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train_no_cv.py
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from src.drone_em_dl.models import *
from src.drone_em_dl.models import Fae
from src.drone_em_dl.data import *
from src.drone_em_dl.data import Data
import matplotlib.pyplot as plt
import argparse
from datetime import datetime
import os
from clearml import Dataset
from clearml import Task
from sys import platform
#python3 train_no_cv.py -data_features="11,12,13,22,23,24,25,26,27,28,29,30,31" -session_name='Qd_Ip_inputs_pitch_roll_head_2' -notes="This is a training run for AE on Ip, Qd and Pitch Roll Heading." -GPU_memory=5000
#11,12,13,22,23,24,25,26,27,28,29,30,31
parser = argparse.ArgumentParser(description="Training drone em dl")
parser.add_argument("-epoch", "--epochs", help="Epoch integer input.", default=1000, type=int)
parser.add_argument("-batchSize", "--batchSize", help="Batch size integer input.", default=50, type=int)
parser.add_argument("-seed", "--seed", help="seed value", default=42, type=int)
parser.add_argument( "-data", "--data", help="relative path to data", default="data/raw/falster_data_Kristian.csv", type=str,)
parser.add_argument( "-data_features", "--data_features", help="list of int. features", default="0,1,2,3,6,11,12,13,20,22,23,24,25,26,27,28,29,30,31", type=str)
parser.add_argument( "-data_split", "--data_split", help="split percentage (train)", default=0.97, type=float,)
parser.add_argument("-verbose", "--verbose", help="verbose", default=1, type=int)
parser.add_argument("-GPU", "--GPU", help="which GPU", default=0, type=int)
parser.add_argument( "-GPU_memory", "--GPU_memory", help="GPU memory", default=20000, type=int)
parser.add_argument( "-hyperturner_name", "--hyperturner_name", help="hyperturner_name", default=0, type=int,)
parser.add_argument( "-latent_space_dim", "--latent_space_dim", help="Latens space dimension for model.", default=15, type=int,)
parser.add_argument( "-neurons", "--neurons", help="neurons", default=[210, 160, 360, 60, 310], type=list)
parser.add_argument( "-model_folder", "--model_folder", help="Folder for model", default="models/cv",)
parser.add_argument( "-dropout_prob", "--dropout_prob", help="dropout_prob", default=0.1, type=float)
parser.add_argument( "-session_name", "--session_name", help="your name to clear ml", default="all_data", type=str,)
parser.add_argument( "-notes", "--notes", help="notes", default="NA", type=str,)
parser.add_argument( "-upload_data", "--upload_data", help="upload_data", default=False, type=bool)
args = parser.parse_args()
name_append = datetime.now().strftime("%d_%m_%Y_%H")
print(name_append)
model_name = f"{args.session_name}" + "_" + name_append
task = Task.create(project_name="Drone_em_dl", task_name=f"Drone_em_dl_{args.session_name}_{model_name}")
if args.verbose > 0:
print(
f"\nTraining models with the following parms: \nEpochs: {args.epochs} \nBatch size: {args.batchSize} \nSeed: {args.seed} \nLatens space dim: {args.latent_space_dim}\n"
)
if platform == "linux" or platform == "linux2":
# linux
strategy = load_gpu(which=int(args.GPU), memory=int(args.GPU_memory))
#### Original Data
if args.verbose > 0:
print("\nloading data")
data = Data()
data.load_data(args.data)
data_features = [int(item) for item in args.data_features.split(',')]
data.get_features(data_features)
data.train_test_split(split=args.data_split)
data.norm_data()
if args.upload_data ==True:
ds = Dataset.create(
dataset_name = f'{args.session_name}',
dataset_project = 'Drone_em_dl'
)
ds.add_files(args.data)
for i in range(len(data.org_test.columns)):
ds.get_logger().report_histogram(
title = f"Histogram of train {data.org_test.columns[i]}",
series = 'train data',
values = data.org_test.iloc[:,i].values
)
fig1 = plt.figure()
plt.scatter(data.org_test.X.values,data.org_test.Y.values, c = data.org_test.iloc[:,-i].values,cmap='jet',s=4)
plt.title(f'{data.org_test.columns[-i]}\n')
cbar = plt.colorbar()
cbar.set_label(f'{data.org_test.columns[-i]}', rotation=270)
plt.xlabel(('X'))
plt.ylabel(('Y'))
ds.get_logger().report_matplotlib_figure(
title = f'Scatter: {data.org_test.columns[-i]}\n',
series = 'training data',
figure = fig1,
report_image= True
)
ds.upload()
ds.finalize()
fig1 = plt.figure()
plt.scatter(data.org_test.X.values,data.org_test.Y.values, c = data.norm_data.norm_test[:,-1],cmap='jet',s=4)
plt.title(f'{data.org_test.columns[-1]}\n')
cbar = plt.colorbar()
cbar.set_label(f'{data.org_test.columns[-1]}', rotation=270)
plt.xlabel(('X'))
plt.ylabel(('Y'))
task.get_logger().report_matplotlib_figure(title='Debug Samples',
series='',
figure=fig1)
strategy = load_gpu(which=int(args.GPU), memory=int(args.GPU_memory))
np.random.seed(args.seed)
task = Task.init(project_name="Drone_em_dl", task_name=f"Drone_em_dl_test2")
#task.connect(parameters,'hyperparameters')
task.connect(args,'args')
if args.verbose > 0:
print(f"\nTime: {name_append}")
os.makedirs(f"{args.model_folder}", exist_ok=True)
with Fae() as ae:
print(model_name)
print('test')
ae = ae.make_model(
input_size=data.norm_data.norm_train[0].shape,
latent_space_dim=args.latent_space_dim,
dense_neurons=args.neurons,
dropout_prob=args.dropout_prob,
name=model_name,
)
print(ae.name)
ae._name = model_name
callbacks = get_callbacks(ae)
model_fit(
ae,
data.norm_data.norm_train,
data.norm_data.norm_train,
batch_size=args.batchSize,
epochs=args.epochs,
verbose=args.verbose,
callbacks=callbacks,
)