diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1f46953
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,21 @@
+venv
+.DS_Store
+local_*
+
+*.pyc
+__pycache__/
+
+data/observations/
+
+.idea/
+*.env
+data
+!data/assets
+dist
+moonwatcher.egg-info
+moonwatcher/coco/*
+moonwatcher/coco
+observations/
+coco/
+stl10_binary.tar.gz
+stl10_binary
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..4947287
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,177 @@
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
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+
+ "You" (or "Your") shall mean an individual or Legal Entity
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+
+ "Work" shall mean the work of authorship, whether in Source or
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+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
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+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
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+ the terms of any separate license agreement you may have executed
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+
+ 6. Trademarks. This License does not grant permission to use the trade
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+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
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+ END OF TERMS AND CONDITIONS
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..4c966a8
--- /dev/null
+++ b/README.md
@@ -0,0 +1,143 @@
+
+
+
+
+The Evaluation & Testing framework for Computer vision models
+Control performance risks, bias and security issues in AI models
+
+
+ [](https://github.com/moonwatcher-ai/moonwatcher/blob/main/LICENSE)
+ [](https://discord.com/invite/xHgSYGXZQK)
+
+
+
+## Install Moonwatcher π
+
+```sh
+pip install moonwatcher
+```
+
+## Try the demos
+> [!WARNING]
+> The demos require `wget` to be installed on your system.
+
+In the demo the performance of a model on unusual values for brightness, contrast and saturation of the underlying
+dataset are checked. To see how to create your own specific test scenarios check out [Quickstart](#quickstart).
+
+Object detection (the demo will download the val2017 set of COCO and use a subset of it):
+```sh
+python -m moonwatcher.demo_detection
+```
+
+Classification (the demo will download STL-10 as a dataset):
+```sh
+python -m moonwatcher.demo_classification
+```
+
+# Contents
+
+- πββοΈ **[Quickstart](#quickstart)**
+ - **1**. π§βπ« [Slices, Checks and Checksuites](#slices_checks_and_checksuites)
+ - π° [Slices](#slices)
+ - β
[Checks](#checks)
+ - π [Checksuites](#checksuites)
+ - **2**. π€ [Run automated checks](#automated-checks)
+ - **3**. π¨βπ» [Write custom checks and checksuites](#write-custom-checks-and-checksuites)
+- π₯οΈ **[Web app](#webapp)**
+
+πββοΈ Quickstart
+
+1. π§βπ« Slices, Checks and Checksuites
+There are three core concepts (apart from models and datasets) to this framework. These concepts are called Checks, Checksuites and Slices.
+
+ Slices
+A slice is a subset of a dataset. There are different methods in the framework to create those subsets for sophisticated evaluation and testing setups.
+
+ Checks
+A check is defining one specific evaluation and/or testing setups. It defines the metric used, the dataset or slice to evaluate/test on and optionally the test comparison.
+When a check is applied on a specific model it returns the evaluation calculated and optionally the testing result (True/False).
+
+ Checksuites
+A checksuite combines multiple checks into one. It is a suite of checks as the name suggests.
+
+
+
+2. π€ Run automated checks
+
+Look into the relevant demo (demo_classification.py or demo_detection.py) to see how to create the MoonwatcherModel and MoonwatcherDataset from your data.
+```python
+from moonwatcher.check import automated_checking
+from moonwatcher.model.model import MoonwatcherModel
+from moonwatcher.dataset.dataset import MoonwatcherDataset
+
+# Your model (your_model) and dataset (your_dataset) loading somewhere
+
+# Look into the relevant demo (demo_classification.py or demo_detection.py)
+# to see how to create the MoonwatcherModel and MoonwatcherDataset from your data.
+mw_model = MoonwatcherModel(
+ model=your_model,
+ ...
+)
+mw_dataset = MoonwatcherDataset(
+ dataset=your_dataset,
+ ...
+)
+
+automated_checking(model=mw_model, dataset=mw_dataset)
+```
+
+3. π¨βπ» Write custom checks and checksuites
+
+Writing a custom check works like this.
+```python
+from moonwatcher.check import Check
+
+accuracy_check = Check(
+ name="AccuracyCheck",
+ dataset_or_slice=mw_dataset,
+ metric="Accuracy",
+ operator=">",
+ value=0.8,
+)
+
+# and run it on your model:
+check_result = accuracy_check(mw_model)
+```
+> [!TIP]
+> You can also slice your dataset and use a slice for the check instead of the whole dataset.
+
+> [!TIP]
+> Class/category based checking is not yet supported, but will be part of the next iteration.
+
+Now adding another check and combining both into a checksuite
+```python
+from moonwatcher.check import Check, CheckSuite
+
+precision_check = Check(
+ name="PrecisionCheck",
+ dataset_or_slice=mw_dataset,
+ metric="Precision",
+ operator=">",
+ value=0.8,
+)
+
+# Combine them into a checksuite
+first_checksuite = CheckSuite(
+ name="AllChecks", checks=[accuracy_check, precision_check]
+)
+
+# and run it on your model:
+checksuite_result = first_checksuite(mw_model)
+```
+
+ π₯οΈ Web app
+The package can be used on its own,
+is open-source and will always be. We additionally developed a web app you
+can use to visualize results in a nice way. To try it out, check out
+
+[Web app instructions](readme/README_webapp.md).
+
+βοΈ Donβt forget to star the project if you want to support open source testing of ML models.
+
+That's it. Have fun! π
diff --git a/moonwatcher/__init__.py b/moonwatcher/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/moonwatcher/annotations.py b/moonwatcher/annotations.py
new file mode 100644
index 0000000..c19cd45
--- /dev/null
+++ b/moonwatcher/annotations.py
@@ -0,0 +1,171 @@
+from typing import List, Union
+
+from torch import Tensor
+import torch
+from moonwatcher.utils.data import DataType
+from moonwatcher.base.base import MoonwatcherObject
+from moonwatcher.utils.data_storage import _prediction_name
+
+
+class Annotation:
+ def __init__(self, datapoint_number):
+ self.datapoint_number = datapoint_number
+
+
+class BoundingBoxes(Annotation):
+ def __init__(
+ self,
+ datapoint_id: int,
+ boxes_xyxy: Tensor,
+ labels: Tensor,
+ ):
+ """
+ Initializes a BoundingBoxes object
+ :param datapoint_id: The unique identifier for the data point.
+ :param boxes_xyxy: A tensor of shape (num_boxes, 4) representing the bounding box coordinates.
+ :param labels: An integer tensor of shape (num_boxes) representing labels for each bounding box.
+ :return:
+ """
+ if not isinstance(boxes_xyxy, Tensor):
+ raise TypeError("bounding boxes must be a Tensor of shape (num_boxes, 4)")
+ if not isinstance(labels, Tensor):
+ raise TypeError("labels must be an int Tensor of shape (num_boxes)")
+
+ super().__init__(datapoint_id)
+ self.boxes_xyxy = boxes_xyxy
+ self.labels = labels
+
+ def to_dict(self):
+ return {
+ "boxes": self.boxes_xyxy,
+ "labels": self.labels,
+ }
+
+
+class PredictedBoundingBoxes(BoundingBoxes):
+ def __init__(
+ self,
+ datapoint_number: int,
+ boxes_xyxy: Tensor,
+ labels: Tensor,
+ scores: Tensor,
+ ):
+ """
+ Initializes a PredictedBoundingBoxes object
+ :param datapoint_number: The unique identifier for the data point.
+ :param boxes_xyxy: A tensor of shape (num_boxes, 4) representing bounding box coordinates.
+ :param labels: An integer tensor of shape (num_boxes) representing labels for each bounding box.
+ :param scores: A float tensor of shape (num_boxes) representing the confidence score for each bounding box.
+ :return:
+ """
+ if not isinstance(scores, Tensor):
+ raise TypeError("scores must be a float Tensor of shape (num_boxes)")
+
+ super().__init__(datapoint_number, boxes_xyxy, labels)
+ self.scores = scores
+
+ def to_dict(self):
+ return {
+ "boxes": self.boxes_xyxy,
+ "scores": self.scores,
+ "labels": self.labels,
+ }
+
+
+class Labels(Annotation):
+ def __init__(self, datapoint_number: int, labels: Tensor):
+ """
+ Initialize a Labels object
+ :param datapoint_number: The unique identifier for the data point.
+ :param labels: A 1-dimensional integer tensor of shape (1) representing the label.
+ :return:
+ """
+ # TODO Check if torchmetrics accepts labels both as torch.tensor([1]) and torch.tensor(1)
+ if (
+ not isinstance(labels, Tensor)
+ or (labels.shape != (1,) and labels.shape != ())
+ or labels.dtype not in (torch.int8, torch.int16, torch.int32, torch.int64)
+ ):
+ raise TypeError("labels must be a 1-dimensional int Tensor")
+
+ super().__init__(datapoint_number)
+ self.labels = labels
+
+
+class PredictedLabels(Labels):
+ def __init__(self, datapoint_number: int, labels: Tensor, scores: Tensor):
+ """
+ Initialize a Labels object
+ :param datapoint_number: The unique identifier for the data point.
+ :param labels: A 1-dimensional integer tensor of shape (1) representing the label.
+ :param scores: A float tensor of shape (num_classes) representing the confidence scores for each class.
+ :return:
+ """
+ # TODO Check if torchmetrics accepts labels both as torch.tensor([1]) and torch.tensor(1)
+ if (
+ not isinstance(scores, Tensor)
+ or (scores.shape != (1,) and scores.shape != ())
+ or scores.dtype not in (torch.float16, torch.float32, torch.float64)
+ ):
+ raise TypeError("scores must be a 1-dimensional float Tensor")
+
+ super().__init__(datapoint_number, labels)
+ self.scores = scores
+
+
+class Annotations:
+ def __init__(self, annotations: List[Annotation] = None):
+ self.annotations = [] if annotations is None else annotations
+ self.datapoint_number_to_annotation_index = {}
+ for annotation_index, annotation in enumerate(self.annotations):
+ self.datapoint_number_to_annotation_index[
+ annotation.datapoint_number
+ ] = annotation_index
+
+ def add(self, annotation: Annotation):
+ self.annotations.append(annotation)
+ self.datapoint_number_to_annotation_index[annotation.datapoint_number] = (
+ len(self.annotations) - 1
+ )
+
+ def get(self, datapoint_number):
+ return self.annotations[
+ self.datapoint_number_to_annotation_index[datapoint_number]
+ ]
+
+ def get_datapoint_ids(self):
+ return list(self.datapoint_number_to_annotation_index.keys())
+
+ def __getitem__(self, datapoint_number):
+ return self.get(datapoint_number=datapoint_number)
+
+ def __len__(self):
+ return len(self.annotations)
+
+ def __iter__(self):
+ return iter(self.annotations)
+
+
+class Predictions(Annotations, MoonwatcherObject):
+ def __init__(
+ self,
+ dataset,
+ model,
+ predictions: List[
+ Union[PredictedBoundingBoxes, BoundingBoxes, PredictedLabels, Labels]
+ ] = None,
+ ):
+ super().__init__(annotations=predictions)
+ name = _prediction_name(model_name=model.name, dataset_name=dataset.name)
+
+ MoonwatcherObject.__init__(self, name=name, datatype=DataType.PREDICTIONS)
+
+
+class GroundTruths(Annotations, MoonwatcherObject):
+ def __init__(
+ self, dataset, groundtruths: List[Union[BoundingBoxes, Labels]] = None
+ ):
+ super().__init__(annotations=groundtruths)
+ MoonwatcherObject.__init__(
+ self, name=dataset.name, datatype=DataType.GROUNDTRUTHS
+ )
diff --git a/moonwatcher/base/base.py b/moonwatcher/base/base.py
new file mode 100644
index 0000000..306fb4c
--- /dev/null
+++ b/moonwatcher/base/base.py
@@ -0,0 +1,70 @@
+import re
+from abc import abstractmethod
+
+from moonwatcher.utils.data import DataType
+from moonwatcher.utils.data_storage import store_file, exists, _slice_name
+
+
+class MoonwatcherObject:
+ def __init__(self, name, datatype: DataType):
+ self.uploaded = False
+ self.name = name
+ self.datatype = datatype
+ if not self.valid_name():
+ raise ValueError(
+ f"Invalid name '{self.name}' for {datatype.value}! Only use a-z, A-Z, 0-9, hyphen (-) or underscore (_)!"
+ )
+ if self.exists_already():
+ raise ValueError(f"{datatype.value} with name {self.name} already exists!")
+ pass
+
+ @abstractmethod
+ def _upload(self) -> bool:
+ raise NotImplementedError
+
+ def upload_if_not(self):
+ if not self.uploaded:
+ upload_successful = self._upload()
+ self.uploaded = upload_successful
+
+ def store(self, overwrite=True):
+ name = self.name
+ if self.datatype.value == DataType.SLICE.value:
+ name = _slice_name(dataset_name=self.dataset_name, name=name)
+ store_file(
+ file=self,
+ datatype=self.datatype,
+ name=name,
+ overwrite=overwrite,
+ )
+
+ def exists_already(self):
+ name = self.name
+ if self.datatype.value == DataType.SLICE.value:
+ name = _slice_name(dataset_name=self.dataset_name, name=name)
+ exists(datatype=self.datatype, name=name)
+
+ def valid_name(self):
+ """
+ Checks if a name contains only the following characters:
+ Alphanumeric (a-z, A-Z, 0-9), underscore (_), and hyphen (-).
+ """
+ pattern = r"^[a-zA-Z0-9\_\-]+$"
+ return bool(re.match(pattern, self.name))
+
+
+# TODO Might be used in some way in the future
+class MoonwatcherWrapper:
+ def __init__(self, object):
+ self.wrapped_object = object
+
+ def __getattr__(self, attr):
+ if attr in self.__dict__:
+ return getattr(self, attr)
+ return getattr(self.wrapped_object, attr)
+
+ def __setattr__(self, attr, value):
+ if attr == "wrapped_object":
+ super().__setattr__(attr, value)
+ else:
+ setattr(self.wrapped_object, attr, value)
diff --git a/moonwatcher/caltech.sh b/moonwatcher/caltech.sh
new file mode 100644
index 0000000..087b15f
--- /dev/null
+++ b/moonwatcher/caltech.sh
@@ -0,0 +1,8 @@
+mkdir caltech101
+cd caltech101
+
+wget https://data.caltech.edu/records/mzrjq-6wc02/files/caltech-101.zip
+
+unzip caltech-101.zip
+
+rm caltech-101.zip
diff --git a/moonwatcher/check.py b/moonwatcher/check.py
new file mode 100644
index 0000000..8dac079
--- /dev/null
+++ b/moonwatcher/check.py
@@ -0,0 +1,387 @@
+import json
+from pathlib import Path
+from typing import Optional, List, Union, Dict, Any
+
+import numpy as np
+
+from moonwatcher.utils.data import OPERATOR_DICT
+from moonwatcher.dataset.dataset import MoonwatcherDataset, Slice
+from moonwatcher.model.model import MoonwatcherModel
+from moonwatcher.metric import calculate_metric
+from moonwatcher.utils.helpers import get_current_timestamp
+from moonwatcher.utils.api_connector import upload_if_possible
+from moonwatcher.utils.data import DataType
+from moonwatcher.base.base import MoonwatcherObject
+from moonwatcher.utils.data import Task
+
+
+class Check(MoonwatcherObject):
+ def __init__(
+ self,
+ name: str,
+ dataset_or_slice: Union[MoonwatcherDataset, Slice],
+ metric: str,
+ metric_parameters: Optional[Dict] = None,
+ description: Optional[str] = None,
+ metadata: Dict[str, Any] = None,
+ operator: Optional[str] = None,
+ value: Optional[float] = None,
+ ):
+ """
+ Creates a check
+
+ :param name: the name you want to give this check
+ :param dataset_or_slice: the dataset or slice to use
+ :param metric: the metric to apply (torchmetric)
+ :param metric_parameters: optional parameters for the metrics
+ :param description: optional description of the check
+ :param metadata: optional tags for the check
+ :param operator: optional if you want to test, compare symbol like >, >= etc.
+ :param value: optional value to compare to
+ """
+ MoonwatcherObject.__init__(self, name=name, datatype=DataType.CHECK)
+ self.testing = False
+ self.description = description
+ self.metadata = metadata
+ self.operator_str = operator
+ if operator is not None and value is not None:
+ self.testing = True
+ self.operator = operator
+ self.value = value
+ self.dataset_or_slice = dataset_or_slice
+ self.metric = metric
+ self.metric_parameters = metric_parameters
+ self.store()
+
+ def _upload(self):
+ if self.metric_parameters is not None:
+ metric_dict = {k: self.metric_parameters[k] for k in self.metric_parameters}
+ else:
+ metric_dict = {}
+ metric_dict["name"] = self.metric
+
+ data = {
+ "name": self.name,
+ "description": self.description,
+ "timestamp": get_current_timestamp(),
+ "metadata": self.metadata,
+ "testing": self.testing,
+ "operator": self.operator_str,
+ "value": self.value,
+ "dataset_name": (
+ self.dataset_or_slice.dataset_name
+ if isinstance(self.dataset_or_slice, Slice)
+ else self.dataset_or_slice.name
+ ),
+ "slice_name": (
+ self.dataset_or_slice.name
+ if isinstance(self.dataset_or_slice, Slice)
+ else None
+ ),
+ "metric": metric_dict,
+ }
+ return upload_if_possible(datatype=DataType.CHECK.value, data=data)
+
+ def __call__(
+ self, model: MoonwatcherModel, show=False, save_report=False
+ ) -> Dict[str, Any]:
+ result = calculate_metric(
+ model=model,
+ dataset_or_slice=self.dataset_or_slice,
+ metric=self.metric,
+ metric_parameters=self.metric_parameters,
+ )
+ report = {
+ "check_name": self.name,
+ "dataset_name": (
+ self.dataset_or_slice.dataset_name
+ if isinstance(self.dataset_or_slice, Slice)
+ else self.dataset_or_slice.name
+ ),
+ "slice_name": (
+ self.dataset_or_slice.name
+ if isinstance(self.dataset_or_slice, Slice)
+ else None
+ ),
+ "model_name": model.name,
+ "metric": self.metric,
+ "operator": self.operator_str,
+ "value": self.value,
+ "result": result,
+ "success": (
+ OPERATOR_DICT[self.operator](result, self.value)
+ if self.testing
+ else None
+ ),
+ "timestamp": get_current_timestamp(),
+ }
+
+ if show:
+ visualize_report(report)
+ self.upload_if_not()
+ self._upload_report(report=report)
+
+ if save_report:
+ with open(
+ f"check_{self.name}_report_for_{model.name}.json", "w", encoding="utf-8"
+ ) as f:
+ json.dump(obj=report, fp=f, indent=4)
+ return report
+
+ def _upload_report(self, report):
+ data = {
+ "model_name": report["model_name"],
+ "check_name": self.name,
+ "timestamp": get_current_timestamp(),
+ "result": report["result"],
+ "passed": report["success"],
+ }
+ return upload_if_possible(datatype=DataType.CHECK_REPORT.value, data=data)
+
+
+class CheckSuite(MoonwatcherObject):
+ def __init__(
+ self,
+ name: str,
+ checks: List[Check],
+ description: Optional[str] = None,
+ metadata: Dict[str, Any] = None,
+ show=False,
+ ):
+ """
+ Creates a checksuite
+
+ :param name: name you want to give the checksuite
+ :param checks: list of checks that are included
+ :param description: optional description
+ :param metadata: optional tags
+ :param show: whether to print results in commandline
+ """
+ MoonwatcherObject.__init__(self, name=name, datatype=DataType.CHECK)
+ self.description = description
+ self.metadata = metadata
+ self.checks = checks
+ self.testing = False
+ for check in self.checks:
+ if check.testing:
+ self.testing = True
+ break
+ self.show = show
+ self.store()
+
+ def _upload(self):
+ data = {
+ "name": self.name,
+ "description": self.description,
+ "timestamp": get_current_timestamp(),
+ "metadata": self.metadata,
+ "testing": self.testing,
+ "check_names": [check.name for check in self.checks],
+ }
+ return upload_if_possible(datatype=DataType.CHECKSUITE.value, data=data)
+
+ def __call__(self, model: MoonwatcherModel, show=None, save_report=False):
+ total_success = None
+ if self.testing:
+ total_success = True
+
+ reports = []
+
+ show = self.show if show is None else show
+ for check in self.checks:
+ report = check(model)
+ reports.append(report)
+ if check.testing:
+ if not report["success"]:
+ total_success = False
+
+ checksuite_report = {
+ "checksuite_name": self.name,
+ "model_name": model.name,
+ "timestamp": get_current_timestamp(),
+ "success": total_success,
+ "checks": reports,
+ }
+
+ if show:
+ visualize_report(checksuite_report)
+ self.upload_if_not()
+ self._upload_report(report=checksuite_report)
+ if save_report:
+ with open(
+ f"checksuite_{self.name}_report_for_{model.name}.json",
+ "w",
+ encoding="utf-8",
+ ) as f:
+ json.dump(obj=checksuite_report, fp=f, indent=4)
+
+ return checksuite_report
+
+ def _upload_report(self, report):
+ data = {
+ "model_name": report["model_name"],
+ "checksuite_name": self.name,
+ "timestamp": get_current_timestamp(),
+ "passed": report["success"],
+ }
+ return upload_if_possible(datatype=DataType.CHECKSUITE_REPORT.value, data=data)
+
+
+class ReportVisualizer:
+ def __init__(self):
+ self.pass_emoji = "\u2705"
+ self.fail_emoji = "\u274C"
+ self.BOLD = "\033[1m"
+ self.END = "\033[0m"
+ self.UNDERLINE = "\033[4m"
+ self.RED = "\033[91m"
+ self.GREEN = "\033[92m"
+
+ def visualize(self, report, spacing=""):
+ if report["success"] is not None:
+ test_output_str = (
+ f"{self.pass_emoji if report['success'] else self.fail_emoji} "
+ )
+ else:
+ test_output_str = f" "
+ if "check_name" in report:
+ name = report["check_name"]
+ else:
+ name = report["checksuite_name"]
+ test_output_str = f"{spacing}{test_output_str}{name}"
+
+ if "checks" in report: # This is CheckSuite
+ print(test_output_str)
+ new_spacing = spacing + " "
+ for sub_report in report["checks"]:
+ self.visualize(report=sub_report, spacing=new_spacing)
+ else: # This is Check
+ report_result = f"{report['result']}".rjust(10)
+ if report["success"] is not None:
+ value = f"{report['value']}".ljust(10)
+ operator = report["operator"]
+ if operator == ">=":
+ operator = "β₯"
+ elif operator == "<=":
+ operator = "β€"
+ elif operator == "==":
+ operator = "="
+ elif operator == "!=":
+ operator = "β "
+
+ operator = f"{operator}".center(3)
+ comparison = f"{operator} {value}"
+ result = f"{self.GREEN if report['success'] else self.RED}{report_result}{self.END}"
+ else:
+ comparison = f""
+ result = f" {report_result}"
+
+ printed_set = report["dataset_name"]
+ if report["slice_name"] is not None:
+ printed_set = report["slice_name"]
+
+ appendix = f" ({report['metric']} on {printed_set})"
+ print_statement = f"{test_output_str.ljust(40)} {self.BOLD}{result}{self.END} {comparison}{appendix}"
+ print(print_statement)
+
+
+def visualize_report(report):
+ visualizer = ReportVisualizer()
+ visualizer.visualize(report)
+
+
+def automated_checking(
+ mw_dataset: MoonwatcherDataset,
+ mw_model: MoonwatcherModel,
+ metadata_keys: List[str] = None,
+ metadata_list: List[Dict[str, Any]] = None,
+ slicing_conditions: List[Dict[str, Any]] = None,
+ checks: List[Dict[str, Any]] = None,
+ demo: bool = True,
+):
+ # Load demo configurations if specified
+ if demo:
+ if mw_dataset.task == Task.CLASSIFICATION.value:
+ filename = "demo_classification.json"
+ elif mw_dataset.task == Task.DETECTION.value:
+ filename = "demo_detection.json"
+ else:
+ raise ValueError(f"Unsupported task: {mw_dataset.task}")
+
+ cur_filepath = Path(__file__)
+ with open(
+ cur_filepath.parent / "configs" / filename, "r", encoding="utf-8"
+ ) as file:
+ config = json.load(file)
+
+ metadata_keys = config["metadata_keys"]
+ slicing_conditions = config["slicing_conditions"]
+ checks = config["checks"]
+
+ # Add Metadata
+ for metadata_key in metadata_keys:
+ mw_dataset.add_predefined_metadata(metadata_key)
+ if metadata_list:
+ mw_dataset.add_metadata_from_list(metadata_list)
+
+ # Create Slices
+ mw_slices = []
+ for slicing_condition in slicing_conditions:
+ if slicing_condition["type"] == "threshold":
+ mw_slice = mw_dataset.slice_by_threshold(
+ slicing_condition["key"],
+ slicing_condition["operator"],
+ slicing_condition["value"],
+ )
+ mw_slices.append(mw_slice)
+ elif slicing_condition["type"] == "percentile":
+ mw_slice = mw_dataset.slice_by_percentile(
+ slicing_condition["key"],
+ slicing_condition["operator"],
+ slicing_condition["value"],
+ )
+ mw_slices.append(mw_slice)
+ else:
+ slices = mw_dataset.slice_by_class(slicing_condition["key"])
+ for mw_slice in slices:
+ mw_slices.append(mw_slice)
+
+ all_test_results = {}
+
+ # Create and run checks for each slice
+ for mw_slice in mw_slices:
+ check_objects = []
+ for check in checks:
+ check_name = f"{check['name']}_{mw_slice.name}"
+ new_check = Check(
+ name=check_name,
+ dataset_or_slice=mw_slice,
+ metric=check["metric"],
+ operator=check.get("operator", None),
+ value=check.get("value", None),
+ )
+ check_objects.append(new_check)
+
+ # Run Checks
+ check_suite = CheckSuite(name=f"Test_{mw_slice.name}", checks=check_objects)
+ test_results = check_suite(model=mw_model, show=True)
+ all_test_results[mw_slice.name] = test_results
+
+ # Create and run checks for the entire dataset
+ check_objects = []
+ for check in checks:
+ check_name = f"{check['name']}_{mw_dataset.name}"
+ new_check = Check(
+ name=check_name,
+ dataset_or_slice=mw_dataset,
+ metric=check["metric"],
+ operator=check.get("operator", None),
+ value=check.get("value", None),
+ )
+ check_objects.append(new_check)
+
+ entire_dataset_check_suite = CheckSuite(name=f"Test_{mw_dataset.name}", checks=check_objects)
+ entire_dataset_test_results = entire_dataset_check_suite(model=mw_model, show=True)
+ all_test_results[mw_dataset.name] = entire_dataset_test_results
+
+ return all_test_results
diff --git a/moonwatcher/coco.sh b/moonwatcher/coco.sh
new file mode 100644
index 0000000..abefd36
--- /dev/null
+++ b/moonwatcher/coco.sh
@@ -0,0 +1,17 @@
+mkdir coco
+cd coco
+mkdir images
+cd images
+
+wget http://images.cocodataset.org/zips/val2017.zip
+
+unzip val2017.zip
+
+rm val2017.zip
+
+cd ../
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+
+unzip annotations_trainval2017.zip
+
+rm annotations_trainval2017.zip
\ No newline at end of file
diff --git a/moonwatcher/configs/demo_classification.json b/moonwatcher/configs/demo_classification.json
new file mode 100644
index 0000000..ec1af73
--- /dev/null
+++ b/moonwatcher/configs/demo_classification.json
@@ -0,0 +1,17 @@
+{
+ "metadata_keys": ["brightness", "contrast", "saturation"],
+ "slicing_conditions": [
+ {"type": "percentile", "key": "brightness", "operator": ">", "value": 80},
+ {"type": "percentile", "key": "brightness", "operator": "<", "value": 20},
+ {"type": "percentile", "key": "contrast", "operator": ">", "value": 80},
+ {"type": "percentile", "key": "contrast", "operator": "<", "value": 20},
+ {"type": "percentile", "key": "saturation", "operator": ">", "value": 80},
+ {"type": "percentile", "key": "saturation", "operator": "<", "value": 20}
+ ],
+ "checks": [
+ {"type": "test", "name": "AccuracyCheck", "metric": "Accuracy", "operator": ">", "value": 0.2},
+ {"type": "test", "name": "F1Check", "metric": "F1_Score", "operator": ">", "value": 0.2},
+ {"type": "test", "name": "Recall", "metric": "Recall", "operator": ">", "value": 0.2},
+ {"type": "test", "name": "Precision", "metric": "Precision", "operator": ">", "value": 0.2}
+ ]
+}
diff --git a/moonwatcher/configs/demo_detection.json b/moonwatcher/configs/demo_detection.json
new file mode 100644
index 0000000..47e4ed0
--- /dev/null
+++ b/moonwatcher/configs/demo_detection.json
@@ -0,0 +1,48 @@
+{
+ "metadata_keys": ["brightness", "contrast", "saturation"],
+ "slicing_conditions": [
+ {"type": "percentile", "key": "brightness", "operator": ">", "value": 90},
+ {"type": "percentile", "key": "brightness", "operator": "<", "value": 10},
+ {"type": "percentile", "key": "contrast", "operator": ">", "value": 90},
+ {"type": "percentile", "key": "contrast", "operator": "<", "value": 10},
+ {"type": "percentile", "key": "saturation", "operator": ">", "value": 90},
+ {"type": "percentile", "key": "saturation", "operator": "<", "value": 10}
+ ],
+ "checks": [
+ {
+ "type": "test",
+ "name": "iouCheck",
+ "metric": "IntersectionOverUnion",
+ "operator": ">",
+ "value": 0.2
+ },
+ {
+ "type": "test",
+ "name": "mapCheck",
+ "metric": "mAP",
+ "operator": ">",
+ "value": 0.2
+ },
+ {
+ "type": "test",
+ "name": "mapCheck_small_objects",
+ "metric": "mAP_small",
+ "operator": ">",
+ "value": 0.2
+ },
+ {
+ "type": "test",
+ "name": "mapCheck_medium_objects",
+ "metric": "mAP_medium",
+ "operator": ">",
+ "value": 0.2
+ },
+ {
+ "type": "test",
+ "name": "mapCheck_large_objects",
+ "metric": "mAP_large",
+ "operator": ">",
+ "value": 0.2
+ }
+ ]
+}
diff --git a/moonwatcher/configs/imagenet_to_stl.json b/moonwatcher/configs/imagenet_to_stl.json
new file mode 100644
index 0000000..23dc179
--- /dev/null
+++ b/moonwatcher/configs/imagenet_to_stl.json
@@ -0,0 +1,72 @@
+{
+ "stl10_classes": [
+ "airplane",
+ "bird",
+ "car",
+ "cat",
+ "deer",
+ "dog",
+ "horse",
+ "monkey",
+ "ship",
+ "truck"
+ ],
+ "mapping": {
+ "404": 0,
+ "405": 0,
+ "409": 0,
+ "818": 0,
+ "10": 1,
+ "11": 1,
+ "12": 1,
+ "13": 1,
+ "14": 1,
+ "15": 1,
+ "16": 1,
+ "17": 1,
+ "18": 1,
+ "19": 1,
+ "436": 2,
+ "511": 2,
+ "656": 9,
+ "817": 2,
+ "281": 3,
+ "282": 3,
+ "283": 3,
+ "284": 3,
+ "285": 3,
+ "340": 4,
+ "341": 4,
+ "342": 4,
+ "151": 5,
+ "152": 5,
+ "153": 5,
+ "154": 5,
+ "155": 5,
+ "156": 5,
+ "157": 5,
+ "158": 5,
+ "159": 5,
+ "222": 6,
+ "223": 6,
+ "224": 6,
+ "225": 6,
+ "365": 7,
+ "366": 7,
+ "367": 7,
+ "368": 7,
+ "369": 7,
+ "778": 8,
+ "779": 8,
+ "780": 8,
+ "781": 8,
+ "782": 8,
+ "783": 8,
+ "784": 8,
+ "785": 8,
+ "786": 8,
+ "787": 8,
+ "555": 9,
+ "569": 9
+ }
+}
diff --git a/moonwatcher/datapoint.py b/moonwatcher/datapoint.py
new file mode 100644
index 0000000..4027931
--- /dev/null
+++ b/moonwatcher/datapoint.py
@@ -0,0 +1,13 @@
+class Datapoint:
+ def __init__(self, number, locator: str = None, metadata: dict = None):
+ self.number = number
+ self.locator = locator
+ if metadata is None:
+ metadata = {}
+ self.metadata = metadata
+
+ def add_metadata(self, key: str, value: any):
+ self.metadata[key] = value
+
+ def get_metadata(self, key):
+ return self.metadata.get(key, None)
diff --git a/moonwatcher/dataset/dataset.py b/moonwatcher/dataset/dataset.py
new file mode 100644
index 0000000..40edd6e
--- /dev/null
+++ b/moonwatcher/dataset/dataset.py
@@ -0,0 +1,552 @@
+from typing import List, Dict, Any, Callable
+
+import torch
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+import torchvision.transforms as transforms
+from torch.utils.data import Dataset, Subset
+
+from moonwatcher.datapoint import Datapoint
+from moonwatcher.utils.data import OPERATOR_DICT
+from moonwatcher.utils.data import DataType, Task
+from moonwatcher.utils.api_connector import is_api_key_and_endpoint_available
+from moonwatcher.base.base import MoonwatcherObject
+from moonwatcher.dataset.metadata import _ATTRIBUTE_FUNCTIONS
+from moonwatcher.utils.api_connector import upload_if_possible
+from moonwatcher.annotations import GroundTruths, Labels, BoundingBoxes
+from moonwatcher.utils.helpers import get_current_timestamp, convert_to_list
+
+
+def find_root_dataset(dataset):
+ """Recursively find the root dataset."""
+ if hasattr(dataset, "dataset"):
+ return find_root_dataset(dataset.dataset)
+ return dataset
+
+
+class MoonwatcherDataset(MoonwatcherObject, Dataset):
+ def __init__(
+ self,
+ dataset: Dataset,
+ name: str,
+ task: str,
+ output_transform: Callable,
+ label_to_name: Dict,
+ metadata: Dict[str, Any] = None,
+ description: str = None,
+ locators: List[str] = None,
+ datapoints_metadata: List[Dict[str, Any]] = None,
+ ):
+ """
+ Creates a moonwatcher dataset wrapper around an existing dataset that can be used with the moonwatcher framework
+
+ :param dataset: the dataset to be wrapped
+ :param name: the name of the dataset
+ :param task: either classification or detection
+ :param output_transform: necessary to transform dataset output into moonwatcher format, see demo files
+ :param label_to_name: dictionary mapping label ids to name
+ :param dataset_transform:
+ :param metadata: dictionary of tags for the dataset, can be ignored
+ :param description: description of the dataset, can be ignored
+ :param locators: necessary only for use with the webapp, urls for every image to display in webapp
+ :param datapoints_metadata: supply metadata for every datapoint, can be used for slicing, optional
+ """
+ MoonwatcherObject.__init__(self, name=name, datatype=DataType.DATASET)
+ Dataset.__init__(self)
+ self.dataset = dataset
+
+ self.label_to_name = label_to_name
+ self.task = task
+ if metadata is None:
+ self.metadata = {}
+ self.metadata["_timestamp"] = get_current_timestamp()
+ self.description = description
+ self.locators = locators
+ self.datapoints = []
+ self.datapoints_metadata = datapoints_metadata
+
+ if self.locators:
+ if not isinstance(self.locators, list):
+ raise ValueError("Locators needs to be a list")
+ if len(self.locators) != len(dataset):
+ raise ValueError(
+ "Locators needs to provide a locator for every image (List with length of dataset)"
+ )
+ for locator in self.locators:
+ if not isinstance(locator, str):
+ raise ValueError("Locators need to be strings")
+
+ for i in range(len(self.dataset)):
+ metadata = (
+ datapoints_metadata[i]
+ if datapoints_metadata is not None and i < len(datapoints_metadata)
+ else {}
+ )
+ if self.locators:
+ datapoint = Datapoint(
+ number=i, metadata=metadata, locator=self.locators[i]
+ )
+ else:
+ datapoint = Datapoint(number=i, metadata=metadata)
+ self.datapoints.append(datapoint)
+
+ self.output_transform = output_transform
+ self.groundtruths = GroundTruths(self)
+
+ for index in tqdm(
+ list(range(len(self.dataset))),
+ desc=f"Saving annotations of dataset {self.name}.",
+ ):
+ data = self.dataset[index]
+ try:
+ transformed_data = self.output_transform(data)
+ except Exception as e:
+ raise Exception(
+ f"Application of output_transform on dataset failed: {e}"
+ )
+
+ if self.task == Task.CLASSIFICATION.value:
+ try:
+ image, label = transformed_data
+ except ValueError as e:
+ raise ValueError(
+ f"Dataset output_transform should return two elements (image, label): {e}"
+ )
+ groundtruth = Labels(datapoint_number=index, labels=label)
+ elif self.task == Task.DETECTION.value:
+ try:
+ image, bounding_boxes, labels = transformed_data
+ except ValueError as e:
+ raise ValueError(
+ f"Dataset output_transform should return three elements (image, bounding_boxes, labels): {e}"
+ )
+ groundtruth = BoundingBoxes(
+ datapoint_id=index, boxes_xyxy=bounding_boxes, labels=labels
+ )
+ else:
+ raise ValueError(
+ f"Unsupported task: {self.task} - Select either 'classification' or 'detection'"
+ )
+
+ self.groundtruths.add(groundtruth)
+
+ self.groundtruths.store()
+ self.store()
+ self.upload_if_not()
+
+ def _upload(self):
+ datapoints = []
+ if not is_api_key_and_endpoint_available():
+ return False
+ if self.datapoints[0].locator is None:
+ raise ValueError(
+ "Please provide locators for the images if you want to upload the dataset."
+ )
+ for datapoint in self.datapoints:
+ datapoints.append(
+ {
+ "locator": datapoint.locator,
+ "metadata": datapoint.metadata,
+ }
+ )
+ data = {
+ "name": self.name,
+ "description": self.description,
+ "timestamp": get_current_timestamp(),
+ "metadata": self.metadata,
+ "label_to_name": self.label_to_name,
+ "datapoints": datapoints,
+ "task": self.task,
+ }
+ upload_if_possible(datatype=DataType.DATASET.value, data=data)
+
+ groundtruths = []
+ for groundtruth in self.groundtruths:
+ groundtruths.append(
+ {
+ "dataset_name": self.name,
+ "datapoint_number": groundtruth.datapoint_number,
+ "boxes": (
+ [convert_to_list(boxes) for boxes in groundtruth.boxes_xyxy]
+ if hasattr(groundtruth, "boxes_xyxy")
+ else None
+ ),
+ "labels": convert_to_list(groundtruth.labels),
+ }
+ )
+ return upload_if_possible(
+ datatype=DataType.GROUNDTRUTHS.value, data=groundtruths
+ )
+
+ def get_datapoint(self, item):
+ return self.datapoints[item]
+
+ def add_predefined_metadata(self, predefined_metadata_key: str):
+ """
+ Use a predefined metadata creation function to add metadata "brightness", "contrast", "saturation", "resolution"
+
+ :param predefined_metadata_key
+ """
+ root_dataset = find_root_dataset(self.dataset)
+ transform = root_dataset.transform
+ root_dataset.transform = None
+ # TODO Check if this works for slices as well (or do we need to use root_dataset)
+ for i in tqdm(range(len(self.dataset)), desc="Adding metadata"):
+ data = self.dataset[i]
+ image = data[0]
+
+ transform_to_tensor = transforms.ToTensor()
+
+ if isinstance(image, Image.Image):
+ image = transform_to_tensor(image)
+ elif isinstance(image, np.ndarray):
+ if image.dtype != np.float32:
+ image = image.astype(np.float32) / 255
+ image = torch.from_numpy(image.transpose((2, 0, 1)))
+ elif isinstance(image, torch.Tensor):
+ if image.shape[-1] == 3:
+ image = image.permute(2, 0, 1)
+ else:
+ image = image
+ else:
+ raise TypeError("Unsupported image type")
+
+ image = image.permute(1, 2, 0).cpu().numpy()
+ image = (image * 255).astype(np.uint8) if image.max() <= 1.0 else image
+
+ if predefined_metadata_key not in self.datapoints[i].metadata:
+ metadata_value = _ATTRIBUTE_FUNCTIONS[predefined_metadata_key](image)
+ self.datapoints[i].add_metadata(
+ key=predefined_metadata_key, value=metadata_value
+ )
+
+ root_dataset.transform = transform
+ self.store(overwrite=True)
+
+ def add_metadata_from_list(self, metadata_list: List[Dict[str, Any]]):
+ """
+ Add metadata for all data points from a list
+
+ :param metadata_list: metadata dicts for all data points.
+ """
+ for i, metadata in enumerate(tqdm(metadata_list, desc="Adding metadata")):
+ if i < len(self.datapoints):
+ for key, value in metadata.items():
+ self.datapoints[i].add_metadata(key=key, value=value)
+ self.store(overwrite=True)
+
+ def add_metadata_custom(self, metadata_key: str, metadata_func: Callable):
+ """
+ Add metadata for all using a metadata function
+ :param metadata_key: name of the metadatum
+ :param metadata_func: function that calculates a metadata value given an image input
+ :return:
+ """
+ root_dataset = find_root_dataset(self.dataset)
+ transform = root_dataset.transform
+ root_dataset.transform = None
+
+ for i in tqdm(range(len(self.dataset)), desc="Adding metadata"):
+ data = self.dataset[i]
+ image = data[0]
+
+ transform_to_tensor = transforms.ToTensor()
+
+ if isinstance(image, Image.Image):
+ image = transform_to_tensor(image)
+ elif isinstance(image, np.ndarray):
+ if image.dtype != np.float32:
+ image = image.astype(np.float32) / 255
+ image = torch.from_numpy(image.transpose((2, 0, 1)))
+ elif isinstance(image, torch.Tensor):
+ if image.shape[-1] == 3:
+ image = image.permute(2, 0, 1)
+ else:
+ image = image
+ else:
+ raise TypeError("Unsupported image type")
+
+ image = image.permute(1, 2, 0).cpu().numpy()
+ image = (image * 255).astype(np.uint8) if image.max() <= 1.0 else image
+
+ if metadata_key not in self.datapoints[i].metadata:
+ metadata_value = metadata_func(image)
+ self.datapoints[i].add_metadata(key=metadata_key, value=metadata_value)
+
+ root_dataset.transform = transform
+ self.store(overwrite=True)
+
+ def _generate_filename(self, metadata_key: str, operator_str: str, value: Any):
+ abbreviations = {
+ ">": "gt",
+ "<": "lt",
+ ">=": "ge",
+ "<=": "le",
+ "==": "eq",
+ "class": "cl",
+ }
+ filename = f"{self.name}_{metadata_key}_{abbreviations[operator_str]}_{str(value).replace('.', '_')}"
+ return filename
+
+ def slice_by_threshold(
+ self,
+ metadata_key: str,
+ operator_str: str,
+ threshold: Any,
+ slice_name: str = None,
+ ):
+ """
+ Create a slice, the metadata key has to exist already: e.g. ("brightness", "<", 0.1)
+
+ :param metadata_key: name of the metadatum
+ :param operator: compare symbol like >, >= etc.
+ :param threshold: threshold for selection of what data should stay inside
+ :param slice_name: name of the slice to create
+ """
+ op_func = OPERATOR_DICT[operator_str]
+
+ indices = [
+ i
+ for i, datapoint in enumerate(self.datapoints)
+ if op_func(datapoint.get_metadata(metadata_key), threshold)
+ ]
+
+ if slice_name is None:
+ slice_name = self._generate_filename(metadata_key, operator_str, threshold)
+
+ return Slice(self, slice_name, indices, self)
+
+ def slice_by_percentile(
+ self,
+ metadata_key: str,
+ operator_str: str,
+ percentile: Any,
+ slice_name: str = None,
+ ):
+ """
+ Create a slice, the metadata key has to exist already: e.g. ("brightness", "<", 99)
+
+ :param metadata_key: name of the metadatum
+ :param operator: compare symbol like >, >= etc.
+ :param percentile: value between 0 and 100
+ :param slice_name: name of the slice to create
+ """
+ op_func = OPERATOR_DICT[operator_str]
+ values = [datapoint.get_metadata(metadata_key) for datapoint in self.datapoints]
+ threshold = np.percentile(values, percentile)
+
+ indices = [
+ i
+ for i, datapoint in enumerate(self.datapoints)
+ if op_func(datapoint.get_metadata(metadata_key), threshold)
+ ]
+
+ if slice_name is None:
+ slice_name = self._generate_filename(metadata_key, operator_str, percentile)
+
+ return Slice(self, slice_name, indices, self)
+
+ def slice_by_class(self, metadata_key: str, slice_names: list[str] = None):
+ """
+ Create slices based on a categorical metadatum (e.g. weather: "sunny", "rainy" ...)
+
+ :param metadata_key: name of the metadatum
+ :param slice_names: list of names for the slices to create (optional)
+ """
+ # Collect indices by class value
+ class_indices = {}
+ for i, datapoint in enumerate(self.datapoints):
+ class_value = datapoint.get_metadata(metadata_key)
+
+ if class_value not in class_indices:
+ class_indices[class_value] = []
+
+ class_indices[class_value].append(i)
+
+ class_values = sorted(class_indices.keys())
+ num_classes = len(class_values)
+
+ if slice_names is None or len(slice_names) != num_classes:
+ # Generate default slice names
+ slice_names = [
+ self._generate_filename(metadata_key, "class", class_value)
+ for class_value in class_values
+ ]
+
+ slices = []
+ for class_value, slice_name in zip(class_values, slice_names):
+ indices = class_indices[class_value]
+ slices.append(Slice(self, slice_name, indices, self))
+
+ return slices
+
+ def __len__(self):
+ return len(self.dataset)
+
+ def __getitem__(self, item):
+ return self.dataset[item]
+
+ def __getattr__(self, attr):
+ return getattr(self.dataset, attr)
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ return state
+
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+
+
+class Slice(MoonwatcherDataset, MoonwatcherObject):
+ def __init__(
+ self,
+ moonwatcher_dataset: MoonwatcherDataset,
+ name: str,
+ indices: List[int],
+ original_dataset: MoonwatcherDataset,
+ description: str = None,
+ ):
+ self.dataset_name = (
+ moonwatcher_dataset.name
+ ) # needs to be here before initialization of MwObject
+ MoonwatcherObject.__init__(self, name=name, datatype=DataType.SLICE)
+
+ # self.dataset_transform = moonwatcher_dataset.dataset_transform
+ self.task = moonwatcher_dataset.task
+ self.output_transform = moonwatcher_dataset.output_transform
+ self.metadata = moonwatcher_dataset.metadata
+ self.locators = moonwatcher_dataset.locators
+ self.datapoints = moonwatcher_dataset.datapoints
+ self.datapoints_metadata = moonwatcher_dataset.datapoints_metadata
+ self.groundtruths = moonwatcher_dataset.groundtruths
+
+ self.description = description
+ self.indices = indices
+ self.dataset = Subset(moonwatcher_dataset.dataset, indices)
+ self.moonwatcher_dataset = moonwatcher_dataset
+ self.original_dataset = original_dataset
+
+ self.datapoints = [self.datapoints[i] for i in self.indices]
+ # self.groundtruths = [self.groundtruths[i] for i in self.indices]
+ self.locators = (
+ [self.locators[i] for i in self.indices] if self.locators else None
+ )
+ # save_groundtruths(self, self.groundtruths)
+ self.store()
+ self.upload_if_not()
+
+ def _upload(self):
+ data = {
+ "dataset_name": self.dataset_name,
+ "name": self.name,
+ "description": self.description,
+ "timestamp": get_current_timestamp(),
+ "metadata": self.metadata,
+ "datapoint_numbers": self.indices,
+ }
+ return upload_if_possible(datatype=DataType.SLICE.value, data=data)
+
+ def slice_by_threshold(
+ self,
+ metadata_key: str,
+ operator_str: str,
+ threshold: Any,
+ slice_name: str = None,
+ ):
+ op_func = OPERATOR_DICT[operator_str]
+
+ indices = [
+ i
+ for i, datapoint in enumerate(self.datapoints)
+ if op_func(datapoint.get_metadata(metadata_key), threshold)
+ ]
+
+ if slice_name is None:
+ slice_name = self._generate_filename(metadata_key, operator_str, threshold)
+
+ return Slice(self, slice_name, indices, self.original_dataset)
+
+ def slice_by_percentile(
+ self,
+ metadata_key: str,
+ operator_str: str,
+ percentile: Any,
+ slice_name: str = None,
+ ):
+ op_func = OPERATOR_DICT[operator_str]
+ values = [datapoint.get_metadata(metadata_key) for datapoint in self.datapoints]
+ threshold = np.percentile(values, percentile)
+
+ indices = [
+ i
+ for i, datapoint in enumerate(self.datapoints)
+ if op_func(datapoint.get_metadata(metadata_key), threshold)
+ ]
+
+ if slice_name is None:
+ slice_name = self._generate_filename(metadata_key, operator_str, percentile)
+
+ return Slice(self, slice_name, indices, self.original_dataset)
+
+ def slice_by_class(self, metadata_key: str, slice_names: list[str] = None):
+ """
+ Create slices based on a categorical metadatum (e.g. weather: "sunny", "rainy" ...)
+
+ :param metadata_key: name of the metadatum
+ :param slice_names: list of names for the slices to create (optional)
+ """
+ # Collect indices by class value
+ class_indices = {}
+ for i, datapoint in enumerate(self.datapoints):
+ class_value = datapoint.get_metadata(metadata_key)
+
+ if class_value not in class_indices:
+ class_indices[class_value] = []
+
+ class_indices[class_value].append(i)
+
+ class_values = sorted(class_indices.keys())
+ num_classes = len(class_values)
+
+ if slice_names is None or len(slice_names) != num_classes:
+ # Generate default slice names
+ slice_names = [
+ self._generate_filename(metadata_key, "class", class_value)
+ for class_value in class_values
+ ]
+
+ slices = []
+ for class_value, slice_name in zip(class_values, slice_names):
+ indices = class_indices[class_value]
+ slices.append(Slice(self, slice_name, indices, self))
+
+ return slices
+
+ def add_predefined_metadata(self, predefined_metadata_key: str):
+ """
+ Use a predefined metadata creation function to add metadata "brightness", "contrast", "saturation", "resolution"
+
+ :param predefined_metadata_key: the key for the predefined metadata to add
+ """
+ super().add_predefined_metadata(predefined_metadata_key)
+ self.original_dataset.add_predefined_metadata(predefined_metadata_key)
+
+ def add_metadata_custom(self, metadata_key: str, metadata_func: Callable):
+ """
+ Add metadata for all using a metadata function
+ :param metadata_key: name of the metadatum
+ :param metadata_func: function that calculates a metadata value given an image input
+ :return:
+ """
+ super().add_metadata_custom(metadata_key, metadata_func)
+ self.original_dataset.add_metadata_custom(metadata_key, metadata_func)
+
+ def add_metadata_from_list(self, metadata_list: List[Dict[str, Any]]):
+ """
+ Add metadata for all data points from a list
+
+ :param metadata_list: metadata dicts for all data points.
+ """
+ super().add_metadata_from_list(metadata_list)
+ self.original_dataset.add_metadata_from_list(metadata_list)
diff --git a/moonwatcher/dataset/metadata.py b/moonwatcher/dataset/metadata.py
new file mode 100644
index 0000000..af4f3bb
--- /dev/null
+++ b/moonwatcher/dataset/metadata.py
@@ -0,0 +1,30 @@
+import cv2
+import numpy as np
+
+
+def compute_brightness(image):
+ hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+ return np.mean(hsv[:, :, 2])
+
+
+def compute_contrast(image):
+ grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+ return grayscale.std()
+
+
+def compute_saturation(image):
+ hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+ return np.mean(hsv[:, :, 1])
+
+
+def compute_resolution(image):
+ height, width = image.shape[:2]
+ return height * width
+
+
+_ATTRIBUTE_FUNCTIONS = {
+ "brightness": compute_brightness,
+ "contrast": compute_contrast,
+ "saturation": compute_saturation,
+ "resolution": compute_resolution,
+}
diff --git a/moonwatcher/demo_classification.py b/moonwatcher/demo_classification.py
new file mode 100644
index 0000000..b5d89c8
--- /dev/null
+++ b/moonwatcher/demo_classification.py
@@ -0,0 +1,104 @@
+import json
+import random
+from pathlib import Path
+
+import torch
+import torchvision.datasets
+from torchvision import transforms
+import torchvision.models as models
+from torch.utils.data import Subset
+from torchvision.models import ResNet50_Weights
+
+from moonwatcher.model.model import MoonwatcherModel, ModelOutputInputTransformation
+from moonwatcher.dataset.dataset import MoonwatcherDataset
+from moonwatcher.utils.data_storage import load_model, load_dataset
+from moonwatcher.utils.data import Task
+from moonwatcher.check import automated_checking
+from moonwatcher.utils.imagenet_to_stl import map_imagenet_to_stl10, stl10_classes
+
+# TODO 1) Choose a Model
+_model = models.resnet50(weights=ResNet50_Weights.DEFAULT)
+_model = _model.eval()
+
+
+# TODO 2) Choose a Dataset
+image_folder = "."
+transform = transforms.Compose(
+ [
+ transforms.Resize((224, 224)),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+_dataset = torchvision.datasets.STL10(
+ root=image_folder,
+ transform=transform,
+ split="test",
+ download=True,
+)
+
+
+# TODO 3) Write transformations for model
+class ModelOutputInputTransformation(ModelOutputInputTransformation):
+ def __init__(self):
+ pass
+
+ def transform_input(self, image):
+ return image.unsqueeze(0)
+
+ def transform_output(self, outputs):
+ outputs = outputs.detach()
+ imagenet_class_idx = torch.argmax(outputs, dim=1).item()
+ # We are using a resnet trained on imagenet to make predictions on stl10
+ # Therefore a remapping is necessary to map the imagenet classes to the stl10 classes
+ stl10_class_idx = map_imagenet_to_stl10(imagenet_class_idx)
+ return torch.tensor([stl10_class_idx])
+
+
+# TODO 4) Write transformations for dataset
+def dataset_output_transform(data):
+ x, label = data
+ return x, torch.tensor(label)
+
+
+# TODO 5) Create Moonwatcher Dataset and Model
+model_name = f"ResNet50"
+dataset_name = f"STL10"
+
+try:
+ mw_model = load_model(model_name)
+except:
+ mw_model = MoonwatcherModel(
+ model=_model,
+ name=model_name,
+ task=Task.CLASSIFICATION.value,
+ output_input_transform=ModelOutputInputTransformation(),
+ device="cpu",
+ )
+
+try:
+ mw_dataset = load_dataset(dataset_name)
+except:
+ # Mapping from numerical labels to strings
+ label_to_name = dict(enumerate(stl10_classes))
+
+ # Select subset of dataset
+ n_samples = 100
+ random.seed(42)
+ random_indices = random.sample(range(len(_dataset)), n_samples)
+ _dataset = Subset(_dataset, random_indices)
+
+ mw_dataset = MoonwatcherDataset(
+ dataset=_dataset,
+ name=dataset_name,
+ task=Task.CLASSIFICATION.value,
+ output_transform=dataset_output_transform,
+ label_to_name=label_to_name,
+ )
+
+
+# TODO 6) Automated Checking
+automated_checking(
+ mw_dataset=mw_dataset,
+ mw_model=mw_model,
+)
diff --git a/moonwatcher/demo_detection.py b/moonwatcher/demo_detection.py
new file mode 100644
index 0000000..973df23
--- /dev/null
+++ b/moonwatcher/demo_detection.py
@@ -0,0 +1,120 @@
+import os
+import json
+from pathlib import Path
+
+import torch
+import numpy as np
+import torchvision.datasets
+from torch.utils.data import Subset
+from torchvision.transforms import functional as F
+from transformers import DetrImageProcessor, DetrForObjectDetection
+
+from moonwatcher.utils.data import Task
+from moonwatcher.check import automated_checking
+from moonwatcher.dataset.dataset import MoonwatcherDataset
+from moonwatcher.utils.bbox_utils import box_xywh_abs_to_xyxy_abs
+from moonwatcher.utils.data_storage import load_model, load_dataset
+from moonwatcher.model.model import MoonwatcherModel, ModelOutputInputTransformation
+
+
+# TODO 1) Choose a Model
+_model = DetrForObjectDetection.from_pretrained(
+ "facebook/detr-resnet-50", revision="no_timm"
+)
+_model = _model.eval()
+
+
+# TODO 2) Choose a Dataset
+cur_filepath = Path(__file__)
+coco_path = Path("coco")
+coco_sh_path = cur_filepath.parent / "coco.sh"
+if not coco_path.exists() or not any(coco_path.iterdir()):
+ os.system(f"sh {coco_sh_path}")
+
+image_folder = "coco/images/val2017/"
+annotations_file = "coco/annotations/instances_val2017.json"
+
+_dataset = torchvision.datasets.CocoDetection(
+ root=image_folder,
+ annFile=annotations_file,
+)
+
+
+# TODO 3) Write transformations for model
+class ModelOutputInputTransformation(ModelOutputInputTransformation):
+ def __init__(self):
+ self.target_sizes = None
+ self.processor = DetrImageProcessor.from_pretrained(
+ "facebook/detr-resnet-50", revision="no_timm"
+ )
+
+ def transform_input(self, images):
+ self.target_sizes = torch.tensor([images.size[::-1]])
+ inputs = self.processor(images=images, return_tensors="pt")
+ return [], inputs
+
+ def transform_output(self, outputs):
+ results = self.processor.post_process_object_detection(
+ outputs, target_sizes=self.target_sizes, threshold=0.5
+ )[0]
+ scores, labels, boxes = results["scores"], results["labels"], results["boxes"]
+ return boxes, labels, scores
+
+
+# TODO 5) Write transformations for dataset
+def dataset_output_transform(data):
+ pil_image, annotation_list = data
+ boxes = []
+ labels = []
+ for annotation in annotation_list:
+ box_xywh_abs = annotation["bbox"]
+ box_xyxy_abs = box_xywh_abs_to_xyxy_abs(box_xywh_abs)
+ label = annotation["category_id"]
+ boxes.append(box_xyxy_abs)
+ labels.append(label)
+ x = F.to_tensor(pil_image)
+ boxes = torch.tensor(boxes)
+ labels = torch.tensor(labels, dtype=torch.int64)
+ return x, boxes, labels
+
+
+# TODO 4) Create Moonwatcher Dataset and Model
+appendix = f"{np.random.randint(0,100)}"
+model_name = f"facebook-detr-resnet50"
+dataset_name = f"COCO_val2017_subset"
+
+try:
+ mw_model = load_model(model_name)
+except:
+ mw_model = MoonwatcherModel(
+ model=_model,
+ name=model_name,
+ task=Task.DETECTION.value,
+ output_input_transform=ModelOutputInputTransformation(),
+ device="cpu",
+ )
+
+try:
+ mw_dataset = load_dataset(dataset_name)
+except:
+ # Mapping from numerical labels to strings
+ label_to_name = {key: _dataset.coco.cats[key]["name"] for key in _dataset.coco.cats}
+
+ # Select Subset of Dataset
+ n_samples = 100
+ _dataset = Subset(_dataset, [i for i in range(n_samples)])
+
+ mw_dataset = MoonwatcherDataset(
+ dataset=_dataset,
+ name=dataset_name,
+ task=Task.DETECTION.value,
+ output_transform=dataset_output_transform,
+ label_to_name=label_to_name,
+ )
+
+
+# TODO 6) Automated Checking
+automated_checking(
+ mw_dataset,
+ mw_model,
+)
diff --git a/moonwatcher/inference/inference.py b/moonwatcher/inference/inference.py
new file mode 100644
index 0000000..2638fcd
--- /dev/null
+++ b/moonwatcher/inference/inference.py
@@ -0,0 +1,124 @@
+import torch
+from tqdm import tqdm
+from moonwatcher.model.model import MoonwatcherModel
+from moonwatcher.dataset.dataset import MoonwatcherDataset
+from moonwatcher.utils.helpers import convert_to_list
+from moonwatcher.utils.api_connector import upload_if_possible
+from moonwatcher.utils.data import DataType, Task
+from moonwatcher.annotations import (
+ Predictions,
+ PredictedLabels,
+ PredictedBoundingBoxes,
+ Labels,
+ BoundingBoxes,
+)
+
+
+def inference(
+ model: MoonwatcherModel, dataset: MoonwatcherDataset, device=torch.device("cpu")
+):
+ model.to(device=device)
+ model.eval()
+
+ predictions = Predictions(model=model, dataset=dataset)
+
+ with tqdm(
+ range(len(dataset)),
+ desc=f"Running model {model.name} on dataset {dataset.name}",
+ unit="Batches",
+ ) as pbar:
+ for id in pbar:
+ task = model.task
+ x = dataset[id][0]
+ try:
+ inputs = model.output_input_transform.transform_input(x)
+ except Exception as e:
+ raise Exception(f"Application of transform_input failed: {e}")
+
+ try:
+ if len(inputs) == 2:
+ args, kwargs = inputs
+ with torch.no_grad():
+ output = model(*args, **kwargs)
+ else:
+ with torch.no_grad():
+ output = model(inputs)
+ except Exception as e:
+ raise Exception(
+ f"Input format after application of transform_input is invalid: {e}"
+ )
+
+ try:
+ transformed_output = model.output_input_transform.transform_output(
+ output
+ )
+ except Exception as e:
+ raise Exception(f"Application of transform_output failed: {e}")
+
+ if task == Task.CLASSIFICATION.value:
+ if len(transformed_output) == 1:
+ label_pred = transformed_output
+ prediction = Labels(datapoint_number=id, labels=label_pred)
+ elif len(transformed_output) == 2:
+ label_pred, scores = transformed_output
+ prediction = PredictedLabels(
+ datapoint_number=id, labels=label_pred, scores=scores
+ )
+ else:
+ raise ValueError(
+ "Length of transformed_output for classification should be 1 (labels) or 2 (labels and scores)!"
+ )
+ elif task == Task.DETECTION.value:
+ if len(transformed_output) == 2:
+ bounding_boxes_pred, labels_pred = transformed_output
+ prediction = BoundingBoxes(
+ datapoint_id=id,
+ boxes_xyxy=bounding_boxes_pred,
+ labels=labels_pred,
+ )
+ elif len(transformed_output) == 3:
+ bounding_boxes_pred, labels_pred, scores = transformed_output
+ prediction = PredictedBoundingBoxes(
+ datapoint_number=id,
+ boxes_xyxy=bounding_boxes_pred,
+ labels=labels_pred,
+ scores=scores,
+ )
+ else:
+ raise ValueError(
+ "Length of transformed_output for object detection must be 2 (bounding boxes and labels) or 3 "
+ "(bounding boxes, labels and scores)!"
+ )
+
+ predictions.add(annotation=prediction)
+
+ model.upload_if_not()
+
+ predictions.store()
+ _upload_predictions(
+ predictions=predictions, dataset_name=dataset.name, model_name=model.name
+ )
+
+
+def _upload_predictions(predictions, dataset_name, model_name):
+ data = []
+ for prediction in predictions:
+ data.append(
+ {
+ "dataset_name": dataset_name,
+ "model_name": model_name,
+ "datapoint_number": prediction.datapoint_number,
+ "boxes": (
+ [convert_to_list(boxes) for boxes in prediction.boxes_xyxy]
+ if hasattr(prediction, "boxes_xyxy")
+ else None
+ ),
+ "labels": convert_to_list(prediction.labels),
+ "scores": (
+ convert_to_list(prediction.scores)
+ if hasattr(prediction, "scores")
+ else None
+ ),
+ }
+ )
+ return upload_if_possible(datatype=DataType.PREDICTIONS.value, data=data)
diff --git a/moonwatcher/main_upload_images.py b/moonwatcher/main_upload_images.py
new file mode 100644
index 0000000..33fc37a
--- /dev/null
+++ b/moonwatcher/main_upload_images.py
@@ -0,0 +1,24 @@
+import boto3
+import torchvision.datasets
+
+from moonwatcher.utils.image_upload import upload_images
+
+
+image_folder = "../data/COCO/val2017/"
+annotations_file = "../data/COCO/annotations/instances_val2017.json"
+
+dataset = torchvision.datasets.CocoDetection(
+ root=image_folder,
+ annFile=annotations_file,
+)
+
+s3 = boto3.client("s3")
+
+
+upload_images(
+ image_folder=image_folder,
+ upload_folder="demo/images",
+ s3_client=s3,
+ bucket_name="moonwatcher-webapp-dev",
+ urls_json_file_path="coco_upload.json",
+)
diff --git a/moonwatcher/metric.py b/moonwatcher/metric.py
new file mode 100644
index 0000000..b60f159
--- /dev/null
+++ b/moonwatcher/metric.py
@@ -0,0 +1,209 @@
+from typing import Union
+
+import torch
+import numpy as np
+import torchmetrics
+from sklearn.preprocessing import LabelEncoder
+
+from moonwatcher.utils.data import Task
+from moonwatcher.inference.inference import inference
+from moonwatcher.dataset.dataset import Slice, MoonwatcherDataset
+from moonwatcher.utils.data_storage import (
+ load_groundtruths,
+ load_predictions,
+ do_predictions_exist,
+)
+
+
+def run_inference_if_necessary(model, dataset):
+ if not do_predictions_exist(dataset_name=dataset.name, model_name=model.name):
+ inference(model=model, dataset=dataset, device=model.device)
+
+
+def get_original_indices(dataset_or_slice):
+ if isinstance(dataset_or_slice, Slice):
+ parent_indices = get_original_indices(dataset_or_slice.moonwatcher_dataset)
+ return [parent_indices[i] for i in dataset_or_slice.indices]
+ elif isinstance(dataset_or_slice, MoonwatcherDataset):
+ return list(range(len(dataset_or_slice.dataset)))
+ else:
+ raise TypeError("Unsupported dataset type")
+
+
+def load_data(model, dataset_or_slice: Union[MoonwatcherDataset, Slice]):
+ relevant_ids = get_original_indices(dataset_or_slice=dataset_or_slice)
+ dataset = (
+ dataset_or_slice.original_dataset
+ if isinstance(dataset_or_slice, Slice)
+ else dataset_or_slice
+ )
+
+ run_inference_if_necessary(model=model, dataset=dataset)
+ groundtruths_loaded = load_groundtruths(dataset_name=dataset.name)
+ predictions_loaded = load_predictions(
+ dataset_name=dataset.name, model_name=model.name
+ )
+
+ return relevant_ids, dataset, groundtruths_loaded, predictions_loaded
+
+
+def calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ metric: str,
+ metric_parameters=None,
+):
+ if metric_parameters is None:
+ metric_parameters = {}
+
+ metric_type = model.task
+ metric_function = _METRIC_FUNCTIONS[metric]
+
+ if metric_type == Task.CLASSIFICATION.value:
+ try:
+ groundtruths = np.array(
+ [groundtruths_loaded[i].labels.item() for i in relevant_ids],
+ dtype=np.int32,
+ )
+ except Exception as e:
+ raise Exception(
+ f"Groundtruths could not be loaded. Dataset output transform should return labels as a 1-dimensional int Tensor of shape (1): {e}"
+ )
+
+ try:
+ predictions = np.array(
+ [predictions_loaded[i].labels.item() for i in relevant_ids],
+ dtype=np.int32,
+ )
+ except Exception as e:
+ raise Exception(
+ f"Predictions could not be loaded. Model output transform should return labels as an int Tensor of shape (num_boxes): {e}"
+ )
+
+ if not groundtruths.size or not predictions.size:
+ raise ValueError(
+ "Ground truths and/or predictions are empty! Ensure your dataset or slice contains data and has been properly processed."
+ )
+
+ try:
+ num_classes = len(dataset.label_to_name)
+ if num_classes == 2:
+ task_type = "binary"
+ else:
+ task_type = "multiclass"
+ label_encoder = LabelEncoder()
+ label_encoder.fit(list(dataset.label_to_name.keys()))
+ groundtruths = label_encoder.transform(groundtruths.ravel())
+ predictions = label_encoder.transform(predictions.ravel())
+
+ groundtruths = torch.tensor(groundtruths).flatten()
+ predictions = torch.tensor(predictions).flatten()
+
+ if "average" not in metric_parameters:
+ metric_parameters["average"] = "macro"
+
+ metric_value = metric_function(
+ predictions,
+ groundtruths,
+ task=task_type,
+ num_classes=num_classes,
+ **metric_parameters,
+ )
+
+ except Exception as e:
+ raise Exception(
+ f"Error occured during metric computation. Check if dataset output_transform and model output_transform return the required format: {e}"
+ )
+ elif metric_type == Task.DETECTION.value:
+ try:
+ groundtruths = [groundtruths_loaded[i].to_dict() for i in relevant_ids]
+ predictions = [predictions_loaded[i].to_dict() for i in relevant_ids]
+
+ groundtruths, predictions = zip(
+ *[
+ (gt, pred)
+ for gt, pred in zip(groundtruths, predictions)
+ if gt["boxes"].numel() > 0 and pred["boxes"].numel() > 0
+ ]
+ )
+
+ if not groundtruths or not predictions:
+ raise ValueError(
+ "Ground truths and/or predictions are empty! Ensure your dataset or slice contains data and has been properly processed."
+ )
+
+ for gt in groundtruths:
+ if "boxes" not in gt or len(gt["boxes"]) == 0:
+ raise ValueError(f"Groundtruth boxes are empty for an entry: {gt}")
+ for pred in predictions:
+ if "boxes" not in pred or len(pred["boxes"]) == 0:
+ raise ValueError(f"Prediction boxes are empty for an entry: {pred}")
+
+ if metric in ["mAP", "mAP_small", "mAP_medium", "mAP_large"]:
+ metric_parameters["iou_type"] = "bbox"
+ metric_function = metric_function(**metric_parameters)
+ metric_function.update(predictions, groundtruths)
+ metric_value = metric_function.compute()
+ metric_value = metric_value[_METRIC_KEYS[metric]]
+ else:
+ metric_value = metric_function(predictions, groundtruths)
+ metric_value = metric_value[_METRIC_KEYS[metric]]
+
+ except Exception as e:
+ raise Exception(
+ f"Error occured during metric computation. Check if dataset output_transform and model output_transform return the required format: {e}"
+ )
+
+ return round(metric_value.item(), 5)
+
+
+def calculate_metric(
+ model,
+ dataset_or_slice: Union[MoonwatcherDataset, Slice],
+ metric: str,
+ metric_parameters=None,
+):
+ relevant_ids, dataset, groundtruths_loaded, predictions_loaded = load_data(
+ model, dataset_or_slice
+ )
+ return calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ metric,
+ metric_parameters,
+ )
+
+
+_METRIC_FUNCTIONS = {
+ "Accuracy": torchmetrics.functional.accuracy,
+ "Precision": torchmetrics.functional.precision,
+ "Recall": torchmetrics.functional.recall,
+ "F1_Score": torchmetrics.functional.f1_score,
+ "HammingDistance": torchmetrics.functional.hamming_distance,
+ "mAP": torchmetrics.detection.MeanAveragePrecision,
+ "mAP_small": torchmetrics.detection.MeanAveragePrecision,
+ "mAP_medium": torchmetrics.detection.MeanAveragePrecision,
+ "mAP_large": torchmetrics.detection.MeanAveragePrecision,
+ "CompleteIntersectionOverUnion": torchmetrics.detection.CompleteIntersectionOverUnion(),
+ "DistanceIntersectionOverUnion": torchmetrics.detection.DistanceIntersectionOverUnion(),
+ "GeneralizedIntersectionOverUnion": torchmetrics.detection.GeneralizedIntersectionOverUnion(),
+ "IntersectionOverUnion": torchmetrics.detection.IntersectionOverUnion(),
+}
+
+
+_METRIC_KEYS = {
+ "mAP": "map",
+ "mAP_small": "map_small",
+ "mAP_medium": "map_medium",
+ "mAP_large": "map_large",
+ "CompleteIntersectionOverUnion": "ciou",
+ "DistanceIntersectionOverUnion": "diou",
+ "GeneralizedIntersectionOverUnion": "giou",
+ "IntersectionOverUnion": "iou",
+}
diff --git a/moonwatcher/model/model.py b/moonwatcher/model/model.py
new file mode 100644
index 0000000..54ef8dd
--- /dev/null
+++ b/moonwatcher/model/model.py
@@ -0,0 +1,98 @@
+from typing import Dict, Any
+from abc import ABC, abstractmethod
+
+from torch.nn import Module
+
+from moonwatcher.utils.data import DataType
+from moonwatcher.base.base import MoonwatcherObject
+from moonwatcher.utils.helpers import get_current_timestamp
+from moonwatcher.utils.api_connector import upload_if_possible
+
+
+class ModelOutputInputTransformation(ABC):
+ def __init__(self):
+ pass
+
+ @abstractmethod
+ def transform_input(self, inputs):
+ """
+ Transform input data into a format that can be directly passed to the model.
+
+ :param inputs: An image from your specified dataset.
+ :return:
+ The transformed input data, formatted to be passed directly to the model.
+ The output can be structured either as positional arguments (*args),
+ or as both positional and keyword arguments (*args and **kwargs).
+
+ Internally, the arguments are directly passed to the model either as model(*args) or model(*args, **kwargs).
+ """
+ pass
+
+ @abstractmethod
+ def transform_output(self, outputs):
+ """
+ Transform the output of the model into the required format.
+
+ :param outputs: The output from the model.
+
+ :return:
+ A tuple containing the model outputs formatted as required:
+ - Classification:
+ labels (torch.Tensor): A 1-dimensional integer tensor of shape (1) representing the label.
+ scores (optional, torch.Tensor): A float tensor of shape (num_classes) representing the confidence scores for each class.
+
+ - Detection:
+ boxes_xyxy (torch.Tensor): A tensor of shape (num_boxes, 4) representing bounding box coordinates.
+ labels (torch.Tensor): An integer tensor of shape (num_boxes) representing labels for each bounding box.
+ scores (optional, torch.Tensor): A float tensor of shape (num_boxes) representing the confidence score for each bounding box.
+ """
+ pass
+
+
+class MoonwatcherModel(MoonwatcherObject, Module):
+ def __init__(
+ self,
+ model: Module,
+ name: str,
+ task: str,
+ output_input_transform: ModelOutputInputTransformation,
+ device: str,
+ metadata: Dict[str, Any] = None,
+ description: str = None,
+ ):
+ """
+ Creates a moonwatcher model wrapper around an existing model that can be used with the moonwatcher framework
+
+ :param model: the model to be wrapped
+ :param name: the name you want to give this model
+ :param task: either classification or detection
+ :param output_input_transform: see ModelOutputInputTransformation class, formatting input output for moonwatcher
+ :param device: only cpu works for now
+ :param metadata: dictionary of tags for the model, can be ignored
+ :param description: description of the model, can be ignored
+ """
+ MoonwatcherObject.__init__(self, name=name, datatype=DataType.MODEL)
+
+ Module.__init__(self)
+
+ self.task = task
+ self.model = model
+ self.metadata = metadata or {}
+ self.timestamp = get_current_timestamp()
+ self.description = description
+ self.output_input_transform = output_input_transform
+ self.device = device
+ self.store()
+
+ def _upload(self):
+ data = {
+ "name": self.name,
+ "description": self.description,
+ "timestamp": self.timestamp,
+ "metadata": self.metadata,
+ "task": self.task,
+ }
+ return upload_if_possible(datatype=DataType.MODEL.value, data=data)
+
+ def forward(self, *args, **kwargs):
+ return self.model.forward(*args, **kwargs)
diff --git a/moonwatcher/utils/api_connector.py b/moonwatcher/utils/api_connector.py
new file mode 100644
index 0000000..b8f3b2a
--- /dev/null
+++ b/moonwatcher/utils/api_connector.py
@@ -0,0 +1,105 @@
+import os
+import json
+from typing import Dict, Union, List
+
+import requests
+from dotenv import load_dotenv
+
+from moonwatcher.utils.data import DataType
+
+
+def check_api_call_success(response):
+ if not response.ok:
+ raise RuntimeError(f"Upload API Call: {response.status_code} - {response.text}")
+
+
+def is_api_key_and_endpoint_available():
+ load_dotenv()
+ base_url = os.getenv("API_ENDPOINT")
+ api_key = os.getenv("API_KEY")
+ if (base_url is None) or (api_key is None):
+ return False
+ return True
+
+
+
+class ApiConnector:
+ def __init__(self):
+ load_dotenv()
+ self.base_url = os.getenv("API_ENDPOINT")
+ self.api_key = os.getenv("API_KEY")
+ self.available = True
+ if (self.base_url is None) or (self.api_key is None):
+ self.available = False
+
+ self.headers = {"X-API-KEY": self.api_key}
+
+ def hello(self):
+ r = requests.get(url=self.base_url + "hello", headers=self.headers)
+ print(r)
+ body = r.content.decode()
+ print(body)
+ pass
+
+ def get_presigned_post(self, filename: str, **kwargs):
+ headers = {key: self.headers[key] for key in self.headers}
+ headers.update({k: kwargs[k] for k in kwargs})
+ headers["filename"] = filename
+ r = requests.get(url=self.base_url + "get_presigned_post", headers=headers)
+ check_api_call_success(response=r)
+ body = json.loads(r.content.decode())
+ return body
+
+
+class DataUploader:
+ def __init__(self):
+ self.api_connector = ApiConnector()
+ self.upload_possible = self.api_connector.available
+
+ def upload(self, datatype: str, upload_data: Union[Dict, List]):
+ accepted_datatypes = [upload_datatype.value for upload_datatype in DataType]
+ kwargs = {}
+ if datatype not in accepted_datatypes:
+ raise ValueError(
+ f"Datatype {datatype} is not accepted for upload. Accepted datatypes are {accepted_datatypes}"
+ )
+ if datatype in [
+ DataType.DATASET.value,
+ DataType.MODEL.value,
+ DataType.CHECK.value,
+ DataType.CHECKSUITE.value,
+ ]:
+ name = upload_data["name"]
+ elif datatype == DataType.SLICE.value:
+ name = f"{upload_data['dataset_name']}_{upload_data['name']}"
+ kwargs["dataset_name"] = upload_data["dataset_name"]
+ kwargs["slice_name"] = upload_data["name"]
+ elif datatype == DataType.CHECK_REPORT.value:
+ name = f"{upload_data['model_name']}_{upload_data['check_name']}"
+ elif datatype == DataType.CHECKSUITE_REPORT.value:
+ name = f"{upload_data['model_name']}_{upload_data['checksuite_name']}"
+ elif datatype == DataType.GROUNDTRUTHS.value:
+ name = f"{upload_data[0]['dataset_name']}"
+ elif datatype == DataType.PREDICTIONS.value:
+ name = f"{upload_data[0]['dataset_name']}_{upload_data[0]['model_name']}"
+
+ filename = f"{datatype}__{name}.json"
+ response = self.api_connector.get_presigned_post(filename=filename, **kwargs)
+ files = {"file": json.dumps(upload_data)}
+ data = response["fields"]
+ r = requests.post(response["url"], data=data, files=files)
+ check_api_call_success(response=r)
+ print(f'Uploaded {datatype} as file "{filename}"')
+
+
+def upload_if_possible(datatype: str, data: Union[Dict, List]):
+ uploader = DataUploader()
+ if uploader.upload_possible:
+ uploader.upload(datatype=datatype, upload_data=data)
+ return True
+ return False
+
+
+if __name__ == "__main__":
+ api_connector = ApiConnector()
+ r = api_connector.hello()
diff --git a/moonwatcher/utils/bbox_utils.py b/moonwatcher/utils/bbox_utils.py
new file mode 100644
index 0000000..632ae9e
--- /dev/null
+++ b/moonwatcher/utils/bbox_utils.py
@@ -0,0 +1,10 @@
+from typing import List
+
+
+def box_xywh_abs_to_xyxy_abs(box_xywh_abs: List[float]):
+ x1_abs, y1_abs, w_abs, h_abs = box_xywh_abs
+ x2_abs = x1_abs + w_abs
+ y2_abs = y1_abs + h_abs
+ box_xyxy_abs = [x1_abs, y1_abs, x2_abs, y2_abs]
+ box_xyxy_abs = [float(item) for item in box_xyxy_abs]
+ return box_xyxy_abs
diff --git a/moonwatcher/utils/data.py b/moonwatcher/utils/data.py
new file mode 100644
index 0000000..32fc694
--- /dev/null
+++ b/moonwatcher/utils/data.py
@@ -0,0 +1,30 @@
+import operator as op
+from enum import Enum
+
+
+OPERATOR_DICT = {
+ "<": op.lt,
+ ">": op.gt,
+ ">=": op.ge,
+ "<=": op.le,
+ "==": op.eq,
+ "=": op.eq,
+ "!=": op.ne,
+}
+
+
+class Task(Enum):
+ CLASSIFICATION = "classification"
+ DETECTION = "detection"
+
+
+class DataType(Enum):
+ DATASET = "dataset"
+ SLICE = "slice"
+ MODEL = "model"
+ CHECK = "check"
+ CHECKSUITE = "checksuite"
+ CHECK_REPORT = "check_report"
+ CHECKSUITE_REPORT = "checksuite_report"
+ PREDICTIONS = "predictions"
+ GROUNDTRUTHS = "groundtruths"
diff --git a/moonwatcher/utils/data_storage.py b/moonwatcher/utils/data_storage.py
new file mode 100644
index 0000000..707e0a3
--- /dev/null
+++ b/moonwatcher/utils/data_storage.py
@@ -0,0 +1,122 @@
+import os
+from pathlib import Path
+
+import torch
+from dotenv import load_dotenv
+
+from moonwatcher.utils.data import DataType
+
+
+class DataStorageManager:
+ def __init__(self):
+ load_dotenv()
+ storage_folder = os.getenv("STORAGE_FOLDER")
+ if storage_folder is None:
+ storage_folder = "."
+
+ self.storage_folder = Path(storage_folder) / "observations"
+ self.storage_folder.mkdir(exist_ok=True)
+ self.accepted_datatypes = [
+ DataType.DATASET.value,
+ DataType.SLICE.value,
+ DataType.MODEL.value,
+ DataType.CHECK.value,
+ DataType.CHECKSUITE.value,
+ DataType.PREDICTIONS.value,
+ DataType.GROUNDTRUTHS.value,
+ ]
+
+ def get_filepath(self, datatype: DataType, name: str):
+ if datatype.value not in self.accepted_datatypes:
+ raise ValueError(
+ f'Datatype: "{datatype.value}" is not an accepted datatype for data storage'
+ )
+ folder = self.storage_folder / datatype.value
+ filepath = folder / (name + ".pt")
+ filepath.parent.mkdir(exist_ok=True, parents=True)
+ return filepath
+
+ def store_file(self, file, datatype: DataType, name: str, overwrite=True):
+ filepath = self.get_filepath(datatype=datatype, name=name)
+ if not overwrite and filepath.exists():
+ raise RuntimeError(
+ f"Cannot store {datatype.value} {name}. It already exists."
+ )
+ torch.save(obj=file, f=filepath)
+ return filepath
+
+ def load_file(self, datatype: DataType, name: str):
+ filepath = self.get_filepath(datatype=datatype, name=name)
+ if not filepath.exists():
+ raise RuntimeError(
+ f"Cannot load {datatype.value} {name}. It does not exist."
+ )
+ return torch.load(f=filepath)
+
+ def exists(self, datatype: DataType, name: str):
+ filepath = self.get_filepath(datatype=datatype, name=name)
+ return filepath.exists()
+
+
+def exists(datatype: DataType, name: str):
+ ds_manager = DataStorageManager()
+ return ds_manager.exists(datatype=datatype, name=name)
+
+
+def _prediction_name(model_name, dataset_name):
+ return dataset_name + "_" + model_name
+
+
+def _slice_name(dataset_name, name):
+ return dataset_name + "_" + name
+
+
+def do_predictions_exist(model_name, dataset_name):
+ name = _prediction_name(model_name=model_name, dataset_name=dataset_name)
+ return exists(datatype=DataType.PREDICTIONS, name=name)
+
+
+def store_file(file, datatype: DataType, name: str, overwrite=False):
+ ds_manager = DataStorageManager()
+ return ds_manager.store_file(
+ file=file, datatype=datatype, name=name, overwrite=overwrite
+ )
+
+
+def load(datatype: DataType, name: str):
+ ds_manager = DataStorageManager()
+ return ds_manager.load_file(datatype=datatype, name=name)
+
+
+def load_model(name):
+ return load(datatype=DataType.MODEL, name=name)
+
+
+def load_dataset(name):
+ dataset = load(datatype=DataType.DATASET, name=name)
+ dataset.upload_if_not()
+ return dataset
+
+
+def load_slice(dataset_name, name):
+ name = _slice_name(dataset_name=dataset_name, name=name)
+ return load(datatype=DataType.SLICE, name=name)
+
+
+def load_check(name):
+ return load(datatype=DataType.CHECK, name=name)
+
+
+def load_checksuite(name):
+ return load(datatype=DataType.CHECKSUITE, name=name)
+
+
+def load_predictions(dataset_name, model_name):
+ return load(
+ datatype=DataType.PREDICTIONS,
+ name=_prediction_name(model_name=model_name, dataset_name=dataset_name),
+ )
+
+
+def load_groundtruths(dataset_name):
+ return load(datatype=DataType.GROUNDTRUTHS, name=dataset_name)
diff --git a/moonwatcher/utils/helpers.py b/moonwatcher/utils/helpers.py
new file mode 100644
index 0000000..f409d08
--- /dev/null
+++ b/moonwatcher/utils/helpers.py
@@ -0,0 +1,18 @@
+import datetime
+
+import pytz
+
+
+def get_current_timestamp() -> str:
+ tz = pytz.timezone("Europe/Berlin")
+ timestamp = datetime.datetime.now(tz=tz).isoformat()
+ return timestamp
+
+
+def convert_to_list(items):
+ result = items.tolist()
+
+ if isinstance(result, list):
+ return result
+ else:
+ return [result]
diff --git a/moonwatcher/utils/image_upload.py b/moonwatcher/utils/image_upload.py
new file mode 100644
index 0000000..13b5972
--- /dev/null
+++ b/moonwatcher/utils/image_upload.py
@@ -0,0 +1,70 @@
+import json
+from pathlib import Path
+from typing import Union, Optional
+
+from tqdm import tqdm
+import boto3
+
+
+def upload_image(
+ s3_client,
+ file_path: Union[Path, str],
+ bucket_name: str,
+ upload_folder: Optional[str] = None,
+ region="eu-central-1",
+):
+ file_path = Path(file_path)
+
+ upload_path = file_path.name
+ if upload_folder is not None:
+ upload_path = f"{upload_folder}/{upload_path}"
+
+ s3_client.upload_file(
+ file_path, bucket_name, upload_path, ExtraArgs={"ContentType": f"image/jpeg"}
+ )
+
+ url = f"https://{bucket_name}.s3.{region}.amazonaws.com/{upload_path}"
+ return url
+
+
+def upload_images(
+ image_folder: Union[Path, str],
+ upload_folder: str,
+ bucket_name: str,
+ s3_client,
+ urls_json_file_path: str = None,
+ region: str = "eu-central-1",
+):
+ image_folder = Path(image_folder)
+
+ image_paths = sorted([image_path for image_path in image_folder.iterdir()])
+
+ urls = []
+ print(
+ f"Uploading images from {image_folder.as_posix()} to s3 bucket {bucket_name} in folder {upload_folder}"
+ )
+ for image_path in tqdm(image_paths, desc="Uploading images"):
+ if image_path.name == ".DS_Store":
+ continue
+ url = upload_image(
+ s3_client=s3_client,
+ file_path=image_path,
+ bucket_name=bucket_name,
+ upload_folder=upload_folder,
+ region=region,
+ )
+ urls.append(url)
+
+ if urls_json_file_path is not None:
+ with open(urls_json_file_path, "w", encoding="utf-8") as f:
+ json.dump(obj=urls, fp=f, indent=4)
+
+
+if __name__ == "__main__":
+ upload_images(
+ "../../data/caltech",
+ "demo/classification/caltech101",
+ "moonwatcher-webapp-dev",
+ boto3.client("s3"),
+ "../configs/caltech_locators.json",
+ )
diff --git a/moonwatcher/utils/imagenet_to_stl.py b/moonwatcher/utils/imagenet_to_stl.py
new file mode 100644
index 0000000..9f77b36
--- /dev/null
+++ b/moonwatcher/utils/imagenet_to_stl.py
@@ -0,0 +1,24 @@
+import json
+from pathlib import Path
+
+
+# Mapping from imagenet classes to slt10 classes
+def load_mapping():
+ cur_filepath = Path(__file__)
+ with open(
+ cur_filepath.parent.parent / "configs" / "imagenet_to_stl.json",
+ "r",
+ encoding="utf-8",
+ ) as f:
+ data = json.load(f)
+
+ stl10_classes = data["stl10_classes"]
+ mapping = data["mapping"]
+
+ def map_imagenet_to_stl10(imagenet_class_idx):
+ return mapping.get(str(imagenet_class_idx), 9)
+
+ return map_imagenet_to_stl10, stl10_classes
+
+
+map_imagenet_to_stl10, stl10_classes = load_mapping()
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..947afa0
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,2128 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
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+version = "4.12.3"
+description = "Screen-scraping library"
+optional = false
+python-versions = ">=3.6.0"
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+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
+[[package]]
+name = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
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+tokenizers = ">=0.19,<0.20"
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+
+[package.extras]
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+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
+torch = ["accelerate (>=0.21.0)", "torch"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "triton"
+version = "2.2.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+files = [
+ {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
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+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
+tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.11.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
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+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
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+[[package]]
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+version = "2.2.1"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "f780aa91036f95764dc4631466ed072dbf8aabd1d61e22e786549c41eb94b65d"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..0958c74
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,44 @@
+#[build-system]
+#requires = ["setuptools", "wheel"]
+#build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+include = ["moonwatcher*"]
+exclude = ["docs*", "tests*"]
+
+[tool.poetry]
+name = "moonwatcher"
+version = "0.1.0-alpha1"
+description = ""
+authors = ["Leon Steffen "]
+readme = "README.md"
+license = "Apache-2.0"
+homepage = "https://www.moonwatcher.ai"
+repository = "https://github.com/moonwatcher-ai/moonwatcher"
+
+[tool.poetry.urls]
+"Issues" = "https://github.com/moonwatcher-ai/moonwatcher/issues"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+torch = "^2.2.2"
+torchvision = "^0.17.2"
+requests = "^2.31.0"
+numpy = "^1.26.4"
+pytz = "^2024.1"
+tqdm = "^4.66.2"
+opencv-python = "^4.9.0.80"
+torchmetrics = "^1.3.2"
+scikit-learn = "^1.4.1.post1"
+transformers = "^4.39.3"
+timm = "^0.9.16"
+setuptools = "^69.2.0"
+pandas = "^2.2.1"
+python-dotenv = "^1.0.1"
+pycocotools = "^2.0.7"
+gdown = "^5.1.0"
+pytest = "^8.1.1"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/readme/README_webapp.md b/readme/README_webapp.md
new file mode 100644
index 0000000..71fbcc9
--- /dev/null
+++ b/readme/README_webapp.md
@@ -0,0 +1,108 @@
+
+
+
+
+
+# Web app
+The web app allows users to investigate the check results and identify instances and patterns that cause your model to fail. Moreover, the check results can be shared with product, sales or customers to foster a common understanding of model quality and performance.
+
+## Try the demo
+You can log into our demo account at https://app.moonwatcher.ai/sign-in to get a better idea of the web app functionality
+with the following credentials:
+- Email: demo@moonwatcher.ai
+- Password: Demo123!
+
+# Contents
+
+- [1.π Sign Up](#sign-up)
+- [2. π API](#API)
+- [3. β¬οΈ Upload](#upload)
+- [4. π Analyze the results](#analyze-the-results)
+ - [π§ 1. Models](#models)
+ - [πΌοΈ 2. Datasets](#datasets)
+
+## 1. π Sign Up
+
+Navigate to [https://app.moonwatcher.ai/sign-up](https://app.moonwatcher.ai/sign-up) to create your account. We have limited the number of accounts in the alpha version. If all accounts are already taken, please contact us.
+
+> [!TIP]
+> If you plan to share the results within your team, we recommend using an email and password that you're comfortable sharing with your colleagues. Currently, we only support one account per team.
+
+## 2. π API
+
+To analyze the results of the open-source library in the web app, you need to upload them via the Moonwatcher API. The upload requires an API Key and the endpoint URL. You can retrieve both from the Settings page.
+
+Next, create an environment file `.env` in your root directory:
+
+```python
+#/.env
+
+API_KEY=
+API_ENDPOINT=
+```
+
+## 3. β¬οΈ Upload
+
+To display a dataset (the associated images and annotations) in the web app, take the following steps:
+
+**1. Add locators to your dataset**
+
+- A locator is a string representing a file URL that points to the image.
+- Add a list of locators to the moonwatcher dataset.
+- Example:
+
+ ```python
+ locators = ["https://path/to/image1","https://path/to/image2","https://path/to/image3",... ]
+
+ mw_dataset = MoonwatcherDataset(
+ dataset=your_dataset,
+ ...
+ locators = locators
+ )
+ ```
+
+> [!WARNING]
+> Locators must be external path strings, i.e., URLs starting with "https://".
+
+> [!WARNING]
+> The web app requires access to the file URL. If the file behind the URL is protected, the web app may not be able to access it.
+
+**2. Upload the data**
+
+- Delete your observations folder.
+- Re-run the checks and check suites that you want to upload.
+- Since you have added the `.env` file, the re-run will automatically trigger an upload of the relevant data.
+
+> [!WARNING]
+> If you don't delete the observations folder and re-run all the checks and check-suites, issues will occur.
+
+## 4. π Analyze the results
+
+The data and results are now uploaded and accessible with the account associated with the API key used for the upload.
+
+You can start your analysis journey from two different starting points:
+
+### π§ 1. Models
+
+Understand where your models fail at a glance. Navigate to the Models tab to display the results of associated checks and check suites.
+
+
+
+To investigate the cause of a test failure, click on the slice or dataset to browse the images with their ground truth and model predictions.
+
+### πΌοΈ 2. Datasets
+
+For an overview of all the datasets in use, navigate to the Datasets tab.
+
+
+
+Explore the dataset, corresponding slices, and annotations to better understand how different models perform on subsets of your data.
+
+
+> [!IMPORTANT]
+> To visualize the images, ensure that Moonwatcher has access to the locator URLs.
+
+# π€ Get in touch
+
+If you have questions, need support, want to share feedback, contribute, or explore collaboration opportunities, feel free to reach out to us at hello@moonwatcher.ai.
diff --git a/readme/logo.png b/readme/logo.png
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diff --git a/readme/logo_white.png b/readme/logo_white.png
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diff --git a/readme/moonwatcher-datasets.gif b/readme/moonwatcher-datasets.gif
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diff --git a/readme/moonwatcher-models.gif b/readme/moonwatcher-models.gif
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a68fea9
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,39 @@
+boto3==1.34.68
+botocore==1.34.68
+certifi==2024.2.2
+charset-normalizer==3.3.2
+dnspython==2.6.1
+dotenv-python==0.0.1
+filelock==3.13.1
+fsspec==2024.3.1
+idna==3.6
+Jinja2==3.1.3
+jmespath==1.0.1
+joblib==1.3.2
+lightning-utilities==0.11.0
+MarkupSafe==2.1.5
+mpmath==1.3.0
+networkx==3.2.1
+numpy~=1.23.5
+opencv-python==4.9.0.80
+packaging==24.0
+pillow~=9.4.0
+pymongo==4.6.2
+python-dateutil==2.9.0.post0
+pytz~=2022.7
+requests~=2.28.1
+s3transfer==0.10.1
+scikit-learn~=1.2.1
+scipy==1.12.0
+six==1.16.0
+sympy==1.12
+threadpoolctl==3.4.0
+torch~=2.1.0
+torchmetrics~=1.3.1
+torchvision~=0.16.0
+tqdm~=4.64.1
+typing_extensions==4.10.0
+urllib3==2.2.1
+pathlib~=1.0.1
+pandas~=1.5.3
+pytest~=7.3.1
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_annotations.py b/tests/test_annotations.py
new file mode 100644
index 0000000..235492f
--- /dev/null
+++ b/tests/test_annotations.py
@@ -0,0 +1,123 @@
+import pytest
+import torch
+
+from moonwatcher.annotations import (
+ BoundingBoxes,
+ PredictedBoundingBoxes,
+ Labels,
+ Annotations,
+ PredictedLabels,
+)
+
+
+def tensor(data, dtype=torch.float32):
+ return torch.tensor(data, dtype=dtype)
+
+
+# Tests for BoundingBoxes
+@pytest.mark.parametrize(
+ "datapoint_id, boxes, labels, valid",
+ [
+ (1, tensor([[1, 2, 3, 4]]), tensor([1]), True),
+ (2, "not a tensor", tensor([1]), False), # Invalid boxes
+ (3, tensor([[1, 2, 3, 4]]), "not a tensor", False), # Invalid labels
+ ],
+)
+def test_bounding_boxes_initialization(datapoint_id, boxes, labels, valid):
+ if valid:
+ bbox = BoundingBoxes(datapoint_id, boxes, labels)
+ assert bbox.datapoint_number == datapoint_id
+ assert torch.equal(bbox.boxes_xyxy, boxes)
+ assert torch.equal(bbox.labels, labels)
+ else:
+ with pytest.raises(TypeError):
+ BoundingBoxes(datapoint_id, boxes, labels)
+
+
+# Tests for PredictedBoundingBoxes
+@pytest.mark.parametrize(
+ "datapoint_id, boxes, labels, scores, valid",
+ [
+ (1, tensor([[1, 2, 3, 4]]), tensor([1]), tensor([0.99]), True),
+ (2, tensor([[1, 2, 3, 4]]), tensor([1]), 0.99, False), # Invalid scores
+ ],
+)
+def test_predicted_bounding_boxes_initialization(
+ datapoint_id, boxes, labels, scores, valid
+):
+ if valid:
+ pred_bbox = PredictedBoundingBoxes(datapoint_id, boxes, labels, scores)
+ assert pred_bbox.scores is not None
+ assert torch.equal(pred_bbox.scores, scores)
+ else:
+ with pytest.raises(TypeError):
+ PredictedBoundingBoxes(datapoint_id, boxes, labels, scores)
+
+
+# Tests for Labels
+@pytest.mark.parametrize(
+ "datapoint_id, labels, expected_exception",
+ [
+ (1, tensor([1], dtype=torch.int32), None), # Correct case
+ (2, tensor([1.3]), TypeError), # Non-integer labels
+ (3, "not a tensor", TypeError), # Non-tensor labels
+ (4, tensor([1, 2], dtype=torch.int32), TypeError), # Incorrect shape
+ ],
+)
+def test_labels_initialization(datapoint_id, labels, expected_exception):
+ if expected_exception:
+ with pytest.raises(expected_exception):
+ Labels(datapoint_id, labels)
+ else:
+ label_obj = Labels(datapoint_id, labels)
+ assert label_obj.labels.shape == (1,)
+ assert label_obj.labels.dtype == torch.int32
+
+
+# Tests for PredictedLabels
+@pytest.mark.parametrize(
+ "datapoint_id, labels, scores, expected_exception",
+ [
+ (1, tensor([1], dtype=torch.int32), tensor([0.9]), None), # Correct case
+ (
+ 2,
+ tensor([1], dtype=torch.int32),
+ "not a tensor",
+ TypeError,
+ ), # Non-tensor scores
+ (
+ 3,
+ tensor([1], dtype=torch.int32),
+ tensor([0.9, 0.1]),
+ TypeError,
+ ), # Incorrect scores shape
+ (4, "not a tensor", tensor([0.9]), TypeError), # Non-tensor labels
+ (
+ 5,
+ tensor([1, 2], dtype=torch.int32),
+ tensor([0.9]),
+ TypeError,
+ ), # Incorrect labels shape
+ ],
+)
+def test_predicted_labels_initialization(
+ datapoint_id, labels, scores, expected_exception
+):
+ if expected_exception:
+ with pytest.raises(expected_exception):
+ PredictedLabels(datapoint_id, labels, scores)
+ else:
+ predicted_label_obj = PredictedLabels(datapoint_id, labels, scores)
+ assert predicted_label_obj.labels.shape == (1,)
+ assert predicted_label_obj.labels.dtype == torch.int32
+ assert predicted_label_obj.scores.shape == (1,)
+ assert predicted_label_obj.scores.dtype == torch.float32
+
+
+# Test for Annotations Class
+def test_annotations():
+ bbox = BoundingBoxes(1, tensor([[0, 1, 2, 3]]), tensor([1]))
+ annotations = Annotations([bbox])
+ retrieved = annotations.get(1)
+ assert retrieved == bbox
+ assert len(annotations) == 1
diff --git a/tests/test_base.py b/tests/test_base.py
new file mode 100644
index 0000000..97181c2
--- /dev/null
+++ b/tests/test_base.py
@@ -0,0 +1,59 @@
+import pytest
+
+from moonwatcher.base.base import MoonwatcherObject, DataType
+
+valid_names = [
+ "exampleName",
+ "example_name",
+ "Example-Name",
+ "example123",
+ "123example",
+ "name_123",
+ "123_name",
+ "example-name123",
+ "name123-name",
+ "abc_xyz-123",
+ "data_set_1",
+ "data-set-2",
+ "Test_Name_3",
+ "Test-Name-4",
+ "dataset5",
+ "dataSet6",
+ "Data_Set-7",
+ "Data-Set_8",
+ "example_9_name",
+ "example_10-name",
+]
+
+invalid_names = [
+ "hello world",
+ "data@set",
+ "profile#1",
+ "update%20",
+ "name*star",
+ "test(case)",
+ "click&collect",
+ "math+science",
+ "value=truth",
+ "key|value",
+ "path/to/file",
+ "user\\admin",
+ "comma,separated",
+ "semicolon;",
+ "colon:colon",
+ 'quote"quote',
+ "apostrophe's",
+ "bracket[1]",
+ "bracket{2}",
+ "less", 5, 3, True),
+ (">", 3, 5, False),
+ (">=", 5, 5, True),
+ (">=", 4, 5, False),
+ ("<=", 3, 3, True),
+ ("<=", 5, 3, False),
+ ("==", 5, 5, True),
+ ("==", 5, 4, False),
+ ("=", 5, 5, True),
+ ("=", 5, 4, False),
+ ("!=", 5, 4, True),
+ ("!=", 5, 5, False),
+ ]
+
+ for operator, left, right, expected in test_cases:
+ assert (
+ OPERATOR_DICT[operator](left, right) == expected
+ ), f"Failed for operator {operator} with values {left} and {right}"
+
+
+def test_task_enum():
+ assert Task.CLASSIFICATION.value == "classification"
+ assert Task.DETECTION.value == "detection"
+
+
+def test_data_type_enum():
+ assert DataType.DATASET.value == "dataset"
+ assert DataType.SLICE.value == "slice"
+ assert DataType.MODEL.value == "model"
+ assert DataType.CHECK.value == "check"
+ assert DataType.CHECKSUITE.value == "checksuite"
+ assert DataType.CHECK_REPORT.value == "check_report"
+ assert DataType.CHECKSUITE_REPORT.value == "checksuite_report"
+ assert DataType.PREDICTIONS.value == "predictions"
+ assert DataType.GROUNDTRUTHS.value == "groundtruths"
+
+
+def test_all_operators_present():
+ expected_operators = {"<", ">", ">=", "<=", "==", "=", "!="}
+ assert (
+ set(OPERATOR_DICT.keys()) == expected_operators
+ ), "Not all expected operators are present in the dictionary"
diff --git a/tests/test_datapoint.py b/tests/test_datapoint.py
new file mode 100644
index 0000000..1f7d142
--- /dev/null
+++ b/tests/test_datapoint.py
@@ -0,0 +1,20 @@
+from moonwatcher.datapoint import Datapoint
+
+
+def test_datapoint_initialization():
+ datapoint = Datapoint(10, "http://fakeurl.com/image_10", {"brightness": 0.8})
+ assert datapoint.number == 10
+ assert datapoint.locator == "http://fakeurl.com/image_10"
+ assert datapoint.metadata == {"brightness": 0.8}
+
+
+def test_add_metadata():
+ datapoint = Datapoint(10)
+ datapoint.add_metadata("brightness", 0.8)
+ assert datapoint.metadata == {"brightness": 0.8}
+
+
+def test_get_metadata():
+ datapoint = Datapoint(10, metadata={"brightness": 0.8})
+ assert datapoint.get_metadata("brightness") == 0.8
+ assert datapoint.get_metadata("nonexistent_key") is None
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
new file mode 100644
index 0000000..434628c
--- /dev/null
+++ b/tests/test_dataset.py
@@ -0,0 +1,139 @@
+import uuid
+
+import torch
+import pytest
+import numpy as np
+from torch.utils.data import Dataset
+
+from moonwatcher.metric import get_original_indices
+from moonwatcher.dataset.dataset import MoonwatcherDataset, Slice
+
+
+class MockDataset(Dataset):
+ def __init__(self, transform=None):
+ self.transform = transform
+
+ def __len__(self):
+ return 100
+
+ def __getitem__(self, idx):
+ dummy_image = np.random.randint(0, 256, (10, 10, 3), dtype=np.uint8)
+ return dummy_image, idx % 10
+
+
+@pytest.fixture
+def simple_dataset():
+ return MockDataset()
+
+
+def output_transform(x):
+ return x[0], torch.tensor(x[1])
+
+
+@pytest.fixture
+def basic_moonwatcher_dataset(simple_dataset):
+ unique_name = f"test_dataset_{uuid.uuid4()}"
+ return MoonwatcherDataset(
+ dataset=simple_dataset,
+ name=unique_name,
+ task="classification",
+ output_transform=output_transform,
+ label_to_name={i: f"class_{i}" for i in range(10)},
+ locators=["http://fakeurl.com/image_{}".format(i) for i in range(100)],
+ )
+
+
+def test_initialization(basic_moonwatcher_dataset):
+ assert basic_moonwatcher_dataset.name.startswith("test_dataset_")
+ assert len(basic_moonwatcher_dataset) == 100
+ assert isinstance(basic_moonwatcher_dataset, MoonwatcherDataset)
+
+
+def test_data_retrieval(basic_moonwatcher_dataset):
+ data_point = basic_moonwatcher_dataset.get_datapoint(10)
+ assert data_point is not None
+ assert data_point.number == 10
+
+
+def test_metadata_addition(basic_moonwatcher_dataset):
+ basic_moonwatcher_dataset.add_predefined_metadata(
+ predefined_metadata_key="brightness",
+ )
+ assert "brightness" in basic_moonwatcher_dataset.datapoints[0].metadata
+
+
+def test_slicing_by_threshold(basic_moonwatcher_dataset):
+ basic_moonwatcher_dataset.add_predefined_metadata(
+ predefined_metadata_key="brightness",
+ )
+ slice_dataset = basic_moonwatcher_dataset.slice_by_threshold("brightness", ">", 190)
+ for idx in slice_dataset.indices:
+ brightness = basic_moonwatcher_dataset.datapoints[idx].metadata["brightness"]
+ assert brightness > 190, f"Expected brightness > 0.1 but got {brightness}"
+
+
+def test_slicing_by_percentile(basic_moonwatcher_dataset):
+ basic_moonwatcher_dataset.add_predefined_metadata(
+ predefined_metadata_key="contrast",
+ )
+ slice_dataset = basic_moonwatcher_dataset.slice_by_percentile("contrast", ">", 90)
+ assert len(slice_dataset) < len(basic_moonwatcher_dataset)
+
+
+def test_slicing_by_class(basic_moonwatcher_dataset):
+ basic_moonwatcher_dataset.add_metadata_custom(
+ metadata_key="class_type",
+ metadata_func=lambda x: 0 if np.random.rand() < 0.5 else 1,
+ )
+ slices = basic_moonwatcher_dataset.slice_by_class("class_type")
+ assert len(slices) == 2
+
+
+def test_get_original_indices(basic_moonwatcher_dataset):
+ slice_indices = [i for i in range(10, 20)]
+ mock_slice = Slice(
+ basic_moonwatcher_dataset, "Slice1", slice_indices, basic_moonwatcher_dataset
+ )
+
+ indices = get_original_indices(mock_slice)
+ assert indices == list(range(10, 20))
+
+ indices = get_original_indices(basic_moonwatcher_dataset)
+ assert indices == list(range(100))
+
+ with pytest.raises(TypeError):
+ get_original_indices("invalid_input")
+
+
+def test_get_original_indices_nested(basic_moonwatcher_dataset):
+ # First-level slice
+ slice_indices = [i for i in range(10, 20)]
+ mock_slice = Slice(
+ basic_moonwatcher_dataset,
+ f"Slice1_{uuid.uuid4()}",
+ slice_indices,
+ basic_moonwatcher_dataset,
+ )
+
+ indices = get_original_indices(mock_slice)
+ assert indices == list(range(10, 20))
+
+ # Second-level slice (slice of a slice)
+ nested_slice_indices = [i for i in range(5, 10)]
+ nested_mock_slice = Slice(
+ mock_slice,
+ f"NestedSlice_{uuid.uuid4()}",
+ nested_slice_indices,
+ basic_moonwatcher_dataset,
+ )
+
+ nested_indices = get_original_indices(nested_mock_slice)
+ assert nested_indices == list(range(15, 20))
+
+ # Full dataset indices
+ indices = get_original_indices(basic_moonwatcher_dataset)
+ assert indices == list(range(100))
+
+ # Invalid input
+ with pytest.raises(TypeError):
+ get_original_indices("invalid_input")
diff --git a/tests/test_helpers.py b/tests/test_helpers.py
new file mode 100644
index 0000000..71ff095
--- /dev/null
+++ b/tests/test_helpers.py
@@ -0,0 +1,32 @@
+import torch
+
+from moonwatcher.utils.helpers import get_current_timestamp, convert_to_list
+
+
+def test_get_current_timestamp():
+ timestamp = get_current_timestamp()
+
+ assert isinstance(timestamp, str)
+ assert len(timestamp) == 32
+ assert timestamp[4] == "-"
+ assert timestamp[7] == "-"
+ assert timestamp[10] == "T"
+ assert timestamp[13] == ":"
+ assert timestamp[16] == ":"
+ assert timestamp[19] == "."
+ assert timestamp[26] == "+"
+ assert timestamp[-6:] == "+02:00"
+
+
+def test_convert_to_list():
+ items = torch.tensor([7, 8, 9])
+ assert convert_to_list(items) == items.tolist()
+
+ items = torch.tensor([7.3, 8.0, 9])
+ assert convert_to_list(items) == items.tolist()
+
+ items = torch.tensor(7)
+ assert convert_to_list(items) == [items]
+
+ items = torch.tensor(2.3)
+ assert convert_to_list(items) == [items]
diff --git a/tests/test_metadata.py b/tests/test_metadata.py
new file mode 100644
index 0000000..2bc889f
--- /dev/null
+++ b/tests/test_metadata.py
@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+
+from moonwatcher.dataset.metadata import (
+ compute_brightness,
+ compute_contrast,
+ compute_saturation,
+ compute_resolution,
+ _ATTRIBUTE_FUNCTIONS,
+)
+
+
+def test_compute_brightness():
+ white_image = np.ones((100, 100, 3), dtype=np.uint8) * 255
+ assert (
+ compute_brightness(white_image) == 255
+ ), "Brightness of a white image should be maximum"
+
+
+def test_compute_contrast():
+ gray_image = np.ones((100, 100, 3), dtype=np.uint8) * 128
+ assert (
+ compute_contrast(gray_image) == 0
+ ), "Contrast of a uniform image should be zero"
+
+
+def test_compute_saturation():
+ bgr_image = np.zeros((100, 100, 3), dtype=np.uint8)
+ bgr_image[:, :, 2] = 255
+ hsv_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2HSV)
+
+ computed_saturation = compute_saturation(hsv_image)
+ assert (
+ computed_saturation == 255
+ ), f"Saturation of a fully red image should be maximum, got {computed_saturation}"
+
+
+def test_compute_resolution():
+ test_image = np.zeros((200, 300, 3), dtype=np.uint8)
+ assert (
+ compute_resolution(test_image) == 60000
+ ), "Resolution should be width multiplied by height"
+
+
+def test_attribute_functions():
+ image = np.ones((100, 100, 3), dtype=np.uint8) * 128
+ assert _ATTRIBUTE_FUNCTIONS["brightness"](image) == compute_brightness(image)
+ assert _ATTRIBUTE_FUNCTIONS["contrast"](image) == compute_contrast(image)
+ assert _ATTRIBUTE_FUNCTIONS["saturation"](image) == compute_saturation(image)
+ assert _ATTRIBUTE_FUNCTIONS["resolution"](image) == compute_resolution(image)
diff --git a/tests/test_metric_classification.py b/tests/test_metric_classification.py
new file mode 100644
index 0000000..cc90427
--- /dev/null
+++ b/tests/test_metric_classification.py
@@ -0,0 +1,151 @@
+import torch
+import pytest
+import numpy as np
+
+from moonwatcher.utils.data import Task
+from moonwatcher.inference.inference import inference
+from moonwatcher.metric import calculate_metric_internal
+from moonwatcher.dataset.dataset import MoonwatcherDataset
+from moonwatcher.utils.data_storage import (
+ load_groundtruths,
+ load_predictions,
+ do_predictions_exist,
+)
+
+
+class MockModel:
+ def __init__(self, name, task, device):
+ self.name = name
+ self.task = task
+ self.device = device
+
+
+class MockDataset(MoonwatcherDataset):
+ def __init__(self, name, labels, label_to_name):
+ self.name = name
+ self.labels = labels
+ self.label_to_name = label_to_name
+ self.dataset = labels
+
+
+class MockLabel:
+ def __init__(self, label):
+ self.labels = torch.tensor([label])
+
+
+def mock_do_predictions_exist(dataset_name, model_name):
+ return True
+
+
+def mock_load_groundtruths(dataset_name):
+ return [MockLabel(0), MockLabel(1), MockLabel(0), MockLabel(1)]
+
+
+def mock_load_predictions(dataset_name, model_name):
+ return [MockLabel(0), MockLabel(1), MockLabel(1), MockLabel(1)]
+
+
+def mock_inference(model, dataset, device):
+ pass
+
+
+@pytest.fixture(autouse=True)
+def patch_functions(monkeypatch):
+ monkeypatch.setattr(
+ "moonwatcher.utils.data_storage.do_predictions_exist", mock_do_predictions_exist
+ )
+ monkeypatch.setattr(
+ "moonwatcher.utils.data_storage.load_groundtruths", mock_load_groundtruths
+ )
+ monkeypatch.setattr(
+ "moonwatcher.utils.data_storage.load_predictions", mock_load_predictions
+ )
+ monkeypatch.setattr("moonwatcher.inference.inference", mock_inference)
+
+
+def load_data_for_testing():
+ model = MockModel(name="mock_model", task=Task.CLASSIFICATION.value, device="cpu")
+ dataset = MockDataset(
+ name="mock_dataset",
+ labels=[0, 1, 0, 1],
+ label_to_name={0: "class0", 1: "class1"},
+ )
+ relevant_ids = list(range(len(dataset.labels)))
+ groundtruths_loaded = mock_load_groundtruths(dataset.name)
+ predictions_loaded = mock_load_predictions(dataset.name, model.name)
+ return model, relevant_ids, dataset, groundtruths_loaded, predictions_loaded
+
+
+def test_calculate_accuracy():
+ (
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ ) = load_data_for_testing()
+ result = calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ "Accuracy",
+ )
+ assert result == 0.75, f"Expected Accuracy to be 0.75 but got {result}"
+
+
+def test_calculate_precision():
+ (
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ ) = load_data_for_testing()
+ result = calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ "Precision",
+ )
+ assert result == 0.66667, f"Expected Precision to be 0.66667 but got {result}"
+
+
+def test_calculate_recall():
+ (
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ ) = load_data_for_testing()
+ result = calculate_metric_internal(
+ model, relevant_ids, dataset, groundtruths_loaded, predictions_loaded, "Recall"
+ )
+ assert result == 1.0, f"Expected Recall to be 1.0 but got {result}"
+
+
+def test_calculate_f1():
+ (
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ ) = load_data_for_testing()
+ result = calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ "F1_Score",
+ )
+ assert result == 0.8, f"Expected F1_Score to be 0.8 but got {result}"
+
+
+if __name__ == "__main__":
+ pytest.main()
diff --git a/tests/test_metric_detection.py b/tests/test_metric_detection.py
new file mode 100644
index 0000000..0bf322f
--- /dev/null
+++ b/tests/test_metric_detection.py
@@ -0,0 +1,130 @@
+import torch
+import pytest
+import numpy as np
+
+from moonwatcher.utils.data import Task
+from moonwatcher.inference.inference import inference
+from moonwatcher.metric import calculate_metric_internal
+from moonwatcher.dataset.dataset import MoonwatcherDataset
+from moonwatcher.utils.data_storage import (
+ load_groundtruths,
+ load_predictions,
+ do_predictions_exist,
+)
+
+
+class MockModel:
+ def __init__(self, name, task, device):
+ self.name = name
+ self.task = task
+ self.device = device
+
+
+class MockDataset(MoonwatcherDataset):
+ def __init__(self, name):
+ self.name = name
+ self.dataset = []
+
+
+class MockDetection:
+ def __init__(self, boxes, labels, scores=None):
+ self.boxes = torch.tensor(boxes)
+ self.labels = torch.tensor(labels)
+ self.scores = torch.tensor(scores) if scores else None
+
+ def to_dict(self):
+ result = {"boxes": self.boxes, "labels": self.labels}
+ if self.scores is not None:
+ result["scores"] = self.scores
+ return result
+
+
+def mock_do_predictions_exist(dataset_name, model_name):
+ return True
+
+
+def mock_load_groundtruths(dataset_name):
+ return [
+ MockDetection([[50, 50, 150, 150]], [1]),
+ MockDetection([[30, 30, 120, 120]], [0]),
+ MockDetection([[10, 10, 100, 100]], [1]),
+ MockDetection([[40, 40, 140, 140]], [1]),
+ ]
+
+
+def mock_load_predictions(dataset_name, model_name):
+ return [
+ MockDetection([[50, 50, 150, 150]], [1], [0.9]),
+ MockDetection([[35, 35, 115, 115]], [0], [0.8]),
+ MockDetection([[15, 15, 105, 105]], [1], [0.75]),
+ MockDetection([[45, 45, 145, 145]], [1], [0.85]),
+ ]
+
+
+def mock_inference(model, dataset, device):
+ pass
+
+
+@pytest.fixture(autouse=True)
+def patch_functions(monkeypatch):
+ monkeypatch.setattr(
+ "moonwatcher.utils.data_storage.do_predictions_exist", mock_do_predictions_exist
+ )
+ monkeypatch.setattr(
+ "moonwatcher.utils.data_storage.load_groundtruths", mock_load_groundtruths
+ )
+ monkeypatch.setattr(
+ "moonwatcher.utils.data_storage.load_predictions", mock_load_predictions
+ )
+ monkeypatch.setattr("moonwatcher.inference.inference", mock_inference)
+
+
+def load_data_for_testing():
+ model = MockModel(name="mock_model", task=Task.DETECTION.value, device="cpu")
+ dataset = MockDataset(name="mock_dataset")
+ relevant_ids = list(range(4))
+ groundtruths_loaded = mock_load_groundtruths(dataset.name)
+ predictions_loaded = mock_load_predictions(dataset.name, model.name)
+ return model, relevant_ids, dataset, groundtruths_loaded, predictions_loaded
+
+
+def test_calculate_iou():
+ (
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ ) = load_data_for_testing()
+ result = calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ "IntersectionOverUnion",
+ )
+ assert result > 0.75, f"Expected IoU to be greater than 0.75 but got {result}"
+
+
+def test_calculate_map():
+ (
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ ) = load_data_for_testing()
+ result = calculate_metric_internal(
+ model,
+ relevant_ids,
+ dataset,
+ groundtruths_loaded,
+ predictions_loaded,
+ "mAP",
+ )
+ assert result > 0.7, f"Expected mAP to be greater than 0.7 but got {result}"
+
+
+if __name__ == "__main__":
+ pytest.main()