Audio data sources + Numpy file support

.gitignore

@@ -163,3 +163,5 @@ logs/cache/*
 flash_examples/data
 flash_examples/cli/*/data
 timit/
+urban8k_images/
+__MACOSX

CHANGELOG.md

@@ -34,12 +34,20 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added Flash Zero, a zero code command line ML platform built with flash ([#611](https://github.com/PyTorchLightning/lightning-flash/pull/611))
 
+- Added support for `.npy` and `.npz` files to `ImageClassificationData` and `AudioClassificationData` ([#651](https://github.com/PyTorchLightning/lightning-flash/pull/651))
+
+- Added support for `from_csv` to the `AudioClassificationData` ([#651](https://github.com/PyTorchLightning/lightning-flash/pull/651))
+
+- Added option to pass a `resolver` to the `from_csv` and `from_pandas` methods of `ImageClassificationData`, which is used to resolve filenames given IDs ([#651](https://github.com/PyTorchLightning/lightning-flash/pull/651))
+
 ### Changed
 
 - Changed how pretrained flag works for loading weights for ImageClassifier task ([#560](https://github.com/PyTorchLightning/lightning-flash/pull/560))
 
 - Removed bolts pretrained weights for SSL from ImageClassifier task ([#560](https://github.com/PyTorchLightning/lightning-flash/pull/560))
 
+- Changed the behaviour of the `sampler` argument of the `DataModule` to take a `Sampler` type rather than instantiated object ([#651](https://github.com/PyTorchLightning/lightning-flash/pull/651))
+
 ### Fixed
 
 - Fixed a bug where serve sanity checking would not be triggered using the latest PyTorchLightning version ([#493](https://github.com/PyTorchLightning/lightning-flash/pull/493))
@@ -50,6 +58,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed a bug where some tasks were not compatible with PyTorch 1.7 due to use of `torch.jit.isinstance` ([#611](https://github.com/PyTorchLightning/lightning-flash/pull/611))
 
+- Fixed a bug where custom samplers would not be properly forwarded to the data loader ([#651](https://github.com/PyTorchLightning/lightning-flash/pull/651))
+
 ## [0.4.0] - 2021-06-22
 
 ### Added

flash/audio/classification/data.py

@@ -13,14 +13,46 @@
 # limitations under the License.
 from typing import Any, Callable, Dict, Optional, Tuple
 
+import numpy as np
+
 from flash.audio.classification.transforms import default_transforms, train_default_transforms
-from flash.core.data.callback import BaseDataFetcher
-from flash.core.data.data_module import DataModule
-from flash.core.data.data_source import DefaultDataSources
+from flash.core.data.data_source import (
+    DefaultDataSources,
+    has_file_allowed_extension,
+    LoaderDataFrameDataSource,
+    PathsDataSource,
+)
 from flash.core.data.process import Deserializer, Preprocess
-from flash.core.utilities.imports import requires_extras
-from flash.image.classification.data import MatplotlibVisualization
-from flash.image.data import ImageDeserializer, ImagePathsDataSource
+from flash.core.utilities.imports import _TORCHVISION_AVAILABLE, requires_extras
+from flash.image.classification.data import ImageClassificationData
+from flash.image.data import ImageDeserializer
+
+if _TORCHVISION_AVAILABLE:
+    from torchvision.datasets.folder import default_loader, IMG_EXTENSIONS
+
+
+NP_EXTENSIONS = (".npy", ".npz")
+
+
+def spectrogram_loader(filepath: str):
+    if has_file_allowed_extension(filepath, IMG_EXTENSIONS):
+        img = default_loader(filepath)
+        data = np.array(img)
+    else:
+        data = np.load(filepath)
+    return data
+
+
+class AudioClassificationPathsDataSource(PathsDataSource):
+    @requires_extras("image")
+    def __init__(self):
+        super().__init__(loader=spectrogram_loader, extensions=IMG_EXTENSIONS + NP_EXTENSIONS)
+
+
+class AudioClassificationDataFrameDataSource(LoaderDataFrameDataSource):
+    @requires_extras("image")
+    def __init__(self):
+        super().__init__(spectrogram_loader)
 
 
 class AudioClassificationPreprocess(Preprocess):
@@ -31,7 +63,7 @@ def __init__(
         val_transform: Optional[Dict[str, Callable]] = None,
         test_transform: Optional[Dict[str, Callable]] = None,
         predict_transform: Optional[Dict[str, Callable]] = None,
-        spectrogram_size: Tuple[int, int] = (196, 196),
+        spectrogram_size: Tuple[int, int] = (128, 128),
         time_mask_param: int = 80,
         freq_mask_param: int = 80,
         deserializer: Optional["Deserializer"] = None,
@@ -46,8 +78,10 @@ def __init__(
             test_transform=test_transform,
             predict_transform=predict_transform,
             data_sources={
-                DefaultDataSources.FILES: ImagePathsDataSource(),
-                DefaultDataSources.FOLDERS: ImagePathsDataSource(),
+                DefaultDataSources.FILES: AudioClassificationPathsDataSource(),
+                DefaultDataSources.FOLDERS: AudioClassificationPathsDataSource(),
+                "data_frame": AudioClassificationDataFrameDataSource(),
+                DefaultDataSources.CSV: AudioClassificationDataFrameDataSource(),
             },
             deserializer=deserializer or ImageDeserializer(),
             default_data_source=DefaultDataSources.FILES,
@@ -72,15 +106,7 @@ def train_default_transforms(self) -> Optional[Dict[str, Callable]]:
         return train_default_transforms(self.spectrogram_size, self.time_mask_param, self.freq_mask_param)
 
 
-class AudioClassificationData(DataModule):
+class AudioClassificationData(ImageClassificationData):
     """Data module for audio classification."""
 
     preprocess_cls = AudioClassificationPreprocess
-
-    def set_block_viz_window(self, value: bool) -> None:
-        """Setter method to switch on/off matplotlib to pop up windows."""
-        self.data_fetcher.block_viz_window = value
-
-    @staticmethod
-    def configure_data_fetcher(*args, **kwargs) -> BaseDataFetcher:
-        return MatplotlibVisualization(*args, **kwargs)

flash/audio/classification/transforms.py

@@ -15,9 +15,10 @@
 
 import torch
 from torch import nn
+from torch.utils.data._utils.collate import default_collate
 
 from flash.core.data.data_source import DefaultDataKeys
-from flash.core.data.transforms import ApplyToKeys, kornia_collate, merge_transforms
+from flash.core.data.transforms import ApplyToKeys, merge_transforms
 from flash.core.utilities.imports import _TORCHAUDIO_AVAILABLE, _TORCHVISION_AVAILABLE
 
 if _TORCHVISION_AVAILABLE:
@@ -32,12 +33,12 @@ def default_transforms(spectrogram_size: Tuple[int, int]) -> Dict[str, Callable]
     """The default transforms for audio classification for spectrograms: resize the spectrogram, convert the
     spectrogram and target to a tensor, and collate the batch."""
     return {
-        "pre_tensor_transform": ApplyToKeys(DefaultDataKeys.INPUT, T.Resize(spectrogram_size)),
         "to_tensor_transform": nn.Sequential(
             ApplyToKeys(DefaultDataKeys.INPUT, torchvision.transforms.ToTensor()),
             ApplyToKeys(DefaultDataKeys.TARGET, torch.as_tensor),
         ),
-        "collate": kornia_collate,
+        "post_tensor_transform": ApplyToKeys(DefaultDataKeys.INPUT, T.Resize(spectrogram_size)),
+        "collate": default_collate,
     }
 
 

flash/core/data/data_module.py

@@ -13,7 +13,20 @@
 # limitations under the License.
 import os
 import platform
-from typing import Any, Callable, Collection, Dict, Iterable, List, Optional, Sequence, Tuple, TYPE_CHECKING, Union
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    TYPE_CHECKING,
+    Union,
+)
 
 import numpy as np
 import pytorch_lightning as pl
@@ -86,7 +99,7 @@ def __init__(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
     ) -> None:
 
         super().__init__()
@@ -281,7 +294,10 @@ def _train_dataloader(self) -> DataLoader:
         pin_memory = True
 
         if self.sampler is None:
+            sampler = None
             shuffle = not isinstance(train_ds, (IterableDataset, IterableAutoDataset))
+        else:
+            sampler = self.sampler(train_ds)
 
         if isinstance(getattr(self, "trainer", None), pl.Trainer):
             return self.trainer.lightning_module.process_train_dataset(
@@ -292,14 +308,14 @@ def _train_dataloader(self) -> DataLoader:
                 shuffle=shuffle,
                 drop_last=drop_last,
                 collate_fn=collate_fn,
-                sampler=self.sampler,
+                sampler=sampler,
             )
 
         return DataLoader(
             train_ds,
             batch_size=self.batch_size,
             shuffle=shuffle,
-            sampler=self.sampler,
+            sampler=sampler,
             num_workers=self.num_workers,
             pin_memory=pin_memory,
             drop_last=drop_last,
@@ -453,7 +469,7 @@ def from_data_source(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given inputs to
@@ -489,7 +505,7 @@ def from_data_source(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
 
@@ -553,7 +569,7 @@ def from_folders(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given folders using the
@@ -582,7 +598,7 @@ def from_folders(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
 
@@ -636,7 +652,7 @@ def from_files(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given sequences of files
@@ -668,7 +684,7 @@ def from_files(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
 
@@ -723,7 +739,7 @@ def from_tensors(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given tensors using the
@@ -755,7 +771,7 @@ def from_tensors(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
 
@@ -810,7 +826,7 @@ def from_numpy(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given numpy array using the
@@ -842,7 +858,7 @@ def from_numpy(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
 
@@ -896,7 +912,7 @@ def from_json(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         field: Optional[str] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
@@ -928,7 +944,7 @@ def from_json(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             field: To specify the field that holds the data in the JSON file.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
@@ -1006,7 +1022,7 @@ def from_csv(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given CSV files using the
@@ -1037,7 +1053,7 @@ def from_csv(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.
 
@@ -1090,7 +1106,7 @@ def from_datasets(
         val_split: Optional[float] = None,
         batch_size: int = 4,
         num_workers: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
+        sampler: Optional[Type[Sampler]] = None,
         **preprocess_kwargs: Any,
     ) -> "DataModule":
         """Creates a :class:`~flash.core.data.data_module.DataModule` object from the given datasets using the
@@ -1119,7 +1135,7 @@ def from_datasets(
             val_split: The ``val_split`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             batch_size: The ``batch_size`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
             num_workers: The ``num_workers`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
-            sampler: The ``sampler`` argument to pass to the :class:`~flash.core.data.data_module.DataModule`.
+            sampler: The ``sampler`` to use for the ``train_dataloader``.
             preprocess_kwargs: Additional keyword arguments to use when constructing the preprocess. Will only be used
                 if ``preprocess = None``.