Add Covid configs (#456)

This PR adds configs to train Covid detection models from Chest-Xray data. 

Co-authored-by: Shruthi42 <[email protected]>

CHANGELOG.md

@@ -84,6 +84,7 @@ console for easier diagnostics.
  Additionally, the `TrainHelloWorldAndHelloContainer` job in the PR build has been split into two jobs, `TrainHelloWorld` and
  `TrainHelloContainer`. A pytest marker `after_training_hello_container` has been added to run tests after training is
  finished in the `TrainHelloContainer` job.
+- ([#456](https://github.com/microsoft/InnerEye-DeepLearning/pull/456)) Adding configs to train Covid detection models.
 
 ### Changed
 

InnerEye/ML/SSL/datamodules_and_datasets/cxr_datasets.py

@@ -4,11 +4,12 @@
 # ------------------------------------------------------------------------------------------
 import logging
 from pathlib import Path
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
 from PIL import Image
+from torch.utils.data import Subset
 from torchvision.datasets import VisionDataset
 
 from InnerEye.Common.type_annotations import PathOrString
@@ -175,3 +176,39 @@ def _prepare_dataset(self) -> None:
  self.dataset_dataframe.Path = self.dataset_dataframe.Path.apply(lambda x: x[strip_n:])
  self.indices = np.arange(len(self.dataset_dataframe))
  self.filenames = [self.root / p for p in self.dataset_dataframe.Path.values]
+
+
+class CovidDataset(InnerEyeCXRDatasetWithReturnIndex):
+ """
+ Dataset class to load CovidDataset dataset as datamodule for monitoring SSL training quality directly on
+ CovidDataset data.
+ We use CVX03 against CVX12 as proxy task.
+ """
+
+ def _prepare_dataset(self) -> None:
+ self.dataset_dataframe = pd.read_csv(self.root / "dataset.csv")
+ mapping = {0: 0, 3: 0, 1: 1, 2: 1}
+ # For monitoring purpose with use binary classification CV03vsCV12
+ self.dataset_dataframe["final_label"] = self.dataset_dataframe.final_label.apply(lambda x: mapping[x])
+ self.indices = np.arange(len(self.dataset_dataframe))
+ self.subject_ids = self.dataset_dataframe.subject.values
+ self.filenames = [self.root / file for file in self.dataset_dataframe.filepath.values]
+ self.targets = self.dataset_dataframe.final_label.values.astype(np.int64).reshape(-1)
+
+ @property
+ def num_classes(self) -> int:
+ return 2
+
+ def _split_dataset(self, val_split: float, seed: int) -> Tuple[Subset, Subset]:
+ """
+ Implements val - train split.
+ :param val_split: proportion to use for validation
+ :param seed: random seed for splitting
+ :return: dataset_train, dataset_val
+ """
+ shuffled_subject_ids = np.random.RandomState(seed).permutation(np.unique(self.subject_ids))
+ n_val = int(len(shuffled_subject_ids) * val_split)
+ val_subjects, train_subjects = shuffled_subject_ids[:n_val], shuffled_subject_ids[n_val:]
+ train_ids, val_ids = np.where(np.isin(self.subject_ids, train_subjects))[0], \
+ np.where(np.isin(self.subject_ids, val_subjects))[0]
+ return Subset(self, train_ids), Subset(self, val_ids)

InnerEye/ML/SSL/lightning_containers/ssl_container.py

@@ -13,7 +13,7 @@
 from yacs.config import CfgNode
 
 from InnerEye.ML.SSL.datamodules_and_datasets.cifar_datasets import InnerEyeCIFAR10, InnerEyeCIFAR100
-from InnerEye.ML.SSL.datamodules_and_datasets.cxr_datasets import CheXpert, NIHCXR, RSNAKaggleCXR
+from InnerEye.ML.SSL.datamodules_and_datasets.cxr_datasets import CheXpert, CovidDataset, NIHCXR, RSNAKaggleCXR
 from InnerEye.ML.SSL.datamodules_and_datasets.datamodules import CombinedDataModule, InnerEyeVisionDataModule
 from InnerEye.ML.SSL.datamodules_and_datasets.transforms_utils import InnerEyeCIFARLinearHeadTransform, \
  InnerEyeCIFARTrainTransform, \
@@ -42,11 +42,12 @@ class EncoderName(Enum):
 
 
 class SSLDatasetName(Enum):
- RSNAKaggleCXR = "RSNAKaggleCXR"
- NIHCXR = "NIHCXR"
  CIFAR10 = "CIFAR10"
  CIFAR100 = "CIFAR100"
+ RSNAKaggleCXR = "RSNAKaggleCXR"
+ NIHCXR = "NIHCXR"
  CheXpert = "CheXpert"
+ Covid = "CovidDataset"
 
 
 InnerEyeDataModuleTypes = Union[InnerEyeVisionDataModule, CombinedDataModule]
@@ -62,11 +63,12 @@ class SSLContainer(LightningContainer):
  Note that this container is also used as the base class for SSLImageClassifier (finetuning container) as they share
  setup and datamodule methods.
  """
- _SSLDataClassMappings = {SSLDatasetName.RSNAKaggleCXR.value: RSNAKaggleCXR,
- SSLDatasetName.NIHCXR.value: NIHCXR,
- SSLDatasetName.CIFAR10.value: InnerEyeCIFAR10,
+ _SSLDataClassMappings = {SSLDatasetName.CIFAR10.value: InnerEyeCIFAR10,
  SSLDatasetName.CIFAR100.value: InnerEyeCIFAR100,
- SSLDatasetName.CheXpert.value: CheXpert}
+ SSLDatasetName.RSNAKaggleCXR.value: RSNAKaggleCXR,
+ SSLDatasetName.NIHCXR.value: NIHCXR,
+ SSLDatasetName.CheXpert.value: CheXpert,
+ SSLDatasetName.Covid.value: CovidDataset}
 
  ssl_augmentation_config = param.ClassSelector(class_=Path, allow_None=True,
  doc="The path to the yaml config defining the parameters of the "
@@ -87,11 +89,13 @@ class SSLContainer(LightningContainer):
  "Used for debugging and tests.")
  linear_head_augmentation_config = param.ClassSelector(class_=Path,
  doc="The path to the yaml config for the linear head "
- "augmentations")
+  "augmentations")
  linear_head_dataset_name = param.ClassSelector(class_=SSLDatasetName,
  doc="Name of the dataset to use for the linear head training")
  linear_head_batch_size = param.Integer(default=256, doc="Batch size for linear head tuning")
- learning_rate_linear_head_during_ssl_training = param.Number(default=1e-4, doc="Learning rate for linear head training during SSL training.")
+ learning_rate_linear_head_during_ssl_training = param.Number(default=1e-4,
+ doc="Learning rate for linear head training during "
+ "SSL training.")
 
  def setup(self) -> None:
  from InnerEye.ML.SSL.lightning_containers.ssl_image_classifier import SSLClassifierContainer
@@ -173,7 +177,8 @@ def get_data_module(self) -> InnerEyeDataModuleTypes:
  return self.data_module
  encoder_data_module = self._create_ssl_data_modules(is_ssl_encoder_module=True)
  linear_data_module = self._create_ssl_data_modules(is_ssl_encoder_module=False)
- return CombinedDataModule(encoder_data_module, linear_data_module, self.use_balanced_binary_loss_for_linear_head)
+ return CombinedDataModule(encoder_data_module, linear_data_module,
+ self.use_balanced_binary_loss_for_linear_head)
 
  def _create_ssl_data_modules(self, is_ssl_encoder_module: bool) -> InnerEyeVisionDataModule:
  """
@@ -220,7 +225,10 @@ def _get_transforms(self, augmentation_config: Optional[CfgNode],
  applied on. If False, return only one transformation.
  :return: training transformation pipeline and validation transformation pipeline.
  """
- if dataset_name in [SSLDatasetName.RSNAKaggleCXR.value, SSLDatasetName.NIHCXR.value, SSLDatasetName.CheXpert.value]:
+ if dataset_name in [SSLDatasetName.RSNAKaggleCXR.value,
+ SSLDatasetName.NIHCXR.value,
+ SSLDatasetName.CheXpert.value,
+ SSLDatasetName.Covid.value]:
  assert augmentation_config is not None
  train_transforms, val_transforms = get_cxr_ssl_transforms(augmentation_config,
  return_two_views_per_sample=is_ssl_encoder_module,