forked from huawei/mindspore2022
1567 lines
56 KiB
Python
1567 lines
56 KiB
Python
# Copyright 2019 Huawei Technologies Co., Ltd
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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 foNtest_resr the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Built-in validators.
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"""
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import inspect as ins
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import os
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import re
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from functools import wraps
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import numpy as np
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from mindspore._c_expression import typing
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from ..core.validator_helpers import parse_user_args, type_check, type_check_list, check_value, \
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INT32_MAX, check_valid_detype, check_dir, check_file, check_sampler_shuffle_shard_options, \
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validate_dataset_param_value, check_padding_options, check_gnn_list_or_ndarray, check_gnn_list_of_pair_or_ndarray, \
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check_num_parallel_workers, check_columns, check_pos_int32, check_valid_str
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from . import datasets
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from . import samplers
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from . import cache_client
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def check_imagefolderdataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(ImageFolderDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle', 'decode']
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nreq_param_list = ['extensions']
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nreq_param_dict = ['class_indexing']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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validate_dataset_param_value(nreq_param_list, param_dict, list)
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validate_dataset_param_value(nreq_param_dict, param_dict, dict)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_mnist_cifar_dataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(ManifestDataset, Cifar10/100Dataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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usage = param_dict.get('usage')
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if usage is not None:
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check_valid_str(usage, ["train", "test", "all"], "usage")
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_manifestdataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(ManifestDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle', 'decode']
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nreq_param_str = ['usage']
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nreq_param_dict = ['class_indexing']
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dataset_file = param_dict.get('dataset_file')
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check_file(dataset_file)
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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validate_dataset_param_value(nreq_param_str, param_dict, str)
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validate_dataset_param_value(nreq_param_dict, param_dict, dict)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_sbu_dataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(SBUDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle', 'decode']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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check_file(os.path.join(dataset_dir, "SBU_captioned_photo_dataset_urls.txt"))
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check_file(os.path.join(dataset_dir, "SBU_captioned_photo_dataset_captions.txt"))
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check_dir(os.path.join(dataset_dir, "sbu_images"))
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_tfrecorddataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(TFRecordDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_list = ['columns_list']
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nreq_param_bool = ['shard_equal_rows']
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dataset_files = param_dict.get('dataset_files')
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if not isinstance(dataset_files, (str, list)):
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raise TypeError("dataset_files should be type str or a list of strings.")
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_list, param_dict, list)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_usps_dataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(USPSDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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usage = param_dict.get('usage')
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if usage is not None:
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check_valid_str(usage, ["train", "test", "all"], "usage")
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_vocdataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(VOCDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle', 'decode']
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nreq_param_dict = ['class_indexing']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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task = param_dict.get('task')
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type_check(task, (str,), "task")
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usage = param_dict.get('usage')
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type_check(usage, (str,), "usage")
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if task == "Segmentation":
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imagesets_file = os.path.join(dataset_dir, "ImageSets", "Segmentation", usage + ".txt")
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if param_dict.get('class_indexing') is not None:
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raise ValueError("class_indexing is not supported in Segmentation task.")
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elif task == "Detection":
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imagesets_file = os.path.join(dataset_dir, "ImageSets", "Main", usage + ".txt")
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else:
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raise ValueError("Invalid task : " + task + ".")
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check_file(imagesets_file)
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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validate_dataset_param_value(nreq_param_dict, param_dict, dict)
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_cocodataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(CocoDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle', 'decode']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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annotation_file = param_dict.get('annotation_file')
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check_file(annotation_file)
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task = param_dict.get('task')
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type_check(task, (str,), "task")
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if task not in {'Detection', 'Stuff', 'Panoptic', 'Keypoint'}:
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raise ValueError("Invalid task type: " + task + ".")
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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sampler = param_dict.get('sampler')
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if sampler is not None and isinstance(sampler, samplers.PKSampler):
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raise ValueError("CocoDataset doesn't support PKSampler.")
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check_sampler_shuffle_shard_options(param_dict)
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_celebadataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(CelebADataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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nreq_param_bool = ['shuffle', 'decode']
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nreq_param_list = ['extensions']
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nreq_param_str = ['dataset_type']
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dataset_dir = param_dict.get('dataset_dir')
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check_dir(dataset_dir)
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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validate_dataset_param_value(nreq_param_list, param_dict, list)
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validate_dataset_param_value(nreq_param_str, param_dict, str)
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usage = param_dict.get('usage')
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if usage is not None and usage not in ('all', 'train', 'valid', 'test'):
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raise ValueError("usage should be 'all', 'train', 'valid' or 'test'.")
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check_sampler_shuffle_shard_options(param_dict)
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sampler = param_dict.get('sampler')
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if sampler is not None and isinstance(sampler, samplers.PKSampler):
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raise ValueError("CelebADataset doesn't support PKSampler.")
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cache = param_dict.get('cache')
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check_cache_option(cache)
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return method(self, *args, **kwargs)
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return new_method
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def check_save(method):
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"""A wrapper that wraps a parameter checker around the saved operator."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_files']
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nreq_param_str = ['file_name', 'file_type']
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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if (param_dict.get('num_files') <= 0 or param_dict.get('num_files') > 1000):
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raise ValueError("num_files should between 0 and 1000.")
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validate_dataset_param_value(nreq_param_str, param_dict, str)
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if param_dict.get('file_type') != 'mindrecord':
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raise ValueError("{} dataset format is not supported.".format(param_dict.get('file_type')))
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return method(self, *args, **kwargs)
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return new_method
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def check_tuple_iterator(method):
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"""A wrapper that wraps a parameter checker around the original create_tuple_iterator and create_dict_iterator."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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[columns, num_epochs, _, _], param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_bool = ['output_numpy']
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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if num_epochs is not None:
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type_check(num_epochs, (int,), "num_epochs")
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check_value(num_epochs, [-1, INT32_MAX], "num_epochs")
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if columns is not None:
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check_columns(columns, "column_names")
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return method(self, *args, **kwargs)
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return new_method
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def check_dict_iterator(method):
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"""A wrapper that wraps a parameter checker around the original create_tuple_iterator and create_dict_iterator."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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[num_epochs, _], param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_bool = ['output_numpy']
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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if num_epochs is not None:
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type_check(num_epochs, (int,), "num_epochs")
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check_value(num_epochs, [-1, INT32_MAX], "num_epochs")
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return method(self, *args, **kwargs)
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return new_method
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def check_minddataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(MindDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'seed', 'num_shards', 'shard_id', 'num_padded']
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nreq_param_list = ['columns_list']
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nreq_param_dict = ['padded_sample']
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dataset_file = param_dict.get('dataset_file')
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if isinstance(dataset_file, list):
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if len(dataset_file) > 4096:
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raise ValueError("length of dataset_file should less than or equal to {}.".format(4096))
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for f in dataset_file:
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check_file(f)
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else:
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check_file(dataset_file)
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_list, param_dict, list)
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validate_dataset_param_value(nreq_param_dict, param_dict, dict)
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check_sampler_shuffle_shard_options(param_dict)
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check_padding_options(param_dict)
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return method(self, *args, **kwargs)
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return new_method
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def check_generatordataset(method):
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"""A wrapper that wraps a parameter checker around the original Dataset(GeneratorDataset)."""
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@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
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source = param_dict.get('source')
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if not callable(source):
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try:
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iter(source)
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except TypeError:
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raise TypeError("Input `source` function of GeneratorDataset should be callable, iterable or random"
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" accessible, commonly it should implement one of the method like yield, __getitem__ or"
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" __next__(__iter__).")
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column_names = param_dict.get('column_names')
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if column_names is not None:
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check_columns(column_names, "column_names")
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schema = param_dict.get('schema')
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if column_names is None and schema is None:
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raise ValueError("Neither columns_names nor schema are provided.")
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if schema is not None:
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if not isinstance(schema, datasets.Schema) and not isinstance(schema, str):
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raise ValueError("schema should be a path to schema file or a schema object.")
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# check optional argument
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nreq_param_int = ["max_rowsize", "num_samples", "num_parallel_workers", "num_shards", "shard_id"]
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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nreq_param_list = ["column_types"]
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validate_dataset_param_value(nreq_param_list, param_dict, list)
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nreq_param_bool = ["shuffle"]
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
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num_shards = param_dict.get("num_shards")
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shard_id = param_dict.get("shard_id")
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if (num_shards is None) != (shard_id is None):
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# These two parameters appear together.
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raise ValueError("num_shards and shard_id need to be passed in together.")
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if num_shards is not None:
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check_pos_int32(num_shards, "num_shards")
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if shard_id >= num_shards:
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raise ValueError("shard_id should be less than num_shards.")
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sampler = param_dict.get("sampler")
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if sampler is not None:
|
|
if isinstance(sampler, samplers.PKSampler):
|
|
raise ValueError("GeneratorDataset doesn't support PKSampler.")
|
|
if not isinstance(sampler, samplers.BuiltinSampler):
|
|
try:
|
|
iter(sampler)
|
|
except TypeError:
|
|
raise TypeError("sampler should be either iterable or from mindspore.dataset.samplers.")
|
|
|
|
if sampler is not None and not hasattr(source, "__getitem__"):
|
|
raise ValueError("sampler is not supported if source does not have attribute '__getitem__'.")
|
|
if num_shards is not None and not hasattr(source, "__getitem__"):
|
|
raise ValueError("num_shards is not supported if source does not have attribute '__getitem__'.")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_random_dataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(RandomDataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id', 'total_rows']
|
|
nreq_param_bool = ['shuffle']
|
|
nreq_param_list = ['columns_list']
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
|
validate_dataset_param_value(nreq_param_list, param_dict, list)
|
|
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
cache = param_dict.get('cache')
|
|
check_cache_option(cache)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_pad_info(key, val):
|
|
"""check the key and value pair of pad_info in batch"""
|
|
type_check(key, (str,), "key in pad_info")
|
|
|
|
if val is not None:
|
|
if len(val) != 2:
|
|
raise ValueError("value of pad_info should be a tuple of size 2.")
|
|
type_check(val, (tuple,), "value in pad_info")
|
|
|
|
if val[0] is not None:
|
|
type_check(val[0], (list,), "shape in pad_info")
|
|
|
|
for dim in val[0]:
|
|
if dim is not None:
|
|
check_pos_int32(dim, "dim of shape in pad_info")
|
|
if val[1] is not None:
|
|
type_check(val[1], (int, float, str, bytes), "pad_value")
|
|
|
|
|
|
def check_bucket_batch_by_length(method):
|
|
"""check the input arguments of bucket_batch_by_length."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[column_names, bucket_boundaries, bucket_batch_sizes, element_length_function, pad_info,
|
|
pad_to_bucket_boundary, drop_remainder], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_list = ['column_names', 'bucket_boundaries', 'bucket_batch_sizes']
|
|
|
|
type_check_list([column_names, bucket_boundaries, bucket_batch_sizes], (list,), nreq_param_list)
|
|
|
|
nbool_param_list = ['pad_to_bucket_boundary', 'drop_remainder']
|
|
type_check_list([pad_to_bucket_boundary, drop_remainder], (bool,), nbool_param_list)
|
|
|
|
# check column_names: must be list of string.
|
|
check_columns(column_names, "column_names")
|
|
|
|
if element_length_function is None and len(column_names) != 1:
|
|
raise ValueError("If element_length_function is not specified, exactly one column name should be passed.")
|
|
|
|
if element_length_function is not None and not callable(element_length_function):
|
|
raise TypeError("element_length_function object is not callable.")
|
|
|
|
# check bucket_boundaries: must be list of int, positive and strictly increasing
|
|
if not bucket_boundaries:
|
|
raise ValueError("bucket_boundaries cannot be empty.")
|
|
|
|
all_int = all(isinstance(item, int) for item in bucket_boundaries)
|
|
if not all_int:
|
|
raise TypeError("bucket_boundaries should be a list of int.")
|
|
|
|
all_non_negative = all(item > 0 for item in bucket_boundaries)
|
|
if not all_non_negative:
|
|
raise ValueError("bucket_boundaries must only contain positive numbers.")
|
|
|
|
for i in range(len(bucket_boundaries) - 1):
|
|
if not bucket_boundaries[i + 1] > bucket_boundaries[i]:
|
|
raise ValueError("bucket_boundaries should be strictly increasing.")
|
|
|
|
# check bucket_batch_sizes: must be list of int and positive
|
|
if len(bucket_batch_sizes) != len(bucket_boundaries) + 1:
|
|
raise ValueError("bucket_batch_sizes must contain one element more than bucket_boundaries.")
|
|
|
|
all_int = all(isinstance(item, int) for item in bucket_batch_sizes)
|
|
if not all_int:
|
|
raise TypeError("bucket_batch_sizes should be a list of int.")
|
|
|
|
all_non_negative = all(item > 0 for item in bucket_batch_sizes)
|
|
if not all_non_negative:
|
|
raise ValueError("bucket_batch_sizes should be a list of positive numbers.")
|
|
|
|
if pad_info is not None:
|
|
type_check(pad_info, (dict,), "pad_info")
|
|
|
|
for k, v in pad_info.items():
|
|
check_pad_info(k, v)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_batch(method):
|
|
"""check the input arguments of batch."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[batch_size, drop_remainder, num_parallel_workers, per_batch_map, input_columns, output_columns,
|
|
column_order, pad_info, python_multiprocessing], param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
if not (isinstance(batch_size, int) or (callable(batch_size))):
|
|
raise TypeError("batch_size should either be an int or a callable.")
|
|
|
|
if callable(batch_size):
|
|
sig = ins.signature(batch_size)
|
|
if len(sig.parameters) != 1:
|
|
raise ValueError("callable batch_size should take one parameter (BatchInfo).")
|
|
else:
|
|
check_pos_int32(int(batch_size), "batch_size")
|
|
|
|
if num_parallel_workers is not None:
|
|
check_num_parallel_workers(num_parallel_workers)
|
|
type_check(drop_remainder, (bool,), "drop_remainder")
|
|
|
|
if (pad_info is not None) and (per_batch_map is not None):
|
|
raise ValueError("pad_info and per_batch_map can't both be set.")
|
|
|
|
if pad_info is not None:
|
|
type_check(param_dict["pad_info"], (dict,), "pad_info")
|
|
for k, v in param_dict.get('pad_info').items():
|
|
check_pad_info(k, v)
|
|
|
|
if (per_batch_map is None) != (input_columns is None):
|
|
# These two parameters appear together.
|
|
raise ValueError("per_batch_map and input_columns need to be passed in together.")
|
|
|
|
if input_columns is not None:
|
|
check_columns(input_columns, "input_columns")
|
|
if len(input_columns) != (len(ins.signature(per_batch_map).parameters) - 1):
|
|
raise ValueError("The signature of per_batch_map should match with input columns.")
|
|
|
|
if output_columns is not None:
|
|
check_columns(output_columns, "output_columns")
|
|
|
|
if column_order is not None:
|
|
check_columns(column_order, "column_order")
|
|
|
|
if python_multiprocessing is not None:
|
|
type_check(python_multiprocessing, (bool,), "python_multiprocessing")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_sync_wait(method):
|
|
"""check the input arguments of sync_wait."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[condition_name, num_batch, _], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(condition_name, (str,), "condition_name")
|
|
type_check(num_batch, (int,), "num_batch")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_shuffle(method):
|
|
"""check the input arguments of shuffle."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[buffer_size], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(buffer_size, (int,), "buffer_size")
|
|
|
|
check_value(buffer_size, [2, INT32_MAX], "buffer_size")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_map(method):
|
|
"""check the input arguments of map."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
from mindspore.dataset.callback import DSCallback
|
|
[_, input_columns, output_columns, column_order, num_parallel_workers, python_multiprocessing, cache,
|
|
callbacks], _ = \
|
|
parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_columns = ['input_columns', 'output_columns', 'column_order']
|
|
|
|
if column_order is not None:
|
|
type_check(column_order, (list,), "column_order")
|
|
if num_parallel_workers is not None:
|
|
check_num_parallel_workers(num_parallel_workers)
|
|
type_check(python_multiprocessing, (bool,), "python_multiprocessing")
|
|
check_cache_option(cache)
|
|
|
|
if callbacks is not None:
|
|
if isinstance(callbacks, (list, tuple)):
|
|
type_check_list(callbacks, (DSCallback,), "callbacks")
|
|
else:
|
|
type_check(callbacks, (DSCallback,), "callbacks")
|
|
|
|
for param_name, param in zip(nreq_param_columns, [input_columns, output_columns, column_order]):
|
|
if param is not None:
|
|
check_columns(param, param_name)
|
|
if callbacks is not None:
|
|
type_check(callbacks, (list, DSCallback), "callbacks")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_filter(method):
|
|
""""check the input arguments of filter."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[predicate, input_columns, num_parallel_workers], _ = parse_user_args(method, *args, **kwargs)
|
|
if not callable(predicate):
|
|
raise TypeError("Predicate should be a Python function or a callable Python object.")
|
|
|
|
if num_parallel_workers is not None:
|
|
check_num_parallel_workers(num_parallel_workers)
|
|
|
|
if input_columns is not None:
|
|
check_columns(input_columns, "input_columns")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_repeat(method):
|
|
"""check the input arguments of repeat."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[count], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(count, (int, type(None)), "repeat")
|
|
if isinstance(count, int):
|
|
if (count <= 0 and count != -1) or count > INT32_MAX:
|
|
raise ValueError("count should be either -1 or positive integer, range[1, INT32_MAX].")
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_skip(method):
|
|
"""check the input arguments of skip."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[count], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(count, (int,), "count")
|
|
check_value(count, (0, INT32_MAX), "count")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_take(method):
|
|
"""check the input arguments of take."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[count], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(count, (int,), "count")
|
|
if (count <= 0 and count != -1) or count > INT32_MAX:
|
|
raise ValueError("count should be either -1 or within the required interval of ({}, {}], got {}."
|
|
.format(0, INT32_MAX, count))
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_positive_int32(method):
|
|
"""check whether the input argument is positive and int, only works for functions with one input."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[count], param_dict = parse_user_args(method, *args, **kwargs)
|
|
para_name = None
|
|
for key in list(param_dict.keys()):
|
|
if key not in ['self', 'cls']:
|
|
para_name = key
|
|
# Need to get default value of param
|
|
if count is not None:
|
|
check_pos_int32(count, para_name)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_device_send(method):
|
|
"""check the input argument for to_device and device_que."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[send_epoch_end, create_data_info_queue], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(send_epoch_end, (bool,), "send_epoch_end")
|
|
type_check(create_data_info_queue, (bool,), "create_data_info_queue")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_zip(method):
|
|
"""check the input arguments of zip."""
|
|
|
|
@wraps(method)
|
|
def new_method(*args, **kwargs):
|
|
[ds], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(ds, (tuple,), "datasets")
|
|
|
|
return method(*args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_zip_dataset(method):
|
|
"""check the input arguments of zip method in `Dataset`."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[ds], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(ds, (tuple, datasets.Dataset), "datasets")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_concat(method):
|
|
"""check the input arguments of concat method in `Dataset`."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[ds], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(ds, (list, datasets.Dataset), "datasets")
|
|
if isinstance(ds, list):
|
|
type_check_list(ds, (datasets.Dataset,), "dataset")
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_rename(method):
|
|
"""check the input arguments of rename."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
values, _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
req_param_columns = ['input_columns', 'output_columns']
|
|
for param_name, param in zip(req_param_columns, values):
|
|
check_columns(param, param_name)
|
|
|
|
input_size, output_size = 1, 1
|
|
input_columns, output_columns = values
|
|
if isinstance(input_columns, list):
|
|
input_size = len(input_columns)
|
|
if isinstance(output_columns, list):
|
|
output_size = len(output_columns)
|
|
if input_size != output_size:
|
|
raise ValueError("Number of column in input_columns and output_columns is not equal.")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_project(method):
|
|
"""check the input arguments of project."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[columns], _ = parse_user_args(method, *args, **kwargs)
|
|
check_columns(columns, 'columns')
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_schema(method):
|
|
"""check the input arguments of Schema.__init__."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[schema_file], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
if schema_file is not None:
|
|
check_file(schema_file)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_add_column(method):
|
|
"""check the input arguments of add_column."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[name, de_type, shape], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(name, (str,), "name")
|
|
|
|
if not name:
|
|
raise TypeError("Expected non-empty string for column name.")
|
|
|
|
if de_type is not None:
|
|
if not isinstance(de_type, typing.Type) and not check_valid_detype(de_type):
|
|
raise TypeError("Unknown column type: {}.".format(de_type))
|
|
else:
|
|
raise TypeError("Expected non-empty string for de_type.")
|
|
|
|
if shape is not None:
|
|
type_check(shape, (list,), "shape")
|
|
type_check_list(shape, (int,), "shape")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_cluedataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(CLUEDataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
|
|
dataset_files = param_dict.get('dataset_files')
|
|
type_check(dataset_files, (str, list), "dataset files")
|
|
|
|
# check task
|
|
task_param = param_dict.get('task')
|
|
if task_param not in ['AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC', 'CSL']:
|
|
raise ValueError("task should be 'AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' or 'CSL'.")
|
|
|
|
# check usage
|
|
usage_param = param_dict.get('usage')
|
|
if usage_param not in ['train', 'test', 'eval']:
|
|
raise ValueError("usage should be 'train', 'test' or 'eval'.")
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
cache = param_dict.get('cache')
|
|
check_cache_option(cache)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_csvdataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(CSVDataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
|
|
# check dataset_files; required argument
|
|
dataset_files = param_dict.get('dataset_files')
|
|
type_check(dataset_files, (str, list), "dataset files")
|
|
|
|
# check field_delim
|
|
field_delim = param_dict.get('field_delim')
|
|
if field_delim is not None:
|
|
type_check(field_delim, (str,), 'field delim')
|
|
if field_delim in ['"', '\r', '\n'] or len(field_delim) > 1:
|
|
raise ValueError("field_delim is invalid.")
|
|
|
|
# check column_defaults
|
|
column_defaults = param_dict.get('column_defaults')
|
|
if column_defaults is not None:
|
|
if not isinstance(column_defaults, list):
|
|
raise TypeError("column_defaults should be type of list.")
|
|
for item in column_defaults:
|
|
if not isinstance(item, (str, int, float)):
|
|
raise TypeError("column type in column_defaults is invalid.")
|
|
|
|
# check column_names: must be list of string.
|
|
column_names = param_dict.get("column_names")
|
|
if column_names is not None:
|
|
all_string = all(isinstance(item, str) for item in column_names)
|
|
if not all_string:
|
|
raise TypeError("column_names should be a list of str.")
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
cache = param_dict.get('cache')
|
|
check_cache_option(cache)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_flowers102dataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(Flowers102Dataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
nreq_param_bool = ['shuffle', 'decode']
|
|
|
|
dataset_dir = param_dict.get('dataset_dir')
|
|
check_dir(dataset_dir)
|
|
|
|
check_dir(os.path.join(dataset_dir, "jpg"))
|
|
|
|
check_file(os.path.join(dataset_dir, "imagelabels.mat"))
|
|
check_file(os.path.join(dataset_dir, "setid.mat"))
|
|
|
|
usage = param_dict.get('usage')
|
|
if usage is not None:
|
|
check_valid_str(usage, ["train", "valid", "test", "all"], "usage")
|
|
|
|
task = param_dict.get('task')
|
|
if task is not None:
|
|
check_valid_str(task, ["Classification", "Segmentation"], "task")
|
|
if task == "Segmentation":
|
|
check_dir(os.path.join(dataset_dir, "segmim"))
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
|
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_textfiledataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(TextFileDataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
|
|
dataset_files = param_dict.get('dataset_files')
|
|
type_check(dataset_files, (str, list), "dataset files")
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
cache = param_dict.get('cache')
|
|
check_cache_option(cache)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_split(method):
|
|
"""check the input arguments of split."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[sizes, randomize], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(sizes, (list,), "sizes")
|
|
type_check(randomize, (bool,), "randomize")
|
|
|
|
# check sizes: must be list of float or list of int
|
|
if not sizes:
|
|
raise ValueError("sizes cannot be empty.")
|
|
|
|
all_int = all(isinstance(item, int) for item in sizes)
|
|
all_float = all(isinstance(item, float) for item in sizes)
|
|
|
|
if not (all_int or all_float):
|
|
raise ValueError("sizes should be list of int or list of float.")
|
|
|
|
if all_int:
|
|
all_positive = all(item > 0 for item in sizes)
|
|
if not all_positive:
|
|
raise ValueError("sizes is a list of int, but there should be no negative or zero numbers.")
|
|
|
|
if all_float:
|
|
all_valid_percentages = all(0 < item <= 1 for item in sizes)
|
|
if not all_valid_percentages:
|
|
raise ValueError("sizes is a list of float, but there should be no numbers outside the range (0, 1].")
|
|
|
|
epsilon = 0.00001
|
|
if not abs(sum(sizes) - 1) < epsilon:
|
|
raise ValueError("sizes is a list of float, but the percentages do not sum up to 1.")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_hostname(hostname):
|
|
if not hostname or len(hostname) > 255:
|
|
return False
|
|
if hostname[-1] == ".":
|
|
hostname = hostname[:-1] # strip exactly one dot from the right, if present
|
|
allowed = re.compile("(?!-)[A-Z\\d-]{1,63}(?<!-)$", re.IGNORECASE)
|
|
return all(allowed.match(x) for x in hostname.split("."))
|
|
|
|
|
|
def check_gnn_graphdata(method):
|
|
"""check the input arguments of graphdata."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[dataset_file, num_parallel_workers, working_mode, hostname,
|
|
port, num_client, auto_shutdown], _ = parse_user_args(method, *args, **kwargs)
|
|
check_file(dataset_file)
|
|
if num_parallel_workers is not None:
|
|
check_num_parallel_workers(num_parallel_workers)
|
|
type_check(hostname, (str,), "hostname")
|
|
if check_hostname(hostname) is False:
|
|
raise ValueError("The hostname is illegal")
|
|
type_check(working_mode, (str,), "working_mode")
|
|
if working_mode not in {'local', 'client', 'server'}:
|
|
raise ValueError("Invalid working mode, please enter 'local', 'client' or 'server'.")
|
|
type_check(port, (int,), "port")
|
|
check_value(port, (1024, 65535), "port")
|
|
type_check(num_client, (int,), "num_client")
|
|
check_value(num_client, (1, 255), "num_client")
|
|
type_check(auto_shutdown, (bool,), "auto_shutdown")
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_all_nodes(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_all_nodes` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[node_type], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(node_type, (int,), "node_type")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_all_edges(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_all_edges` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[edge_type], _ = parse_user_args(method, *args, **kwargs)
|
|
type_check(edge_type, (int,), "edge_type")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_nodes_from_edges(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_nodes_from_edges` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[edge_list], _ = parse_user_args(method, *args, **kwargs)
|
|
check_gnn_list_or_ndarray(edge_list, "edge_list")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_edges_from_nodes(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_edges_from_nodes` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[node_list], _ = parse_user_args(method, *args, **kwargs)
|
|
check_gnn_list_of_pair_or_ndarray(node_list, "node_list")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_all_neighbors(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_all_neighbors` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[node_list, neighbour_type, _], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
check_gnn_list_or_ndarray(node_list, 'node_list')
|
|
type_check(neighbour_type, (int,), "neighbour_type")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_sampled_neighbors(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_sampled_neighbors` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[node_list, neighbor_nums, neighbor_types, _], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
check_gnn_list_or_ndarray(node_list, 'node_list')
|
|
|
|
check_gnn_list_or_ndarray(neighbor_nums, 'neighbor_nums')
|
|
if not neighbor_nums or len(neighbor_nums) > 6:
|
|
raise ValueError("Wrong number of input members for {0}, should be between 1 and 6, got {1}.".format(
|
|
'neighbor_nums', len(neighbor_nums)))
|
|
|
|
check_gnn_list_or_ndarray(neighbor_types, 'neighbor_types')
|
|
if not neighbor_types or len(neighbor_types) > 6:
|
|
raise ValueError("Wrong number of input members for {0}, should be between 1 and 6, got {1}.".format(
|
|
'neighbor_types', len(neighbor_types)))
|
|
|
|
if len(neighbor_nums) != len(neighbor_types):
|
|
raise ValueError(
|
|
"The number of members of neighbor_nums and neighbor_types is inconsistent.")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_neg_sampled_neighbors(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_neg_sampled_neighbors` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[node_list, neg_neighbor_num, neg_neighbor_type], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
check_gnn_list_or_ndarray(node_list, 'node_list')
|
|
type_check(neg_neighbor_num, (int,), "neg_neighbor_num")
|
|
type_check(neg_neighbor_type, (int,), "neg_neighbor_type")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_random_walk(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `random_walk` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[target_nodes, meta_path, step_home_param, step_away_param, default_node], _ = parse_user_args(method, *args,
|
|
**kwargs)
|
|
check_gnn_list_or_ndarray(target_nodes, 'target_nodes')
|
|
check_gnn_list_or_ndarray(meta_path, 'meta_path')
|
|
type_check(step_home_param, (float,), "step_home_param")
|
|
type_check(step_away_param, (float,), "step_away_param")
|
|
type_check(default_node, (int,), "default_node")
|
|
check_value(default_node, (-1, INT32_MAX), "default_node")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_aligned_list(param, param_name, member_type):
|
|
"""Check whether the structure of each member of the list is the same."""
|
|
|
|
type_check(param, (list,), "param")
|
|
if not param:
|
|
raise TypeError(
|
|
"Parameter {0} or its members are empty".format(param_name))
|
|
member_have_list = None
|
|
list_len = None
|
|
for member in param:
|
|
if isinstance(member, list):
|
|
check_aligned_list(member, param_name, member_type)
|
|
|
|
if member_have_list not in (None, True):
|
|
raise TypeError("The type of each member of the parameter {0} is inconsistent.".format(
|
|
param_name))
|
|
if list_len is not None and len(member) != list_len:
|
|
raise TypeError("The size of each member of parameter {0} is inconsistent.".format(
|
|
param_name))
|
|
member_have_list = True
|
|
list_len = len(member)
|
|
else:
|
|
type_check(member, (member_type,), param_name)
|
|
if member_have_list not in (None, False):
|
|
raise TypeError("The type of each member of the parameter {0} is inconsistent.".format(
|
|
param_name))
|
|
member_have_list = False
|
|
|
|
|
|
def check_gnn_get_node_feature(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_node_feature` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[node_list, feature_types], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(node_list, (list, np.ndarray), "node_list")
|
|
if isinstance(node_list, list):
|
|
check_aligned_list(node_list, 'node_list', int)
|
|
elif isinstance(node_list, np.ndarray):
|
|
if not node_list.dtype == np.int32:
|
|
raise TypeError("Each member in {0} should be of type int32. Got {1}.".format(
|
|
node_list, node_list.dtype))
|
|
|
|
check_gnn_list_or_ndarray(feature_types, 'feature_types')
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_gnn_get_edge_feature(method):
|
|
"""A wrapper that wraps a parameter checker around the GNN `get_edge_feature` function."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[edge_list, feature_types], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
type_check(edge_list, (list, np.ndarray), "edge_list")
|
|
if isinstance(edge_list, list):
|
|
check_aligned_list(edge_list, 'edge_list', int)
|
|
elif isinstance(edge_list, np.ndarray):
|
|
if not edge_list.dtype == np.int32:
|
|
raise TypeError("Each member in {0} should be of type int32. Got {1}.".format(
|
|
edge_list, edge_list.dtype))
|
|
|
|
check_gnn_list_or_ndarray(feature_types, 'feature_types')
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_numpyslicesdataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(NumpySlicesDataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
data = param_dict.get("data")
|
|
column_names = param_dict.get("column_names")
|
|
type_check(data, (list, tuple, dict, np.ndarray), "data")
|
|
if data is None or len(data) == 0: # pylint: disable=len-as-condition
|
|
raise ValueError("Argument data cannot be empty")
|
|
if isinstance(data, tuple):
|
|
type_check(data[0], (list, np.ndarray), "data[0]")
|
|
|
|
# check column_names
|
|
if column_names is not None:
|
|
check_columns(column_names, "column_names")
|
|
|
|
# check num of input column in column_names
|
|
column_num = 1 if isinstance(column_names, str) else len(column_names)
|
|
if isinstance(data, dict):
|
|
data_column = len(list(data.keys()))
|
|
if column_num != data_column:
|
|
raise ValueError("Num of input column names is {0}, but required is {1}."
|
|
.format(column_num, data_column))
|
|
|
|
elif isinstance(data, tuple):
|
|
if column_num != len(data):
|
|
raise ValueError("Num of input column names is {0}, but required is {1}."
|
|
.format(column_num, len(data)))
|
|
else:
|
|
if column_num != 1:
|
|
raise ValueError("Num of input column names is {0}, but required is {1} as data is list."
|
|
.format(column_num, 1))
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_paddeddataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(PaddedDataset)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
padded_samples = param_dict.get("padded_samples")
|
|
if not padded_samples:
|
|
raise ValueError("padded_samples cannot be empty.")
|
|
type_check(padded_samples, (list,), "padded_samples")
|
|
type_check(padded_samples[0], (dict,), "padded_element")
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_cache_option(cache):
|
|
"""Sanity check for cache parameter"""
|
|
if cache is not None:
|
|
type_check(cache, (cache_client.DatasetCache,), "cache")
|
|
|
|
|
|
def check_to_device_send(method):
|
|
"""Check the input arguments of send function for TransferDataset."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
[num_epochs], _ = parse_user_args(method, *args, **kwargs)
|
|
|
|
if num_epochs is not None:
|
|
type_check(num_epochs, (int,), "num_epochs")
|
|
check_value(num_epochs, [-1, INT32_MAX], "num_epochs")
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_flickr_dataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original Dataset(Flickr8k, Flickr30k)."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
nreq_param_bool = ['shuffle', 'decode']
|
|
|
|
dataset_dir = param_dict.get('dataset_dir')
|
|
annotation_file = param_dict.get('annotation_file')
|
|
check_dir(dataset_dir)
|
|
check_file(annotation_file)
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validate_dataset_param_value(nreq_param_int, param_dict, int)
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validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
|
|
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check_sampler_shuffle_shard_options(param_dict)
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|
|
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cache = param_dict.get('cache')
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check_cache_option(cache)
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|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_sb_dataset(method):
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"""A wrapper that wraps a parameter checker around the original Semantic Boundaries Dataset."""
|
|
|
|
@wraps(method)
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def new_method(self, *args, **kwargs):
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_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
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nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
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|
nreq_param_bool = ['shuffle', 'decode']
|
|
|
|
dataset_dir = param_dict.get('dataset_dir')
|
|
check_dir(dataset_dir)
|
|
|
|
usage = param_dict.get('usage')
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|
if usage is not None:
|
|
check_valid_str(usage, ["train", "val", "train_noval", "all"], "usage")
|
|
|
|
task = param_dict.get('task')
|
|
if task is not None:
|
|
check_valid_str(task, ["Boundaries", "Segmentation"], "task")
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
|
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_cityscapes_dataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original CityScapesDataset."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
nreq_param_bool = ['shuffle', 'decode']
|
|
|
|
dataset_dir = param_dict.get('dataset_dir')
|
|
check_dir(dataset_dir)
|
|
|
|
task = param_dict.get('task')
|
|
check_valid_str(task, ["instance", "semantic", "polygon", "color"], "task")
|
|
|
|
quality_mode = param_dict.get('quality_mode')
|
|
check_valid_str(quality_mode, ["fine", "coarse"], "quality_mode")
|
|
|
|
usage = param_dict.get('usage')
|
|
if quality_mode == "fine":
|
|
valid_strings = ["train", "test", "val", "all"]
|
|
else:
|
|
valid_strings = ["train", "train_extra", "val", "all"]
|
|
check_valid_str(usage, valid_strings, "usage")
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
|
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|
|
|
|
|
|
def check_div2k_dataset(method):
|
|
"""A wrapper that wraps a parameter checker around the original DIV2KDataset."""
|
|
|
|
@wraps(method)
|
|
def new_method(self, *args, **kwargs):
|
|
_, param_dict = parse_user_args(method, *args, **kwargs)
|
|
|
|
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
|
nreq_param_bool = ['shuffle', 'decode']
|
|
|
|
dataset_dir = param_dict.get('dataset_dir')
|
|
check_dir(dataset_dir)
|
|
|
|
usage = param_dict.get('usage')
|
|
check_valid_str(usage, ['train', 'valid', 'all'], "usage")
|
|
|
|
downgrade = param_dict.get('downgrade')
|
|
check_valid_str(downgrade, ['bicubic', 'unknown', 'mild', 'difficult', 'wild'], 'downgrade')
|
|
|
|
validate_dataset_param_value(['scale'], param_dict, int)
|
|
scale = param_dict.get('scale')
|
|
scale_values = [2, 3, 4, 8]
|
|
if scale not in scale_values:
|
|
raise ValueError("Input scale is not within the valid set of {0}.".format(str(scale_values)))
|
|
|
|
if scale == 8 and downgrade != "bicubic":
|
|
raise ValueError("DIV2KNode: scale equal to 8 is allowed only in bicubic downgrade.")
|
|
|
|
downgrade_2018 = ["mild", "difficult", "wild"]
|
|
if downgrade in downgrade_2018 and scale != 4:
|
|
raise ValueError("DIV2KNode: {0} downgrade requires scale equal to 4.".format(downgrade))
|
|
|
|
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
|
validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
|
|
|
check_sampler_shuffle_shard_options(param_dict)
|
|
|
|
return method(self, *args, **kwargs)
|
|
|
|
return new_method
|