142 lines
4.8 KiB
Python
142 lines
4.8 KiB
Python
import os
|
|
|
|
import numpy as np
|
|
import onnxruntime
|
|
import pytest
|
|
import torch
|
|
|
|
import tests.base.develop_pipelines as tpipes
|
|
import tests.base.develop_utils as tutils
|
|
from pytorch_lightning import Trainer
|
|
from tests.base import EvalModelTemplate
|
|
|
|
|
|
def test_model_saves_with_input_sample(tmpdir):
|
|
"""Test that ONNX model saves with input sample and size is greater than 3 MB"""
|
|
model = EvalModelTemplate()
|
|
trainer = Trainer(max_epochs=1)
|
|
trainer.fit(model)
|
|
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
input_sample = torch.randn((1, 28 * 28))
|
|
model.to_onnx(file_path, input_sample)
|
|
assert os.path.isfile(file_path)
|
|
assert os.path.getsize(file_path) > 3e+06
|
|
|
|
|
|
@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
|
|
def test_model_saves_on_gpu(tmpdir):
|
|
"""Test that model saves on gpu"""
|
|
model = EvalModelTemplate()
|
|
trainer = Trainer(gpus=1, max_epochs=1)
|
|
trainer.fit(model)
|
|
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
input_sample = torch.randn((1, 28 * 28))
|
|
model.to_onnx(file_path, input_sample)
|
|
assert os.path.isfile(file_path)
|
|
assert os.path.getsize(file_path) > 3e+06
|
|
|
|
|
|
def test_model_saves_with_example_output(tmpdir):
|
|
"""Test that ONNX model saves when provided with example output"""
|
|
model = EvalModelTemplate()
|
|
trainer = Trainer(max_epochs=1)
|
|
trainer.fit(model)
|
|
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
input_sample = torch.randn((1, 28 * 28))
|
|
model.eval()
|
|
example_outputs = model.forward(input_sample)
|
|
model.to_onnx(file_path, input_sample, example_outputs=example_outputs)
|
|
assert os.path.exists(file_path) is True
|
|
|
|
|
|
def test_model_saves_with_example_input_array(tmpdir):
|
|
"""Test that ONNX model saves with_example_input_array and size is greater than 3 MB"""
|
|
model = EvalModelTemplate()
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
model.to_onnx(file_path)
|
|
assert os.path.exists(file_path) is True
|
|
assert os.path.getsize(file_path) > 3e+06
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
|
|
def test_model_saves_on_multi_gpu(tmpdir):
|
|
"""Test that ONNX model saves on a distributed backend"""
|
|
tutils.set_random_master_port()
|
|
|
|
trainer_options = dict(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=1,
|
|
limit_train_batches=10,
|
|
limit_val_batches=10,
|
|
gpus=[0, 1],
|
|
distributed_backend='ddp_spawn',
|
|
progress_bar_refresh_rate=0
|
|
)
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
tpipes.run_model_test(trainer_options, model)
|
|
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
model.to_onnx(file_path)
|
|
assert os.path.exists(file_path) is True
|
|
|
|
|
|
def test_verbose_param(tmpdir, capsys):
|
|
"""Test that output is present when verbose parameter is set"""
|
|
model = EvalModelTemplate()
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
model.to_onnx(file_path, verbose=True)
|
|
captured = capsys.readouterr()
|
|
assert "graph(%" in captured.out
|
|
|
|
|
|
def test_error_if_no_input(tmpdir):
|
|
"""Test that an exception is thrown when there is no input tensor"""
|
|
model = EvalModelTemplate()
|
|
model.example_input_array = None
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
with pytest.raises(ValueError, match=r'Could not export to ONNX since neither `input_sample` nor'
|
|
r' `model.example_input_array` attribute is set.'):
|
|
model.to_onnx(file_path)
|
|
|
|
|
|
def test_error_if_input_sample_is_not_tensor(tmpdir):
|
|
"""Test that an exception is thrown when there is no input tensor"""
|
|
model = EvalModelTemplate()
|
|
model.example_input_array = None
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
input_sample = np.random.randn(1, 28 * 28)
|
|
with pytest.raises(ValueError, match=f'Received `input_sample` of type {type(input_sample)}. Expected type is '
|
|
f'`Tensor`'):
|
|
model.to_onnx(file_path, input_sample)
|
|
|
|
|
|
def test_if_inference_output_is_valid(tmpdir):
|
|
"""Test that the output inferred from ONNX model is same as from PyTorch"""
|
|
model = EvalModelTemplate()
|
|
trainer = Trainer(max_epochs=5)
|
|
trainer.fit(model)
|
|
|
|
model.eval()
|
|
with torch.no_grad():
|
|
torch_out = model(model.example_input_array)
|
|
|
|
file_path = os.path.join(tmpdir, "model.onnx")
|
|
model.to_onnx(file_path, model.example_input_array, export_params=True)
|
|
|
|
ort_session = onnxruntime.InferenceSession(file_path)
|
|
|
|
def to_numpy(tensor):
|
|
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
|
|
|
|
# compute ONNX Runtime output prediction
|
|
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(model.example_input_array)}
|
|
ort_outs = ort_session.run(None, ort_inputs)
|
|
|
|
# compare ONNX Runtime and PyTorch results
|
|
assert np.allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-05)
|