mindspore.dataset.SBUDataset
- class mindspore.dataset.SBUDataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None)[source]
SBU(SBU Captioned Photo) dataset.
The generated dataset has two columns
[image, caption]. The tensor of columnimageis of the uint8 type. The tensor of columncaptionis of the string type.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of images to be included in the dataset. Default: None, will read 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 or not 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: 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.
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 does not contain data files.
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 num_parallel_workers exceeds the max thread numbers.
ValueError – If shard_id is not in range of [0, num_shards ).
Note
This dataset can take in a sampler. ‘sampler’ and ‘shuffle’ are mutually exclusive. The table below shows what input arguments are allowed and their expected behavior.
Expected Order Behavior of Using sampler and shuffle Parameter ‘sampler’
Parameter ‘shuffle’
Expected Order Behavior
None
None
random order
None
True
random order
None
False
sequential order
Sampler object
None
order defined by sampler
Sampler object
True
not allowed
Sampler object
False
not allowed
Examples
>>> sbu_dataset_dir = "/path/to/sbu_dataset_directory" >>> # Read 3 samples from SBU dataset >>> dataset = ds.SBUDataset(dataset_dir=sbu_dataset_dir, num_samples=3)
About SBU dataset:
SBU dataset is a large captioned photo collection. It contains one million images with associated visually relevant captions.
You should manually download the images using official download.m by replacing ‘urls{i}(24, end)’ with ‘urls{i}(24:1:end)’ and keep the directory as below.
. └─ dataset_dir ├── SBU_captioned_photo_dataset_captions.txt ├── SBU_captioned_photo_dataset_urls.txt └── sbu_images ├── m_3326_3596303505_3ce4c20529.jpg ├── ...... └── m_2522_4182181099_c3c23ab1cc.jpgCitation:
@inproceedings{Ordonez:2011:im2text, Author = {Vicente Ordonez and Girish Kulkarni and Tamara L. Berg}, Title = {Im2Text: Describing Images Using 1 Million Captioned Photographs}, Booktitle = {Neural Information Processing Systems ({NIPS})}, Year = {2011}, }
Pre-processing Operation
Apply a function in this dataset. |
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Concatenate the dataset objects in the input list. |
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Filter dataset by prediction. |
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Map func to each row in dataset and flatten the result. |
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Apply each operation in operations to this dataset. |
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The specified columns will be selected from the dataset and passed into the pipeline with the order specified. |
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Rename the columns in input datasets. |
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Repeat this dataset count times. |
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Reset the dataset for next epoch. |
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Save the dynamic data processed by the dataset pipeline in common dataset format. |
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Shuffle the dataset by creating a cache with the size of buffer_size . |
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Skip the first N elements of this dataset. |
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Split the dataset into smaller, non-overlapping datasets. |
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Takes at most given numbers of elements from the dataset. |
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Zip the datasets in the sense of input tuple of datasets. |
Batch
Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first. |
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Bucket elements according to their lengths. |
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Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first. |
Iterator
Create an iterator over the dataset. |
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Create an iterator over the dataset. |
Attribute
Return the size of batch. |
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Return the class index. |
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Return the names of the columns in dataset. |
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Return the number of batches in an epoch. |
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Get the replication times in RepeatDataset. |
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Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode. |
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Get the number of classes in a dataset. |
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Get the shapes of output data. |
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Get the types of output data. |
Apply Sampler
Add a child sampler for the current dataset. |
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Replace the last child sampler of the current dataset, remaining the parent sampler unchanged. |
Others
Return a transferred Dataset that transfers data through a device. |
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Release a blocking condition and trigger callback with given data. |
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Add a blocking condition to the input Dataset and a synchronize action will be applied. |
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Serialize a pipeline into JSON string and dump into file if filename is provided. |