154 lines
5.0 KiB
Python
154 lines
5.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import numpy as np
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import onnxruntime
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import pytest
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import torch
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import tests.helpers.pipelines as tpipes
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import tests.helpers.utils as tutils
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from pytorch_lightning import Trainer
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from tests.helpers import BoringModel
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def test_model_saves_with_input_sample(tmpdir):
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"""Test that ONNX model saves with input sample and size is greater than 3 MB"""
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model = BoringModel()
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trainer = Trainer(fast_dev_run=True)
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trainer.fit(model)
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file_path = os.path.join(tmpdir, "model.onnx")
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input_sample = torch.randn((1, 32))
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model.to_onnx(file_path, input_sample)
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assert os.path.isfile(file_path)
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assert os.path.getsize(file_path) > 4e2
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_model_saves_on_gpu(tmpdir):
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"""Test that model saves on gpu"""
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model = BoringModel()
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trainer = Trainer(gpus=1, fast_dev_run=True)
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trainer.fit(model)
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file_path = os.path.join(tmpdir, "model.onnx")
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input_sample = torch.randn((1, 32))
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model.to_onnx(file_path, input_sample)
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assert os.path.isfile(file_path)
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assert os.path.getsize(file_path) > 4e2
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def test_model_saves_with_example_output(tmpdir):
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"""Test that ONNX model saves when provided with example output"""
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model = BoringModel()
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trainer = Trainer(fast_dev_run=True)
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trainer.fit(model)
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file_path = os.path.join(tmpdir, "model.onnx")
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input_sample = torch.randn((1, 32))
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model.eval()
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example_outputs = model.forward(input_sample)
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model.to_onnx(file_path, input_sample, example_outputs=example_outputs)
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assert os.path.exists(file_path) is True
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def test_model_saves_with_example_input_array(tmpdir):
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"""Test that ONNX model saves with_example_input_array and size is greater than 3 MB"""
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model = BoringModel()
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model.example_input_array = torch.randn(5, 32)
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file_path = os.path.join(tmpdir, "model.onnx")
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model.to_onnx(file_path)
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assert os.path.exists(file_path) is True
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assert os.path.getsize(file_path) > 4e2
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_model_saves_on_multi_gpu(tmpdir):
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"""Test that ONNX model saves on a distributed backend"""
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tutils.set_random_master_port()
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trainer_options = dict(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0, 1],
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accelerator='ddp_spawn',
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progress_bar_refresh_rate=0,
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)
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model = BoringModel()
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model.example_input_array = torch.randn(5, 32)
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tpipes.run_model_test(trainer_options, model, min_acc=0.08)
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file_path = os.path.join(tmpdir, "model.onnx")
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model.to_onnx(file_path)
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assert os.path.exists(file_path) is True
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def test_verbose_param(tmpdir, capsys):
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"""Test that output is present when verbose parameter is set"""
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model = BoringModel()
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model.example_input_array = torch.randn(5, 32)
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file_path = os.path.join(tmpdir, "model.onnx")
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model.to_onnx(file_path, verbose=True)
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captured = capsys.readouterr()
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assert "graph(%" in captured.out
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def test_error_if_no_input(tmpdir):
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"""Test that an error is thrown when there is no input tensor"""
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model = BoringModel()
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model.example_input_array = None
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file_path = os.path.join(tmpdir, "model.onnx")
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with pytest.raises(
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ValueError,
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match=r'Could not export to ONNX since neither `input_sample` nor'
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r' `model.example_input_array` attribute is set.'
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):
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model.to_onnx(file_path)
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def test_if_inference_output_is_valid(tmpdir):
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"""Test that the output inferred from ONNX model is same as from PyTorch"""
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model = BoringModel()
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model.example_input_array = torch.randn(5, 32)
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trainer = Trainer(fast_dev_run=True)
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trainer.fit(model)
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model.eval()
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with torch.no_grad():
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torch_out = model(model.example_input_array)
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file_path = os.path.join(tmpdir, "model.onnx")
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model.to_onnx(file_path, model.example_input_array, export_params=True)
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ort_session = onnxruntime.InferenceSession(file_path)
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def to_numpy(tensor):
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return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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# compute ONNX Runtime output prediction
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ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(model.example_input_array)}
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ort_outs = ort_session.run(None, ort_inputs)
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# compare ONNX Runtime and PyTorch results
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assert np.allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-05)
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