Merge branch 'rwightman:master' into master

README.md

@@ -23,10 +23,11 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
-### Nov 19, 2021
+### Nov 22, 2021
 * A number of updated weights anew new model defs
   * `eca_halonext26ts` - 79.5 @ 256
   * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288
+  * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth))
   * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288
   * `sebotnet33ts_256` (new) - 81.2 @ 224
   * `lamhalobotnet50ts_256` - 81.5 @ 256
@@ -35,6 +36,8 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
   * `resnet101` - 82.0 @ 224, 82.8 @ 288
   * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288
   * `resnet152` - 82.8 @ 224, 83.5 @ 288
+  * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320
+  * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320
 * `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris)
 * Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare)
   * models updated for tracing compatibility (almost full support with some distlled transformer exceptions)

tests/test_models.py

@@ -339,15 +339,18 @@ EXCLUDE_FX_FILTERS = []
 if 'GITHUB_ACTIONS' in os.environ:
     EXCLUDE_FX_FILTERS += [
         'beit_large*',
-        'swin_large*',
+        'mixer_l*',
+        '*nfnet_f2*',
         '*resnext101_32x32d',
         'resnetv2_152x2*',
-        '*nfnet_f2*',
         'resmlp_big*',
         'resnetrs270',
+        'swin_large*',
         'vgg*',
         'vit_large*',
+        'vit_base_patch8*',
         'xcit_large*',
+        '*evob', '*evos', # remove experimental evonorm models, seem to cause issues with dtype manipulation
     ]
 
 
@@ -368,81 +371,89 @@ def test_model_forward_fx(model_name, batch_size):
     input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
     if max(input_size) > MAX_FWD_FX_SIZE:
         pytest.skip("Fixed input size model > limit.")
-    inputs = torch.randn((batch_size, *input_size))
-    outputs = model(inputs)
-    if isinstance(outputs, tuple):
-        outputs = torch.cat(outputs)
+    with torch.no_grad():
+        inputs = torch.randn((batch_size, *input_size))
+        outputs = model(inputs)
+        if isinstance(outputs, tuple):
+            outputs = torch.cat(outputs)
 
-    model = _create_fx_model(model)
-    fx_outputs = tuple(model(inputs).values())
-    if isinstance(fx_outputs, tuple):
-        fx_outputs = torch.cat(fx_outputs)
+        model = _create_fx_model(model)
+        fx_outputs = tuple(model(inputs).values())
+        if isinstance(fx_outputs, tuple):
+            fx_outputs = torch.cat(fx_outputs)
 
     assert torch.all(fx_outputs == outputs)
     assert outputs.shape[0] == batch_size
     assert not torch.isnan(outputs).any(), 'Output included NaNs'
 
 
-@pytest.mark.timeout(120)
-@pytest.mark.parametrize('model_name', list_models(
-    exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
-@pytest.mark.parametrize('batch_size', [2])
-def test_model_backward_fx(model_name, batch_size):
-    """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
-    if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
-
-    input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
-    if max(input_size) > MAX_BWD_FX_SIZE:
-        pytest.skip("Fixed input size model > limit.")
-
-    model = create_model(model_name, pretrained=False, num_classes=42)
-    num_params = sum([x.numel() for x in model.parameters()])
-    model.train()
-
-    model = _create_fx_model(model, train=True)
-    outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
-    if isinstance(outputs, tuple):
-        outputs = torch.cat(outputs)
-    outputs.mean().backward()
-    for n, x in model.named_parameters():
-        assert x.grad is not None, f'No gradient for {n}'
-    num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
-
-    assert outputs.shape[-1] == 42
-    assert num_params == num_grad, 'Some parameters are missing gradients'
-    assert not torch.isnan(outputs).any(), 'Output included NaNs'
-
-# reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
-EXCLUDE_FX_JIT_FILTERS = [
-    'deit_*_distilled_patch16_224',
-    'levit*',
-    'pit_*_distilled_224',
-] + EXCLUDE_FX_FILTERS
-
-
-@pytest.mark.timeout(120)
-@pytest.mark.parametrize(
-    'model_name', list_models(
-        exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
-@pytest.mark.parametrize('batch_size', [1])
-def test_model_forward_fx_torchscript(model_name, batch_size):
-    """Symbolically trace each model, script it, and run single forward pass"""
-    if not has_fx_feature_extraction:
-        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
-
-    input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
-    if max(input_size) > MAX_JIT_SIZE:
-        pytest.skip("Fixed input size model > limit.")
+if 'GITHUB_ACTIONS' not in os.environ:
+    # FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process
 
-    with set_scriptable(True):
-        model = create_model(model_name, pretrained=False)
-    model.eval()
+    @pytest.mark.timeout(120)
+    @pytest.mark.parametrize('model_name', list_models(
+        exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
+    @pytest.mark.parametrize('batch_size', [2])
+    def test_model_backward_fx(model_name, batch_size):
+        """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
+        if not has_fx_feature_extraction:
+            pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+
+        input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
+        if max(input_size) > MAX_BWD_FX_SIZE:
+            pytest.skip("Fixed input size model > limit.")
+
+        model = create_model(model_name, pretrained=False, num_classes=42)
+        model.train()
+        num_params = sum([x.numel() for x in model.parameters()])
+        if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
+            pytest.skip("Skipping FX backward test on model with more than 100M params.")
+
+        model = _create_fx_model(model, train=True)
+        outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
+        if isinstance(outputs, tuple):
+            outputs = torch.cat(outputs)
+        outputs.mean().backward()
+        for n, x in model.named_parameters():
+            assert x.grad is not None, f'No gradient for {n}'
+        num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
+
+        assert outputs.shape[-1] == 42
+        assert num_params == num_grad, 'Some parameters are missing gradients'
+        assert not torch.isnan(outputs).any(), 'Output included NaNs'
+
+
+    # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
+    EXCLUDE_FX_JIT_FILTERS = [
+        'deit_*_distilled_patch16_224',
+        'levit*',
+        'pit_*_distilled_224',
+    ] + EXCLUDE_FX_FILTERS
 
-    model = torch.jit.script(_create_fx_model(model))
-    outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
-    if isinstance(outputs, tuple):
-        outputs = torch.cat(outputs)
 
-    assert outputs.shape[0] == batch_size
-    assert not torch.isnan(outputs).any(), 'Output included NaNs'
+    @pytest.mark.timeout(120)
+    @pytest.mark.parametrize(
+        'model_name', list_models(
+            exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
+    @pytest.mark.parametrize('batch_size', [1])
+    def test_model_forward_fx_torchscript(model_name, batch_size):
+        """Symbolically trace each model, script it, and run single forward pass"""
+        if not has_fx_feature_extraction:
+            pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+
+        input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
+        if max(input_size) > MAX_JIT_SIZE:
+            pytest.skip("Fixed input size model > limit.")
+
+        with set_scriptable(True):
+            model = create_model(model_name, pretrained=False)
+        model.eval()
+
+        model = torch.jit.script(_create_fx_model(model))
+        with torch.no_grad():
+            outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
+            if isinstance(outputs, tuple):
+                outputs = torch.cat(outputs)
+
+        assert outputs.shape[0] == batch_size
+        assert not torch.isnan(outputs).any(), 'Output included NaNs'

timm/models/helpers.py

@@ -11,11 +11,11 @@ from typing import Any, Callable, Optional, Tuple
 
 import torch
 import torch.nn as nn
-
+from torch.hub import load_state_dict_from_url
 
 from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
 from .fx_features import FeatureGraphNet
-from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf, load_state_dict_from_url
+from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf
 from .layers import Conv2dSame, Linear
 
 
@@ -184,12 +184,12 @@ def load_pretrained(model, default_cfg=None, num_classes=1000, in_chans=3, filte
     if not pretrained_url and not hf_hub_id:
         _logger.warning("No pretrained weights exist for this model. Using random initialization.")
         return
-    if hf_hub_id and has_hf_hub(necessary=not pretrained_url):
-        _logger.info(f'Loading pretrained weights from Hugging Face hub ({hf_hub_id})')
-        state_dict = load_state_dict_from_hf(hf_hub_id)
-    else:
+    if pretrained_url:
         _logger.info(f'Loading pretrained weights from url ({pretrained_url})')
         state_dict = load_state_dict_from_url(pretrained_url, progress=progress, map_location='cpu')
+    elif hf_hub_id and has_hf_hub(necessary=True):
+        _logger.info(f'Loading pretrained weights from Hugging Face hub ({hf_hub_id})')
+        state_dict = load_state_dict_from_hf(hf_hub_id)
     if filter_fn is not None:
         # for backwards compat with filter fn that take one arg, try one first, the two
         try:

timm/models/hub.py

@@ -2,10 +2,11 @@ import json
 import logging
 import os
 from functools import partial
-from typing import Union, Optional
+from pathlib import Path
+from typing import Union
 
 import torch
-from torch.hub import load_state_dict_from_url, download_url_to_file, urlparse, HASH_REGEX
+from torch.hub import HASH_REGEX, download_url_to_file, urlparse
 try:
     from torch.hub import get_dir
 except ImportError:
@@ -13,12 +14,12 @@ except ImportError:
 
 from timm import __version__
 try:
-    from huggingface_hub import hf_hub_url
-    from huggingface_hub import cached_download
+    from huggingface_hub import HfApi, HfFolder, Repository, cached_download, hf_hub_url
     cached_download = partial(cached_download, library_name="timm", library_version=__version__)
+    _has_hf_hub = True
 except ImportError:
-    hf_hub_url = None
     cached_download = None
+    _has_hf_hub = False
 
 _logger = logging.getLogger(__name__)
 
@@ -53,11 +54,11 @@ def download_cached_file(url, check_hash=True, progress=False):
 
 
 def has_hf_hub(necessary=False):
-    if hf_hub_url is None and necessary:
+    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 hf_hub_url is not None
+    return _has_hf_hub
 
 
 def hf_split(hf_id):
@@ -94,3 +95,77 @@ def load_state_dict_from_hf(model_id: str):
     cached_file = _download_from_hf(model_id, 'pytorch_model.bin')
     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 = model.default_cfg
+    hf_config['num_classes'] = model_config.pop('num_classes', model.num_classes)
+    hf_config['num_features'] = model_config.pop('num_features', model.num_features)
+    hf_config['labels'] = model_config.pop('labels', [f"LABEL_{i}" for i in range(hf_config['num_classes'])])
+    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,
+    local_dir,
+    repo_namespace_or_url=None,
+    commit_message='Add model',
+    use_auth_token=True,
+    git_email=None,
+    git_user=None,
+    revision=None,
+    model_config=None,
+):
+    if repo_namespace_or_url:
+        repo_owner, repo_name = repo_namespace_or_url.rstrip('/').split('/')[-2:]
+    else:
+        if isinstance(use_auth_token, str):
+            token = use_auth_token
+        else:
+            token = HfFolder.get_token()
+
+        if token is None:
+            raise ValueError(
+                "You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and "
+                "entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
+                "token as the `use_auth_token` argument."
+            )
+
+        repo_owner = HfApi().whoami(token)['name']
+        repo_name = Path(local_dir).name
+
+    repo_url = f'https://huggingface.co/{repo_owner}/{repo_name}'
+
+    repo = Repository(
+        local_dir,
+        clone_from=repo_url,
+        use_auth_token=use_auth_token,
+        git_user=git_user,
+        git_email=git_email,
+        revision=revision,
+    )
+
+    # Prepare a default model card that includes the necessary tags to enable inference.
+    readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}'
+    with repo.commit(commit_message):
+        # Save model weights and config.
+        save_for_hf(model, repo.local_dir, model_config=model_config)
+
+        # Save a model card if it doesn't exist.
+        readme_path = Path(repo.local_dir) / 'README.md'
+        if not readme_path.exists():
+            readme_path.write_text(readme_text)
+
+    return repo.git_remote_url()

timm/models/layers/evo_norm.py

@@ -34,18 +34,17 @@ class EvoNormBatch2d(nn.Module):
             nn.init.ones_(self.v)
 
     def forward(self, x):
-        assert x.dim() == 4, 'expected 4D input'
+        _assert(x.dim() == 4, 'expected 4D input')
         x_type = x.dtype
-        running_var = self.running_var.view(1, -1, 1, 1)
-        if self.training:
-            var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
-            n = x.numel() / x.shape[1]
-            running_var = var.detach() * self.momentum * (n / (n - 1)) + running_var * (1 - self.momentum)
-            self.running_var.copy_(running_var.view(self.running_var.shape))
-        else:
-            var = running_var
-
         if self.v is not None:
+            running_var = self.running_var.view(1, -1, 1, 1)
+            if self.training:
+                var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
+                n = x.numel() / x.shape[1]
+                running_var = var.detach() * self.momentum * (n / (n - 1)) + running_var * (1 - self.momentum)
+                self.running_var.copy_(running_var.view(self.running_var.shape))
+            else:
+                var = running_var
             v = self.v.to(dtype=x_type).reshape(1, -1, 1, 1)
             d = x * v + (x.var(dim=(2, 3), unbiased=False, keepdim=True) + self.eps).sqrt().to(dtype=x_type)
             d = d.max((var + self.eps).sqrt().to(dtype=x_type))

timm/models/mlp_mixer.py

@@ -128,6 +128,13 @@ default_cfgs = dict(
         url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
 
+    resmlp_12_224_dino=_cfg(
+        url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dino.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+    resmlp_24_224_dino=_cfg(
+        url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dino.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+
     gmlp_ti16_224=_cfg(),
     gmlp_s16_224=_cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
@@ -589,6 +596,33 @@ def resmlp_big_24_224_in22ft1k(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def resmlp_12_224_dino(pretrained=False, **kwargs):
+    """ ResMLP-12
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+
+    Model pretrained via DINO (self-supervised) - https://arxiv.org/abs/2104.14294
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_12_224_dino', pretrained=pretrained, **model_args)
+    return model
+
+
+@register_model
+def resmlp_24_224_dino(pretrained=False, **kwargs):
+    """ ResMLP-24
+    Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
+
+    Model pretrained via DINO (self-supervised) - https://arxiv.org/abs/2104.14294
+    """
+    model_args = dict(
+        patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
+        block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
+    model = _create_mixer('resmlp_24_224_dino', pretrained=pretrained, **model_args)
+    return model
+
+
 @register_model
 def gmlp_ti16_224(pretrained=False, **kwargs):
     """ gMLP-Tiny

timm/scheduler/poly_lr.py

@@ -37,7 +37,7 @@ class PolyLRScheduler(Scheduler):
                  noise_pct=0.67,
                  noise_std=1.0,
                  noise_seed=42,
-                 k_decay=.5,
+                 k_decay=1.0,
                  initialize=True) -> None:
         super().__init__(
             optimizer, param_group_field="lr",