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train_simulator.py
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train_simulator.py
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import argparse
import os
import torch
from TenSim.utils.model import Net
from TenSim.utils.data_reader import TomatoDataset
from TenSim.utils.trainer import data_prepare, train_nn
number_of_hidden_dims1 = 300
number_of_hidden_dims2 = 300
number_of_hidden_dims3 = 600
BATCH_SIZE = 512
LEARNING_RATE = 1e-4
DAY_IN_LIFE_CYCLE = 160
EPOCHS = 1
def train_greenhouse(wur_tomato_reader, full_train_x, full_train_y, tmp_folder):
for g_path in [tmp_folder + '/model', tmp_folder + '/scaler', tmp_folder + '/log']:
if not os.path.exists(g_path):
os.makedirs(g_path)
x_scaler_path = tmp_folder + '/scaler/greenhouse_x_scaler.pkl'
y_scaler_path = tmp_folder + '/scaler/greenhouse_y_scaler.pkl'
save_model = tmp_folder + '/model/simulator_greenhouse.pkl'
train_log = tmp_folder + '/log/trainlog_greenhouse.log'
net = Net(input_dim=13, output_dim=3, hidden_dim=number_of_hidden_dims1)
train_x, train_y = wur_tomato_reader.greenhouse_x_y(
full_train_x, full_train_y)
dealDataset, train_loader, val_loader, test_loader = data_prepare(
train_x, train_y,
x_scaler_path=x_scaler_path,
y_scaler_path=y_scaler_path,
batch_size=BATCH_SIZE,
train_shuffle=True,
test_shuffle=False)
train_nn(net=net,
train_loader=train_loader,
val_loader=val_loader,
lr=LEARNING_RATE,
Epoch=EPOCHS,
save_model=save_model,
train_log=train_log)
def train_crop_front(wur_tomato_reader, full_train_x, full_train_y, tmp_folder):
for g_path in [tmp_folder + '/model', tmp_folder + '/scaler', tmp_folder + '/log']:
if not os.path.exists(g_path):
os.makedirs(g_path)
x_scaler_path = tmp_folder + '/scaler/crop_front_x_scaler.pkl'
y_scaler_path = tmp_folder + '/scaler/crop_front_y_scaler.pkl'
save_model = tmp_folder + '/model/simulator_crop_front.pkl'
train_log = tmp_folder + '/log/trainlog_crop_front.log'
net = Net(input_dim=7, output_dim=3, hidden_dim=number_of_hidden_dims2)
train_x, train_y = wur_tomato_reader.crop_front_x_y(
full_train_x, full_train_y)
dealDataset, train_loader, val_loader, test_loader = data_prepare(
train_x, train_y,
x_scaler_path=x_scaler_path,
y_scaler_path=y_scaler_path,
batch_size=BATCH_SIZE,
train_shuffle=True,
test_shuffle=False)
train_nn(net=net,
train_loader=train_loader,
val_loader=val_loader,
lr=LEARNING_RATE,
Epoch=EPOCHS,
save_model=save_model,
train_log=train_log)
def train_crop_back(wur_tomato_reader, full_train_x, full_train_y, tmp_folder):
for g_path in [tmp_folder + '/model', tmp_folder + '/scaler', tmp_folder + '/log']:
if not os.path.exists(g_path):
os.makedirs(g_path)
x_scaler_path = tmp_folder + '/scaler/crop_back_x_scaler.pkl'
y_scaler_path = tmp_folder + '/scaler/crop_back_y_scaler.pkl'
save_model = tmp_folder + '/model/simulator_crop_back.pkl'
train_log = tmp_folder + '/log/trainlog_crop_back.log'
net = Net(input_dim=4, output_dim=1, hidden_dim=number_of_hidden_dims3)
train_x, train_y = wur_tomato_reader.crop_back_x_y(
full_train_x, full_train_y)
dealDataset, train_loader, val_loader, test_loader = data_prepare(
train_x, train_y,
x_scaler_path=x_scaler_path,
y_scaler_path=y_scaler_path,
batch_size=BATCH_SIZE,
train_shuffle=True,
test_shuffle=False)
train_nn(net=net,
train_loader=train_loader,
val_loader=val_loader,
lr=LEARNING_RATE,
Epoch=EPOCHS,
save_model=save_model,
train_log=train_log)
def train_model(args):
traj_train_files = os.path.join(
args.base_input_path, args.traj_train_files)
tmp_folder = os.path.join(args.model_dir, args.version)
if not os.path.exists(tmp_folder):
os.makedirs(tmp_folder)
wur_tomato_reader = TomatoDataset(
train_file=traj_train_files, tmp_folder=tmp_folder)
train_data = wur_tomato_reader.read_data(traj_train_files)
full_train_x, full_train_y = wur_tomato_reader.data_process(train_data)
print("train simulator:")
print('start greenhouse model training')
train_greenhouse(wur_tomato_reader, full_train_x, full_train_y, tmp_folder)
print('end greenhouse model training')
print('--------------------------------')
print('start front crop model training')
train_crop_front(wur_tomato_reader, full_train_x, full_train_y, tmp_folder)
print('end front crop model training')
print('--------------------------------')
print('start back crop model training')
train_crop_back(wur_tomato_reader, full_train_x, full_train_y, tmp_folder)
print('end back crop model training')
print('--------------------------------')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--base_input_path", default="./input", type=str)
parser.add_argument(
"--model_dir", default="./result/models_new/", type=str)
parser.add_argument("--traj_train_files",
default="test-sim.txt", type=str)
parser.add_argument("--version", default="baseline", type=str)
args = parser.parse_args()
train_model(args)