Bug fix in the MLP layer: torch.squeeze was removing the batch size w…

…hen equal to 1 (facebookresearch#179)

Summary:
The fix comes with unit tests so that error will be caught up if it comes back.

Pull Request resolved: facebookresearch#179

Reviewed By: prigoyal

Differential Revision: D26338689

Pulled By: QuentinDuval

fbshipit-source-id: be9aaf668faf046060df321b62a462d79c7b0d8b

tests/test_mlp.py

@@ -0,0 +1,61 @@
+import unittest
+
+import torch
+from vissl.models.heads import LinearEvalMLP, MLP
+from vissl.utils.hydra_config import AttrDict
+
+
+class TestMLP(unittest.TestCase):
+    """
+    Unit test to verify that correct construction of MLP layers
+    and linear evaluation MLP layers
+    """
+
+    MODEL_CONFIG = AttrDict({
+        "HEAD": {
+            "BATCHNORM_EPS": 1e-6,
+            "BATCHNORM_MOMENTUM": 0.99,
+            "PARAMS_MULTIPLIER": 1.0,
+        }
+    })
+
+    def test_mlp(self):
+        mlp = MLP(self.MODEL_CONFIG, dims=[2048, 100])
+
+        x = torch.randn(size=(4, 2048))
+        out = mlp(x)
+        assert out.shape == torch.Size([4, 100])
+
+        x = torch.randn(size=(1, 2048))
+        out = mlp(x)
+        assert out.shape == torch.Size([1, 100])
+
+    def test_mlp_reshaping(self):
+        mlp = MLP(self.MODEL_CONFIG, dims=[2048, 100])
+
+        x = torch.randn(size=(1, 2048, 1, 1))
+        out = mlp(x)
+        assert out.shape == torch.Size([1, 100])
+
+    def test_mlp_catch_bad_shapes(self):
+        mlp = MLP(self.MODEL_CONFIG, dims=[2048, 100])
+
+        x = torch.randn(size=(1, 2048, 2, 1))
+        with self.assertRaises(AssertionError) as context:
+            mlp(x)
+        assert context.exception is not None
+
+    def test_eval_mlp_shape(self):
+        eval_mlp = LinearEvalMLP(
+            self.MODEL_CONFIG,
+            in_channels=2048,
+            dims=[2048 * 2 * 2, 1000],
+        )
+
+        resnet_feature_map = torch.randn(size=(4, 2048, 2, 2))
+        out = eval_mlp(resnet_feature_map)
+        assert out.shape == torch.Size([4, 1000])
+
+        resnet_feature_map = torch.randn(size=(1, 2048, 2, 2))
+        out = eval_mlp(resnet_feature_map)
+        assert out.shape == torch.Size([1, 1000])

vissl/models/heads/mlp.py

@@ -91,9 +91,10 @@ def forward(self, batch: torch.Tensor):
                 len(batch) == 1
             ), "MLP input should be either a tensor (2D, 4D) or list containing 1 tensor."
             batch = batch[0]
-        batch = torch.squeeze(batch)
-        assert (
-            len(batch.shape) <= 2
-        ), f"MLP expected 2D input tensor or 4D tensor of shape NxCx1x1. got: {batch.shape}"
+        if batch.ndim > 2:
+            assert (
+                    all(d == 1 for d in batch.shape[2:])
+            ), f"MLP expected 2D input tensor or 4D tensor of shape NxCx1x1. got: {batch.shape}"
+            batch = batch.reshape((batch.size(0), batch.size(1)))
         out = self.clf(batch)
         return out

vissl/models/model_helpers.py

@@ -46,7 +46,7 @@ def is_feature_extractor_model(model_config):
     If the model is a feature extractor model:
         - evaluation model is on
         - trunk is frozen
-        - number of features specified for features extratction > 0
+        - number of features specified for features extraction > 0
     """
     if (
         model_config.FEATURE_EVAL_SETTINGS.EVAL_MODE_ON