def create_trainer_and_start(self,
out_path,
epochs=1,
load_test_set=False):
dlds = DataloaderFlowfrontSensor(sensor_indizes=((1, 8), (1, 8)))
m = ModelTrainer(
lambda: S20DryspotModelFCWide(),
data_source_paths=tr_resources.get_data_paths_debug(),
save_path=out_path,
load_datasets_path=self.torch_dataset_resources /
"reference_datasets",
cache_path=None,
num_validation_samples=8,
num_test_samples=8,
num_workers=0,
epochs=epochs,
data_processing_function=dlds.get_flowfront_sensor_bool_dryspot,
data_gather_function=dg.get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.BCELoss(),
optimizer_function=lambda params: torch.optim.AdamW(params,
lr=1e-4),
classification_evaluator_function=lambda summary_writer:
BinaryClassificationEvaluator(summary_writer=summary_writer),
load_test_set_in_training_mode=load_test_set,
data_root=test_resources.test_src_dir,
)
return m
import pickle
from pathlib import Path
import numpy as np
import Resources.training as r
from Pipeline import torch_datagenerator as td
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_loader_flowfront_sensor import DataloaderFlowfrontSensor
if __name__ == "__main__":
dlds = DataloaderFlowfrontSensor(sensor_indizes=((1, 8), (1, 8)))
generator = td.LoopingDataGenerator(r.get_data_paths_debug(),
get_filelist_within_folder_blacklisted,
dlds.get_flowfront_sensor_bool_dryspot,
num_validation_samples=131072,
num_test_samples=1048576,
batch_size=131072,
split_load_path=r.dataset_split,
split_save_path=Path(),
num_workers=75,
looping_strategy=None)
mean = 0.
std = 0.
j = 0
for i, (inputs, _, _) in enumerate(generator):
abs_speed_at_sensors = np.linalg.norm(inputs, axis=2)
mean_at_sensors = abs_speed_at_sensors.mean(axis=0)
mean = mean + mean_at_sensors
std_at_sensors = abs_speed_at_sensors.std(axis=0)
std = std + std_at_sensors
import torch
import Resources.training as r
from Models.erfh5_DeconvModel import DeconvModelEfficient
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_loader_flowfront_sensor import DataloaderFlowfrontSensor
from Trainer.ModelTrainer import ModelTrainer
from Trainer.evaluation import SensorToFlowfrontEvaluator
from Utils.training_utils import read_cmd_params
if __name__ == "__main__":
args = read_cmd_params()
dl = DataloaderFlowfrontSensor(image_size=(149, 117),
frame_count=1,
use_binary_sensor_only=True)
m = ModelTrainer(
lambda: DeconvModelEfficient(),
data_source_paths=r.get_data_paths_base_0(),
save_path=r.save_path,
dataset_split_path=r.dataset_split,
cache_path=r.cache_path,
batch_size=2048,
train_print_frequency=100,
epochs=100,
num_workers=75,
num_validation_samples=131072,
num_test_samples=1048576,
data_processing_function=dl.get_flowfront_sensor_and_flowfront_label,
data_gather_function=get_filelist_within_folder_blacklisted,
import torch
import Resources.training as r
from Models.erfh5_fullyConnected import S1140DryspotModelFCWide
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_loader_flowfront_sensor import DataloaderFlowfrontSensor
from Trainer.ModelTrainer import ModelTrainer
from Trainer.evaluation import BinaryClassificationEvaluator
from Utils.training_utils import read_cmd_params
if __name__ == "__main__":
args = read_cmd_params()
dlds = DataloaderFlowfrontSensor(sensor_indizes=((0, 1), (0, 1)),
frame_count=1,
use_binary_sensor_only=True)
m = ModelTrainer(lambda: S1140DryspotModelFCWide(),
data_source_paths=r.get_data_paths_debug(),
save_path=r.save_path,
dataset_split_path=r.dataset_split,
cache_path=r.cache_path,
batch_size=2048,
train_print_frequency=100,
epochs=100,
num_workers=75,
num_validation_samples=512, # 131072,
num_test_samples=1024, # 1048576,
data_processing_function=dlds.get_flowfront_sensor_bool_dryspot,
data_gather_function=get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.BCELoss(),
from pathlib import Path
import torch
import Resources.training as r
from Models.erfh5_ConvModel import S20Channel4toDrySpot
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_loader_flowfront_sensor import DataloaderFlowfrontSensor
from Trainer.ModelTrainer import ModelTrainer
from Trainer.evaluation import BinaryClassificationEvaluator
from Utils.training_utils import read_cmd_params
if __name__ == "__main__":
args = read_cmd_params()
dlds = DataloaderFlowfrontSensor(sensor_indizes=((1, 8), (1, 8)),
frame_count=4)
m = ModelTrainer(
lambda: S20Channel4toDrySpot(),
data_source_paths=r.get_data_paths_base_0(),
save_path=r.save_path,
load_datasets_path=r.datasets_dryspots,
cache_path=r.cache_path,
batch_size=2048,
train_print_frequency=100,
epochs=1000,
num_workers=75,
num_validation_samples=131072,
num_test_samples=1048576,
data_processing_function=dlds.get_flowfront_sensor_bool_dryspot,
data_gather_function=get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.BCELoss(),