[PT FE]: handle prim::ListConstruct + aten::pad case (#15288)

tests/layer_tests/pytorch_tests/test_pad.py

@@ -108,3 +108,95 @@ class TestPad(PytorchLayerTest):
     def test_pad2d(self, pads, mode, value, ie_device, precision, ir_version):
         self._test(*self.create_model(pads, mode, value), ie_device, precision, ir_version,
                    kwargs_to_prepare_input={'ndim': 2}, trace_model=True)
+
+
+class TestPadListPaddingings(PytorchLayerTest):
+    def _prepare_input(self, ndim=4, pad_w=0, pad_h=0):
+        import numpy as np
+        input_5d_shape = [1, 3, 14, 14, 18]
+        return (np.random.randn(*input_5d_shape[:ndim]).astype(np.float32), np.array(pad_w, dtype=np.int32), np.array(pad_h, dtype=np.int32))
+
+    def create_model(self, mode, value=None):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_pad(torch.nn.Module):
+            def __init__(self, mode, value=None):
+                super().__init__()
+                self.mode = mode
+                self.value = value
+
+            def forward(self, x, pad_w:int, pad_h:int):
+                return F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2], value=self.value)
+
+        ref_net = None
+
+        return aten_pad(mode, value), ref_net, "aten::pad"
+
+    @pytest.mark.parametrize("pad_w,pad_h,mode,value", [
+        (2, 0, "reflect", None),
+        (0, 2, "reflect", None),
+        (10, 10, "reflect", None),
+        (0, 0, "reflect", None),
+        (5, 3, "reflect", None),
+        (2, 0, "replicate", None),
+        (0, 2, "replicate", None),
+        (10, 10, "replicate", None),
+        (5, 3, "replicate", None),
+        (0, 0, "replicate", None),
+        (2, 0, "constant", None),
+        (0, 3, "constant", 42.),
+        (4, 4, "constant", -0.57),
+        (1, 2, "constant", None),
+        (0, 0, "constant", -0.57),
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_pad4d(self, pad_w, pad_h, mode, value, ie_device, precision, ir_version):
+        self._test(*self.create_model(mode, value), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={'ndim': 4, "pad_w": pad_w, "pad_h": pad_h})
+
+    @pytest.mark.parametrize("pad_w,pad_h,mode,value", [
+        (2, 0, "reflect", None),
+        (0, 2, "reflect", None),
+        (10, 10, "reflect", None),
+        (0, 0, "reflect", None),
+        (5, 3, "reflect", None),
+        (2, 0, "replicate", None),
+        (0, 2, "replicate", None),
+        (10, 10, "replicate", None),
+        (5, 3, "replicate", None),
+        (0, 0, "replicate", None),
+        (2, 0, "constant", None),
+        (0, 3, "constant", 42.),
+        (4, 4, "constant", -0.57),
+        (1, 2, "constant", None),
+        (0, 0, "constant", -0.57)
+    ])
+    @pytest.mark.nightly
+    def test_pad5d(self, pad_w, pad_h, mode, value, ie_device, precision, ir_version):
+        self._test(*self.create_model(mode, value), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={'ndim': 5, "pad_w": pad_w, "pad_h": pad_h})
+
+    @pytest.mark.parametrize("pad_w,pad_h,mode,value", [
+        (2, 0, "reflect", None),
+        (0, 2, "reflect", None),
+        (10, 10, "reflect", None),
+        (0, 0, "reflect", None),
+        (5, 3, "reflect", None),
+        (2, 0, "replicate", None),
+        (0, 2, "replicate", None),
+        (10, 10, "replicate", None),
+        (5, 3, "replicate", None),
+        (0, 0, "replicate", None),
+        (2, 0, "constant", None),
+        (0, 3, "constant", 42.),
+        (4, 4, "constant", -0.57),
+        (1, 2, "constant", None),
+        (0, 0, "constant", -0.57)
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_pad2d(self, pad_w, pad_h, mode, value, ie_device, precision, ir_version):
+        self._test(*self.create_model(mode, value), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={'ndim': 2, "pad_w": pad_w, "pad_h": pad_h})