mindspore.dataset.Caltech101Dataset
- class mindspore.dataset.Caltech101Dataset(dataset_dir, target_type=None, num_samples=None, num_parallel_workers=1, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None)[source]
Caltech 101 dataset.
The columns of the generated dataset depend on the value of target_type .
When target_type is
'category'
, the columns are[image, category]
.When target_type is
'annotation'
, the columns are[image, annotation]
.When target_type is
'all'
, the columns are[image, category, annotation]
.
The tensor of column
image
is of the uint8 type. The tensor of columncategory
is of the uint32 type. The tensor of columnannotation
is a 2-dimensional ndarray that stores the contour of the image and consists of a series of points.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset. This root directory contains two subdirectories, one is called 101_ObjectCategories, which stores images, and the other is called Annotations, which stores annotations.
target_type (str, optional) – Target of the image. If target_type is
'category'
, return category represents the target class. If target_type is'annotation'
, return annotation. If target_type is'all'
, return category and annotation. Default:None
, means'category'
.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 subprocesses to read the data. Default:
1
.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) – Whether or not to 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 maximum 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.
- 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 shard_id is not in range of [0, num_shards ).
ValueError – If target_type is not
'category'
,'annotation'
or'all'
.ValueError – If num_parallel_workers exceeds the max thread numbers.
- 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 >>> caltech101_dataset_directory = "/path/to/caltech101_dataset_directory" >>> >>> # 1) Read all samples (image files) in caltech101_dataset_directory with 8 threads >>> dataset = ds.Caltech101Dataset(dataset_dir=caltech101_dataset_directory, num_parallel_workers=8) >>> >>> # 2) Read all samples (image files) with the target_type "annotation" >>> dataset = ds.Caltech101Dataset(dataset_dir=caltech101_dataset_directory, target_type="annotation")
About Caltech101Dataset:
Pictures of objects belonging to 101 categories, about 40 to 800 images per category. Most categories have about 50 images. The size of each image is roughly 300 x 200 pixels. The official provides the contour data of each object in each picture, which is the annotation.
Here is the original Caltech101 dataset structure, and you can unzip the dataset files into the following directory structure, which are read by MindSpore API.
. └── caltech101_dataset_directory ├── 101_ObjectCategories │ ├── Faces │ │ ├── image_0001.jpg │ │ ├── image_0002.jpg │ │ ... │ ├── Faces_easy │ │ ├── image_0001.jpg │ │ ├── image_0002.jpg │ │ ... │ ├── ... └── Annotations ├── Airplanes_Side_2 │ ├── annotation_0001.mat │ ├── annotation_0002.mat │ ... ├── Faces_2 │ ├── annotation_0001.mat │ ├── annotation_0002.mat │ ... ├── ...
Citation:
@article{FeiFei2004LearningGV, author = {Li Fei-Fei and Rob Fergus and Pietro Perona}, title = {Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories}, journal = {Computer Vision and Pattern Recognition Workshop}, year = {2004}, url = {https://data.caltech.edu/records/mzrjq-6wc02}, }
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. |