Fix the PPLCNetV3 detection backbone channel count

luxonis_train/nodes/backbones/pplcnet_v3/pplcnet_v3.py

@@ -82,10 +82,7 @@ def __init__(
         self.blocks = nn.ModuleList(blocks)
 
         if self.use_detection_backbone:
-            blocks_out_channels = [
-                scale_up(blocks[i].out_channels, self.scale)
-                for i in range(1, 5)
-            ]
+            blocks_out_channels = [blocks[i].out_channels for i in range(1, 5)]
 
             detecion_out_channels = [
                 int(c * self.scale) for c in [16, 24, 56, 480]

tests/unittests/test_callbacks/test_ema.py

@@ -57,6 +57,11 @@ def test_ema_initialization(model: LightningModule, ema_callback: EMACallback):
     assert ema_callback.ema.use_dynamic_decay == ema_callback.use_dynamic_decay
 
 
+def test_ema_before_fit_start(ema_callback: EMACallback):
+    with pytest.raises(ValueError, match="not yet init"):
+        _ = ema_callback.ema
+
+
 def test_ema_update_on_batch_end(
     model: LightningModule, ema_callback: EMACallback
 ):

tests/unittests/test_pplcnet_v3.py

@@ -0,0 +1,31 @@
+import torch
+from torch import Size
+
+from luxonis_train.nodes.backbones import PPLCNetV3
+
+
+def test_recognition_backbone():
+    backbone = _build_backbone(use_detection_backbone=False)
+    out = backbone(torch.rand(2, 3, 48, 320))
+    assert len(out) == 5
+    assert out[-1].shape[-2:] == (1, 40)
+
+
+def test_detection_backbone():
+    backbone = _build_backbone(use_detection_backbone=True)
+    out = backbone(torch.rand(2, 3, 48, 320))
+    assert [f.shape[1] for f in out] == [15, 22, 53, 456]
+
+
+def _build_backbone(use_detection_backbone: bool) -> PPLCNetV3:
+    # `variant=` hides the injected parameters from pyright.
+    _, variants = PPLCNetV3.get_variants()
+    variant = variants["rec-light"]
+    return PPLCNetV3(
+        input_shapes=[{"features": [Size((3, 48, 320))]}],
+        scale=variant["scale"],
+        n_branches=variant["n_branches"],
+        layer_params=variant["layer_params"],
+        use_detection_backbone=use_detection_backbone,
+        max_text_len=40,
+    )