Merge branch 'master' of https://github.com/computational-cell-analytics/micro-sam

Author: constantinpape

doc/installation.md

@@ -1,29 +1,23 @@
 # Installation
 
-We provide three different ways of installing `micro_sam`:
+There are three ways to install `micro_sam`:
 - [From mamba](#from-mamba) is the recommended way if you want to use all functionality.
 - [From source](#from-source) for setting up a development environment to use the latest version and be able to change and contribute to our software.
-- [From installer](#from-installer) to install without having to use conda. This mode of installation is still experimental! It only provides the annotation tools and does not enable model finetuning.
-
-Our software requires the following dependencies:
-- [PyTorch](https://pytorch.org/get-started/locally/)
-- [SegmentAnything](https://github.com/facebookresearch/segment-anything#installation)
-- [elf](https://github.com/constantinpape/elf)
-- [torch_em](https://github.com/constantinpape/torch-em)
-- [napari](https://napari.org/stable/) (for the interactive annotation tools)
-
+- [From installer](#from-installer) to install without having to use conda (supported platforms: Windows and Linux, only for CPU users). 
 
 ## From mamba
 
-[mamba](https://mamba.readthedocs.io/en/latest/) is a drop-in replacement for conda, but is much faster than it.
+[mamba](https://mamba.readthedocs.io/en/latest/) is a drop-in replacement for conda, but much faster.
 While the steps below may also work with `conda`, we highly recommend using `mamba`.
-You can follow the instrutions [here](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) to install `mamba`.
+You can follow the instructions [here](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html) to install `mamba`.
+
+**IMPORTANT**: Make sure to avoid installing anything in the base environment.
 
-`micro_sam` can then be installed in an existing environment via
+`micro_sam` can be installed in an existing environment via:
 ```
 $ mamba install -c conda-forge micro_sam
 ```
-or you can create a new environment (here called `micro-sam`) via
+or you should create a new environment (here called `micro-sam`) via:
 ```
 $ mamba create -c conda-forge -n micro-sam micro_sam
 ```

micro_sam/training/joint_sam_trainer.py

@@ -25,15 +25,17 @@ def __init__(
         self.instance_loss = instance_loss
         self.instance_metric = instance_metric
 
-    def save_checkpoint(self, name, best_metric, **extra_save_dict):
+    def save_checkpoint(self, name, current_metric, best_metric, **extra_save_dict):
         current_unetr_state = self.unetr.state_dict()
         decoder_state = []
         for k, v in current_unetr_state.items():
             if not k.startswith("encoder"):
                 decoder_state.append((k, v))
         decoder_state = OrderedDict(decoder_state)
 
-        super().save_checkpoint(name, best_metric, decoder_state=decoder_state, **extra_save_dict)
+        super().save_checkpoint(
+            name, current_metric=current_metric, best_metric=best_metric, decoder_state=decoder_state, **extra_save_dict
+        )
 
     def load_checkpoint(self, checkpoint="best"):
         save_dict = super().load_checkpoint(checkpoint)

micro_sam/util.py

@@ -812,7 +812,7 @@ def segmentation_to_one_hot(
         n_ids = int(segmentation.max())
 
     else:
-        assert segmentation_ids[0] != 0
+        assert segmentation_ids[0] != 0, "No objects were found."
 
         # the segmentation ids have to be sorted
         segmentation_ids = np.sort(segmentation_ids)