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David Rotermund
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157
test_it.py
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157
test_it.py
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# %%
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import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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import sys
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import torch
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import dataconf
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import logging
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from datetime import datetime
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from network.Parameter import Config
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from network.build_network import build_network
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from network.build_optimizer import build_optimizer
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from network.build_lr_scheduler import build_lr_scheduler
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from network.build_datasets import build_datasets
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from network.load_previous_weights import load_previous_weights
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from network.loop_train_test import (
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loop_test,
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loop_train,
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run_lr_scheduler,
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loop_test_reconstruction,
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)
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from network.SbSReconstruction import SbSReconstruction
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from torch.utils.tensorboard import SummaryWriter
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# ######################################################################
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# We want to log what is going on into a file and screen
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# ######################################################################
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now = datetime.now()
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dt_string_filename = now.strftime("%Y_%m_%d_%H_%M_%S")
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logging.basicConfig(
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filename="log_" + dt_string_filename + ".txt",
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filemode="w",
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level=logging.INFO,
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format="%(asctime)s %(message)s",
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)
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logging.getLogger().addHandler(logging.StreamHandler())
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# ######################################################################
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# Load the config data from the json file
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# ######################################################################
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if os.path.exists("def.json") is False:
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raise Exception("Config file not found! def.json")
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if os.path.exists("network.json") is False:
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raise Exception("Config file not found! network.json")
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if os.path.exists("dataset.json") is False:
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raise Exception("Config file not found! dataset.json")
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cfg = dataconf.multi.file("network.json").file("dataset.json").file("def.json").on(Config)
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logging.info(cfg)
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logging.info(f"Number of spikes: {cfg.number_of_spikes}")
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logging.info(f"Cooldown after spikes: {cfg.cooldown_after_number_of_spikes}")
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logging.info(f"Reduction cooldown: {cfg.reduction_cooldown}")
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logging.info("")
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logging.info(f"Epsilon 0: {cfg.epsilon_0}")
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logging.info(f"Batch size: {cfg.batch_size}")
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logging.info(f"Data mode: {cfg.data_mode}")
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logging.info("")
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logging.info("*** Config loaded.")
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logging.info("")
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tb = SummaryWriter(log_dir=cfg.log_path)
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# ###########################################
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# GPU Yes / NO ?
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# ###########################################
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default_dtype = torch.float32
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torch.set_default_dtype(default_dtype)
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torch_device: str = "cuda:0" if torch.cuda.is_available() else "cpu"
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use_gpu: bool = True if torch.cuda.is_available() else False
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logging.info(f"Using {torch_device} device")
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device = torch.device(torch_device)
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# ######################################################################
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# Prepare the test and training data
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# ######################################################################
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the_dataset_train, the_dataset_test, my_loader_test, my_loader_train = build_datasets(
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cfg
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)
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logging.info("*** Data loaded.")
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# ######################################################################
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# Build the network, Optimizer, and LR Scheduler #
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# ######################################################################
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network = build_network(
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cfg=cfg, device=device, default_dtype=default_dtype, logging=logging
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)
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logging.info("")
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optimizer = build_optimizer(network=network, cfg=cfg, logging=logging)
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lr_scheduler = build_lr_scheduler(optimizer=optimizer, cfg=cfg, logging=logging)
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logging.info("*** Network generated.")
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load_previous_weights(
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network=network,
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overload_path=cfg.learning_parameters.overload_path,
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logging=logging,
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device=device,
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default_dtype=default_dtype,
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)
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logging.info("")
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last_test_performance: float = -1.0
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# ##############################################
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# Run test data
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# ##############################################
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network.eval()
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if isinstance(network[-1], SbSReconstruction) is False:
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last_test_performance = loop_test(
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epoch_id=cfg.epoch_id,
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cfg=cfg,
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network=network,
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my_loader_test=my_loader_test,
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the_dataset_test=the_dataset_test,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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)
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else:
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last_test_performance = loop_test_reconstruction(
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epoch_id=cfg.epoch_id,
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cfg=cfg,
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network=network,
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my_loader_test=my_loader_test,
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the_dataset_test=the_dataset_test,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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)
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tb.close()
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# %%
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228
train_it.py
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228
train_it.py
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@ -0,0 +1,228 @@
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# %%
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import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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import sys
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import torch
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import dataconf
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import logging
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from datetime import datetime
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from network.Parameter import Config
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from network.build_network import build_network
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from network.build_optimizer import build_optimizer
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from network.build_lr_scheduler import build_lr_scheduler
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from network.build_datasets import build_datasets
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from network.load_previous_weights import load_previous_weights
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from network.loop_train_test import (
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loop_test,
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loop_train,
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run_lr_scheduler,
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loop_test_reconstruction,
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)
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from network.SbSReconstruction import SbSReconstruction
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from torch.utils.tensorboard import SummaryWriter
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# ######################################################################
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# We want to log what is going on into a file and screen
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# ######################################################################
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now = datetime.now()
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dt_string_filename = now.strftime("%Y_%m_%d_%H_%M_%S")
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logging.basicConfig(
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filename="log_" + dt_string_filename + ".txt",
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filemode="w",
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level=logging.INFO,
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format="%(asctime)s %(message)s",
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)
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logging.getLogger().addHandler(logging.StreamHandler())
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# ######################################################################
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# Load the config data from the json file
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# ######################################################################
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if os.path.exists("def.json") is False:
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raise Exception("Config file not found! def.json")
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if os.path.exists("network.json") is False:
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raise Exception("Config file not found! network.json")
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if os.path.exists("dataset.json") is False:
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raise Exception("Config file not found! dataset.json")
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cfg = dataconf.multi.file("network.json").file("dataset.json").file("def.json").on(Config)
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logging.info(cfg)
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logging.info(f"Number of spikes: {cfg.number_of_spikes}")
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logging.info(f"Cooldown after spikes: {cfg.cooldown_after_number_of_spikes}")
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logging.info(f"Reduction cooldown: {cfg.reduction_cooldown}")
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logging.info("")
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logging.info(f"Epsilon 0: {cfg.epsilon_0}")
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logging.info(f"Batch size: {cfg.batch_size}")
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logging.info(f"Data mode: {cfg.data_mode}")
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logging.info("")
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logging.info("*** Config loaded.")
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logging.info("")
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tb = SummaryWriter(log_dir=cfg.log_path)
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# ###########################################
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# GPU Yes / NO ?
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# ###########################################
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default_dtype = torch.float32
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torch.set_default_dtype(default_dtype)
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torch_device: str = "cuda:0" if torch.cuda.is_available() else "cpu"
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use_gpu: bool = True if torch.cuda.is_available() else False
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logging.info(f"Using {torch_device} device")
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device = torch.device(torch_device)
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# ######################################################################
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# Prepare the test and training data
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# ######################################################################
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the_dataset_train, the_dataset_test, my_loader_test, my_loader_train = build_datasets(
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cfg
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)
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logging.info("*** Data loaded.")
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# ######################################################################
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# Build the network, Optimizer, and LR Scheduler #
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# ######################################################################
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network = build_network(
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cfg=cfg, device=device, default_dtype=default_dtype, logging=logging
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)
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logging.info("")
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optimizer = build_optimizer(network=network, cfg=cfg, logging=logging)
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lr_scheduler = build_lr_scheduler(optimizer=optimizer, cfg=cfg, logging=logging)
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logging.info("*** Network generated.")
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load_previous_weights(
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network=network,
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overload_path=cfg.learning_parameters.overload_path,
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logging=logging,
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device=device,
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default_dtype=default_dtype,
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)
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logging.info("")
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last_test_performance: float = -1.0
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with torch.no_grad():
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if cfg.learning_parameters.learning_active is True:
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while cfg.epoch_id < cfg.epoch_id_max:
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# ##############################################
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# Run a training data epoch
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# ##############################################
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network.train()
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(
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my_loss_for_batch,
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performance_for_batch,
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full_loss,
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full_correct,
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) = loop_train(
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cfg=cfg,
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network=network,
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my_loader_train=my_loader_train,
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the_dataset_train=the_dataset_train,
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optimizer=optimizer,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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adapt_learning_rate=cfg.learning_parameters.adapt_learning_rate_after_minibatch,
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lr_scheduler=lr_scheduler,
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last_test_performance=last_test_performance,
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)
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# Let the torch learning rate scheduler update the
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# learning rates of the optimiers
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if cfg.learning_parameters.adapt_learning_rate_after_minibatch is False:
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run_lr_scheduler(
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cfg=cfg,
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lr_scheduler=lr_scheduler,
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optimizer=optimizer,
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performance_for_batch=performance_for_batch,
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my_loss_for_batch=my_loss_for_batch,
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tb=tb,
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logging=logging,
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)
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# ##############################################
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# Run test data
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# ##############################################
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network.eval()
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if isinstance(network[-1], SbSReconstruction) is False:
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last_test_performance = loop_test(
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epoch_id=cfg.epoch_id,
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cfg=cfg,
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network=network,
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my_loader_test=my_loader_test,
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the_dataset_test=the_dataset_test,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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)
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else:
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last_test_performance = loop_test_reconstruction(
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epoch_id=cfg.epoch_id,
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cfg=cfg,
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network=network,
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my_loader_test=my_loader_test,
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the_dataset_test=the_dataset_test,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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)
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# Next epoch
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cfg.epoch_id += 1
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else:
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# ##############################################
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# Run test data
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# ##############################################
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network.eval()
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if isinstance(network[-1], SbSReconstruction) is False:
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last_test_performance = loop_test(
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epoch_id=cfg.epoch_id,
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cfg=cfg,
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network=network,
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my_loader_test=my_loader_test,
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the_dataset_test=the_dataset_test,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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)
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else:
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last_test_performance = loop_test_reconstruction(
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epoch_id=cfg.epoch_id,
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cfg=cfg,
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network=network,
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my_loader_test=my_loader_test,
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the_dataset_test=the_dataset_test,
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device=device,
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default_dtype=default_dtype,
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logging=logging,
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tb=tb,
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)
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tb.close()
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# %%
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