mindspore.dataset.DIV2KDataset

class mindspore.dataset.DIV2KDataset(dataset_dir, usage='train', downgrade='bicubic', scale=2, num_samples=None, num_parallel_workers=None, shuffle=None, decode=None, sampler=None, num_shards=None, shard_id=None, cache=None)[source]

DIV2K(DIVerse 2K resolution image) dataset.

The generated dataset has two columns [hr_image, lr_image] . The tensor of column hr_image and the tensor of column lr_image are of the uint8 type.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Acceptable usages include 'train', 'valid' or 'all'. Default: 'train'.
downgrade (str, optional) – Acceptable downgrades include 'bicubic', 'unknown', 'mild', 'difficult' or 'wild'. Default: 'bicubic'.
scale (int, optional) – Acceptable scales include 2, 3, 4 or 8. Default: 2. When downgrade is 'bicubic', scale can be 2, 3, 4, 8. When downgrade is 'unknown', scale can only be 2, 3, 4. When downgrade is 'mild', 'difficult' or 'wild', scale can only be 4.
num_samples (int, optional) – The number of images to be included in the dataset. Default: None , all images.
num_parallel_workers (int, optional) – Number of worker threads to read the data. Default: None , will use global default workers(8), it can be set by mindspore.dataset.config.set_num_parallel_workers() .
shuffle (bool, optional) – Whether to perform shuffle on the dataset. Default: None , expected order behavior shown in the table below.
decode (bool, optional) – Decode the images after reading. Default: None, set to False.
sampler (Sampler, optional) – Object used to choose samples from the dataset. Default: None , expected order behavior shown in the table below.
num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the max sample number of per shard. Used in data parallel training .
shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.
cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default: None , which means no cache is used.

Raises

RuntimeError – If dataset_dir is invalid or does not contain data files.
ValueError – If num_parallel_workers exceeds the max thread numbers.
RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and num_shards/shard_id are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If dataset_dir is not exist.
ValueError – If usage is invalid.
ValueError – If downgrade is invalid.
ValueError – If scale is invalid.
ValueError – If scale equal to 8 and downgrade not equal to 'bicubic'.
ValueError – If downgrade is 'mild', 'difficult' or 'wild', and scale not equal to 4.
ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

Load & Process Data With Dataset Pipeline

Note

The parameters num_samples , shuffle , num_shards , shard_id can be used to control the sampler used in the dataset, and their effects when combined with parameter sampler are as follows.

Sampler obtained by different combinations of parameters sampler and num_samples , shuffle , num_shards , shard_id
Parameter sampler	Parameter num_shards / shard_id	Parameter shuffle	Parameter num_samples	Sampler Used
mindspore.dataset.Sampler type	None	None	None	sampler
numpy.ndarray,list,tuple,int type	/	/	num_samples	SubsetSampler(indices = sampler , num_samples = num_samples )
iterable type	/	/	num_samples	IterSampler(sampler = sampler , num_samples = num_samples )
None	num_shards / shard_id	None / True	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = True , num_samples = num_samples )
None	num_shards / shard_id	False	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = False , num_samples = num_samples )
None	None	None / True	None	RandomSampler(num_samples = num_samples )
None	None	None / True	num_samples	RandomSampler(replacement = True , num_samples = num_samples )
None	None	False	num_samples	SequentialSampler(num_samples = num_samples )

Examples

>>> import mindspore.dataset as ds
>>> div2k_dataset_dir = "/path/to/div2k_dataset_directory"
>>>
>>> # 1) Get all samples from DIV2K dataset in sequence
>>> dataset = ds.DIV2KDataset(dataset_dir=div2k_dataset_dir, usage="train", scale=2, downgrade="bicubic",
...                           shuffle=False)
>>>
>>> # 2) Randomly select 350 samples from DIV2K dataset
>>> dataset = ds.DIV2KDataset(dataset_dir=div2k_dataset_dir, usage="train", scale=2, downgrade="bicubic",
...                           num_samples=350, shuffle=True)
>>>
>>> # 3) Get samples from DIV2K dataset for shard 0 in a 2-way distributed training
>>> dataset = ds.DIV2KDataset(dataset_dir=div2k_dataset_dir, usage="train", scale=2, downgrade="bicubic",
...                           num_shards=2, shard_id=0)
>>>
>>> # In DIV2K dataset, each dictionary has keys "hr_image" and "lr_image"

About DIV2K dataset:

The DIV2K dataset consists of 1000 2K resolution images, among which 800 images are for training, 100 images are for validation and 100 images are for testing. NTIRE 2017 and NTIRE 2018 include only training dataset and validation dataset.

You can unzip the dataset files into the following directory structure and read by MindSpore's API.

Take the training set as an example.

.
└── DIV2K
     ├── DIV2K_train_HR
     |    ├── 0001.png
     |    ├── 0002.png
     |    ├── ...
     ├── DIV2K_train_LR_bicubic
     |    ├── X2
     |    |    ├── 0001x2.png
     |    |    ├── 0002x2.png
     |    |    ├── ...
     |    ├── X3
     |    |    ├── 0001x3.png
     |    |    ├── 0002x3.png
     |    |    ├── ...
     |    └── X4
     |         ├── 0001x4.png
     |         ├── 0002x4.png
     |         ├── ...
     ├── DIV2K_train_LR_unknown
     |    ├── X2
     |    |    ├── 0001x2.png
     |    |    ├── 0002x2.png
     |    |    ├── ...
     |    ├── X3
     |    |    ├── 0001x3.png
     |    |    ├── 0002x3.png
     |    |    ├── ...
     |    └── X4
     |         ├── 0001x4.png
     |         ├── 0002x4.png
     |         ├── ...
     ├── DIV2K_train_LR_mild
     |    ├── 0001x4m.png
     |    ├── 0002x4m.png
     |    ├── ...
     ├── DIV2K_train_LR_difficult
     |    ├── 0001x4d.png
     |    ├── 0002x4d.png
     |    ├── ...
     ├── DIV2K_train_LR_wild
     |    ├── 0001x4w.png
     |    ├── 0002x4w.png
     |    ├── ...
     └── DIV2K_train_LR_x8
          ├── 0001x8.png
          ├── 0002x8.png
          ├── ...

Citation:

@InProceedings{Agustsson_2017_CVPR_Workshops,
author    = {Agustsson, Eirikur and Timofte, Radu},
title     = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url       = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
month     = {July},
year      = {2017}
}

Pre-processing Operation

`mindspore.dataset.Dataset.apply`	Apply a function in this dataset.
`mindspore.dataset.Dataset.concat`	Concatenate the dataset objects in the input list.
`mindspore.dataset.Dataset.filter`	Filter dataset by prediction.
`mindspore.dataset.Dataset.flat_map`	Map func to each row in dataset and flatten the result.
`mindspore.dataset.Dataset.map`	Apply each operation in operations to this dataset.
`mindspore.dataset.Dataset.project`	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
`mindspore.dataset.Dataset.rename`	Rename the columns in input datasets.
`mindspore.dataset.Dataset.repeat`	Repeat this dataset count times.
`mindspore.dataset.Dataset.reset`	Reset the dataset for next epoch.
`mindspore.dataset.Dataset.save`	Save the dynamic data processed by the dataset pipeline in common dataset format.
`mindspore.dataset.Dataset.shuffle`	Shuffle the dataset by creating a cache with the size of buffer_size .
`mindspore.dataset.Dataset.skip`	Skip the first N elements of this dataset.
`mindspore.dataset.Dataset.split`	Split the dataset into smaller, non-overlapping datasets.
`mindspore.dataset.Dataset.take`	Take the first specified number of samples from the dataset.
`mindspore.dataset.Dataset.zip`	Zip the datasets in the sense of input tuple of datasets.

Batch

`mindspore.dataset.Dataset.batch`	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
`mindspore.dataset.Dataset.bucket_batch_by_length`	Bucket elements according to their lengths.
`mindspore.dataset.Dataset.padded_batch`	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

`mindspore.dataset.Dataset.create_dict_iterator`	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Get the mapping dictionary from category names to category indexes.
`mindspore.dataset.Dataset.get_col_names`	Return the names of the columns in dataset.
`mindspore.dataset.Dataset.get_dataset_size`	Return the number of batches in an epoch.
`mindspore.dataset.Dataset.get_repeat_count`	Get the replication times in RepeatDataset.
`mindspore.dataset.Dataset.input_indexs`	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
`mindspore.dataset.Dataset.num_classes`	Get the number of classes in a dataset.
`mindspore.dataset.Dataset.output_shapes`	Get the shapes of output data.
`mindspore.dataset.Dataset.output_types`	Get the types of output data.

Apply Sampler

`mindspore.dataset.MappableDataset.add_sampler`	Add a child sampler for the current dataset.
`mindspore.dataset.MappableDataset.use_sampler`	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

`mindspore.dataset.Dataset.sync_update`	Release a blocking condition and trigger callback with given data.
`mindspore.dataset.Dataset.sync_wait`	Add a blocking condition to the input Dataset and a synchronize action will be applied.
`mindspore.dataset.Dataset.to_json`	Serialize a pipeline into JSON string and dump into file if filename is provided.