mindspore.dataset.FlickrDataset
- class mindspore.dataset.FlickrDataset(dataset_dir, annotation_file, num_samples=None, num_parallel_workers=None, shuffle=None, decode=None, sampler=None, num_shards=None, shard_id=None, cache=None)[source]
Flickr8k and Flickr30k datasets.
The generated dataset has two columns
[image, annotation]
. The tensor of columnimage
is of the uint8 type. The tensor of columnannotation
is a tensor which contains 5 annotations string, such as ["a", "b", "c", "d", "e"].- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
annotation_file (str) – Path to the root directory that contains the annotation.
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 bymindspore.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
.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 is not valid 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 annotation_file is not exist.
ValueError – If shard_id is not in range of [0, num_shards ).
- Tutorial Examples:
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.
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 >>> flickr_dataset_dir = "/path/to/flickr_dataset_directory" >>> annotation_file = "/path/to/flickr_annotation_file" >>> >>> # 1) Get all samples from FLICKR dataset in sequence >>> dataset = ds.FlickrDataset(dataset_dir=flickr_dataset_dir, ... annotation_file=annotation_file, ... shuffle=False) >>> >>> # 2) Randomly select 350 samples from FLICKR dataset >>> dataset = ds.FlickrDataset(dataset_dir=flickr_dataset_dir, ... annotation_file=annotation_file, ... num_samples=350, ... shuffle=True) >>> >>> # 3) Get samples from FLICKR dataset for shard 0 in a 2-way distributed training >>> dataset = ds.FlickrDataset(dataset_dir=flickr_dataset_dir, ... annotation_file=annotation_file, ... num_shards=2, ... shard_id=0) >>> >>> # In FLICKR dataset, each dictionary has keys "image" and "annotation"
About Flickr8k dataset:
The Flickr8k dataset consists of 8092 color images. There are 40460 annotations in the Flickr8k.token.txt, each image has 5 annotations.
You can unzip the dataset files into the following directory structure and read by MindSpore's API.
. └── Flickr8k ├── Flickr8k_Dataset │ ├── 1000268201_693b08cb0e.jpg │ ├── 1001773457_577c3a7d70.jpg │ ├── ... └── Flickr8k.token.txt
Citation:
@article{DBLP:journals/jair/HodoshYH13, author = {Micah Hodosh and Peter Young and Julia Hockenmaier}, title = {Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics}, journal = {J. Artif. Intell. Res.}, volume = {47}, pages = {853--899}, year = {2013}, url = {https://doi.org/10.1613/jair.3994}, doi = {10.1613/jair.3994}, timestamp = {Mon, 21 Jan 2019 15:01:17 +0100}, biburl = {https://dblp.org/rec/journals/jair/HodoshYH13.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
About Flickr30k dataset:
The Flickr30k dataset consists of 31783 color images. There are 158915 annotations in the results_20130124.token, each image has 5 annotations.
You can unzip the dataset files into the following directory structure and read by MindSpore's API.
. └── Flickr30k ├── flickr30k-images │ ├── 1000092795.jpg │ ├── 10002456.jpg │ ├── ... └── results_20130124.token
Citation:
@article{DBLP:journals/tacl/YoungLHH14, author = {Peter Young and Alice Lai and Micah Hodosh and Julia Hockenmaier}, title = {From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions}, journal = {Trans. Assoc. Comput. Linguistics}, volume = {2}, pages = {67--78}, year = {2014}, url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/229}, timestamp = {Wed, 17 Feb 2021 21:55:25 +0100}, biburl = {https://dblp.org/rec/journals/tacl/YoungLHH14.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
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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Take the first specified number of samples 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 that yields samples of type dict, while the key is the column name and the value is the data. |
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Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column. |
Attribute
Return the size of batch. |
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Get the mapping dictionary from category names to category indexes. |
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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
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. |