def process_line(line, jitterer):
parts = line.split(
','
) # expects the chip path and the corresponding coefficient file path separated by comma
chip_path = parts[0]
coeff_path = parts[1]
if not os.path.isfile(coeff_path):
# print('coeff for %s does not exist' % chip_path)
return
id = category_index(coeff_path)
# print("ID found %s" % id)
output_dir = os.path.join(msceleb1m_jitter_dir, id)
if not os.path.isdir(output_dir):
os.makdir(output_dir)
chipID = chip_id(chip_path)
output_f = os.path.join(output_dir, "%s_jitter_0.jpg" % chipID)
if os.path.isfile(output_f) and not force_rewrite:
# print("%s already exists !" % output_f)
return
else:
image = np.array(Image.open(chip_path))
coeffs = face3d.subject_perspective_sighting_coefficients(coeff_path)
ims = jitterer.multiple_random_jitters([image], coeffs, N_jitters)
for i, im in enumerate(ims):
output_f = os.path.join(output_dir,
"%s_jitter_%s.jpg" % (chipID, i))
Image.fromarray(im).save(output_f)
def test__compile_correction_potential():
""" test varecof_io.writer.corr_potentials.compile_correction_pot
"""
os.makdir(TMP_DIR)
varecof_io.writer.corr_potentials.compile_corr_pot(TMP_DIR)
def __init__(self):
global logPath, resultPath,proDir
proDir = readConfig.proDir
resultPath = os.path.join(proDir,"result")
#create result file if it doesn't exit
if not os.path.exists(resultPath):
os.mkdir(resultPath)
#defined test result file name by localtime
logPath = os.pathjoin(resultPath,str(datetime.now().strftime("%Y%m%d%H%M%S")))
#create test result file if it doesn't exist
if not os.path.exists(logPath):
os.makdir(logPath)
#defined logger
self.logger = logging.getLogger()
#defined log level
self.logger.setevel(logging.INFO)
#defined handler
handler = logging.FileHandler(os.path.join(logPath,"output.log"))
#defined formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
#defined formatter
handler.setFormatter(formatter)
#add handler
self.logger.addHandler(handler)
def _upload(file):
if file:
file_name = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),'uploadDirectory')
if not os.path.isdir(file_name):
os.makdir(file_name)
file_name = os.path.join(file_name,'' + time.strftime("%m_%d_%H_%M_%S_")+ file.name)
with open(file_name,'wb+') as destination:
for chunk in file.chunks():
destination.write(chunk)
destination.close()
return file_name
return None
def save(self):
# file path
if not os.path.exists(DATABASE_PATH):
os.makdir(DATABASE_PATH)
os.chdir(DATABASE_PATH)
obj = {"notice": self.notice_singles, "assignments": self.assignment_singles, "files": self.file_singles}
fin = open("database.db", "w")
fin.write(json.dumps(obj))
fin.close()
return
def __init__(self):
self.maxX,self.maxY = 0,0
self.savePath = './simulatePictures'
if not os.path.exists(self.savePath):
os.makdir(self.savePath)
# ** cross param **#
self.crossRadius = 14
self.crossDistance = 150
self.crossColor = [25,200,0]
# ** road param **#
self.roadColor = [0,0,0] #black
self.roadLineType = 4
self.channelWidth = 5
self.channelDistance = 3
self.lineWidth = 2
self.time = 0
def _upload(file):
if file:
file_name = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
'uploadDirectory')
if not os.path.isdir(file_name):
os.makdir(file_name)
file_name = os.path.join(
file_name, '' + time.strftime("%m_%d_%H_%M_%S_") + file.name)
with open(file_name, 'wb+') as destination:
for chunk in file.chunks():
destination.write(chunk)
destination.close()
return file_name
return None
def save(self):
# file path
if not os.path.exists(DATABASE_PATH):
os.makdir(DATABASE_PATH)
os.chdir(DATABASE_PATH)
obj = {
"notice": self.notice_singles,
"assignments": self.assignment_singles,
"files": self.file_singles
}
fin = open("database.db", "w")
fin.write(json.dumps(obj))
fin.close()
return
def __init__(self, name, logfile_name=None, level=logging.DEBUG):
self.logger = logging.getLogger(name)
self.logger.setLevel(level)
formatter = logging.Formatter(
"%(asctime)s [%(levelname)s] %(name)s - %(message)s")
ch = None
if logfile_name is None:
ch = logging.StreamHandler()
else:
logDir = os.path.dirname(logfile_name)
if logDir != "" and not os.path.exists(logDir):
os.makdir(logDir)
pass
now = time.localtime()
suffix = '.%d%02d%02d' % (now.tm_year, now.tm_mon, now.tm_mday)
ch = logging.FileHandler(logfile_name + suffix)
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
self.logger.addHandler(ch)
data = DOGSCATS()
data.data_augmentation(augment_size=5000)
data.data_preprocessing(preprocess_mode='MinMax')
x_train_splitted, x_val, y_train_splitted, y_val = data.get_splitted_train_validation_set()
x_train, y_train = data.get_train_set()
x_test, y_test = data.get_test_set()
num_classes = data.num_classes
modelID = str('DogsCats_CNN_LRsched_PlateauCB')
zeit = str(time.time())
# Save Path
dir_path = os.path.abspath('/home/phil/MachineLearning/models/')
if not os.path.exists(dir_path):
os.makdir(dir_path)
model_path = os.path.join(dir_path, str(modelID) + str(zeit) + '.h5')
# Log Dir
log_dir = os.path.abspath('/home/phil/MachineLearning/logs/')
if not os.path.exists(log_dir):
os.mkdir(log_dir)
model_log_dir = os.path.join(log_dir, str(modelID) + str(zeit))
# Define the DNN
def model_fn(optimizer, learning_rate, filter_block1, kernel_size_block1, filter_block2,
kernel_size_block2, filter_block3, kernel_size_block3, dense_layer_size,
kernel_initializer, bias_initializer, activation_str, dropout_rate, use_bn):
# Input
input_img = Input(shape=x_train.shape[1:])
# Conv Block 1
# - To denoise with block-wise denoiser, set pad to some scalar (5 by default).
# We set the step-size to be 1/(L+2*tau)
alg_args['step'] = 1 / (2 + 2 * tau)
alg_args['num_processes'] = pnum
time_start = time.time()
asyncRED_recon, asyncRED_out, path = asyncRED_solver(
dObj, rObj, **alg_args)
time_end = time.time() - time_start
print(f"Total time used: {time_end:.{4}}")
asyncRED_out['recon'] = asyncRED_recon
asyncRED_out['tau'] = tau
# save out info
asyncRED_output['img_{}'.format(i)] = asyncRED_out
if not os.path.exists('results'):
os.makdir('results')
sio.savemat(
'results/AsyncRED_{}-{}_proc={}.mat'.format(startIndex, endIndex,
pnum), asyncRED_output)
####################################################
#### PlOT CONVERGENCE ###
####################################################
# asyncRED_dist = asyncRED_out['dist']
asyncRED_snr = asyncRED_out['snr']
# compute the averaged distance to fixed points
avgSnrAsyncRED = np.squeeze(asyncRED_snr)
xRange = np.linspace(0, alg_args['num_iter'], alg_args['num_iter'])
fig, (ax1, ax2) = plt.subplots(1, 2)
Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:04:45) [MSC v.1900 32 bit (Intel)] on win32
Type "copyright", "credits" or "license()" for more information.
>>>
RESTART: C:\Users\pavan.badveli\Desktop\canarabank docs\PYTHON\04172018\RMS.py
>>> import os
>>> print (dir (os))
['DirEntry', 'F_OK', 'MutableMapping', 'O_APPEND', 'O_BINARY', 'O_CREAT', 'O_EXCL', 'O_NOINHERIT', 'O_RANDOM', 'O_RDONLY', 'O_RDWR', 'O_SEQUENTIAL', 'O_SHORT_LIVED', 'O_TEMPORARY', 'O_TEXT', 'O_TRUNC', 'O_WRONLY', 'P_DETACH', 'P_NOWAIT', 'P_NOWAITO', 'P_OVERLAY', 'P_WAIT', 'PathLike', 'R_OK', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', 'TMP_MAX', 'W_OK', 'X_OK', '_Environ', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_execvpe', '_exists', '_exit', '_fspath', '_get_exports_list', '_putenv', '_unsetenv', '_wrap_close', 'abc', 'abort', 'access', 'altsep', 'chdir', 'chmod', 'close', 'closerange', 'cpu_count', 'curdir', 'defpath', 'device_encoding', 'devnull', 'dup', 'dup2', 'environ', 'errno', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'extsep', 'fdopen', 'fsdecode', 'fsencode', 'fspath', 'fstat', 'fsync', 'ftruncate', 'get_exec_path', 'get_handle_inheritable', 'get_inheritable', 'get_terminal_size', 'getcwd', 'getcwdb', 'getenv', 'getlogin', 'getpid', 'getppid', 'isatty', 'kill', 'linesep', 'link', 'listdir', 'lseek', 'lstat', 'makedirs', 'mkdir', 'name', 'open', 'pardir', 'path', 'pathsep', 'pipe', 'popen', 'putenv', 'read', 'readlink', 'remove', 'removedirs', 'rename', 'renames', 'replace', 'rmdir', 'scandir', 'sep', 'set_handle_inheritable', 'set_inheritable', 'spawnl', 'spawnle', 'spawnv', 'spawnve', 'st', 'startfile', 'stat', 'stat_float_times', 'stat_result', 'statvfs_result', 'strerror', 'supports_bytes_environ', 'supports_dir_fd', 'supports_effective_ids', 'supports_fd', 'supports_follow_symlinks', 'symlink', 'sys', 'system', 'terminal_size', 'times', 'times_result', 'truncate', 'umask', 'uname_result', 'unlink', 'urandom', 'utime', 'waitpid', 'walk', 'write']
>>> print (os.getcwd())
C:\Users\pavan.badveli\Desktop\canarabank docs\PYTHON\04172018
>>> os.chdir()
Traceback (most recent call last):
File "<pyshell#3>", line 1, in <module>
os.chdir()
TypeError: Required argument 'path' (pos 1) not found
>>> os.makdir('')
Traceback (most recent call last):
File "<pyshell#4>", line 1, in <module>
os.makdir('')
AttributeError: module 'os' has no attribute 'makdir'
>>> os.makedirs('')
Traceback (most recent call last):
File "<pyshell#5>", line 1, in <module>
os.makedirs('')
File "C:\Users\pavan.badveli\AppData\Local\Programs\Python\Python36-32\lib\os.py", line 220, in makedirs
mkdir(name, mode)
FileNotFoundError: [WinError 3] The system cannot find the path specified: ''
>>> os.removedirs(folder name in the working directory)
M = Model(args.z_dims)
M._build_graph(images_batch)
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
train_op = tf.train.AdamOptimizer(args.lr).minimize(M.cost, global_step=global_step)
saver = tf.train.Saver(max_to_keep=20)
config = get_session_config(0.3, multiprocessing.cpu_count()/2)
sess = tf.Session(config=config)
init_op = tf.global_variables_initializer()
sess.run(init_op)
if args.task != 'train':
saver.restore(sess, tf.train.latest_checkpoint('./checkpoints'))
else:
if not os.path.exists(bin_filepath):
os.makdir('./logs')
summary_writer = tf.summary.FileWriter('./logs')
summary_op = tf.summary.merge_all()
# creates threads to start all queue runners collected in the graph
# [remember] always call init_op before start the runner
tf.train.start_queue_runners(sess=sess)
if args.task == 'train':
step = 0
while True:
_, summary_str, loss= sess.run([train_op, summary_op, M.cost])
summary_writer.add_summary(summary_str, step)
if step%1000 == 0:
if not os.path.exists('./checkpoints'):
os.mkdir('./checkpoints')
saver.save(sess, os.path.join('./checkpoints', 'mnist'), global_step=global_step)
print "==================================="
