only copare top 100 classes in image classification

tests/onnxruntime/test_modeling.py

@@ -2867,9 +2867,6 @@ class ORTModelForImageClassificationIntegrationTest(ORTModelTestMixin):
     ORTMODEL_CLASS = ORTModelForImageClassification
     TASK = "image-classification"
 
-    ATOL = 2e-3  # 0.02 difference in logits
-    RTOL = 1e-2  # 1% difference in logits
-
     def _get_model_ids(self, model_arch):
         model_ids = MODEL_NAMES[model_arch]
         if isinstance(model_ids, dict):
@@ -3040,8 +3037,16 @@ def test_compare_to_io_binding(self, model_arch):
         onnx_outputs = onnx_model(**inputs)
         io_outputs = io_model(**inputs)
 
+        print("shape of logits", io_outputs.logits.shape)
+
         self.assertTrue("logits" in io_outputs)
         self.assertIsInstance(io_outputs.logits, torch.Tensor)
+        self.assertEqual(io_outputs.logits.shape, onnx_outputs.logits.shape)
+
+        if io_outputs.logits.shape[1] > 100:
+            # we compare only the top 100 classes (biggest 100 values in order)
+            io_outputs.logits = torch.topk(io_outputs.logits, 100, dim=1).values
+            onnx_outputs.logits = torch.topk(onnx_outputs.logits, 100, dim=1).values
 
         # compare tensor outputs
         torch.testing.assert_close(onnx_outputs.logits, io_outputs.logits, atol=self.ATOL, rtol=self.RTOL)