Feat s3 img reader (#289)

* refactor: anistropy not hardcoded

* refactor: anisotropy not built-in

* refactor: simplified graph loader

* refactor: simplified swc_util

* refactor: image reader class

---------

Co-authored-by: anna-grim <anna.grim@alleninstitute.org>

Author: anna-grim

src/deep_neurographs/config.py

@@ -23,7 +23,8 @@ class GraphConfig:
     ----------
     anisotropy : list[float], optional
         Scaling factors applied to xyz coordinates to account for anisotropy
-        of microscope. The default is [1.0, 1.0, 1.0].
+        of microscope. Note this instance of "anisotropy" is only used while
+        reading fragments (i.e. swcs). The default is [1.0, 1.0, 1.0].
     complex_bool : bool
         Indication of whether to generate complex proposals, meaning proposals
         between leaf and non-leaf nodes. The default is False.
@@ -74,12 +75,15 @@ class MLConfig:
 
     Attributes
     ----------
+    anisotropy : list[float], optional
+        Scaling factors applied to xyz coordinates to account for anisotropy
+        of microscope. Note this instance of "anisotropy" is only used while
+        generating features. The default is [1.0, 1.0, 1.0].
     batch_size : int
         The number of samples processed in one batch during training or
         inference. Default is 1000.
-    downsample_factor : int
-        Downsampling factor that accounts for which level in the image pyramid
-        the voxel coordinates must index into. The default is 0.
+    multiscale : int
+        Level in the image pyramid that voxel coordinates must index into.
     high_threshold : float
         A threshold value used for classification, above which predictions are
         considered to be high-confidence. Default is 0.9.
@@ -89,14 +93,14 @@ class MLConfig:
         Type of machine learning model to use. Default is "GraphNeuralNet".
 
     """
-
+    anisotropy: List[float] = field(default_factory=list)
     batch_size: int = 2000
-    downsample_factor: int = 1
     high_threshold: float = 0.9
     lr: float = 1e-3
-    threshold: float = 0.6
     model_type: str = "GraphNeuralNet"
+    multiscale: int = 1
     n_epochs: int = 1000
+    threshold: float = 0.6
     validation_split: float = 0.15
     weight_decay: float = 1e-3
 

src/deep_neurographs/inference.py

@@ -68,7 +68,7 @@ def __init__(
         config,
         device="cpu",
         is_multimodal=False,
-        label_path=None,
+        labels_path=None,
         log_runtimes=True,
         save_to_s3_bool=False,
         s3_dict=None,
@@ -98,7 +98,7 @@ def __init__(
             ...
         is_multimodal : bool, optional
             ...
-        label_path : str, optional
+        labels_path : str, optional
             Path to the segmentation assumed to be stored on a GCS bucket. The
             default is None.
         log_runtimes : bool, optional
@@ -132,11 +132,12 @@ def __init__(
             self.model_path,
             self.ml_config.model_type,
             self.graph_config.search_radius,
+            anisotropy=self.ml_config.anisotropy,
             batch_size=self.ml_config.batch_size,
             confidence_threshold=self.ml_config.threshold,
             device=device,
-            downsample_factor=self.ml_config.downsample_factor,
-            label_path=label_path,
+            multiscale=self.ml_config.multiscale,
+            labels_path=labels_path,
             is_multimodal=is_multimodal,
         )
 
@@ -474,11 +475,12 @@ def __init__(
         model_path,
         model_type,
         radius,
+        anisotropy=[1.0, 1.0, 1.0],
         batch_size=BATCH_SIZE,
         confidence_threshold=CONFIDENCE_THRESHOLD,
         device=None,
-        downsample_factor=1,
-        label_path=None,
+        multiscale=1,
+        labels_path=None,
         is_multimodal=False
     ):
         """
@@ -501,9 +503,9 @@ def __init__(
         confidence_threshold : float, optional
             Threshold on acceptance probability for proposals. The default is
             the global variable "CONFIDENCE_THRESHOLD".
-        downsample_factor : int, optional
-            Downsampling factor that accounts for which level in the image
-            pyramid the voxel coordinates must index into. The default is 0.
+        multiscale : int, optional
+            Level in the image pyramid that voxel coordinates must index into.
+            The default is 1.
 
         Returns
         -------
@@ -520,8 +522,9 @@ def __init__(
         # Features
         self.feature_generator = FeatureGenerator(
             img_path,
-            downsample_factor,
-            label_path=label_path,
+            multiscale,
+            anisotropy=anisotropy,
+            labels_path=labels_path,
             is_multimodal=is_multimodal
         )