[PT FE]: support aten::instance_norm (#15213)

tests/layer_tests/pytorch_tests/test_instance_norm.py

@@ -0,0 +1,64 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestInstanceNorm(PytorchLayerTest):
+    def _prepare_input(self, ndim=4):
+        import numpy as np
+        shape5d = [3, 6, 10, 5, 2]
+        shape = shape5d[:ndim]
+        return (np.random.randn(*shape).astype(np.float32),)
+
+    def create_model(self, weights=False, bias=False, mean_var=False, eps=1e-05):
+        import torch
+
+        class aten_instance_norm(torch.nn.Module):
+            def __init__(self, weights=False, bias=False, mean_var=False, eps=1e-05):
+                super(aten_instance_norm, self).__init__()
+                weights_shape = (6, )
+                self.weight = torch.randn(weights_shape) if weights else None
+                self.bias = None
+                self.use_input_stats = not mean_var
+                if bias:
+                    self.bias = torch.randn(weights_shape)
+                self.mean = None
+                self.var = None
+                if mean_var:
+                    self.mean = torch.randn(weights_shape)
+                    self.var = torch.randn(weights_shape)
+                
+                self.eps = eps
+
+            def forward(self, x):
+                return torch.instance_norm(x, self.weight, self.bias, self.mean, self.var,  self.use_input_stats, 0.1, self.eps, False)
+
+        ref_net = None
+
+        return aten_instance_norm(weights, bias, mean_var, eps), ref_net, "aten::instance_norm"
+
+    @pytest.mark.parametrize("params",
+                             [
+                              {"eps": 0.0001},
+                              {'weights': True, 'eps': -0.05},
+                              {'weights': True},
+                              {'weights': True, 'bias': True},
+                              {"weights": True, 'bias': False, "mean_var": True},
+                              {"weights": True, 'bias': True, "mean_var": True},
+                              {"weights": False, 'bias': True, "mean_var": True},
+                              {"weights": False, 'bias': False, "mean_var": True},
+                              {"weights": False, 'bias': False, "mean_var": True, "eps": 1.5}
+                             ])
+    @pytest.mark.parametrize("kwargs_to_prepare_input", [
+        {"ndim": 3},
+        {'ndim': 4},
+        {"ndim": 5}
+    ])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_group_norm(self, params, ie_device, precision, ir_version, kwargs_to_prepare_input):
+        self._test(*self.create_model(**params),
+                   ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input, dynamic_shapes=not params.get("mean_var", False))