[PoC] refactor transforms v2 tests (#7562)

Co-authored-by: Nicolas Hug <[email protected]>
Co-authored-by: Nicolas Hug <[email protected]>

Author: pmeier

test/common_utils.py

@@ -7,9 +7,11 @@
 import os
 import pathlib
 import random
+import re
 import shutil
 import sys
 import tempfile
+import warnings
 from collections import defaultdict
 from subprocess import CalledProcessError, check_output, STDOUT
 from typing import Callable, Sequence, Tuple, Union
@@ -880,3 +882,23 @@ def assert_run_python_script(source_code):
             raise RuntimeError(f"script errored with output:\n{e.output.decode()}")
         if out != b"":
             raise AssertionError(out.decode())
+
+
+@contextlib.contextmanager
+def assert_no_warnings():
+    # The name `catch_warnings` is a misnomer as the context manager does **not** catch any warnings, but rather scopes
+    # the warning filters. All changes that are made to the filters while in this context, will be reset upon exit.
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        yield
+
+
+@contextlib.contextmanager
+def ignore_jit_no_profile_information_warning():
+    # Calling a scripted object often triggers a warning like
+    # `UserWarning: operator() profile_node %$INT1 : int[] = prim::profile_ivalue($INT2) does not have profile information`
+    # with varying `INT1` and `INT2`. Since these are uninteresting for us and only clutter the test summary, we ignore
+    # them.
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message=re.escape("operator() profile_node %"), category=UserWarning)
+        yield

test/test_transforms_v2.py

@@ -1711,8 +1711,6 @@ def test_antialias_warning():
     tensor_video = torch.randint(0, 256, size=(2, 3, 10, 10), dtype=torch.uint8)
 
     match = "The default value of the antialias parameter"
-    with pytest.warns(UserWarning, match=match):
-        transforms.Resize((20, 20))(tensor_img)
     with pytest.warns(UserWarning, match=match):
         transforms.RandomResizedCrop((20, 20))(tensor_img)
     with pytest.warns(UserWarning, match=match):
@@ -1722,18 +1720,6 @@ def test_antialias_warning():
     with pytest.warns(UserWarning, match=match):
         transforms.RandomResize(10, 20)(tensor_img)
 
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize(tensor_img, (20, 20))
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize_image_tensor(tensor_img, (20, 20))
-
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize(tensor_video, (20, 20))
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize_video(tensor_video, (20, 20))
-
-    with pytest.warns(UserWarning, match=match):
-        datapoints.Image(tensor_img).resize((20, 20))
     with pytest.warns(UserWarning, match=match):
         datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20))
 
@@ -1744,27 +1730,17 @@ def test_antialias_warning():
 
     with warnings.catch_warnings():
         warnings.simplefilter("error")
-        transforms.Resize((20, 20))(pil_img)
         transforms.RandomResizedCrop((20, 20))(pil_img)
         transforms.ScaleJitter((20, 20))(pil_img)
         transforms.RandomShortestSize((20, 20))(pil_img)
         transforms.RandomResize(10, 20)(pil_img)
-        transforms.functional.resize(pil_img, (20, 20))
 
-        transforms.Resize((20, 20), antialias=True)(tensor_img)
         transforms.RandomResizedCrop((20, 20), antialias=True)(tensor_img)
         transforms.ScaleJitter((20, 20), antialias=True)(tensor_img)
         transforms.RandomShortestSize((20, 20), antialias=True)(tensor_img)
         transforms.RandomResize(10, 20, antialias=True)(tensor_img)
 
-        transforms.functional.resize(tensor_img, (20, 20), antialias=True)
-        transforms.functional.resize_image_tensor(tensor_img, (20, 20), antialias=True)
-        transforms.functional.resize(tensor_video, (20, 20), antialias=True)
-        transforms.functional.resize_video(tensor_video, (20, 20), antialias=True)
-
-        datapoints.Image(tensor_img).resize((20, 20), antialias=True)
         datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
-        datapoints.Video(tensor_video).resize((20, 20), antialias=True)
         datapoints.Video(tensor_video).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)