slurm cluster is running, will see whether it is correct or not

chunkflow/chunk/image/convnet/inferencer.py

@@ -60,7 +60,10 @@ def __init__(self,
         self.input_size = input_size
 
         if output_crop_margin is None:
-            self.output_crop_margin = self.output_patch_overlap
+            if mask_output_chunk:
+                self.output_crop_margin = (0,0,0)
+            else:
+                self.output_crop_margin = self.output_patch_overlap
         else:
             self.output_crop_margin = output_crop_margin
             # we should always crop more than the patch overlap 

chunkflow/flow/flow.py

@@ -227,25 +227,24 @@ def create_info(tasks,layer_path, channel_num, layer_type, data_type, encoding,
               type=click.Path(file_okay=True, dir_okay=False, exists=True, 
                               readable=True, resolve_path=True),
               help='file contains bounding boxes or tasks.')
-@click.option('--task-index', '-i', 
+@click.option('--job-index', '-i', 
               type=int, default=None,
               help='index of task in the tasks.')
 @click.option('--slurm-job-array/--no-slurm-job-array',
               default=False, help='use the slurm job array '+
               'environment variable to identify task index.')
 @click.option('--granularity', '-g',
               type=int, default=1, help='number of tasks to do in one run.')
-@operator
-def fetch_task_from_file(tasks, file_path, task_index, slurm_job_array, granularity):
+@generator
+def fetch_task_from_file(file_path: str, job_index: int, slurm_job_array: bool, granularity: int):
     if(slurm_job_array):
-        assert os.environ['SLURM_ARRAY_JOB_ID'] == 0
-        assert os.environ['SLURM_ARRAY_TASK_ID'] >= 0
-        task_index = os.environ['SLURM_ARRAY_TASK_ID']
-    assert task_index is not None
+        job_index = int(os.environ['SLURM_ARRAY_TASK_ID'])
+    assert job_index is not None
 
     bbox_array = np.load(file_path)
-    task_stop = min(bbox_array.shape[0], task_index + granularity)
-    for idx in range(task_index, task_stop):
+    task_start = job_index * granularity 
+    task_stop = min(bbox_array.shape[0], task_start + granularity)
+    for idx in range(task_start, task_stop):
         bbox = Bbox.from_list(bbox_array[idx, :])
         task = get_initial_task()
         task['bbox'] = bbox
@@ -1230,6 +1229,7 @@ def neuroglancer(tasks, name, voxel_size, port, chunk_names):
             state['operators'][name](task, selected=chunk_names)
         yield task
 
+
 @main.command('quantize')
 @click.option('--name', type=str, default='quantize', help='name of this operator')
 @click.option('--input-chunk-name', '-i', type=str, default='chunk', help = 'input chunk name')

chunkflow/plugins/mapto01.py

@@ -1,6 +1,9 @@
 import numpy as np
 
 def exec(chunk: np.ndarray):
+    if np.issubdtype(chunk.dtype, np.uint8):
+        chunk = chunk.astype(np.float32)
+        chunk = chunk / 255.0
     assert np.issubdtype(chunk.dtype, np.float32)
     assert chunk.min() >= -1
     assert chunk.max() <= 1

requirements.txt

@@ -1,4 +1,3 @@
-python-dateutil==2.8.0
 boto3
 numpy>=1.16
 six>=1.13.0