Skip to content

labeled_dir_dataset

LabeledDirDataset

Bases: InMemoryDataset

A dataset which reads files from a folder hierarchy like root/class(/es)/data.file.

Parameters:

Name Type Description Default
root_dir str

The path to the directory containing data sorted by folders.

 required
data_key str

What key to assign to the data values in the data dictionary.

 'x'
label_key str

What key to assign to the label values in the data dictionary.

 'y'
label_mapping Optional[Dict[str, Any]]

A dictionary defining the mapping to use. If not provided will map classes to int labels.

 None
file_extension Optional[str]

If provided then only files ending with the file_extension will be included.

 None
Source code in fastestimator\fastestimator\dataset\labeled_dir_dataset.py 
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
@traceable()
class LabeledDirDataset(InMemoryDataset):
    """A dataset which reads files from a folder hierarchy like root/class(/es)/data.file.

    Args:
        root_dir: The path to the directory containing data sorted by folders.
        data_key: What key to assign to the data values in the data dictionary.
        label_key: What key to assign to the label values in the data dictionary.
        label_mapping: A dictionary defining the mapping to use. If not provided will map classes to int labels.
        file_extension: If provided then only files ending with the file_extension will be included.
    """
    data: Dict[int, Dict[str, Any]]
    mapping: Dict[str, Any]
    label_key: str

    def __init__(self,
                 root_dir: str,
                 data_key: str = "x",
                 label_key: str = "y",
                 label_mapping: Optional[Dict[str, Any]] = None,
                 file_extension: Optional[str] = None) -> None:
        # Recursively find all the data
        root_dir = os.path.normpath(root_dir)
        data = {}
        keys = deque([""])
        for _, dirs, entries in os.walk(root_dir):
            key = keys.popleft()
            dirs = [os.path.join(key, d) for d in dirs]
            dirs.reverse()
            keys.extendleft(dirs)
            entries = [
                os.path.join(key, e) for e in entries if not e.startswith(".") and e.endswith(file_extension or "")
            ]
            if entries:
                data[key] = entries
        # Compute label mappings
        self.mapping = label_mapping or {label: idx for idx, label in enumerate(sorted(data.keys()))}
        assert self.mapping.keys() >= data.keys(), \
            "Mapping provided to LabeledDirDataset is missing key(s): {}".format(
                data.keys() - self.mapping.keys())
        # Store the data by index
        parsed_data = {}
        idx = 0
        for key, values in data.items():
            label = self.mapping[key]
            # Sort the values so that deterministic splitting works
            values.sort()
            for value in values:
                parsed_data[idx] = {data_key: os.path.join(root_dir, value), label_key: label}
                idx += 1
        self.label_key = label_key
        super().__init__(parsed_data)

    def summary(self) -> DatasetSummary:
        """Generate a summary representation of this dataset.
        Returns:
            A summary representation of this dataset.
        """
        summary = super().summary()
        summary.class_key = self.label_key
        summary.class_key_mapping = self.mapping
        summary.num_classes = len(self.mapping)
        return summary

summary

Generate a summary representation of this dataset.

Returns:

Type Description
DatasetSummary

A summary representation of this dataset.
Source code in fastestimator\fastestimator\dataset\labeled_dir_dataset.py 
76
77
78
79
80
81
82
83
84
85
def summary(self) -> DatasetSummary:
    """Generate a summary representation of this dataset.
    Returns:
        A summary representation of this dataset.
    """
    summary = super().summary()
    summary.class_key = self.label_key
    summary.class_key_mapping = self.mapping
    summary.num_classes = len(self.mapping)
    return summary