Fix incorrectly frozen BN on ResNet FPN backbone (#3396)

* Avoid freezing bn1 if all layers are trainable.

* Remove misleading comments.

torchvision/models/detection/backbone_utils.py

@@ -96,7 +96,8 @@ def resnet_fpn_backbone(
     # select layers that wont be frozen
     assert 0 <= trainable_layers <= 5
     layers_to_train = ['layer4', 'layer3', 'layer2', 'layer1', 'conv1'][:trainable_layers]
-    # freeze layers only if pretrained backbone is used
+    if trainable_layers == 5:
+        layers_to_train.append('bn1')
     for name, parameter in backbone.named_parameters():
         if all([not name.startswith(layer) for layer in layers_to_train]):
             parameter.requires_grad_(False)
@@ -152,7 +153,6 @@ def mobilenet_backbone(
     assert 0 <= trainable_layers <= num_stages
     freeze_before = num_stages if trainable_layers == 0 else stage_indices[num_stages - trainable_layers]
 
-    # freeze layers only if pretrained backbone is used
     for b in backbone[:freeze_before]:
         for parameter in b.parameters():
             parameter.requires_grad_(False)