[PT FE] Add EDSR models to GHA tests (#21350)

* [PT FE] Add EDSR models to GHA tests

* Apply suggestions from code review

tests/model_hub_tests/torch_tests/requirements.txt

@@ -14,3 +14,4 @@ pyctcdecode
 protobuf
 soundfile
 pandas
+super-image

tests/model_hub_tests/torch_tests/test_edsr.py

@@ -0,0 +1,85 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+import pytest
+import random
+import torch
+from models_hub_common.constants import hf_hub_cache_dir
+from models_hub_common.utils import cleanup_dir
+
+from torch_utils import TestTorchConvertModel
+from super_image import ImageLoader, EdsrModel, MsrnModel, A2nModel, PanModel, CarnModel, DrlnModel, MdsrModel, HanModel, AwsrnModel, RnanModel, MasaModel, JiifModel, LiifModel, SmsrModel, RcanModel, DrnModel, PhysicssrModel, DdbpnModel
+from PIL import Image
+import requests
+
+name_to_class = {
+    "a2n": A2nModel,
+    "awsrn-bam": AwsrnModel,
+    "carn": CarnModel,
+    "carn-bam": CarnModel,
+    "drln": DrlnModel,
+    "drln-bam": DrlnModel,
+    "edsr": EdsrModel,
+    "edsr-base": EdsrModel,
+    "msrn": MsrnModel,
+    "mdsr": MdsrModel,
+    "msrn-bam": MsrnModel,
+    "mdsr-bam": MdsrModel,
+    "pan": PanModel,
+    "pan-bam": PanModel,
+    "rcan-bam": RcanModel,
+    "han": HanModel,
+}
+
+# To make tests reproducible we seed the random generator
+torch.manual_seed(0)
+
+
+class TestEdsrConvertModel(TestTorchConvertModel):
+    def load_model(self, model_name, model_link):
+        # image link from https://github.com/eugenesiow/super-image
+        url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
+        image = Image.open(requests.get(url, stream=True).raw)
+        assert model_name in name_to_class, "Unexpected model name"
+        model = name_to_class[model_name].from_pretrained(
+            f'eugenesiow/{model_name}', scale=self.scale)
+        inputs = ImageLoader.load_image(image)
+        self.example = (torch.randn_like(inputs),)
+        self.inputs = (inputs,)
+        return model
+
+    def teardown_method(self):
+        # remove all downloaded files from cache
+        cleanup_dir(hf_hub_cache_dir)
+        super().teardown_method()
+
+    @pytest.mark.parametrize("name,scale", [("edsr", 2)])
+    @pytest.mark.precommit
+    def test_convert_model_precommit(self, name, scale, ie_device):
+        self.scale = scale
+        self.run(name, None, ie_device)
+
+    @pytest.mark.nightly
+    @pytest.mark.parametrize("name,scale", [
+        ("a2n", random.randint(2, 4)),
+        ("awsrn-bam", random.randint(2, 4)),
+        ("carn", random.randint(2, 4)),
+        ("carn-bam", random.randint(2, 4)),
+        ("drln", random.randint(2, 4)),
+        ("drln-bam", random.randint(2, 4)),
+        ("edsr", random.randint(2, 4)),
+        ("edsr-base", random.randint(2, 4)),
+        ("msrn", random.randint(2, 4)),
+        ("msrn-bam", random.randint(2, 4)),
+        ("mdsr", random.randint(2, 4)),
+        ("mdsr-bam", random.randint(2, 4)),
+        ("pan", random.randint(2, 4)),
+        ("pan-bam", random.randint(2, 4)),
+        ("han", 4),
+        ("rcan-bam", 4),
+    ])
+    def test_convert_model_all_models(self, name, scale, ie_device):
+        self.scale = scale
+        self.run(name, None, ie_device)