Working cnn transformer.

1 files changed, 4 insertions, 16 deletions

diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 0510d5c..e6ae84c 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -9,7 +9,6 @@ import re
 from typing import Callable, Dict, List, Optional, Tuple, Type
 import warnings
 
-import adabelief_pytorch
 import click
 from loguru import logger
 import numpy as np
@@ -17,19 +16,17 @@ import torch
 from torchsummary import summary
 from tqdm import tqdm
 from training.gpu_manager import GPUManager
-from training.trainer.callbacks import Callback, CallbackList
+from training.trainer.callbacks import CallbackList
 from training.trainer.train import Trainer
 import wandb
 import yaml
 
 
 from text_recognizer.models import Model
-from text_recognizer.networks import loss as custom_loss_module
+from text_recognizer.networks.loss import loss as custom_loss_module
 
 EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
 
-DEFAULT_TRAIN_ARGS = {"batch_size": 64, "epochs": 16}
-
 
 def _get_level(verbose: int) -> int:
     """Sets the logger level."""
@@ -107,11 +104,7 @@ def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dic
     criterion_args = experiment_config["criterion"].get("args", {}) or {}
 
     # Optimizers
-    if experiment_config["optimizer"]["type"] == "AdaBelief":
-        warnings.filterwarnings("ignore", category=UserWarning)
-        optimizer_ = getattr(adabelief_pytorch, experiment_config["optimizer"]["type"])
-    else:
-        optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"])
+    optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"])
     optimizer_args = experiment_config["optimizer"].get("args", {})
 
     # Learning rate scheduler
@@ -277,11 +270,6 @@ def run_experiment(
         # Lets W&B save the model and track the gradients and optional parameters.
         wandb.watch(model.network)
 
-    experiment_config["train_args"] = {
-        **DEFAULT_TRAIN_ARGS,
-        **experiment_config.get("train_args", {}),
-    }
-
     experiment_config["experiment_group"] = experiment_config.get(
         "experiment_group", None
     )
@@ -351,7 +339,7 @@ def run_experiment(
     "--pretrained_weights", type=str, help="Path to pretrained model weights."
 )
 @click.option(
-    "--notrain", is_flag=False, is_eager=True, help="Do not train the model.",
+    "--notrain", is_flag=False, help="Do not train the model.",
 )
 def run_cli(
     experiment_config: str,