Skip big model in test to reduce memory usage in CI (#5903)

* Skip big model in test

* Let get_models_from_module func to read skipped_models from global directly instead of function param to reduce changes

* Also skip regnet_y_128gf

* Only skip test for test_classification_model and create toggle using env var

* Remove unnecessary comment

* Fix comparison of device to use str

* Add logic to test_classification_model directly

* Add kprcnn in autocast flaky list

Co-authored-by: Vasilis Vryniotis <[email protected]>

Author: YosuaMichael

test/test_models.py

@@ -18,6 +18,7 @@
 from torchvision import models
 
 ACCEPT = os.getenv("EXPECTTEST_ACCEPT", "0") == "1"
+SKIP_BIG_MODEL = os.getenv("SKIP_BIG_MODEL", "1") == "1"
 
 
 def get_models_from_module(module):
@@ -231,6 +232,7 @@ def _check_input_backprop(model, inputs):
     "lraspp_mobilenet_v3_large",
     "maskrcnn_resnet50_fpn",
     "maskrcnn_resnet50_fpn_v2",
+    "keypointrcnn_resnet50_fpn",
 )
 
 # The tests for the following quantized models are flaky possibly due to inconsistent
@@ -329,6 +331,12 @@ def _check_input_backprop(model, inputs):
     _model_params[m] = {"input_shape": (1, 3, 64, 64)}
 
 
+# skip big models to reduce memory usage on CI test
+skipped_big_models = {
+    "vit_h_14",
+    "regnet_y_128gf",
+}
+
 # The following contains configuration and expected values to be used tests that are model specific
 _model_tests_values = {
     "retinanet_resnet50_fpn": {
@@ -592,6 +600,8 @@ def test_classification_model(model_fn, dev):
         "input_shape": (1, 3, 224, 224),
     }
     model_name = model_fn.__name__
+    if dev == "cuda" and SKIP_BIG_MODEL and model_name in skipped_big_models:
+        pytest.skip("Skipped to reduce memory usage. Set env var SKIP_BIG_MODEL=0 to enable test for this model")
     kwargs = {**defaults, **_model_params.get(model_name, {})}
     num_classes = kwargs.get("num_classes")
     input_shape = kwargs.pop("input_shape")
@@ -606,7 +616,7 @@ def test_classification_model(model_fn, dev):
     _check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
     _check_fx_compatible(model, x, eager_out=out)
 
-    if dev == torch.device("cuda"):
+    if dev == "cuda":
         with torch.cuda.amp.autocast():
             out = model(x)
             # See autocast_flaky_numerics comment at top of file.
@@ -659,7 +669,7 @@ def check_out(out):
     _check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
     _check_fx_compatible(model, x, eager_out=out)
 
-    if dev == torch.device("cuda"):
+    if dev == "cuda":
         with torch.cuda.amp.autocast():
             out = model(x)
             # See autocast_flaky_numerics comment at top of file.
@@ -757,7 +767,7 @@ def compute_mean_std(tensor):
     full_validation = check_out(out)
     _check_jit_scriptable(model, ([x],), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
 
-    if dev == torch.device("cuda"):
+    if dev == "cuda":
         with torch.cuda.amp.autocast():
             out = model(model_input)
             # See autocast_flaky_numerics comment at top of file.
@@ -823,7 +833,7 @@ def test_video_model(model_fn, dev):
     _check_fx_compatible(model, x, eager_out=out)
     assert out.shape[-1] == 50
 
-    if dev == torch.device("cuda"):
+    if dev == "cuda":
         with torch.cuda.amp.autocast():
             out = model(x)
             assert out.shape[-1] == 50