def do_print(stream=None):
print(util.log.get_date_str(), file=stream)
print('\n# =========================================================================== #', file=stream)
print('# Training flags:', file=stream)
print('# =========================================================================== #', file=stream)
def print_ckpt(path):
ckpt = util.tf.get_latest_ckpt(path)
if ckpt is not None:
print('Resume Training from : %s' % (ckpt), file=stream)
return True
return False
print('\n# =========================================================================== #', file=stream)
print('# pixel_link net parameters:', file=stream)
print('# =========================================================================== #', file=stream)
vars = globals()
for key in vars:
var = vars[key]
if util.dtype.is_number(var) or util.dtype.is_str(var) or util.dtype.is_list(var) or util.dtype.is_tuple(
var):
pprint('%s=%s' % (key, str(var)), stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print('# =========================================================================== #', file=stream)
data_files = parallel_reader.get_data_files(dataset.data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def print_config(stream=None):
print(
'\n# =========================================================================== #',
file=stream)
print('# Training | Evaluation flags:', file=stream)
print(
'# =========================================================================== #',
file=stream)
#pprint(flags, stream=stream)
for k in flags:
v = flags[k].value
print("param [ {} ]: {}".format(k, v), file=stream)
print(
'\n# =========================================================================== #',
file=stream)
print('# SSD net parameters:', file=stream)
print(
'# =========================================================================== #',
file=stream)
pprint(dict(ssd_params._asdict()), stream=stream)
print(
'\n# =========================================================================== #',
file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print(
'# =========================================================================== #',
file=stream)
data_files = parallel_reader.get_data_files(data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def print_config(stream=None):
print(
'\n# =========================================================================== #',
file=stream)
print('# Training | Evaluation flags:', file=stream)
print(
'# =========================================================================== #',
file=stream)
pprint(flags, stream=stream)
print(
'\n# =========================================================================== #',
file=stream)
print('# SSD net parameters:', file=stream)
print(
'# =========================================================================== #',
file=stream)
pprint(dict(ssd_params._asdict()), stream=stream)
print(
'\n# =========================================================================== #',
file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print(
'# =========================================================================== #',
file=stream)
data_files = parallel_reader.get_data_files(data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def do_print(stream=None):
print(logger.get_date_str(), file = stream)
print('\n# =========================================================================== #', file=stream)
print('# Training flags:', file=stream)
print('# =========================================================================== #', file=stream)
def print_ckpt(path):
ckpt = ckpt_util.get_latest_ckpt(path)
if ckpt is not None:
print('Resume Training from : %s'%(ckpt), file = stream)
return True
return False
if not print_ckpt(flags.train_dir):
print_ckpt(flags.checkpoint_path)
pprint(flags.__flags, stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# pixel_link net parameters:', file=stream)
print('# =========================================================================== #', file=stream)
vars = globals()
for key in vars:
var = vars[key]
if is_number(var) or type(var)==str or type(var)==list or type(var)==tuple:
pprint('%s=%s'%(key, str(var)), stream = stream)
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print('# =========================================================================== #', file=stream)
data_files = parallel_reader.get_data_files(dataset.data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def do_print(stream=None):
print(
'\n# =========================================================================== #',
file=stream)
print('# Training flags:', file=stream)
print(
'# =========================================================================== #',
file=stream)
pprint(flags.__flags, stream=stream)
print(
'\n# =========================================================================== #',
file=stream)
print('# seglink net parameters:', file=stream)
print(
'# =========================================================================== #',
file=stream)
vars = globals()
for key in vars:
var = vars[key]
if util.dtype.is_number(var) or util.dtype.is_str(
var) or util.dtype.is_list(var) or util.dtype.is_tuple(
var):
pprint('%s=%s' % (key, str(var)), stream=stream)
print(
'\n# =========================================================================== #',
file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print(
'# =========================================================================== #',
file=stream)
data_files = parallel_reader.get_data_files(dataset.data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def do_print(stream=None):
print(util.log.get_date_str(), file = stream)
print('\n# =========================================================================== #', file=stream)
print('# Training flags:', file=stream)
print('# =========================================================================== #', file=stream)
def print_ckpt(path):
ckpt = util.tf.get_latest_ckpt(path)
if ckpt is not None:
print('Resume Training from : %s'%(ckpt), file = stream)
return True
return False
if not print_ckpt(flags.train_dir):
print_ckpt(flags.checkpoint_path)
pprint(flags.__flags, stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# pixel_link net parameters:', file=stream)
print('# =========================================================================== #', file=stream)
vars = globals()
for key in vars:
var = vars[key]
if util.dtype.is_number(var) or util.dtype.is_str(var) or util.dtype.is_list(var) or util.dtype.is_tuple(var):
pprint('%s=%s'%(key, str(var)), stream = stream)
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print('# =========================================================================== #', file=stream)
data_files = parallel_reader.get_data_files(dataset.data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def print_config(stream=None):
print('\n# =========================================================================== #', file=stream)
print('# SSD 网络参数:', file=stream)
print('# =========================================================================== #', file=stream)
pprint(dict(ssd_params._asdict()), stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# 训练数据dataset files:', file=stream)
print('# =========================================================================== #', file=stream)
data_files = parallel_reader.get_data_files(data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def _verify_read_up_to_out(self, shared_queue):
with self.test_session():
num_files = 3
num_records_per_file = 7
tfrecord_paths = test_utils.create_tfrecord_files(
self.get_temp_dir(),
num_files=num_files,
num_records_per_file=num_records_per_file)
p_reader = parallel_reader.ParallelReader(
io_ops.TFRecordReader, shared_queue, num_readers=5)
data_files = parallel_reader.get_data_files(tfrecord_paths)
filename_queue = input_lib.string_input_producer(data_files, num_epochs=1)
key, value = p_reader.read_up_to(filename_queue, 4)
count0 = 0
count1 = 0
count2 = 0
all_keys_count = 0
all_values_count = 0
sv = supervisor.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
while True:
try:
current_keys, current_values = sess.run([key, value])
self.assertEquals(len(current_keys), len(current_values))
all_keys_count += len(current_keys)
all_values_count += len(current_values)
for current_key in current_keys:
if '0-of-3' in str(current_key):
count0 += 1
if '1-of-3' in str(current_key):
count1 += 1
if '2-of-3' in str(current_key):
count2 += 1
except errors_impl.OutOfRangeError:
break
self.assertEquals(count0, num_records_per_file)
self.assertEquals(count1, num_records_per_file)
self.assertEquals(count2, num_records_per_file)
self.assertEquals(
all_keys_count,
num_files * num_records_per_file)
self.assertEquals(all_values_count, all_keys_count)
self.assertEquals(
count0 + count1 + count2,
all_keys_count)
def _verify_read_up_to_out(self, shared_queue):
with self.test_session():
num_files = 3
num_records_per_file = 7
tfrecord_paths = test_utils.create_tfrecord_files(
self.get_temp_dir(),
num_files=num_files,
num_records_per_file=num_records_per_file)
p_reader = parallel_reader.ParallelReader(io_ops.TFRecordReader,
shared_queue,
num_readers=5)
data_files = parallel_reader.get_data_files(tfrecord_paths)
filename_queue = input_lib.string_input_producer(data_files,
num_epochs=1)
key, value = p_reader.read_up_to(filename_queue, 4)
count0 = 0
count1 = 0
count2 = 0
all_keys_count = 0
all_values_count = 0
sv = supervisor.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
while True:
try:
current_keys, current_values = sess.run([key, value])
self.assertEquals(len(current_keys), len(current_values))
all_keys_count += len(current_keys)
all_values_count += len(current_values)
for current_key in current_keys:
if '0-of-3' in str(current_key):
count0 += 1
if '1-of-3' in str(current_key):
count1 += 1
if '2-of-3' in str(current_key):
count2 += 1
except errors_impl.OutOfRangeError:
break
self.assertEquals(count0, num_records_per_file)
self.assertEquals(count1, num_records_per_file)
self.assertEquals(count2, num_records_per_file)
self.assertEquals(all_keys_count, num_files * num_records_per_file)
self.assertEquals(all_values_count, all_keys_count)
self.assertEquals(count0 + count1 + count2, all_keys_count)
def print_config(stream=None):
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation flags:', file=stream)
print('# =========================================================================== #', file=stream)
pprint(flags, stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# SSD net parameters:', file=stream)
print('# =========================================================================== #', file=stream)
pprint(dict(ssd_params._asdict()), stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print('# =========================================================================== #', file=stream)
data_files = parallel_reader.get_data_files(data_sources)
pprint(sorted(data_files), stream=stream)
print('', file=stream)
def _verify_all_data_sources_read(self, shared_queue):
with self.cached_session():
tfrecord_paths = test_utils.create_tfrecord_files(
self.get_temp_dir(), num_files=3)
num_readers = len(tfrecord_paths)
p_reader = parallel_reader.ParallelReader(io_ops.TFRecordReader,
shared_queue,
num_readers=num_readers)
data_files = parallel_reader.get_data_files(tfrecord_paths)
filename_queue = input_lib.string_input_producer(data_files)
key, value = p_reader.read(filename_queue)
count0 = 0
count1 = 0
count2 = 0
num_reads = 50
sv = supervisor.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
for _ in range(num_reads):
current_key, _ = sess.run([key, value])
if '0-of-3' in str(current_key):
count0 += 1
if '1-of-3' in str(current_key):
count1 += 1
if '2-of-3' in str(current_key):
count2 += 1
self.assertGreater(count0, 0)
self.assertGreater(count1, 0)
self.assertGreater(count2, 0)
self.assertEquals(count0 + count1 + count2, num_reads)
def _verify_all_data_sources_read(self, shared_queue):
with self.test_session():
tfrecord_paths = test_utils.create_tfrecord_files(
self.get_temp_dir(), num_files=3)
num_readers = len(tfrecord_paths)
p_reader = parallel_reader.ParallelReader(
io_ops.TFRecordReader, shared_queue, num_readers=num_readers)
data_files = parallel_reader.get_data_files(tfrecord_paths)
filename_queue = input_lib.string_input_producer(data_files)
key, value = p_reader.read(filename_queue)
count0 = 0
count1 = 0
count2 = 0
num_reads = 50
sv = supervisor.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
for _ in range(num_reads):
current_key, _ = sess.run([key, value])
if '0-of-3' in str(current_key):
count0 += 1
if '1-of-3' in str(current_key):
count1 += 1
if '2-of-3' in str(current_key):
count2 += 1
self.assertGreater(count0, 0)
self.assertGreater(count1, 0)
self.assertGreater(count2, 0)
self.assertEquals(count0 + count1 + count2, num_reads)
