Making use of LightningDataModule and simplification of data loading

This merge request adds LightningDataModule to better organize the code and make better use of lightning's features. This centralizes common tasks such as DataLoader creation and application of transforms into a base class to be inherited from.

Data loading was also simplified by removing custom Sample classes and maker functions, and the addition of RuntimeDataset and CachedDataset.

Remaining tasks:

• (@dcarron) Create a common/default DataModule for the shenzhen dataset that takes protocols and transforms as parameters to avoid copying code for each protocol configuration
• (@dcarron) Add typehints to ShenzenDataModule
• (@dcarron) Investigate issue where training a new model with ElasticDeformation as a transform converges more slowly if the data is not cached.
• (@andre.anjos) Update documentation on ShenzenDataModule
• (@biosignal) Update all datasets, using Shenzhen as a reference
  • (@mdelitroz) Montgomery
  • (@mdelitroz) Hivtb
  • (@andre.anjos) Indian
  • (@andre.anjos) Padchest
  • (@mdelitroz) Tbpoc
  • (@andre.anjos) tbx11_simplified -> renamed as tbx11k, protocol v1 (uses original dataset organisation)
  • (@andre.anjos) tbx11_simplified_v2 -> renamed as tbx11k, protocol v2 (uses original dataset organisation)
  • (@andre.anjos) mc_ch -> renamed as montgomery-shenzhen
  • (@andre.anjos) mc_ch_in -> renamed as montgomery-shenzhen-indian
  • (@andre.anjos) mc_ch_in_11k -> renamed as montgomery-shenzhen-indian-tbx11k-v1
  • (@andre.anjos) mc_ch_in_11k_v2 -> renamed as montgomery-shenzhen-indian-tbx11k-v2
  • (@andre.anjos) mc_ch_in_pc -> renamed as montgomery-shenzhen-indian-padchest
  • (@andre.anjos) nih_cxr14_re -> renamed as nih-cxr14 (n.b.: multi-class dataset, radiological findings)
  • (@andre.anjos) nih_cxr14_re_pc -> renamed as nih-cxr14-padchest
• (@dcarron) Update models
• (@dcarron) Update evaluation scripts
• (@dcarron) Update support for extra_validation datasets
• (@biosignal) Update full documentation
• (@biosignal) Update unit tests

Addresses the following issues:

• Closes #14 (closed)
• Closes #18 (closed)
• Closes #19 (closed)
• Closes #21 (closed)
• Closes #29 (closed)

src/ptbench/data/shenzhen/default.py

@@ -14,7 +14,7 @@ from clapper.logging import setup
 from torchvision import transforms
 
 from ..base_datamodule import BaseDataModule
-from ..dataset import JSONProtocol, TBDataset
+from ..dataset import CachedDataset, JSONProtocol, RuntimeDataset
 from ..shenzhen import _protocols, _raw_data_loader
 from ..transforms import ElasticDeformation, RemoveBlackBorders
 
@@ -59,43 +59,44 @@ class DefaultModule(BaseDataModule):
             fieldnames=("data", "label"),
         )
 
+        if self._cache_samples:
+            dataset = CachedDataset
+        else:
+            dataset = RuntimeDataset
+
         if not self._has_setup_fit and stage == "fit":
-            self.train_dataset = TBDataset(
+            self.train_dataset = dataset(
                 json_protocol,
                 self._protocol,
                 "train",
                 _raw_data_loader,
                 self._build_transforms(is_train=True),
-                cache_samples=self._cache_samples,
             )
 
-            self.validation_dataset = TBDataset(
+            self.validation_dataset = dataset(
                 json_protocol,
                 self._protocol,
                 "validation",
                 _raw_data_loader,
                 self._build_transforms(is_train=False),
-                cache_samples=self._cache_samples,
             )
 
             self._has_setup_fit = True
 
         if not self._has_setup_predict and stage == "predict":
-            self.train_dataset = TBDataset(
+            self.train_dataset = dataset(
                 json_protocol,
                 self._protocol,
                 "train",
                 _raw_data_loader,
                 self._build_transforms(is_train=False),
-                cache_samples=self._cache_samples,
             )
-            self.validation_dataset = TBDataset(
+            self.validation_dataset = dataset(
                 json_protocol,
                 self._protocol,
                 "validation",
                 _raw_data_loader,
                 self._build_transforms(is_train=False),
-                cache_samples=self._cache_samples,
             )
 
             self._has_setup_predict = True