test_checkpoint.py

#!/usr/bin/env python3
# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import pytest
import torch
import torch.nn as nn

from icefall.checkpoint import average_checkpoints, load_checkpoint, save_checkpoint


@pytest.fixture
def checkpoints1(tmp_path):
    f = tmp_path / "f.pt"
    m = nn.Module()
    m.p1 = nn.Parameter(torch.tensor([10.0, 20.0]), requires_grad=False)
    m.register_buffer("p2", torch.tensor([10, 100]))

    params = {"a": 10, "b": 20}
    save_checkpoint(f, m, params=params)
    return f


@pytest.fixture
def checkpoints2(tmp_path):
    f = tmp_path / "f2.pt"
    m = nn.Module()
    m.p1 = nn.Parameter(torch.Tensor([50, 30.0]))
    m.register_buffer("p2", torch.tensor([1, 3]))
    params = {"a": 100, "b": 200}

    save_checkpoint(f, m, params=params)
    return f


def test_load_checkpoints(checkpoints1):
    m = nn.Module()
    m.p1 = nn.Parameter(torch.Tensor([0, 0.0]))
    m.p2 = nn.Parameter(torch.Tensor([0, 0]))
    params = load_checkpoint(checkpoints1, m)
    assert torch.allclose(m.p1, torch.Tensor([10.0, 20]))
    assert params["a"] == 10
    assert params["b"] == 20


def test_average_checkpoints(checkpoints1, checkpoints2):
    state_dict = average_checkpoints([checkpoints1, checkpoints2])
    assert torch.allclose(state_dict["p1"], torch.Tensor([30, 25.0]))
    assert torch.allclose(state_dict["p2"], torch.tensor([5, 51]))