Configured codecov

.github/workflows/ci.yaml

@@ -128,48 +128,20 @@ jobs:
         token_format: access_token
 
     - name: Run pytest
-      uses: pavelzw/pytest-action@v2
       env:
         LUXONISML_BUCKET: luxonis-test-bucket
-        PYTORCH_MPS_HIGH_WATERMARK_RATIO: 0.0
-      with:
-        emoji: false
-        custom-arguments: --junit-xml pytest.xml --cov luxonis_train --cov-report xml
+      run: pytest --cov --junitxml=junit.xml -o junit_family=legacy
 
-    - name: Create Test Report
-      uses: EnricoMi/publish-unit-test-result-action@v2
+    - name: Upload test results to Codecov
       if: matrix.os == 'ubuntu-latest'
+      uses: codecov/test-results-action@v1
       with:
-        files: pytest.xml
+        token: ${{ secrets.CODECOV_TOKEN }}
+        fail_ci_if_error: false
 
-    - name: Generate coverage badge
-      uses: tj-actions/coverage-badge-py@v2
+    - name: Upload coverage results to Codecov
       if: matrix.os == 'ubuntu-latest'
+      uses: codecov/codecov-action@v4
       with:
-        output: media/coverage_badge.svg
-
-    - name: Generate coverage report
-      uses: orgoro/[email protected]
-      if: matrix.os == 'ubuntu-latest'
-      with:
-        coverageFile: coverage.xml
-        token: ${{ secrets.GITHUB_TOKEN }}
-        thresholdAll: 0.9
-        thresholdNew: 0.8
-
-    - name: Commit coverage badge
-      if: matrix.os == 'ubuntu-latest'
-      run: |
-        git config --global user.name 'GitHub Actions'
-        git config --global user.email '[email protected]'
-        git diff --quiet media/coverage_badge.svg || {
-          git add media/coverage_badge.svg
-          git commit -m "[Automated] Updated coverage badge"
-        }
-
-    - name: Push changes
-      uses: ad-m/github-push-action@master
-      if: matrix.os == 'ubuntu-latest'
-      with:
-        branch: ${{ github.head_ref }}
-
+        token: ${{ secrets.CODECOV_TOKEN }}
+        fail_ci_if_error: false

README.md

@@ -9,7 +9,7 @@
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 ![CI](https://github.com/luxonis/luxonis-train/actions/workflows/ci.yaml/badge.svg)
 ![Docs](https://github.com/luxonis/luxonis-train/actions/workflows/docs.yaml/badge.svg)
-[![Coverage](media/coverage_badge.svg)](https://github.com/luxonis/luxonis-train/actions)
+[![codecov](https://codecov.io/gh/luxonis/luxonis-train/graph/badge.svg?token=647MTHBYD5)](https://codecov.io/gh/luxonis/luxonis-train)
 
 Luxonis training framework (`luxonis-train`) is intended for training deep learning models that can run fast on OAK products.
 

tests/integration/conftest.py

@@ -248,7 +248,7 @@ def CIFAR10_subset_generator():
 
 @pytest.fixture
 def config(train_overfit: bool) -> dict[str, Any]:
-    if train_overfit:
+    if train_overfit:  # pragma: no cover
         epochs = 100
     else:
         epochs = 1

tests/integration/multi_input_modules.py

@@ -20,7 +20,7 @@ def input_shapes(self):
             "pointcloud": torch.Size([1000, 3]),
         }
 
-    def __getitem__(self, _):
+    def __getitem__(self, _):  # pragma: no cover
         # Fake data
         left = torch.rand(3, 224, 224, dtype=torch.float32)
         right = torch.rand(3, 224, 224, dtype=torch.float32)

tests/integration/test_detection.py

@@ -77,8 +77,8 @@ def train_and_test(
 ):
     model = LuxonisModel(config, opts)
     model.train()
-    results = model.test(view="val")
-    if train_overfit:
+    if train_overfit:  # pragma: no cover
+        results = model.test(view="val")
         for name, value in results.items():
             if "/map_50" in name or "/kpt_map_medium" in name:
                 assert value > 0.8, f"{name} = {value} (expected > 0.8)"

tests/integration/test_segmentation.py

@@ -116,8 +116,8 @@ def train_and_test(
 ):
     model = LuxonisModel(config, opts)
     model.train()
-    results = model.test(view="val")
-    if train_overfit:
+    if train_overfit:  # pragma: no cover
+        results = model.test(view="val")
         for name, value in results.items():
             if "metric" in name:
                 assert value > 0.8, f"{name} = {value} (expected > 0.8)"

tests/unittests/test_base_node.py

@@ -76,7 +76,7 @@ def test_invalid(packet: Packet[Tensor]):
         node.wrap({"inp": torch.rand(3, 224, 224)})
 
 
-def tets_in_sizes():
+def test_in_sizes():
     node = DummyNode(
         input_shapes=[{"features": [Size((3, 224, 224)) for _ in range(3)]}]
     )

tests/unittests/test_utils/test_boxutils.py

@@ -1,3 +1,5 @@
+from typing import Literal
+
 import pytest
 import torch
 
@@ -13,7 +15,10 @@
 
 
 def generate_random_bboxes(
-    n_bboxes: int, max_width: int, max_height: int, format: str = "xyxy"
+    n_bboxes: int,
+    max_width: int,
+    max_height: int,
+    format: Literal["xyxy", "xywh", "cxcywh"],
 ):
     x1y1 = torch.rand(n_bboxes, 2) * torch.tensor(
         [max_width - 1, max_height - 1]
@@ -33,10 +38,6 @@ def generate_random_bboxes(
     elif format == "cxcywh":
         cxcy = x1y1 + wh / 2
         bboxes = torch.cat((cxcy, wh), dim=1)
-    else:
-        raise ValueError(
-            "Unsupported format. Choose from 'xyxy', 'xywh', 'cxcywh'."
-        )
 
     return bboxes
 
@@ -62,27 +63,29 @@ def test_bbox2dist():
 
 
 @pytest.mark.parametrize("iou_type", ["none", "giou", "diou", "ciou", "siou"])
-def test_bbox_iou(iou_type: IoUType):
-    for format in ["xyxy", "cxcywh", "xywh"]:
-        bbox1 = generate_random_bboxes(5, 640, 640, format)
-        if iou_type == "siou":
-            bbox2 = generate_random_bboxes(5, 640, 640, format)
-        else:
-            bbox2 = generate_random_bboxes(8, 640, 640, format)
-
-        iou = bbox_iou(
-            bbox1,
-            bbox2,
-            bbox_format=format,  # type: ignore
-            iou_type=iou_type,
-        )
-
-        assert iou.shape == (bbox1.shape[0], bbox2.shape[0])
-        if iou_type == "none":
-            min = 0
-        else:
-            min = -1.5
-        assert iou.min() >= min and iou.max() <= 1
+@pytest.mark.parametrize("format", ["xyxy", "xywh", "cxcywh"])
+def test_bbox_iou(
+    iou_type: IoUType, format: Literal["xyxy", "xywh", "cxcywh"]
+):
+    bbox1 = generate_random_bboxes(5, 640, 640, format)
+    if iou_type == "siou":
+        bbox2 = generate_random_bboxes(5, 640, 640, format)
+    else:
+        bbox2 = generate_random_bboxes(8, 640, 640, format)
+
+    iou = bbox_iou(
+        bbox1,
+        bbox2,
+        bbox_format=format,  # type: ignore
+        iou_type=iou_type,
+    )
+
+    assert iou.shape == (bbox1.shape[0], bbox2.shape[0])
+    if iou_type == "none":
+        min = 0
+    else:
+        min = -1.5
+    assert iou.min() >= min and iou.max() <= 1
 
     if iou_type == "none":
         with pytest.raises(ValueError):