1 files changed, 74 insertions, 0 deletions
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 8033f47..8296e59 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -1 +1,75 @@
 """Script to run experiments."""
+import importlib
+import os
+from typing import Dict
+
+import click
+import torch
+from training.train import Trainer
+
+
+def run_experiment(
+    experiment_config: Dict, save_weights: bool, gpu_index: int, use_wandb: bool = False
+) -> None:
+    """Short summary."""
+    # Import the data loader module and arguments.
+    datasets_module = importlib.import_module("text_recognizer.datasets")
+    data_loader_ = getattr(datasets_module, experiment_config["dataloader"])
+    data_loader_args = experiment_config.get("data_loader_args", {})
+
+    # Import the model module and model arguments.
+    models_module = importlib.import_module("text_recognizer.models")
+    model_class_ = getattr(models_module, experiment_config["model"])
+
+    # Import metric.
+    metric_fn_ = getattr(models_module, experiment_config["metric"])
+
+    # Import network module and arguments.
+    network_module = importlib.import_module("text_recognizer.networks")
+    network_fn_ = getattr(network_module, experiment_config["network"])
+    network_args = experiment_config.get("network_args", {})
+
+    # Criterion
+    criterion_ = getattr(torch.nn, experiment_config["criterion"])
+    criterion_args = experiment_config.get("criterion_args", {})
+
+    # Optimizer
+    optimizer_ = getattr(torch.optim, experiment_config["optimizer"])
+    optimizer_args = experiment_config.get("optimizer_args", {})
+
+    # Learning rate scheduler
+    lr_scheduler_ = None
+    lr_scheduler_args = None
+    if experiment_config["lr_scheduler"] is not None:
+        lr_scheduler_ = getattr(
+            torch.optim.lr_scheduler, experiment_config["lr_scheduler"]
+        )
+        lr_scheduler_args = experiment_config.get("lr_scheduler_args", {})
+
+    # Device
+    # TODO fix gpu manager
+    device = None
+
+    model = model_class_(
+        network_fn=network_fn_,
+        network_args=network_args,
+        data_loader=data_loader_,
+        data_loader_args=data_loader_args,
+        metrics=metric_fn_,
+        criterion=criterion_,
+        criterion_args=criterion_args,
+        optimizer=optimizer_,
+        optimizer_args=optimizer_args,
+        lr_scheduler=lr_scheduler_,
+        lr_scheduler_args=lr_scheduler_args,
+        device=device,
+    )
+
+    # TODO: Fix checkpoint path and wandb
+    trainer = Trainer(
+        model=model,
+        epochs=experiment_config["epochs"],
+        val_metric=experiment_config["metric"],
+    )
+
+    trainer.fit()