added .csv image loading utils (#116)

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

* added .csv image loading utils

.gitignore

@@ -137,6 +137,5 @@ docs/api/
 titanic.csv
 .vscode
 data_folder
-data
 *.pt
 *.zip

docs/source/reference/image_classification.rst

@@ -158,6 +158,7 @@ Available backbones:
 * densenet121
 * densenet169
 * densenet161
+* swav-imagenet
 
 ------
 

flash/data/__init__.py

@@ -0,0 +1 @@
+from flash.data.data_utils import labels_from_categorical_csv

flash/data/data_utils.py

@@ -0,0 +1,28 @@
+from typing import Dict, List, Union
+
+import pandas as pd
+
+
+def labels_from_categorical_csv(csv: str, index_col: str, return_dict: bool = True) -> Union[Dict, List]:
+    """
+    Returns a dictionary with {index_col: label} for each entry in the csv.
+
+    Expects a csv of this form:
+
+    index_col,    b,     c,     d
+    some_name,    0      0      1
+    some_name_b,  1      0      0
+
+    """
+    df = pd.read_csv(csv)
+    # get names
+    names = df[index_col].to_list()
+    del df[index_col]
+
+    # everything else is binary
+    labels = df.to_numpy().argmax(1).tolist()
+
+    if return_dict:
+        labels = {name: label for name, label in zip(names, labels)}
+
+    return labels

flash/vision/classification/data.py

@@ -15,6 +15,7 @@
 import pathlib
 from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
 
+import pandas as pd
 import torch
 from PIL import Image
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -55,9 +56,13 @@ def __init__(
         self.labels = labels or []
         self.transform = transform
         self.loader = loader
-        if self.has_labels:
+        if not self.has_dict_labels and self.has_labels:
             self.label_to_class_mapping = dict(map(reversed, enumerate(sorted(set(self.labels)))))
 
+    @property
+    def has_dict_labels(self) -> bool:
+        return isinstance(self.labels, dict)
+
     @property
     def has_labels(self) -> bool:
         return self.labels is not None
@@ -71,7 +76,11 @@ def __getitem__(self, index: int) -> Tuple[Any, Optional[int]]:
         if self.transform is not None:
             img = self.transform(img)
         label = None
-        if self.has_labels:
+        if self.has_dict_labels:
+            name = os.path.basename(filename)
+            label = self.labels[name]
+
+        elif self.has_labels:
             label = self.labels[index]
             label = self.label_to_class_mapping[label]
         return img, label
@@ -244,13 +253,13 @@ class ImageClassificationData(DataModule):
     @classmethod
     def from_filepaths(
         cls,
-        train_filepaths: Optional[Sequence[Union[str, pathlib.Path]]] = None,
+        train_filepaths: Union[str, Optional[Sequence[Union[str, pathlib.Path]]]] = None,
         train_labels: Optional[Sequence] = None,
         train_transform: Optional[Callable] = _default_train_transforms,
-        valid_filepaths: Optional[Sequence[Union[str, pathlib.Path]]] = None,
+        valid_filepaths: Union[str, Optional[Sequence[Union[str, pathlib.Path]]]] = None,
         valid_labels: Optional[Sequence] = None,
         valid_transform: Optional[Callable] = _default_valid_transforms,
-        test_filepaths: Optional[Sequence[Union[str, pathlib.Path]]] = None,
+        test_filepaths: Union[str, Optional[Sequence[Union[str, pathlib.Path]]]] = None,
         test_labels: Optional[Sequence] = None,
         loader: Callable = _pil_loader,
         batch_size: int = 64,
@@ -260,13 +269,13 @@ def from_filepaths(
         """Creates a ImageClassificationData object from lists of image filepaths and labels
 
         Args:
-            train_filepaths: sequence of file paths for training dataset. Defaults to ``None``.
+            train_filepaths: string or sequence of file paths for training dataset. Defaults to ``None``.
             train_labels: sequence of labels for training dataset. Defaults to ``None``.
             train_transform: transforms for training dataset. Defaults to ``None``.
-            valid_filepaths: sequence of file paths for validation dataset. Defaults to ``None``.
+            valid_filepaths: string or sequence of file paths for validation dataset. Defaults to ``None``.
             valid_labels: sequence of labels for validation dataset. Defaults to ``None``.
             valid_transform: transforms for validation and testing dataset. Defaults to ``None``.
-            test_filepaths: sequence of file paths for test dataset. Defaults to ``None``.
+            test_filepaths: string or sequence of file paths for test dataset. Defaults to ``None``.
             test_labels: sequence of labels for test dataset. Defaults to ``None``.
             loader: function to load an image file. Defaults to ``None``.
             batch_size: the batchsize to use for parallel loading. Defaults to ``64``.
@@ -278,7 +287,32 @@ def from_filepaths(
 
         Examples:
             >>> img_data = ImageClassificationData.from_filepaths(["a.png", "b.png"], [0, 1]) # doctest: +SKIP
+
+        Example when labels are in .csv file::
+
+            train_labels = labels_from_categorical_csv('path/to/train.csv', 'my_id')
+            valid_labels = labels_from_categorical_csv(path/to/valid.csv', 'my_id')
+            test_labels = labels_from_categorical_csv(path/to/tests.csv', 'my_id')
+
+            data = ImageClassificationData.from_filepaths(
+                batch_size=2,
+                train_filepaths='path/to/train',
+                train_labels=train_labels,
+                valid_filepaths='path/to/valid',
+                valid_labels=valid_labels,
+                test_filepaths='path/to/test',
+                test_labels=test_labels,
+            )
+
         """
+        # enable passing in a string which loads all files in that folder as a list
+        if isinstance(train_filepaths, str):
+            train_filepaths = [os.path.join(train_filepaths, x) for x in os.listdir(train_filepaths)]
+        if isinstance(valid_filepaths, str):
+            valid_filepaths = [os.path.join(valid_filepaths, x) for x in os.listdir(valid_filepaths)]
+        if isinstance(test_filepaths, str):
+            test_filepaths = [os.path.join(test_filepaths, x) for x in os.listdir(test_filepaths)]
+
         train_ds = FilepathDataset(
             filepaths=train_filepaths,
             labels=train_labels,

tests/vision/classification/test_data.py

@@ -11,13 +11,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import os
 from pathlib import Path
 
 import numpy as np
 import torch
 from PIL import Image
 from torchvision import transforms as T
 
+from flash.data.data_utils import labels_from_categorical_csv
 from flash.vision import ImageClassificationData
 
 
@@ -72,6 +74,67 @@ def test_from_filepaths(tmpdir):
     assert labels.shape == (1, )
 
 
+def test_categorical_csv_labels(tmpdir):
+    train_dir = Path(tmpdir / "some_dataset")
+    train_dir.mkdir()
+
+    (train_dir / "train").mkdir()
+    _rand_image().save(train_dir / "train" / "train_1.png")
+    _rand_image().save(train_dir / "train" / "train_2.png")
+
+    (train_dir / "valid").mkdir()
+    _rand_image().save(train_dir / "valid" / "valid_1.png")
+    _rand_image().save(train_dir / "valid" / "valid_2.png")
+
+    (train_dir / "test").mkdir()
+    _rand_image().save(train_dir / "test" / "test_1.png")
+    _rand_image().save(train_dir / "test" / "test_2.png")
+
+    train_csv = os.path.join(tmpdir, 'some_dataset', 'train.csv')
+    text_file = open(train_csv, 'w')
+    text_file.write(
+        'my_id, label_a, label_b, label_c\n"train_1.png", 0, 1, 0\n"train_2.png", 0, 0, 1\n"train_2.png", 1, 0, 0\n'
+    )
+    text_file.close()
+
+    valid_csv = os.path.join(tmpdir, 'some_dataset', 'valid.csv')
+    text_file = open(valid_csv, 'w')
+    text_file.write(
+        'my_id, label_a, label_b, label_c\n"valid_1.png", 0, 1, 0\n"valid_2.png", 0, 0, 1\n"valid_3.png", 1, 0, 0\n'
+    )
+    text_file.close()
+
+    test_csv = os.path.join(tmpdir, 'some_dataset', 'test.csv')
+    text_file = open(test_csv, 'w')
+    text_file.write(
+        'my_id, label_a, label_b, label_c\n"test_1.png", 0, 1, 0\n"test_2.png", 0, 0, 1\n"test_3.png", 1, 0, 0\n'
+    )
+    text_file.close()
+
+    train_labels = labels_from_categorical_csv(train_csv, 'my_id')
+    valid_labels = labels_from_categorical_csv(valid_csv, 'my_id')
+    test_labels = labels_from_categorical_csv(test_csv, 'my_id')
+
+    data = ImageClassificationData.from_filepaths(
+        batch_size=2,
+        train_filepaths=os.path.join(tmpdir, 'some_dataset', 'train'),
+        train_labels=train_labels,
+        valid_filepaths=os.path.join(tmpdir, 'some_dataset', 'valid'),
+        valid_labels=valid_labels,
+        test_filepaths=os.path.join(tmpdir, 'some_dataset', 'test'),
+        test_labels=test_labels,
+    )
+
+    for (x, y) in data.train_dataloader():
+        assert len(x) == 2
+
+    for (x, y) in data.val_dataloader():
+        assert len(x) == 2
+
+    for (x, y) in data.test_dataloader():
+        assert len(x) == 2
+
+
 def test_from_folders(tmpdir):
     train_dir = Path(tmpdir / "train")
     train_dir.mkdir()