feat: 🚧 Incorporate LeibNetz.

dacapo/experiments/architectures/leibnetz_architecture.py

@@ -0,0 +1,31 @@
+# TODO
+from .architecture import Architecture
+
+from funlib.geometry import Coordinate
+
+import torch
+
+
+class DummyArchitecture(Architecture):
+    def __init__(self, architecture_config):
+        super().__init__()
+
+        self.channels_in = architecture_config.num_in_channels
+        self.channels_out = architecture_config.num_out_channels
+
+        self.conv = torch.nn.Conv3d(self.channels_in, self.channels_out, kernel_size=3)
+
+    @property
+    def input_shape(self):
+        return Coordinate(40, 20, 20)
+
+    @property
+    def num_in_channels(self):
+        return self.channels_in
+
+    @property
+    def num_out_channels(self):
+        return self.channels_out
+
+    def forward(self, x):
+        return self.conv(x)

dacapo/experiments/architectures/leibnetz_architecture_config.py

@@ -0,0 +1,22 @@
+# TODO
+import attr
+
+from .dummy_architecture import DummyArchitecture
+from .architecture_config import ArchitectureConfig
+
+from typing import Tuple
+
+
+@attr.s
+class DummyArchitectureConfig(ArchitectureConfig):
+    """This is just a dummy architecture config used for testing. None of the
+    attributes have any particular meaning."""
+
+    architecture_type = DummyArchitecture
+
+    num_in_channels: int = attr.ib(metadata={"help_text": "Dummy attribute."})
+
+    num_out_channels: int = attr.ib(metadata={"help_text": "Dummy attribute."})
+
+    def verify(self) -> Tuple[bool, str]:
+        return False, "This is a DummyArchitectureConfig and is never valid"

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

@@ -22,6 +22,6 @@ class ResampledArrayConfig(ArrayConfig):
     downsample: Coordinate = attr.ib(
         metadata={"help_text": "The amount by which to downsample!"}
     )
-    interp_order: bool = attr.ib(
+    interp_order: int = attr.ib(
         metadata={"help_text": "The order of the interpolation!"}
     )

dacapo/experiments/trainers/leibnetz_trainer.py

@@ -34,7 +34,7 @@ def __init__(self, trainer_config):
         self.learning_rate = trainer_config.learning_rate
         self.batch_size = trainer_config.batch_size
         self.num_data_fetchers = trainer_config.num_data_fetchers
-        self.print_profiling = 100
+        self.print_profiling = 100  # TODO: remove all hard coded values
         self.snapshot_iteration = trainer_config.snapshot_interval
         self.min_masked = trainer_config.min_masked
         self.reject_probability = trainer_config.reject_probability
@@ -47,7 +47,7 @@ def __init__(self, trainer_config):
 
         self.scheduler = None
 
-    def create_optimizer(self, model):
+    def create_optimizer(self, model):  # TODO: add optimizer to config
         optimizer = torch.optim.RAdam(lr=self.learning_rate, params=model.parameters())
         self.scheduler = (
             torch.optim.lr_scheduler.LinearLR(  # TODO: add scheduler to config

setup.py

@@ -32,10 +32,10 @@
         "funlib.math>=0.1",
         "funlib.geometry>=0.2",
         "mwatershed>=0.1",
-        "funlib.persistence>=0.1",
+        "funlib.persistence @ git+https://github.com/janelia-cellmap/funlib.persistence.git@ome-zarr-reading",
         "funlib.evaluate @ git+https://github.com/pattonw/funlib.evaluate",
         "gunpowder>=1.3",
-        "lsds>=0.1.3",
+        # "lsds>=0.1.3",
         "reloading",
     ],
 )