Make test precision stricter for Classification (#6380)

* Make test precision stricter for Classification

* Update classification threshold.

* Update quantized classification threshold.

test/test_models.py

@@ -614,7 +614,7 @@ def test_classification_model(model_fn, dev):
     # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
     x = torch.rand(input_shape).to(device=dev)
     out = model(x)
-    _assert_expected(out.cpu(), model_name, prec=0.1)
+    _assert_expected(out.cpu(), model_name, prec=1e-3)
     assert out.shape[-1] == num_classes
     _check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
     _check_fx_compatible(model, x, eager_out=out)
@@ -841,7 +841,7 @@ def test_video_model(model_fn, dev):
     # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
     x = torch.rand(input_shape).to(device=dev)
     out = model(x)
-    _assert_expected(out.cpu(), model_name, prec=0.1)
+    _assert_expected(out.cpu(), model_name, prec=1e-5)
     assert out.shape[-1] == num_classes
     _check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
     _check_fx_compatible(model, x, eager_out=out)
@@ -884,7 +884,7 @@ def test_quantized_classification_model(model_fn):
     out = model(x)
 
     if model_name not in quantized_flaky_models:
-        _assert_expected(out, model_name + "_quantized", prec=0.1)
+        _assert_expected(out, model_name + "_quantized", prec=2e-2)
         assert out.shape[-1] == 5
         _check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
         _check_fx_compatible(model, x, eager_out=out)