Cleanup

added requirements.txt and startet using pip only
removed version from dgs.__init__ file and use importlib to obtain it

Signed-off-by: Martin <[email protected]>

dgs/__init__.py

@@ -1,11 +1,3 @@
 """
 Tracking via Dynamically Gated Similarities
-
-TODO Provide more package information
 """
-
-__version__ = "0.0.2"
-__author__ = "Martin Steinborn"
-__homepage__ = "https://bmmtstb.github.io/dynamically-gated-similarities/"
-__description__ = 'Code for Paper "Tracking with Dynamically Gated Similarities"'
-__url__ = "https://github.com/bmmtstb/dynamically-gated-similarities"

dgs/default_config.py

@@ -3,6 +3,7 @@
 
 These values are used, iff the given config does not set own values.
 """
+
 import torch
 from easydict import EasyDict
 
@@ -12,7 +13,7 @@
 # General #
 # ####### #
 
-# cfg.name = "DEFAULT"  # shouldn't be set, to force user to give it a name
+cfg.name = "DEFAULT"
 cfg.print_prio = "normal"
 cfg.working_memory_size = 30
 

dgs/models/engine/engine.py

@@ -121,7 +121,6 @@ class EngineModule(BaseModule):
     lr_sched: list[optim.lr_scheduler.LRScheduler]
     """The learning-rate sheduler(s) can be changed by setting ``engine.lr_scheduler = [..., ...]``."""
 
-    @torch.enable_grad
     def __init__(
         self,
         config: Config,

dgs/models/engine/visual_sim_engine.py

@@ -34,6 +34,9 @@ class VisualSimilarityEngine(EngineModule):
     val_dl: TorchDataLoader
     """The torch DataLoader containing the validation (query) data."""
 
+    # The heart of the project might get a little larger...
+    # pylint: disable=too-many-arguments,too-many-locals
+
     def __init__(
         self,
         config: Config,

dgs/models/loss.py

@@ -1,6 +1,7 @@
 """
 Functions to load and manage torch loss functions.
 """
+
 from typing import Type, Union
 
 from torch import nn

dgs/models/metric.py

@@ -1,11 +1,13 @@
 """
 Methods for handling the computation of distances and other metrics.
 """
+
 import warnings
 from typing import Type, Union
 
 import torch
 from torch import nn
+from torch.linalg import vector_norm
 
 from dgs.utils.types import Metric
 
@@ -71,8 +73,8 @@ def compute_cmc(
 def custom_cosine_similarity(input1: torch.Tensor, input2: torch.Tensor, dim: int, eps: float) -> torch.Tensor:
     """See https://github.com/pytorch/pytorch/issues/104564#issuecomment-1625348908"""
     # get normalization value
-    t1_div = torch.linalg.vector_norm(input1, dim=dim, keepdims=True)
-    t2_div = torch.linalg.vector_norm(input2, dim=dim, keepdims=True)
+    t1_div = vector_norm(input1, dim=dim, keepdim=True)  # pylint: disable=not-callable
+    t2_div = vector_norm(input2, dim=dim, keepdim=True)  # pylint: disable=not-callable
 
     t1_div = t1_div.clone()
     t2_div = t2_div.clone()

dgs/models/module.py

@@ -1,6 +1,7 @@
 """
 Base model class as lowest building block for dynamic modules
 """
+
 import inspect
 from abc import ABC, abstractmethod
 from functools import wraps
@@ -17,7 +18,7 @@
 module_validations: Validations = {
     "name": ["str", ("longer", 2)],
     "print_prio": [("in", PRINT_PRIORITY)],
-    "device": ["str", ("or", (("in", ["cuda", "cpu"]), ("instance", torch.device)))],
+    "device": [("or", (("in", ["cuda", "cpu"]), ("type", torch.device)))],
     "gpus": ["optional", lambda gpus: isinstance(gpus, list) and all(isinstance(gpu, int) for gpu in gpus)],
     "num_workers": ["optional", "int", ("gte", 0)],
     "sp": [("instance", bool)],
@@ -97,7 +98,7 @@ class BaseModule(ABC):
     """
 
     @enable_keyboard_interrupt
-    def __init__(self, config: Config, path: NodePath, validate_base: bool = False):
+    def __init__(self, config: Config, path: NodePath):
         self.config: Config = config
         self.params: Config = get_sub_config(config, path)
         self._path: NodePath = path
@@ -106,16 +107,13 @@ def __init__(self, config: Config, path: NodePath, validate_base: bool = False):
         if not self.config["gpus"]:
             self.config["gpus"] = [-1]
         elif isinstance(self.config["gpus"], str):
-            self.config["gpus"] = (
-                [int(i) for i in self.config["gpus"].split(",")] if torch.cuda.device_count() >= 1 else [-1]
-            )
+            self.config["gpus"] = [int(i) for i in self.config["gpus"].split(",")]
+
         # set default value of num_workers
         if not self.config["num_workers"]:
             self.config["num_workers"] = 0
 
-        # validate config when calling BaseModule class and flag is True
-        if validate_base:
-            self.validate_params(module_validations, "config")
+        self.validate_params(module_validations, "config")
 
     def validate_params(self, validations: Validations, attrib_name: str = "params") -> None:
         """Given per key validations, validate this module's parameters.
@@ -173,7 +171,9 @@ def validate_params(self, validations: Validations, attrib_name: str = "params")
             if param_name not in getattr(self, attrib_name):
                 if "optional" in list_of_validations:
                     continue  # value is optional and does not exist, skip validation
-                raise InvalidParameterException(f"{param_name} is expected to be in module {self.__class__.__name__}")
+                raise InvalidParameterException(
+                    f"'{param_name}' is expected to be in module '{self.__class__.__name__}'"
+                )
 
             # it is now safe to get the value
             value = getattr(self, attrib_name)[param_name]
@@ -202,13 +202,13 @@ def validate_params(self, validations: Validations, attrib_name: str = "params")
                     if validate_value(value=value, data=data, validation=validation_name):
                         continue
                     raise InvalidParameterException(
-                        f"In module {self.__class__.__name__}, parameter {param_name} is not valid. "
-                        f"Value is {value} and is expected to have validation(s) {list_of_validations}."
+                        f"In module '{self.__class__.__name__}', parameter '{param_name}' is not valid. "
+                        f"Value is '{value}' and is expected to have validation(s) '{list_of_validations}'."
                     )
                 # no other case was true
                 raise ValidationException(
-                    f"Validation is expected to be callable or tuple, but is {type(validation)}. "
-                    f"Current module: {self.__class__.__name__}, Parameter: {param_name}"
+                    f"Validation is expected to be callable or tuple, but is '{type(validation)}'. "
+                    f"Current module: '{self.__class__.__name__}', Parameter: '{param_name}'"
                 )
 
     @abstractmethod

dgs/models/optimizer.py

@@ -1,6 +1,7 @@
 """
 Functions to load and manage torch optimizers.
 """
+
 from typing import Type, Union
 
 from torch import optim

dgs/models/pose_warping/__init__.py

@@ -2,6 +2,7 @@
 Module to warp a given pose, pose-state -
 or more generally to predict the next pose of a person given previous time steps.
 """
+
 from typing import Type
 
 from dgs.utils.exceptions import InvalidParameterException

dgs/models/pose_warping/kalman.py

@@ -1,6 +1,7 @@
 """
 Implementation if kalman filter for basic pose warping
 """
+
 import torch
 
 from dgs.models.pose_warping.pose_warping import PoseWarpingModule

dgs/models/pose_warping/pose_warping.py

@@ -1,6 +1,7 @@
 """
 Helpers and models for warping the pose-state of a track into the next time frame.
 """
+
 from abc import abstractmethod
 
 import torch

dgs/models/similarity/__init__.py

@@ -1,6 +1,7 @@
 """
 Modules for handling similarity functions or other models that return similarity scores between two (or more) inputs.
 """
+
 from typing import Type
 
 from dgs.utils.exceptions import InvalidParameterException

dgs/models/similarity/combined.py

@@ -1,6 +1,7 @@
 """
 Models that combine the results of two or more similarity matrices.
 """
+
 from abc import abstractmethod
 
 import torch

dgs/models/similarity/pose_similarity.py

@@ -1,6 +1,7 @@
 """
 Modules for computing the similarity between two poses.
 """
+
 import torch
 
 from dgs.models.similarity.similarity import SimilarityModule

dgs/models/similarity/similarity.py

@@ -3,6 +3,7 @@
 
 Similarity functions compute a similarity score "likeness" between two equally sized inputs.
 """
+
 from typing import Callable
 
 import torch

dgs/models/states.py

@@ -1,6 +1,7 @@
 """
 definitions and helpers for pose-state(s)
 """
+
 from collections import UserDict
 from typing import Union
 

dgs/utils/config.py

@@ -3,6 +3,7 @@
 
 Contains functions for validating configuration and parameter of modules.
 """
+
 from copy import deepcopy
 from typing import Union
 

dgs/utils/constants.py

@@ -1,6 +1,7 @@
 """
 Predefined constants that will not change and might be used at different places.
 """
+
 import os
 
 import torch

dgs/utils/files.py

@@ -1,6 +1,7 @@
 """
 Contains helper functions for loading and interacting with files and paths.
 """
+
 import json
 import os
 

dgs/utils/image.py

@@ -14,6 +14,7 @@
 RGB Images in cv2 have a shape of ``[h x w x C]`` and the channels are in order GBR.
 Grayscale Images in cv2 have a shape of ``[h x w]``.
 """
+
 from typing import Iterable, Union
 
 import torch

dgs/utils/timer.py

@@ -1,6 +1,7 @@
 """
 Models, functions and helpers for timing operations.
 """
+
 import time
 from collections import UserList
 from datetime import timedelta

dgs/utils/torchtools.py

@@ -2,6 +2,7 @@
 Tools for handling recurring torch tasks. Mostly taken from the `torchreid package
 <https://kaiyangzhou.github.io/deep-person-reid/_modules/torchreid/utils/torchtools.html#load_pretrained_weights>`_
 """
+
 import os
 import pickle
 import shutil

dgs/utils/types.py

@@ -1,6 +1,7 @@
 """
 definition of regularly used types
 """
+
 from typing import Callable, Union
 
 import torch

dgs/utils/validation.py

@@ -1,6 +1,7 @@
 """
 Utilities for validating recurring data types.
 """
+
 import os
 from collections.abc import Iterable, Sized
 from typing import Union
@@ -30,6 +31,7 @@
     "callable": (lambda x, _: callable(x)),
     "instance": isinstance,  # alias
     "isinstance": isinstance,
+    "type": (lambda x, d: isinstance(d, type) and isinstance(x, d)),
     "iterable": (lambda x, _: isinstance(x, Iterable)),
     "sized": (lambda x, _: isinstance(x, Sized)),
     # number

dgs/utils/visualization.py

@@ -6,6 +6,7 @@
 Matplotlib uses a different order for the images: `[B x H x W x C]`.
 At least, the channel for matplotlib is RGB too.
 """
+
 from typing import Union
 
 import numpy as np

docs/conf.py

@@ -12,6 +12,7 @@
 
 import os
 import sys
+from importlib.metadata import PackageNotFoundError, version as ilib_version
 
 sys.path.insert(0, os.path.abspath(".."))
 
@@ -23,10 +24,11 @@
 copyright = "2023, Martin Steinborn"
 author = "Martin Steinborn"
 
-version_file = "../dgs/__init__.py"
-with open(version_file, "r") as f:
-    exec(compile(f.read(), version_file, "exec"))
-__version__ = locals()["__version__"]
+try:
+    __version__ = ilib_version("dynamically_gated_similarities")
+except PackageNotFoundError:
+    __version__ = "0.0.0"
+
 # The short X.Y version
 version = __version__[: __version__.find(".", __version__.find(".") + 1)]
 # The full version, including alpha/beta/rc tags

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dynamically_gated_similarities"
-version = "0.0.2"
+version = "0.0.3"
 authors = [
     { name = "Martin Steinborn", email = "[email protected]" },
 ]