from tvtk.api import tvtk, write_data
import sys
sys.path.append('../')
import model.flow_net as flow_net
from inputs.flow_data_queue import Sailfish_data
from utils.experiment_manager import make_checkpoint_path
from utils.boundary_utils import get_random_params
import matplotlib.pyplot as plt
FLAGS = tf.app.flags.FLAGS
FLOW_DIR = make_checkpoint_path(FLAGS.base_dir_flow, FLAGS, network="flow")
BOUNDARY_DIR = make_checkpoint_path(FLAGS.base_dir_boundary_flow, FLAGS, network="boundary")
print(BOUNDARY_DIR)
print(FLOW_DIR)
shape = FLAGS.shape.split('x')
shape = map(int, shape)
def evaluate():
"""Run Eval once.
"""
with tf.Session() as sess:
# Make image placeholder
param_inputs, _ = flow_net.inputs_boundary(FLAGS.nr_boundary_params, 1, shape)
# Make boundary
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('base_dir', '../checkpoints',
"""dir to store trained net """)
tf.app.flags.DEFINE_integer('batch_size', 8, """ training batch size """)
tf.app.flags.DEFINE_integer('max_steps', 500000,
""" max number of steps to train """)
tf.app.flags.DEFINE_float('keep_prob', 0.7,
""" keep probability for dropout """)
tf.app.flags.DEFINE_float('learning_rate', 1e-4,
""" keep probability for dropout """)
tf.app.flags.DEFINE_bool('display_test', True, """ display the test images """)
tf.app.flags.DEFINE_string('test_set', "car", """ either car or random """)
TEST_DIR = make_checkpoint_path(FLAGS.base_dir, FLAGS)
def tryint(s):
try:
return int(s)
except:
return s
def alphanum_key(s):
return [tryint(c) for c in re.split('([0-9]+)', s)]
def evaluate():
"""Run Eval once.
import sys
sys.path.append('../')
import model.flow_net as flow_net
from inputs.flow_data_queue import Sailfish_data
from utils.experiment_manager import make_checkpoint_path
from model.pressure import calc_force
from model.velocity_norm import calc_velocity_norm
import matplotlib.pyplot as plt
from tqdm import *
FLAGS = tf.app.flags.FLAGS
FLOW_DIR = make_checkpoint_path(FLAGS.base_dir_flow, FLAGS, network="flow")
shape = FLAGS.shape.split('x')
shape = map(int, shape)
batch_size = 10
def tryint(s):
try:
return int(s)
except:
return s
def alphanum_key(s):
import matplotlib.pyplot as plt
#SMALL_SIZE = 12
#MEDIUM_SIZE = 16
#BIGGER_SIZE = 18
#plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
#plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title
#plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
#plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
#plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
FLAGS = tf.app.flags.FLAGS
FLOW_DIR = make_checkpoint_path(FLAGS.base_dir_heat, FLAGS, network="heat")
BOUNDARY_DIR = make_checkpoint_path(FLAGS.base_dir_boundary_heat,
FLAGS,
network="boundary")
print(FLOW_DIR)
print(BOUNDARY_DIR)
shape = FLAGS.shape.split('x')
shape = map(int, shape)
batch_size = 1
def evaluate():
"""Run Eval once.
Args:
import numpy as np
import tensorflow as tf
import sys
sys.path.append('../')
import model.flow_net as flow_net
from inputs.vtk_data import VTK_data
from inputs.boundary_data_queue import Boundary_data
from utils.experiment_manager import make_checkpoint_path
import matplotlib.pyplot as plt
FLAGS = tf.app.flags.FLAGS
TRAIN_DIR = make_checkpoint_path(FLAGS.base_dir_boundary_flow,
FLAGS,
network="boundary")
shape = FLAGS.dims * [FLAGS.obj_size]
def train():
"""Train ring_net for a number of steps."""
with tf.Graph().as_default():
# global step counter
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# make inputs
input_dims = FLAGS.nr_boundary_params
import numpy as np
import tensorflow as tf
import sys
sys.path.append('../')
import model.flow_net as flow_net
from inputs.vtk_data import VTK_data
from utils.experiment_manager import make_checkpoint_path
import matplotlib.pyplot as plt
FLAGS = tf.app.flags.FLAGS
BOUNDARY_DIR = make_checkpoint_path(FLAGS.base_dir_boundary,
FLAGS,
network="boundary")
# video init
shape = FLAGS.shape.split('x')
shape = map(int, shape)
batch_size = 1
def tryint(s):
try:
return int(s)
except:
return s
import tensorflow as tf
import sys
sys.path.append('../')
import model.flow_net as flow_net
from inputs.heat_data_queue import Heat_Sink_data
from utils.experiment_manager import make_checkpoint_path
from model.optimizer import *
from tqdm import *
import matplotlib.pyplot as plt
FLAGS = tf.app.flags.FLAGS
TRAIN_DIR = make_checkpoint_path(FLAGS.base_dir_heat, FLAGS, network="heat")
shape = FLAGS.shape.split('x')
shape = map(int, shape)
def train():
"""Train ring_net for a number of steps."""
with tf.Graph().as_default():
# store grad and loss values
grads = []
loss_gen = []
# global step counter
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
