mirror of
https://github.com/macaodha/batdetect2.git
synced 2026-04-04 15:20:19 +02:00
226 lines
7.2 KiB
Python
226 lines
7.2 KiB
Python
from pathlib import Path
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import lightning as L
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import torch
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from deepdiff import DeepDiff
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from soundevent import data
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from torch.optim import Adam
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from batdetect2.api_v2 import BatDetect2API
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from batdetect2.config import BatDetect2Config
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from batdetect2.models import ModelConfig
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from batdetect2.train import (
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TrainingConfig,
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TrainingModule,
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load_model_from_checkpoint,
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run_train,
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)
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from batdetect2.train.optimizers import AdamOptimizerConfig
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from batdetect2.train.schedulers import CosineAnnealingSchedulerConfig
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from batdetect2.train.train import build_training_module
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from batdetect2.typing.preprocess import AudioLoader
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def build_default_module(config: BatDetect2Config | None = None):
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config = config or BatDetect2Config()
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return build_training_module(
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model_config=config.model,
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train_config=config.train,
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)
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def test_can_initialize_default_module():
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module = build_default_module()
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assert isinstance(module, L.LightningModule)
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def test_can_save_checkpoint(
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tmp_path: Path,
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clip: data.Clip,
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sample_audio_loader: AudioLoader,
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):
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module = build_default_module()
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trainer = L.Trainer()
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path = tmp_path / "example.ckpt"
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trainer.strategy.connect(module)
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trainer.save_checkpoint(path)
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recovered = TrainingModule.load_from_checkpoint(path)
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wav = torch.tensor(sample_audio_loader.load_clip(clip)).unsqueeze(0)
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spec1 = module.model.preprocessor(wav)
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spec2 = recovered.model.preprocessor(wav)
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torch.testing.assert_close(spec1, spec2, rtol=0, atol=0)
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output1 = module.model(wav.unsqueeze(0))
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output2 = recovered.model(wav.unsqueeze(0))
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torch.testing.assert_close(output1, output2, rtol=0, atol=0)
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def test_load_model_from_checkpoint_returns_model_and_config(
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tmp_path: Path,
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):
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input_model_config = ModelConfig(samplerate=192_000)
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expected_model_config = ModelConfig.model_validate(
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input_model_config.model_dump(mode="json")
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)
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train_config = TrainingConfig()
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module = build_training_module(
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model_config=input_model_config,
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train_config=train_config,
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)
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trainer = L.Trainer()
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path = tmp_path / "example.ckpt"
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trainer.strategy.connect(module)
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trainer.save_checkpoint(path)
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model, loaded_model_config = load_model_from_checkpoint(path)
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assert model is not None
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assert loaded_model_config.model_dump(
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mode="json"
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) == expected_model_config.model_dump(mode="json")
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recovered = TrainingModule.load_from_checkpoint(path)
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assert recovered.train_config.model_dump(
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mode="json"
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) == train_config.model_dump(mode="json")
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def test_checkpoint_stores_train_config_hyperparameters(tmp_path: Path):
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model_config = ModelConfig(samplerate=384_000)
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expected_model_config = ModelConfig.model_validate(
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model_config.model_dump(mode="json")
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)
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train_config = TrainingConfig()
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train_config.optimizer = AdamOptimizerConfig(learning_rate=5e-4)
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train_config.scheduler = CosineAnnealingSchedulerConfig(t_max=123)
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train_config.trainer.max_epochs = 3
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train_config.train_loader.batch_size = 2
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module = build_training_module(
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model_config=model_config,
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train_config=train_config,
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)
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trainer = L.Trainer()
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path = tmp_path / "example.ckpt"
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trainer.strategy.connect(module)
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trainer.save_checkpoint(path)
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recovered = TrainingModule.load_from_checkpoint(path)
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assert not DeepDiff(
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recovered.model_config.model_dump(mode="json"),
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expected_model_config.model_dump(mode="json"),
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)
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assert not DeepDiff(
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recovered.train_config.model_dump(mode="json"),
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train_config.model_dump(mode="json"),
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)
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def test_configure_optimizers_uses_train_config_values(tmp_path: Path):
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model_config = ModelConfig()
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expected_model_config = ModelConfig.model_validate(
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model_config.model_dump(mode="json")
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)
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train_config = TrainingConfig()
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train_config.optimizer = AdamOptimizerConfig(learning_rate=5e-4)
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train_config.scheduler = CosineAnnealingSchedulerConfig(t_max=321)
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module = build_training_module(
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model_config=model_config,
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train_config=train_config,
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)
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optimization_config = module.configure_optimizers()
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optimizer = optimization_config["optimizer"]
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scheduler = optimization_config["lr_scheduler"]["scheduler"]
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assert isinstance(optimizer, Adam)
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assert isinstance(scheduler, CosineAnnealingLR)
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assert optimizer.param_groups[0]["lr"] == 5e-4
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assert scheduler.T_max == 321
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trainer = L.Trainer()
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path = tmp_path / "example.ckpt"
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trainer.strategy.connect(module)
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trainer.save_checkpoint(path)
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recovered = TrainingModule.load_from_checkpoint(path)
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assert recovered.model_config.model_dump(
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mode="json"
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) == expected_model_config.model_dump(mode="json")
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assert recovered.train_config.model_dump(
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mode="json"
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) == train_config.model_dump(mode="json")
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loaded_optimization_config = recovered.configure_optimizers()
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loaded_optimizer = loaded_optimization_config["optimizer"]
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loaded_scheduler = loaded_optimization_config["lr_scheduler"]["scheduler"]
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assert loaded_optimizer.param_groups[0]["lr"] == 5e-4
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assert loaded_scheduler.T_max == 321
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def test_api_from_checkpoint_reconstructs_model_config(tmp_path: Path):
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module = build_default_module()
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trainer = L.Trainer()
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path = tmp_path / "example.ckpt"
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trainer.strategy.connect(module)
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trainer.save_checkpoint(path)
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api = BatDetect2API.from_checkpoint(path)
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assert api.config.model.model_dump(
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mode="json"
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) == module.model_config.model_dump(mode="json")
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assert api.config.audio.samplerate == module.model_config.samplerate
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def test_train_smoke_produces_loadable_checkpoint(
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tmp_path: Path,
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example_annotations: list[data.ClipAnnotation],
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sample_audio_loader: AudioLoader,
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):
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config = BatDetect2Config()
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config.train.trainer.limit_train_batches = 1
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config.train.trainer.limit_val_batches = 1
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config.train.trainer.log_every_n_steps = 1
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config.train.train_loader.batch_size = 1
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config.train.train_loader.augmentations.enabled = False
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run_train(
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train_annotations=example_annotations[:1],
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val_annotations=example_annotations[:1],
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train_config=config.train,
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model_config=config.model,
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audio_config=config.audio,
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num_epochs=1,
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train_workers=0,
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val_workers=0,
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checkpoint_dir=tmp_path,
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seed=0,
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)
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checkpoints = list(tmp_path.rglob("*.ckpt"))
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assert checkpoints
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model, model_config = load_model_from_checkpoint(checkpoints[0])
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assert model_config.samplerate == config.model.samplerate
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assert model_config.architecture.name == config.model.architecture.name
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assert model_config.preprocess.model_dump(
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mode="json"
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) == config.model.preprocess.model_dump(mode="json")
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assert model_config.postprocess.model_dump(
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mode="json"
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) == config.model.postprocess.model_dump(mode="json")
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wav = torch.tensor(
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sample_audio_loader.load_clip(example_annotations[0].clip)
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).unsqueeze(0)
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outputs = model(wav.unsqueeze(0))
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assert outputs is not None
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