86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright (C) 2018-2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import os
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import pytest
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import random
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import torch
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from models_hub_common.constants import hf_hub_cache_dir
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from models_hub_common.utils import cleanup_dir
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from torch_utils import TestTorchConvertModel
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from super_image import ImageLoader, EdsrModel, MsrnModel, A2nModel, PanModel, CarnModel, DrlnModel, MdsrModel, HanModel, AwsrnModel, RnanModel, MasaModel, JiifModel, LiifModel, SmsrModel, RcanModel, DrnModel, PhysicssrModel, DdbpnModel
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from PIL import Image
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import requests
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name_to_class = {
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"a2n": A2nModel,
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"awsrn-bam": AwsrnModel,
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"carn": CarnModel,
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"carn-bam": CarnModel,
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"drln": DrlnModel,
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"drln-bam": DrlnModel,
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"edsr": EdsrModel,
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"edsr-base": EdsrModel,
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"msrn": MsrnModel,
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"mdsr": MdsrModel,
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"msrn-bam": MsrnModel,
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"mdsr-bam": MdsrModel,
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"pan": PanModel,
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"pan-bam": PanModel,
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"rcan-bam": RcanModel,
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"han": HanModel,
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}
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# To make tests reproducible we seed the random generator
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torch.manual_seed(0)
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class TestEdsrConvertModel(TestTorchConvertModel):
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def load_model(self, model_name, model_link):
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# image link from https://github.com/eugenesiow/super-image
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url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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assert model_name in name_to_class, "Unexpected model name"
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model = name_to_class[model_name].from_pretrained(
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f'eugenesiow/{model_name}', scale=self.scale)
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inputs = ImageLoader.load_image(image)
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self.example = (torch.randn_like(inputs),)
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self.inputs = (inputs,)
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return model
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def teardown_method(self):
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# remove all downloaded files from cache
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cleanup_dir(hf_hub_cache_dir)
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super().teardown_method()
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@pytest.mark.parametrize("name,scale", [("edsr", 2)])
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@pytest.mark.precommit
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def test_convert_model_precommit(self, name, scale, ie_device):
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self.scale = scale
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self.run(name, None, ie_device)
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@pytest.mark.nightly
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@pytest.mark.parametrize("name,scale", [
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("a2n", random.randint(2, 4)),
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("awsrn-bam", random.randint(2, 4)),
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("carn", random.randint(2, 4)),
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("carn-bam", random.randint(2, 4)),
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("drln", random.randint(2, 4)),
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("drln-bam", random.randint(2, 4)),
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("edsr", random.randint(2, 4)),
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("edsr-base", random.randint(2, 4)),
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("msrn", random.randint(2, 4)),
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("msrn-bam", random.randint(2, 4)),
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("mdsr", random.randint(2, 4)),
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("mdsr-bam", random.randint(2, 4)),
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("pan", random.randint(2, 4)),
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("pan-bam", random.randint(2, 4)),
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("han", 4),
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("rcan-bam", 4),
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])
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def test_convert_model_all_models(self, name, scale, ie_device):
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self.scale = scale
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self.run(name, None, ie_device)
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