37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
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import pytest
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import os
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import torch
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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from pytorch_lightning.utilities.upgrade_checkpoint import upgrade_checkpoint
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@pytest.mark.parametrize(
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"old_checkpoint, new_checkpoint",
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[
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(
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{"epoch": 1, "global_step": 23, "checkpoint_callback_best": 0.34},
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{"epoch": 1, "global_step": 23, "callbacks": {ModelCheckpoint: {"best_model_score": 0.34}}},
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),
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(
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{"epoch": 1, "global_step": 23, "checkpoint_callback_best_model_score": 0.99},
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{"epoch": 1, "global_step": 23, "callbacks": {ModelCheckpoint: {"best_model_score": 0.99}}},
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),
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(
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{"epoch": 1, "global_step": 23, "checkpoint_callback_best_model_path": 'path'},
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{"epoch": 1, "global_step": 23, "callbacks": {ModelCheckpoint: {"best_model_path": 'path'}}},
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),
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(
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{"epoch": 1, "global_step": 23, "early_stop_callback_wait": 2, "early_stop_callback_patience": 4},
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{"epoch": 1, "global_step": 23, "callbacks": {EarlyStopping: {"wait_count": 2, "patience": 4}}},
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),
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],
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)
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def test_upgrade_checkpoint(tmpdir, old_checkpoint, new_checkpoint):
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filepath = os.path.join(tmpdir, "model.ckpt")
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torch.save(old_checkpoint, filepath)
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upgrade_checkpoint(filepath)
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updated_checkpoint = torch.load(filepath)
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assert updated_checkpoint == new_checkpoint
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