add test_wandb_init + test_wandb_log_frequency

tests/test_trainer.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
+from unittest.mock import patch
 
 import numpy as np
 import pytest
@@ -36,13 +37,13 @@
     stresses=stresses,
     magmoms=magmoms,
 )
+train_loader, val_loader, _test_loader = get_train_val_test_loader(
+    data, batch_size=16, train_ratio=0.9, val_ratio=0.05
+)
+chgnet = CHGNet.load()
 
 
 def test_trainer(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
-    chgnet = CHGNet.load()
-    train_loader, val_loader, _test_loader = get_train_val_test_loader(
-        data, batch_size=16, train_ratio=0.9, val_ratio=0.05
-    )
     extra_run_config = dict(some_other_hyperparam=42)
     trainer = Trainer(
         model=chgnet,
@@ -81,12 +82,8 @@ def test_trainer(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
 
 
 def test_trainer_composition_model(tmp_path: Path) -> None:
-    chgnet = CHGNet.load()
     for param in chgnet.composition_model.parameters():
         assert param.requires_grad is False
-    train_loader, val_loader, _test_loader = get_train_val_test_loader(
-        data, batch_size=16, train_ratio=0.9, val_ratio=0.05
-    )
     trainer = Trainer(
         model=chgnet,
         targets="efsm",
@@ -115,3 +112,76 @@ def test_trainer_composition_model(tmp_path: Path) -> None:
     expect[0][10] = 0
     expect[0][16] = 0
     assert torch.all(comparison == expect)
+
+
+@pytest.fixture()
+def mock_wandb():
+    with patch("chgnet.trainer.trainer.wandb") as mock:
+        yield mock
+
+
+def test_wandb_init(mock_wandb):
+    chgnet = CHGNet.load()
+    _trainer = Trainer(
+        model=chgnet,
+        wandb_path="test-project/test-run",
+        wandb_init_kwargs={"tags": ["test"]},
+    )
+    expected_config = {
+        "targets": "ef",
+        "energy_loss_ratio": 1,
+        "force_loss_ratio": 1,
+        "stress_loss_ratio": 0.1,
+        "mag_loss_ratio": 0.1,
+        "optimizer": "Adam",
+        "scheduler": "CosLR",
+        "criterion": "MSE",
+        "epochs": 50,
+        "starting_epoch": 0,
+        "learning_rate": 0.001,
+        "print_freq": 100,
+        "torch_seed": None,
+        "data_seed": None,
+        "use_device": None,
+        "check_cuda_mem": False,
+        "wandb_path": "test-project/test-run",
+        "wandb_init_kwargs": {"tags": ["test"]},
+        "extra_run_config": None,
+    }
+    mock_wandb.init.assert_called_once_with(
+        project="test-project", name="test-run", config=expected_config, tags=["test"]
+    )
+
+
+def test_wandb_log_frequency(mock_wandb):
+    trainer = Trainer(model=chgnet, wandb_path="test-project/test-run", epochs=1)
+
+    # Test epoch logging
+    trainer.train(train_loader, val_loader, wandb_log_freq="epoch", save_dir="")
+    assert (
+        mock_wandb.log.call_count == 2 * trainer.epochs
+    ), "Expected one train and one val log per epoch"
+
+    mock_wandb.log.reset_mock()
+
+    # Test batch logging
+    trainer.train(train_loader, val_loader, wandb_log_freq="batch", save_dir="")
+    expected_batch_calls = trainer.epochs * len(train_loader)
+    assert (
+        mock_wandb.log.call_count > expected_batch_calls
+    ), "Expected more calls for batch logging"
+
+    # Test log content (for both epoch and batch logging)
+    for call_args in mock_wandb.log.call_args_list:
+        logged_data = call_args[0][0]
+        assert isinstance(logged_data, dict), "Logged data should be a dictionary"
+        assert any(
+            key.endswith("_mae") for key in logged_data
+        ), "Logged data should contain MAE metrics"
+
+    mock_wandb.log.reset_mock()
+
+    # Test no logging when wandb_path is not provided
+    trainer_no_wandb = Trainer(model=chgnet, epochs=1)
+    trainer_no_wandb.train(train_loader, val_loader)
+    mock_wandb.log.assert_not_called()