make auxiliary heads in pretrained models optional (#828)

* add aux_logits support to inception

it is related to pytorch/pytorch#18668

* instantiate InceptionAux only when requested

it is related to pytorch/pytorch#18668

* revert googlenet

* support and aux_logits in pretrained models

* return namedtuple when aux_logit is True

torchvision/models/googlenet.py

+import warnings
+from collections import namedtuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -10,6 +12,8 @@ model_urls = {
     'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
 }
 
+_GoogLeNetOuputs = namedtuple('GoogLeNetOuputs', ['logits', 'aux_logits2', 'aux_logits1'])
+
 
 def googlenet(pretrained=False, **kwargs):
     r"""GoogLeNet (Inception v1) model architecture from
@@ -18,7 +22,7 @@ def googlenet(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         aux_logits (bool): If True, adds two auxiliary branches that can improve training.
-            Automatically set to False if 'pretrained' is True. Default: *True*
+            Default: *False* when pretrained is True otherwise *True*
         transform_input (bool): If True, preprocesses the input according to the method with which it
             was trained on ImageNet. Default: *False*
     """
@@ -27,9 +31,16 @@ def googlenet(pretrained=False, **kwargs):
             kwargs['transform_input'] = True
         if 'aux_logits' not in kwargs:
             kwargs['aux_logits'] = False
+        if kwargs['aux_logits']:
+            warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them')
+        original_aux_logits = kwargs['aux_logits']
+        kwargs['aux_logits'] = True
         kwargs['init_weights'] = False
         model = GoogLeNet(**kwargs)
         model.load_state_dict(model_zoo.load_url(model_urls['googlenet']))
+        if not original_aux_logits:
+            model.aux_logits = False
+            del model.aux1, model.aux2
         return model
 
     return GoogLeNet(**kwargs)
@@ -62,6 +73,7 @@ class GoogLeNet(nn.Module):
         self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
         self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
 
+        if aux_logits:
             self.aux1 = InceptionAux(512, num_classes)
             self.aux2 = InceptionAux(528, num_classes)
 
@@ -141,7 +153,7 @@ class GoogLeNet(nn.Module):
         x = self.fc(x)
         # N x 1000 (num_classes)
         if self.training and self.aux_logits:
-            return aux1, aux2, x
+            return _GoogLeNetOuputs(x, aux2, aux1)
         return x
 
 

torchvision/models/inception.py

+from collections import namedtuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -12,6 +13,8 @@ model_urls = {
     'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
 }
 
+_InceptionOuputs = namedtuple('InceptionOuputs', ['logits', 'aux_logits'])
+
 
 def inception_v3(pretrained=False, **kwargs):
     r"""Inception v3 model architecture from
@@ -23,14 +26,24 @@ def inception_v3(pretrained=False, **kwargs):
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
+        aux_logits (bool): If True, add an auxiliary branch that can improve training.
+            Default: *True*
         transform_input (bool): If True, preprocesses the input according to the method with which it
             was trained on ImageNet. Default: *False*
     """
     if pretrained:
         if 'transform_input' not in kwargs:
             kwargs['transform_input'] = True
+        if 'aux_logits' in kwargs:
+            original_aux_logits = kwargs['aux_logits']
+            kwargs['aux_logits'] = True
+        else:
+            original_aux_logits = True
         model = Inception3(**kwargs)
         model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google']))
+        if not original_aux_logits:
+            model.aux_logits = False
+            del model.AuxLogits
         return model
 
     return Inception3(**kwargs)
@@ -131,7 +144,7 @@ class Inception3(nn.Module):
         x = self.fc(x)
         # N x 1000 (num_classes)
         if self.training and self.aux_logits:
-            return x, aux
+            return _InceptionOuputs(x, aux)
         return x