57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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# 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 pytest
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from torch.utils.data import BatchSampler, SequentialSampler
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from pytorch_lightning import seed_everything
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from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper, UnrepeatedDistributedSampler
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@pytest.mark.parametrize("shuffle", [False, True])
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def test_unrepeated_distributed_sampler(shuffle, tmpdir):
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"""Test each rank will receive a different number of elements."""
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seed_everything(42)
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world_size = 4
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samplers = []
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dataset = range(103)
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for rank in range(world_size):
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samplers.append(UnrepeatedDistributedSampler(dataset, rank=rank, num_replicas=world_size, shuffle=shuffle))
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indices = [[v for v in s] for s in samplers]
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assert len(indices[0]) == 26
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assert len(indices[1]) == 26
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assert len(indices[2]) == 26
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assert len(indices[3]) == 25
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assert indices[0][-1] == 18 if shuffle else 100
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assert indices[1][-1] == 30 if shuffle else 101
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assert indices[2][-1] == 29 if shuffle else 102
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assert indices[3][-1] == 35 if shuffle else 99
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def test_index_batch_sampler(tmpdir):
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"""Test `IndexBatchSampler` properly extracts indices."""
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dataset = range(15)
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sampler = SequentialSampler(dataset)
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batch_sampler = BatchSampler(sampler, 3, False)
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index_batch_sampler = IndexBatchSamplerWrapper(batch_sampler)
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assert batch_sampler.batch_size == index_batch_sampler.batch_size
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assert batch_sampler.drop_last == index_batch_sampler.drop_last
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assert batch_sampler.sampler is sampler
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for batch in index_batch_sampler:
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assert index_batch_sampler.batch_indices == batch
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