Feature/image saving callback

configs/experiment/im2im/gan.yaml

@@ -42,3 +42,18 @@ callbacks:
 
   early_stopping:
     monitor: val/loss/generator_loss
+
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/gan_superres.yaml

@@ -42,3 +42,18 @@ callbacks:
 
   early_stopping:
     monitor: val/loss/generator_loss
+
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/instance_seg.yaml

@@ -37,3 +37,18 @@ data:
     # patch_shape: [64, 64]
     # 3D
     patch_shape: [16, 32, 32]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/labelfree.yaml

@@ -35,3 +35,19 @@ data:
     # patch_shape: [64, 64]
     # 3D
     patch_shape: [16, 32, 32]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/mae.yaml

@@ -35,3 +35,19 @@ data:
     # patch_shape: [16, 16]
     # 3D
     patch_shape: [16, 16, 16]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/segmentation.yaml

@@ -34,3 +34,19 @@ data:
     # patch_shape: [64, 64]
     # 3D
     patch_shape: [16, 32, 32]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/segmentation_plugin.yaml

@@ -61,3 +61,19 @@ model:
   _aux:
     filters: MUST_OVERRIDE
     overlap: 0
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/segmentation_superres.yaml

@@ -34,3 +34,19 @@ data:
     # patch_shape: [64, 64]
     # 3D
     patch_shape: [16, 32, 32]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/experiment/im2im/vit_segmentation.yaml

@@ -37,3 +37,19 @@ data:
     # patch_shape: [16, 16]
     # 3D
     patch_shape: [16, 16, 16]
+
+callbacks:
+  # prediction
+  # saving:
+  #   _target_: cyto_dl.callbacks.ImageSaver
+  #   save_dir: ${paths.output_dir}
+  #   save_every_n_epochs: ${model.save_images_every_n_epochs}
+  #   stages: ["predict"]
+  #   save_input: False
+  # training
+  saving:
+    _target_: cyto_dl.callbacks.ImageSaver
+    save_dir: ${paths.output_dir}
+    save_every_n_epochs: ${model.save_images_every_n_epochs}
+    stages: ["train", "test", "val"]
+    save_input: True

configs/model/im2im/gan.yaml

@@ -66,7 +66,6 @@ _aux:
           scales: 1
         reconstruction_loss:
           _target_: torch.nn.MSELoss
-        save_input: True
         postprocess:
           input:
             _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel

configs/model/im2im/gan_superres.yaml

@@ -66,7 +66,6 @@ _aux:
           scales: 1
         reconstruction_loss:
           _target_: torch.nn.MSELoss
-        save_input: True
         postprocess:
           input:
             _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel

configs/model/im2im/instance_seg.yaml

@@ -44,7 +44,6 @@ _aux:
         loss:
           _target_: cyto_dl.models.im2im.utils.InstanceSegLoss
           dim: ${spatial_dims}
-        save_input: True
         postprocess:
           input:
             _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel

configs/model/im2im/labelfree.yaml

@@ -51,4 +51,3 @@ _aux:
           prediction:
             _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel
             rescale_dtype: numpy.uint8
-        save_input: True

configs/model/im2im/segmentation.yaml

@@ -54,4 +54,3 @@ _aux:
             activation:
               _target_: torch.nn.Sigmoid
             rescale_dtype: numpy.uint8
-        save_input: True

configs/model/im2im/segmentation_plugin.yaml

@@ -32,8 +32,6 @@ task_heads:
         _target_: cyto_dl.models.im2im.utils.postprocessing.AutoThreshold
         method: "threshold_otsu"
 
-    save_input: True
-
 optimizer:
   generator:
     _partial_: True

configs/model/im2im/segmentation_superres.yaml

@@ -1,6 +1,6 @@
 _target_: cyto_dl.models.im2im.MultiTaskIm2Im
 
-save_images_every_n_epochs: 1
+save_images_every_n_epochs: 10
 save_dir: ${paths.output_dir}
 
 x_key: ${source_col}
@@ -54,7 +54,6 @@ _aux:
             activation:
               _target_: torch.nn.Sigmoid
             rescale_dtype: numpy.uint8
-        save_input: True
         in_channels: 1
         out_channels: 1
         upsample_ratio: 4

configs/model/im2im/vit_segmentation_decoder.yaml

@@ -47,7 +47,6 @@ _aux:
         loss:
           _target_: cyto_dl.models.im2im.utils.InstanceSegLoss
           dim: ${spatial_dims}
-        save_input: True
         postprocess:
           input:
             _target_: cyto_dl.models.im2im.utils.postprocessing.ActThreshLabel

cyto_dl/callbacks/__init__.py

@@ -2,6 +2,7 @@
 # from .callback_utils import GetKLDRanks
 # from .latent_walk import LatentWalk
 
+from .image_saver import ImageSaver
 from .layer_freeze import LayerFreeze
 
 # raise NotImplementedError("TODO: refactor callbacks")

cyto_dl/callbacks/image_saver.py

@@ -0,0 +1,80 @@
+from pathlib import Path
+from typing import List, Union
+
+from bioio.writers import OmeTiffWriter
+from lightning.pytorch.callbacks import Callback
+
+VALID_STAGES = ("train", "val", "test", "predict")
+
+
+class ImageSaver(Callback):
+    def __init__(
+        self,
+        save_dir: Union[str, Path],
+        save_every_n_epochs: int = 1,
+        stages: List[str] = ["train", "val"],
+        save_input: bool = False,
+    ):
+        """Callback for saving images after postprocessing by eads.
+
+        Parameters
+        ----------
+        save_dir: Union[str, Path]
+            Directory to save images
+        save_every_n_epochs:int=1
+            Frequency to save images
+        stages:List[str]=["train", "val"]
+            Stages to save images
+        save_input:bool =False
+            Whether to save input images
+        """
+        self.save_dir = Path(save_dir)
+        for stage in stages:
+            assert stage in VALID_STAGES, f"Invalid stage {stage}, must be one of {VALID_STAGES}"
+        self.save_every_n_epochs = save_every_n_epochs
+        self.stages = stages
+        self.save_keys = ["pred", "target"]
+        if save_input:
+            self.save_keys.append("input")
+
+    def _save(self, fn, data):
+        fn.parent.mkdir(exist_ok=True, parents=True)
+        OmeTiffWriter.save(uri=fn, data=data)
+
+    def on_predict_batch_end(
+        self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0
+    ):
+        if "predict" in self.stages:
+            io_map, outputs = outputs
+            if outputs is None:
+                # image has already been saved
+                return
+            for i, head_io_map in enumerate(io_map.values()):
+                for k, save_path in head_io_map.items():
+                    self._save(save_path, outputs[k]["pred"][i])
+
+    # train/test/val
+    def save(self, outputs, stage=None, step=None):
+        for k in self.save_keys:
+            for head in outputs[k]:
+                self._save(
+                    self.save_dir / f"{stage}_images/{step}_{head}_{k}.tif", outputs[k][head]
+                )
+
+    def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0):
+        if "test" in self.stages:
+            # save all test outputs
+            self.save(outputs, "test", trainer.global_step)
+
+    def _should_save(self, batch_idx, epoch):
+        return batch_idx == (epoch + 1) % self.save_every_n_epochs == 0
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0):
+        if "train" in self.stages and self._should_save(batch_idx, trainer.current_epoch):
+            self.save(outputs, "train", trainer.global_step)
+
+    def on_validation_batch_end(
+        self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0
+    ):
+        if "val" in self.stages and self._should_save(batch_idx, trainer.current_epoch):
+            self.save(outputs, "val", trainer.global_step)

cyto_dl/callbacks/outlier_detection.py

@@ -6,7 +6,7 @@
 import pandas as pd
 from einops import reduce
 from lightning.pytorch.callbacks import Callback
-from ostats import add_sample
+from online_stats import add_sample
 from scipy.spatial.distance import mahalanobis
 
 

cyto_dl/models/im2im/gan.py

@@ -141,8 +141,10 @@ def _extract_loss(self, outs, loss_type):
 
     def model_step(self, stage, batch, batch_idx):
         run_heads, _ = self._get_run_heads(batch, stage, batch_idx)
+        n_postprocess = self.get_n_postprocess_image(batch, batch_idx, stage)
+
         batch = self._to_tensor(batch)
-        outs = self.run_forward(batch, stage, self.should_save_image(batch_idx, stage), run_heads)
+        outs = self.run_forward(batch, stage, n_postprocess, run_heads)
 
         loss_D = self._extract_loss(outs, "loss_D")
         loss_G = self._extract_loss(outs, "loss_G")
@@ -157,17 +159,24 @@ def model_step(self, stage, batch, batch_idx):
             d_opt.zero_grad()
             self.manual_backward(loss_D["loss"])
             d_opt.step()
-        loss_dict = {f"discriminator_{key}": loss for key, loss in loss_D.items()}
-        loss_dict.update({f"generator_{key}": loss for key, loss in loss_G.items()})
-        loss_dict["loss"] = loss_dict["generator_loss"]
+        results = {f"discriminator_{key}": loss for key, loss in loss_D.items()}
+        results.update({f"generator_{key}": loss for key, loss in loss_G.items()})
+        results["loss"] = results["generator_loss"]
+
+        if n_postprocess > 0:
+            # add postprocessed images to return dict
+            for k in ("pred", "target", "input"):
+                results[k] = self.get_per_head(outs, k)
 
-        return loss_dict, None, None
+        self.compute_metrics(results, None, None, stage)
+        return results
 
     def predict_step(self, batch, batch_idx):
         stage = "predict"
         run_heads, io_map = self._get_run_heads(batch, stage, batch_idx)
+        outs = None
         if len(run_heads) > 0:
+            n_postprocess = self.get_n_postprocess_image(batch, batch_idx, stage)
             batch = self._to_tensor(batch)
-            save_image = self.should_save_image(batch_idx, stage)
-            self.run_forward(batch, stage, save_image, run_heads)
-        return io_map
+            outs = self.run_forward(batch, stage, n_postprocess, run_heads)
+        return io_map, outs