[Py OV] Fix issue with passing (h, w) sequence to PreprocessConverter…

….Resize (#26687)

### Details:
- The output image size after resize should match the dimensions
specified in the sequence (h, w) passed as `size` argument. Adjustments
should be done if `size` is an int.
[docs](https://pytorch.org/vision/stable/generated/torchvision.transforms.Resize.html)
- Add more combinations of parameters to
`test_torchvision_preprocessor/test_resize`

### Tickets:
 - [CVS-149919](https://jira.devtools.intel.com/browse/CVS-149919)

---------

Co-authored-by: Anastasia Kuporosova <[email protected]>

src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py

@@ -294,12 +294,13 @@ def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, me
 
         target_h, target_w = _setup_size(transform.size, "Incorrect size type for Resize operation")
 
-        # rescale the smaller image edge
-        current_h, current_w = meta["image_dimensions"]
-        if current_h > current_w:
-            target_h = int(transform.size * (current_h / current_w))
-        elif current_w > current_h:
-            target_w = int(transform.size * (current_w / current_h))
+        if isinstance(transform.size, int):
+            # rescale the smaller image edge
+            current_h, current_w = meta["image_dimensions"]
+            if current_h > current_w:
+                target_h = int(transform.size * (current_h / current_w))
+            elif current_w > current_h:
+                target_w = int(transform.size * (current_w / current_h))
 
         ppp.input(input_idx).tensor().set_layout(Layout("NCHW"))
 

tests/layer_tests/py_frontend_tests/test_torchvision_preprocessor.py

@@ -87,21 +87,27 @@ def test_normalize():
     reason="Ticket: 114816",
 )
 @pytest.mark.parametrize(
-    ("target_size", "interpolation", "tolerance"),
+    ("target_size", "current_size", "interpolation", "tolerance"),
     [
-        ((220, 220, 3), transforms.InterpolationMode.NEAREST, 4e-05),
-        ((200, 240, 3), transforms.InterpolationMode.NEAREST, 0.3),  # Ticket 127670
-        ((220, 220, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
-        ((200, 240, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
-        ((220, 220, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
-        ((200, 240, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        (224, (220, 220, 3), transforms.InterpolationMode.NEAREST, 4e-05),
+        (224, (200, 240, 3), transforms.InterpolationMode.NEAREST, 0.3),  # Ticket 127670
+        (224, (220, 220, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        (224, (200, 240, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        (224, (220, 220, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        (224, (200, 240, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        ((224, 224), (220, 220, 3), transforms.InterpolationMode.NEAREST, 4e-05),
+        ((224, 224), (200, 240, 3), transforms.InterpolationMode.NEAREST, 4e-05),
+        ((224, 224), (220, 220, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        ((224, 224), (200, 240, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        ((224, 224), (220, 220, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        ((224, 224), (200, 240, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
     ],
 )
-def test_resize(target_size, interpolation, tolerance):
-    test_input = np.random.randint(255, size=target_size, dtype=np.uint8)
+def test_resize(target_size, current_size, interpolation, tolerance):
+    test_input = np.random.randint(255, size=current_size, dtype=np.uint8)
     preprocess_pipeline = transforms.Compose(
         [
-            transforms.Resize(224, interpolation=interpolation),
+            transforms.Resize(target_size, interpolation=interpolation),
             transforms.ToTensor(),
         ],
     )