Refactor peft test (#810)

Refactor PEFT test

Author: constantinpape

test/test_models/test_peft_sam.py

@@ -8,12 +8,7 @@
 class TestPEFTSam(unittest.TestCase):
     model_type = "vit_b"
 
-    def test_lora_sam(self):
-        from micro_sam.models.peft_sam import PEFT_Sam, LoRASurgery
-
-        _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
-        peft_sam = PEFT_Sam(sam, rank=2, peft_module=LoRASurgery)
-
+    def _check_output(self, peft_sam):
         shape = (3, 1024, 1024)
         expected_shape = (1, 3, 1024, 1024)
         with torch.no_grad():
@@ -22,90 +17,54 @@ def test_lora_sam(self):
             masks = output[0]["masks"]
             self.assertEqual(masks.shape, expected_shape)
 
+    def test_lora_sam(self):
+        from micro_sam.models.peft_sam import PEFT_Sam, LoRASurgery
+
+        _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
+        peft_sam = PEFT_Sam(sam, rank=2, peft_module=LoRASurgery)
+        self._check_output(peft_sam)
+
     def test_fact_sam(self):
         from micro_sam.models.peft_sam import PEFT_Sam, FacTSurgery
 
         _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
         peft_sam = PEFT_Sam(sam, rank=2, peft_module=FacTSurgery)
-
-        shape = (3, 1024, 1024)
-        expected_shape = (1, 3, 1024, 1024)
-        with torch.no_grad():
-            batched_input = [{"image": torch.rand(*shape), "original_size": shape[1:]}]
-            output = peft_sam(batched_input, multimask_output=True)
-            masks = output[0]["masks"]
-            self.assertEqual(masks.shape, expected_shape)
+        self._check_output(peft_sam)
 
     def test_attention_layer_peft_sam(self):
         from micro_sam.models.peft_sam import PEFT_Sam, AttentionSurgery
 
         _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
         peft_sam = PEFT_Sam(sam, rank=2, peft_module=AttentionSurgery)
-
-        shape = (3, 1024, 1024)
-        expected_shape = (1, 3, 1024, 1024)
-        with torch.no_grad():
-            batched_input = [{"image": torch.rand(*shape), "original_size": shape[1:]}]
-            output = peft_sam(batched_input, multimask_output=True)
-            masks = output[0]["masks"]
-            self.assertEqual(masks.shape, expected_shape)
+        self._check_output(peft_sam)
 
     def test_norm_layer_peft_sam(self):
         from micro_sam.models.peft_sam import PEFT_Sam, LayerNormSurgery
 
         _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
         peft_sam = PEFT_Sam(sam, rank=2, peft_module=LayerNormSurgery)
-
-        shape = (3, 1024, 1024)
-        expected_shape = (1, 3, 1024, 1024)
-        with torch.no_grad():
-            batched_input = [{"image": torch.rand(*shape), "original_size": shape[1:]}]
-            output = peft_sam(batched_input, multimask_output=True)
-            masks = output[0]["masks"]
-            self.assertEqual(masks.shape, expected_shape)
+        self._check_output(peft_sam)
 
     def test_bias_layer_peft_sam(self):
         from micro_sam.models.peft_sam import PEFT_Sam, BiasSurgery
 
         _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
         peft_sam = PEFT_Sam(sam, rank=2, peft_module=BiasSurgery)
-
-        shape = (3, 1024, 1024)
-        expected_shape = (1, 3, 1024, 1024)
-        with torch.no_grad():
-            batched_input = [{"image": torch.rand(*shape), "original_size": shape[1:]}]
-            output = peft_sam(batched_input, multimask_output=True)
-            masks = output[0]["masks"]
-            self.assertEqual(masks.shape, expected_shape)
+        self._check_output(peft_sam)
 
     def test_ssf_peft_sam(self):
         from micro_sam.models.peft_sam import PEFT_Sam, SSFSurgery
 
         _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
         peft_sam = PEFT_Sam(sam, rank=2, peft_module=SSFSurgery)
-        
-        shape = (3, 1024, 1024)
-        expected_shape = (1, 3, 1024, 1024)
-        with torch.no_grad():
-            batched_input = [{"image": torch.rand(*shape), "original_size": shape[1:]}]
-            output = peft_sam(batched_input, multimask_output=True)
-            masks = output[0]["masks"]
-            self.assertEqual(masks.shape, expected_shape)
+        self._check_output(peft_sam)
 
     def test_adaptformer_peft_sam(self):
         from micro_sam.models.peft_sam import PEFT_Sam, AdaptFormer
 
         _, sam = util.get_sam_model(model_type=self.model_type, return_sam=True, device="cpu")
         peft_sam = PEFT_Sam(sam, rank=2, peft_module=AdaptFormer, projection_size=64, alpha=2.0, dropout=0.5)
-
-
-        shape = (3, 1024, 1024)
-        expected_shape = (1, 3, 1024, 1024)
-        with torch.no_grad():
-            batched_input = [{"image": torch.rand(*shape), "original_size": shape[1:]}]
-            output = peft_sam(batched_input, multimask_output=True)
-            masks = output[0]["masks"]
-            self.assertEqual(masks.shape, expected_shape)
+        self._check_output(peft_sam)
 
 
 if __name__ == "__main__":