Merge pull request #1581 from rwightman/refactor-imports

Major module / path restructure

avg_checkpoints.py

@@ -16,7 +16,7 @@ import argparse
 import os
 import glob
 import hashlib
-from timm.models.helpers import load_state_dict
+from timm.models import load_state_dict
 
 parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
 parser.add_argument('--input', default='', type=str, metavar='PATH',

benchmark.py

@@ -19,7 +19,8 @@ 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, set_fast_norm
+from timm.layers import set_fast_norm
+from timm.models import create_model, is_model, list_models
 from timm.optim import create_optimizer_v2
 from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry
 

clean_checkpoint.py

@@ -13,7 +13,7 @@ import os
 import hashlib
 import shutil
 from collections import OrderedDict
-from timm.models.helpers import load_state_dict
+from timm.models import load_state_dict
 
 parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
 parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',

hubconf.py

@@ -1,4 +1,3 @@
 dependencies = ['torch']
-from timm.models import registry
-
-globals().update(registry._model_entrypoints)
+import timm
+globals().update(timm.models._registry._model_entrypoints)

inference.py

@@ -5,11 +5,11 @@ An example inference script that outputs top-k class ids for images in a folder
 
 Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
 """
-import os
-import time
 import argparse
 import json
 import logging
+import os
+import time
 from contextlib import suppress
 from functools import partial
 
@@ -17,12 +17,11 @@ import numpy as np
 import pandas as pd
 import torch
 
-from timm.models import create_model, apply_test_time_pool, load_checkpoint
 from timm.data import create_dataset, create_loader, resolve_data_config
+from timm.layers import apply_test_time_pool
+from timm.models import create_model
 from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser
 
-
-
 try:
     from apex import amp
     has_apex = True

tests/test_layers.py

@@ -1,10 +1,7 @@
-import pytest
 import torch
 import torch.nn as nn
-import platform
-import os
 
-from timm.models.layers import create_act_layer, get_act_layer, set_layer_config
+from timm.layers import create_act_layer, set_layer_config
 
 
 class MLP(nn.Module):

tests/test_models.py

@@ -14,7 +14,7 @@ except ImportError:
 
 import timm
 from timm import list_models, create_model, set_scriptable, get_pretrained_cfg_value
-from timm.models.fx_features import _leaf_modules, _autowrap_functions    
+from timm.models._features_fx import _leaf_modules, _autowrap_functions
 
 if hasattr(torch._C, '_jit_set_profiling_executor'):
     # legacy executor is too slow to compile large models for unit tests

timm/__init__.py

@@ -1,4 +1,4 @@
 from .version import __version__
+from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
 from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
-    is_scriptable, is_exportable, set_scriptable, set_exportable, \
     is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value

timm/data/readers/class_map.py

@@ -1,6 +1,7 @@
 import os
 import pickle
 
+
 def load_class_map(map_or_filename, root=''):
     if isinstance(map_or_filename, dict):
         assert dict, 'class_map dict must be non-empty'
@@ -14,7 +15,7 @@ def load_class_map(map_or_filename, root=''):
         with open(class_map_path) as f:
             class_to_idx = {v.strip(): k for k, v in enumerate(f)}
     elif class_map_ext == '.pkl':
-        with open(class_map_path,'rb') as f:
+        with open(class_map_path, 'rb') as f:
             class_to_idx = pickle.load(f)
     else:
         assert False, f'Unsupported class map file extension ({class_map_ext}).'

timm/layers/__init__.py

@@ -0,0 +1,44 @@
+from .activations import *
+from .adaptive_avgmax_pool import \
+    adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
+from .blur_pool import BlurPool2d
+from .classifier import ClassifierHead, create_classifier
+from .cond_conv2d import CondConv2d, get_condconv_initializer
+from .config import is_exportable, is_scriptable, is_no_jit, set_exportable, set_scriptable, set_no_jit,\
+    set_layer_config
+from .conv2d_same import Conv2dSame, conv2d_same
+from .conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
+from .create_act import create_act_layer, get_act_layer, get_act_fn
+from .create_attn import get_attn, create_attn
+from .create_conv2d import create_conv2d
+from .create_norm import get_norm_layer, create_norm_layer
+from .create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
+from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
+from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
+from .evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
+    EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
+from .fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
+from .filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
+from .gather_excite import GatherExcite
+from .global_context import GlobalContext
+from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
+from .inplace_abn import InplaceAbn
+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, GroupNorm1, LayerNorm, 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
+from .pool2d_same import AvgPool2dSame, create_pool2d
+from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
+from .selective_kernel import SelectiveKernel
+from .separable_conv import SeparableConv2d, SeparableConvNormAct
+from .space_to_depth import SpaceToDepthModule
+from .split_attn import SplitAttn
+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_, trunc_normal_tf_, variance_scaling_, lecun_normal_

timm/models/__init__.py

@@ -64,12 +64,18 @@ from .xception import *
 from .xception_aligned import *
 from .xcit import *
 
-from .factory import create_model, parse_model_name, safe_model_name
-from .helpers import load_checkpoint, resume_checkpoint, model_parameters
-from .layers import TestTimePoolHead, apply_test_time_pool
-from .layers import convert_splitbn_model, convert_sync_batchnorm
-from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable, is_no_jit, set_no_jit
-from .layers import set_fast_norm
-from .pretrained import PretrainedCfg, filter_pretrained_cfg, generate_default_cfgs, split_model_name_tag
-from .registry import register_model, model_entrypoint, list_models, list_pretrained, is_model, list_modules,\
+from ._builder import build_model_with_cfg, load_pretrained, load_custom_pretrained, resolve_pretrained_cfg, \
+    set_pretrained_download_progress, set_pretrained_check_hash
+from ._factory import create_model, parse_model_name, safe_model_name
+from ._features import FeatureInfo, FeatureHooks, FeatureHookNet, FeatureListNet, FeatureDictNet
+from ._features_fx import FeatureGraphNet, GraphExtractNet, create_feature_extractor, \
+    register_notrace_module, register_notrace_function
+from ._helpers import clean_state_dict, load_state_dict, load_checkpoint, remap_checkpoint, resume_checkpoint
+from ._hub import load_model_config_from_hf, load_state_dict_from_hf, push_to_hf_hub
+from ._manipulate import model_parameters, named_apply, named_modules, named_modules_with_params, \
+    group_modules, group_parameters, checkpoint_seq, adapt_input_conv
+from ._pretrained import PretrainedCfg, DefaultCfg, \
+    filter_pretrained_cfg, generate_default_cfgs, split_model_name_tag
+from ._prune import adapt_model_from_string
+from ._registry import register_model, model_entrypoint, list_models, list_pretrained, is_model, list_modules, \
     is_model_in_modules, is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value

timm/models/_builder.py

@@ -0,0 +1,399 @@
+import dataclasses
+import logging
+from copy import deepcopy
+from typing import Optional, Dict, Callable, Any, Tuple
+
+from torch import nn as nn
+from torch.hub import load_state_dict_from_url
+
+from timm.models._features import FeatureListNet, FeatureHookNet
+from timm.models._features_fx import FeatureGraphNet
+from timm.models._helpers import load_state_dict
+from timm.models._hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
+from timm.models._manipulate import adapt_input_conv
+from timm.models._pretrained import PretrainedCfg
+from timm.models._prune import adapt_model_from_file
+from timm.models._registry import get_pretrained_cfg
+
+_logger = logging.getLogger(__name__)
+
+# Global variables for rarely used pretrained checkpoint download progress and hash check.
+# Use set_pretrained_download_progress / set_pretrained_check_hash functions to toggle.
+_DOWNLOAD_PROGRESS = False
+_CHECK_HASH = False
+
+
+__all__ = ['set_pretrained_download_progress', 'set_pretrained_check_hash', 'load_custom_pretrained', 'load_pretrained',
+           'pretrained_cfg_for_features', 'resolve_pretrained_cfg', 'build_model_with_cfg']
+
+
+def _resolve_pretrained_source(pretrained_cfg):
+    cfg_source = pretrained_cfg.get('source', '')
+    pretrained_url = pretrained_cfg.get('url', None)
+    pretrained_file = pretrained_cfg.get('file', None)
+    hf_hub_id = pretrained_cfg.get('hf_hub_id', None)
+    # resolve where to load pretrained weights from
+    load_from = ''
+    pretrained_loc = ''
+    if cfg_source == 'hf-hub' and has_hf_hub(necessary=True):
+        # hf-hub specified as source via model identifier
+        load_from = 'hf-hub'
+        assert hf_hub_id
+        pretrained_loc = hf_hub_id
+    else:
+        # default source == timm or unspecified
+        if pretrained_file:
+            load_from = 'file'
+            pretrained_loc = pretrained_file
+        elif pretrained_url:
+            load_from = 'url'
+            pretrained_loc = pretrained_url
+        elif hf_hub_id and has_hf_hub(necessary=True):
+            # hf-hub available as alternate weight source in default_cfg
+            load_from = 'hf-hub'
+            pretrained_loc = hf_hub_id
+    if load_from == 'hf-hub' and pretrained_cfg.get('hf_hub_filename', None):
+        # if a filename override is set, return tuple for location w/ (hub_id, filename)
+        pretrained_loc = pretrained_loc, pretrained_cfg['hf_hub_filename']
+    return load_from, pretrained_loc
+
+
+def set_pretrained_download_progress(enable=True):
+    """ Set download progress for pretrained weights on/off (globally). """
+    global _DOWNLOAD_PROGRESS
+    _DOWNLOAD_PROGRESS = enable
+
+
+def set_pretrained_check_hash(enable=True):
+    """ Set hash checking for pretrained weights on/off (globally). """
+    global _CHECK_HASH
+    _CHECK_HASH = enable
+
+
+def load_custom_pretrained(
+        model: nn.Module,
+        pretrained_cfg: Optional[Dict] = None,
+        load_fn: Optional[Callable] = None,
+):
+    r"""Loads a custom (read non .pth) weight file
+
+    Downloads checkpoint file into cache-dir like torch.hub based loaders, but calls
+    a passed in custom load fun, or the `load_pretrained` model member fn.
+
+    If the object is already present in `model_dir`, it's deserialized and returned.
+    The default value of `model_dir` is ``<hub_dir>/checkpoints`` where
+    `hub_dir` is the directory returned by :func:`~torch.hub.get_dir`.
+
+    Args:
+        model: The instantiated model to load weights into
+        pretrained_cfg (dict): Default pretrained model cfg
+        load_fn: An external standalone fn that loads weights into provided model, otherwise a fn named
+            'laod_pretrained' on the model will be called if it exists
+    """
+    pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
+    if not pretrained_cfg:
+        _logger.warning("Invalid pretrained config, cannot load weights.")
+        return
+
+    load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
+    if not load_from:
+        _logger.warning("No pretrained weights exist for this model. Using random initialization.")
+        return
+    if load_from == 'hf-hub':  # FIXME
+        _logger.warning("Hugging Face hub not currently supported for custom load pretrained models.")
+    elif load_from == 'url':
+        pretrained_loc = download_cached_file(
+            pretrained_loc,
+            check_hash=_CHECK_HASH,
+            progress=_DOWNLOAD_PROGRESS
+        )
+
+    if load_fn is not None:
+        load_fn(model, pretrained_loc)
+    elif hasattr(model, 'load_pretrained'):
+        model.load_pretrained(pretrained_loc)
+    else:
+        _logger.warning("Valid function to load pretrained weights is not available, using random initialization.")
+
+
+def load_pretrained(
+        model: nn.Module,
+        pretrained_cfg: Optional[Dict] = None,
+        num_classes: int = 1000,
+        in_chans: int = 3,
+        filter_fn: Optional[Callable] = None,
+        strict: bool = True,
+):
+    """ Load pretrained checkpoint
+
+    Args:
+        model (nn.Module) : PyTorch model module
+        pretrained_cfg (Optional[Dict]): configuration for pretrained weights / target dataset
+        num_classes (int): num_classes for target model
+        in_chans (int): in_chans for target model
+        filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args)
+        strict (bool): strict load of checkpoint
+
+    """
+    pretrained_cfg = pretrained_cfg or getattr(model, 'pretrained_cfg', None)
+    if not pretrained_cfg:
+        _logger.warning("Invalid pretrained config, cannot load weights.")
+        return
+
+    load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
+    if load_from == 'file':
+        _logger.info(f'Loading pretrained weights from file ({pretrained_loc})')
+        state_dict = load_state_dict(pretrained_loc)
+    elif load_from == 'url':
+        _logger.info(f'Loading pretrained weights from url ({pretrained_loc})')
+        state_dict = load_state_dict_from_url(
+            pretrained_loc,
+            map_location='cpu',
+            progress=_DOWNLOAD_PROGRESS,
+            check_hash=_CHECK_HASH,
+        )
+    elif load_from == 'hf-hub':
+        _logger.info(f'Loading pretrained weights from Hugging Face hub ({pretrained_loc})')
+        if isinstance(pretrained_loc, (list, tuple)):
+            state_dict = load_state_dict_from_hf(*pretrained_loc)
+        else:
+            state_dict = load_state_dict_from_hf(pretrained_loc)
+    else:
+        _logger.warning("No pretrained weights exist or were found for this model. Using random initialization.")
+        return
+
+    if filter_fn is not None:
+        # for backwards compat with filter fn that take one arg, try one first, the two
+        try:
+            state_dict = filter_fn(state_dict)
+        except TypeError:
+            state_dict = filter_fn(state_dict, model)
+
+    input_convs = pretrained_cfg.get('first_conv', None)
+    if input_convs is not None and in_chans != 3:
+        if isinstance(input_convs, str):
+            input_convs = (input_convs,)
+        for input_conv_name in input_convs:
+            weight_name = input_conv_name + '.weight'
+            try:
+                state_dict[weight_name] = adapt_input_conv(in_chans, state_dict[weight_name])
+                _logger.info(
+                    f'Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s)')
+            except NotImplementedError as e:
+                del state_dict[weight_name]
+                strict = False
+                _logger.warning(
+                    f'Unable to convert pretrained {input_conv_name} weights, using random init for this layer.')
+
+    classifiers = pretrained_cfg.get('classifier', None)
+    label_offset = pretrained_cfg.get('label_offset', 0)
+    if classifiers is not None:
+        if isinstance(classifiers, str):
+            classifiers = (classifiers,)
+        if num_classes != pretrained_cfg['num_classes']:
+            for classifier_name in classifiers:
+                # completely discard fully connected if model num_classes doesn't match pretrained weights
+                state_dict.pop(classifier_name + '.weight', None)
+                state_dict.pop(classifier_name + '.bias', None)
+            strict = False
+        elif label_offset > 0:
+            for classifier_name in classifiers:
+                # special case for pretrained weights with an extra background class in pretrained weights
+                classifier_weight = state_dict[classifier_name + '.weight']
+                state_dict[classifier_name + '.weight'] = classifier_weight[label_offset:]
+                classifier_bias = state_dict[classifier_name + '.bias']
+                state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
+
+    model.load_state_dict(state_dict, strict=strict)
+
+
+def pretrained_cfg_for_features(pretrained_cfg):
+    pretrained_cfg = deepcopy(pretrained_cfg)
+    # remove default pretrained cfg fields that don't have much relevance for feature backbone
+    to_remove = ('num_classes', 'classifier', 'global_pool')  # add default final pool size?
+    for tr in to_remove:
+        pretrained_cfg.pop(tr, None)
+    return pretrained_cfg
+
+
+def _filter_kwargs(kwargs, names):
+    if not kwargs or not names:
+        return
+    for n in names:
+        kwargs.pop(n, None)
+
+
+def _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter):
+    """ Update the default_cfg and kwargs before passing to model
+
+    Args:
+        pretrained_cfg: input pretrained cfg (updated in-place)
+        kwargs: keyword args passed to model build fn (updated in-place)
+        kwargs_filter: keyword arg keys that must be removed before model __init__
+    """
+    # Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs)
+    default_kwarg_names = ('num_classes', 'global_pool', 'in_chans')
+    if pretrained_cfg.get('fixed_input_size', False):
+        # if fixed_input_size exists and is True, model takes an img_size arg that fixes its input size
+        default_kwarg_names += ('img_size',)
+
+    for n in default_kwarg_names:
+        # for legacy reasons, model __init__args uses img_size + in_chans as separate args while
+        # pretrained_cfg has one input_size=(C, H ,W) entry
+        if n == 'img_size':
+            input_size = pretrained_cfg.get('input_size', None)
+            if input_size is not None:
+                assert len(input_size) == 3
+                kwargs.setdefault(n, input_size[-2:])
+        elif n == 'in_chans':
+            input_size = pretrained_cfg.get('input_size', None)
+            if input_size is not None:
+                assert len(input_size) == 3
+                kwargs.setdefault(n, input_size[0])
+        else:
+            default_val = pretrained_cfg.get(n, None)
+            if default_val is not None:
+                kwargs.setdefault(n, pretrained_cfg[n])
+
+    # Filter keyword args for task specific model variants (some 'features only' models, etc.)
+    _filter_kwargs(kwargs, names=kwargs_filter)
+
+
+def resolve_pretrained_cfg(
+        variant: str,
+        pretrained_cfg=None,
+        pretrained_cfg_overlay=None,
+) -> PretrainedCfg:
+    model_with_tag = variant
+    pretrained_tag = None
+    if pretrained_cfg:
+        if isinstance(pretrained_cfg, dict):
+            # pretrained_cfg dict passed as arg, validate by converting to PretrainedCfg
+            pretrained_cfg = PretrainedCfg(**pretrained_cfg)
+        elif isinstance(pretrained_cfg, str):
+            pretrained_tag = pretrained_cfg
+            pretrained_cfg = None
+
+    # fallback to looking up pretrained cfg in model registry by variant identifier
+    if not pretrained_cfg:
+        if pretrained_tag:
+            model_with_tag = '.'.join([variant, pretrained_tag])
+        pretrained_cfg = get_pretrained_cfg(model_with_tag)
+
+    if not pretrained_cfg:
+        _logger.warning(
+            f"No pretrained configuration specified for {model_with_tag} model. Using a default."
+            f" Please add a config to the model pretrained_cfg registry or pass explicitly.")
+        pretrained_cfg = PretrainedCfg()  # instance with defaults
+
+    pretrained_cfg_overlay = pretrained_cfg_overlay or {}
+    if not pretrained_cfg.architecture:
+        pretrained_cfg_overlay.setdefault('architecture', variant)
+    pretrained_cfg = dataclasses.replace(pretrained_cfg, **pretrained_cfg_overlay)
+
+    return pretrained_cfg
+
+
+def build_model_with_cfg(
+        model_cls: Callable,
+        variant: str,
+        pretrained: bool,
+        pretrained_cfg: Optional[Dict] = None,
+        pretrained_cfg_overlay: Optional[Dict] = None,
+        model_cfg: Optional[Any] = None,
+        feature_cfg: Optional[Dict] = None,
+        pretrained_strict: bool = True,
+        pretrained_filter_fn: Optional[Callable] = None,
+        kwargs_filter: Optional[Tuple[str]] = None,
+        **kwargs,
+):
+    """ Build model with specified default_cfg and optional model_cfg
+
+    This helper fn aids in the construction of a model including:
+      * handling default_cfg and associated pretrained weight loading
+      * passing through optional model_cfg for models with config based arch spec
+      * features_only model adaptation
+      * pruning config / model adaptation
+
+    Args:
+        model_cls (nn.Module): model class
+        variant (str): model variant name
+        pretrained (bool): load pretrained weights
+        pretrained_cfg (dict): model's pretrained weight/task config
+        model_cfg (Optional[Dict]): model's architecture config
+        feature_cfg (Optional[Dict]: feature extraction adapter config
+        pretrained_strict (bool): load pretrained weights strictly
+        pretrained_filter_fn (Optional[Callable]): filter callable for pretrained weights
+        kwargs_filter (Optional[Tuple]): kwargs to filter before passing to model
+        **kwargs: model args passed through to model __init__
+    """
+    pruned = kwargs.pop('pruned', False)
+    features = False
+    feature_cfg = feature_cfg or {}
+
+    # resolve and update model pretrained config and model kwargs
+    pretrained_cfg = resolve_pretrained_cfg(
+        variant,
+        pretrained_cfg=pretrained_cfg,
+        pretrained_cfg_overlay=pretrained_cfg_overlay
+    )
+
+    # FIXME converting back to dict, PretrainedCfg use should be propagated further, but not into model
+    pretrained_cfg = pretrained_cfg.to_dict()
+
+    _update_default_kwargs(pretrained_cfg, kwargs, kwargs_filter)
+
+    # Setup for feature extraction wrapper done at end of this fn
+    if kwargs.pop('features_only', False):
+        features = True
+        feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
+        if 'out_indices' in kwargs:
+            feature_cfg['out_indices'] = kwargs.pop('out_indices')
+
+    # Instantiate the model
+    if model_cfg is None:
+        model = model_cls(**kwargs)
+    else:
+        model = model_cls(cfg=model_cfg, **kwargs)
+    model.pretrained_cfg = pretrained_cfg
+    model.default_cfg = model.pretrained_cfg  # alias for backwards compat
+
+    if pruned:
+        model = adapt_model_from_file(model, variant)
+
+    # For classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
+    num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
+    if pretrained:
+        if pretrained_cfg.get('custom_load', False):
+            load_custom_pretrained(
+                model,
+                pretrained_cfg=pretrained_cfg,
+            )
+        else:
+            load_pretrained(
+                model,
+                pretrained_cfg=pretrained_cfg,
+                num_classes=num_classes_pretrained,
+                in_chans=kwargs.get('in_chans', 3),
+                filter_fn=pretrained_filter_fn,
+                strict=pretrained_strict,
+            )
+
+    # Wrap the model in a feature extraction module if enabled
+    if features:
+        feature_cls = FeatureListNet
+        if 'feature_cls' in feature_cfg:
+            feature_cls = feature_cfg.pop('feature_cls')
+            if isinstance(feature_cls, str):
+                feature_cls = feature_cls.lower()
+                if 'hook' in feature_cls:
+                    feature_cls = FeatureHookNet
+                elif feature_cls == 'fx':
+                    feature_cls = FeatureGraphNet
+                else:
+                    assert False, f'Unknown feature class {feature_cls}'
+        model = feature_cls(model, **feature_cfg)
+        model.pretrained_cfg = pretrained_cfg_for_features(pretrained_cfg)  # add back default_cfg
+        model.default_cfg = model.pretrained_cfg  # alias for backwards compat
+
+    return model

timm/models/_efficientnet_blocks.py

@@ -2,13 +2,12 @@
 
 Hacked together by / Copyright 2019, Ross Wightman
 """
-import math
 
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
 
-from .layers import create_conv2d, DropPath, make_divisible, create_act_layer, get_norm_act_layer
+from timm.layers import create_conv2d, DropPath, make_divisible, create_act_layer, get_norm_act_layer
 
 __all__ = [
     'SqueezeExcite', 'ConvBnAct', 'DepthwiseSeparableConv', 'InvertedResidual', 'CondConvResidual', 'EdgeResidual']

timm/models/_efficientnet_builder.py

@@ -14,8 +14,8 @@ from functools import partial
 
 import torch.nn as nn
 
-from .efficientnet_blocks import *
-from .layers import CondConv2d, get_condconv_initializer, get_act_layer, get_attn, make_divisible
+from ._efficientnet_blocks import *
+from timm.layers import CondConv2d, get_condconv_initializer, get_act_layer, get_attn, make_divisible
 
 __all__ = ["EfficientNetBuilder", "decode_arch_def", "efficientnet_init_weights",
            'resolve_bn_args', 'resolve_act_layer', 'round_channels', 'BN_MOMENTUM_TF_DEFAULT', 'BN_EPS_TF_DEFAULT']

timm/models/_factory.py

@@ -0,0 +1,103 @@
+import os
+from typing import Any, Dict, Optional, Union
+from urllib.parse import urlsplit
+
+from timm.layers import set_layer_config
+from ._pretrained import PretrainedCfg, split_model_name_tag
+from ._helpers import load_checkpoint
+from ._hub import load_model_config_from_hf
+from ._registry import is_model, model_entrypoint
+
+
+__all__ = ['parse_model_name', 'safe_model_name', 'create_model']
+
+
+def parse_model_name(model_name):
+    if model_name.startswith('hf_hub'):
+        # NOTE for backwards compat, deprecate hf_hub use
+        model_name = model_name.replace('hf_hub', 'hf-hub')
+    parsed = urlsplit(model_name)
+    assert parsed.scheme in ('', 'timm', 'hf-hub')
+    if parsed.scheme == 'hf-hub':
+        # FIXME may use fragment as revision, currently `@` in URI path
+        return parsed.scheme, parsed.path
+    else:
+        model_name = os.path.split(parsed.path)[-1]
+        return 'timm', model_name
+
+
+def safe_model_name(model_name, remove_source=True):
+    # return a filename / path safe model name
+    def make_safe(name):
+        return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
+    if remove_source:
+        model_name = parse_model_name(model_name)[-1]
+    return make_safe(model_name)
+
+
+def create_model(
+        model_name: str,
+        pretrained: bool = False,
+        pretrained_cfg: Optional[Union[str, Dict[str, Any], PretrainedCfg]] = None,
+        pretrained_cfg_overlay:  Optional[Dict[str, Any]] = None,
+        checkpoint_path: str = '',
+        scriptable: Optional[bool] = None,
+        exportable: Optional[bool] = None,
+        no_jit: Optional[bool] = None,
+        **kwargs,
+):
+    """Create a model
+
+    Lookup model's entrypoint function and pass relevant args to create a new model.
+
+    **kwargs will be passed through entrypoint fn to timm.models.build_model_with_cfg()
+    and then the model class __init__(). kwargs values set to None are pruned before passing.
+
+    Args:
+        model_name (str): name of model to instantiate
+        pretrained (bool): load pretrained ImageNet-1k weights if true
+        pretrained_cfg (Union[str, dict, PretrainedCfg]): pass in external pretrained_cfg for model
+        pretrained_cfg_overlay (dict): replace key-values in base pretrained_cfg with these
+        checkpoint_path (str): path of checkpoint to load _after_ the model is initialized
+        scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
+        exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
+        no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
+
+    Keyword Args:
+        drop_rate (float): dropout rate for training (default: 0.0)
+        global_pool (str): global pool type (default: 'avg')
+        **: other kwargs are consumed by builder or model __init__()
+    """
+    # Parameters that aren't supported by all models or are intended to only override model defaults if set
+    # should default to None in command line args/cfg. Remove them if they are present and not set so that
+    # non-supporting models don't break and default args remain in effect.
+    kwargs = {k: v for k, v in kwargs.items() if v is not None}
+
+    model_source, model_name = parse_model_name(model_name)
+    if model_source == 'hf-hub':
+        assert not pretrained_cfg, 'pretrained_cfg should not be set when sourcing model from Hugging Face Hub.'
+        # For model names specified in the form `hf-hub:path/architecture_name@revision`,
+        # load model weights + pretrained_cfg from Hugging Face hub.
+        pretrained_cfg, model_name = load_model_config_from_hf(model_name)
+    else:
+        model_name, pretrained_tag = split_model_name_tag(model_name)
+        if not pretrained_cfg:
+            # a valid pretrained_cfg argument takes priority over tag in model name
+            pretrained_cfg = pretrained_tag
+
+    if not is_model(model_name):
+        raise RuntimeError('Unknown model (%s)' % model_name)
+
+    create_fn = model_entrypoint(model_name)
+    with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
+        model = create_fn(
+            pretrained=pretrained,
+            pretrained_cfg=pretrained_cfg,
+            pretrained_cfg_overlay=pretrained_cfg_overlay,
+            **kwargs,
+        )
+
+    if checkpoint_path:
+        load_checkpoint(model, checkpoint_path)
+
+    return model

timm/models/_features.py

@@ -0,0 +1,287 @@
+""" PyTorch Feature Extraction Helpers
+
+A collection of classes, functions, modules to help extract features from models
+and provide a common interface for describing them.
+
+The return_layers, module re-writing idea inspired by torchvision IntermediateLayerGetter
+https://github.com/pytorch/vision/blob/d88d8961ae51507d0cb680329d985b1488b1b76b/torchvision/models/_utils.py
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from collections import OrderedDict, defaultdict
+from copy import deepcopy
+from functools import partial
+from typing import Dict, List, Tuple
+
+import torch
+import torch.nn as nn
+
+
+__all__ = ['FeatureInfo', 'FeatureHooks', 'FeatureDictNet', 'FeatureListNet', 'FeatureHookNet']
+
+
+class FeatureInfo:
+
+    def __init__(self, feature_info: List[Dict], out_indices: Tuple[int]):
+        prev_reduction = 1
+        for fi in feature_info:
+            # sanity check the mandatory fields, there may be additional fields depending on the model
+            assert 'num_chs' in fi and fi['num_chs'] > 0
+            assert 'reduction' in fi and fi['reduction'] >= prev_reduction
+            prev_reduction = fi['reduction']
+            assert 'module' in fi
+        self.out_indices = out_indices
+        self.info = feature_info
+
+    def from_other(self, out_indices: Tuple[int]):
+        return FeatureInfo(deepcopy(self.info), out_indices)
+
+    def get(self, key, idx=None):
+        """ Get value by key at specified index (indices)
+        if idx == None, returns value for key at each output index
+        if idx is an integer, return value for that feature module index (ignoring output indices)
+        if idx is a list/tupple, return value for each module index (ignoring output indices)
+        """
+        if idx is None:
+            return [self.info[i][key] for i in self.out_indices]
+        if isinstance(idx, (tuple, list)):
+            return [self.info[i][key] for i in idx]
+        else:
+            return self.info[idx][key]
+
+    def get_dicts(self, keys=None, idx=None):
+        """ return info dicts for specified keys (or all if None) at specified indices (or out_indices if None)
+        """
+        if idx is None:
+            if keys is None:
+                return [self.info[i] for i in self.out_indices]
+            else:
+                return [{k: self.info[i][k] for k in keys} for i in self.out_indices]
+        if isinstance(idx, (tuple, list)):
+            return [self.info[i] if keys is None else {k: self.info[i][k] for k in keys} for i in idx]
+        else:
+            return self.info[idx] if keys is None else {k: self.info[idx][k] for k in keys}
+
+    def channels(self, idx=None):
+        """ feature channels accessor
+        """
+        return self.get('num_chs', idx)
+
+    def reduction(self, idx=None):
+        """ feature reduction (output stride) accessor
+        """
+        return self.get('reduction', idx)
+
+    def module_name(self, idx=None):
+        """ feature module name accessor
+        """
+        return self.get('module', idx)
+
+    def __getitem__(self, item):
+        return self.info[item]
+
+    def __len__(self):
+        return len(self.info)
+
+
+class FeatureHooks:
+    """ Feature Hook Helper
+
+    This module helps with the setup and extraction of hooks for extracting features from
+    internal nodes in a model by node name. This works quite well in eager Python but needs
+    redesign for torchscript.
+    """
+
+    def __init__(self, hooks, named_modules, out_map=None, default_hook_type='forward'):
+        # setup feature hooks
+        modules = {k: v for k, v in named_modules}
+        for i, h in enumerate(hooks):
+            hook_name = h['module']
+            m = modules[hook_name]
+            hook_id = out_map[i] if out_map else hook_name
+            hook_fn = partial(self._collect_output_hook, hook_id)
+            hook_type = h.get('hook_type', default_hook_type)
+            if hook_type == 'forward_pre':
+                m.register_forward_pre_hook(hook_fn)
+            elif hook_type == 'forward':
+                m.register_forward_hook(hook_fn)
+            else:
+                assert False, "Unsupported hook type"
+        self._feature_outputs = defaultdict(OrderedDict)
+
+    def _collect_output_hook(self, hook_id, *args):
+        x = args[-1]  # tensor we want is last argument, output for fwd, input for fwd_pre
+        if isinstance(x, tuple):
+            x = x[0]  # unwrap input tuple
+        self._feature_outputs[x.device][hook_id] = x
+
+    def get_output(self, device) -> Dict[str, torch.tensor]:
+        output = self._feature_outputs[device]
+        self._feature_outputs[device] = OrderedDict()  # clear after reading
+        return output
+
+
+def _module_list(module, flatten_sequential=False):
+    # a yield/iter would be better for this but wouldn't be compatible with torchscript
+    ml = []
+    for name, module in module.named_children():
+        if flatten_sequential and isinstance(module, nn.Sequential):
+            # first level of Sequential containers is flattened into containing model
+            for child_name, child_module in module.named_children():
+                combined = [name, child_name]
+                ml.append(('_'.join(combined), '.'.join(combined), child_module))
+        else:
+            ml.append((name, name, module))
+    return ml
+
+
+def _get_feature_info(net, out_indices):
+    feature_info = getattr(net, 'feature_info')
+    if isinstance(feature_info, FeatureInfo):
+        return feature_info.from_other(out_indices)
+    elif isinstance(feature_info, (list, tuple)):
+        return FeatureInfo(net.feature_info, out_indices)
+    else:
+        assert False, "Provided feature_info is not valid"
+
+
+def _get_return_layers(feature_info, out_map):
+    module_names = feature_info.module_name()
+    return_layers = {}
+    for i, name in enumerate(module_names):
+        return_layers[name] = out_map[i] if out_map is not None else feature_info.out_indices[i]
+    return return_layers
+
+
+class FeatureDictNet(nn.ModuleDict):
+    """ Feature extractor with OrderedDict return
+
+    Wrap a model and extract features as specified by the out indices, the network is
+    partially re-built from contained modules.
+
+    There is a strong assumption that the modules have been registered into the model in the same
+    order as they are used. There should be no reuse of the same nn.Module more than once, including
+    trivial modules like `self.relu = nn.ReLU`.
+
+    Only submodules that are directly assigned to the model class (`model.feature1`) or at most
+    one Sequential container deep (`model.features.1`, with flatten_sequent=True) can be captured.
+    All Sequential containers that are directly assigned to the original model will have their
+    modules assigned to this module with the name `model.features.1` being changed to `model.features_1`
+
+    Arguments:
+        model (nn.Module): model from which we will extract the features
+        out_indices (tuple[int]): model output indices to extract features for
+        out_map (sequence): list or tuple specifying desired return id for each out index,
+            otherwise str(index) is used
+        feature_concat (bool): whether to concatenate intermediate features that are lists or tuples
+            vs select element [0]
+        flatten_sequential (bool): whether to flatten sequential modules assigned to model
+    """
+    def __init__(
+            self, model,
+            out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
+        super(FeatureDictNet, self).__init__()
+        self.feature_info = _get_feature_info(model, out_indices)
+        self.concat = feature_concat
+        self.return_layers = {}
+        return_layers = _get_return_layers(self.feature_info, out_map)
+        modules = _module_list(model, flatten_sequential=flatten_sequential)
+        remaining = set(return_layers.keys())
+        layers = OrderedDict()
+        for new_name, old_name, module in modules:
+            layers[new_name] = module
+            if old_name in remaining:
+                # return id has to be consistently str type for torchscript
+                self.return_layers[new_name] = str(return_layers[old_name])
+                remaining.remove(old_name)
+            if not remaining:
+                break
+        assert not remaining and len(self.return_layers) == len(return_layers), \
+            f'Return layers ({remaining}) are not present in model'
+        self.update(layers)
+
+    def _collect(self, x) -> (Dict[str, torch.Tensor]):
+        out = OrderedDict()
+        for name, module in self.items():
+            x = module(x)
+            if name in self.return_layers:
+                out_id = self.return_layers[name]
+                if isinstance(x, (tuple, list)):
+                    # If model tap is a tuple or list, concat or select first element
+                    # FIXME this may need to be more generic / flexible for some nets
+                    out[out_id] = torch.cat(x, 1) if self.concat else x[0]
+                else:
+                    out[out_id] = x
+        return out
+
+    def forward(self, x) -> Dict[str, torch.Tensor]:
+        return self._collect(x)
+
+
+class FeatureListNet(FeatureDictNet):
+    """ Feature extractor with list return
+
+    See docstring for FeatureDictNet above, this class exists only to appease Torchscript typing constraints.
+    In eager Python we could have returned List[Tensor] vs Dict[id, Tensor] based on a member bool.
+    """
+    def __init__(
+            self, model,
+            out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
+        super(FeatureListNet, self).__init__(
+            model, out_indices=out_indices, out_map=out_map, feature_concat=feature_concat,
+            flatten_sequential=flatten_sequential)
+
+    def forward(self, x) -> (List[torch.Tensor]):
+        return list(self._collect(x).values())
+
+
+class FeatureHookNet(nn.ModuleDict):
+    """ FeatureHookNet
+
+    Wrap a model and extract features specified by the out indices using forward/forward-pre hooks.
+
+    If `no_rewrite` is True, features are extracted via hooks without modifying the underlying
+    network in any way.
+
+    If `no_rewrite` is False, the model will be re-written as in the
+    FeatureList/FeatureDict case by folding first to second (Sequential only) level modules into this one.
+
+    FIXME this does not currently work with Torchscript, see FeatureHooks class
+    """
+    def __init__(
+            self, model,
+            out_indices=(0, 1, 2, 3, 4), out_map=None, out_as_dict=False, no_rewrite=False,
+            feature_concat=False, flatten_sequential=False, default_hook_type='forward'):
+        super(FeatureHookNet, self).__init__()
+        assert not torch.jit.is_scripting()
+        self.feature_info = _get_feature_info(model, out_indices)
+        self.out_as_dict = out_as_dict
+        layers = OrderedDict()
+        hooks = []
+        if no_rewrite:
+            assert not flatten_sequential
+            if hasattr(model, 'reset_classifier'):  # make sure classifier is removed?
+                model.reset_classifier(0)
+            layers['body'] = model
+            hooks.extend(self.feature_info.get_dicts())
+        else:
+            modules = _module_list(model, flatten_sequential=flatten_sequential)
+            remaining = {f['module']: f['hook_type'] if 'hook_type' in f else default_hook_type
+                         for f in self.feature_info.get_dicts()}
+            for new_name, old_name, module in modules:
+                layers[new_name] = module
+                for fn, fm in module.named_modules(prefix=old_name):
+                    if fn in remaining:
+                        hooks.append(dict(module=fn, hook_type=remaining[fn]))
+                        del remaining[fn]
+                if not remaining:
+                    break
+            assert not remaining, f'Return layers ({remaining}) are not present in model'
+        self.update(layers)
+        self.hooks = FeatureHooks(hooks, model.named_modules(), out_map=out_map)
+
+    def forward(self, x):
+        for name, module in self.items():
+            x = module(x)
+        out = self.hooks.get_output(x.device)
+        return out if self.out_as_dict else list(out.values())

timm/models/_features_fx.py

@@ -0,0 +1,110 @@
+""" PyTorch FX Based Feature Extraction Helpers
+Using https://pytorch.org/vision/stable/feature_extraction.html
+"""
+from typing import Callable, List, Dict, Union, Type
+
+import torch
+from torch import nn
+
+from ._features import _get_feature_info
+
+try:
+    from torchvision.models.feature_extraction import create_feature_extractor as _create_feature_extractor
+    has_fx_feature_extraction = True
+except ImportError:
+    has_fx_feature_extraction = False
+
+# Layers we went to treat as leaf modules
+from timm.layers import Conv2dSame, ScaledStdConv2dSame, CondConv2d, StdConv2dSame
+from timm.layers.non_local_attn import BilinearAttnTransform
+from timm.layers.pool2d_same import MaxPool2dSame, AvgPool2dSame
+
+# NOTE: By default, any modules from timm.models.layers that we want to treat as leaf modules go here
+# BUT modules from timm.models should use the registration mechanism below
+_leaf_modules = {
+    BilinearAttnTransform,  # reason: flow control t <= 1
+    # Reason: get_same_padding has a max which raises a control flow error
+    Conv2dSame, MaxPool2dSame, ScaledStdConv2dSame, StdConv2dSame, AvgPool2dSame,
+    CondConv2d,  # reason: TypeError: F.conv2d received Proxy in groups=self.groups * B (because B = x.shape[0])
+}
+
+try:
+    from timm.layers import InplaceAbn
+    _leaf_modules.add(InplaceAbn)
+except ImportError:
+    pass
+
+
+__all__ = ['register_notrace_module', 'register_notrace_function', 'create_feature_extractor',
+           'FeatureGraphNet', 'GraphExtractNet']
+
+
+def register_notrace_module(module: Type[nn.Module]):
+    """
+    Any module not under timm.models.layers should get this decorator if we don't want to trace through it.
+    """
+    _leaf_modules.add(module)
+    return module
+
+
+# Functions we want to autowrap (treat them as leaves)
+_autowrap_functions = set()
+
+
+def register_notrace_function(func: Callable):
+    """
+    Decorator for functions which ought not to be traced through
+    """
+    _autowrap_functions.add(func)
+    return func
+
+
+def create_feature_extractor(model: nn.Module, return_nodes: Union[Dict[str, str], List[str]]):
+    assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
+    return _create_feature_extractor(
+        model, return_nodes,
+        tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)}
+    )
+
+
+class FeatureGraphNet(nn.Module):
+    """ A FX Graph based feature extractor that works with the model feature_info metadata
+    """
+    def __init__(self, model, out_indices, out_map=None):
+        super().__init__()
+        assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
+        self.feature_info = _get_feature_info(model, out_indices)
+        if out_map is not None:
+            assert len(out_map) == len(out_indices)
+        return_nodes = {
+            info['module']: out_map[i] if out_map is not None else info['module']
+            for i, info in enumerate(self.feature_info) if i in out_indices}
+        self.graph_module = create_feature_extractor(model, return_nodes)
+
+    def forward(self, x):
+        return list(self.graph_module(x).values())
+
+
+class GraphExtractNet(nn.Module):
+    """ A standalone feature extraction wrapper that maps dict -> list or single tensor
+    NOTE:
+      * one can use feature_extractor directly if dictionary output is desired
+      * unlike FeatureGraphNet, this is intended to be used standalone and not with model feature_info
+      metadata for builtin feature extraction mode
+      * create_feature_extractor can be used directly if dictionary output is acceptable
+
+    Args:
+        model: model to extract features from
+        return_nodes: node names to return features from (dict or list)
+        squeeze_out: if only one output, and output in list format, flatten to single tensor
+    """
+    def __init__(self, model, return_nodes: Union[Dict[str, str], List[str]], squeeze_out: bool = True):
+        super().__init__()
+        self.squeeze_out = squeeze_out
+        self.graph_module = create_feature_extractor(model, return_nodes)
+
+    def forward(self, x) -> Union[List[torch.Tensor], torch.Tensor]:
+        out = list(self.graph_module(x).values())
+        if self.squeeze_out and len(out) == 1:
+            return out[0]
+        return out

timm/models/_helpers.py

@@ -0,0 +1,115 @@
+""" Model creation / weight loading / state_dict helpers
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import logging
+import os
+from collections import OrderedDict
+
+import torch
+
+import timm.models._builder
+
+_logger = logging.getLogger(__name__)
+
+__all__ = ['clean_state_dict', 'load_state_dict', 'load_checkpoint', 'remap_checkpoint', 'resume_checkpoint']
+
+
+def clean_state_dict(state_dict):
+    # 'clean' checkpoint by removing .module prefix from state dict if it exists from parallel training
+    cleaned_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        name = k[7:] if k.startswith('module.') else k
+        cleaned_state_dict[name] = v
+    return cleaned_state_dict
+
+
+def load_state_dict(checkpoint_path, use_ema=True):
+    if checkpoint_path and os.path.isfile(checkpoint_path):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        state_dict_key = ''
+        if isinstance(checkpoint, dict):
+            if use_ema and checkpoint.get('state_dict_ema', None) is not None:
+                state_dict_key = 'state_dict_ema'
+            elif use_ema and checkpoint.get('model_ema', None) is not None:
+                state_dict_key = 'model_ema'
+            elif 'state_dict' in checkpoint:
+                state_dict_key = 'state_dict'
+            elif 'model' in checkpoint:
+                state_dict_key = 'model'
+        state_dict = clean_state_dict(checkpoint[state_dict_key] if state_dict_key else checkpoint)
+        _logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
+        return state_dict
+    else:
+        _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
+        raise FileNotFoundError()
+
+
+def load_checkpoint(model, checkpoint_path, use_ema=True, strict=True, remap=False):
+    if os.path.splitext(checkpoint_path)[-1].lower() in ('.npz', '.npy'):
+        # numpy checkpoint, try to load via model specific load_pretrained fn
+        if hasattr(model, 'load_pretrained'):
+            timm.models._model_builder.load_pretrained(checkpoint_path)
+        else:
+            raise NotImplementedError('Model cannot load numpy checkpoint')
+        return
+    state_dict = load_state_dict(checkpoint_path, use_ema)
+    if remap:
+        state_dict = remap_checkpoint(model, state_dict)
+    incompatible_keys = model.load_state_dict(state_dict, strict=strict)
+    return incompatible_keys
+
+
+def remap_checkpoint(model, state_dict, allow_reshape=True):
+    """ remap checkpoint by iterating over state dicts in order (ignoring original keys).
+    This assumes models (and originating state dict) were created with params registered in same order.
+    """
+    out_dict = {}
+    for (ka, va), (kb, vb) in zip(model.state_dict().items(), state_dict.items()):
+        assert va.numel == vb.numel, f'Tensor size mismatch {ka}: {va.shape} vs {kb}: {vb.shape}. Remap failed.'
+        if va.shape != vb.shape:
+            if allow_reshape:
+                vb = vb.reshape(va.shape)
+            else:
+                assert False,  f'Tensor shape mismatch {ka}: {va.shape} vs {kb}: {vb.shape}. Remap failed.'
+        out_dict[ka] = vb
+    return out_dict
+
+
+def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None, log_info=True):
+    resume_epoch = None
+    if os.path.isfile(checkpoint_path):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
+            if log_info:
+                _logger.info('Restoring model state from checkpoint...')
+            state_dict = clean_state_dict(checkpoint['state_dict'])
+            model.load_state_dict(state_dict)
+
+            if optimizer is not None and 'optimizer' in checkpoint:
+                if log_info:
+                    _logger.info('Restoring optimizer state from checkpoint...')
+                optimizer.load_state_dict(checkpoint['optimizer'])
+
+            if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint:
+                if log_info:
+                    _logger.info('Restoring AMP loss scaler state from checkpoint...')
+                loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key])
+
+            if 'epoch' in checkpoint:
+                resume_epoch = checkpoint['epoch']
+                if 'version' in checkpoint and checkpoint['version'] > 1:
+                    resume_epoch += 1  # start at the next epoch, old checkpoints incremented before save
+
+            if log_info:
+                _logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
+        else:
+            model.load_state_dict(checkpoint)
+            if log_info:
+                _logger.info("Loaded checkpoint '{}'".format(checkpoint_path))
+        return resume_epoch
+    else:
+        _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
+        raise FileNotFoundError()
+
+

timm/models/_hub.py

@@ -0,0 +1,220 @@
+import json
+import logging
+import os
+from functools import partial
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Optional, Union
+
+import torch
+from torch.hub import HASH_REGEX, download_url_to_file, urlparse
+
+try:
+    from torch.hub import get_dir
+except ImportError:
+    from torch.hub import _get_torch_home as get_dir
+
+from timm import __version__
+from timm.models._pretrained import filter_pretrained_cfg
+
+try:
+    from huggingface_hub import (
+        create_repo, get_hf_file_metadata,
+        hf_hub_download, hf_hub_url,
+        repo_type_and_id_from_hf_id, upload_folder)
+    from huggingface_hub.utils import EntryNotFoundError
+    hf_hub_download = partial(hf_hub_download, library_name="timm", library_version=__version__)
+    _has_hf_hub = True
+except ImportError:
+    hf_hub_download = None
+    _has_hf_hub = False
+
+_logger = logging.getLogger(__name__)
+
+__all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'load_model_config_from_hf',
+           'load_state_dict_from_hf', 'save_for_hf', 'push_to_hf_hub']
+
+
+def get_cache_dir(child_dir=''):
+    """
+    Returns the location of the directory where models are cached (and creates it if necessary).
+    """
+    # Issue warning to move data if old env is set
+    if os.getenv('TORCH_MODEL_ZOO'):
+        _logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
+
+    hub_dir = get_dir()
+    child_dir = () if not child_dir else (child_dir,)
+    model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir)
+    os.makedirs(model_dir, exist_ok=True)
+    return model_dir
+
+
+def download_cached_file(url, check_hash=True, progress=False):
+    if isinstance(url, (list, tuple)):
+        url, filename = url
+    else:
+        parts = urlparse(url)
+        filename = os.path.basename(parts.path)
+    cached_file = os.path.join(get_cache_dir(), filename)
+    if not os.path.exists(cached_file):
+        _logger.info('Downloading: "{}" to {}\n'.format(url, cached_file))
+        hash_prefix = None
+        if check_hash:
+            r = HASH_REGEX.search(filename)  # r is Optional[Match[str]]
+            hash_prefix = r.group(1) if r else None
+        download_url_to_file(url, cached_file, hash_prefix, progress=progress)
+    return cached_file
+
+
+def has_hf_hub(necessary=False):
+    if not _has_hf_hub and necessary:
+        # if no HF Hub module installed, and it is necessary to continue, raise error
+        raise RuntimeError(
+            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
+    return _has_hf_hub
+
+
+def hf_split(hf_id):
+    # FIXME I may change @ -> # and be parsed as fragment in a URI model name scheme
+    rev_split = hf_id.split('@')
+    assert 0 < len(rev_split) <= 2, 'hf_hub id should only contain one @ character to identify revision.'
+    hf_model_id = rev_split[0]
+    hf_revision = rev_split[-1] if len(rev_split) > 1 else None
+    return hf_model_id, hf_revision
+
+
+def load_cfg_from_json(json_file: Union[str, os.PathLike]):
+    with open(json_file, "r", encoding="utf-8") as reader:
+        text = reader.read()
+    return json.loads(text)
+
+
+def _download_from_hf(model_id: str, filename: str):
+    hf_model_id, hf_revision = hf_split(model_id)
+    return hf_hub_download(hf_model_id, filename, revision=hf_revision)
+
+
+def load_model_config_from_hf(model_id: str):
+    assert has_hf_hub(True)
+    cached_file = _download_from_hf(model_id, 'config.json')
+
+    hf_config = load_cfg_from_json(cached_file)
+    if 'pretrained_cfg' not in hf_config:
+        # old form, pull pretrain_cfg out of the base dict
+        pretrained_cfg = hf_config
+        hf_config = {}
+        hf_config['architecture'] = pretrained_cfg.pop('architecture')
+        hf_config['num_features'] = pretrained_cfg.pop('num_features', None)
+        if 'labels' in pretrained_cfg:
+            hf_config['label_name'] = pretrained_cfg.pop('labels')
+        hf_config['pretrained_cfg'] = pretrained_cfg
+
+    # NOTE currently discarding parent config as only arch name and pretrained_cfg used in timm right now
+    pretrained_cfg = hf_config['pretrained_cfg']
+    pretrained_cfg['hf_hub_id'] = model_id  # insert hf_hub id for pretrained weight load during model creation
+    pretrained_cfg['source'] = 'hf-hub'
+    if 'num_classes' in hf_config:
+        # model should be created with parent num_classes if they exist
+        pretrained_cfg['num_classes'] = hf_config['num_classes']
+    model_name = hf_config['architecture']
+
+    return pretrained_cfg, model_name
+
+
+def load_state_dict_from_hf(model_id: str, filename: str = 'pytorch_model.bin'):
+    assert has_hf_hub(True)
+    cached_file = _download_from_hf(model_id, filename)
+    state_dict = torch.load(cached_file, map_location='cpu')
+    return state_dict
+
+
+def save_for_hf(model, save_directory, model_config=None):
+    assert has_hf_hub(True)
+    model_config = model_config or {}
+    save_directory = Path(save_directory)
+    save_directory.mkdir(exist_ok=True, parents=True)
+
+    weights_path = save_directory / 'pytorch_model.bin'
+    torch.save(model.state_dict(), weights_path)
+
+    config_path = save_directory / 'config.json'
+    hf_config = {}
+    pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True)
+    # set some values at root config level
+    hf_config['architecture'] = pretrained_cfg.pop('architecture')
+    hf_config['num_classes'] = model_config.get('num_classes', model.num_classes)
+    hf_config['num_features'] = model_config.get('num_features', model.num_features)
+    hf_config['global_pool'] = model_config.get('global_pool', getattr(model, 'global_pool', None))
+
+    if 'label' in model_config:
+        _logger.warning(
+            "'label' as a config field for timm models is deprecated. Please use 'label_name' and 'display_name'. "
+            "Using provided 'label' field as 'label_name'.")
+        model_config['label_name'] = model_config.pop('label')
+
+    label_name = model_config.pop('label_name', None)
+    if label_name:
+        assert isinstance(label_name, (dict, list, tuple))
+        # map label id (classifier index) -> unique label name (ie synset for ImageNet, MID for OpenImages)
+        # can be a dict id: name if there are id gaps, or tuple/list if no gaps.
+        hf_config['label_name'] = model_config['label_name']
+
+    display_name = model_config.pop('display_name', None)
+    if display_name:
+        assert isinstance(display_name, dict)
+        # map label_name -> user interface display name
+        hf_config['display_name'] = model_config['display_name']
+
+    hf_config['pretrained_cfg'] = pretrained_cfg
+    hf_config.update(model_config)
+
+    with config_path.open('w') as f:
+        json.dump(hf_config, f, indent=2)
+
+
+def push_to_hf_hub(
+    model,
+    repo_id: str,
+    commit_message: str = 'Add model',
+    token: Optional[str] = None,
+    revision: Optional[str] = None,
+    private: bool = False,
+    create_pr: bool = False,
+    model_config: Optional[dict] = None,
+):
+    # Create repo if it doesn't exist yet
+    repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
+
+    # Infer complete repo_id from repo_url
+    # Can be different from the input `repo_id` if repo_owner was implicit
+    _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
+    repo_id = f"{repo_owner}/{repo_name}"
+
+    # Check if README file already exist in repo
+    try:
+        get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
+        has_readme = True
+    except EntryNotFoundError:
+        has_readme = False
+
+    # Dump model and push to Hub
+    with TemporaryDirectory() as tmpdir:
+        # Save model weights and config.
+        save_for_hf(model, tmpdir, model_config=model_config)
+
+        # Add readme if it does not exist
+        if not has_readme:
+            model_name = repo_id.split('/')[-1]
+            readme_path = Path(tmpdir) / "README.md"
+            readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {model_name}'
+            readme_path.write_text(readme_text)
+
+        # Upload model and return
+        return upload_folder(
+            repo_id=repo_id,
+            folder_path=tmpdir,
+            revision=revision,
+            create_pr=create_pr,
+            commit_message=commit_message,
+        )

timm/models/_manipulate.py

@@ -0,0 +1,258 @@
+import collections.abc
+import math
+import re
+from collections import defaultdict
+from itertools import chain
+from typing import Callable, Union, Dict
+
+import torch
+from torch import nn as nn
+from torch.utils.checkpoint import checkpoint
+
+__all__ = ['model_parameters', 'named_apply', 'named_modules', 'named_modules_with_params', 'adapt_input_conv',
+           'group_with_matcher', 'group_modules', 'group_parameters', 'flatten_modules', 'checkpoint_seq']
+
+
+def model_parameters(model, exclude_head=False):
+    if exclude_head:
+        # FIXME this a bit of a quick and dirty hack to skip classifier head params based on ordering
+        return [p for p in model.parameters()][:-2]
+    else:
+        return model.parameters()
+
+
+def named_apply(fn: Callable, module: nn.Module, name='', depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = '.'.join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+def named_modules(module: nn.Module, name='', depth_first=True, include_root=False):
+    if not depth_first and include_root:
+        yield name, module
+    for child_name, child_module in module.named_children():
+        child_name = '.'.join((name, child_name)) if name else child_name
+        yield from named_modules(
+            module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        yield name, module
+
+
+def named_modules_with_params(module: nn.Module, name='', depth_first=True, include_root=False):
+    if module._parameters and not depth_first and include_root:
+        yield name, module
+    for child_name, child_module in module.named_children():
+        child_name = '.'.join((name, child_name)) if name else child_name
+        yield from named_modules_with_params(
+            module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if module._parameters and depth_first and include_root:
+        yield name, module
+
+
+MATCH_PREV_GROUP = (99999,)
+
+
+def group_with_matcher(
+        named_objects,
+        group_matcher: Union[Dict, Callable],
+        output_values: bool = False,
+        reverse: bool = False
+):
+    if isinstance(group_matcher, dict):
+        # dictionary matcher contains a dict of raw-string regex expr that must be compiled
+        compiled = []
+        for group_ordinal, (group_name, mspec) in enumerate(group_matcher.items()):
+            if mspec is None:
+                continue
+            # map all matching specifications into 3-tuple (compiled re, prefix, suffix)
+            if isinstance(mspec, (tuple, list)):
+                # multi-entry match specifications require each sub-spec to be a 2-tuple (re, suffix)
+                for sspec in mspec:
+                    compiled += [(re.compile(sspec[0]), (group_ordinal,), sspec[1])]
+            else:
+                compiled += [(re.compile(mspec), (group_ordinal,), None)]
+        group_matcher = compiled
+
+    def _get_grouping(name):
+        if isinstance(group_matcher, (list, tuple)):
+            for match_fn, prefix, suffix in group_matcher:
+                r = match_fn.match(name)
+                if r:
+                    parts = (prefix, r.groups(), suffix)
+                    # map all tuple elem to int for numeric sort, filter out None entries
+                    return tuple(map(float, chain.from_iterable(filter(None, parts))))
+            return float('inf'),  # un-matched layers (neck, head) mapped to largest ordinal
+        else:
+            ord = group_matcher(name)
+            if not isinstance(ord, collections.abc.Iterable):
+                return ord,
+            return tuple(ord)
+
+    # map layers into groups via ordinals (ints or tuples of ints) from matcher
+    grouping = defaultdict(list)
+    for k, v in named_objects:
+        grouping[_get_grouping(k)].append(v if output_values else k)
+
+    # remap to integers
+    layer_id_to_param = defaultdict(list)
+    lid = -1
+    for k in sorted(filter(lambda x: x is not None, grouping.keys())):
+        if lid < 0 or k[-1] != MATCH_PREV_GROUP[0]:
+            lid += 1
+        layer_id_to_param[lid].extend(grouping[k])
+
+    if reverse:
+        assert not output_values, "reverse mapping only sensible for name output"
+        # output reverse mapping
+        param_to_layer_id = {}
+        for lid, lm in layer_id_to_param.items():
+            for n in lm:
+                param_to_layer_id[n] = lid
+        return param_to_layer_id
+
+    return layer_id_to_param
+
+
+def group_parameters(
+        module: nn.Module,
+        group_matcher,
+        output_values=False,
+        reverse=False,
+):
+    return group_with_matcher(
+        module.named_parameters(), group_matcher, output_values=output_values, reverse=reverse)
+
+
+def group_modules(
+        module: nn.Module,
+        group_matcher,
+        output_values=False,
+        reverse=False,
+):
+    return group_with_matcher(
+        named_modules_with_params(module), group_matcher, output_values=output_values, reverse=reverse)
+
+
+def flatten_modules(named_modules, depth=1, prefix='', module_types='sequential'):
+    prefix_is_tuple = isinstance(prefix, tuple)
+    if isinstance(module_types, str):
+        if module_types == 'container':
+            module_types = (nn.Sequential, nn.ModuleList, nn.ModuleDict)
+        else:
+            module_types = (nn.Sequential,)
+    for name, module in named_modules:
+        if depth and isinstance(module, module_types):
+            yield from flatten_modules(
+                module.named_children(),
+                depth - 1,
+                prefix=(name,) if prefix_is_tuple else name,
+                module_types=module_types,
+            )
+        else:
+            if prefix_is_tuple:
+                name = prefix + (name,)
+                yield name, module
+            else:
+                if prefix:
+                    name = '.'.join([prefix, name])
+                yield name, module
+
+
+def checkpoint_seq(
+        functions,
+        x,
+        every=1,
+        flatten=False,
+        skip_last=False,
+        preserve_rng_state=True
+):
+    r"""A helper function for checkpointing sequential models.
+
+    Sequential models execute a list of modules/functions in order
+    (sequentially). Therefore, we can divide such a sequence into segments
+    and checkpoint each segment. All segments except run in :func:`torch.no_grad`
+    manner, i.e., not storing the intermediate activations. The inputs of each
+    checkpointed segment will be saved for re-running the segment in the backward pass.
+
+    See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works.
+
+    .. warning::
+        Checkpointing currently only supports :func:`torch.autograd.backward`
+        and only if its `inputs` argument is not passed. :func:`torch.autograd.grad`
+        is not supported.
+
+    .. warning:
+        At least one of the inputs needs to have :code:`requires_grad=True` if
+        grads are needed for model inputs, otherwise the checkpointed part of the
+        model won't have gradients.
+
+    Args:
+        functions: A :class:`torch.nn.Sequential` or the list of modules or functions to run sequentially.
+        x: A Tensor that is input to :attr:`functions`
+        every: checkpoint every-n functions (default: 1)
+        flatten (bool): flatten nn.Sequential of nn.Sequentials
+        skip_last (bool): skip checkpointing the last function in the sequence if True
+        preserve_rng_state (bool, optional, default=True):  Omit stashing and restoring
+            the RNG state during each checkpoint.
+
+    Returns:
+        Output of running :attr:`functions` sequentially on :attr:`*inputs`
+
+    Example:
+        >>> model = nn.Sequential(...)
+        >>> input_var = checkpoint_seq(model, input_var, every=2)
+    """
+    def run_function(start, end, functions):
+        def forward(_x):
+            for j in range(start, end + 1):
+                _x = functions[j](_x)
+            return _x
+        return forward
+
+    if isinstance(functions, torch.nn.Sequential):
+        functions = functions.children()
+    if flatten:
+        functions = chain.from_iterable(functions)
+    if not isinstance(functions, (tuple, list)):
+        functions = tuple(functions)
+
+    num_checkpointed = len(functions)
+    if skip_last:
+        num_checkpointed -= 1
+    end = -1
+    for start in range(0, num_checkpointed, every):
+        end = min(start + every - 1, num_checkpointed - 1)
+        x = checkpoint(run_function(start, end, functions), x, preserve_rng_state=preserve_rng_state)
+    if skip_last:
+        return run_function(end + 1, len(functions) - 1, functions)(x)
+    return x
+
+
+def adapt_input_conv(in_chans, conv_weight):
+    conv_type = conv_weight.dtype
+    conv_weight = conv_weight.float()  # Some weights are in torch.half, ensure it's float for sum on CPU
+    O, I, J, K = conv_weight.shape
+    if in_chans == 1:
+        if I > 3:
+            assert conv_weight.shape[1] % 3 == 0
+            # For models with space2depth stems
+            conv_weight = conv_weight.reshape(O, I // 3, 3, J, K)
+            conv_weight = conv_weight.sum(dim=2, keepdim=False)
+        else:
+            conv_weight = conv_weight.sum(dim=1, keepdim=True)
+    elif in_chans != 3:
+        if I != 3:
+            raise NotImplementedError('Weight format not supported by conversion.')
+        else:
+            # NOTE this strategy should be better than random init, but there could be other combinations of
+            # the original RGB input layer weights that'd work better for specific cases.
+            repeat = int(math.ceil(in_chans / 3))
+            conv_weight = conv_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
+            conv_weight *= (3 / float(in_chans))
+    conv_weight = conv_weight.to(conv_type)
+    return conv_weight

timm/models/_pretrained.py

@@ -4,6 +4,9 @@ from dataclasses import dataclass, field, replace, asdict
 from typing import Any, Deque, Dict, Tuple, Optional, Union
 
 
+__all__ = ['PretrainedCfg', 'filter_pretrained_cfg', 'DefaultCfg', 'split_model_name_tag', 'generate_default_cfgs']
+
+
 @dataclass
 class PretrainedCfg:
     """

timm/models/_prune.py

@@ -0,0 +1,113 @@
+import os
+from copy import deepcopy
+
+from torch import nn as nn
+
+from timm.layers import Conv2dSame, BatchNormAct2d, Linear
+
+__all__ = ['extract_layer', 'set_layer', 'adapt_model_from_string', 'adapt_model_from_file']
+
+
+def extract_layer(model, layer):
+    layer = layer.split('.')
+    module = model
+    if hasattr(model, 'module') and layer[0] != 'module':
+        module = model.module
+    if not hasattr(model, 'module') and layer[0] == 'module':
+        layer = layer[1:]
+    for l in layer:
+        if hasattr(module, l):
+            if not l.isdigit():
+                module = getattr(module, l)
+            else:
+                module = module[int(l)]
+        else:
+            return module
+    return module
+
+
+def set_layer(model, layer, val):
+    layer = layer.split('.')
+    module = model
+    if hasattr(model, 'module') and layer[0] != 'module':
+        module = model.module
+    lst_index = 0
+    module2 = module
+    for l in layer:
+        if hasattr(module2, l):
+            if not l.isdigit():
+                module2 = getattr(module2, l)
+            else:
+                module2 = module2[int(l)]
+            lst_index += 1
+    lst_index -= 1
+    for l in layer[:lst_index]:
+        if not l.isdigit():
+            module = getattr(module, l)
+        else:
+            module = module[int(l)]
+    l = layer[lst_index]
+    setattr(module, l, val)
+
+
+def adapt_model_from_string(parent_module, model_string):
+    separator = '***'
+    state_dict = {}
+    lst_shape = model_string.split(separator)
+    for k in lst_shape:
+        k = k.split(':')
+        key = k[0]
+        shape = k[1][1:-1].split(',')
+        if shape[0] != '':
+            state_dict[key] = [int(i) for i in shape]
+
+    new_module = deepcopy(parent_module)
+    for n, m in parent_module.named_modules():
+        old_module = extract_layer(parent_module, n)
+        if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
+            if isinstance(old_module, Conv2dSame):
+                conv = Conv2dSame
+            else:
+                conv = nn.Conv2d
+            s = state_dict[n + '.weight']
+            in_channels = s[1]
+            out_channels = s[0]
+            g = 1
+            if old_module.groups > 1:
+                in_channels = out_channels
+                g = in_channels
+            new_conv = conv(
+                in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
+                bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
+                groups=g, stride=old_module.stride)
+            set_layer(new_module, n, new_conv)
+        elif isinstance(old_module, BatchNormAct2d):
+            new_bn = BatchNormAct2d(
+                state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
+                affine=old_module.affine, track_running_stats=True)
+            new_bn.drop = old_module.drop
+            new_bn.act = old_module.act
+            set_layer(new_module, n, new_bn)
+        elif isinstance(old_module, nn.BatchNorm2d):
+            new_bn = nn.BatchNorm2d(
+                num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
+                affine=old_module.affine, track_running_stats=True)
+            set_layer(new_module, n, new_bn)
+        elif isinstance(old_module, nn.Linear):
+            # FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer?
+            num_features = state_dict[n + '.weight'][1]
+            new_fc = Linear(
+                in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None)
+            set_layer(new_module, n, new_fc)
+            if hasattr(new_module, 'num_features'):
+                new_module.num_features = num_features
+    new_module.eval()
+    parent_module.eval()
+
+    return new_module
+
+
+def adapt_model_from_file(parent_module, model_variant):
+    adapt_file = os.path.join(os.path.dirname(__file__), '_pruned', model_variant + '.txt')
+    with open(adapt_file, 'r') as f:
+        return adapt_model_from_string(parent_module, f.read().strip())

timm/models/_registry.py

@@ -0,0 +1,212 @@
+""" Model Registry
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+
+import fnmatch
+import re
+import sys
+from collections import defaultdict, deque
+from copy import deepcopy
+from typing import List, Optional, Union, Tuple
+
+from ._pretrained import PretrainedCfg, DefaultCfg, split_model_name_tag
+
+__all__ = [
+    'list_models', 'list_pretrained', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules',
+    'get_pretrained_cfg_value', 'is_model_pretrained', 'get_arch_name']
+
+_module_to_models = defaultdict(set)  # dict of sets to check membership of model in module
+_model_to_module = {}  # mapping of model names to module names
+_model_entrypoints = {}  # mapping of model names to architecture entrypoint fns
+_model_has_pretrained = set()  # set of model names that have pretrained weight url present
+_model_default_cfgs = dict()  # central repo for model arch -> default cfg objects
+_model_pretrained_cfgs = dict()  # central repo for model arch + tag -> pretrained cfgs
+_model_with_tags = defaultdict(list)  # shortcut to map each model arch to all model + tag names
+
+
+def get_arch_name(model_name: str) -> Tuple[str, Optional[str]]:
+    return split_model_name_tag(model_name)[0]
+
+
+def register_model(fn):
+    # lookup containing module
+    mod = sys.modules[fn.__module__]
+    module_name_split = fn.__module__.split('.')
+    module_name = module_name_split[-1] if len(module_name_split) else ''
+
+    # add model to __all__ in module
+    model_name = fn.__name__
+    if hasattr(mod, '__all__'):
+        mod.__all__.append(model_name)
+    else:
+        mod.__all__ = [model_name]
+
+    # add entries to registry dict/sets
+    _model_entrypoints[model_name] = fn
+    _model_to_module[model_name] = module_name
+    _module_to_models[module_name].add(model_name)
+    if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs:
+        # this will catch all models that have entrypoint matching cfg key, but miss any aliasing
+        # entrypoints or non-matching combos
+        cfg = mod.default_cfgs[model_name]
+        if not isinstance(cfg, DefaultCfg):
+            # new style default cfg dataclass w/ multiple entries per model-arch
+            assert isinstance(cfg, dict)
+            # old style cfg dict per model-arch
+            cfg = PretrainedCfg(**cfg)
+            cfg = DefaultCfg(tags=deque(['']), cfgs={'': cfg})
+
+        for tag_idx, tag in enumerate(cfg.tags):
+            is_default = tag_idx == 0
+            pretrained_cfg = cfg.cfgs[tag]
+            if is_default:
+                _model_pretrained_cfgs[model_name] = pretrained_cfg
+                if pretrained_cfg.has_weights:
+                    # add tagless entry if it's default and has weights
+                    _model_has_pretrained.add(model_name)
+            if tag:
+                model_name_tag = '.'.join([model_name, tag])
+                _model_pretrained_cfgs[model_name_tag] = pretrained_cfg
+                if pretrained_cfg.has_weights:
+                    # add model w/ tag if tag is valid
+                    _model_has_pretrained.add(model_name_tag)
+                _model_with_tags[model_name].append(model_name_tag)
+            else:
+                _model_with_tags[model_name].append(model_name)  # has empty tag (to slowly remove these instances)
+
+        _model_default_cfgs[model_name] = cfg
+
+    return fn
+
+
+def _natural_key(string_):
+    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
+
+
+def list_models(
+        filter: Union[str, List[str]] = '',
+        module: str = '',
+        pretrained=False,
+        exclude_filters: str = '',
+        name_matches_cfg: bool = False,
+        include_tags: Optional[bool] = None,
+):
+    """ Return list of available model names, sorted alphabetically
+
+    Args:
+        filter (str) - Wildcard filter string that works with fnmatch
+        module (str) - Limit model selection to a specific submodule (ie 'vision_transformer')
+        pretrained (bool) - Include only models with valid pretrained weights if True
+        exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
+        name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
+        include_tags (Optional[boo]) - Include pretrained tags in model names (model.tag). If None, defaults
+            set to True when pretrained=True else False (default: None)
+    Example:
+        model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
+        model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
+    """
+    if include_tags is None:
+        # FIXME should this be default behaviour? or default to include_tags=True?
+        include_tags = pretrained
+
+    if module:
+        all_models = list(_module_to_models[module])
+    else:
+        all_models = _model_entrypoints.keys()
+
+    if include_tags:
+        # expand model names to include names w/ pretrained tags
+        models_with_tags = []
+        for m in all_models:
+            models_with_tags.extend(_model_with_tags[m])
+        all_models = models_with_tags
+
+    if filter:
+        models = []
+        include_filters = filter if isinstance(filter, (tuple, list)) else [filter]
+        for f in include_filters:
+            include_models = fnmatch.filter(all_models, f)  # include these models
+            if len(include_models):
+                models = set(models).union(include_models)
+    else:
+        models = all_models
+
+    if exclude_filters:
+        if not isinstance(exclude_filters, (tuple, list)):
+            exclude_filters = [exclude_filters]
+        for xf in exclude_filters:
+            exclude_models = fnmatch.filter(models, xf)  # exclude these models
+            if len(exclude_models):
+                models = set(models).difference(exclude_models)
+
+    if pretrained:
+        models = _model_has_pretrained.intersection(models)
+
+    if name_matches_cfg:
+        models = set(_model_pretrained_cfgs).intersection(models)
+
+    return list(sorted(models, key=_natural_key))
+
+
+def list_pretrained(
+        filter: Union[str, List[str]] = '',
+        exclude_filters: str = '',
+):
+    return list_models(
+        filter=filter,
+        pretrained=True,
+        exclude_filters=exclude_filters,
+        include_tags=True,
+    )
+
+
+def is_model(model_name):
+    """ Check if a model name exists
+    """
+    arch_name = get_arch_name(model_name)
+    return arch_name in _model_entrypoints
+
+
+def model_entrypoint(model_name, module_filter: Optional[str] = None):
+    """Fetch a model entrypoint for specified model name
+    """
+    arch_name = get_arch_name(model_name)
+    if module_filter and arch_name not in _module_to_models.get(module_filter, {}):
+        raise RuntimeError(f'Model ({model_name} not found in module {module_filter}.')
+    return _model_entrypoints[arch_name]
+
+
+def list_modules():
+    """ Return list of module names that contain models / model entrypoints
+    """
+    modules = _module_to_models.keys()
+    return list(sorted(modules))
+
+
+def is_model_in_modules(model_name, module_names):
+    """Check if a model exists within a subset of modules
+    Args:
+        model_name (str) - name of model to check
+        module_names (tuple, list, set) - names of modules to search in
+    """
+    arch_name = get_arch_name(model_name)
+    assert isinstance(module_names, (tuple, list, set))
+    return any(arch_name in _module_to_models[n] for n in module_names)
+
+
+def is_model_pretrained(model_name):
+    return model_name in _model_has_pretrained
+
+
+def get_pretrained_cfg(model_name):
+    if model_name in _model_pretrained_cfgs:
+        return deepcopy(_model_pretrained_cfgs[model_name])
+    raise RuntimeError(f'No pretrained config exists for model {model_name}.')
+
+
+def get_pretrained_cfg_value(model_name, cfg_key):
+    """ Get a specific model default_cfg value by key. None if key doesn't exist.
+    """
+    if model_name in _model_pretrained_cfgs:
+        return getattr(_model_pretrained_cfgs[model_name], cfg_key, None)
+    raise RuntimeError(f'No pretrained config exist for model {model_name}.')

timm/models/beit.py

@@ -61,12 +61,14 @@ import torch.nn.functional as F
 from torch.utils.checkpoint import checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
-from .helpers import build_model_with_cfg
-from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
-from .pretrained import generate_default_cfgs
-from .registry import register_model
+from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_
+from ._builder import build_model_with_cfg
+from ._pretrained import generate_default_cfgs
+from ._registry import register_model
 from .vision_transformer import checkpoint_filter_fn
 
+__all__ = ['Beit']
+
 
 def gen_relative_position_index(window_size: Tuple[int, int]) -> torch.Tensor:
     num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3

timm/models/byoanet.py

@@ -13,9 +13,9 @@ Consider all of the models definitions here as experimental WIP and likely to ch
 Hacked together by / copyright Ross Wightman, 2021.
 """
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from ._builder import build_model_with_cfg
+from ._registry import register_model
 from .byobnet import ByoBlockCfg, ByoModelCfg, ByobNet, interleave_blocks
-from .helpers import build_model_with_cfg
-from .registry import register_model
 
 __all__ = []
 

timm/models/byobnet.py

@@ -26,18 +26,18 @@ Hacked together by / copyright Ross Wightman, 2021.
 """
 import math
 from dataclasses import dataclass, field, replace
-from typing import Tuple, List, Dict, Optional, Union, Any, Callable, Sequence
 from functools import partial
+from typing import Tuple, List, Dict, Optional, Union, Any, Callable, Sequence
 
 import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg, named_apply, checkpoint_seq
-from .layers import ClassifierHead, ConvNormAct, BatchNormAct2d, DropPath, AvgPool2dSame, \
-    create_conv2d, get_act_layer, get_norm_act_layer, get_attn, make_divisible, to_2tuple, EvoNorm2dS0, EvoNorm2dS0a,\
-    EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a, FilterResponseNormAct2d, FilterResponseNormTlu2d
-from .registry import register_model
+from timm.layers import ClassifierHead, ConvNormAct, BatchNormAct2d, DropPath, AvgPool2dSame, \
+    create_conv2d, get_act_layer, get_norm_act_layer, get_attn, make_divisible, to_2tuple, EvoNorm2dS0a
+from ._builder import build_model_with_cfg
+from ._manipulate import named_apply, checkpoint_seq
+from ._registry import register_model
 
 __all__ = ['ByobNet', 'ByoModelCfg', 'ByoBlockCfg', 'create_byob_stem', 'create_block']
 

timm/models/cait.py

@@ -8,17 +8,16 @@ Modifications and additions for timm hacked together by / Copyright 2021, Ross W
 """
 # Copyright (c) 2015-present, Facebook, Inc.
 # All rights reserved.
-from copy import deepcopy
 from functools import partial
 
 import torch
 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 PatchEmbed, Mlp, DropPath, trunc_normal_
-from .registry import register_model
-
+from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_
+from ._builder import build_model_with_cfg
+from ._manipulate import checkpoint_seq
+from ._registry import register_model
 
 __all__ = ['Cait', 'ClassAttn', 'LayerScaleBlockClassAttn', 'LayerScaleBlock', 'TalkingHeadAttn']
 

timm/models/coat.py

@@ -7,7 +7,6 @@ Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
 
 Modified from timm/models/vision_transformer.py
 """
-from copy import deepcopy
 from functools import partial
 from typing import Tuple, List, Union
 
@@ -16,19 +15,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_
-from .registry import register_model
-from .layers import _assert
-
-
-__all__ = [
-    "coat_tiny",
-    "coat_mini",
-    "coat_lite_tiny",
-    "coat_lite_mini",
-    "coat_lite_small"
-]
+from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert
+from ._builder import build_model_with_cfg
+from ._registry import register_model
+
+__all__ = ['CoaT']
 
 
 def _cfg_coat(url='', **kwargs):

timm/models/convit.py

@@ -22,20 +22,20 @@ Modifications and additions for timm hacked together by / Copyright 2021, Ross W
 https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
 '''
 
+from functools import partial
+
 import torch
 import torch.nn as nn
-from functools import partial
-import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp
-from .registry import register_model
+from timm.layers import DropPath, trunc_normal_, PatchEmbed, Mlp
+from ._builder import build_model_with_cfg
+from ._features_fx import register_notrace_module
+from ._registry import register_model
 from .vision_transformer_hybrid import HybridEmbed
-from .fx_features import register_notrace_module
 
-import torch
-import torch.nn as nn
+
+__all__ = ['ConViT']
 
 
 def _cfg(url='', **kwargs):

timm/models/convmixer.py

@@ -5,9 +5,12 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.models.registry import register_model
-from .helpers import build_model_with_cfg, checkpoint_seq
-from .layers import SelectAdaptivePool2d
+from timm.layers import SelectAdaptivePool2d
+from ._registry import register_model
+from ._builder import build_model_with_cfg
+from ._manipulate import checkpoint_seq
+
+__all__ = ['ConvMixer']
 
 
 def _cfg(url='', **kwargs):

timm/models/convnext.py

@@ -18,12 +18,12 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import named_apply, build_model_with_cfg, checkpoint_seq
-from .layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \
+from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \
     create_conv2d, get_act_layer, make_divisible, to_ntuple
-from .pretrained import generate_default_cfgs
-from .registry import register_model
-
+from ._builder import build_model_with_cfg
+from ._manipulate import named_apply, checkpoint_seq
+from ._pretrained import generate_default_cfgs
+from ._registry import register_model
 
 __all__ = ['ConvNeXt']  # model_registry will add each entrypoint fn to this
 

timm/models/crossvit.py

@@ -24,21 +24,22 @@ Modifications and additions for timm hacked together by / Copyright 2021, Ross W
 Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
 
 """
+from functools import partial
+from typing import List
 from typing import Tuple
 
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
 import torch.hub
-from functools import partial
-from typing import List
+import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .fx_features import register_notrace_function
-from .helpers import build_model_with_cfg
-from .layers import DropPath, to_2tuple, trunc_normal_, _assert
-from .registry import register_model
-from .vision_transformer import Mlp, Block
+from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
+from ._builder import build_model_with_cfg
+from ._features_fx import register_notrace_function
+from ._registry import register_model
+from .vision_transformer import Block
+
+__all__ = ['CrossViT']  # model_registry will add each entrypoint fn to this
 
 
 def _cfg(url='', **kwargs):

timm/models/cspnet.py

@@ -12,20 +12,18 @@ Reference impl via darknet cfg files at https://github.com/WongKinYiu/CrossStage
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
-import collections.abc
-from dataclasses import dataclass, field, asdict
+from dataclasses import dataclass, asdict
 from functools import partial
-from typing import Any, Callable, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg, named_apply, MATCH_PREV_GROUP
-from .layers import ClassifierHead, ConvNormAct, ConvNormActAa, DropPath, get_attn, create_act_layer, make_divisible
-from .registry import register_model
-
+from timm.layers import ClassifierHead, ConvNormAct, ConvNormActAa, DropPath, get_attn, create_act_layer, make_divisible
+from ._builder import build_model_with_cfg
+from ._manipulate import named_apply, MATCH_PREV_GROUP
+from ._registry import register_model
 
 __all__ = ['CspNet']  # model_registry will add each entrypoint fn to this
 

timm/models/deit.py

@@ -17,9 +17,11 @@ from torch import nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.models.vision_transformer import VisionTransformer, trunc_normal_, checkpoint_filter_fn
+from ._builder import build_model_with_cfg
+from ._manipulate import checkpoint_seq
+from ._registry import register_model
 
-from .helpers import build_model_with_cfg, checkpoint_seq
-from .registry import register_model
+__all__ = ['VisionTransformerDistilled']  # model_registry will add each entrypoint fn to this
 
 
 def _cfg(url='', **kwargs):

timm/models/densenet.py

@@ -4,7 +4,6 @@ fixed kwargs passthrough and addition of dynamic global avg/max pool.
 """
 import re
 from collections import OrderedDict
-from functools import partial
 
 import torch
 import torch.nn as nn
@@ -13,9 +12,10 @@ import torch.utils.checkpoint as cp
 from torch.jit.annotations import List
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg, MATCH_PREV_GROUP
-from .layers import BatchNormAct2d, create_norm_act_layer, BlurPool2d, create_classifier
-from .registry import register_model
+from timm.layers import BatchNormAct2d, create_norm_act_layer, BlurPool2d, create_classifier
+from ._builder import build_model_with_cfg
+from ._manipulate import MATCH_PREV_GROUP
+from ._registry import register_model
 
 __all__ = ['DenseNet']
 

timm/models/dla.py

@@ -13,9 +13,9 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import create_classifier
-from .registry import register_model
+from timm.layers import create_classifier
+from ._builder import build_model_with_cfg
+from ._registry import register_model
 
 __all__ = ['DLA']
 

timm/models/dpn.py

@@ -15,9 +15,9 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier
-from .registry import register_model
+from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier
+from ._builder import build_model_with_cfg
+from ._registry import register_model
 
 __all__ = ['DPN']
 

timm/models/edgenext.py

@@ -8,20 +8,20 @@ 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
 import torch.nn.functional as F
+from torch import nn
 
 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
-
+from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d
+from ._builder import build_model_with_cfg
+from ._features_fx import register_notrace_module
+from ._manipulate import named_apply, checkpoint_seq
+from ._registry import register_model
 
 __all__ = ['EdgeNeXt']  # model_registry will add each entrypoint fn to this
 

timm/models/efficientformer.py

@@ -18,9 +18,11 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import DropPath, trunc_normal_, to_2tuple, Mlp
-from .registry import register_model
+from timm.layers import DropPath, trunc_normal_, to_2tuple, Mlp
+from ._builder import build_model_with_cfg
+from ._registry import register_model
+
+__all__ = ['EfficientFormer']  # model_registry will add each entrypoint fn to this
 
 
 def _cfg(url='', **kwargs):

timm/models/efficientnet.py

@@ -42,15 +42,15 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
-from .efficientnet_blocks import SqueezeExcite
-from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights,\
+from timm.layers import create_conv2d, create_classifier, get_norm_act_layer, GroupNormAct
+from ._builder import build_model_with_cfg, pretrained_cfg_for_features
+from ._efficientnet_blocks import SqueezeExcite
+from ._efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, \
     round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
-from .features import FeatureInfo, FeatureHooks
-from .helpers import build_model_with_cfg, pretrained_cfg_for_features, checkpoint_seq
-from .layers import create_conv2d, create_classifier, get_norm_act_layer, EvoNorm2dS0, GroupNormAct
-from .registry import register_model
+from ._features import FeatureInfo, FeatureHooks
+from ._manipulate import checkpoint_seq
+from ._registry import register_model
 
 __all__ = ['EfficientNet', 'EfficientNetFeatures']
 

timm/models/factory.py

@@ -1,100 +1,4 @@
-import os
-from typing import Any, Dict, Optional, Union
-from urllib.parse import urlsplit
+from ._factory import *
 
-from .pretrained import PretrainedCfg, split_model_name_tag
-from .helpers import load_checkpoint
-from .hub import load_model_config_from_hf
-from .layers import set_layer_config
-from .registry import is_model, model_entrypoint
-
-
-def parse_model_name(model_name):
-    if model_name.startswith('hf_hub'):
-        # NOTE for backwards compat, deprecate hf_hub use
-        model_name = model_name.replace('hf_hub', 'hf-hub')
-    parsed = urlsplit(model_name)
-    assert parsed.scheme in ('', 'timm', 'hf-hub')
-    if parsed.scheme == 'hf-hub':
-        # FIXME may use fragment as revision, currently `@` in URI path
-        return parsed.scheme, parsed.path
-    else:
-        model_name = os.path.split(parsed.path)[-1]
-        return 'timm', model_name
-
-
-def safe_model_name(model_name, remove_source=True):
-    # return a filename / path safe model name
-    def make_safe(name):
-        return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
-    if remove_source:
-        model_name = parse_model_name(model_name)[-1]
-    return make_safe(model_name)
-
-
-def create_model(
-        model_name: str,
-        pretrained: bool = False,
-        pretrained_cfg: Optional[Union[str, Dict[str, Any], PretrainedCfg]] = None,
-        pretrained_cfg_overlay:  Optional[Dict[str, Any]] = None,
-        checkpoint_path: str = '',
-        scriptable: Optional[bool] = None,
-        exportable: Optional[bool] = None,
-        no_jit: Optional[bool] = None,
-        **kwargs,
-):
-    """Create a model
-
-    Lookup model's entrypoint function and pass relevant args to create a new model.
-
-    **kwargs will be passed through entrypoint fn to timm.models.build_model_with_cfg()
-    and then the model class __init__(). kwargs values set to None are pruned before passing.
-
-    Args:
-        model_name (str): name of model to instantiate
-        pretrained (bool): load pretrained ImageNet-1k weights if true
-        pretrained_cfg (Union[str, dict, PretrainedCfg]): pass in external pretrained_cfg for model
-        pretrained_cfg_overlay (dict): replace key-values in base pretrained_cfg with these
-        checkpoint_path (str): path of checkpoint to load _after_ the model is initialized
-        scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
-        exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
-        no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
-
-    Keyword Args:
-        drop_rate (float): dropout rate for training (default: 0.0)
-        global_pool (str): global pool type (default: 'avg')
-        **: other kwargs are consumed by builder or model __init__()
-    """
-    # Parameters that aren't supported by all models or are intended to only override model defaults if set
-    # should default to None in command line args/cfg. Remove them if they are present and not set so that
-    # non-supporting models don't break and default args remain in effect.
-    kwargs = {k: v for k, v in kwargs.items() if v is not None}
-
-    model_source, model_name = parse_model_name(model_name)
-    if model_source == 'hf-hub':
-        assert not pretrained_cfg, 'pretrained_cfg should not be set when sourcing model from Hugging Face Hub.'
-        # For model names specified in the form `hf-hub:path/architecture_name@revision`,
-        # load model weights + pretrained_cfg from Hugging Face hub.
-        pretrained_cfg, model_name = load_model_config_from_hf(model_name)
-    else:
-        model_name, pretrained_tag = split_model_name_tag(model_name)
-        if not pretrained_cfg:
-            # a valid pretrained_cfg argument takes priority over tag in model name
-            pretrained_cfg = pretrained_tag
-
-    if not is_model(model_name):
-        raise RuntimeError('Unknown model (%s)' % model_name)
-
-    create_fn = model_entrypoint(model_name)
-    with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
-        model = create_fn(
-            pretrained=pretrained,
-            pretrained_cfg=pretrained_cfg,
-            pretrained_cfg_overlay=pretrained_cfg_overlay,
-            **kwargs,
-        )
-
-    if checkpoint_path:
-        load_checkpoint(model, checkpoint_path)
-
-    return model
+import warnings
+warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)

timm/models/features.py

@@ -1,284 +1,4 @@
-""" PyTorch Feature Extraction Helpers
+from ._features import *
 
-A collection of classes, functions, modules to help extract features from models
-and provide a common interface for describing them.
-
-The return_layers, module re-writing idea inspired by torchvision IntermediateLayerGetter
-https://github.com/pytorch/vision/blob/d88d8961ae51507d0cb680329d985b1488b1b76b/torchvision/models/_utils.py
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-from collections import OrderedDict, defaultdict
-from copy import deepcopy
-from functools import partial
-from typing import Dict, List, Tuple
-
-import torch
-import torch.nn as nn
-
-
-class FeatureInfo:
-
-    def __init__(self, feature_info: List[Dict], out_indices: Tuple[int]):
-        prev_reduction = 1
-        for fi in feature_info:
-            # sanity check the mandatory fields, there may be additional fields depending on the model
-            assert 'num_chs' in fi and fi['num_chs'] > 0
-            assert 'reduction' in fi and fi['reduction'] >= prev_reduction
-            prev_reduction = fi['reduction']
-            assert 'module' in fi
-        self.out_indices = out_indices
-        self.info = feature_info
-
-    def from_other(self, out_indices: Tuple[int]):
-        return FeatureInfo(deepcopy(self.info), out_indices)
-
-    def get(self, key, idx=None):
-        """ Get value by key at specified index (indices)
-        if idx == None, returns value for key at each output index
-        if idx is an integer, return value for that feature module index (ignoring output indices)
-        if idx is a list/tupple, return value for each module index (ignoring output indices)
-        """
-        if idx is None:
-            return [self.info[i][key] for i in self.out_indices]
-        if isinstance(idx, (tuple, list)):
-            return [self.info[i][key] for i in idx]
-        else:
-            return self.info[idx][key]
-
-    def get_dicts(self, keys=None, idx=None):
-        """ return info dicts for specified keys (or all if None) at specified indices (or out_indices if None)
-        """
-        if idx is None:
-            if keys is None:
-                return [self.info[i] for i in self.out_indices]
-            else:
-                return [{k: self.info[i][k] for k in keys} for i in self.out_indices]
-        if isinstance(idx, (tuple, list)):
-            return [self.info[i] if keys is None else {k: self.info[i][k] for k in keys} for i in idx]
-        else:
-            return self.info[idx] if keys is None else {k: self.info[idx][k] for k in keys}
-
-    def channels(self, idx=None):
-        """ feature channels accessor
-        """
-        return self.get('num_chs', idx)
-
-    def reduction(self, idx=None):
-        """ feature reduction (output stride) accessor
-        """
-        return self.get('reduction', idx)
-
-    def module_name(self, idx=None):
-        """ feature module name accessor
-        """
-        return self.get('module', idx)
-
-    def __getitem__(self, item):
-        return self.info[item]
-
-    def __len__(self):
-        return len(self.info)
-
-
-class FeatureHooks:
-    """ Feature Hook Helper
-
-    This module helps with the setup and extraction of hooks for extracting features from
-    internal nodes in a model by node name. This works quite well in eager Python but needs
-    redesign for torchscript.
-    """
-
-    def __init__(self, hooks, named_modules, out_map=None, default_hook_type='forward'):
-        # setup feature hooks
-        modules = {k: v for k, v in named_modules}
-        for i, h in enumerate(hooks):
-            hook_name = h['module']
-            m = modules[hook_name]
-            hook_id = out_map[i] if out_map else hook_name
-            hook_fn = partial(self._collect_output_hook, hook_id)
-            hook_type = h.get('hook_type', default_hook_type)
-            if hook_type == 'forward_pre':
-                m.register_forward_pre_hook(hook_fn)
-            elif hook_type == 'forward':
-                m.register_forward_hook(hook_fn)
-            else:
-                assert False, "Unsupported hook type"
-        self._feature_outputs = defaultdict(OrderedDict)
-
-    def _collect_output_hook(self, hook_id, *args):
-        x = args[-1]  # tensor we want is last argument, output for fwd, input for fwd_pre
-        if isinstance(x, tuple):
-            x = x[0]  # unwrap input tuple
-        self._feature_outputs[x.device][hook_id] = x
-
-    def get_output(self, device) -> Dict[str, torch.tensor]:
-        output = self._feature_outputs[device]
-        self._feature_outputs[device] = OrderedDict()  # clear after reading
-        return output
-
-
-def _module_list(module, flatten_sequential=False):
-    # a yield/iter would be better for this but wouldn't be compatible with torchscript
-    ml = []
-    for name, module in module.named_children():
-        if flatten_sequential and isinstance(module, nn.Sequential):
-            # first level of Sequential containers is flattened into containing model
-            for child_name, child_module in module.named_children():
-                combined = [name, child_name]
-                ml.append(('_'.join(combined), '.'.join(combined), child_module))
-        else:
-            ml.append((name, name, module))
-    return ml
-
-
-def _get_feature_info(net, out_indices):
-    feature_info = getattr(net, 'feature_info')
-    if isinstance(feature_info, FeatureInfo):
-        return feature_info.from_other(out_indices)
-    elif isinstance(feature_info, (list, tuple)):
-        return FeatureInfo(net.feature_info, out_indices)
-    else:
-        assert False, "Provided feature_info is not valid"
-
-
-def _get_return_layers(feature_info, out_map):
-    module_names = feature_info.module_name()
-    return_layers = {}
-    for i, name in enumerate(module_names):
-        return_layers[name] = out_map[i] if out_map is not None else feature_info.out_indices[i]
-    return return_layers
-
-
-class FeatureDictNet(nn.ModuleDict):
-    """ Feature extractor with OrderedDict return
-
-    Wrap a model and extract features as specified by the out indices, the network is
-    partially re-built from contained modules.
-
-    There is a strong assumption that the modules have been registered into the model in the same
-    order as they are used. There should be no reuse of the same nn.Module more than once, including
-    trivial modules like `self.relu = nn.ReLU`.
-
-    Only submodules that are directly assigned to the model class (`model.feature1`) or at most
-    one Sequential container deep (`model.features.1`, with flatten_sequent=True) can be captured.
-    All Sequential containers that are directly assigned to the original model will have their
-    modules assigned to this module with the name `model.features.1` being changed to `model.features_1`
-
-    Arguments:
-        model (nn.Module): model from which we will extract the features
-        out_indices (tuple[int]): model output indices to extract features for
-        out_map (sequence): list or tuple specifying desired return id for each out index,
-            otherwise str(index) is used
-        feature_concat (bool): whether to concatenate intermediate features that are lists or tuples
-            vs select element [0]
-        flatten_sequential (bool): whether to flatten sequential modules assigned to model
-    """
-    def __init__(
-            self, model,
-            out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
-        super(FeatureDictNet, self).__init__()
-        self.feature_info = _get_feature_info(model, out_indices)
-        self.concat = feature_concat
-        self.return_layers = {}
-        return_layers = _get_return_layers(self.feature_info, out_map)
-        modules = _module_list(model, flatten_sequential=flatten_sequential)
-        remaining = set(return_layers.keys())
-        layers = OrderedDict()
-        for new_name, old_name, module in modules:
-            layers[new_name] = module
-            if old_name in remaining:
-                # return id has to be consistently str type for torchscript
-                self.return_layers[new_name] = str(return_layers[old_name])
-                remaining.remove(old_name)
-            if not remaining:
-                break
-        assert not remaining and len(self.return_layers) == len(return_layers), \
-            f'Return layers ({remaining}) are not present in model'
-        self.update(layers)
-
-    def _collect(self, x) -> (Dict[str, torch.Tensor]):
-        out = OrderedDict()
-        for name, module in self.items():
-            x = module(x)
-            if name in self.return_layers:
-                out_id = self.return_layers[name]
-                if isinstance(x, (tuple, list)):
-                    # If model tap is a tuple or list, concat or select first element
-                    # FIXME this may need to be more generic / flexible for some nets
-                    out[out_id] = torch.cat(x, 1) if self.concat else x[0]
-                else:
-                    out[out_id] = x
-        return out
-
-    def forward(self, x) -> Dict[str, torch.Tensor]:
-        return self._collect(x)
-
-
-class FeatureListNet(FeatureDictNet):
-    """ Feature extractor with list return
-
-    See docstring for FeatureDictNet above, this class exists only to appease Torchscript typing constraints.
-    In eager Python we could have returned List[Tensor] vs Dict[id, Tensor] based on a member bool.
-    """
-    def __init__(
-            self, model,
-            out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
-        super(FeatureListNet, self).__init__(
-            model, out_indices=out_indices, out_map=out_map, feature_concat=feature_concat,
-            flatten_sequential=flatten_sequential)
-
-    def forward(self, x) -> (List[torch.Tensor]):
-        return list(self._collect(x).values())
-
-
-class FeatureHookNet(nn.ModuleDict):
-    """ FeatureHookNet
-
-    Wrap a model and extract features specified by the out indices using forward/forward-pre hooks.
-
-    If `no_rewrite` is True, features are extracted via hooks without modifying the underlying
-    network in any way.
-
-    If `no_rewrite` is False, the model will be re-written as in the
-    FeatureList/FeatureDict case by folding first to second (Sequential only) level modules into this one.
-
-    FIXME this does not currently work with Torchscript, see FeatureHooks class
-    """
-    def __init__(
-            self, model,
-            out_indices=(0, 1, 2, 3, 4), out_map=None, out_as_dict=False, no_rewrite=False,
-            feature_concat=False, flatten_sequential=False, default_hook_type='forward'):
-        super(FeatureHookNet, self).__init__()
-        assert not torch.jit.is_scripting()
-        self.feature_info = _get_feature_info(model, out_indices)
-        self.out_as_dict = out_as_dict
-        layers = OrderedDict()
-        hooks = []
-        if no_rewrite:
-            assert not flatten_sequential
-            if hasattr(model, 'reset_classifier'):  # make sure classifier is removed?
-                model.reset_classifier(0)
-            layers['body'] = model
-            hooks.extend(self.feature_info.get_dicts())
-        else:
-            modules = _module_list(model, flatten_sequential=flatten_sequential)
-            remaining = {f['module']: f['hook_type'] if 'hook_type' in f else default_hook_type
-                         for f in self.feature_info.get_dicts()}
-            for new_name, old_name, module in modules:
-                layers[new_name] = module
-                for fn, fm in module.named_modules(prefix=old_name):
-                    if fn in remaining:
-                        hooks.append(dict(module=fn, hook_type=remaining[fn]))
-                        del remaining[fn]
-                if not remaining:
-                    break
-            assert not remaining, f'Return layers ({remaining}) are not present in model'
-        self.update(layers)
-        self.hooks = FeatureHooks(hooks, model.named_modules(), out_map=out_map)
-
-    def forward(self, x):
-        for name, module in self.items():
-            x = module(x)
-        out = self.hooks.get_output(x.device)
-        return out if self.out_as_dict else list(out.values())
+import warnings
+warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)

timm/models/fx_features.py

@@ -1,106 +1,4 @@
-""" PyTorch FX Based Feature Extraction Helpers
-Using https://pytorch.org/vision/stable/feature_extraction.html
-"""
-from typing import Callable, List, Dict, Union, Type
+from ._features_fx import *
 
-import torch
-from torch import nn
-
-from .features import _get_feature_info
-
-try:
-    from torchvision.models.feature_extraction import create_feature_extractor as _create_feature_extractor
-    has_fx_feature_extraction = True
-except ImportError:
-    has_fx_feature_extraction = False
-
-# Layers we went to treat as leaf modules
-from .layers import Conv2dSame, ScaledStdConv2dSame, CondConv2d, StdConv2dSame
-from .layers.non_local_attn import BilinearAttnTransform
-from .layers.pool2d_same import MaxPool2dSame, AvgPool2dSame
-
-# NOTE: By default, any modules from timm.models.layers that we want to treat as leaf modules go here
-# BUT modules from timm.models should use the registration mechanism below
-_leaf_modules = {
-    BilinearAttnTransform,  # reason: flow control t <= 1
-    # Reason: get_same_padding has a max which raises a control flow error
-    Conv2dSame, MaxPool2dSame, ScaledStdConv2dSame, StdConv2dSame, AvgPool2dSame,
-    CondConv2d,  # reason: TypeError: F.conv2d received Proxy in groups=self.groups * B (because B = x.shape[0])
-}
-
-try:
-    from .layers import InplaceAbn
-    _leaf_modules.add(InplaceAbn)
-except ImportError:
-    pass
-
-
-def register_notrace_module(module: Type[nn.Module]):
-    """
-    Any module not under timm.models.layers should get this decorator if we don't want to trace through it.
-    """
-    _leaf_modules.add(module)
-    return module
-
-
-# Functions we want to autowrap (treat them as leaves)
-_autowrap_functions = set()
-
-
-def register_notrace_function(func: Callable):
-    """
-    Decorator for functions which ought not to be traced through
-    """
-    _autowrap_functions.add(func)
-    return func
-
-
-def create_feature_extractor(model: nn.Module, return_nodes: Union[Dict[str, str], List[str]]):
-    assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
-    return _create_feature_extractor(
-        model, return_nodes,
-        tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)}
-    )
-
-
-class FeatureGraphNet(nn.Module):
-    """ A FX Graph based feature extractor that works with the model feature_info metadata
-    """
-    def __init__(self, model, out_indices, out_map=None):
-        super().__init__()
-        assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
-        self.feature_info = _get_feature_info(model, out_indices)
-        if out_map is not None:
-            assert len(out_map) == len(out_indices)
-        return_nodes = {
-            info['module']: out_map[i] if out_map is not None else info['module']
-            for i, info in enumerate(self.feature_info) if i in out_indices}
-        self.graph_module = create_feature_extractor(model, return_nodes)
-
-    def forward(self, x):
-        return list(self.graph_module(x).values())
-
-
-class GraphExtractNet(nn.Module):
-    """ A standalone feature extraction wrapper that maps dict -> list or single tensor
-    NOTE:
-      * one can use feature_extractor directly if dictionary output is desired
-      * unlike FeatureGraphNet, this is intended to be used standalone and not with model feature_info
-      metadata for builtin feature extraction mode
-      * create_feature_extractor can be used directly if dictionary output is acceptable
-
-    Args:
-        model: model to extract features from
-        return_nodes: node names to return features from (dict or list)
-        squeeze_out: if only one output, and output in list format, flatten to single tensor
-    """
-    def __init__(self, model, return_nodes: Union[Dict[str, str], List[str]], squeeze_out: bool = True):
-        super().__init__()
-        self.squeeze_out = squeeze_out
-        self.graph_module = create_feature_extractor(model, return_nodes)
-
-    def forward(self, x) -> Union[List[torch.Tensor], torch.Tensor]:
-        out = list(self.graph_module(x).values())
-        if self.squeeze_out and len(out) == 1:
-            return out[0]
-        return out
+import warnings
+warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.models", DeprecationWarning)

timm/models/gcvit.py

@@ -28,12 +28,13 @@ import torch.nn as nn
 import torch.utils.checkpoint as checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .fx_features import register_notrace_function
-from .helpers import build_model_with_cfg, named_apply
-from .layers import DropPath, to_2tuple, to_ntuple, Mlp, ClassifierHead, LayerNorm2d,\
+from timm.layers import DropPath, to_2tuple, to_ntuple, Mlp, ClassifierHead, LayerNorm2d, \
     get_attn, get_act_layer, get_norm_layer, _assert
-from .registry import register_model
-from .vision_transformer_relpos import RelPosMlp, RelPosBias  # FIXME move to common location
+from ._builder import build_model_with_cfg
+from ._features_fx import register_notrace_function
+from ._manipulate import named_apply
+from ._registry import register_model
+from .vision_transformer_relpos import RelPosBias  # FIXME move to common location
 
 __all__ = ['GlobalContextVit']