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David Rotermund 2023-01-15 14:57:02 +01:00 committed by GitHub
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# %%
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import sys
import torch
import dataconf
import logging
from datetime import datetime
from network.Parameter import Config
from network.build_network import build_network
from network.build_optimizer import build_optimizer
from network.build_lr_scheduler import build_lr_scheduler
from network.build_datasets import build_datasets
from network.load_previous_weights import load_previous_weights
from network.loop_train_test import (
loop_test,
loop_train,
run_lr_scheduler,
loop_test_reconstruction,
)
from network.SbSReconstruction import SbSReconstruction
from torch.utils.tensorboard import SummaryWriter
# ######################################################################
# We want to log what is going on into a file and screen
# ######################################################################
now = datetime.now()
dt_string_filename = now.strftime("%Y_%m_%d_%H_%M_%S")
logging.basicConfig(
filename="log_" + dt_string_filename + ".txt",
filemode="w",
level=logging.INFO,
format="%(asctime)s %(message)s",
)
logging.getLogger().addHandler(logging.StreamHandler())
# ######################################################################
# Load the config data from the json file
# ######################################################################
if len(sys.argv) < 2:
raise Exception("Argument: Config file name is missing")
filename: str = sys.argv[1]
if os.path.exists(filename) is False:
raise Exception(f"Config file not found! {filename}")
if os.path.exists("network.json") is False:
raise Exception("Config file not found! network.json")
if os.path.exists("dataset.json") is False:
raise Exception("Config file not found! dataset.json")
cfg = dataconf.multi.file("network.json").file("dataset.json").file(filename).on(Config)
logging.info(cfg)
logging.info(f"Using configuration file: {filename}")
logging.info(f"Number of spikes: {cfg.number_of_spikes}")
logging.info(f"Cooldown after spikes: {cfg.cooldown_after_number_of_spikes}")
logging.info(f"Reduction cooldown: {cfg.reduction_cooldown}")
logging.info("")
logging.info(f"Epsilon 0: {cfg.epsilon_0}")
logging.info(f"Batch size: {cfg.batch_size}")
logging.info(f"Data mode: {cfg.data_mode}")
logging.info("")
logging.info("*** Config loaded.")
logging.info("")
tb = SummaryWriter(log_dir=cfg.log_path)
# ###########################################
# GPU Yes / NO ?
# ###########################################
default_dtype = torch.float32
torch.set_default_dtype(default_dtype)
torch_device: str = "cuda:0" if torch.cuda.is_available() else "cpu"
use_gpu: bool = True if torch.cuda.is_available() else False
logging.info(f"Using {torch_device} device")
device = torch.device(torch_device)
# ######################################################################
# Prepare the test and training data
# ######################################################################
the_dataset_train, the_dataset_test, my_loader_test, my_loader_train = build_datasets(
cfg
)
logging.info("*** Data loaded.")
# ######################################################################
# Build the network, Optimizer, and LR Scheduler #
# ######################################################################
network = build_network(
cfg=cfg, device=device, default_dtype=default_dtype, logging=logging
)
logging.info("")
optimizer = build_optimizer(network=network, cfg=cfg, logging=logging)
lr_scheduler = build_lr_scheduler(optimizer=optimizer, cfg=cfg, logging=logging)
logging.info("*** Network generated.")
load_previous_weights(
network=network,
overload_path=cfg.learning_parameters.overload_path,
logging=logging,
device=device,
default_dtype=default_dtype,
)
logging.info("")
last_test_performance: float = -1.0
with torch.no_grad():
if cfg.learning_parameters.learning_active is True:
while cfg.epoch_id < cfg.epoch_id_max:
# ##############################################
# Run a training data epoch
# ##############################################
network.train()
(
my_loss_for_batch,
performance_for_batch,
full_loss,
full_correct,
) = loop_train(
cfg=cfg,
network=network,
my_loader_train=my_loader_train,
the_dataset_train=the_dataset_train,
optimizer=optimizer,
device=device,
default_dtype=default_dtype,
logging=logging,
tb=tb,
adapt_learning_rate=cfg.learning_parameters.adapt_learning_rate_after_minibatch,
lr_scheduler=lr_scheduler,
last_test_performance=last_test_performance,
)
# Let the torch learning rate scheduler update the
# learning rates of the optimiers
if cfg.learning_parameters.adapt_learning_rate_after_minibatch is False:
run_lr_scheduler(
cfg=cfg,
lr_scheduler=lr_scheduler,
optimizer=optimizer,
performance_for_batch=performance_for_batch,
my_loss_for_batch=my_loss_for_batch,
tb=tb,
logging=logging,
)
# ##############################################
# Run test data
# ##############################################
network.eval()
if isinstance(network[-1], SbSReconstruction) is False:
last_test_performance = loop_test(
epoch_id=cfg.epoch_id,
cfg=cfg,
network=network,
my_loader_test=my_loader_test,
the_dataset_test=the_dataset_test,
device=device,
default_dtype=default_dtype,
logging=logging,
tb=tb,
)
else:
last_test_performance = loop_test_reconstruction(
epoch_id=cfg.epoch_id,
cfg=cfg,
network=network,
my_loader_test=my_loader_test,
the_dataset_test=the_dataset_test,
device=device,
default_dtype=default_dtype,
logging=logging,
tb=tb,
)
# Next epoch
cfg.epoch_id += 1
else:
# ##############################################
# Run test data
# ##############################################
network.eval()
if isinstance(network[-1], SbSReconstruction) is False:
last_test_performance = loop_test(
epoch_id=cfg.epoch_id,
cfg=cfg,
network=network,
my_loader_test=my_loader_test,
the_dataset_test=the_dataset_test,
device=device,
default_dtype=default_dtype,
logging=logging,
tb=tb,
)
else:
last_test_performance = loop_test_reconstruction(
epoch_id=cfg.epoch_id,
cfg=cfg,
network=network,
my_loader_test=my_loader_test,
the_dataset_test=the_dataset_test,
device=device,
default_dtype=default_dtype,
logging=logging,
tb=tb,
)
tb.close()
# %%