Fix resnet fpn backbone for resnet18 and resnet34 (#1147)

* in_channels_stage2 from backbone.inplanes

* remove type for backward compatible

test/test_backbone_utils.py

+import unittest
+
+
+import torch
+from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
+
+
+class ResnetFPNBackboneTester(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.dtype = torch.float32
+
+    def test_resnet18_fpn_backbone(self):
+        device = torch.device('cpu')
+        x = torch.rand(1, 3, 300, 300, dtype=self.dtype, device=device)
+        resnet18_fpn = resnet_fpn_backbone(backbone_name='resnet18', pretrained=False)
+        y = resnet18_fpn(x)
+        assert list(y.keys()) == [0, 1, 2, 3, 'pool']
+
+    def test_resnet50_fpn_backbone(self):
+        device = torch.device('cpu')
+        x = torch.rand(1, 3, 300, 300, dtype=self.dtype, device=device)
+        resnet50_fpn = resnet_fpn_backbone(backbone_name='resnet50', pretrained=False)
+        y = resnet50_fpn(x)
+        assert list(y.keys()) == [0, 1, 2, 3, 'pool']

torchvision/models/detection/backbone_utils.py

@@ -51,7 +51,7 @@ def resnet_fpn_backbone(backbone_name, pretrained):
 
     return_layers = {'layer1': 0, 'layer2': 1, 'layer3': 2, 'layer4': 3}
 
-    in_channels_stage2 = 256
+    in_channels_stage2 = backbone.inplanes // 8
     in_channels_list = [
         in_channels_stage2,
         in_channels_stage2 * 2,