diff --git a/mmseg/models/backbones/fast_scnn.py b/mmseg/models/backbones/fast_scnn.py
index fb9df6383..d69bc5bed 100644
--- a/mmseg/models/backbones/fast_scnn.py
+++ b/mmseg/models/backbones/fast_scnn.py
@@ -227,6 +227,13 @@ class FastSCNN(nn.Module):
         """
 
         super(FastSCNN, self).__init__()
+        if global_in_channels != higher_in_channels:
+            raise AssertionError('Global Input Channels must be the same with Higher Input Channels!')
+        elif global_out_channels != lower_in_channels:
+            raise AssertionError('Global Output Channels must be the same with Lower Input Channels!')
+        if scale_factor != 4:
+            raise AssertionError('Scale-factor must compensate the downsampling factor in the GFE module!')
+
         self.in_channels = in_channels
         self.downsample_dw_channels1 = downsample_dw_channels1
         self.downsample_dw_channels2 = downsample_dw_channels2
diff --git a/tests/test_models/test_backbone.py b/tests/test_models/test_backbone.py
index 00ae43d00..9ba33d283 100644
--- a/tests/test_models/test_backbone.py
+++ b/tests/test_models/test_backbone.py
@@ -4,7 +4,7 @@ from mmcv.ops import DeformConv2dPack
 from mmcv.utils.parrots_wrapper import _BatchNorm
 from torch.nn.modules import AvgPool2d, GroupNorm
 
-from mmseg.models.backbones import ResNet, ResNetV1d, ResNeXt
+from mmseg.models.backbones import ResNet, ResNetV1d, ResNeXt, FastSCNN
 from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
 from mmseg.models.backbones.resnext import Bottleneck as BottleneckX
 from mmseg.models.utils import ResLayer
@@ -664,3 +664,26 @@ def test_resnext_backbone():
     assert feat[1].shape == torch.Size([1, 512, 28, 28])
     assert feat[2].shape == torch.Size([1, 1024, 14, 14])
     assert feat[3].shape == torch.Size([1, 2048, 7, 7])
+
+
+def test_fastscnn_backbone():
+    with pytest.raises(AssertionError):
+        # Fast-SCNN channel constraints.
+        FastSCNN(3, 32, 48, 64, (64, 96, 128), 127, 64, 128)
+
+    # Test FastSCNN Standard Forward
+    model = FastSCNN()
+    model.init_weights()
+    model.train()
+    imgs = torch.randn(1, 3, 1024, 2048)
+    feat = model(imgs)
+
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size([1, 64, 128, 256])   # higher-res
+    assert feat[1].shape == torch.Size([1, 128, 32, 64])    # lower-res
+    assert feat[2].shape == torch.Size([1, 128, 128, 256])  # FFM output
+
+
+
+
+