chore: import upstream snapshot with attribution

tests/training/test_metrics_csv.py

@@ -0,0 +1,159 @@
+# ------------------------------------------------------------------------
+# RF-DETR
+# Copyright (c) 2025 Roboflow. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+"""Integration tests: metrics.csv contains all columns used by plot_metrics().
+
+Runs a minimal PTL training loop (1 epoch, 2 batches each) using mocked model internals so no real dataset or GPU is
+required.  After training, reads the CSVLogger output and asserts that every metric column that ``plot_metrics()`` needs
+is present and has at least one non-NaN value.
+
+Also verifies that ``train/loss`` is logged at the same scale as ``val/loss`` (i.e. NOT divided by ``grad_accum_steps``
+before logging).
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pandas as pd
+import torch
+
+from rfdetr.config import RFDETRBaseConfig, TrainConfig
+from rfdetr.training import build_trainer
+from rfdetr.training.module_data import RFDETRDataModule
+from rfdetr.training.module_model import RFDETRModelModule
+
+from .helpers import _fake_postprocess, _FakeCriterion, _FakeDataset, _make_param_dicts, _TinyModel
+
+# ---------------------------------------------------------------------------
+# Helpers local to this module
+# ---------------------------------------------------------------------------
+
+
+def _fit_and_read_csv(mc: RFDETRBaseConfig, tc: TrainConfig, criterion=None) -> pd.DataFrame:
+    """Run 1 epoch (2 train + 2 val batches) and return the resulting metrics.csv."""
+    fake_criterion = criterion or _FakeCriterion()
+    with (
+        patch("rfdetr.training.module_model.build_model_from_config", return_value=_TinyModel()),
+        patch(
+            "rfdetr.training.module_model.build_criterion_from_config",
+            return_value=(fake_criterion, MagicMock(side_effect=_fake_postprocess)),
+        ),
+        patch("rfdetr.training.module_data.build_dataset", return_value=_FakeDataset(length=20)),
+        patch(
+            "rfdetr.training.module_model.get_param_dict",
+            side_effect=lambda args, model: _make_param_dicts(model),
+        ),
+    ):
+        module = RFDETRModelModule(mc, tc)
+        datamodule = RFDETRDataModule(mc, tc)
+        trainer = build_trainer(
+            tc,
+            mc,
+            accelerator="cpu",
+            max_epochs=1,
+            limit_train_batches=2,
+            limit_val_batches=2,
+            log_every_n_steps=1,
+        )
+        trainer.fit(module, datamodule=datamodule)
+
+    csv_path = Path(tc.output_dir) / "metrics.csv"
+    assert csv_path.exists(), "CSVLogger must write metrics.csv to output_dir"
+    return pd.read_csv(csv_path)
+
+
+# ---------------------------------------------------------------------------
+# Expected columns (must exist and have ≥1 non-NaN row after one epoch)
+# ---------------------------------------------------------------------------
+
+_REQUIRED_DETECTION = frozenset(
+    {
+        "train/loss",
+        "train/lr",
+        "val/loss",
+        "val/mAP_50",
+        "val/mAP_50_95",
+        "val/mAR",
+    }
+)
+
+_REQUIRED_DETECTION_EMA = _REQUIRED_DETECTION | frozenset(
+    {
+        "val/ema_mAP_50",
+        "val/ema_mAP_50_95",
+        "val/ema_mAR",
+    }
+)
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+class TestDetectionMetricsCSV:
+    """metrics.csv contains all columns that plot_metrics() needs for detection."""
+
+    def test_base_metrics_present_without_ema(self, base_model_config, base_train_config):
+        """Without EMA all core val/* columns must appear in metrics.csv with non-NaN data."""
+        mc = base_model_config()
+        tc = base_train_config(use_ema=False, run_test=False)
+        df = _fit_and_read_csv(mc, tc)
+
+        missing = _REQUIRED_DETECTION - set(df.columns)
+        assert not missing, f"Missing columns in metrics.csv: {sorted(missing)}"
+
+        all_nan = {c for c in _REQUIRED_DETECTION if df[c].isna().all()}
+        assert not all_nan, f"Columns with all-NaN values: {sorted(all_nan)}"
+
+    def test_ema_metrics_present_with_ema_enabled(self, base_model_config, base_train_config):
+        """With use_ema=True the ema_* aliases must also appear in metrics.csv."""
+        mc = base_model_config()
+        tc = base_train_config(use_ema=True, run_test=False)
+        df = _fit_and_read_csv(mc, tc)
+
+        missing = _REQUIRED_DETECTION_EMA - set(df.columns)
+        assert not missing, f"Missing EMA columns in metrics.csv: {sorted(missing)}"
+
+        all_nan = {c for c in _REQUIRED_DETECTION_EMA if df[c].isna().all()}
+        assert not all_nan, f"EMA columns with all-NaN values: {sorted(all_nan)}"
+
+    def test_train_loss_is_unscaled(self, base_model_config, base_train_config):
+        """Train/loss must be logged at the raw criterion scale, not divided by grad_accum_steps.
+
+        With grad_accum_steps=4 the old code divided the logged value by 4, making train/loss ~4× smaller than val/loss.
+        After the fix the logged value equals the raw weighted criterion output so both losses are on the same scale.
+        """
+        fixed_loss_value = 5.0
+        grad_accum_steps = 4
+
+        class _FixedCriterion:
+            weight_dict = {"loss_ce": 1.0}
+
+            def num_boxes_for_targets(self, outputs, targets):
+                dummy = outputs.get("dummy", torch.zeros(1))
+                return torch.ones((), dtype=dummy.dtype, device=dummy.device)
+
+            def __call__(self, outputs, targets, num_boxes=None):
+                # Loss is always fixed_loss_value, connected to model params for gradient.
+                dummy = outputs.get("dummy", torch.zeros(1))
+                denominator = self.num_boxes_for_targets(outputs, targets) if num_boxes is None else num_boxes
+                return {"loss_ce": (dummy.mean() * 0 + fixed_loss_value) / denominator}
+
+        mc = base_model_config()
+        tc = base_train_config(use_ema=False, run_test=False, grad_accum_steps=grad_accum_steps)
+        df = _fit_and_read_csv(mc, tc, criterion=_FixedCriterion())
+
+        logged = df["train/loss"].dropna().mean()
+        expected_unscaled = fixed_loss_value
+        expected_if_divided = fixed_loss_value / grad_accum_steps
+
+        assert abs(logged - expected_unscaled) < abs(logged - expected_if_divided), (
+            f"train/loss={logged:.4f} is closer to the grad-accum-divided value "
+            f"({expected_if_divided:.4f}) than the raw criterion output "
+            f"({expected_unscaled:.4f}). The division must have been removed."
+        )