forked from huawei/mindspore2022
!19436 Update python docstring
Merge pull request !19436 from luoyang/code_docs_python-doc
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commit
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@ -107,9 +107,9 @@ def set_seed(seed):
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def get_seed():
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"""
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Get random number seed. If seed has been set, then get_seed will
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get the seed value that has been set, if the seed is not set,
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it will return std::mt19937::default_seed.
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Get random number seed. If the seed has been set, then will
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return the set value, otherwise it will return the default seed value
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which equals to std::mt19937::default_seed.
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Returns:
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int, random number seed.
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@ -1510,7 +1510,7 @@ class Dataset:
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def get_col_names(self):
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"""
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Renturn the names of the columns in dataset
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Renturn the names of the columns in dataset.
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Returns:
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list, list of column names in the dataset.
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@ -1757,7 +1757,7 @@ class Dataset:
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Returns:
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dict, a str-to-int mapping from label name to index.
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dict, a str-to-list<int> mapping from label name to index for Coco ONLY. The second number
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in the list is used to indicate the super category
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in the list is used to indicate the super category.
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"""
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if self.children:
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return self.children[0].get_class_indexing()
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@ -3181,24 +3181,27 @@ class ImageFolderDataset(MappableDataset):
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... extensions=[".JPEG", ".png"])
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About ImageFolderDataset:
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| You can construct the following directory structure from your dataset files and read by MindSpore's API.
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| .
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| └── image_folder_dataset_directory
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| ├── class1
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| │ ├── 000000000001.jpg
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| │ ├── 000000000002.jpg
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| │ ├── ...
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| ├── class2
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| │ ├── 000000000001.jpg
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| │ ├── 000000000002.jpg
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| │ ├── ...
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| ├── class3
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| │ ├── 000000000001.jpg
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| │ ├── 000000000002.jpg
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| │ ├── ...
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| ├── classN
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| ├── ...
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You can construct the following directory structure from your dataset files and read by MindSpore's API.
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.. code-block::
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.
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└── image_folder_dataset_directory
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├── class1
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│ ├── 000000000001.jpg
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│ ├── 000000000002.jpg
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│ ├── ...
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├── class2
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│ ├── 000000000001.jpg
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│ ├── 000000000002.jpg
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│ ├── ...
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├── class3
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│ ├── 000000000001.jpg
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│ ├── 000000000002.jpg
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│ ├── ...
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├── classN
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├── ...
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"""
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@check_imagefolderdataset
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@ -3292,29 +3295,35 @@ class MnistDataset(MappableDataset):
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>>> # Note: In mnist_dataset dataset, each dictionary has keys "image" and "label"
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About MNIST dataset:
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| The MNIST database of handwritten digits has a training set of 60,000 examples,
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and a test set of 10,000 examples. It is a subset of a larger set available from
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NIST. The digits have been size-normalized and centered in a fixed-size image.
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| Here is the original MNIST dataset structure.
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| You can unzip the dataset files into this directory structure and read by MindSpore's API.
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| .
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| └── mnist_dataset_dir
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| ├── t10k-images-idx3-ubyte
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| ├── t10k-labels-idx1-ubyte
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| ├── train-images-idx3-ubyte
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| └── train-labels-idx1-ubyte
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The MNIST database of handwritten digits has a training set of 60,000 examples,
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and a test set of 10,000 examples. It is a subset of a larger set available from
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NIST. The digits have been size-normalized and centered in a fixed-size image.
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.. code-block::
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Here is the original MNIST dataset structure.
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You can unzip the dataset files into this directory structure and read by MindSpore's API.
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@article{lecun2010mnist,
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title = {MNIST handwritten digit database},
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author = {LeCun, Yann and Cortes, Corinna and Burges, CJ},
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journal = {ATT Labs [Online]},
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volume = {2},
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year = {2010},
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howpublished = {http://yann.lecun.com/exdb/mnist}
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}
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.. code-block::
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.
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└── mnist_dataset_dir
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├── t10k-images-idx3-ubyte
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├── t10k-labels-idx1-ubyte
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├── train-images-idx3-ubyte
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└── train-labels-idx1-ubyte
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Citation:
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.. code-block::
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@article{lecun2010mnist,
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title = {MNIST handwritten digit database},
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author = {LeCun, Yann and Cortes, Corinna and Burges, CJ},
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journal = {ATT Labs [Online]},
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volume = {2},
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year = {2010},
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howpublished = {http://yann.lecun.com/exdb/mnist}
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}
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"""
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@check_mnist_cifar_dataset
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@ -4021,8 +4030,8 @@ class TFRecordDataset(SourceDataset):
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pattern of files. The list will be sorted in a lexicographical order.
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schema (Union[str, Schema], optional): Path to the JSON schema file or schema object (default=None).
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If the schema is not provided, the meta data from the TFData file is considered the schema.
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columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
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num_samples (int, optional): Number of samples (rows) to read (default=None).
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columns_list (list[str], optional): List of columns to be read (default=None, read all columns).
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num_samples (int, optional): The number of samples (rows) to be included in the dataset (default=None).
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If num_samples is None and numRows(parsed from schema) does not exist, read the full dataset;
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If num_samples is None and numRows(parsed from schema) is greater than 0, read numRows rows;
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If both num_samples and numRows(parsed from schema) are greater than 0, read num_samples rows.
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@ -4222,9 +4231,9 @@ class Cifar10Dataset(MappableDataset):
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Args:
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dataset_dir (str): Path to the root directory that contains the dataset.
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usage (str, optional): Usage of this dataset, can be `train`, `test` or `all` . `train` will read from 50,000
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train samples, `test` will read from 10,000 test samples, `all` will read from all 60,000 samples.
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(default=None, all samples)
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num_samples (int, optional): The number of images to be included in the dataset.
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train samples, `test` will read from 10,000 test samples, `all` will read from all 60,000 samples
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(default=None, all samples).
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num_samples (int, optional): The number of images to be included in the dataset
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(default=None, all images).
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num_parallel_workers (int, optional): Number of workers to read the data
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(default=None, number set in the config).
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@ -4294,32 +4303,38 @@ class Cifar10Dataset(MappableDataset):
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>>> # In CIFAR10 dataset, each dictionary has keys "image" and "label"
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About CIFAR-10 dataset:
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| The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
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with 6000 images per class. There are 50000 training images and 10000 test images.
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The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
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| Here is the original CIFAR-10 dataset structure.
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| You can unzip the dataset files into the following directory structure and read by MindSpore's API.
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| .
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| └── cifar-10-batches-bin
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| ├── data_batch_1.bin
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| ├── data_batch_2.bin
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| ├── data_batch_3.bin
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| ├── data_batch_4.bin
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| ├── data_batch_5.bin
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| ├── test_batch.bin
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| ├── readme.html
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| └── batches.meta.txt
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The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
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with 6000 images per class. There are 50000 training images and 10000 test images.
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The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
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.. code-block::
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Here is the original CIFAR-10 dataset structure.
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You can unzip the dataset files into the following directory structure and read by MindSpore's API.
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@techreport{Krizhevsky09,
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author = {Alex Krizhevsky},
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title = {Learning multiple layers of features from tiny images},
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institution = {},
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year = {2009},
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howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html}
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}
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.. code-block::
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.
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└── cifar-10-batches-bin
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├── data_batch_1.bin
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├── data_batch_2.bin
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├── data_batch_3.bin
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├── data_batch_4.bin
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├── data_batch_5.bin
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├── test_batch.bin
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├── readme.html
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└── batches.meta.txt
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Citation:
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.. code-block::
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@techreport{Krizhevsky09,
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author = {Alex Krizhevsky},
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title = {Learning multiple layers of features from tiny images},
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institution = {},
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year = {2009},
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howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html}
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}
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"""
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@check_mnist_cifar_dataset
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@ -4346,9 +4361,9 @@ class Cifar100Dataset(MappableDataset):
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Args:
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dataset_dir (str): Path to the root directory that contains the dataset.
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usage (str, optional): Usage of this dataset, can be `train`, `test` or `all` . `train` will read from 50,000
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train samples, `test` will read from 10,000 test samples, `all` will read from all 60,000 samples.
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(default=None, all samples)
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num_samples (int, optional): The number of images to be included in the dataset.
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train samples, `test` will read from 10,000 test samples, `all` will read from all 60,000 samples
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(default=None, all samples).
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num_samples (int, optional): The number of images to be included in the dataset
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(default=None, all images).
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num_parallel_workers (int, optional): Number of workers to read the data
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(default=None, number set in the config).
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@ -4415,29 +4430,35 @@ class Cifar100Dataset(MappableDataset):
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>>> # In CIFAR100 dataset, each dictionary has 3 keys: "image", "fine_label" and "coarse_label"
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About CIFAR-100 dataset:
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| This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images
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each. There are 500 training images and 100 testing images per class. The 100 classes in
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the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the
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class to which it belongs) and a "coarse" label (the superclass to which it belongs).
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| Here is the original CIFAR-100 dataset structure.
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| You can unzip the dataset files into the following directory structure and read by MindSpore's API.
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| .
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| └── cifar-100-binary
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| ├── train.bin
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| ├── test.bin
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| ├── fine_label_names.txt
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| └── coarse_label_names.txt
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This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images
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each. There are 500 training images and 100 testing images per class. The 100 classes in
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the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the
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class to which it belongs) and a "coarse" label (the superclass to which it belongs).
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.. code-block::
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Here is the original CIFAR-100 dataset structure.
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You can unzip the dataset files into the following directory structure and read by MindSpore's API.
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@techreport{Krizhevsky09,
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author = {Alex Krizhevsky},
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title = {Learning multiple layers of features from tiny images},
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institution = {},
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year = {2009},
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howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html}
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}
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.. code-block::
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.
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└── cifar-100-binary
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├── train.bin
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├── test.bin
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├── fine_label_names.txt
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└── coarse_label_names.txt
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Citation:
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.. code-block::
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@techreport{Krizhevsky09,
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author = {Alex Krizhevsky},
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title = {Learning multiple layers of features from tiny images},
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institution = {},
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year = {2009},
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howpublished = {http://www.cs.toronto.edu/~kriz/cifar.html}
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}
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"""
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@check_mnist_cifar_dataset
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@ -4458,11 +4479,13 @@ class RandomDataset(SourceDataset):
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A source dataset that generates random data.
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Args:
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total_rows (int): Number of rows for the dataset to generate (default=None, number of rows is random)
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total_rows (int, optional): Number of samples for the dataset to generate
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(default=None, number of samples is random).
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schema (Union[str, Schema], optional): Path to the JSON schema file or schema object (default=None).
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If the schema is not provided, the random dataset generates a random schema.
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columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
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num_samples (int): number of samples to draw from the total. (default=None, which means all rows)
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num_samples (int, optional): The number of samples to be included in the dataset
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(default=None, all samples).
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num_parallel_workers (int, optional): Number of workers to read the data
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(default=None, number set in the config).
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cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
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@ -4614,10 +4637,10 @@ class VOCDataset(MappableDataset):
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Args:
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dataset_dir (str): Path to the root directory that contains the dataset.
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task (str): Set the task type of reading voc data, now only support `Segmentation` or `Detection`
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task (str, optional): Set the task type of reading voc data, now only support `Segmentation` or `Detection`
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(default=`Segmentation`).
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usage (str): Set the task type of ImageSets(default=`train`). If task is `Segmentation`, image and annotation
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list will be loaded in ./ImageSets/Segmentation/usage + ".txt"; If task is `Detection`, image and
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usage (str, optional): Set the task type of ImageSets(default=`train`). If task is `Segmentation`, image and
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annotation list will be loaded in ./ImageSets/Segmentation/usage + ".txt"; If task is `Detection`, image and
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annotation list will be loaded in ./ImageSets/Main/usage + ".txt"; if task and usage is not set, image and
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annotation list will be loaded in ./ImageSets/Segmentation/train.txt as default.
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class_indexing (dict, optional): A str-to-int mapping from label name to index, only valid in
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|
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@ -4710,50 +4733,56 @@ class VOCDataset(MappableDataset):
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>>> # In VOC dataset, if task='Detection', each dictionary has keys "image" and "annotation"
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About VOC dataset.
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| The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual
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object category recognition and detection, providing the vision and machine
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learning communities with a standard dataset of images and annotation, and
|
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standard evaluation procedures.
|
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|
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| You can unzip the original VOC-2012 dataset files into this directory structure and read by MindSpore's API.
|
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| .
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| └── voc2012_dataset_dir
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| ├── Annotations
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| │ ├── 2007_000027.xml
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| │ ├── 2007_000032.xml
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| │ ├── ...
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| ├── ImageSets
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| │ ├── Action
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| │ ├── Layout
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| │ ├── Main
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| │ └── Segmentation
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| ├── JPEGImages
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| │ ├── 2007_000027.jpg
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| │ ├── 2007_000032.jpg
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| │ ├── ...
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| ├── SegmentationClass
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| │ ├── 2007_000032.png
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| │ ├── 2007_000033.png
|
||||
| │ ├── ...
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||||
| └── SegmentationObject
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||||
| ├── 2007_000032.png
|
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| ├── 2007_000033.png
|
||||
| ├── ...
|
||||
The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual
|
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object category recognition and detection, providing the vision and machine
|
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learning communities with a standard dataset of images and annotation, and
|
||||
standard evaluation procedures.
|
||||
|
||||
.. code-block::
|
||||
You can unzip the original VOC-2012 dataset files into this directory structure and read by MindSpore's API.
|
||||
|
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@article{Everingham10,
|
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author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
|
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title = {The Pascal Visual Object Classes (VOC) Challenge},
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journal = {International Journal of Computer Vision},
|
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volume = {88},
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year = {2010},
|
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number = {2},
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month = {jun},
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pages = {303--338},
|
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biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
|
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howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc{year}/index.html}
|
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}
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.. code-block::
|
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|
||||
.
|
||||
└── voc2012_dataset_dir
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├── Annotations
|
||||
│ ├── 2007_000027.xml
|
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│ ├── 2007_000032.xml
|
||||
│ ├── ...
|
||||
├── ImageSets
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│ ├── Action
|
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│ ├── Layout
|
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│ ├── Main
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│ └── Segmentation
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├── JPEGImages
|
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│ ├── 2007_000027.jpg
|
||||
│ ├── 2007_000032.jpg
|
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│ ├── ...
|
||||
├── SegmentationClass
|
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│ ├── 2007_000032.png
|
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│ ├── 2007_000033.png
|
||||
│ ├── ...
|
||||
└── SegmentationObject
|
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├── 2007_000032.png
|
||||
├── 2007_000033.png
|
||||
├── ...
|
||||
|
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Citation:
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.. code-block::
|
||||
|
||||
@article{Everingham10,
|
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author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
|
||||
title = {The Pascal Visual Object Classes (VOC) Challenge},
|
||||
journal = {International Journal of Computer Vision},
|
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volume = {88},
|
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year = {2010},
|
||||
number = {2},
|
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month = {jun},
|
||||
pages = {303--338},
|
||||
biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
|
||||
howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc{year}/index.html}
|
||||
}
|
||||
"""
|
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|
||||
@check_vocdataset
|
||||
|
|
@ -4812,8 +4841,8 @@ class CocoDataset(MappableDataset):
|
|||
|
||||
Args:
|
||||
dataset_dir (str): Path to the root directory that contains the dataset.
|
||||
annotation_file (str): Path to the annotation JSON.
|
||||
task (str): Set the task type for reading COCO data. Supported task types:
|
||||
annotation_file (str): Path to the annotation JSON file.
|
||||
task (str, optional): Set the task type for reading COCO data. Supported task types:
|
||||
`Detection`, `Stuff`, `Panoptic` and `Keypoint` (default=`Detection`).
|
||||
num_samples (int, optional): The number of images to be included in the dataset
|
||||
(default=None, all images).
|
||||
|
|
@ -4907,51 +4936,57 @@ class CocoDataset(MappableDataset):
|
|||
>>> # In COCO dataset, each dictionary has keys "image" and "annotation"
|
||||
|
||||
About COCO dataset:
|
||||
| COCO is a large-scale object detection, segmentation, and captioning dataset.
|
||||
It contains 91 common object categories with 82 of them having more than 5,000
|
||||
labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer
|
||||
categories but more instances per category.
|
||||
|
||||
| You can unzip the original COCO-2017 dataset files into this directory structure and read by MindSpore's API.
|
||||
| .
|
||||
| └── coco_dataset_directory
|
||||
| ├── train2017
|
||||
| │ ├── 000000000009.jpg
|
||||
| │ ├── 000000000025.jpg
|
||||
| │ ├── ...
|
||||
| ├── test2017
|
||||
| │ ├── 000000000001.jpg
|
||||
| │ ├── 000000058136.jpg
|
||||
| │ ├── ...
|
||||
| ├── val2017
|
||||
| │ ├── 000000000139.jpg
|
||||
| │ ├── 000000057027.jpg
|
||||
| │ ├── ...
|
||||
| └── annotations
|
||||
| ├── captions_train2017.json
|
||||
| ├── captions_val2017.json
|
||||
| ├── instances_train2017.json
|
||||
| ├── instances_val2017.json
|
||||
| ├── person_keypoints_train2017.json
|
||||
| └── person_keypoints_val2017.json
|
||||
COCO is a large-scale object detection, segmentation, and captioning dataset.
|
||||
It contains 91 common object categories with 82 of them having more than 5,000
|
||||
labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer
|
||||
categories but more instances per category.
|
||||
|
||||
.. code-block::
|
||||
You can unzip the original COCO-2017 dataset files into this directory structure and read by MindSpore's API.
|
||||
|
||||
@article{DBLP:journals/corr/LinMBHPRDZ14,
|
||||
author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and
|
||||
Lubomir D. Bourdev and Ross B. Girshick and James Hays and
|
||||
Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick},
|
||||
title = {Microsoft {COCO:} Common Objects in Context},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1405.0312},
|
||||
year = {2014},
|
||||
url = {http://arxiv.org/abs/1405.0312},
|
||||
archivePrefix = {arXiv},
|
||||
eprint = {1405.0312},
|
||||
timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
|
||||
biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
|
||||
bibsource = {dblp computer science bibliography, https://dblp.org}
|
||||
}
|
||||
.. code-block::
|
||||
|
||||
.
|
||||
└── coco_dataset_directory
|
||||
├── train2017
|
||||
│ ├── 000000000009.jpg
|
||||
│ ├── 000000000025.jpg
|
||||
│ ├── ...
|
||||
├── test2017
|
||||
│ ├── 000000000001.jpg
|
||||
│ ├── 000000058136.jpg
|
||||
│ ├── ...
|
||||
├── val2017
|
||||
│ ├── 000000000139.jpg
|
||||
│ ├── 000000057027.jpg
|
||||
│ ├── ...
|
||||
└── annotations
|
||||
├── captions_train2017.json
|
||||
├── captions_val2017.json
|
||||
├── instances_train2017.json
|
||||
├── instances_val2017.json
|
||||
├── person_keypoints_train2017.json
|
||||
└── person_keypoints_val2017.json
|
||||
|
||||
Citation:
|
||||
|
||||
.. code-block::
|
||||
|
||||
@article{DBLP:journals/corr/LinMBHPRDZ14,
|
||||
author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and
|
||||
Lubomir D. Bourdev and Ross B. Girshick and James Hays and
|
||||
Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick},
|
||||
title = {Microsoft {COCO:} Common Objects in Context},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1405.0312},
|
||||
year = {2014},
|
||||
url = {http://arxiv.org/abs/1405.0312},
|
||||
archivePrefix = {arXiv},
|
||||
eprint = {1405.0312},
|
||||
timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
|
||||
biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
|
||||
bibsource = {dblp computer science bibliography, https://dblp.org}
|
||||
}
|
||||
"""
|
||||
|
||||
@check_cocodataset
|
||||
|
|
@ -4999,7 +5034,8 @@ class CelebADataset(MappableDataset):
|
|||
num_parallel_workers (int, optional): Number of workers to read the data (default=None, will use value set in
|
||||
the config).
|
||||
shuffle (bool, optional): Whether to perform shuffle on the dataset (default=None).
|
||||
usage (str): one of `all`, `train`, `valid` or `test` (default=`all`, will read all samples).
|
||||
usage (str, optional): Specify the `train`, `valid`, `test` part or `all` parts of dataset
|
||||
(default=`all`, will read all samples).
|
||||
sampler (Sampler, optional): Object used to choose samples from the dataset (default=None).
|
||||
decode (bool, optional): decode the images after reading (default=False).
|
||||
extensions (list[str], optional): List of file extensions to be included in the dataset (default=None).
|
||||
|
|
@ -5061,61 +5097,71 @@ class CelebADataset(MappableDataset):
|
|||
>>> # Note: In celeba dataset, each data dictionary owns keys "image" and "attr"
|
||||
|
||||
About CelebA dataset:
|
||||
| CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset
|
||||
with more than 200K celebrity images, each with 40 attribute annotations.
|
||||
|
|
||||
| The images in this dataset cover large pose variations and background clutter.
|
||||
CelebA has large diversities, large quantities, and rich annotations, including
|
||||
| * 10,177 number of identities,
|
||||
| * 202,599 number of face images, and
|
||||
| * 5 landmark locations, 40 binary attributes annotations per image.
|
||||
|
|
||||
| The dataset can be employed as the training and test sets for the following computer
|
||||
vision tasks: face attribute recognition, face detection, landmark (or facial part)
|
||||
localization, and face editing & synthesis.
|
||||
|
||||
| Original CelebA dataset structure:
|
||||
| .
|
||||
| └── CelebA
|
||||
| ├── README.md
|
||||
| ├── Img
|
||||
| │ ├── img_celeba.7z
|
||||
| │ ├── img_align_celeba_png.7z
|
||||
| │ └── img_align_celeba.zip
|
||||
| ├── Eval
|
||||
| │ └── list_eval_partition.txt
|
||||
| └── Anno
|
||||
| ├── list_landmarks_celeba.txt
|
||||
| ├── list_landmarks_align_celeba.txt
|
||||
| ├── list_bbox_celeba.txt
|
||||
| ├── list_attr_celeba.txt
|
||||
| └── identity_CelebA.txt
|
||||
CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset
|
||||
with more than 200K celebrity images, each with 40 attribute annotations.
|
||||
|
||||
| You can unzip the dataset files into the following structure and read by MindSpore's API.
|
||||
| .
|
||||
| └── celeba_dataset_directory
|
||||
| ├── list_attr_celeba.txt
|
||||
| ├── 000001.jpg
|
||||
| ├── 000002.jpg
|
||||
| ├── 000003.jpg
|
||||
| ├── ...
|
||||
The images in this dataset cover large pose variations and background clutter.
|
||||
CelebA has large diversities, large quantities, and rich annotations, including
|
||||
|
||||
.. code-block::
|
||||
* 10,177 number of identities,
|
||||
* 202,599 number of face images, and
|
||||
* 5 landmark locations, 40 binary attributes annotations per image.
|
||||
|
||||
@article{DBLP:journals/corr/LiuLWT14,
|
||||
author = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
|
||||
title = {Deep Learning Face Attributes in the Wild},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1411.7766},
|
||||
year = {2014},
|
||||
url = {http://arxiv.org/abs/1411.7766},
|
||||
archivePrefix = {arXiv},
|
||||
eprint = {1411.7766},
|
||||
timestamp = {Tue, 10 Dec 2019 15:37:26 +0100},
|
||||
biburl = {https://dblp.org/rec/journals/corr/LiuLWT14.bib},
|
||||
bibsource = {dblp computer science bibliography, https://dblp.org},
|
||||
howpublished = {http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html},
|
||||
}
|
||||
The dataset can be employed as the training and test sets for the following computer
|
||||
vision tasks: face attribute recognition, face detection, landmark (or facial part)
|
||||
localization, and face editing & synthesis.
|
||||
|
||||
Original CelebA dataset structure:
|
||||
|
||||
.. code-block::
|
||||
|
||||
.
|
||||
└── CelebA
|
||||
├── README.md
|
||||
├── Img
|
||||
│ ├── img_celeba.7z
|
||||
│ ├── img_align_celeba_png.7z
|
||||
│ └── img_align_celeba.zip
|
||||
├── Eval
|
||||
│ └── list_eval_partition.txt
|
||||
└── Anno
|
||||
├── list_landmarks_celeba.txt
|
||||
├── list_landmarks_align_celeba.txt
|
||||
├── list_bbox_celeba.txt
|
||||
├── list_attr_celeba.txt
|
||||
└── identity_CelebA.txt
|
||||
|
||||
You can unzip the dataset files into the following structure and read by MindSpore's API.
|
||||
|
||||
.. code-block::
|
||||
|
||||
.
|
||||
└── celeba_dataset_directory
|
||||
├── list_attr_celeba.txt
|
||||
├── 000001.jpg
|
||||
├── 000002.jpg
|
||||
├── 000003.jpg
|
||||
├── ...
|
||||
|
||||
Citation:
|
||||
|
||||
.. code-block::
|
||||
|
||||
@article{DBLP:journals/corr/LiuLWT14,
|
||||
author = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
|
||||
title = {Deep Learning Face Attributes in the Wild},
|
||||
journal = {CoRR},
|
||||
volume = {abs/1411.7766},
|
||||
year = {2014},
|
||||
url = {http://arxiv.org/abs/1411.7766},
|
||||
archivePrefix = {arXiv},
|
||||
eprint = {1411.7766},
|
||||
timestamp = {Tue, 10 Dec 2019 15:37:26 +0100},
|
||||
biburl = {https://dblp.org/rec/journals/corr/LiuLWT14.bib},
|
||||
bibsource = {dblp computer science bibliography, https://dblp.org},
|
||||
howpublished = {http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html}
|
||||
}
|
||||
"""
|
||||
|
||||
@check_celebadataset
|
||||
|
|
@ -5202,8 +5248,9 @@ class CLUEDataset(SourceDataset):
|
|||
a pattern of files. The list will be sorted in a lexicographical order.
|
||||
task (str, optional): The kind of task, one of `AFQMC`, `TNEWS`, `IFLYTEK`, `CMNLI`, `WSC` and `CSL`.
|
||||
(default=AFQMC).
|
||||
usage (str, optional): Need train, test or eval data (default="train").
|
||||
num_samples (int, optional): Number of samples (rows) to read (default=None, reads the full dataset).
|
||||
usage (str, optional): Specify the `train`, `test` or `eval` part of dataset (default="train").
|
||||
num_samples (int, optional): The number of samples to be included in the dataset
|
||||
(default=None, will include all images).
|
||||
num_parallel_workers (int, optional): Number of workers to read the data
|
||||
(default=None, number set in the config).
|
||||
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
|
||||
|
|
@ -5233,31 +5280,37 @@ class CLUEDataset(SourceDataset):
|
|||
>>> clue_dataset_dir = ["/path/to/clue_dataset_file"] # contains 1 or multiple clue files
|
||||
>>> dataset = ds.CLUEDataset(dataset_files=clue_dataset_dir, task='AFQMC', usage='train')
|
||||
|
||||
About CLUE dataset.
|
||||
| CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different
|
||||
tasks, including single-sentence classification, sentence pair classification, and machine
|
||||
reading comprehension.
|
||||
About CLUE dataset:
|
||||
|
||||
| You can unzip the dataset files into the following structure and read by MindSpore's API,
|
||||
such as afqmc dataset:
|
||||
| .
|
||||
| └── afqmc_public
|
||||
| ├── train.json
|
||||
| ├── test.json
|
||||
| └── dev.json
|
||||
CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different
|
||||
tasks, including single-sentence classification, sentence pair classification, and machine
|
||||
reading comprehension.
|
||||
|
||||
.. code-block::
|
||||
You can unzip the dataset files into the following structure and read by MindSpore's API,
|
||||
such as afqmc dataset:
|
||||
|
||||
@article{CLUEbenchmark,
|
||||
title = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
|
||||
author = {Liang Xu, Xuanwei Zhang, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Junyi Li, Yudong Li,
|
||||
Kai Sun, Yechen Xu, Yiming Cui, Cong Yu, Qianqian Dong, Yin Tian, Dian Yu, Bo Shi, Jun Zeng,
|
||||
Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou,
|
||||
Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Zhenzhong Lan},
|
||||
journal = {arXiv preprint arXiv:2004.05986},
|
||||
year = {2020},
|
||||
howpublished = {https://github.com/CLUEbenchmark/CLUE}
|
||||
}
|
||||
.. code-block::
|
||||
|
||||
.
|
||||
└── afqmc_public
|
||||
├── train.json
|
||||
├── test.json
|
||||
└── dev.json
|
||||
|
||||
Citation:
|
||||
|
||||
.. code-block::
|
||||
|
||||
@article{CLUEbenchmark,
|
||||
title = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
|
||||
author = {Liang Xu, Xuanwei Zhang, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Junyi Li, Yudong Li,
|
||||
Kai Sun, Yechen Xu, Yiming Cui, Cong Yu, Qianqian Dong, Yin Tian, Dian Yu, Bo Shi, Jun Zeng,
|
||||
Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou,
|
||||
Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Zhenzhong Lan},
|
||||
journal = {arXiv preprint arXiv:2004.05986},
|
||||
year = {2020},
|
||||
howpublished = {https://github.com/CLUEbenchmark/CLUE}
|
||||
}
|
||||
"""
|
||||
|
||||
@check_cluedataset
|
||||
|
|
@ -5288,7 +5341,8 @@ class CSVDataset(SourceDataset):
|
|||
columns as string type.
|
||||
column_names (list[str], optional): List of column names of the dataset (default=None). If this
|
||||
is not provided, infers the column_names from the first row of CSV file.
|
||||
num_samples (int, optional): Number of samples (rows) to read (default=None, reads the full dataset).
|
||||
num_samples (int, optional): The number of samples to be included in the dataset
|
||||
(default=None, will include all images).
|
||||
num_parallel_workers (int, optional): Number of workers to read the data
|
||||
(default=None, number set in the config).
|
||||
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
|
||||
|
|
@ -5343,7 +5397,8 @@ class TextFileDataset(SourceDataset):
|
|||
Args:
|
||||
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
|
||||
pattern of files. The list will be sorted in a lexicographical order.
|
||||
num_samples (int, optional): Number of samples (rows) to read (default=None, reads the full dataset).
|
||||
num_samples (int, optional): The number of samples to be included in the dataset
|
||||
(default=None, will include all images).
|
||||
num_parallel_workers (int, optional): Number of workers to read the data
|
||||
(default=None, number set in the config).
|
||||
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
|
||||
|
|
@ -5465,8 +5520,9 @@ class NumpySlicesDataset(GeneratorDataset):
|
|||
list, there will be one column in each row, otherwise there tends to be multi columns. Large data is not
|
||||
recommended to be loaded in this way as data is loading into memory.
|
||||
column_names (list[str], optional): List of column names of the dataset (default=None). If column_names is not
|
||||
provided, when data is dict, column_names will be its keys, otherwise it will be like column_0, column_1 ...
|
||||
num_samples (int, optional): The number of samples to be included in the dataset (default=None, all images).
|
||||
provided, the output column names will be named as the keys of dict when the input data is a dict,
|
||||
otherwise they will be named like column_0, column_1 ...
|
||||
num_samples (int, optional): The number of samples to be included in the dataset (default=None, all samples).
|
||||
num_parallel_workers (int, optional): Number of subprocesses used to fetch the dataset in parallel (default=1).
|
||||
shuffle (bool, optional): Whether or not to perform shuffle on the dataset. Random accessible input is required.
|
||||
(default=None, expected order behavior shown in the table).
|
||||
|
|
|
|||
|
|
@ -228,7 +228,7 @@ DE_C_RELATIONAL = {Relational.EQ: cde.RelationalOp.EQ,
|
|||
|
||||
|
||||
class Mask(TensorOperation):
|
||||
"""
|
||||
r"""
|
||||
Mask content of the input tensor with the given predicate.
|
||||
Any element of the tensor that matches the predicate will be evaluated to True, otherwise False.
|
||||
|
||||
|
|
@ -237,7 +237,7 @@ class Mask(TensorOperation):
|
|||
Relational.GT, Relational.LE, Relational.GE], take Relational.EQ as example, EQ refers to equal.
|
||||
constant (Union[str, int, float, bool]): Constant to be compared to.
|
||||
Constant will be cast to the type of the input tensor.
|
||||
dtype (mindspore.dtype, optional): Type of the generated mask (Default mstype.bool_).
|
||||
dtype (mindspore.dtype, optional): Type of the generated mask (Default mstype.bool\_).
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset.transforms.c_transforms import Relational
|
||||
|
|
|
|||
Loading…
Reference in New Issue