Fix some test failures, torchscript issues

tests/test_models.py

@@ -27,7 +27,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
 NON_STD_FILTERS = [
     'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
     'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit_*',
-    'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*']
+    'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/efficientformer.py

@@ -26,7 +26,7 @@ from .registry import register_model
 def _cfg(url='', **kwargs):
     return {
         'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'fixed_input_size': True,
         'crop_pct': .95, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'stem.conv1', 'classifier': 'head',

timm/models/gcvit.py

@@ -209,8 +209,7 @@ class WindowAttentionGlobal(nn.Module):
 
     def forward(self, x, q_global: Optional[torch.Tensor] = None):
         B, N, C = x.shape
-        if self.use_global:
-            _assert(q_global is not None, 'q_global must be passed in global mode')
+        if self.use_global and q_global is not None:
             _assert(x.shape[-1] == q_global.shape[-1], 'x and q_global seq lengths should be equal')
 
             kv = self.qkv(x)

timm/models/pvt_v2.py

@@ -286,7 +286,6 @@ class PyramidVisionTransformerV2(nn.Module):
         self.num_classes = num_classes
         assert global_pool in ('avg', '')
         self.global_pool = global_pool
-        self.img_size = to_2tuple(img_size) if img_size is not None else None
         self.depths = depths
         num_stages = len(depths)
         mlp_ratios = to_ntuple(num_stages)(mlp_ratios)
@@ -324,7 +323,8 @@ class PyramidVisionTransformerV2(nn.Module):
             cur += depths[i]
 
         # classification head
-        self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
+        self.num_features = embed_dims[-1]
+        self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
 
         self.apply(self._init_weights)