diff --git a/direct/cli/__init__.py b/direct/cli/__init__.py
index 36518f18..d83a81fd 100644
--- a/direct/cli/__init__.py
+++ b/direct/cli/__init__.py
@@ -1,6 +1,7 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
 """DIRECT Command-line interface. This is the file which builds the main parser."""
+
 import argparse
 import sys
 
@@ -16,11 +17,14 @@ def main():
     root_subparsers.required = True
     root_subparsers.dest = "subcommand"
 
-    # Prevent circular import
+    # Prevent circular imports
     from direct.cli.train import register_parser as register_train_subcommand
+    from direct.cli.predict import register_parser as register_predict_subcommand
 
     # Training images related commands.
     register_train_subcommand(root_subparsers)
+    # Inference images related commands.
+    register_predict_subcommand(root_subparsers)
 
     args = root_parser.parse_args()
     args.subcommand(args)
diff --git a/direct/cli/predict.py b/direct/cli/predict.py
new file mode 100644
index 00000000..310dc621
--- /dev/null
+++ b/direct/cli/predict.py
@@ -0,0 +1,71 @@
+# coding=utf-8
+# Copyright (c) DIRECT Contributors
+
+import argparse
+import pathlib
+
+from direct.cli.utils import file_or_url
+from direct.environment import Args
+from direct.predict import predict_from_argparse
+
+
+def register_parser(parser: argparse._SubParsersAction):
+    """Register wsi commands to a root parser."""
+
+    epilog = f"""
+        Examples:
+        ---------
+        Run on single machine:
+            $ direct predict <data_root> <output_directory> <experiment_directory> --checkpoint <checkpoint> \
+                            --num-gpus <num_gpus> [ --cfg <cfg_filename>.yaml --other-flags <other_flags>]
+
+        Run on multiple machines:
+            (machine0)$ direct predict <data_root> <output_directory> <experiment_dir> --checkpoint <checkpoint> \
+                            --cfg <cfg_filename>.yaml --machine-rank 0 --num-machines 2 [--other-flags]
+            (machine1)$ direct predict <data_root> <output_directory> <experiment_dir> --checkpoint <checkpoint> \
+                            --cfg <cfg_filename>.yaml --machine-rank 1 --num-machines 2 [--other-flags]
+        """
+    common_parser = Args(add_help=False)
+    predict_parser = parser.add_parser(
+        "predict",
+        help="Run inference using direct.",
+        parents=[common_parser],
+        epilog=epilog,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    predict_parser.add_argument("data_root", type=pathlib.Path, help="Path to the inference data directory.")
+    predict_parser.add_argument("output_directory", type=pathlib.Path, help="Path to the output directory.")
+    predict_parser.add_argument(
+        "experiment_directory",
+        type=pathlib.Path,
+        help="Path to the directory with checkpoints and config.",
+    )
+    predict_parser.add_argument(
+        "--checkpoint",
+        type=int,
+        required=True,
+        help="Number of an existing checkpoint in experiment directory.",
+    )
+    predict_parser.add_argument(
+        "--filenames-filter",
+        type=pathlib.Path,
+        help="Path to list of filenames to parse.",
+    )
+    predict_parser.add_argument(
+        "--name",
+        dest="name",
+        help="Run name if this is different than the experiment directory.",
+        required=False,
+        type=str,
+        default="",
+    )
+    predict_parser.add_argument(
+        "--cfg",
+        dest="cfg_file",
+        help="Config file for inference. Can be either a local file or a remote URL."
+        "Only use it to overwrite the standard loading of the config in the project directory.",
+        required=False,
+        type=file_or_url,
+    )
+
+    predict_parser.set_defaults(subcommand=predict_from_argparse)
diff --git a/direct/functionals/challenges.py b/direct/functionals/challenges.py
index df72d8ef..f1f953bc 100644
--- a/direct/functionals/challenges.py
+++ b/direct/functionals/challenges.py
@@ -83,8 +83,17 @@ def calgary_campinas_psnr(gt, pred):
 
 def calgary_campinas_vif(gt, pred):
     def vif_func(gt, target, data_range):  # noqa
-        from sewar.full_ref import vifp
-
-        return vifp(gt, target, sigma_nsq=0.4)
+        from direct.utils.imports import _module_available
+
+        # Calgary Campinas VIF metric requires 'sewar' module. Check if it exists
+        if not _module_available("sewar"):
+            raise RuntimeError(
+                "'sewar' module required for calgary_campinas_vif metric, but not found. "
+                "Please use 'pip3 install sewar' and run again."
+            )
+        else:
+            from sewar.full_ref import vifp
+
+            return vifp(gt, target, sigma_nsq=0.4)
 
     return _calgary_campinas_metric(gt, pred, vif_func)
diff --git a/direct/inference.py b/direct/inference.py
index a0cab418..c6bc1d20 100644
--- a/direct/inference.py
+++ b/direct/inference.py
@@ -30,7 +30,6 @@ def setup_inference_save_to_h5(
     machine_rank: int,
     cfg_file=None,
     process_per_chunk: Optional[int] = None,
-    volume_processing_func: Callable = None,
     mixed_precision: bool = False,
     debug: bool = False,
 ):
@@ -51,7 +50,6 @@ def setup_inference_save_to_h5(
     machine_rank :
     cfg_file :
     process_per_chunk :
-    volume_processing_func :
     mixed_precision :
     debug :
 
@@ -94,7 +92,6 @@ def setup_inference_save_to_h5(
         write_output_to_h5(
             output,
             output_directory,
-            volume_processing_func,
             output_key="reconstruction",
         )
 
diff --git a/direct/predict.py b/direct/predict.py
new file mode 100644
index 00000000..c0f632ef
--- /dev/null
+++ b/direct/predict.py
@@ -0,0 +1,58 @@
+# coding=utf-8
+# Copyright (c) DIRECT Contributors
+import argparse
+import functools
+import logging
+import os
+
+import torch
+
+from direct.common.subsample import build_masking_function
+from direct.environment import Args
+from direct.inference import build_inference_transforms, setup_inference_save_to_h5
+from direct.launch import launch
+from direct.utils import set_all_seeds
+
+logger = logging.getLogger(__name__)
+
+
+def _get_transforms(env):
+    dataset_cfg = env.cfg.inference.dataset
+    mask_func = build_masking_function(**dataset_cfg.transforms.masking)
+    transforms = build_inference_transforms(env, mask_func, dataset_cfg)
+    return dataset_cfg, transforms
+
+
+setup_inference_save_to_h5 = functools.partial(
+    setup_inference_save_to_h5,
+    functools.partial(_get_transforms),
+)
+
+
+def predict_from_argparse(args: argparse.Namespace):
+    # This sets MKL threads to 1.
+    # DataLoader can otherwise bring a lot of difficulties when computing CPU FFTs in the transforms.
+    torch.set_num_threads(1)
+    os.environ["OMP_NUM_THREADS"] = "1"
+
+    set_all_seeds(args.seed)
+
+    launch(
+        setup_inference_save_to_h5,
+        args.num_machines,
+        args.num_gpus,
+        args.machine_rank,
+        args.dist_url,
+        args.name,
+        args.data_root,
+        args.experiment_directory,
+        args.output_directory,
+        args.filenames_filter,
+        args.checkpoint,
+        args.device,
+        args.num_workers,
+        args.machine_rank,
+        args.cfg_file,
+        args.mixed_precision,
+        args.debug,
+    )
diff --git a/direct/utils/imports.py b/direct/utils/imports.py
new file mode 100644
index 00000000..7a107262
--- /dev/null
+++ b/direct/utils/imports.py
@@ -0,0 +1,21 @@
+# coding=utf-8
+# Copyright (c) DIRECT Contributors
+"""General utilities for module imports"""
+
+from importlib.util import find_spec
+
+
+def _module_available(module_path: str) -> bool:
+    """
+    Check if a path is available in your environment
+    >>> _module_available('os')
+    True
+    >>> _module_available('bla.bla')
+    False
+    Adapted from: https://github.com/PyTorchLightning/pytorch-lightning/blob/ef7d41692ca04bb9877da5c743f80fceecc6a100/pytorch_lightning/utilities/imports.py#L27
+    Under Apache 2.0 license.
+    """
+    try:
+        return find_spec(module_path) is not None
+    except ModuleNotFoundError:
+        return False
diff --git a/projects/calgary_campinas/predict_test.py b/projects/calgary_campinas/predict_test.py
index b8cb392a..57f42d5c 100644
--- a/projects/calgary_campinas/predict_test.py
+++ b/projects/calgary_campinas/predict_test.py
@@ -1,5 +1,6 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
+
 import functools
 import logging
 import os
@@ -16,8 +17,6 @@
 from direct.inference import build_inference_transforms, setup_inference_save_to_h5
 from direct.utils import set_all_seeds
 
-from .utils import volume_post_processing_func
-
 logger = logging.getLogger(__name__)
 
 
@@ -150,7 +149,6 @@ def _get_transforms(masks_dict, env):
         args.device,
         args.num_workers,
         args.machine_rank,
-        volume_post_processing_func,
         args.mixed_precision,
         args.debug,
     )
diff --git a/projects/calgary_campinas/predict_val.py b/projects/calgary_campinas/predict_val.py
deleted file mode 100644
index 0acb1d70..00000000
--- a/projects/calgary_campinas/predict_val.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# coding=utf-8
-# Copyright (c) DIRECT Contributors
-import functools
-import logging
-import os
-import pathlib
-import sys
-
-import torch
-
-import direct.launch
-from direct.common.subsample import build_masking_function
-from direct.environment import Args
-from direct.inference import build_inference_transforms, setup_inference_save_to_h5
-from direct.utils import set_all_seeds
-
-from utils import volume_post_processing_func as calgary_campinas_post_processing_func
-
-logger = logging.getLogger(__name__)
-
-
-def _get_transforms(validation_index, env):
-    dataset_cfg = env.cfg.validation.datasets[validation_index]
-    mask_func = build_masking_function(**dataset_cfg.transforms.masking)
-    transforms = build_inference_transforms(env, mask_func, dataset_cfg)
-    return dataset_cfg, transforms
-
-
-if __name__ == "__main__":
-    # This sets MKL threads to 1.
-    # DataLoader can otherwise bring a lot of difficulties when computing CPU FFTs in the transforms.
-    torch.set_num_threads(1)
-    os.environ["OMP_NUM_THREADS"] = "1"
-
-    epilog = f"""
-        Examples:
-        Run on single machine:
-            $ {sys.argv[0]} data_root output_directory --checkpoint <checkpoint_num> --name <name> [--other-flags]
-        Run on multiple machines:
-            (machine0)$ {sys.argv[0]} data_root output_directory --checkpoint <checkpoint_num> --name <name> --machine-rank 0 --num-machines 2 --dist-url <URL> [--other-flags]
-            (machine1)$ {sys.argv[0]} data_root output_directory --checkpoint <checkpoint_num> --name <name> --machine-rank 1 --num-machines 2 --dist-url <URL> [--other-flags]
-        """
-
-    parser = Args(epilog=epilog)
-    parser.add_argument("data_root", type=pathlib.Path, help="Path to the DoIterationOutput directory.")
-    parser.add_argument("output_directory", type=pathlib.Path, help="Path to the DoIterationOutput directory.")
-    parser.add_argument(
-        "experiment_directory",
-        type=pathlib.Path,
-        help="Path to the directory with checkpoints and config.",
-    )
-    parser.add_argument(
-        "--checkpoint",
-        type=int,
-        required=True,
-        help="Number of an existing checkpoint.",
-    )
-    parser.add_argument(
-        "--validation-index",
-        type=int,
-        required=True,
-        help="This is the index of the validation set in the config, e.g., 0 will select the first validation set.",
-    )
-    parser.add_argument(
-        "--filenames-filter",
-        type=pathlib.Path,
-        help="Path to list of filenames to parse.",
-    )
-    parser.add_argument("--name", help="Run name.", required=True, type=str)
-    parser.add_argument(
-        "--cfg",
-        dest="cfg_file",
-        help="Config file for inference. "
-        "Only use it to overwrite the standard loading of the config in the project directory.",
-        required=False,
-        type=pathlib.Path,
-    )
-    parser.add_argument(
-        "--use-orthogonal-normalization",
-        dest="use_orthogonal_normalization",
-        help="If set, an orthogonal normalization (e.g. ortho in numpy.fft) will be used. "
-        "The Calgary-Campinas challenge does not use this, therefore the volumes will be"
-        " normalized to their expected outputs.",
-        default="store_true",
-    )
-
-    args = parser.parse_args()
-    set_all_seeds(args.seed)
-
-    setup_inference_save_to_h5 = functools.partial(
-        setup_inference_save_to_h5,
-        functools.partial(_get_transforms, args.validation_index),
-    )
-    volume_post_processing_func = None
-    if not args.use_orthogonal_normalization:
-        volume_post_processing_func = calgary_campinas_post_processing_func
-
-    direct.launch.launch(
-        setup_inference_save_to_h5,
-        args.num_machines,
-        args.num_gpus,
-        args.machine_rank,
-        args.dist_url,
-        args.name,
-        args.data_root,
-        args.experiment_directory,
-        args.output_directory,
-        args.filenames_filter,
-        args.checkpoint,
-        args.device,
-        args.num_workers,
-        args.machine_rank,
-        volume_post_processing_func,
-        args.mixed_precision,
-        args.debug,
-    )
diff --git a/projects/spie_radial_subsampling/predict_val.py b/projects/predict_val.py
similarity index 83%
rename from projects/spie_radial_subsampling/predict_val.py
rename to projects/predict_val.py
index 209c63ce..d4399aaa 100644
--- a/projects/spie_radial_subsampling/predict_val.py
+++ b/projects/predict_val.py
@@ -1,12 +1,12 @@
 # coding=utf-8
 # Copyright (c) DIRECT Contributors
+
 import functools
 import logging
 import os
 import pathlib
 import sys
 
-import numpy as np
 import torch
 
 import direct.launch
@@ -18,11 +18,6 @@
 logger = logging.getLogger(__name__)
 
 
-def _calgary_volume_post_processing_func(volume):
-    volume = volume / np.sqrt(np.prod(volume.shape[1:]))
-    return volume
-
-
 def _get_transforms(validation_index, env):
     dataset_cfg = env.cfg.validation.datasets[validation_index]
     mask_func = build_masking_function(**dataset_cfg.transforms.masking)
@@ -86,14 +81,6 @@ def _get_transforms(validation_index, env):
         required=False,
         type=pathlib.Path,
     )
-    parser.add_argument(
-        "--use-orthogonal-normalization",
-        dest="use_orthogonal_normalization",
-        help="If set, an orthogonal normalization (e.g. ortho in numpy.fft) will be used. "
-        "The Calgary-Campinas challenge does not use this, therefore the volumes will be"
-        " normalized to their expected outputs.",
-        default="store_true",
-    )
 
     args = parser.parse_args()
     set_all_seeds(args.seed)
@@ -102,9 +89,6 @@ def _get_transforms(validation_index, env):
         setup_inference_save_to_h5,
         functools.partial(_get_transforms, args.validation_index),
     )
-    volume_post_processing_func = None
-    if not args.use_orthogonal_normalization:
-        volume_post_processing_func = _calgary_volume_post_processing_func
 
     direct.launch.launch(
         setup_inference_save_to_h5,
@@ -122,7 +106,6 @@ def _get_transforms(validation_index, env):
         args.num_workers,
         args.machine_rank,
         args.cfg_file,
-        volume_post_processing_func,
         args.mixed_precision,
         args.debug,
     )
diff --git a/projects/spie_radial_subsampling/predict_test.py b/projects/spie_radial_subsampling/predict_test.py
deleted file mode 100644
index 1a6a9d6f..00000000
--- a/projects/spie_radial_subsampling/predict_test.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# coding=utf-8
-# Copyright (c) DIRECT Contributors
-import functools
-import logging
-import os
-import pathlib
-import sys
-
-import numpy as np
-import torch
-
-import direct.launch
-from direct.common.subsample import build_masking_function
-from direct.environment import Args
-from direct.inference import build_inference_transforms, setup_inference_save_to_h5
-from direct.utils import set_all_seeds
-
-logger = logging.getLogger(__name__)
-
-
-def _calgary_volume_post_processing_func(volume):
-    volume = volume / np.sqrt(np.prod(volume.shape[1:]))
-    return volume
-
-
-def _get_transforms(env):
-    dataset_cfg = env.cfg.inference.dataset
-    mask_func = build_masking_function(**dataset_cfg.transforms.masking)
-    transforms = build_inference_transforms(env, mask_func, dataset_cfg)
-    return dataset_cfg, transforms
-
-
-if __name__ == "__main__":
-    # This sets MKL threads to 1.
-    # DataLoader can otherwise bring a lot of difficulties when computing CPU FFTs in the transforms.
-    torch.set_num_threads(1)
-    os.environ["OMP_NUM_THREADS"] = "1"
-
-    epilog = f"""
-        Examples:
-        Run on single machine:
-            $ {sys.argv[0]} data_root output_directory --checkpoint <checkpoint_num> --name <name> [--other-flags]
-        Run on multiple machines:
-            (machine0)$ {sys.argv[0]} data_root output_directory --checkpoint <checkpoint_num> --name <name> --machine-rank 0 --num-machines 2 --dist-url <URL> [--other-flags]
-            (machine1)$ {sys.argv[0]} data_root output_directory --checkpoint <checkpoint_num> --name <name> --machine-rank 1 --num-machines 2 --dist-url <URL> [--other-flags]
-        """
-
-    parser = Args(epilog=epilog)
-    parser.add_argument("data_root", type=pathlib.Path, help="Path to the data directory.")
-    parser.add_argument("output_directory", type=pathlib.Path, help="Path to the DoIterationOutput directory.")
-    parser.add_argument(
-        "experiment_directory",
-        type=pathlib.Path,
-        help="Path to the directory with checkpoints and config.",
-    )
-    parser.add_argument(
-        "--checkpoint",
-        type=int,
-        required=True,
-        help="Number of an existing checkpoint.",
-    )
-    parser.add_argument(
-        "--filenames-filter",
-        type=pathlib.Path,
-        help="Path to list of filenames to parse.",
-    )
-    parser.add_argument(
-        "--name",
-        dest="name",
-        help="Run name if this is different experiment directory.",
-        required=False,
-        type=str,
-        default="",
-    )
-    parser.add_argument(
-        "--cfg",
-        dest="cfg_file",
-        help="Config file for inference. "
-        "Only use it to overwrite the standard loading of the config in the project directory.",
-        required=False,
-        type=pathlib.Path,
-    )
-    parser.add_argument(
-        "--use-orthogonal-normalization",
-        dest="use_orthogonal_normalization",
-        help="If set, an orthogonal normalization (e.g. ortho in numpy.fft) will be used. "
-        "The Calgary-Campinas challenge does not use this, therefore the volumes will be"
-        " normalized to their expected outputs.",
-        default="store_true",
-    )
-
-    args = parser.parse_args()
-    set_all_seeds(args.seed)
-
-    setup_inference_save_to_h5 = functools.partial(
-        setup_inference_save_to_h5,
-        functools.partial(_get_transforms),
-    )
-
-    volume_post_processing_func = None
-    if not args.use_orthogonal_normalization:
-        volume_post_processing_func = _calgary_volume_post_processing_func
-
-    direct.launch.launch(
-        setup_inference_save_to_h5,
-        args.num_machines,
-        args.num_gpus,
-        args.machine_rank,
-        args.dist_url,
-        args.name,
-        args.data_root,
-        args.experiment_directory,
-        args.output_directory,
-        args.filenames_filter,
-        args.checkpoint,
-        args.device,
-        args.num_workers,
-        args.machine_rank,
-        args.cfg_file,
-        volume_post_processing_func,
-        args.mixed_precision,
-        args.debug,
-    )
diff --git a/tests/tests_utils/test_imports.py b/tests/tests_utils/test_imports.py
new file mode 100644
index 00000000..96c66d34
--- /dev/null
+++ b/tests/tests_utils/test_imports.py
@@ -0,0 +1,19 @@
+# coding=utf-8
+# Copyright (c) DIRECT Contributors
+
+import pytest
+
+from direct.utils.imports import _module_available
+
+
+@pytest.mark.parametrize(
+    ["module", "is_available"],
+    [
+        ("torch", True),
+        ("numpy", True),
+        ("non-existent", False),
+    ],
+)
+def test_module_available(module, is_available):
+
+    assert _module_available(module) == is_available