[Datumaro] Refactor explain and models CLI (#1714)

* Update explain cli

* Update model cli

* Update config for models

* Remove input size hint for models

datumaro/datumaro/cli/commands/explain.py

@@ -59,7 +59,7 @@ def build_parser(parser_ctor=argparse.ArgumentParser):
         help="Confidence threshold for detections (default: include all)")
     rise_parser.add_argument('-b', '--batch-size', default=1, type=int,
         help="Inference batch size (default: %(default)s)")
-    rise_parser.add_argument('--progressive', action='store_true',
+    rise_parser.add_argument('--display', action='store_true',
         help="Visualize results during computations")
 
     parser.add_argument('-p', '--project', dest='project_dir', default='.',
@@ -108,16 +108,13 @@ def explain_command(args):
     if args.target[0] == TargetKinds.image:
         image_path = args.target[1]
         image = load_image(image_path)
-        if model.preferred_input_size() is not None:
-            h, w = model.preferred_input_size()
-            image = cv2.resize(image, (w, h))
 
         log.info("Running inference explanation for '%s'" % image_path)
-        heatmap_iter = rise.apply(image, progressive=args.progressive)
+        heatmap_iter = rise.apply(image, progressive=args.display)
 
         image = image / 255.0
         file_name = osp.splitext(osp.basename(image_path))[0]
-        if args.progressive:
+        if args.display:
             for i, heatmaps in enumerate(heatmap_iter):
                 for j, heatmap in enumerate(heatmaps):
                     hm_painted = cm.jet(heatmap)[:, :, 2::-1]
@@ -154,35 +151,31 @@ def explain_command(args):
             log.info("Running inference explanation for '%s'" % project_name)
 
         for item in dataset:
-            image = item.image
+            image = item.image.data
             if image is None:
                 log.warn(
                     "Dataset item %s does not have image data. Skipping." % \
                     (item.id))
                 continue
 
-            if model.preferred_input_size() is not None:
-                h, w = model.preferred_input_size()
-                image = cv2.resize(image, (w, h))
             heatmap_iter = rise.apply(image)
 
             image = image / 255.0
-            file_name = osp.splitext(osp.basename(image_path))[0]
             heatmaps = next(heatmap_iter)
 
             if args.save_dir is not None:
-                log.info("Saving inference heatmaps at '%s'" % args.save_dir)
+                log.info("Saving inference heatmaps to '%s'" % args.save_dir)
                 os.makedirs(args.save_dir, exist_ok=True)
 
                 for j, heatmap in enumerate(heatmaps):
-                    save_path = osp.join(args.save_dir,
-                        file_name + '-heatmap-%s.png' % j)
-                    save_image(save_path, heatmap * 255.0)
+                    save_image(osp.join(args.save_dir,
+                            item.id + '-heatmap-%s.png' % j),
+                        heatmap * 255.0, create_dir=True)
 
-            if args.progressive:
+            if not args.save_dir or args.display:
                 for j, heatmap in enumerate(heatmaps):
                     disp = (image + cm.jet(heatmap)[:, :, 2::-1]) / 2
-                    cv2.imshow(file_name + '-heatmap-%s' % j, disp)
+                    cv2.imshow(item.id + '-heatmap-%s' % j, disp)
                 cv2.waitKey(0)
     else:
         raise NotImplementedError()

datumaro/datumaro/cli/contexts/model/__init__.py

@@ -11,8 +11,9 @@ import re
 
 from datumaro.components.config import DEFAULT_FORMAT
 from datumaro.components.project import Environment
-from ...util import add_subparser, MultilineFormatter
-from ...util.project import load_project
+
+from ...util import CliException, MultilineFormatter, add_subparser
+from ...util.project import load_project, generate_next_dir_name
 
 
 def build_add_parser(parser_ctor=argparse.ArgumentParser):
@@ -63,19 +64,20 @@ def add_command(args):
     except KeyError:
         raise CliException("Launcher '%s' is not found" % args.launcher)
 
-    cli_plugin = launcher.cli_plugin
+    cli_plugin = getattr(launcher, 'cli_plugin', launcher)
     model_args = cli_plugin.from_cmdline(args.extra_args)
 
     if args.copy:
-        try:
         log.info("Copying model data")
 
         model_dir = project.local_model_dir(args.name)
         os.makedirs(model_dir, exist_ok=False)
+
+        try:
             cli_plugin.copy_model(model_dir, model_args)
-        except NotImplementedError:
+        except (AttributeError, NotImplementedError):
             log.error("Can't copy: copying is not available for '%s' models" % \
-                (args.launcher))
+                args.launcher)
 
     log.info("Adding the model")
     project.add_model(args.name, {
@@ -115,12 +117,14 @@ def remove_command(args):
 def build_run_parser(parser_ctor=argparse.ArgumentParser):
     parser = parser_ctor()
 
-    parser.add_argument('-o', '--output-dir', dest='dst_dir', required=True,
+    parser.add_argument('-o', '--output-dir', dest='dst_dir',
         help="Directory to save output")
     parser.add_argument('-m', '--model', dest='model_name', required=True,
         help="Model to apply to the project")
     parser.add_argument('-p', '--project', dest='project_dir', default='.',
         help="Directory of the project to operate on (default: current dir)")
+    parser.add_argument('--overwrite', action='store_true',
+        help="Overwrite if exists")
     parser.set_defaults(command=run_command)
 
     return parser
@@ -128,10 +132,17 @@ def build_run_parser(parser_ctor=argparse.ArgumentParser):
 def run_command(args):
     project = load_project(args.project_dir)
 
-    dst_dir = osp.abspath(args.dst_dir)
-    os.makedirs(dst_dir, exist_ok=False)
+    dst_dir = args.dst_dir
+    if dst_dir:
+        if not args.overwrite and osp.isdir(dst_dir) and os.listdir(dst_dir):
+            raise CliException("Directory '%s' already exists "
+                "(pass --overwrite overwrite)" % dst_dir)
+    else:
+        dst_dir = generate_next_dir_name('%s-inference' % \
+            (project.config.project_name))
+
     project.make_dataset().apply_model(
-        save_dir=dst_dir,
+        save_dir=osp.abspath(dst_dir),
         model=args.model_name)
 
     log.info("Inference results have been saved to '%s'" % dst_dir)

datumaro/datumaro/components/config.py

@@ -130,7 +130,7 @@ class Config:
         return len(self.items())
 
     def __iter__(self):
-        return iter(zip(self.keys(), self.values()))
+        return iter(self.keys())
 
     def __getitem__(self, key):
         default = object()

datumaro/datumaro/components/config_model.py

@@ -21,7 +21,6 @@ class Source(Config):
 
 MODEL_SCHEMA = _SchemaBuilder() \
     .add('launcher', str) \
-    .add('model_dir', str, internal=True) \
     .add('options', dict) \
     .build()
 

datumaro/datumaro/components/launcher.py

@@ -17,9 +17,6 @@ class Launcher:
     def launch(self, inputs):
         raise NotImplementedError()
 
-    def preferred_input_size(self):
-        return None
-
     def categories(self):
         return None
 # pylint: enable=no-self-use

datumaro/datumaro/components/project.py

@@ -817,9 +817,8 @@ class Project:
 
     def make_executable_model(self, name):
         model = self.get_model(name)
-        model.model_dir = self.local_model_dir(name)
         return self.env.make_launcher(model.launcher,
-            **model.options, model_dir=model.model_dir)
+            **model.options, model_dir=self.local_model_dir(name))
 
     def make_source_project(self, name):
         source = self.get_source(name)

datumaro/datumaro/plugins/openvino_launcher.py

@@ -186,6 +186,3 @@ class OpenVinoLauncher(Launcher):
     def process_outputs(self, inputs, outputs):
         return self._interpreter.process_outputs(inputs, outputs)
 
-    def preferred_input_size(self):
-        _, _, h, w = self._input_layout
-        return (h, w)