feat: 🚧 Incorporate simple change from rhoadesj/dev

dacapo/cli.py

@@ -42,7 +42,7 @@ def validate(run_name, iteration):
 
 @cli.command()
 @click.option(
-    "-r", "--run_name", required=True, type=str, help="The name of the run to use."
+    "-r", "--run-name", required=True, type=str, help="The name of the run to apply."
 )
 @click.option(
     "-ic",

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

@@ -52,7 +52,7 @@ def axes(self):
             logger.debug(
                 "DaCapo expects Zarr datasets to have an 'axes' attribute!\n"
                 f"Zarr {self.file_name} and dataset {self.dataset} has attributes: {list(self._attributes.items())}\n"
-                f"Using default {['t', 'z', 'y', 'x'][-self.dims::]}",
+                f"Using default {['c', 'z', 'y', 'x'][-self.dims::]}",
             )
             return ["c", "z", "y", "x"][-self.dims : :]
 

dacapo/predict.py

@@ -24,7 +24,7 @@ def predict(
     num_cpu_workers: int = 4,
     compute_context: ComputeContext = LocalTorch(),
     output_roi: Optional[Roi] = None,
-    output_dtype: Optional[np.dtype] = np.uint8,
+    output_dtype: Optional[np.dtype] = np.float32,  # add necessary type conversions
     overwrite: bool = False,
 ):
     # get the model's input and output size
@@ -71,7 +71,6 @@ def predict(
 
     # prepare data source
     pipeline = DaCapoArraySource(raw_array, raw)
-    pipeline += gp.Normalize(raw)
     # raw: (c, d, h, w)
     pipeline += gp.Pad(raw, Coordinate((None,) * input_voxel_size.dims))
     # raw: (c, d, h, w)

dacapo/train.py

@@ -1,4 +1,3 @@
-from copy import deepcopy
 from dacapo.store.create_store import create_array_store
 from .experiments import Run
 from .compute_context import LocalTorch, ComputeContext
@@ -11,7 +10,6 @@
 import logging
 
 logger = logging.getLogger(__name__)
-logger.setLevel("INFO")
 
 
 def train(run_name: str, compute_context: ComputeContext = LocalTorch()):
@@ -103,16 +101,7 @@ def train_run(
             logger.error(
                 f"Found weights for iteration {latest_weights_iteration}, but "
                 f"run {run.name} was only trained until {trained_until}. "
-                "Filling stats with last observed values."
             )
-            last_iteration_stats = run.training_stats.iteration_stats[-1]
-            for i in range(
-                last_iteration_stats.iteration, latest_weights_iteration - 1
-            ):
-                new_iteration_stats = deepcopy(last_iteration_stats)
-                new_iteration_stats.iteration = i + 1
-                run.training_stats.add_iteration_stats(new_iteration_stats)
-                trained_until = run.training_stats.trained_until()
 
     # start/resume training