Change models with mean 0,0,0 std 1,1,1 from int to float for consistency as mentioned in #1355

timm/models/mlp_mixer.py

@@ -85,11 +85,11 @@ default_cfgs = dict(
     # Mixer ImageNet-21K-P pretraining
     mixer_b16_224_miil_in21k=_cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
+        mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
     ),
     mixer_b16_224_miil=_cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
+        mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
     ),
 
     gmixer_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),

timm/models/mobilenetv3.py

@@ -41,10 +41,10 @@ default_cfgs = {
         interpolation='bicubic',
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'),
     'mobilenetv3_large_100_miil': _cfg(
-        interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
+        interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.),
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_1k_miil_78_0.pth'),
     'mobilenetv3_large_100_miil_in21k': _cfg(
-        interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
+        interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.),
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_in21k_miil.pth', num_classes=11221),
 
     'mobilenetv3_small_050': _cfg(

timm/models/mobilevit.py

@@ -34,7 +34,7 @@ def _cfg(url='', **kwargs):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
         'crop_pct': 0.9, 'interpolation': 'bicubic',
-        'mean': (0, 0, 0), 'std': (1, 1, 1),
+        'mean': (0., 0., 0.), 'std': (1., 1., 1.),
         'first_conv': 'stem.conv', 'classifier': 'head.fc',
         'fixed_input_size': False,
         **kwargs

timm/models/tresnet.py

@@ -21,7 +21,7 @@ def _cfg(url='', **kwargs):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
-        'mean': (0, 0, 0), 'std': (1, 1, 1),
+        'mean': (0., 0., 0.), 'std': (1., 1., 1.),
         'first_conv': 'body.conv1.0', 'classifier': 'head.fc',
         **kwargs
     }

timm/models/vision_transformer.py

@@ -163,12 +163,12 @@ default_cfgs = {
     # ViT ImageNet-21K-P pretraining by MILL
     'vit_base_patch16_224_miil_in21k': _cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
+        mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
     ),
     'vit_base_patch16_224_miil': _cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
             '/vit_base_patch16_224_1k_miil_84_4.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
+        mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
     ),
 
     'vit_base_patch16_rpn_224': _cfg(