Merge pull request huggingface#1799 from huggingface/dot_nine_cleanup

Final cleanup before .9 release

timm/layers/__init__.py

@@ -34,7 +34,7 @@
     SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d
 from .padding import get_padding, get_same_padding, pad_same
 from .patch_dropout import PatchDropout
-from .patch_embed import PatchEmbed, resample_patch_embed
+from .patch_embed import PatchEmbed, PatchEmbedWithSize, resample_patch_embed
 from .pool2d_same import AvgPool2dSame, create_pool2d
 from .pos_embed import resample_abs_pos_embed
 from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords

timm/layers/patch_embed.py

@@ -9,7 +9,7 @@
 Hacked together by / Copyright 2020 Ross Wightman
 """
 import logging
-from typing import List, Optional, Callable
+from typing import Callable, List, Optional, Tuple, Union
 
 import torch
 from torch import nn as nn
@@ -75,6 +75,49 @@ def forward(self, x):
         return x
 
 
+class PatchEmbedWithSize(PatchEmbed):
+    """ 2D Image to Patch Embedding
+    """
+    output_fmt: Format
+
+    def __init__(
+            self,
+            img_size: Optional[int] = 224,
+            patch_size: int = 16,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            norm_layer: Optional[Callable] = None,
+            flatten: bool = True,
+            output_fmt: Optional[str] = None,
+            bias: bool = True,
+    ):
+        super().__init__(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer,
+            flatten=flatten,
+            output_fmt=output_fmt,
+            bias=bias,
+        )
+
+    def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
+        B, C, H, W = x.shape
+        if self.img_size is not None:
+            _assert(H % self.patch_size[0] == 0, f"Input image height ({H}) must be divisible by patch size ({self.patch_size[0]}).")
+            _assert(W % self.patch_size[1] == 0, f"Input image width ({W}) must be divisible by patch size ({self.patch_size[1]}).")
+
+        x = self.proj(x)
+        grid_size = x.shape[-2:]
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        elif self.output_fmt != Format.NCHW:
+            x = nchw_to(x, self.output_fmt)
+        x = self.norm(x)
+        return x, grid_size
+
+
 def resample_patch_embed(
         patch_embed,
         new_size: List[int],

timm/layers/pos_embed.py

@@ -24,29 +24,31 @@ def resample_abs_pos_embed(
         verbose: bool = False,
 ):
     # sort out sizes, assume square if old size not provided
-    new_size = to_2tuple(new_size)
-    new_ntok = new_size[0] * new_size[1]
-    if not old_size:
-        old_size = int(math.sqrt(posemb.shape[1] - num_prefix_tokens))
-    old_size = to_2tuple(old_size)
-    if new_size == old_size:  # might not both be same container type
+    num_pos_tokens = posemb.shape[1]
+    num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
+    if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
         return posemb
 
+    if not old_size:
+        hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
+        old_size = hw, hw
+
     if num_prefix_tokens:
         posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:]
     else:
         posemb_prefix, posemb = None, posemb
 
     # do the interpolation
+    embed_dim = posemb.shape[-1]
     posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
     posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
-    posemb = posemb.permute(0, 2, 3, 1).reshape(1, new_ntok, -1)
-
-    if verbose:
-        _logger.info(f'Resized position embedding: {old_size} to {new_size}.')
+    posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
 
     # add back extra (class, etc) prefix tokens
     if posemb_prefix is not None:
-        print(posemb_prefix.shape, posemb.shape)
         posemb = torch.cat([posemb_prefix, posemb], dim=1)
+
+    if not torch.jit.is_scripting() and verbose:
+        _logger.info(f'Resized position embedding: {old_size} to {new_size}.')
+
     return posemb

timm/models/_builder.py

@@ -22,7 +22,7 @@
 # Use set_pretrained_download_progress / set_pretrained_check_hash functions to toggle.
 _DOWNLOAD_PROGRESS = False
 _CHECK_HASH = False
-
+_USE_OLD_CACHE = int(os.environ.get('TIMM_USE_OLD_CACHE', 0)) > 0
 
 __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']
@@ -32,6 +32,7 @@ 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)
+    pretrained_sd = pretrained_cfg.get('state_dict', None)
     hf_hub_id = pretrained_cfg.get('hf_hub_id', None)
 
     # resolve where to load pretrained weights from
@@ -44,14 +45,21 @@ def _resolve_pretrained_source(pretrained_cfg):
         pretrained_loc = hf_hub_id
     else:
         # default source == timm or unspecified
-        if pretrained_file:
-            # file load override is the highest priority if set
+        if pretrained_sd:
+            # direct state_dict pass through is the highest priority
+            load_from = 'state_dict'
+            pretrained_loc = pretrained_sd
+            assert isinstance(pretrained_loc, dict)
+        elif pretrained_file:
+            # file load override is the second-highest priority if set
             load_from = 'file'
             pretrained_loc = pretrained_file
         else:
-            # next, HF hub is prioritized unless a valid cached version of weights exists already
-            cached_url_valid = check_cached_file(pretrained_url) if pretrained_url else False
-            if hf_hub_id and has_hf_hub(necessary=True) and not cached_url_valid:
+            old_cache_valid = False
+            if _USE_OLD_CACHE:
+                # prioritized old cached weights if exists and env var enabled
+                old_cache_valid = check_cached_file(pretrained_url) if pretrained_url else False
+            if not old_cache_valid and 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
@@ -106,7 +114,7 @@ def load_custom_pretrained(
     if not load_from:
         _logger.warning("No pretrained weights exist for this model. Using random initialization.")
         return
-    if load_from == 'hf-hub':  # FIXME
+    if load_from == 'hf-hub':
         _logger.warning("Hugging Face hub not currently supported for custom load pretrained models.")
     elif load_from == 'url':
         pretrained_loc = download_cached_file(
@@ -148,7 +156,10 @@ def load_pretrained(
         return
 
     load_from, pretrained_loc = _resolve_pretrained_source(pretrained_cfg)
-    if load_from == 'file':
+    if load_from == 'state_dict':
+        _logger.info(f'Loading pretrained weights from state dict')
+        state_dict = pretrained_loc  # pretrained_loc is the actual state dict for this override
+    elif load_from == 'file':
         _logger.info(f'Loading pretrained weights from file ({pretrained_loc})')
         state_dict = load_state_dict(pretrained_loc)
     elif load_from == 'url':

timm/models/_pretrained.py

@@ -11,11 +11,12 @@
 class PretrainedCfg:
     """
     """
-    # weight locations
-    url: Optional[Union[str, Tuple[str, str]]] = None
-    file: Optional[str] = None
-    hf_hub_id: Optional[str] = None
-    hf_hub_filename: Optional[str] = None
+    # weight source locations
+    url: Optional[Union[str, Tuple[str, str]]] = None  # remote URL
+    file: Optional[str] = None  # local / shared filesystem path
+    state_dict: Optional[Dict[str, Any]] = None  # in-memory state dict
+    hf_hub_id: Optional[str] = None  # Hugging Face Hub model id ('organization/model')
+    hf_hub_filename: Optional[str] = None  # Hugging Face Hub filename (overrides default)
 
     source: Optional[str] = None  # source of cfg / weight location used (url, file, hf-hub)
     architecture: Optional[str] = None  # architecture variant can be set when not implicit

timm/models/beit.py

@@ -477,6 +477,11 @@ def _cfg(url='', **kwargs):
         hf_hub_id='timm/',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
     ),
+    'beitv2_base_patch16_224.in1k_ft_in1k': _cfg(
+        url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft1k.pth',
+        hf_hub_id='timm/',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
+    ),
     'beitv2_base_patch16_224.in1k_ft_in22k': _cfg(
         url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21k.pth',
         hf_hub_id='timm/',
@@ -487,6 +492,11 @@ def _cfg(url='', **kwargs):
         hf_hub_id='timm/',
         crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
     ),
+    'beitv2_large_patch16_224.in1k_ft_in1k': _cfg(
+        url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft1k.pth',
+        hf_hub_id='timm/',
+        crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD
+    ),
     'beitv2_large_patch16_224.in1k_ft_in22k': _cfg(
         url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth',
         hf_hub_id='timm/',
@@ -515,7 +525,7 @@ def _create_beit(variant, pretrained=False, **kwargs):
 
 
 @register_model
-def beit_base_patch16_224(pretrained=False, **kwargs):
+def beit_base_patch16_224(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1)
@@ -524,7 +534,7 @@ def beit_base_patch16_224(pretrained=False, **kwargs):
 
 
 @register_model
-def beit_base_patch16_384(pretrained=False, **kwargs):
+def beit_base_patch16_384(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1)
@@ -533,7 +543,7 @@ def beit_base_patch16_384(pretrained=False, **kwargs):
 
 
 @register_model
-def beit_large_patch16_224(pretrained=False, **kwargs):
+def beit_large_patch16_224(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         patch_size=16, embed_dim=1024, depth=24, num_heads=16,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
@@ -542,7 +552,7 @@ def beit_large_patch16_224(pretrained=False, **kwargs):
 
 
 @register_model
-def beit_large_patch16_384(pretrained=False, **kwargs):
+def beit_large_patch16_384(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
@@ -551,7 +561,7 @@ def beit_large_patch16_384(pretrained=False, **kwargs):
 
 
 @register_model
-def beit_large_patch16_512(pretrained=False, **kwargs):
+def beit_large_patch16_512(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
@@ -560,7 +570,7 @@ def beit_large_patch16_512(pretrained=False, **kwargs):
 
 
 @register_model
-def beitv2_base_patch16_224(pretrained=False, **kwargs):
+def beitv2_base_patch16_224(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)
@@ -569,7 +579,7 @@ def beitv2_base_patch16_224(pretrained=False, **kwargs):
 
 
 @register_model
-def beitv2_large_patch16_224(pretrained=False, **kwargs):
+def beitv2_large_patch16_224(pretrained=False, **kwargs) -> Beit:
     model_args = dict(
         patch_size=16, embed_dim=1024, depth=24, num_heads=16,
         use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5)