✅ [Pass] test for v7 structure!

tests/conftest.py

@@ -8,7 +8,7 @@ from hydra import compose, initialize
 project_root = Path(__file__).resolve().parent.parent
 sys.path.append(str(project_root))
 
-from yolo import Config, Vec2Box, create_model
+from yolo import Anc2Box, Config, Vec2Box, create_converter, create_model
 from yolo.model.yolo import YOLO
 from yolo.tools.data_loader import StreamDataLoader, YoloDataLoader
 from yolo.tools.dataset_preparation import prepare_dataset
@@ -42,6 +42,11 @@ def inference_cfg():
     return get_cfg(overrides=["task=inference"])
 
 
+@pytest.fixture(scope="session")
+def inference_v7_cfg():
+    return get_cfg(overrides=["task=inference", "model=v7"])
+
+
 @pytest.fixture(scope="session")
 def device():
     return torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -65,12 +70,26 @@ def model(train_cfg: Config, device) -> YOLO:
     return model.to(device)
 
 
+@pytest.fixture(scope="session")
+def model_v7(inference_v7_cfg: Config, device) -> YOLO:
+    model = create_model(inference_v7_cfg.model)
+    return model.to(device)
+
+
 @pytest.fixture(scope="session")
 def vec2box(train_cfg: Config, model: YOLO, device) -> Vec2Box:
-    vec2box = Vec2Box(model, train_cfg.model.anchor, train_cfg.image_size, device)
+    vec2box = create_converter(train_cfg.model.name, model, train_cfg.model.anchor, train_cfg.image_size, device)
     return vec2box
 
 
+@pytest.fixture(scope="session")
+def anc2box(inference_v7_cfg: Config, model: YOLO, device) -> Anc2Box:
+    anc2box = create_converter(
+        inference_v7_cfg.model.name, model, inference_v7_cfg.model.anchor, inference_v7_cfg.image_size, device
+    )
+    return anc2box
+
+
 @pytest.fixture(scope="session")
 def train_dataloader(train_cfg: Config):
     prepare_dataset(train_cfg.dataset, task="train")
@@ -88,6 +107,11 @@ def file_stream_data_loader(inference_cfg: Config):
     return StreamDataLoader(inference_cfg.task.data)
 
 
+@pytest.fixture(scope="session")
+def file_stream_data_loader_v7(inference_v7_cfg: Config):
+    return StreamDataLoader(inference_v7_cfg.task.data)
+
+
 @pytest.fixture(scope="session")
 def directory_stream_data_loader(inference_cfg: Config):
     inference_cfg.task.data.source = "tests/data/images/train"

tests/test_model/test_yolo.py

@@ -36,6 +36,16 @@ def test_build_model_v9m():
         assert len(model.model) == 39
 
 
+def test_build_model_v7():
+    with initialize(config_path=config_path, version_base=None):
+        cfg: Config = compose(config_name=config_name, overrides=[f"model=v7"])
+
+        OmegaConf.set_struct(cfg.model, False)
+        cfg.weight = None
+        model = YOLO(cfg.model)
+        assert len(model.model) == 106
+
+
 @pytest.fixture
 def cfg() -> Config:
     with initialize(config_path="../../yolo/config", version_base=None):

tests/test_tools/test_solver.py

@@ -11,7 +11,7 @@ from yolo.config.config import Config
 from yolo.model.yolo import YOLO
 from yolo.tools.data_loader import StreamDataLoader, YoloDataLoader
 from yolo.tools.solver import ModelTester, ModelTrainer, ModelValidator
-from yolo.utils.bounding_box_utils import Vec2Box
+from yolo.utils.bounding_box_utils import Anc2Box, Vec2Box
 
 
 @pytest.fixture
@@ -41,6 +41,12 @@ def model_tester(inference_cfg: Config, model: YOLO, vec2box: Vec2Box, validatio
     return tester
 
 
+@pytest.fixture
+def modelv7_tester(inference_v7_cfg: Config, model_v7: YOLO, anc2box: Anc2Box, validation_progress_logger, device):
+    tester = ModelTester(inference_v7_cfg, model_v7, anc2box, validation_progress_logger, device)
+    return tester
+
+
 def test_model_tester_initialization(model_tester: ModelTester):
     assert isinstance(model_tester.model, YOLO)
     assert hasattr(model_tester, "solve")
@@ -50,6 +56,10 @@ def test_model_tester_solve_single_image(model_tester: ModelTester, file_stream_
     model_tester.solve(file_stream_data_loader)
 
 
+def test_modelv7_tester_solve_single_image(modelv7_tester: ModelTester, file_stream_data_loader_v7: StreamDataLoader):
+    modelv7_tester.solve(file_stream_data_loader_v7)
+
+
 @pytest.fixture
 def model_trainer(train_cfg: Config, model: YOLO, vec2box: Vec2Box, train_progress_logger, device):
     train_cfg.task.epoch = 2

tests/test_utils/test_bounding_box_utils.py

@@ -9,7 +9,9 @@ from torch import allclose, float32, isclose, tensor
 project_root = Path(__file__).resolve().parent.parent.parent
 sys.path.append(str(project_root))
 from yolo import Config, NMSConfig, create_model
+from yolo.config.config import AnchorConfig
 from yolo.utils.bounding_box_utils import (
+    Anc2Box,
     Vec2Box,
     bbox_nms,
     calculate_iou,
@@ -125,6 +127,24 @@ def test_vec2box_autoanchor():
     assert vec2box.scaler.shape == tuple([4200])
 
 
+def test_anc2box_autoanchor(inference_v7_cfg: Config):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = create_model(inference_v7_cfg.model, weight_path=None).to(device)
+    anchor_cfg: AnchorConfig = inference_v7_cfg.model.anchor.copy()
+    del anchor_cfg.strides
+    anc2box = Anc2Box(model, anchor_cfg, inference_v7_cfg.image_size, device)
+    assert anc2box.strides == [8, 16, 32]
+
+    anc2box.update((320, 640))
+    anchor_grids_shape = [anchor_grid.shape for anchor_grid in anc2box.anchor_grids]
+    assert anchor_grids_shape == [
+        torch.Size([1, 1, 80, 80, 2]),
+        torch.Size([1, 1, 40, 40, 2]),
+        torch.Size([1, 1, 20, 20, 2]),
+    ]
+    assert anc2box.anchor_scale.shape == torch.Size([3, 1, 3, 1, 1, 2])
+
+
 def test_bbox_nms():
     cls_dist = tensor(
         [[[0.1, 0.7, 0.2], [0.6, 0.3, 0.1]], [[0.4, 0.4, 0.2], [0.5, 0.4, 0.1]]]  # Example class distribution