check disconnected inputs in torch

model_compression_toolkit/core/common/graph/base_graph.py

@@ -449,7 +449,7 @@ def remove_node(self,
         if node_to_remove in self.get_inputs():  # If node is in the graph's inputs, the inputs should be updated
             if new_graph_inputs is None:
                 Logger.critical(
-                    f'{node_to_remove.name} s among the graph inputs; however, it cannot be removed without providing a new input.')  # pragma: no cover
+                    f'{node_to_remove.name} is among the graph inputs; however, it cannot be removed without providing a new input.')  # pragma: no cover
             self.set_inputs(new_graph_inputs)
 
         # Make sure there are no connected edges left to the node before removing it.

model_compression_toolkit/core/pytorch/reader/reader.py

@@ -152,5 +152,8 @@ def model_reader(model: torch.nn.Module,
     logging.info("Start Model Reading...")
     fx_model = fx_graph_module_generation(model, representative_data_gen, to_tensor)
     graph = build_graph(fx_model, to_numpy)
+    disconnected_inputs = [n.name for n in graph.get_inputs() if not graph.out_edges(n)]
+    if disconnected_inputs:
+        raise ValueError(f'The network contains disconnected input(s): {disconnected_inputs}.')
     graph = remove_broken_nodes_from_graph(graph)
     return graph

tests/pytorch_tests/graph_tests/test_fx_errors.py → tests/pytorch_tests/graph_tests/test_graph_reading.py

@@ -17,9 +17,10 @@
 import unittest
 import torch
 import numpy as np
+from model_compression_toolkit.core.pytorch import pytorch_implementation
 
 from model_compression_toolkit.core.pytorch.reader.reader import fx_graph_module_generation
-from model_compression_toolkit.core.pytorch.pytorch_implementation import to_torch_tensor
+from model_compression_toolkit.core.pytorch.pytorch_implementation import to_torch_tensor, PytorchImplementation
 
 
 class BadFxModel(torch.nn.Module):
@@ -37,16 +38,32 @@ def forward(self, inputs, flag=False):
         return x
 
 
-class TestGraphReading(unittest.TestCase):
+def data_gen():
+    yield [np.zeros((1, 3, 20, 20))]
+
 
-    def test_graph_reading(self):
+class TestGraphReading(unittest.TestCase):
+    def test_fx_tracer_error(self):
         model = BadFxModel()
-        try:
-            graph = fx_graph_module_generation(model,
-                                               lambda : np.zeros((1, 3, 20, 20)),
-                                               to_torch_tensor)
-        except Exception as e:
-            self.assertEqual(str(e).split('\n')[0], 'Error parsing model with torch.fx')
+
+        with self.assertRaises(Exception) as e:
+            fx_graph_module_generation(model,
+                                       data_gen,
+                                       to_torch_tensor)
+        self.assertEqual(str(e.exception).split('\n')[0], 'Error parsing model with torch.fx')
+
+    def test_disconnected_input(self):
+        class Model(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = torch.nn.Conv2d(3, 5, 3)
+
+            def forward(self, x, unused=None):
+                return self.conv(x)
+
+        with self.assertRaises(ValueError) as e:
+            PytorchImplementation().model_reader(Model(), data_gen)
+        self.assertEqual(str(e.exception), r"The network contains disconnected input(s): ['unused'].")
 
 
 if __name__ == '__main__':