feat: docstrings + typing

Former-commit-id: 38dfddce97808be4077aa7d943f34096429bc49c [formerly e5d46bbdd39dbd70b65f8deb5fe9ad66d0ecd9b0]
Former-commit-id: 35f5b2cd55cc38756e03c013ee705b137b425239

src/modules/__init__.py

@@ -0,0 +1 @@
+from .mrcnn import MRCNNModule

src/modules/mrcnn.py

@@ -1,19 +1,31 @@
-"""Pytorch lightning wrapper for model."""
+"""Mask R-CNN Pytorch Lightning Module for Object Detection and Segmentation."""
+
+from typing import Any, Dict, List
 
 import pytorch_lightning as pl
 import torch
 import torchvision
 import wandb
-from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
 from torchmetrics.detection.mean_ap import MeanAveragePrecision
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 from torchvision.models.detection.mask_rcnn import (
+    MaskRCNN,
     MaskRCNN_ResNet50_FPN_Weights,
     MaskRCNNPredictor,
 )
 
+Prediction = List[Dict[str, torch.Tensor]]
 
-def get_model_instance_segmentation(num_classes):
+
+def get_model_instance_segmentation(n_classes: int) -> MaskRCNN:
+    """Returns a Torchvision MaskRCNN model for finetunning.
+
+    Args:
+        n_classes (int): number of classes the model should predict, background included
+
+    Returns:
+        MaskRCNN: the model ready to be used
+    """
     # load an instance segmentation model pre-trained on COCO
     model = torchvision.models.detection.maskrcnn_resnet50_fpn(
         weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT,
@@ -23,19 +35,26 @@ def get_model_instance_segmentation(num_classes):
     # get number of input features for the classifier
     in_features = model.roi_heads.box_predictor.cls_score.in_features
     # replace the pre-trained head with a new one
-    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
+    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, n_classes)
 
     # now get the number of input features for the mask classifier
     in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
     hidden_layer = 256
     # and replace the mask predictor with a new one
-    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, hidden_layer, num_classes)
+    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, hidden_layer, n_classes)
 
     return model
 
 
 class MRCNNModule(pl.LightningModule):
-    def __init__(self, n_classes):
+    """Mask R-CNN Pytorch Lightning Module encapsulating commong PyTorch functions."""
+
+    def __init__(self, n_classes: int) -> None:
+        """Constructor, build model, save hyperparameters.
+
+        Args:
+            n_classes (int): number of classes the model should predict, background included
+        """
         super().__init__()
 
         # Hyperparameters
@@ -50,16 +69,38 @@ class MRCNNModule(pl.LightningModule):
         # onnx export
         self.example_input_array = torch.randn(1, 3, 1024, 1024, requires_grad=True).half()
 
-    def forward(self, imgs):
-        self.model.eval()
-        return self.model(imgs)
+        # torchmetrics
+        self.metric_bbox = MeanAveragePrecision(iou_type="bbox")
+        self.metric_segm = MeanAveragePrecision(iou_type="segm")
 
-    def training_step(self, batch, batch_idx):
+    def forward(self, imgs: torch.Tensor) -> Prediction:  # type: ignore
+        """Make a forward pass (prediction), usefull for onnx export.
+
+        Args:
+            imgs (torch.Tensor): the images whose prediction we wish to make
+
+        Returns:
+            torch.Tensor: the predictions
+        """
+        self.model.eval()
+        pred: Prediction = self.model(imgs)
+        return pred
+
+    def training_step(self, batch: torch.Tensor, batch_idx: int) -> float:  # type: ignore
+        """PyTorch training step.
+
+        Args:
+            batch (torch.Tensor): the batch to train the model on
+            batch_idx (int): the batch index number
+
+        Returns:
+            float: the training loss of this step
+        """
         # unpack batch
         images, targets = batch
 
         # compute loss
-        loss_dict = self.model(images, targets)
+        loss_dict: dict[str, float] = self.model(images, targets)
         loss_dict = {f"train/{key}": val for key, val in loss_dict.items()}
         loss = sum(loss_dict.values())
         loss_dict["train/loss"] = loss
@@ -69,15 +110,28 @@ class MRCNNModule(pl.LightningModule):
 
         return loss
 
-    def on_validation_epoch_start(self):
-        self.metric_bbox = MeanAveragePrecision(iou_type="bbox")
-        self.metric_segm = MeanAveragePrecision(iou_type="segm")
+    def on_validation_epoch_start(self) -> None:
+        """Reset TorchMetrics."""
+        self.metric_bbox.reset()
+        self.metric_segm.reset()
 
-    def validation_step(self, batch, batch_idx):
+    def validation_step(self, batch: torch.Tensor, batch_idx: int) -> Prediction:  # type: ignore
+        """PyTorch validation step.
+
+        Args:
+            batch (torch.Tensor): the batch to evaluate the model on
+            batch_idx (int): the batch index number
+
+        Returns:
+            torch.Tensor: the predictions
+        """
         # unpack batch
         images, targets = batch
 
-        preds = self.model(images)
+        # make prediction
+        preds: Prediction = self.model(images)
+
+        # update TorchMetrics from predictions
         for pred, target in zip(preds, targets):
             pred["masks"] = pred["masks"].squeeze(1).int().bool()
             target["masks"] = target["masks"].squeeze(1).int().bool()
@@ -86,17 +140,28 @@ class MRCNNModule(pl.LightningModule):
 
         return preds
 
-    def validation_epoch_end(self, outputs):
-        # log metrics
+    def validation_epoch_end(self, outputs: List[Prediction]) -> None:  # type: ignore
+        """Compute TorchMetrics.
+
+        Args:
+            outputs (List[Prediction]): list of predictions from validation steps
+        """
+        # compute and log bounding boxes metrics
         metric_dict = self.metric_bbox.compute()
         metric_dict = {f"valid/bbox/{key}": val for key, val in metric_dict.items()}
         self.log_dict(metric_dict)
 
+        # compute and log semgentation metrics
         metric_dict = self.metric_segm.compute()
         metric_dict = {f"valid/segm/{key}": val for key, val in metric_dict.items()}
         self.log_dict(metric_dict)
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> Dict[str, Any]:
+        """PyTorch optimizers and Schedulers.
+
+        Returns:
+            Dict[str, Any]: dictionnary for PyTorch Lightning optimizer/scheduler configuration
+        """
         optimizer = torch.optim.Adam(
             self.parameters(),
             lr=wandb.config.LEARNING_RATE,

src/mrcnn/__init__.py

@@ -1 +0,0 @@
-from .module import MRCNNModule