def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100,
data_dir='./data',
model_dir='./model')
flags = parser.parse_args(args=argv[1:])
train_path = os.path.join(flags.data_dir, 'train.tfrecord')
test_path = os.path.join(flags.data_dir, 'test.tfrecord')
_NUM_IMAGES['train'] = sum(
1 for _ in tf.python_io.tf_record_iterator(train_path))
_NUM_IMAGES['test'] = sum(
1 for _ in tf.python_io.tf_record_iterator(test_path))
# batch_size=32
# data_dir = './data',
# model_dir = './model'
# resnet_size = 50
# version = 2
# train_epochs = 100
# epochs_between_evals = 1
# max_train_steps = None
resnet_run_loop.resnet_main(flags, model_fn, input_fn)
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 26, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=90, version=1)
flags = parser.parse_args(args=argv[2:])
if flags.oss_load:
auth = oss2.Auth(_ACCESS_ID, _ACCESS_KEY)
bucket = oss2.Bucket(auth, _HOST, _BUCKET)
seed = int(argv[1])
print('Setting random seed = ', seed)
print('special seeding')
mlperf_log.resnet_print(key=mlperf_log.RUN_SET_RANDOM_SEED, value=seed)
random.seed(seed)
tf.set_random_seed(seed)
np.random.seed(seed)
mlperf_log.resnet_print(key=mlperf_log.PREPROC_NUM_TRAIN_EXAMPLES,
value=_NUM_IMAGES['train'])
mlperf_log.resnet_print(key=mlperf_log.PREPROC_NUM_EVAL_EXAMPLES,
value=_NUM_IMAGES['validation'])
input_function = input_fn
resnet_run_loop.resnet_main(
seed,
flags,
imagenet_model_fn,
input_function,
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100, data_dir='./data')
flags = parser.parse_args(args=argv[1:])
flags.model_dir = './no-lmk-model' if flags.no_lmk else './lmk-model'
_NUM_IMAGES['train'] = sum(1 for _ in tf.python_io.tf_record_iterator(
get_filenames(True, flags.data_dir)[0]))
_NUM_IMAGES['test'] = sum(1 for _ in tf.python_io.tf_record_iterator(
get_filenames(False, flags.data_dir)[0]))
# batch_size=32
# no-lmk = False
# data_dir = './data',
# model_dir = './lmk-model'
# resnet_size = 50
# version = 2
# train_epochs = 100
# epochs_between_evals = 1
# max_train_steps = None
resnet_run_loop.resnet_main(flags, model_fn, input_fn)
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100,
data_dir='../dataset',
model_dir='./model')
flags = parser.parse_args(args=argv[1:])
train_path = os.path.join(flags.data_dir, 'train.tfrecord')
validation_path = os.path.join(flags.data_dir, 'validation.tfrecord')
predict_path = os.path.join(flags.data_dir, 'predict.tfrecord')
_NUM_IMAGES['train'] = sum(
1 for _ in tf.python_io.tf_record_iterator(train_path))
_NUM_IMAGES['validation'] = sum(
1 for _ in tf.python_io.tf_record_iterator(validation_path))
_NUM_IMAGES['predict'] = sum(
1 for _ in tf.python_io.tf_record_iterator(predict_path))
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(
flags,
model_fn,
input_function,
shape=[_IMAGE_SIZE, _IMAGE_SIZE, _NUM_CHANNELS])
def run_cifar(flags_obj):
input_function = input_fn
resnet_run_loop.resnet_main(flags_obj,
cifar_10_model_fn,
input_function,
DATASET_NAME,
shape=[_HEIGHT, _WIDTH, _NUM_CLASSES])
def run_imagenet(flags_obj):
"""Run ResNet ImageNet training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
"""
input_function = input_fn
resnet_run_loop.resnet_main(
flags_obj, imagenet_model_fn, input_function, DATASET_NAME,
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(train_epochs=100)
flags = parser.parse_args(args=argv[1:])
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(flags, imagenet_model_fn, input_function)
def run_imagenet(flags_obj): """Run ResNet ImageNet training and eval loop. Args: flags_obj: An object containing parsed flag values. """ input_function = (flags_obj.use_synthetic_data and get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or input_fn) resnet_run_loop.resnet_main( flags_obj, imagenet_model_fn, input_function, DATASET_NAME, shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser()
# Set defaults that are reasonable for this model.
parser.set_defaults(data_dir='/tmp/cifar10_data',
model_dir='/tmp/cifar10_model',
resnet_size=32,
train_epochs=250,
epochs_between_evals=10,
batch_size=128,
select_device='NGRAPH')
flags = parser.parse_args(args=argv[1:])
input_function = flags.use_synthetic_data and get_synth_input_fn() or input_fn
resnet_run_loop.resnet_main(flags, cifar10_model_fn, input_function)
def run_imagenet(flags_obj):
"""Run ResNet ImageNet training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
Returns:
Dict of results of the run. Contains the keys `eval_results` and
`train_hooks`. `eval_results` contains accuracy (top_1) and
accuracy_top_5. `train_hooks` is a list the instances of hooks used during
training.
"""
# 选择输入数据还是合成数据，get_synth_input_fn是随机合成的数据
# input_fn是输入数据
input_function = (flags_obj.use_synthetic_data and get_synth_input_fn(
flags_core.get_tf_dtype(flags_obj)) or input_fn)
result = resnet_run_loop.resnet_main(
flags_obj,
imagenet_model_fn,
input_function,
DATASET_NAME,
shape=[DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE, NUM_CHANNELS])
return result
def run_cifar(flags_obj, config, conf_matrix):
"""Run ResNet CIFAR-10 training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
"""
if config._mode == 'predict':
input_function = input_fn_predict
else:
input_function = (flags_obj.use_synthetic_data
and get_synth_input_fn() or input_fn)
resnet_run_loop.resnet_main(flags_obj,
config,
conf_matrix,
cifar10_model_fn,
input_function,
DATASET_NAME,
shape=[_HEIGHT, _WIDTH, _NUM_CHANNELS])
def main(argv):
parser = resnet_run_loop.ResnetArgParser()
# Set defaults that are reasonable for this model.
parser.set_defaults(data_dir='cifar10_data',
model_dir='cifar10_model',
resnet_size=32,
train_epochs=250,
epochs_between_evals=10,
batch_size=128)
flags = parser.parse_args(args=argv[1:])
import pdb
pdb.set_trace()
input_function = flags.use_synthetic_data and get_synth_input_fn(
) or input_fn
resnet_run_loop.resnet_main(flags,
cifar10_model_fn,
input_function,
shape=[_HEIGHT, _WIDTH, _NUM_CHANNELS])
def run_cifar(flags_obj):
"""Run ResNet CIFAR-10 training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
"""
input_function = (flags_obj.use_synthetic_data and get_synth_input_fn()
or input_fn)
eval_accuracy = resnet_run_loop.resnet_main( # Xinyi modified
flags_obj, cifar10_model_fn, input_function, DATASET_NAME,
shape=[_HEIGHT, _WIDTH, _NUM_CHANNELS])
return eval_accuracy # Xinyi modified
def run_cifar(flags_obj):
"""Run ResNet CIFAR-10 training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
Returns:
Dictionary of results. Including final accuracy.
"""
if flags_obj.image_bytes_as_serving_input:
tf.compat.v1.logging.fatal(
'--image_bytes_as_serving_input cannot be set to True for CIFAR. '
'This flag is only applicable to ImageNet.')
return
input_function = (flags_obj.use_synthetic_data and get_synth_input_fn(
flags_core.get_tf_dtype(flags_obj)) or input_fn)
result = resnet_run_loop.resnet_main(flags_obj,
cifar10_model_fn,
input_function,
DATASET_NAME,
shape=[HEIGHT, WIDTH, NUM_CHANNELS])
return result