added .csv image loading utils (#118)

* 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

flash/core/model.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import functools
+import os
 from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Type, Union
 
 import pytorch_lightning as pl
@@ -126,7 +127,7 @@ def predict(
 
         Args:
 
-            x: Input to predict. Can be raw data or processed data.
+            x: Input to predict. Can be raw data or processed data. If str, assumed to be a folder of data.
 
             batch_idx: Batch index
 
@@ -142,6 +143,12 @@ def predict(
             The post-processed model predictions
 
         """
+        # enable x to be a path to a folder
+        if isinstance(x, str):
+            files = os.listdir(x)
+            files = [os.path.join(x, y) for y in files]
+            x = files
+
         data_pipeline = data_pipeline or self.data_pipeline
         batch = x if skip_collate_fn else data_pipeline.collate_fn(x)
         batch_x, batch_y = batch if len(batch) == 2 and isinstance(batch, (list, tuple)) else (batch, None)

flash/vision/classification/data.py

@@ -17,7 +17,7 @@
 
 import pandas as pd
 import torch
-from PIL import Image
+from PIL import Image, UnidentifiedImageError
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torchvision import transforms as T
 from torchvision.datasets import VisionDataset
@@ -241,9 +241,13 @@ def before_collate(self, samples: Any) -> Any:
         if isinstance(samples, (list, tuple)) and all(isinstance(p, str) for p in samples):
             outputs = []
             for sample in samples:
-                output = self._loader(sample)
-                transform = self._valid_transform if self._use_valid_transform else self._train_transform
-                outputs.append(transform(output))
+                try:
+                    output = self._loader(sample)
+                    transform = self._valid_transform if self._use_valid_transform else self._train_transform
+                    outputs.append(transform(output))
+                except UnidentifiedImageError:
+                    print(f'Skipping: could not read file {sample}')
+
             return outputs
         raise MisconfigurationException("The samples should either be a tensor or a list of paths.")
 

tests/core/test_model.py

@@ -11,11 +11,14 @@
 # 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.
+from pathlib import Path
 from typing import Any
 
+import numpy as np
 import pytest
 import pytorch_lightning as pl
 import torch
+from PIL import Image
 from torch import nn
 from torch.nn import functional as F
 
@@ -68,6 +71,18 @@ def test_classificationtask_task_predict():
     assert pred0[0] == pred1[0]
 
 
+def test_classification_task_predict_folder_path(tmpdir):
+    train_dir = Path(tmpdir / "train")
+    train_dir.mkdir()
+
+    _rand_image().save(train_dir / "1.png")
+    _rand_image().save(train_dir / "2.png")
+
+    task = ImageClassifier(num_classes=10)
+    predictions = task.predict(str(train_dir))
+    assert len(predictions) == 2
+
+
 def test_classificationtask_trainer_predict(tmpdir):
     model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
     task = ClassificationTask(model)
@@ -127,3 +142,7 @@ def test_model_download(tmpdir, cls, filename):
     with tmpdir.as_cwd():
         task = cls.load_from_checkpoint(url + filename)
         assert isinstance(task, cls)
+
+
+def _rand_image():
+    return Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))