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[PT FE] Add support for GFPGAN model (openvinotoolkit#21371)
* [PT FE] Add support for GFPGAN model * Remove logs * Fix codestyle * Add support for aten::normal
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| # Copyright (C) 2018-2023 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import pytest | ||
| import torch | ||
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| from pytorch_layer_test_class import PytorchLayerTest | ||
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| class TestInplaceNormal(PytorchLayerTest): | ||
| def _prepare_input(self): | ||
| import numpy as np | ||
| return (np.random.randn(1, 3, 224, 224).astype(np.float32),) | ||
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| def create_model(self, mean, std): | ||
| class aten_normal(torch.nn.Module): | ||
| def __init__(self, mean, std): | ||
| super(aten_normal, self).__init__() | ||
| self.mean = mean | ||
| self.std = std | ||
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| def forward(self, x): | ||
| x = x.to(torch.float32) | ||
| return x.normal_(mean=self.mean, std=self.std), x | ||
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| return aten_normal(mean, std), None, "aten::normal_" | ||
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| @pytest.mark.parametrize("mean,std", [(0., 1.), (5., 20.)]) | ||
| @pytest.mark.nightly | ||
| @pytest.mark.precommit | ||
| def test_inplace_normal(self, mean, std, ie_device, precision, ir_version): | ||
| self._test(*self.create_model(mean, std), | ||
| ie_device, precision, ir_version, custom_eps=1e30) | ||
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| class TestNormal(PytorchLayerTest): | ||
| def _prepare_input(self): | ||
| import numpy as np | ||
| if isinstance(self.inputs, list): | ||
| return (np.random.randn(*self.inputs).astype(np.float32),) | ||
| return self.inputs | ||
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| class aten_normal1(torch.nn.Module): | ||
| def forward(self, mean, std): | ||
| return torch.normal(mean, std) | ||
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| class aten_normal2(torch.nn.Module): | ||
| def forward(self, mean, std): | ||
| x = torch.empty_like(mean, dtype=torch.float32) | ||
| return torch.normal(mean, std, out=x), x | ||
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| class aten_normal3(torch.nn.Module): | ||
| def forward(self, mean): | ||
| return torch.normal(mean) | ||
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| class aten_normal4(torch.nn.Module): | ||
| def forward(self, mean): | ||
| x = torch.empty_like(mean, dtype=torch.float32) | ||
| return torch.normal(mean, out=x), x | ||
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| class aten_normal5(torch.nn.Module): | ||
| def forward(self, mean): | ||
| x = torch.empty_like(mean, dtype=torch.float32) | ||
| return torch.normal(mean, 2., out=x), x | ||
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| class aten_normal6(torch.nn.Module): | ||
| def forward(self, x): | ||
| x = x.to(torch.float32) | ||
| return torch.normal(0., 1., x.shape) | ||
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| class aten_normal7(torch.nn.Module): | ||
| def forward(self, x): | ||
| x = x.to(torch.float32) | ||
| return torch.normal(0., 1., x.shape, out=x), x | ||
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| @pytest.mark.nightly | ||
| @pytest.mark.precommit | ||
| @pytest.mark.parametrize("model,inputs", [ | ||
| (aten_normal1(), (torch.arange(1., 11.).numpy(), torch.arange(1, 0, -0.1).numpy())), | ||
| (aten_normal2(), (torch.arange(1., 11.).numpy(), torch.arange(1, 0, -0.1).numpy())), | ||
| (aten_normal3(), (torch.arange(1., 11.).numpy(),)), | ||
| (aten_normal4(), (torch.arange(1., 11.).numpy(),)), | ||
| (aten_normal5(), (torch.arange(1., 11.).numpy(),)), | ||
| (aten_normal6(), [1, 3, 224, 224]), | ||
| (aten_normal7(), [1, 3, 224, 224]), | ||
| ]) | ||
| def test_inplace_normal(self, model, inputs, ie_device, precision, ir_version): | ||
| self.inputs = inputs | ||
| self._test(model, None, "aten::normal", | ||
| ie_device, precision, ir_version, custom_eps=1e30) |
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@@ -15,3 +15,5 @@ protobuf | |
| soundfile | ||
| pandas | ||
| super-image | ||
| basicsr | ||
| facexlib | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,81 @@ | ||
| # Copyright (C) 2018-2023 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import os | ||
| import subprocess | ||
| import sys | ||
| import tempfile | ||
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| import pytest | ||
| import torch | ||
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| from torch_utils import TestTorchConvertModel | ||
| from openvino import convert_model | ||
| import numpy as np | ||
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| # To make tests reproducible we seed the random generator | ||
| torch.manual_seed(0) | ||
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| class TestGFPGANConvertModel(TestTorchConvertModel): | ||
| def setup_class(self): | ||
| self.repo_dir = tempfile.TemporaryDirectory() | ||
| os.system( | ||
| f"git clone https://github.com/TencentARC/GFPGAN.git {self.repo_dir.name}") | ||
| subprocess.check_call( | ||
| ["git", "checkout", "bc5a5deb95a4a9653851177985d617af1b9bfa8b"], cwd=self.repo_dir.name) | ||
| checkpoint_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth" | ||
| subprocess.check_call( | ||
| ["wget", "-nv", checkpoint_url], cwd=self.repo_dir.name) | ||
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| def load_model(self, model_name, model_link): | ||
| sys.path.append(self.repo_dir.name) | ||
| from gfpgan import GFPGANer | ||
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| filename = os.path.join(self.repo_dir.name, 'GFPGANv1.3.pth') | ||
| arch = 'clean' | ||
| channel_multiplier = 2 | ||
| restorer = GFPGANer( | ||
| model_path=filename, | ||
| upscale=2, | ||
| arch=arch, | ||
| channel_multiplier=channel_multiplier, | ||
| bg_upsampler=None) | ||
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| self.example = (torch.randn(1, 3, 512, 512),) | ||
| self.inputs = (torch.randn(1, 3, 512, 512),) | ||
| return restorer.gfpgan | ||
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| def convert_model(self, model_obj): | ||
| ov_model = convert_model( | ||
| model_obj, example_input=self.example, input=[1, 3, 512, 512], verbose=True) | ||
| return ov_model | ||
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| def compare_results(self, fw_outputs, ov_outputs): | ||
| assert len(fw_outputs) == len(ov_outputs), \ | ||
| "Different number of outputs between framework and OpenVINO:" \ | ||
| " {} vs. {}".format(len(fw_outputs), len(ov_outputs)) | ||
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| fw_eps = 5e-2 | ||
| is_ok = True | ||
| for i in range(len(ov_outputs)): | ||
| cur_fw_res = fw_outputs[i] | ||
| cur_ov_res = ov_outputs[i] | ||
| try: | ||
| np.testing.assert_allclose( | ||
| cur_ov_res, cur_fw_res, fw_eps, fw_eps) | ||
| except AssertionError as e: | ||
| print(e) | ||
| # The model has aten::normal_ operation which produce random numbers. | ||
| # Cannot reliably validate the output 0 | ||
| if i != 0: | ||
| is_ok = False | ||
| assert is_ok, "Accuracy validation failed" | ||
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| def teardown_class(self): | ||
| # remove all downloaded files from cache | ||
| self.repo_dir.cleanup() | ||
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| @pytest.mark.nightly | ||
| def test_convert_model(self, ie_device): | ||
| self.run("GFPGAN", None, ie_device) |