Reordered functions in cli module

Signed-off-by: Jim Martens <github@2martens.de>

src/twomartens/masterthesis/cli.py

@@ -40,6 +40,21 @@ def config(args: argparse.Namespace) -> None:
         conf.list_property_values()
 
 
+def prepare(args: argparse.Namespace) -> None:
+    import pickle
+    
+    from twomartens.masterthesis import data
+    
+    file_names_photos, file_names_instances, instances = data.prepare_scenenet_data(args.scenenet_path,
+                                                                                    args.protobuf_path)
+    with open(f"{args.ground_truth_path}/photo_paths.bin", "wb") as file:
+        pickle.dump(file_names_photos, file)
+    with open(f"{args.ground_truth_path}/instance_paths.bin", "wb") as file:
+        pickle.dump(file_names_instances, file)
+    with open(f"{args.ground_truth_path}/instances.bin", "wb") as file:
+        pickle.dump(instances, file)
+
+
 def train(args: argparse.Namespace) -> None:
     if args.network == "ssd" or args.network == "bayesian_ssd":
         _ssd_train(args)
@@ -47,6 +62,96 @@ def train(args: argparse.Namespace) -> None:
         _auto_encoder_train(args)
 
 
+def test(args: argparse.Namespace) -> None:
+    if args.network == "ssd" or args.network == "bayesian_ssd":
+        _ssd_test(args)
+    elif args.network == "auto_encoder":
+        _auto_encoder_test(args)
+
+
+def evaluate(args: argparse.Namespace) -> None:
+    if args.network == "ssd":
+        _ssd_evaluate(args)
+    else:
+        raise NotImplementedError
+
+
+def visualise(args: argparse.Namespace) -> None:
+    import pickle
+    
+    from matplotlib import pyplot
+    import numpy as np
+    from PIL import Image
+    
+    from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
+    
+    with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file:
+        file_names = pickle.load(file)
+    with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
+        instances = pickle.load(file)
+    
+    output_path = f"{args.output_path}/visualise/{args.trajectory}"
+    annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json"
+    cats_to_classes, _, cats_to_names, _ = coco_utils.get_coco_category_maps(annotation_file_train)
+    
+    colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist()
+    
+    i = 0
+    nr_images = len(file_names[args.trajectory])
+    nr_digits = math.ceil(math.log10(nr_images))
+    for file_name, labels in zip(file_names[args.trajectory], instances[args.trajectory]):
+        if not labels:
+            continue
+        
+        # only loop through selected trajectory
+        with Image.open(file_name) as image:
+            figure = pyplot.figure(figsize=(20, 12))
+            pyplot.imshow(image)
+            
+            current_axis = pyplot.gca()
+            
+            for instance in labels:
+                bbox = instance['bbox']
+                # Transform the predicted bounding boxes for the 300x300 image to the original image dimensions.
+                xmin = bbox[0]
+                ymin = bbox[1]
+                xmax = bbox[2]
+                ymax = bbox[3]
+                color = colors[cats_to_classes[int(instance['coco_id'])]]
+                label = f"{cats_to_names[int(instance['coco_id'])]}: {instance['wordnet_class_name']}, " \
+                    f"{instance['wordnet_id']}"
+                current_axis.add_patch(
+                    pyplot.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, color=color, fill=False, linewidth=2))
+                current_axis.text(xmin, ymin, label, size='x-large', color='white',
+                                  bbox={'facecolor': color, 'alpha': 1.0})
+            pyplot.savefig(f"{output_path}/{str(i).zfill(nr_digits)}")
+            pyplot.close(figure)
+        
+        i += 1
+
+
+def measure_mapping(args: argparse.Namespace) -> None:
+    import pickle
+    
+    from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
+    
+    with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
+        instances = pickle.load(file)
+    
+    output_path = f"{args.output_path}/measure/{args.tarball_id}"
+    annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json"
+    cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train)
+    
+    for i, trajectory in enumerate(instances):
+        counts = {cat_id: 0 for cat_id in cats_to_classes.keys()}
+        for labels in trajectory:
+            for instance in labels:
+                counts[instance['coco_id']] += 1
+        
+        with open(f"{output_path}/{i}.bin", "wb") as file:
+            pickle.dump(counts, file)
+
+
 def _ssd_train(args: argparse.Namespace) -> None:
     import os
     import pickle
@@ -200,11 +305,87 @@ def _auto_encoder_train(args: argparse.Namespace) -> None:
                            channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
 
 
-def evaluate(args: argparse.Namespace) -> None:
+def _ssd_test(args: argparse.Namespace) -> None:
-    if args.network == "ssd":
+    import pickle
-        _ssd_evaluate(args)
+    import os
+    
+    import tensorflow as tf
+    
+    from twomartens.masterthesis import data
+    from twomartens.masterthesis import ssd
+    
+    config = tf.ConfigProto()
+    config.log_device_placement = False
+    config.gpu_options.allow_growth = False
+    tf.enable_eager_execution(config=config)
+    
+    batch_size = 16
+    image_size = (300, 300)
+    forward_passes_per_image = 10
+    use_dropout = False if args.network == "ssd" else True
+    
+    checkpoint_path = f"{args.weights_path}/train/{args.network}/{args.train_iteration}"
+    model_file = f"{checkpoint_path}/ssd300.h5"
+    output_path = f"{args.output_path}/val/{args.network}/{args.iteration}/"
+    os.makedirs(output_path, exist_ok=True)
+    
+    # load prepared ground truth
+    with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file:
+        file_names_photos = pickle.load(file)
+    with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
+        instances = pickle.load(file)
+
+    # model
+    ssd_model = tf.keras.models.load_model(model_file)
+    
+    test_generator, length_dataset = \
+        data.load_scenenet_data(file_names_photos, instances, args.coco_path,
+                                predictor_sizes=ssd_model.predictor_sizes,
+                                batch_size=batch_size,
+                                resized_shape=image_size,
+                                training=False, evaluation=True)
+    del file_names_photos, instances
+
+    nr_digits = math.ceil(math.log10(math.ceil(length_dataset / batch_size)))
+    steps_per_epoch = int(math.ceil(length_dataset / batch_size))
+    ssd.predict_keras(test_generator,
+                      steps_per_epoch,
+                      ssd_model,
+                      use_dropout,
+                      forward_passes_per_image,
+                      image_size,
+                      output_path,
+                      nr_digits)
+
+
+def _auto_encoder_test(args: argparse.Namespace) -> None:
+    from twomartens.masterthesis import data
+    from twomartens.masterthesis.aae import run
+    import tensorflow as tf
+    from tensorflow.python.ops import summary_ops_v2
+    
+    tf.enable_eager_execution()
+    coco_path = args.coco_path
+    category = args.category
+    category_trained = args.category_trained
+    batch_size = 16
+    image_size = 256
+    coco_data = data.load_coco_val(coco_path, category, num_epochs=1,
+                                   batch_size=batch_size, resized_shape=(image_size, image_size))
+    use_summary_writer = summary_ops_v2.create_file_writer(
+        f"{args.summary_path}/val/category-{category}/{args.iteration}"
+    )
+    if args.debug:
+        with use_summary_writer.as_default():
+            run.run_simple(coco_data, iteration=args.iteration_trained,
+                           weights_prefix=f"{args.weights_path}/category-{category_trained}",
+                           zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
+                           image_size=image_size)
     else:
-        raise NotImplementedError
+        run.run_simple(coco_data, iteration=args.iteration_trained,
+                       weights_prefix=f"{args.weights_path}/category-{category_trained}",
+                       zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
+                       image_size=image_size)
 
 
 def _ssd_evaluate(args: argparse.Namespace) -> None:
@@ -294,183 +475,3 @@ def _ssd_evaluate(args: argparse.Namespace) -> None:
     
     with open(result_file, "wb") as file:
         pickle.dump(results, file)
-
-
-def test(args: argparse.Namespace) -> None:
-    if args.network == "ssd" or args.network == "bayesian_ssd":
-        _ssd_test(args)
-    elif args.network == "auto_encoder":
-        _auto_encoder_test(args)
-
-
-def _ssd_test(args: argparse.Namespace) -> None:
-    import pickle
-    import os
-    
-    import tensorflow as tf
-    
-    from twomartens.masterthesis import data
-    from twomartens.masterthesis import ssd
-    
-    config = tf.ConfigProto()
-    config.log_device_placement = False
-    config.gpu_options.allow_growth = False
-    tf.enable_eager_execution(config=config)
-    
-    batch_size = 16
-    image_size = (300, 300)
-    forward_passes_per_image = 10
-    use_dropout = False if args.network == "ssd" else True
-    
-    checkpoint_path = f"{args.weights_path}/train/{args.network}/{args.train_iteration}"
-    model_file = f"{checkpoint_path}/ssd300.h5"
-    output_path = f"{args.output_path}/val/{args.network}/{args.iteration}/"
-    os.makedirs(output_path, exist_ok=True)
-    
-    # load prepared ground truth
-    with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file:
-        file_names_photos = pickle.load(file)
-    with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
-        instances = pickle.load(file)
-
-    # model
-    ssd_model = tf.keras.models.load_model(model_file)
-    
-    test_generator, length_dataset = \
-        data.load_scenenet_data(file_names_photos, instances, args.coco_path,
-                                predictor_sizes=ssd_model.predictor_sizes,
-                                batch_size=batch_size,
-                                resized_shape=image_size,
-                                training=False, evaluation=True)
-    del file_names_photos, instances
-
-    nr_digits = math.ceil(math.log10(math.ceil(length_dataset / batch_size)))
-    steps_per_epoch = int(math.ceil(length_dataset / batch_size))
-    ssd.predict_keras(test_generator,
-                      steps_per_epoch,
-                      ssd_model,
-                      use_dropout,
-                      forward_passes_per_image,
-                      image_size,
-                      output_path,
-                      nr_digits)
-
-
-def _auto_encoder_test(args: argparse.Namespace) -> None:
-    from twomartens.masterthesis import data
-    from twomartens.masterthesis.aae import run
-    import tensorflow as tf
-    from tensorflow.python.ops import summary_ops_v2
-    
-    tf.enable_eager_execution()
-    coco_path = args.coco_path
-    category = args.category
-    category_trained = args.category_trained
-    batch_size = 16
-    image_size = 256
-    coco_data = data.load_coco_val(coco_path, category, num_epochs=1,
-                                   batch_size=batch_size, resized_shape=(image_size, image_size))
-    use_summary_writer = summary_ops_v2.create_file_writer(
-        f"{args.summary_path}/val/category-{category}/{args.iteration}"
-    )
-    if args.debug:
-        with use_summary_writer.as_default():
-            run.run_simple(coco_data, iteration=args.iteration_trained,
-                           weights_prefix=f"{args.weights_path}/category-{category_trained}",
-                           zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
-                           image_size=image_size)
-    else:
-        run.run_simple(coco_data, iteration=args.iteration_trained,
-                       weights_prefix=f"{args.weights_path}/category-{category_trained}",
-                       zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
-                       image_size=image_size)
-
-
-def prepare(args: argparse.Namespace) -> None:
-    import pickle
-    
-    from twomartens.masterthesis import data
-    
-    file_names_photos, file_names_instances, instances = data.prepare_scenenet_data(args.scenenet_path,
-                                                                                    args.protobuf_path)
-    with open(f"{args.ground_truth_path}/photo_paths.bin", "wb") as file:
-        pickle.dump(file_names_photos, file)
-    with open(f"{args.ground_truth_path}/instance_paths.bin", "wb") as file:
-        pickle.dump(file_names_instances, file)
-    with open(f"{args.ground_truth_path}/instances.bin", "wb") as file:
-        pickle.dump(instances, file)
-
-
-def visualise(args: argparse.Namespace) -> None:
-    import pickle
-    
-    from matplotlib import pyplot
-    import numpy as np
-    from PIL import Image
-
-    from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
-    
-    with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file:
-        file_names = pickle.load(file)
-    with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
-        instances = pickle.load(file)
-
-    output_path = f"{args.output_path}/visualise/{args.trajectory}"
-    annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json"
-    cats_to_classes, _, cats_to_names, _ = coco_utils.get_coco_category_maps(annotation_file_train)
-
-    colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist()
-    
-    i = 0
-    nr_images = len(file_names[args.trajectory])
-    nr_digits = math.ceil(math.log10(nr_images))
-    for file_name, labels in zip(file_names[args.trajectory], instances[args.trajectory]):
-        if not labels:
-            continue
-        
-        # only loop through selected trajectory
-        with Image.open(file_name) as image:
-            figure = pyplot.figure(figsize=(20, 12))
-            pyplot.imshow(image)
-        
-            current_axis = pyplot.gca()
-        
-            for instance in labels:
-                bbox = instance['bbox']
-                # Transform the predicted bounding boxes for the 300x300 image to the original image dimensions.
-                xmin = bbox[0]
-                ymin = bbox[1]
-                xmax = bbox[2]
-                ymax = bbox[3]
-                color = colors[cats_to_classes[int(instance['coco_id'])]]
-                label = f"{cats_to_names[int(instance['coco_id'])]}: {instance['wordnet_class_name']}, {instance['wordnet_id']}"
-                current_axis.add_patch(
-                    pyplot.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, color=color, fill=False, linewidth=2))
-                current_axis.text(xmin, ymin, label, size='x-large', color='white',
-                                  bbox={'facecolor': color, 'alpha': 1.0})
-            pyplot.savefig(f"{output_path}/{str(i).zfill(nr_digits)}")
-            pyplot.close(figure)
-        
-        i += 1
-
-
-def measure_mapping(args: argparse.Namespace) -> None:
-    import pickle
-
-    from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
-
-    with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
-        instances = pickle.load(file)
-
-    output_path = f"{args.output_path}/measure/{args.tarball_id}"
-    annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json"
-    cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train)
-    
-    for i, trajectory in enumerate(instances):
-        counts = {cat_id: 0 for cat_id in cats_to_classes.keys()}
-        for labels in trajectory:
-            for instance in labels:
-                counts[instance['coco_id']] += 1
-        
-        with open(f"{output_path}/{i}.bin", "wb") as file:
-            pickle.dump(counts, file)