update test

tests/test_onnx_nets.py

@@ -82,4 +82,4 @@ def test_timm(self, request, model_name):
 
 if __name__ == "__main__":
     warnings.filterwarnings("ignore")
-    pytest.main(["-p", "no:warnings", "-n", "10", "-v", "tests/test_onnx_nets.py"])
+    pytest.main(["-p", "no:warnings", "-v", "tests/test_onnx_nets.py"])

tests/test_pattern_generator.py

@@ -72,7 +72,10 @@ def rewrite(self):
                 raise Exception("Pattern Matched")
 
         register_fusion_pattern(GeluMatcher(pattern, 1))
-        slim(model_filename, f"{directory}/{request.node.name}_slim.onnx")
+        with pytest.raises(Exception) as excinfo:
+            slim(model_filename, f"{directory}/{request.node.name}_slim.onnx")
+
+        assert str(excinfo.value) == "Pattern Matched"
 
 
 if __name__ == "__main__":

tests/test_pattern_matcher.py

@@ -5,6 +5,7 @@
 import torch.nn as nn
 
 from onnxslim import slim
+from onnxslim.utils import summarize_model, print_model_info_as_table
 
 
 class TestPatternMatcher:
@@ -35,7 +36,9 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+
 
     def test_pad_conv(self, request):
         """Test padding followed by 2D convolution within a neural network module."""
@@ -67,7 +70,12 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+
+        assert summary["op_type_counts"]['Conv'] == 2
+        assert summary["op_type_counts"]['Add'] == 1
+
 
     def test_conv_bn(self, request):
         """Test the convolutional layer followed by batch normalization export and re-import via ONNX."""
@@ -92,7 +100,10 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename, do_constant_folding=False)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+        assert summary["op_type_counts"]['Conv'] == 1
+
 
     def test_consecutive_slice(self, request):
         """Tests consecutive slicing operations on a model by exporting it to ONNX format and then slimming the ONNX
@@ -117,7 +128,10 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+        assert summary["op_type_counts"]['Slice'] == 1
+
 
     def test_consecutive_reshape(self, request):
         """Test the functionality of consecutive reshape operations in a model and export it to ONNX format."""
@@ -138,7 +152,10 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+        assert summary["op_type_counts"]['Reshape'] == 1
+
 
     def test_matmul_add(self, request):
         """Tests matrix multiplication followed by an addition operation within a neural network model."""
@@ -162,7 +179,10 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+        assert summary["op_type_counts"]['Gemm'] == 1
+
 
     def test_reduce(self, request):
         """Tests model reduction by exporting a PyTorch model to ONNX format, slimming it, and saving to a specified
@@ -189,7 +209,9 @@ def forward(self, x):
         filename = f"{directory}/{request.node.name}.onnx"
         torch.onnx.export(m, input, filename, opset_version=11)
 
-        slim(filename, filename)
+        summary = summarize_model(slim(filename))
+        print_model_info_as_table(request.node.name, [summary])
+        assert summary["op_type_counts"]['ReduceSum'] == 1
 
 
 if __name__ == "__main__":