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Revert the config dicts

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This commit is contained in:
henryruhs
2025-03-06 23:28:11 +01:00
parent e5f983b2bf
commit 1dfd230fc5
7 changed files with 60 additions and 96 deletions
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embedding_converter/src/dataset.py
+2 -5
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@@ -10,11 +10,8 @@ from .types import Batch
class StaticDataset(Dataset[Tensor]):
def __init__(self, config_parser : ConfigParser) -> None:
self.config =\
{
'file_pattern': config_parser.get('training.dataset', 'file_pattern')
}
self.file_paths = glob.glob(self.config.get('file_pattern'))
self.config_file_pattern = config_parser.get('training.dataset', 'file_pattern')
self.file_paths = glob.glob(self.config_file_pattern)
self.transforms = self.compose_transforms()
def __getitem__(self, index : int) -> Batch:
embedding_converter/src/exporting.py
+9 -12
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@@ -10,18 +10,15 @@ CONFIG_PARSER.read('config.ini')
def export() -> None:
config =\
{
'directory_path': CONFIG_PARSER.get('exporting', 'directory_path'),
'source_path': CONFIG_PARSER.get('exporting', 'source_path'),
'target_path': CONFIG_PARSER.get('exporting', 'target_path'),
'ir_version': CONFIG_PARSER.getint('exporting', 'ir_version'),
'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version')
}
config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path')
config_source_path = CONFIG_PARSER.get('exporting', 'source_path')
config_target_path = CONFIG_PARSER.get('exporting', 'target_path')
config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version')
config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version')
os.makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type]
model = EmbeddingConverterTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu')
os.makedirs(config_directory_path, exist_ok = True)
model = EmbeddingConverterTrainer.load_from_checkpoint(config_source_path, map_location = 'cpu')
model.eval()
model.ir_version = torch.tensor(config.get('ir_version'))
model.ir_version = torch.tensor(config_ir_version)
input_tensor = torch.randn(1, 512)
torch.onnx.export(model, input_tensor, config.get('target_path'), input_names = [ 'input' ], output_names = [ 'output' ], opset_version = config.get('opset_version'))
torch.onnx.export(model, input_tensor, config_target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = config_opset_version)
embedding_converter/src/training.py
+23 -38
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@@ -21,15 +21,12 @@ CONFIG_PARSER.read('config.ini')
class EmbeddingConverterTrainer(LightningModule):
def __init__(self, config_parser : ConfigParser) -> None:
super().__init__()
self.config =\
{
'source_path': config_parser.get('training.model', 'source_path'),
'target_path': config_parser.get('training.model', 'target_path'),
'learning_rate': config_parser.getfloat('training.trainer', 'learning_rate')
}
self.config_source_path = config_parser.get('training.model', 'source_path')
self.config_target_path = config_parser.get('training.model', 'target_path')
self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate')
self.embedding_converter = EmbeddingConverter()
self.source_embedder = torch.jit.load(self.config.get('source_path'), map_location = 'cpu') # type:ignore[no-untyped-call]
self.target_embedder = torch.jit.load(self.config.get('target_path'), map_location = 'cpu') # type:ignore[no-untyped-call]
self.source_embedder = torch.jit.load(self.config_source_path, map_location = 'cpu')
self.target_embedder = torch.jit.load(self.config_target_path, map_location = 'cpu')
self.mse_loss = nn.MSELoss()
def forward(self, source_embedding : Embedding) -> Embedding:
@@ -53,7 +50,7 @@ class EmbeddingConverterTrainer(LightningModule):
return validation_score
def configure_optimizers(self) -> OptimizerSet:
optimizer = torch.optim.Adam(self.parameters(), lr = self.config.get('learning_rate'))
optimizer = torch.optim.Adam(self.parameters(), lr = self.config_learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
optimizer_set =\
{
@@ -71,52 +68,43 @@ class EmbeddingConverterTrainer(LightningModule):
def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]:
config =\
{
'batch_size': CONFIG_PARSER.getint('training.loader', 'batch_size'),
'num_workers': CONFIG_PARSER.getint('training.loader', 'num_workers')
}
config_batch_size = CONFIG_PARSER.getint('training.loader', 'batch_size')
config_num_workers = CONFIG_PARSER.getint('training.loader', 'num_workers')
training_dataset, validate_dataset = split_dataset(dataset)
training_loader = StatefulDataLoader(training_dataset, batch_size = config.get('batch_size'), shuffle = True, num_workers = config.get('num_workers'), drop_last = True, pin_memory = True, persistent_workers = True)
validation_loader = StatefulDataLoader(validate_dataset, batch_size = config.get('batch_size'), shuffle = False, num_workers = config.get('num_workers'), pin_memory = True, persistent_workers = True)
training_loader = StatefulDataLoader(training_dataset, batch_size = config_batch_size, shuffle = True, num_workers = config_num_workers, drop_last = True, pin_memory = True, persistent_workers = True)
validation_loader = StatefulDataLoader(validate_dataset, batch_size = config_batch_size, shuffle = False, num_workers = config_num_workers, pin_memory = True, persistent_workers = True)
return training_loader, validation_loader
def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]:
config =\
{
'split_ratio': CONFIG_PARSER.getfloat('training.loader', 'split_ratio')
}
config_split_ratio = CONFIG_PARSER.getfloat('training.loader', 'split_ratio')
dataset_size = len(dataset) # type:ignore[arg-type]
training_size = int(dataset_size * config.get('split_ratio'))
training_size = int(dataset_size * config_split_ratio)
validation_size = int(dataset_size - training_size)
training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ])
return training_dataset, validate_dataset
def create_trainer() -> Trainer:
config =\
{
'max_epochs': CONFIG_PARSER.getint('training.trainer', 'max_epochs'),
'precision': CONFIG_PARSER.get('training.trainer', 'precision'),
'directory_path': CONFIG_PARSER.get('training.output', 'directory_path'),
'file_pattern': CONFIG_PARSER.get('training.output', 'file_pattern')
}
config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs')
config_precision = CONFIG_PARSER.get('training.trainer', 'precision')
config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path')
config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern')
logger = TensorBoardLogger('.logs', name = 'embedding_converter')
return Trainer(
logger = logger,
log_every_n_steps = 10,
max_epochs = config.get('max_epochs'),
precision = config.get('precision'),
max_epochs = config_max_epochs,
precision = config_precision,
callbacks =
[
ModelCheckpoint(
monitor = 'training_loss',
dirpath = config.get('directory_path'),
filename = config.get('file_pattern'),
dirpath = config_directory_path,
filename = config_file_pattern,
every_n_epochs = 1,
save_top_k = 3,
save_last = True
@@ -126,10 +114,7 @@ def create_trainer() -> Trainer:
def train() -> None:
config =\
{
'resume_path': CONFIG_PARSER.get('training.output', 'resume_path')
}
config_resume_path = CONFIG_PARSER.get('training.output', 'resume_path')
if torch.cuda.is_available():
torch.set_float32_matmul_precision('high')
@@ -139,7 +124,7 @@ def train() -> None:
embedding_converter_trainer = EmbeddingConverterTrainer(CONFIG_PARSER)
trainer = create_trainer()
if os.path.exists(config.get('resume_path')):
trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path'))
if os.path.exists(config_resume_path):
trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_resume_path)
else:
trainer.fit(embedding_converter_trainer, training_loader, validation_loader)