Fix network args

2 files changed, 18 insertions, 23 deletions

diff --git a/training/experiments/image_transformer.yaml b/training/experiments/image_transformer.yaml
index 012a19b..9e8f9fc 100644
--- a/training/experiments/image_transformer.yaml
+++ b/training/experiments/image_transformer.yaml
@@ -1,12 +1,9 @@
 network:
         type: ImageTransformer
         args:
-                input_shape: None
-                output_shape: None
                 encoder:
                         type: None
                         args: None
-                mapping: sentence_piece
                 num_decoder_layers: 4
                 hidden_dim: 256
                 num_heads: 4
@@ -60,7 +57,7 @@ callbacks:
 trainer:
         args:
                 stochastic_weight_avg: true
-                auto_scale_batch_size: power
+                auto_scale_batch_size: binsearch
                 gradient_clip_val: 0
                 fast_dev_run: false
                 gpus: 1
diff --git a/training/run_experiment.py b/training/run_experiment.py
index 0a67bfa..ea9f512 100644
--- a/training/run_experiment.py
+++ b/training/run_experiment.py
@@ -6,7 +6,6 @@ from typing import Dict, List, NamedTuple, Optional, Union, Type
 
 import click
 from loguru import logger
-import numpy as np
 from omegaconf import OmegaConf
 import pytorch_lightning as pl
 import torch
@@ -23,10 +22,10 @@ EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
 def _configure_logging(log_dir: Optional[Path], verbose: int = 0) -> None:
     """Configure the loguru logger for output to terminal and disk."""
 
-    def _get_level(verbose: int) -> int:
+    def _get_level(verbose: int) -> str:
         """Sets the logger level."""
-        levels = {0: 40, 1: 20, 2: 10}
-        verbose = verbose if verbose <= 2 else 2
+        levels = {0: "WARNING", 1: "INFO", 2: "DEBUG"}
+        verbose = min(verbose, 2)
         return levels[verbose]
 
     # Have to remove default logger to get tqdm to work properly.
@@ -50,7 +49,7 @@ def _import_class(module_and_class_name: str) -> type:
     return getattr(module, class_name)
 
 
-def _configure_pl_callbacks(
+def _configure_callbacks(
     args: List[Union[OmegaConf, NamedTuple]]
 ) -> List[Type[pl.callbacks.Callback]]:
     """Configures PyTorch Lightning callbacks."""
@@ -60,13 +59,16 @@ def _configure_pl_callbacks(
     return pl_callbacks
 
 
-def _configure_wandb_callback(
-    network: Type[nn.Module], args: Dict
+def _configure_logger(
+        network: Type[nn.Module], args: Dict, use_wandb: bool
 ) -> pl.loggers.WandbLogger:
-    """Configures wandb logger."""
-    pl_logger = pl.loggers.WandbLogger()
-    pl_logger.watch(network)
-    pl_logger.log_hyperparams(vars(args))
+    """Configures PyTorch Lightning logger."""
+    if use_wandb:
+        pl_logger = pl.loggers.WandbLogger()
+        pl_logger.watch(network)
+        pl_logger.log_hyperparams(vars(args))
+    else:
+        pl_logger = pl.logger.TensorBoardLogger("training/logs")
     return pl_logger
 
 
@@ -106,15 +108,11 @@ def run(path: str, train: bool, test: bool, tune: bool, use_wandb: bool) -> None
 
     # Initialize data object and network.
     data_module = data_module_class(**config.data.args)
-    network = network_class(**config.network.args)
+    network = network_class(**data_module.config(), **config.network.args)
 
     # Load callback and logger
-    callbacks = _configure_pl_callbacks(config.callbacks)
-    pl_logger = (
-        _configure_wandb_callback(network, config.network.args)
-        if use_wandb
-        else pl.logger.TensorBoardLogger("training/logs")
-    )
+    callbacks = _configure_callbacks(config.callbacks)
+    pl_logger = _configure_logger(network, config, use_wandb)
 
     # Checkpoint
     if config.load_checkpoint is not None:
@@ -125,7 +123,7 @@ def run(path: str, train: bool, test: bool, tune: bool, use_wandb: bool) -> None
             config.load_checkpoint, network=network, **config.model.args
         )
     else:
-        lit_model = lit_model_class(**config.model.args)
+        lit_model = lit_model_class(network=network, **config.model.args)
 
     trainer = pl.Trainer(
         **config.trainer,