Dev/main (#198)

.gitignore

@@ -2,6 +2,7 @@
 *.hdf
 *.h5
 # *.ipynb
+.ipynb_checkpoints/
 *.pyc
 *.egg-info
 *.dat

dacapo/blockwise/predict_worker.py

@@ -1,5 +1,6 @@
 import sys
 from pathlib import Path
+from typing import Optional
 
 import torch
 from dacapo.experiments.datasplits.datasets.arrays import ZarrArray
@@ -45,9 +46,9 @@ def cli(log_level):
 @click.option(
     "-i",
     "--iteration",
-    required=True,
     type=int,
     help="The training iteration of the model to use for prediction.",
+    default=None,
 )
 @click.option(
     "-ic",
@@ -62,7 +63,7 @@ def cli(log_level):
 @click.option("-od", "--output_dataset", required=True, type=str)
 def start_worker(
     run_name: str,
-    iteration: int,
+    iteration: int | None,
     input_container: Path | str,
     input_dataset: str,
     output_container: Path | str,
@@ -76,11 +77,12 @@ def start_worker(
     run_config = config_store.retrieve_run_config(run_name)
     run = Run(run_config)
 
-    # create weights store
-    weights_store = create_weights_store()
+    if iteration is not None:
+        # create weights store
+        weights_store = create_weights_store()
 
-    # load weights
-    weights_store.retrieve_weights(run_name, iteration)
+        # load weights
+        weights_store.retrieve_weights(run_name, iteration)
 
     # get arrays
     input_array_identifier = LocalArrayIdentifier(Path(input_container), input_dataset)
@@ -178,15 +180,15 @@ def start_worker(
 
 def spawn_worker(
     run_name: str,
-    iteration: int,
+    iteration: int | None,
     input_array_identifier: "LocalArrayIdentifier",
     output_array_identifier: "LocalArrayIdentifier",
 ):
     """Spawn a worker to predict on a given dataset.
 
     Args:
         run_name (str): The name of the run to apply.
-        iteration (int): The training iteration of the model to use for prediction.
+        iteration (int or None): The training iteration of the model to use for prediction.
         input_array_identifier (LocalArrayIdentifier): The raw data to predict on.
         output_array_identifier (LocalArrayIdentifier): The identifier of the prediction array.
     """
@@ -200,8 +202,6 @@ def spawn_worker(
         "start-worker",
         "--run-name",
         run_name,
-        "--iteration",
-        iteration,
         "--input_container",
         input_array_identifier.container,
         "--input_dataset",
@@ -211,6 +211,8 @@ def spawn_worker(
         "--output_dataset",
         output_array_identifier.dataset,
     ]
+    if iteration is not None:
+        command.extend(["--iteration", str(iteration)])
 
     print("Defining worker with command: ", compute_context.wrap_command(command))
 

dacapo/examples/distance_task/cosem_example.ipynb

@@ -252,7 +252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

dacapo/examples/distance_task/cosem_example.py

@@ -1,4 +1,3 @@
-
 # %% [markdown]
 # # Dacapo
 #

dacapo/examples/distance_task/cosem_example_fill_in_the_blank.py

@@ -26,8 +26,7 @@
 # Create the datasplit, produce the neuroglancer link and store the datasplit
 datasplit = ...
 viewer = ...
-config_store...
-
+config_store
 # %% [markdown]
 # ## Task
 # What do you want to learn? An instance segmentation? If so, how? Affinities,
@@ -40,9 +39,8 @@
 
 # Create a distance task config where the clip_distance=tol_distance=10x the output resolution,
 # and scale_factor = 20x the output resolution
-task_config = 
-config_store....
-
+task_config = ...
+config_store
 # %% [markdown]
 # ## Architecture
 #
@@ -97,14 +95,14 @@
         # Create a gamma augment config with range .5 to 2
         ...,
         # Create an intensity scale shift agument config to rescale data from the range 0->1 to -1->1
-       ...,
+        ...,
     ],
     snapshot_interval=10000,
     min_masked=0.05,
     clip_raw=True,
 )
 # Store the trainer
-config_store....
+config_store
 
 # %% [markdown]
 # ## Run
@@ -128,7 +126,7 @@
 run_config = ...
 
 print(run_config.name)
-config_store...
+config_store
 
 # %% [markdown]
 # ## Train
@@ -138,6 +136,7 @@
 # %%
 from dacapo.train import train_run
 from dacapo.experiments.run import Run
+
 # load the run and train it
-run = Run(config_store...)
+run = Run(config_store)
 train_run(run)

dacapo/examples/distance_task/cosem_finetune_example.ipynb

@@ -10,8 +10,7 @@
     "from dacapo.store.create_store import create_config_store\n",
     "\n",
     "config_store = create_config_store()\n",
-    "\n",
-    ""
+    "\n"
    ]
   },
   {
@@ -43,8 +42,7 @@
     "\n",
     "datasplit = datasplit_config.datasplit_type(datasplit_config)\n",
     "viewer = datasplit._neuroglancer()\n",
-    "config_store.store_datasplit_config(datasplit_config)\n",
-    ""
+    "config_store.store_datasplit_config(datasplit_config)\n"
    ]
   },
   {
@@ -73,8 +71,7 @@
     "    tol_distance=40.0,\n",
     "    scale_factor=80.0,\n",
     ")\n",
-    "config_store.store_task_config(task_config)\n",
-    ""
+    "config_store.store_task_config(task_config)\n"
    ]
   },
   {
@@ -106,8 +103,7 @@
     "    constant_upsample=True,\n",
     "    upsample_factors=[(2, 2, 2)],\n",
     ")\n",
-    "config_store.store_architecture_config(architecture_config)\n",
-    ""
+    "config_store.store_architecture_config(architecture_config)\n"
    ]
   },
   {
@@ -154,8 +150,7 @@
     "    min_masked=0.05,\n",
     "    clip_raw=True,\n",
     ")\n",
-    "config_store.store_trainer_config(trainer_config)\n",
-    ""
+    "config_store.store_trainer_config(trainer_config)\n"
    ]
   },
   {
@@ -173,7 +168,6 @@
    "outputs": [],
    "source": [
     "from dacapo.experiments import RunConfig\n",
-    "from dacapo.experiments.run import Run\n",
     "\n",
     "from dacapo.experiments.starts import CosemStartConfig\n",
     "\n",
@@ -199,8 +193,7 @@
     "    )\n",
     "\n",
     "    print(run_config.name)\n",
-    "    config_store.store_run_config(run_config)\n",
-    ""
+    "    config_store.store_run_config(run_config)\n"
    ]
   },
   {
@@ -225,8 +218,7 @@
     "config_store = create_config_store()\n",
     "\n",
     "run = Run(config_store.retrieve_run_config(\"cosem_distance_run_4nm_finetune\"))\n",
-    "train_run(run)\n",
-    ""
+    "train_run(run)\n"
    ]
   },
   {
@@ -237,8 +229,6 @@
    ]
   }
  ],
- "nbformat": 4,
- "nbformat_minor": 2,
  "metadata": {
   "language_info": {
    "codemirror_mode": {
@@ -252,5 +242,7 @@
    "pygments_lexer": "ipython3",
    "version": 3
   }
- }
-}
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}