Fix MobileNetV3 crash with global_pool='', output consistent with other models but not equivalent due to efficient head.

tests/test_models.py

@@ -99,10 +99,11 @@ def test_model_default_cfgs(model_name, batch_size):
         assert outputs.shape[-1] == model.num_features
 
         # test model forward without pooling and classifier
-        if not isinstance(model, timm.models.MobileNetV3):
         model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
         outputs = model.forward(input_tensor)
         assert len(outputs.shape) == 4
+        if not isinstance(model, timm.models.MobileNetV3):
+            # FIXME mobilenetv3 forward_features vs removed pooling differ
             assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
 
     # check classifier and first convolution names match those in default_cfg

timm/models/mobilenetv3.py

@@ -101,7 +101,7 @@ class MobileNetV3(nn.Module):
         head_chs = builder.in_chs
 
         # Head + Pooling
-        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) if global_pool else nn.Identity()
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         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)
@@ -122,7 +122,7 @@ class MobileNetV3(nn.Module):
     def reset_classifier(self, num_classes, global_pool='avg'):
         self.num_classes = num_classes
         # cannot meaningfully change pooling of efficient head after creation
-        assert global_pool == self.global_pool.pool_type
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.classifier = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
@@ -136,7 +136,9 @@ class MobileNetV3(nn.Module):
         return x
 
     def forward(self, x):
-        x = self.forward_features(x).flatten(1)
+        x = self.forward_features(x)
+        if not self.global_pool.is_identity():
+            x = x.flatten(1)
         if self.drop_rate > 0.:
             x = F.dropout(x, p=self.drop_rate, training=self.training)
         return self.classifier(x)