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io_Cosmo.py
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io_Cosmo.py
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import numpy as np
import tensorflow as tf
import hyper_parameters_Cosmo
import os
import itertools
def _float64_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
class loadNpyData:
def __init__(self,data,label,num):
self.data = data
self.label = label
self.num = num
def convert_to(self):
filename = str(self.num)+'.tfrecord'
print('Writing ', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(len(self.data)):
data_raw = self.data[index].tostring()
label_raw = self.label[index].tostring()
example = tf.train.Example(features = tf.train.Features(feature={'label_raw': _bytes_feature(label_raw),'data_raw': _bytes_feature(data_raw)}))
writer.write(example.SerializeToString())
writer.close()
class loadTfrecordData:
def __init__(self,fileBuffer,num):
self.fileBuffer = fileBuffer
def reconstruct_from(self):
for filename in record_iterator:
example = tf.train.Example()
example.ParseFromString(filename)
data_raw = (example.features.feature['data_raw'].bytes_list.value[0])
data = np.fromstring(data_raw, dtype=np.float).reshape([-1,64,64,64,1])
label_raw = (example.features.feature['label_raw'].bytes_list.value[0])
label = np.fromstring(label_raw,dtype=np.float).reshape([-1,2])
return data,label
def read_tfrecord(filename_queue):
reader = tf.TFRecordReader()
_,single_example = reader.read(filename_queue)
parsed_example = tf.parse_single_example(
single_example,
features = {
"data_raw": tf.FixedLenFeature([],tf.string),
"label_raw": tf.FixedLenFeature([],tf.string)
}
)
NbodySimuDecode = tf.decode_raw(parsed_example['data_raw'],tf.float64)
labelDecode = tf.decode_raw(parsed_example['label_raw'],tf.float64)
NbodySimus = tf.reshape(NbodySimuDecode,[64,64,64])
#augment
NbodySimus = tf.cond(tf.random_uniform([1],maxval=1)[0] < tf.constant(.5),lambda:NbodySimus,lambda:NbodySimus[::-1,:,...])
NbodySimus = tf.cond(tf.random_uniform([1],maxval=1)[0] < tf.constant(.5),lambda:NbodySimus,lambda:NbodySimus[:,::-1,...])
NbodySimus = tf.cond(tf.random_uniform([1],maxval=1)[0] < tf.constant(.5),lambda:NbodySimus,lambda:NbodySimus[:,:,::-1])
prand = tf.random_uniform([1],maxval=1)[0]
NbodySimus = tf.cond(prand < tf.constant(1./6),lambda:tf.transpose(NbodySimus, perm = (1,2,0)),lambda:NbodySimus)
NbodySimus = tf.cond(tf.logical_and(prand < tf.constant(2./6) , prand > tf.constant(1./6)), lambda:tf.transpose(NbodySimus, perm = (1,0,2)),lambda:NbodySimus)
NbodySimus = tf.cond(tf.logical_and(prand < tf.constant(3./6) , prand > tf.constant(2./6)), lambda:tf.transpose(NbodySimus, perm = (0,2,1)),lambda:NbodySimus)
NbodySimus = tf.cond(tf.logical_and(prand < tf.constant(4./6) , prand > tf.constant(3./6)), lambda:tf.transpose(NbodySimus, perm = (2,0,1)),lambda:NbodySimus)
NbodySimus = tf.cond(tf.logical_and(prand < tf.constant(5./6) , prand > tf.constant(4./6)), lambda:tf.transpose(NbodySimus, perm = (2,1,0)),lambda:NbodySimus)
#normalize
NbodySimus /= (tf.reduce_sum(NbodySimus)/64**3+0.)
NbodySimuAddDim = tf.expand_dims(NbodySimus,axis = 3)
label = tf.reshape(labelDecode,[2])
label = (label - tf.constant([2.995679839999998983e-01,8.610806619999996636e-01],dtype = tf.float64))/tf.constant([2.905168635566176411e-02,4.023372385668218254e-02],dtype = tf.float64)
return NbodySimuAddDim,label
def readDataSet(filenames):
filename_queue = tf.train.string_input_producer(filenames,num_epochs=None,shuffle=True)
NbodySimus,label= read_tfrecord(filename_queue)
#NbodyList = [read_tfrecord(filename_queue) for _ in range(hyper_parameters_Cosmo.Input["NUM_THREADS"])]
NbodySimus_batch, label_batch = tf.train.shuffle_batch(
[NbodySimus,label],
#NbodyList,
batch_size = hyper_parameters_Cosmo.Input["BATCH_SIZE"],
num_threads = hyper_parameters_Cosmo.Input["NUM_THREADS"],
capacity = hyper_parameters_Cosmo.Input["CAPACITY"],
min_after_dequeue = hyper_parameters_Cosmo.Input["MIN_AFTER_DEQUEUE"],
allow_smaller_final_batch=True)
return NbodySimus_batch, label_batch
def read_test_tfrecord(filename_queue):
reader = tf.TFRecordReader()
_,single_example = reader.read(filename_queue)
parsed_example = tf.parse_single_example(
single_example,
features = {
"data_raw": tf.FixedLenFeature([],tf.string),
"label_raw": tf.FixedLenFeature([],tf.string)
}
)
NbodySimuDecode = tf.decode_raw(parsed_example['data_raw'],tf.float64)
labelDecode = tf.decode_raw(parsed_example['label_raw'],tf.float64)
NbodySimus = tf.reshape(NbodySimuDecode,[64,64,64])
NbodySimus /= (tf.reduce_sum(NbodySimus)/64**3+0.)
NbodySimuAddDim = tf.expand_dims(NbodySimus,3)
label = tf.reshape(labelDecode,[2])
labelAddDim = (label - tf.constant([2.995679839999998983e-01,8.610806619999996636e-01],dtype = tf.float64))/tf.constant([2.905168635566176411e-02,4.023372385668218254e-02],dtype = tf.float64)
print NbodySimuAddDim.shape
'''
NbodySimuAddDimList = []
#augment
for trans in itertools.permutations([0,1,2]):
NbodySimuTrans = tf.transpose(NbodySimus, perm = tuple(trans))
for flips in list(itertools.product([True, False], repeat=3)):
Nbody = tf.identity(NbodySimuTrans)
if flips[0]: Nbody = Nbody[::-1,...]
if flips[1]: Nbody = Nbody[:,::-1,...]
if flips[2]: Nbody = Nbody[:,:,::-1]
NbodySimuAddDimList.append(tf.expand_dims(Nbody,0))
NbodySimuAddDim = tf.concat(NbodySimuAddDimList,0);
#normalize
label = tf.reshape(labelDecode,[2])
label = (label - tf.constant([2.995679839999998983e-01,8.610806619999996636e-01],dtype = tf.float64))/tf.constant([2.905168635566176411e-02,4.023372385668218254e-02],dtype = tf.float64)
labelAddDim = tf.tile(tf.expand_dims(label,0),tf.constant([48,1]))
'''
return NbodySimuAddDim,labelAddDim
def readTestSet(filenames):
filename_queue = tf.train.string_input_producer(filenames,num_epochs=None,shuffle=False)
NbodySimus,label= read_test_tfrecord(filename_queue)
NbodySimus_batch, label_batch = tf.train.batch(
[NbodySimus,label],
#NbodyList,
batch_size = hyper_parameters_Cosmo.Input_Test["BATCH_SIZE"],
num_threads = hyper_parameters_Cosmo.Input_Test["NUM_THREADS"],
capacity = hyper_parameters_Cosmo.Input_Test["CAPACITY"],
enqueue_many=False,
allow_smaller_final_batch=True)
return NbodySimus_batch, label_batch
if __name__ == '__main__':
order = np.random.permutation(64*400)
order = np.split(np.append(order,np.arange(64*400,64*499)),499)
label_path = os.path.join('/zfsauton/home/siyuh/256_64','basics_infos_1000_1499.txt')
labels = np.loadtxt(label_path,delimiter=',')
for i in range(0,499):
data = []
label = []
for j in order[i]:
numDirectory = int(j/64)
numFile = j%64
data_path = os.path.join(hyper_parameters_Cosmo.Path["init_data"],str('01')+str(numDirectory).rjust(3,'0'),str(numFile)+'.npy')
data = np.append(data,np.load(data_path))
label = np.append(label,labels[numDirectory][[1,3]])
loadNpyData(data.reshape(-1,64,64,64,1),label.reshape(-1,2),i).convert_to()
'''
NbodySimu, NbodyLabel = readTestSet(filenames=["400.tfrecord","401.tfrecord"])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator();
threads = tf.train.start_queue_runners(coord=coord);
for i in range(65):
_NbodySimu,_NbodyLabel = sess.run([NbodySimu, NbodyLabel])
print (_NbodySimu[0])
print ("----------------");
coord.request_stop()
coord.join()
'''