CLIs: move --device option to group level

src/eynollah/cli/cli.py

@@ -15,6 +15,7 @@ class EynollahCliCtx:
     Holds options relevant for all eynollah subcommands
     """
     model_zoo: EynollahModelZoo
+    device: str = ''
     log_level : Union[str, None] = 'INFO'
 
 
@@ -35,6 +36,11 @@ class EynollahCliCtx:
     type=(str, str, str),
     multiple=True,
 )
+@click.option(
+    "--device",
+    "-D",
+    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
+)
 @click.option(
     "--log_level",
     "-l",
@@ -42,7 +48,7 @@ class EynollahCliCtx:
     help="Override log level globally to this",
 )
 @click.pass_context
-def main(ctx, model_basedir, model_overrides, log_level):
+def main(ctx, model_basedir, model_overrides, device, log_level):
     """
     eynollah - Document Layout Analysis, Image Enhancement, OCR
     """
@@ -58,6 +64,7 @@ def main(ctx, model_basedir, model_overrides, log_level):
     # Initialize CLI context
     ctx.obj = EynollahCliCtx(
         model_zoo=model_zoo,
+        device=device,
         log_level=log_level,
     )
 

src/eynollah/cli/cli_binarize.py

@@ -33,11 +33,6 @@ import click
     help="overwrite (instead of skipping) if output xml exists",
     is_flag=True,
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
 def binarize_cli(
         ctx,
@@ -46,14 +41,14 @@ def binarize_cli(
         dir_in,
         output,
         overwrite,
-        device,
 ):
     """
     Binarize images with a ML model
     """
     from ..sbb_binarize import SbbBinarizer
     assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo, device=device)
+    binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo,
+                             device=ctx.obj.device)
     binarizer.run(
         image_filename=input_image,
         use_patches=patches,

src/eynollah/cli/cli_enhance.py

@@ -48,13 +48,8 @@ import click
     is_flag=True,
     help="save the enhanced image in original image size",
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
-def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale, device):
+def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale):
     """
     Enhance image
     """
@@ -62,10 +57,10 @@ def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower
     from ..image_enhancer import Enhancer
     enhancer = Enhancer(
         model_zoo=ctx.obj.model_zoo,
+        device=ctx.obj.device,
         num_col_upper=num_col_upper,
         num_col_lower=num_col_lower,
         save_org_scale=save_org_scale,
-        device=device,
     )
     enhancer.run(overwrite=overwrite,
                  dir_in=dir_in,

src/eynollah/cli/cli_layout.py

@@ -172,11 +172,6 @@ import click
     type=click.FloatRange(min=0),
     help="abort when number of failed images exceeds this value (if >=1) or ratio of failed over total images exceeds this value (if <1); 0 means ignore failures",
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
 def layout_cli(
     ctx,
@@ -207,7 +202,6 @@ def layout_cli(
     ignore_page_extraction,
     num_jobs,
     halt_fail,
-    device,
 ):
     """
     Detect Layout (with optional image enhancement and reading order detection)
@@ -223,7 +217,7 @@ def layout_cli(
     assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
     eynollah = Eynollah(
         model_zoo=ctx.obj.model_zoo,
-        device=device,
+        device=ctx.obj.device,
         enable_plotting=enable_plotting,
         allow_enhancement=allow_enhancement,
         curved_line=curved_line,

src/eynollah/cli/cli_ocr.py

@@ -73,11 +73,6 @@ import click
     type=click.FloatRange(min=0.0, max=1.0),
     help="minimum OCR confidence threshold. Text lines with a lower confidence value will not be included in the output XML file.",
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
 def ocr_cli(
     ctx,
@@ -92,7 +87,6 @@ def ocr_cli(
     do_not_mask_with_textline_contour,
     batch_size,
     min_conf_value_of_textline_text,
-    device,
 ):
     """
     Recognize text with a CNN/RNN or transformer ML model.
@@ -101,11 +95,12 @@ def ocr_cli(
     from ..eynollah_ocr import Eynollah_ocr
     eynollah_ocr = Eynollah_ocr(
         model_zoo=ctx.obj.model_zoo,
+        device=ctx.obj.device,
         tr_ocr=tr_ocr,
         do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
         batch_size=batch_size,
         min_conf_value_of_textline_text=min_conf_value_of_textline_text,
-        device=device)
+    )
     eynollah_ocr.run(overwrite=overwrite,
                      dir_in=dir_in,
                      dir_in_bin=dir_in_bin,

src/eynollah/cli/cli_readingorder.py

@@ -22,19 +22,15 @@ import click
     type=click.Path(exists=True, file_okay=False),
     required=True,
 )
-@click.option(
-    "--device",
-    "-D",
-    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
-)
 @click.pass_context
-def readingorder_cli(ctx, input, dir_in, out, device):
+def readingorder_cli(ctx, input, dir_in, out):
     """
     Generate ReadingOrder with a ML model
     """
     from ..mb_ro_on_layout import Reorder
     assert bool(input) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    orderer = Reorder(model_zoo=ctx.obj.model_zoo, device=device)
+    orderer = Reorder(model_zoo=ctx.obj.model_zoo,
+                      device=ctx.obj.device)
     orderer.run(xml_filename=input,
                 dir_in=dir_in,
                 dir_out=out,