A few more changes for 0.3.2 maint release. Linear layer change for mobilenetv3 and inception_v3, support no bias for linear wrapper.

tests/test_models.py

@@ -121,7 +121,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
         create_model(model_name, pretrained=True, in_chans=in_chans)
 
     @pytest.mark.timeout(120)
-    @pytest.mark.parametrize('model_name', list_models(pretrained=True))
+    @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=['vit_*']))
     @pytest.mark.parametrize('batch_size', [1])
     def test_model_features_pretrained(model_name, batch_size):
         """Create that pretrained weights load when features_only==True."""

timm/models/helpers.py

@@ -14,7 +14,7 @@ import torch.nn as nn
 import torch.utils.model_zoo as model_zoo
 
 from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
-from .layers import Conv2dSame
+from .layers import Conv2dSame, Linear
 
 
 _logger = logging.getLogger(__name__)
@@ -234,7 +234,7 @@ def adapt_model_from_string(parent_module, model_string):
         if 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 = nn.Linear(
+            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'):

timm/models/inception_v3.py

@@ -10,7 +10,7 @@ import torch.nn.functional as F
 from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 from .helpers import build_model_with_cfg
 from .registry import register_model
-from .layers import trunc_normal_, create_classifier
+from .layers import trunc_normal_, create_classifier, Linear
 
 
 def _cfg(url='', **kwargs):
@@ -250,7 +250,7 @@ class InceptionAux(nn.Module):
         self.conv0 = conv_block(in_channels, 128, kernel_size=1)
         self.conv1 = conv_block(128, 768, kernel_size=5)
         self.conv1.stddev = 0.01
-        self.fc = nn.Linear(768, num_classes)
+        self.fc = Linear(768, num_classes)
         self.fc.stddev = 0.001
 
     def forward(self, x):

timm/models/layers/__init__.py

@@ -18,6 +18,7 @@ from .eca import EcaModule, CecaModule
 from .evo_norm import EvoNormBatch2d, EvoNormSample2d
 from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple
 from .inplace_abn import InplaceAbn
+from .linear import Linear
 from .mixed_conv2d import MixedConv2d
 from .norm_act import BatchNormAct2d
 from .padding import get_padding

timm/models/layers/linear.py

@@ -13,6 +13,7 @@ class Linear(nn.Linear):
     """
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         if torch.jit.is_scripting():
-            return F.linear(input, self.weight.to(dtype=input.dtype), self.bias.to(dtype=input.dtype))
+            bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None
+            return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias)
         else:
             return F.linear(input, self.weight, self.bias)

timm/models/mobilenetv3.py

@@ -18,7 +18,7 @@ from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_la
 from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
 from .features import FeatureInfo, FeatureHooks
 from .helpers import build_model_with_cfg
-from .layers import SelectAdaptivePool2d, create_conv2d, get_act_fn, hard_sigmoid
+from .layers import SelectAdaptivePool2d, Linear, create_conv2d, get_act_fn, hard_sigmoid
 from .registry import register_model
 
 __all__ = ['MobileNetV3']
@@ -105,7 +105,7 @@ class MobileNetV3(nn.Module):
         num_pooled_chs = head_chs * self.global_pool.feat_mult()
         self.conv_head = create_conv2d(num_pooled_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
         self.act2 = act_layer(inplace=True)
-        self.classifier = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+        self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
         efficientnet_init_weights(self)
 
@@ -123,7 +123,7 @@ class MobileNetV3(nn.Module):
         self.num_classes = num_classes
         # cannot meaningfully change pooling of efficient head after creation
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.classifier = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+        self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
         x = self.conv_stem(x)

train.py

@@ -327,6 +327,7 @@ def main():
         bn_tf=args.bn_tf,
         bn_momentum=args.bn_momentum,
         bn_eps=args.bn_eps,
+        scriptable=args.torchscript,
         checkpoint_path=args.initial_checkpoint)
 
     if args.local_rank == 0: