relative path

flash/vision/segmentation/data.py

@@ -22,6 +22,7 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch.utils.data import Dataset
 
+import flash.vision.segmentation.transforms as T
 from flash.core.classification import SegmentationLabels
 from flash.data.auto_dataset import AutoDataset
 from flash.data.base_viz import BaseVisualization  # for viz
@@ -30,8 +31,6 @@
 from flash.data.process import Preprocess
 from flash.utils.imports import _MATPLOTLIB_AVAILABLE
 
-from . import transforms as T
-
 if _MATPLOTLIB_AVAILABLE:
     import matplotlib.pyplot as plt
 else:

flash_examples/finetuning/semantic_segmentation.py

@@ -57,7 +57,7 @@ def load_data(data_root: str = 'data/') -> Tuple[List[str], List[str]]:
 
 # 2.2 Visualise the samples
 labels_map = SegmentationLabels.create_random_labels_map(num_classes=21)
-datamodule.set_map_labels(labels_map)
+datamodule.set_labels_map(labels_map)
 datamodule.show_train_batch("load_sample")
 datamodule.show_train_batch("post_tensor_transform")
 
@@ -69,7 +69,7 @@ def load_data(data_root: str = 'data/') -> Tuple[List[str], List[str]]:
 
 # 4. Create the trainer.
 trainer = flash.Trainer(
-    max_epochs=20,
+    max_epochs=5,
     gpus=1,
     # precision=16,  # why slower ? :)
 )