Add a few more test model defs in prep for weight upload

timm/models/_efficientnet_builder.py

@@ -20,7 +20,7 @@ from timm.layers import CondConv2d, get_condconv_initializer, get_act_layer, get
 from ._efficientnet_blocks import *
 from ._manipulate import named_modules
 
-__all__ = ["EfficientNetBuilder", "decode_arch_def", "efficientnet_init_weights",
+__all__ = ["EfficientNetBuilder", "BlockArgs", "decode_arch_def", "efficientnet_init_weights",
            'resolve_bn_args', 'resolve_act_layer', 'round_channels', 'BN_MOMENTUM_TF_DEFAULT', 'BN_EPS_TF_DEFAULT']
 
 _logger = logging.getLogger(__name__)

timm/models/efficientnet.py

@@ -44,7 +44,8 @@ import torch.nn.functional as F
 from torch.utils.checkpoint import checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
-from timm.layers import create_conv2d, create_classifier, get_norm_act_layer, GroupNormAct, LayerType
+from timm.layers import create_conv2d, create_classifier, get_norm_act_layer, LayerType, \
+    GroupNormAct, LayerNormAct2d, EvoNorm2dS0
 from ._builder import build_model_with_cfg, pretrained_cfg_for_features
 from ._efficientnet_blocks import SqueezeExcite
 from ._efficientnet_builder import BlockArgs, EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, \
@@ -1808,6 +1809,14 @@ default_cfgs = generate_default_cfgs({
         hf_hub_id='timm/',
         mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
         input_size=(3, 160, 160), pool_size=(5, 5)),
+    "test_efficientnet_ln.r160_in1k": _cfg(
+        #hf_hub_id='timm/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 160, 160), pool_size=(5, 5)),
+    "test_efficientnet_evos.r160_in1k": _cfg(
+        #hf_hub_id='timm/',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 160, 160), pool_size=(5, 5)),
 })
 
 
@@ -2802,6 +2811,21 @@ def test_efficientnet_gn(pretrained=False, **kwargs) -> EfficientNet:
         'test_efficientnet_gn', pretrained=pretrained, norm_layer=partial(GroupNormAct, group_size=8), **kwargs)
     return model
 
+
+@register_model
+def test_efficientnet_ln(pretrained=False, **kwargs) -> EfficientNet:
+    model = _gen_test_efficientnet(
+        'test_efficientnet_ln', pretrained=pretrained, norm_layer=LayerNormAct2d, **kwargs)
+    return model
+
+
+@register_model
+def test_efficientnet_evos(pretrained=False, **kwargs) -> EfficientNet:
+    model = _gen_test_efficientnet(
+        'test_efficientnet_evos', pretrained=pretrained, norm_layer=partial(EvoNorm2dS0, group_size=8), **kwargs)
+    return model
+
+
 register_model_deprecations(__name__, {
     'tf_efficientnet_b0_ap': 'tf_efficientnet_b0.ap_in1k',
     'tf_efficientnet_b1_ap': 'tf_efficientnet_b1.ap_in1k',

timm/models/vision_transformer.py

@@ -2015,6 +2015,12 @@ default_cfgs = {
     'test_vit.r160_in1k': _cfg(
         hf_hub_id='timm/',
         input_size=(3, 160, 160), crop_pct=0.875),
+    'test_vit2.r160_in1k': _cfg(
+        #hf_hub_id='timm/',
+        input_size=(3, 160, 160), crop_pct=0.875),
+    'test_vit3.r160_in1k': _cfg(
+        #hf_hub_id='timm/',
+        input_size=(3, 160, 160), crop_pct=0.875),
 }
 
 _quick_gelu_cfgs = [
@@ -3216,6 +3222,26 @@ def test_vit(pretrained: bool = False, **kwargs) -> VisionTransformer:
     return model
 
 
+def test_vit2(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ ViT Test
+    """
+    model_args = dict(
+        patch_size=16, embed_dim=64, depth=8, num_heads=2, mlp_ratio=3,
+        class_token=False, reg_tokens=1, global_pool='avg', init_values=1e-5)
+    model = _create_vision_transformer('test_vit2', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+def test_vit3(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    """ ViT Test
+    """
+    model_args = dict(
+        patch_size=16, embed_dim=96, depth=10, num_heads=3, mlp_ratio=2,
+        class_token=False, reg_tokens=1, global_pool='map', init_values=1e-5)
+    model = _create_vision_transformer('test_vit3', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 register_model_deprecations(__name__, {
     'vit_tiny_patch16_224_in21k': 'vit_tiny_patch16_224.augreg_in21k',
     'vit_small_patch32_224_in21k': 'vit_small_patch32_224.augreg_in21k',