[TF FE] Stabilize Conv2DBackpropInput tests on all platforms

tests/layer_tests/tensorflow_tests/test_tf_Conv2DBackpropInput.py

@@ -0,0 +1,68 @@
+# Copyright (C) 2023-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+
+rng = np.random.default_rng(475912)
+
+
+class TestConv2DBackpropInput(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'filter:0' in inputs_info, "Test error: inputs_info must contain `filter`"
+        assert 'out_backprop:0' in inputs_info, "Test error: inputs_info must contain `out_backprop`"
+
+        filter_shape = inputs_info['filter:0']
+        out_backprop_shape = inputs_info['out_backprop:0']
+        inputs_data = {}
+        if np.issubdtype(self.input_type, np.floating):
+            inputs_data['filter:0'] = rng.uniform(-1.0, 1.0, filter_shape).astype(self.input_type)
+            inputs_data['out_backprop:0'] = rng.uniform(-1.0, 1.0, out_backprop_shape).astype(self.input_type)
+        return inputs_data
+
+    def create_conv2d_backprop_input_net(self, input_sizes, filter_shape, out_backprop_shape, strides,
+                                         padding, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        with tf.compat.v1.Session() as sess:
+            input_sizes = tf.constant(input_sizes, dtype=tf.int32)
+            filter = tf.compat.v1.placeholder(input_type, filter_shape, "filter")
+            out_backprop = tf.compat.v1.placeholder(input_type, out_backprop_shape, "out_backprop")
+
+            tf.raw_ops.Conv2DBackpropInput(input_sizes=input_sizes, filter=filter, out_backprop=out_backprop,
+                                           strides=strides, padding=padding)
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        ref_net = None
+
+        return tf_net, ref_net
+
+    test_data = [
+        dict(input_sizes=[1, 10, 10, 1], filter_shape=[1, 1, 1, 1], out_backprop_shape=[1, 10, 10, 1],
+             strides=[1, 1, 1, 1]),
+        dict(input_sizes=[1, 10, 10, 3], filter_shape=[2, 2, 3, 3], out_backprop_shape=[1, 5, 5, 3],
+             strides=[1, 2, 2, 1]),
+        dict(input_sizes=[1, 20, 20, 3], filter_shape=[2, 2, 3, 3], out_backprop_shape=[1, 10, 10, 3],
+             strides=[1, 2, 2, 1]),
+        dict(input_sizes=[1, 20, 20, 1], filter_shape=[1, 1, 1, 1], out_backprop_shape=[1, 20, 20, 1],
+             strides=[1, 1, 1, 1]),
+    ]
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("padding", ['SAME', 'VALID'])
+    @pytest.mark.parametrize("input_type", [np.float16, np.float32, np.float64])
+    @pytest.mark.precommit
+    @pytest.mark.nightly
+    def test_create_conv2d_backprop_input(self, params, padding, input_type,
+                                          ie_device, precision, ir_version, temp_dir, use_legacy_frontend):
+        custom_eps = None
+        if input_type == np.float16:
+            custom_eps = 1e-3
+        self._test(*self.create_conv2d_backprop_input_net(**params, padding=padding, input_type=input_type),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend, custom_eps=custom_eps)