96 lines
2.6 KiB
Python
96 lines
2.6 KiB
Python
import pytest
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import torch
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import pytorch_lightning as pl
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import tests.base.develop_utils as tutils
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from tests.base import EvalModelTemplate
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_single_gpu_test(tmpdir):
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tutils.set_random_master_port()
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model = EvalModelTemplate()
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trainer = pl.Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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limit_train_batches=10,
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limit_val_batches=10,
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gpus=[0],
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)
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trainer.fit(model)
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assert 'ckpt' in trainer.checkpoint_callback.best_model_path
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results = trainer.test()
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assert 'test_acc' in results[0]
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old_weights = model.c_d1.weight.clone().detach().cpu()
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results = trainer.test(model)
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assert 'test_acc' in results[0]
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# make sure weights didn't change
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new_weights = model.c_d1.weight.clone().detach().cpu()
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assert torch.all(torch.eq(old_weights, new_weights))
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_dp_test(tmpdir):
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tutils.set_random_master_port()
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
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model = EvalModelTemplate()
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trainer = pl.Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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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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distributed_backend='dp',
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)
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trainer.fit(model)
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assert 'ckpt' in trainer.checkpoint_callback.best_model_path
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results = trainer.test()
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assert 'test_acc' in results[0]
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old_weights = model.c_d1.weight.clone().detach().cpu()
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results = trainer.test(model)
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assert 'test_acc' in results[0]
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# make sure weights didn't change
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new_weights = model.c_d1.weight.clone().detach().cpu()
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assert torch.all(torch.eq(old_weights, new_weights))
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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def test_ddp_spawn_test(tmpdir):
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tutils.set_random_master_port()
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model = EvalModelTemplate()
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trainer = pl.Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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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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distributed_backend='ddp_spawn',
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)
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trainer.fit(model)
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assert 'ckpt' in trainer.checkpoint_callback.best_model_path
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results = trainer.test()
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assert 'test_acc' in results[0]
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old_weights = model.c_d1.weight.clone().detach().cpu()
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results = trainer.test(model)
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assert 'test_acc' in results[0]
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# make sure weights didn't change
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new_weights = model.c_d1.weight.clone().detach().cpu()
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assert torch.all(torch.eq(old_weights, new_weights))
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