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David Rotermund
GitHub
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2 changed files with 436 additions and 407 deletions
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@ -5,7 +5,9 @@ import argh
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import time
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import os
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import json
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import glob
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from jsmin import jsmin
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from natsort import natsorted
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from functions.alicorn_data_loader import alicorn_data_loader
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from functions.train import train
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@ -81,6 +83,11 @@ def main(
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scheduler_factor=float(config["scheduler_factor"]),
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precision_100_percent=int(config["precision_100_percent"]),
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scheduler_threshold=float(config["scheduler_threshold"]),
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model_continue=bool(config["model_continue"]),
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initial_model_path=str(config["initial_model_path"]),
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tb_runs_path=str(config["tb_runs_path"]),
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trained_models_path=str(config["trained_models_path"]),
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performance_data_path=str(config["performance_data_path"]),
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)
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@ -118,6 +125,11 @@ def run_network(
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scheduler_factor: float,
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precision_100_percent: int,
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scheduler_threshold: float,
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model_continue: bool,
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initial_model_path: str,
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tb_runs_path: str,
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trained_models_path: str,
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performance_data_path: str,
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) -> None:
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# define device:
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device_str: str = "cuda:0" if torch.cuda.is_available() else "cpu"
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@ -189,8 +201,9 @@ def run_network(
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current = datetime.datetime.now().strftime("%d%m-%H%M")
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# new tb session
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os.makedirs("tb_runs", exist_ok=True)
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path: str = os.path.join("tb_runs", f"{model_name}")
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os.makedirs(tb_runs_path, exist_ok=True)
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path: str = os.path.join(tb_runs_path, f"{model_name}")
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tb = SummaryWriter(path)
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# --------------------------------------------------------------------------
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@ -208,20 +221,30 @@ def run_network(
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logger.info(f"Pooling layer kernel: {mp_1_kernel_size}, stride: {mp_1_stride}")
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# define model:
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model = make_cnn(
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conv_out_channels_list=out_channels,
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conv_kernel_size=kernel_size,
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conv_stride_size=stride,
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conv_activation_function=activation_function,
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train_conv_0=train_first_layer,
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conv_0_kernel_size=conv_0_kernel_size,
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mp_1_kernel_size=mp_1_kernel_size,
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mp_1_stride=mp_1_stride,
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logger=logger,
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pooling_type=pooling_type,
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conv_0_enable_softmax=conv_0_enable_softmax,
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l_relu_negative_slope=leak_relu_negative_slope,
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).to(device)
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if model_continue:
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filename_list: list = natsorted(
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glob.glob(os.path.join(initial_model_path, str("*.pt")))
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)
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assert len(filename_list) > 0
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model_filename: str = filename_list[-1]
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logger.info(f"Load filename: {model_filename}")
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else:
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model = make_cnn(
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conv_out_channels_list=out_channels,
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conv_kernel_size=kernel_size,
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conv_stride_size=stride,
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conv_activation_function=activation_function,
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train_conv_0=train_first_layer,
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conv_0_kernel_size=conv_0_kernel_size,
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mp_1_kernel_size=mp_1_kernel_size,
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mp_1_stride=mp_1_stride,
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logger=logger,
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pooling_type=pooling_type,
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conv_0_enable_softmax=conv_0_enable_softmax,
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l_relu_negative_slope=leak_relu_negative_slope,
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).to(device)
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model = torch.load(model_filename, map_location=device)
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logger.info(model)
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@ -321,11 +344,12 @@ def run_network(
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pt_filename: str = f"{model_name}_{epoch}Epoch_{current}.pt"
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logger.info("")
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logger.info(f"Saved model: {pt_filename}")
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os.makedirs("trained_models", exist_ok=True)
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os.makedirs(trained_models_path, exist_ok=True)
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torch.save(
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model,
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os.path.join(
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"trained_models",
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trained_models_path,
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pt_filename,
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),
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)
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@ -370,9 +394,9 @@ def run_network(
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save_name=model_name,
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)
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os.makedirs("performance_data", exist_ok=True)
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os.makedirs(performance_data_path, exist_ok=True)
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np.savez(
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os.path.join("performance_data", f"performances_{model_name}.npz"),
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os.path.join(performance_data_path, f"performances_{model_name}.npz"),
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train_accuracy=np.array(train_accuracy),
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test_accuracy=np.array(test_accuracy),
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train_losses=np.array(train_losses),
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@ -386,11 +410,11 @@ def run_network(
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logger.info("")
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logger.info(f"Saved model: {model_name}_{epoch}Epoch_{current}")
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if save_model:
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os.makedirs("trained_models", exist_ok=True)
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os.makedirs(trained_models_path, exist_ok=True)
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torch.save(
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model,
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os.path.join(
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"trained_models",
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trained_models_path,
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f"{model_name}_{epoch}Epoch_{current}.pt",
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),
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)
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19
config.json
19
config.json
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@ -1,10 +1,11 @@
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{
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"data_path": "/home/kk/Documents/Semester4/code/RenderStimuli/Output/",
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"model_continue": false, // true, (false)
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"save_logging_messages": true, // (true), false
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"display_logging_messages": true, // (true), false
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"batch_size_train": 250,
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"batch_size_test": 500,
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"max_epochs": 2000,
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"max_epochs": 5000,
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"save_model": true,
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"conv_0_kernel_size": 11,
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"mp_1_kernel_size": 3,
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@ -22,14 +23,14 @@
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// LR Scheduler ->
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"use_scheduler": true, // (true), false
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"scheduler_verbose": true,
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"scheduler_factor": 0.1, //(0.1)
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"scheduler_patience": 10, // (10)
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"scheduler_factor": 0.025, //(0.1)
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"scheduler_patience": 100, // (10)
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"scheduler_threshold": 1e-5, // (1e-4)
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"minimum_learning_rate": 1e-8,
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"learning_rate": 0.0001,
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"minimum_learning_rate": 1e-10,
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"learning_rate": 1e-5,
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// <- LR Scheduler
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"pooling_type": "max", // (max), average, none
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"conv_0_enable_softmax": false, // true, (false)
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"conv_0_enable_softmax": true, // true, (false)
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"use_adam": true, // (true) => adam, false => SGD
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"condition": "Coignless",
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"scale_data": 255.0, // (255.0)
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@ -48,5 +49,9 @@
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],
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"conv_stride_sizes": [
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1
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]
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],
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"initial_model_path": "initial_models",
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"tb_runs_path": "tb_runs",
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"trained_models_path": "trained_models",
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"performance_data_path": "performance_data"
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}
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