212 lines
8.8 KiB
Python
212 lines
8.8 KiB
Python
# 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 dataclasses
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import numbers
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from collections import namedtuple, OrderedDict
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from typing import List
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import numpy as np
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import pytest
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import torch
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from pytorch_lightning.utilities.apply_func import apply_to_collection, apply_to_collections
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def test_recursive_application_to_collection():
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ntc = namedtuple('Foo', ['bar'])
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@dataclasses.dataclass
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class Feature:
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input_ids: torch.Tensor
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segment_ids: np.ndarray
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@dataclasses.dataclass
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class ModelExample:
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example_ids: List[str]
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feature: Feature
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label: torch.Tensor
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to_reduce = {
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'a': torch.tensor([1.]), # Tensor
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'b': [torch.tensor([2.])], # list
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'c': (torch.tensor([100.]), ), # tuple
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'd': ntc(bar=5.), # named tuple
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'e': np.array([10.]), # numpy array
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'f': 'this_is_a_dummy_str', # string
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'g': 12., # number
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'h': Feature(input_ids=torch.tensor([1., 2., 3.]), segment_ids=np.array([4., 5., 6.])), # dataclass
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'i': ModelExample(
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example_ids=['i-1', 'i-2', 'i-3'],
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feature=Feature(input_ids=torch.tensor([1., 2., 3.]), segment_ids=np.array([4., 5., 6.])),
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label=torch.tensor([7., 8., 9.])
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) # nested dataclass
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}
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expected_result = {
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'a': torch.tensor([2.]),
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'b': [torch.tensor([4.])],
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'c': (torch.tensor([200.]), ),
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'd': ntc(bar=torch.tensor([10.])),
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'e': np.array([20.]),
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'f': 'this_is_a_dummy_str',
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'g': 24.,
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'h': Feature(input_ids=torch.tensor([2., 4., 6.]), segment_ids=np.array([8., 10., 12.])),
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'i': ModelExample(
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example_ids=['i-1', 'i-2', 'i-3'],
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feature=Feature(input_ids=torch.tensor([2., 4., 6.]), segment_ids=np.array([8., 10., 12.])),
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label=torch.tensor([14., 16., 18.])
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)
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}
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reduced = apply_to_collection(to_reduce, (torch.Tensor, numbers.Number, np.ndarray), lambda x: x * 2)
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assert isinstance(reduced, dict), ' Type Consistency of dict not preserved'
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assert all([x in reduced for x in to_reduce]), 'Not all entries of the dict were preserved'
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assert all([isinstance(reduced[k], type(expected_result[k])) for k in to_reduce]), \
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'At least one type was not correctly preserved'
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assert isinstance(reduced['a'], torch.Tensor), 'Reduction Result of a Tensor should be a Tensor'
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assert torch.allclose(expected_result['a'], reduced['a']), \
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'Reduction of a tensor does not yield the expected value'
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assert isinstance(reduced['b'], list), 'Reduction Result of a list should be a list'
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assert all([torch.allclose(x, y) for x, y in zip(reduced['b'], expected_result['b'])]), \
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'At least one value of list reduction did not come out as expected'
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assert isinstance(reduced['c'], tuple), 'Reduction Result of a tuple should be a tuple'
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assert all([torch.allclose(x, y) for x, y in zip(reduced['c'], expected_result['c'])]), \
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'At least one value of tuple reduction did not come out as expected'
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assert isinstance(reduced['d'], ntc), 'Type Consistency for named tuple not given'
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assert isinstance(reduced['d'].bar, numbers.Number), \
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'Failure in type promotion while reducing fields of named tuples'
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assert reduced['d'].bar == expected_result['d'].bar
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assert isinstance(reduced['e'], np.ndarray), 'Type Promotion in reduction of numpy arrays failed'
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assert reduced['e'] == expected_result['e'], \
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'Reduction of numpy array did not yield the expected result'
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assert isinstance(reduced['f'], str), 'A string should not be reduced'
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assert reduced['f'] == expected_result['f'], 'String not preserved during reduction'
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assert isinstance(reduced['g'], numbers.Number), 'Reduction of a number should result in a number'
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assert reduced['g'] == expected_result['g'], 'Reduction of a number did not yield the desired result'
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assert dataclasses.is_dataclass(reduced['h']) and not isinstance(reduced['h'], type), \
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'Reduction of a dataclass should result in a dataclass'
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assert torch.allclose(reduced['h'].input_ids, expected_result['h'].input_ids), \
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'Reduction of a dataclass did not yield the desired result'
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assert np.allclose(reduced['h'].segment_ids, expected_result['h'].segment_ids), \
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'Reduction of a dataclass did not yield the desired result'
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assert dataclasses.is_dataclass(reduced['i']) and not isinstance(reduced['i'], type), \
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'Reduction of a dataclass should result in a dataclass'
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assert dataclasses.is_dataclass(reduced['i'].feature) and not isinstance(reduced['i'].feature, type), \
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'Reduction of a nested dataclass should result in a nested dataclass'
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assert reduced['i'].example_ids == expected_result['i'].example_ids, \
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'Reduction of a nested dataclass did not yield the desired result'
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assert torch.allclose(reduced['i'].label, expected_result['i'].label), \
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'Reduction of a nested dataclass did not yield the desired result'
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assert torch.allclose(reduced['i'].feature.input_ids, expected_result['i'].feature.input_ids), \
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'Reduction of a nested dataclass did not yield the desired result'
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assert np.allclose(reduced['i'].feature.segment_ids, expected_result['i'].feature.segment_ids), \
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'Reduction of a nested dataclass did not yield the desired result'
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# mapping support
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reduced = apply_to_collection({'a': 1, 'b': 2}, int, lambda x: str(x))
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assert reduced == {'a': '1', 'b': '2'}
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reduced = apply_to_collection(OrderedDict([('b', 2), ('a', 1)]), int, lambda x: str(x))
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assert reduced == OrderedDict([('b', '2'), ('a', '1')])
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# custom mappings
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class _CustomCollection(dict):
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def __init__(self, initial_dict):
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super().__init__(initial_dict)
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to_reduce = _CustomCollection({'a': 1, 'b': 2, 'c': 3})
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reduced = apply_to_collection(to_reduce, int, lambda x: str(x))
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assert reduced == _CustomCollection({'a': '1', 'b': '2', 'c': '3'})
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def test_apply_to_collection_include_none():
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to_reduce = [1, 2, 3.4, 5.6, 7]
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def fn(x):
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if isinstance(x, float):
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return x
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reduced = apply_to_collection(to_reduce, (int, float), fn)
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assert reduced == [None, None, 3.4, 5.6, None]
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reduced = apply_to_collection(to_reduce, (int, float), fn, include_none=False)
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assert reduced == [3.4, 5.6]
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def test_apply_to_collections():
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to_reduce_1 = {'a': {'b': [1, 2]}, 'c': 5}
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to_reduce_2 = {'a': {'b': [3, 4]}, 'c': 6}
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def fn(a, b):
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return a + b
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# basic test
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reduced = apply_to_collections(to_reduce_1, to_reduce_2, int, fn)
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assert reduced == {'a': {'b': [4, 6]}, 'c': 11}
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with pytest.raises(KeyError):
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# strict mode - if a key does not exist in both we fail
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apply_to_collections({**to_reduce_2, 'd': 'foo'}, to_reduce_1, float, fn)
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# multiple dtypes
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reduced = apply_to_collections(to_reduce_1, to_reduce_2, (list, int), fn)
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assert reduced == {'a': {'b': [1, 2, 3, 4]}, 'c': 11}
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# wrong dtype
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reduced = apply_to_collections(to_reduce_1, to_reduce_2, (list, int), fn, wrong_dtype=int)
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assert reduced == {'a': {'b': [1, 2, 3, 4]}, 'c': 5}
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# list takes precedence because it is the type of data1
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reduced = apply_to_collections([1, 2, 3], [4], (int, list), fn)
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assert reduced == [1, 2, 3, 4]
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# different sizes
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with pytest.raises(AssertionError, match='Sequence collections have different sizes'):
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apply_to_collections([[1, 2], [3]], [4], int, fn)
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def fn(a, b):
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return a.keys() | b.keys()
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# base case
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reduced = apply_to_collections(to_reduce_1, to_reduce_2, dict, fn)
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assert reduced == {'a', 'c'}
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# type conversion
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to_reduce = [(1, 2), (3, 4)]
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reduced = apply_to_collections(to_reduce, to_reduce, int, lambda *x: sum(x))
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assert reduced == [(2, 4), (6, 8)]
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# named tuple
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foo = namedtuple('Foo', ['bar'])
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to_reduce = [foo(1), foo(2), foo(3)]
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reduced = apply_to_collections(to_reduce, to_reduce, int, lambda *x: sum(x))
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assert reduced == [foo(2), foo(4), foo(6)]
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# passing none
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reduced1 = apply_to_collections([1, 2, 3], None, int, lambda x: x * x)
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reduced2 = apply_to_collections(None, [1, 2, 3], int, lambda x: x * x)
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assert reduced1 == reduced2 == [1, 4, 9]
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reduced = apply_to_collections(None, None, int, lambda x: x * x)
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assert reduced is None
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