Fix a few issues that came up in tests

timm/models/layers/pool2d_same.py

@@ -44,6 +44,7 @@ class MaxPool2dSame(nn.MaxPool2d):
     def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False, count_include_pad=True):
         kernel_size = tup_pair(kernel_size)
         stride = tup_pair(stride)
+        dilation = tup_pair(dilation)
         super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode, count_include_pad)
 
     def forward(self, x):

timm/models/nasnet.py

@@ -13,7 +13,7 @@ default_cfgs = {
         'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
         'input_size': (3, 331, 331),
         'pool_size': (11, 11),
-        'crop_pct': 0.875,
+        'crop_pct': 0.911,
         'interpolation': 'bicubic',
         'mean': (0.5, 0.5, 0.5),
         'std': (0.5, 0.5, 0.5),

timm/models/pnasnet.py

@@ -24,7 +24,7 @@ default_cfgs = {
         'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth',
         'input_size': (3, 331, 331),
         'pool_size': (11, 11),
-        'crop_pct': 0.875,
+        'crop_pct': 0.911,
         'interpolation': 'bicubic',
         'mean': (0.5, 0.5, 0.5),
         'std': (0.5, 0.5, 0.5),

timm/models/resnet.py

@@ -521,20 +521,23 @@ class ResNet(nn.Module):
 
 def _create_resnet_with_cfg(variant, default_cfg, pretrained=False, **kwargs):
     assert isinstance(default_cfg, dict)
-    load_strict, features = True, False
+    features = False
     out_indices = None
     if kwargs.pop('features_only', False):
-        load_strict, features = False, True
+        features = True
         kwargs.pop('num_classes', 0)
         out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4))
+    pruned = kwargs.pop('pruned', False)
+
     model = ResNet(**kwargs)
     model.default_cfg = copy.deepcopy(default_cfg)
-    if kwargs.pop('pruned', False):
+
+    if pruned:
         model = adapt_model_from_file(model, variant)
     if pretrained:
         load_pretrained(
             model,
-            num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), strict=load_strict)
+            num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), strict=not features)
     if features:
         model = FeatureNet(model, out_indices=out_indices)
     return model