style: 🎨 Black format

dacapo/experiments/datasplits/datasets/arrays/concat_array.py

@@ -9,6 +9,7 @@
 
 logger = logging.getLogger(__file__)
 
+
 class ConcatArray(Array):
     """This is a wrapper around other `source_arrays` that concatenates
     them along the channel dimension."""
@@ -118,5 +119,7 @@ def __getitem__(self, roi: Roi) -> np.ndarray:
             axis=0,
         )
         if concatenated.shape[0] == 1:
-            logger.info(f"Concatenated array has only one channel: {self.name} {concatenated.shape}")
+            logger.info(
+                f"Concatenated array has only one channel: {self.name} {concatenated.shape}"
+            )
         return concatenated

dacapo/experiments/run.py

@@ -11,6 +11,7 @@
 
 logger = logging.getLogger(__file__)
 
+
 class Run:
     name: str
     train_until: int
@@ -58,28 +59,34 @@ def __init__(self, run_config):
             return
         try:
             from ..store import create_config_store
+
             start_config_store = create_config_store()
-            starter_config = start_config_store.retrieve_run_config(run_config.start_config.run)
+            starter_config = start_config_store.retrieve_run_config(
+                run_config.start_config.run
+            )
         except Exception as e:
-            logger.error(f"could not load start config: {e} Should be added to the database config store RUN")
+            logger.error(
+                f"could not load start config: {e} Should be added to the database config store RUN"
+            )
             raise e
-        
+
         # preloaded weights from previous run
         if run_config.task_config.name == starter_config.task_config.name:
             self.start = Start(run_config.start_config)
         else:
             # Match labels between old and new head
-            if hasattr(run_config.task_config,"channels"):
+            if hasattr(run_config.task_config, "channels"):
                 # Map old head and new head
                 old_head = starter_config.task_config.channels
                 new_head = run_config.task_config.channels
-                self.start = Start(run_config.start_config,old_head=old_head,new_head=new_head)
+                self.start = Start(
+                    run_config.start_config, old_head=old_head, new_head=new_head
+                )
             else:
                 logger.warning("Not implemented channel match for this task")
-                self.start = Start(run_config.start_config,remove_head=True)
+                self.start = Start(run_config.start_config, remove_head=True)
         self.start.initialize_weights(self.model)
 
-
     @staticmethod
     def get_validation_scores(run_config) -> ValidationScores:
         """

dacapo/experiments/starts/start.py

@@ -3,15 +3,21 @@
 
 logger = logging.getLogger(__file__)
 
-        # self.old_head   =["ecs","plasma_membrane","mito","mito_membrane","vesicle","vesicle_membrane","mvb","mvb_membrane","er","er_membrane","eres","nucleus","microtubules","microtubules_out"]
-        # self.new_head = ["mito","nucleus","ld","ecs","peroxisome"] 
-head_keys = ["prediction_head.weight","prediction_head.bias","chain.1.weight","chain.1.bias"]
+# self.old_head   =["ecs","plasma_membrane","mito","mito_membrane","vesicle","vesicle_membrane","mvb","mvb_membrane","er","er_membrane","eres","nucleus","microtubules","microtubules_out"]
+# self.new_head = ["mito","nucleus","ld","ecs","peroxisome"]
+head_keys = [
+    "prediction_head.weight",
+    "prediction_head.bias",
+    "chain.1.weight",
+    "chain.1.bias",
+]
 
 # Hack
 # if label is mito_peroxisome or peroxisome then change it to mito
-mitos = ["mito_proxisome","peroxisome"]
+mitos = ["mito_proxisome", "peroxisome"]
 
-def match_heads(model, head_weights, old_head, new_head ):
+
+def match_heads(model, head_weights, old_head, new_head):
     # match the heads
     for label in new_head:
         old_label = label
@@ -30,8 +36,9 @@ def match_heads(model, head_weights, old_head, new_head ):
                     model.state_dict()[key][new_index] = n_val
             logger.warning(f"matched head for {label} with {old_label}")
 
+
 class Start(ABC):
-    def __init__(self, start_config,remove_head = False, old_head= None, new_head = None):
+    def __init__(self, start_config, remove_head=False, old_head=None, new_head=None):
         self.run = start_config.run
         self.criterion = start_config.criterion
         self.remove_head = remove_head
@@ -44,7 +51,9 @@ def initialize_weights(self, model):
         weights_store = create_weights_store()
         weights = weights_store._retrieve_weights(self.run, self.criterion)
 
-        logger.warning(f"loading weights from run {self.run}, criterion: {self.criterion}")
+        logger.warning(
+            f"loading weights from run {self.run}, criterion: {self.criterion}"
+        )
 
         try:
             if self.old_head and self.new_head:
@@ -61,15 +70,21 @@ def initialize_weights(self, model):
             logger.warning(f"ERROR starter: {e}")
 
     def load_model_using_head_removal(self, model, weights):
-        logger.warning(f"removing head from run {self.run}, criterion: {self.criterion}")
+        logger.warning(
+            f"removing head from run {self.run}, criterion: {self.criterion}"
+        )
         for key in head_keys:
             weights.model.pop(key, None)
         logger.warning(f"removed head from run {self.run}, criterion: {self.criterion}")
         model.load_state_dict(weights.model, strict=False)
-        logger.warning(f"loaded weights in non strict mode from run {self.run}, criterion: {self.criterion}")
+        logger.warning(
+            f"loaded weights in non strict mode from run {self.run}, criterion: {self.criterion}"
+        )
 
     def load_model_using_head_matching(self, model, weights):
-        logger.warning(f"matching heads from run {self.run}, criterion: {self.criterion}")
+        logger.warning(
+            f"matching heads from run {self.run}, criterion: {self.criterion}"
+        )
         logger.warning(f"old head: {self.old_head}")
         logger.warning(f"new head: {self.new_head}")
         head_weights = {}
@@ -79,6 +94,3 @@ def load_model_using_head_matching(self, model, weights):
             weights.model.pop(key, None)
         model.load_state_dict(weights.model, strict=False)
         model = match_heads(model, head_weights, self.old_head, self.new_head)
-
-
-

dacapo/experiments/tasks/predictors/distance_predictor.py

@@ -27,7 +27,13 @@ class DistancePredictor(Predictor):
     in the channels argument.
     """
 
-    def __init__(self, channels: List[str], scale_factor: float, mask_distances: bool,extra_conv :bool):
+    def __init__(
+        self,
+        channels: List[str],
+        scale_factor: float,
+        mask_distances: bool,
+        extra_conv: bool,
+    ):
         self.channels = channels
         self.norm = "tanh"
         self.dt_scale_factor = scale_factor
@@ -37,7 +43,7 @@ def __init__(self, channels: List[str], scale_factor: float, mask_distances: boo
         self.epsilon = 5e-2
         self.threshold = 0.8
         self.extra_conv = extra_conv
-        self.extra_conv_dims =len(self.channels) *2
+        self.extra_conv_dims = len(self.channels) * 2
 
     @property
     def embedding_dims(self):

dacapo/experiments/trainers/gunpowder_trainer.py

@@ -43,7 +43,9 @@ def __init__(self, trainer_config):
         self.clip_raw = trainer_config.clip_raw
 
         # Testing out if calculating multiple times and multiplying is necessary
-        self.add_predictor_nodes_to_dataset = trainer_config.add_predictor_nodes_to_dataset
+        self.add_predictor_nodes_to_dataset = (
+            trainer_config.add_predictor_nodes_to_dataset
+        )
         self.finetune_head_only = trainer_config.finetune_head_only
 
         self.scheduler = None
@@ -177,7 +179,9 @@ def build_batch_provider(self, datasets, model, task, snapshot_container=None):
             task.predictor,
             gt_key=gt_key,
             target_key=target_key,
-            weights_key=datasets_weight_key if self.add_predictor_nodes_to_dataset else weight_key,
+            weights_key=datasets_weight_key
+            if self.add_predictor_nodes_to_dataset
+            else weight_key,
             mask_key=mask_key,
         )
 
@@ -230,7 +234,9 @@ def iterate(self, num_iterations, model, optimizer, device):
                 f"Trainer fetch batch took {time.time() - t_start_fetch} seconds"
             )
 
-            for param in model.parameters(): #  TODO: get parameters from optimizer instead
+            for (
+                param
+            ) in model.parameters():  #  TODO: get parameters from optimizer instead
                 param.grad = None
 
             t_start_prediction = time.time()
@@ -352,4 +358,4 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         pass
 
     def can_train(self, datasets) -> bool:
-        return all([dataset.gt is not None for dataset in datasets])
+        return all([dataset.gt is not None for dataset in datasets])

dacapo/experiments/trainers/gunpowder_trainer_config.py

@@ -32,10 +32,12 @@ class GunpowderTrainerConfig(TrainerConfig):
 
     add_predictor_nodes_to_dataset: Optional[bool] = attr.ib(
         default=True,
-        metadata={"help_text": "Whether to add a predictor node to dataset_source and apply product of weights"}
+        metadata={
+            "help_text": "Whether to add a predictor node to dataset_source and apply product of weights"
+        },
     )
 
     finetune_head_only: Optional[bool] = attr.ib(
         default=False,
-        metadata={"help_text": "Whether to fine-tune head only or all layers"}
-    )
+        metadata={"help_text": "Whether to fine-tune head only or all layers"},
+    )

dacapo/train.py

@@ -178,7 +178,7 @@ def train_run(
             stats_store.store_training_stats(run.name, run.training_stats)
 
             # make sure to move optimizer back to the correct device
-            run.move_optimizer(compute_context.device)            
+            run.move_optimizer(compute_context.device)
             run.model.train()
 
             weights_store.store_weights(run, run.training_stats.trained_until())

dacapo/utils/balance_weights.py

@@ -77,4 +77,4 @@ def balance_weights(
         # scale_slab the masked-in scale_slab with the class weights
         scale_slab *= np.take(w, labels_slab)
 
-    return error_scale, moving_counts
+    return error_scale, moving_counts

docs/source/conf.py

@@ -12,43 +12,44 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../..'))
+
+sys.path.insert(0, os.path.abspath("../.."))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'DaCapo'
-copyright = '2022, William Patton, David Ackerman, Jan Funke'
-author = 'William Patton, David Ackerman, Jan Funke'
+project = "DaCapo"
+copyright = "2022, William Patton, David Ackerman, Jan Funke"
+author = "William Patton, David Ackerman, Jan Funke"
 
 
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx_autodoc_typehints']
+extensions = ["sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx_autodoc_typehints"]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_material'
+html_theme = "sphinx_material"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 html_css_files = [
-    'css/custom.css',
-]
+    "css/custom.css",
+]