[Eye-9] Extend MO with Eye-9 op (#11555)

tests/layer_tests/tensorflow_tests/test_tf_Eye.py

@@ -0,0 +1,64 @@
+# Copyright (C) 2018-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
+
+
+class TestTFEye(CommonTFLayerTest):
+    eye_output_type_param = np.float32
+
+    # Overload inputs generation to fill dummy Add input with 0
+    def _prepare_input(self, inputs_dict):
+        for input in inputs_dict.keys():
+            inputs_dict[input] = np.zeros(inputs_dict[input]).astype(self.eye_output_type_param)
+        return inputs_dict
+
+
+    def create_tf_eye_net(self, num_rows, num_columns, batch_shape, output_type):
+        tf.compat.v1.reset_default_graph()
+
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            tf.compat.v1.global_variables_initializer()
+            # batch_shape_input = tf.constant(constant_value)
+            if output_type is None:
+                eye = tf.eye(num_rows=num_rows, num_columns=num_columns, batch_shape=batch_shape)
+            else:
+                self.eye_output_type_param = output_type
+                eye = tf.eye(num_rows=num_rows, num_columns=num_columns, batch_shape=batch_shape, dtype=tf.as_dtype(output_type))
+
+            # Dummy Add layer to prevent fully const network
+            input_zero = tf.compat.v1.placeholder(tf.as_dtype(self.eye_output_type_param), [1], 'Input')
+            add = tf.add(eye, input_zero)
+
+            tf_net = sess.graph_def
+
+        ref_net = None
+        return tf_net, ref_net
+
+    test_data = [dict(num_rows=5, num_columns=None, batch_shape=None, output_type=None),
+                 dict(num_rows=5, num_columns=5, batch_shape=[2, 3], output_type=np.float32),
+                 dict(num_rows=5, num_columns=5, batch_shape=[2, 3], output_type=np.float32),
+                 dict(num_rows=5, num_columns=5, batch_shape=[2, 3], output_type=np.float16),
+                 dict(num_rows=5, num_columns=5, batch_shape=[2, 3], output_type=np.int32),
+                 dict(num_rows=5, num_columns=5, batch_shape=[2, 3], output_type=np.int8),
+                 dict(num_rows=8, num_columns=5, batch_shape=None, output_type=np.float32),
+                 dict(num_rows=5, num_columns=8, batch_shape=None, output_type=np.float32),
+                 dict(num_rows=2, num_columns=2, batch_shape=None, output_type=np.float32),
+                 dict(num_rows=6, num_columns=6, batch_shape=[2], output_type=np.float32)]
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_tf_eye(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
+                     api_2=True):
+        if ie_device == 'GPU':
+            pytest.skip("Roll is not supported on GPU")
+        self._test(*self.create_tf_eye_net(**params), ie_device,
+                   precision,
+                   temp_dir=temp_dir, ir_version=ir_version, use_new_frontend=use_new_frontend,
+                   api_2=api_2, **params)