variant, cfg_variant=None, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        ByobNet, variant, pretrained,
+        model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
+        feature_cfg=dict(flatten_sequential=True),
+        **kwargs)
+
+
 @register_model
 def mobilevit_xxs(pretrained=False, **kwargs):
     return _create_mobilevit('mobilevit_xxs', pretrained=pretrained, **kwargs)
@@ -270,3 +626,74 @@ def mobilevit_s(pretrained=False, **kwargs):
 @register_model
 def semobilevit_s(pretrained=False, **kwargs):
     return _create_mobilevit('semobilevit_s', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_050(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_050', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_075(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_075', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_100(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_100', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_125(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_125', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_150(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_150', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_175(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_175', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_200(pretrained=False, **kwargs):
+    return _create_mobilevit('mobilevitv2_200', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_150_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_150_in22ft1k', cfg_variant='mobilevitv2_150', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_175_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_175_in22ft1k', cfg_variant='mobilevitv2_175', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_200_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_200_in22ft1k', cfg_variant='mobilevitv2_200', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_150_384_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_150_384_in22ft1k', cfg_variant='mobilevitv2_150', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_175_384_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_175_384_in22ft1k', cfg_variant='mobilevitv2_175', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def mobilevitv2_200_384_in22ft1k(pretrained=False, **kwargs):
+    return _create_mobilevit(
+        'mobilevitv2_200_384_in22ft1k', cfg_variant='mobilevitv2_200', pretrained=pretrained, **kwargs)

timm/models/poolformer.py

@@ -26,7 +26,7 @@ import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg, checkpoint_seq
-from .layers import DropPath, trunc_normal_, to_2tuple, ConvMlp
+from .layers import DropPath, trunc_normal_, to_2tuple, ConvMlp, GroupNorm1
 from .registry import register_model
 
 
@@ -80,15 +80,6 @@ class PatchEmbed(nn.Module):
         return x
 
 
-class GroupNorm1(nn.GroupNorm):
-    """ Group Normalization with 1 group.
-    Input: tensor in shape [B, C, H, W]
-    """
-
-    def __init__(self, num_channels, **kwargs):
-        super().__init__(1, num_channels, **kwargs)
-
-
 class Pooling(nn.Module):
     def __init__(self, pool_size=3):
         super().__init__()

timm/models/resnet.py

@@ -35,6 +35,16 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = {
     # ResNet and Wide ResNet
+    'resnet10t': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet10t_176_c3-f3215ab1.pth',
+        input_size=(3, 176, 176), pool_size=(6, 6),
+        test_crop_pct=0.95, test_input_size=(3, 224, 224),
+        first_conv='conv1.0'),
+    'resnet14t': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet14t_176_c3-c4ed2c37.pth',
+        input_size=(3, 176, 176), pool_size=(6, 6),
+        test_crop_pct=0.95, test_input_size=(3, 224, 224),
+        first_conv='conv1.0'),
     'resnet18': _cfg(url='https://download.pytorch.org/models/resnet18-5c106cde.pth'),
     'resnet18d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth',
@@ -262,6 +272,9 @@ default_cfgs = {
     'resnetblur101d': _cfg(
         url='',
         interpolation='bicubic', first_conv='conv1.0'),
+    'resnetaa50': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetaa50_a1h-4cf422b3.pth',
+        test_input_size=(3, 288, 288), test_crop_pct=1.0, interpolation='bicubic'),
     'resnetaa50d': _cfg(
         url='',
         interpolation='bicubic', first_conv='conv1.0'),
@@ -723,6 +736,24 @@ def _create_resnet(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)
 
 
+@register_model
+def resnet10t(pretrained=False, **kwargs):
+    """Constructs a ResNet-10-T model.
+    """
+    model_args = dict(
+        block=BasicBlock, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs)
+    return _create_resnet('resnet10t', pretrained, **model_args)
+
+
+@register_model
+def resnet14t(pretrained=False, **kwargs):
+    """Constructs a ResNet-14-T model.
+    """
+    model_args = dict(
+        block=Bottleneck, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs)
+    return _create_resnet('resnet14t', pretrained, **model_args)
+
+
 @register_model
 def resnet18(pretrained=False, **kwargs):
     """Constructs a ResNet-18 model.
@@ -1436,6 +1467,14 @@ def resnetblur101d(pretrained=False, **kwargs):
     return _create_resnet('resnetblur101d', pretrained, **model_args)
 
 
+@register_model
+def resnetaa50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model with avgpool anti-aliasing
+    """
+    model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, **kwargs)
+    return _create_resnet('resnetaa50', pretrained, **model_args)
+
+
 @register_model
 def resnetaa50d(pretrained=False, **kwargs):
     """Constructs a ResNet-50-D model with avgpool anti-aliasing

timm/models/vision_transformer.py

@@ -325,8 +325,8 @@ class VisionTransformer(nn.Module):
     def __init__(
             self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',
             embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
-            class_token=True, fc_norm=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='',
-            embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
+            class_token=True, no_embed_class=False, fc_norm=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
+            weight_init='', embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=Block):
         """
         Args:
             img_size (int, tuple): input image size
@@ -360,15 +360,17 @@ class VisionTransformer(nn.Module):
         self.num_classes = num_classes
         self.global_pool = global_pool
         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        self.num_tokens = 1 if class_token else 0
+        self.num_prefix_tokens = 1 if class_token else 0
+        self.no_embed_class = no_embed_class
         self.grad_checkpointing = False
 
         self.patch_embed = embed_layer(
             img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
         num_patches = self.patch_embed.num_patches
 
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if self.num_tokens > 0 else None
-        self.pos_embed = nn.Parameter(torch.randn(1, num_patches + self.num_tokens, embed_dim) * .02)
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
+        embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
         self.pos_drop = nn.Dropout(p=drop_rate)
 
         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
@@ -428,11 +430,24 @@ class VisionTransformer(nn.Module):
             self.global_pool = global_pool
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
 
-    def forward_features(self, x):
-        x = self.patch_embed(x)
+    def _pos_embed(self, x):
+        if self.no_embed_class:
+            # deit-3, updated JAX (big vision)
+            # position embedding does not overlap with class token, add then concat
+            x = x + self.pos_embed
+            if self.cls_token is not None:
+                x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        else:
+            # original timm, JAX, and deit vit impl
+            # pos_embed has entry for class token, concat then add
             if self.cls_token is not None:
                 x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
-        x = self.pos_drop(x + self.pos_embed)
+            x = x + self.pos_embed
+        return self.pos_drop(x)
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
         if self.grad_checkpointing and not torch.jit.is_scripting():
             x = checkpoint_seq(self.blocks, x)
         else:
@@ -442,7 +457,7 @@ class VisionTransformer(nn.Module):
 
     def forward_head(self, x, pre_logits: bool = False):
         if self.global_pool:
-            x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
+            x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
         x = self.fc_norm(x)
         return x if pre_logits else self.head(x)
 
@@ -556,7 +571,11 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str =
     pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
     if pos_embed_w.shape != model.pos_embed.shape:
         pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
-            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
+            pos_embed_w,
+            model.pos_embed,
+            getattr(model, 'num_prefix_tokens', 1),
+            model.patch_embed.grid_size
+        )
     model.pos_embed.copy_(pos_embed_w)
     model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
     model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
@@ -585,16 +604,16 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str =
         block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
 
 
-def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
+def resize_pos_embed(posemb, posemb_new, num_prefix_tokens=1, gs_new=()):
     # Rescale the grid of position embeddings when loading from state_dict. Adapted from
     # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
     _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
     ntok_new = posemb_new.shape[1]
-    if num_tokens:
-        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
-        ntok_new -= num_tokens
+    if num_prefix_tokens:
+        posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
+        ntok_new -= num_prefix_tokens
     else:
-        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
+        posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
     gs_old = int(math.sqrt(len(posemb_grid)))
     if not len(gs_new):  # backwards compatibility
         gs_new = [int(math.sqrt(ntok_new))] * 2
@@ -603,25 +622,34 @@ def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
     posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
     posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
     posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+    posemb = torch.cat([posemb_prefix, posemb_grid], dim=1)
     return posemb
 
 
 def checkpoint_filter_fn(state_dict, model):
     """ convert patch embedding weight from manual patchify + linear proj to conv"""
+    import re
     out_dict = {}
     if 'model' in state_dict:
         # For deit models
         state_dict = state_dict['model']
+
     for k, v in state_dict.items():
         if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
             # For old models that I trained prior to conv based patchification
             O, I, H, W = model.patch_embed.proj.weight.shape
             v = v.reshape(O, -1, H, W)
-        elif k == 'pos_embed' and v.shape != model.pos_embed.shape:
+        elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
             # To resize pos embedding when using model at different size from pretrained weights
             v = resize_pos_embed(
-                v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
+                v,
+                model.pos_embed,
+                getattr(model, 'num_prefix_tokens', 1),
+                model.patch_embed.grid_size
+            )
+        elif 'gamma_' in k:
+            # remap layer-scale gamma into sub-module (deit3 models)
+            k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
         elif 'pre_logits' in k:
             # NOTE representation layer removed as not used in latest 21k/1k pretrained weights
             continue

timm/models/vision_transformer_relpos.py

@@ -8,6 +8,7 @@ import math
 import logging
 from functools import partial
 from collections import OrderedDict
+from dataclasses import dataclass
 from typing import Optional, Tuple
 
 import torch
@@ -16,7 +17,7 @@ import torch.nn.functional as F
 from torch.utils.checkpoint import checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
-from .helpers import build_model_with_cfg, named_apply
+from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply
 from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, to_2tuple
 from .registry import register_model
 
@@ -47,9 +48,16 @@ default_cfgs = {
     'vit_relpos_base_patch16_224': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'),
 
+    'vit_srelpos_small_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth'),
+    'vit_srelpos_medium_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth'),
+
+    'vit_relpos_medium_patch16_cls_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth'),
     'vit_relpos_base_patch16_cls_224': _cfg(
         url=''),
-    'vit_relpos_base_patch16_gapcls_224': _cfg(
+    'vit_relpos_base_patch16_clsgap_224': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth'),
 
     'vit_relpos_small_patch16_rpn_224': _cfg(url=''),
@@ -59,35 +67,43 @@ default_cfgs = {
 }
 
 
-def gen_relative_position_index(win_size: Tuple[int, int], class_token: int = 0) -> torch.Tensor:
-    # cut and paste w/ modifications from swin / beit codebase
-    # cls to token & token 2 cls & cls to cls
+def gen_relative_position_index(
+        q_size: Tuple[int, int],
+        k_size: Tuple[int, int] = None,
+        class_token: bool = False) -> torch.Tensor:
+    # Adapted with significant modifications from Swin / BeiT codebases
     # get pair-wise relative position index for each token inside the window
-    window_area = win_size[0] * win_size[1]
-    coords = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1)  # 2, Wh, Ww
-    relative_coords = coords[:, :, None] - coords[:, None, :]  # 2, Wh*Ww, Wh*Ww
-    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-    relative_coords[:, :, 0] += win_size[0] - 1  # shift to start from 0
-    relative_coords[:, :, 1] += win_size[1] - 1
-    relative_coords[:, :, 0] *= 2 * win_size[1] - 1
+    q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1)  # 2, Wh, Ww
+    if k_size is None:
+        k_coords = q_coords
+        k_size = q_size
+    else:
+        # different q vs k sizes is a WIP
+        k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1)
+    relative_coords = q_coords[:, :, None] - k_coords[:, None, :]  # 2, Wh*Ww, Wh*Ww
+    relative_coords = relative_coords.permute(1, 2, 0)  # Wh*Ww, Wh*Ww, 2
+    _, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0)
+
     if class_token:
-        num_relative_distance = (2 * win_size[0] - 1) * (2 * win_size[1] - 1) + 3
-        relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
-        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        # handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias
+        # NOTE not intended or tested with MLP log-coords
+        max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1]))
+        num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3
+        relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0])
         relative_position_index[0, 0:] = num_relative_distance - 3
         relative_position_index[0:, 0] = num_relative_distance - 2
         relative_position_index[0, 0] = num_relative_distance - 1
-    else:
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-    return relative_position_index
+
+    return relative_position_index.contiguous()
 
 
 def gen_relative_log_coords(
         win_size: Tuple[int, int],
         pretrained_win_size: Tuple[int, int] = (0, 0),
-        mode='swin'
+        mode='swin',
 ):
-    # as per official swin-v2 impl, supporting timm swin-v2-cr coords as well
+    assert mode in ('swin', 'cr', 'rw')
+    # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well
     relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32)
     relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32)
     relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
@@ -100,12 +116,22 @@ def gen_relative_log_coords(
             relative_coords_table[:, :, 0] /= (win_size[0] - 1)
             relative_coords_table[:, :, 1] /= (win_size[1] - 1)
         relative_coords_table *= 8  # normalize to -8, 8
-        scale = math.log2(8)
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+            1.0 + relative_coords_table.abs()) / math.log2(8)
     else:
-        # FIXME we should support a form of normalization (to -1/1) for this mode?
-        scale = math.log2(math.e)
+        if mode == 'rw':
+            # cr w/ window size normalization -> [-1,1] log coords
+            relative_coords_table[:, :, 0] /= (win_size[0] - 1)
+            relative_coords_table[:, :, 1] /= (win_size[1] - 1)
+            relative_coords_table *= 8  # scale to -8, 8
             relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-        1.0 + relative_coords_table.abs()) / scale
+                1.0 + relative_coords_table.abs())
+            relative_coords_table /= math.log2(9)   # -> [-1, 1]
+        else:
+            # mode == 'cr'
+            relative_coords_table = torch.sign(relative_coords_table) * torch.log(
+                1.0 + relative_coords_table.abs())
+
     return relative_coords_table
 
 
@@ -115,19 +141,29 @@ class RelPosMlp(nn.Module):
             window_size,
             num_heads=8,
             hidden_dim=128,
-            class_token=False,
+            prefix_tokens=0,
             mode='cr',
             pretrained_window_size=(0, 0)
     ):
         super().__init__()
         self.window_size = window_size
         self.window_area = self.window_size[0] * self.window_size[1]
-        self.class_token = 1 if class_token else 0
+        self.prefix_tokens = prefix_tokens
         self.num_heads = num_heads
         self.bias_shape = (self.window_area,) * 2 + (num_heads,)
-        self.apply_sigmoid = mode == 'swin'
+        if mode == 'swin':
+            self.bias_act = nn.Sigmoid()
+            self.bias_gain = 16
+            mlp_bias = (True, False)
+        elif mode == 'rw':
+            self.bias_act = nn.Tanh()
+            self.bias_gain = 4
+            mlp_bias = True
+        else:
+            self.bias_act = nn.Identity()
+            self.bias_gain = None
+            mlp_bias = True
 
-        mlp_bias = (True, False) if mode == 'swin' else True
         self.mlp = Mlp(
             2,  # x, y
             hidden_features=hidden_dim,
@@ -155,10 +191,11 @@ class RelPosMlp(nn.Module):
                 self.relative_position_index.view(-1)]  # Wh*Ww,Wh*Ww,nH
             relative_position_bias = relative_position_bias.view(self.bias_shape)
         relative_position_bias = relative_position_bias.permute(2, 0, 1)
-        if self.apply_sigmoid:
-            relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        if self.class_token:
-            relative_position_bias = F.pad(relative_position_bias, [self.class_token, 0, self.class_token, 0])
+        relative_position_bias = self.bias_act(relative_position_bias)
+        if self.bias_gain is not None:
+            relative_position_bias = self.bias_gain * relative_position_bias
+        if self.prefix_tokens:
+            relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0])
         return relative_position_bias.unsqueeze(0).contiguous()
 
     def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
@@ -167,18 +204,18 @@ class RelPosMlp(nn.Module):
 
 class RelPosBias(nn.Module):
 
-    def __init__(self, window_size, num_heads, class_token=False):
+    def __init__(self, window_size, num_heads, prefix_tokens=0):
         super().__init__()
+        assert prefix_tokens <= 1
         self.window_size = window_size
         self.window_area = window_size[0] * window_size[1]
-        self.class_token = 1 if class_token else 0
-        self.bias_shape = (self.window_area + self.class_token,) * 2 + (num_heads,)
+        self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,)
 
-        num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * self.class_token
+        num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens
         self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
         self.register_buffer(
             "relative_position_index",
-            gen_relative_position_index(self.window_size, class_token=self.class_token),
+            gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0),
             persistent=False,
         )
 
@@ -306,11 +343,32 @@ class VisionTransformerRelPos(nn.Module):
     """
 
     def __init__(
-            self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
-            embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-6,
-            class_token=False, fc_norm=False, rel_pos_type='mlp', shared_rel_pos=False, rel_pos_dim=None,
-            drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip',
-            embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
+            self,
+            img_size=224,
+            patch_size=16,
+            in_chans=3,
+            num_classes=1000,
+            global_pool='avg',
+            embed_dim=768,
+            depth=12,
+            num_heads=12,
+            mlp_ratio=4.,
+            qkv_bias=True,
+            init_values=1e-6,
+            class_token=False,
+            fc_norm=False,
+            rel_pos_type='mlp',
+            rel_pos_dim=None,
+            shared_rel_pos=False,
+            drop_rate=0.,
+            attn_drop_rate=0.,
+            drop_path_rate=0.,
+            weight_init='skip',
+            embed_layer=PatchEmbed,
+            norm_layer=None,
+            act_layer=None,
+            block_fn=RelPosBlock
+    ):
         """
         Args:
             img_size (int, tuple): input image size
@@ -345,19 +403,22 @@ class VisionTransformerRelPos(nn.Module):
         self.num_classes = num_classes
         self.global_pool = global_pool
         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        self.num_tokens = 1 if class_token else 0
+        self.num_prefix_tokens = 1 if class_token else 0
         self.grad_checkpointing = False
 
         self.patch_embed = embed_layer(
             img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
         feat_size = self.patch_embed.grid_size
 
-        rel_pos_args = dict(window_size=feat_size, class_token=class_token)
+        rel_pos_args = dict(window_size=feat_size, prefix_tokens=self.num_prefix_tokens)
         if rel_pos_type.startswith('mlp'):
             if rel_pos_dim:
                 rel_pos_args['hidden_dim'] = rel_pos_dim
+            # FIXME experimenting with different relpos log coord configs
             if 'swin' in rel_pos_type:
                 rel_pos_args['mode'] = 'swin'
+            elif 'rw' in rel_pos_type:
+                rel_pos_args['mode'] = 'rw'
             rel_pos_cls = partial(RelPosMlp, **rel_pos_args)
         else:
             rel_pos_cls = partial(RelPosBias, **rel_pos_args)
@@ -367,7 +428,7 @@ class VisionTransformerRelPos(nn.Module):
             # NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both...
             rel_pos_cls = None
 
-        self.cls_token = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim)) if self.num_tokens else None
+        self.cls_token = nn.Parameter(torch.zeros(1, self.num_prefix_tokens, embed_dim)) if class_token else None
 
         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
         self.blocks = nn.ModuleList([
@@ -434,7 +495,7 @@ class VisionTransformerRelPos(nn.Module):
 
     def forward_head(self, x, pre_logits: bool = False):
         if self.global_pool:
-            x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
+            x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
         x = self.fc_norm(x)
         return x if pre_logits else self.head(x)
 
@@ -502,6 +563,41 @@ def vit_relpos_base_patch16_224(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_srelpos_small_patch16_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=False,
+        rel_pos_dim=384, shared_rel_pos=True, **kwargs)
+    model = _create_vision_transformer_relpos('vit_srelpos_small_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_srelpos_medium_patch16_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False,
+        rel_pos_dim=512, shared_rel_pos=True, **kwargs)
+    model = _create_vision_transformer_relpos(
+        'vit_srelpos_medium_patch16_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_relpos_medium_patch16_cls_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-M/16) w/ relative log-coord position, class token present
+    """
+    model_kwargs = dict(
+        patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False,
+        rel_pos_dim=256, class_token=True, global_pool='token', **kwargs)
+    model = _create_vision_transformer_relpos(
+        'vit_relpos_medium_patch16_cls_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
@@ -514,14 +610,14 @@ def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs):
 
 
 @register_model
-def vit_relpos_base_patch16_gapcls_224(pretrained=False, **kwargs):
+def vit_relpos_base_patch16_clsgap_224(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
     NOTE this config is a bit of a mistake, class token was enabled but global avg-pool w/ fc-norm was not disabled
     Leaving here for comparisons w/ a future re-train as it performs quite well.
     """
     model_kwargs = dict(
         patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True, **kwargs)
-    model = _create_vision_transformer_relpos('vit_relpos_base_patch16_gapcls_224', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer_relpos('vit_relpos_base_patch16_clsgap_224', pretrained=pretrained, **model_kwargs)
     return model
 
 

timm/version.py

@@ -1 +1 @@
-__version__ = '0.6.2.dev0'
+__version__ = '0.6.3.dev0'

validate.py

@@ -38,6 +38,12 @@ try:
 except AttributeError:
     pass
 
+try:
+    from functorch.compile import memory_efficient_fusion
+    has_functorch = True
+except ImportError as e:
+    has_functorch = False
+
 torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('validate')
 
@@ -61,6 +67,8 @@ parser.add_argument('--img-size', default=None, type=int,
                     metavar='N', help='Input image dimension, uses model default if empty')
 parser.add_argument('--input-size', default=None, nargs=3, type=int,
                     metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
+parser.add_argument('--use-train-size', action='store_true', default=False,
+                    help='force use of train input size, even when test size is specified in pretrained cfg')
 parser.add_argument('--crop-pct', default=None, type=float,
                     metavar='N', help='Input image center crop pct')
 parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
@@ -101,8 +109,11 @@ parser.add_argument('--tf-preprocessing', action='store_true', default=False,
                     help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
 parser.add_argument('--use-ema', dest='use_ema', action='store_true',
                     help='use ema version of weights if present')
-parser.add_argument('--torchscript', dest='torchscript', action='store_true',
-                    help='convert model torchscript for inference')
+scripting_group = parser.add_mutually_exclusive_group()
+scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
+                    help='torch.jit.script the full model')
+scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
+                    help="Enable AOT Autograd support. (It's recommended to use this option with `--fuser nvfuser` together)")
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
 parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
@@ -155,14 +166,22 @@ def validate(args):
     param_count = sum([m.numel() for m in model.parameters()])
     _logger.info('Model %s created, param count: %d' % (args.model, param_count))
 
-    data_config = resolve_data_config(vars(args), model=model, use_test_size=True, verbose=True)
+    data_config = resolve_data_config(
+        vars(args),
+        model=model,
+        use_test_size=not args.use_train_size,
+        verbose=True
+    )
     test_time_pool = False
     if args.test_pool:
-        model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True)
+        model, test_time_pool = apply_test_time_pool(model, data_config)
 
     if args.torchscript:
         torch.jit.optimized_execution(True)
-        model = torch.jit.script(model)
+        model = torch.jit.trace(model, example_inputs=torch.randn((args.batch_size,) + data_config['input_size']))
+    if args.aot_autograd:
+        assert has_functorch, "functorch is needed for --aot-autograd"
+        model = memory_efficient_fusion(model)
 
     model = model.cuda()
     if args.apex_amp: