def main(_):
# Initialize Horovod (TODO: Remove dependency of horovod for freezing graphs)
hvd.init()
if not FLAGS.output_file:
raise ValueError(
'You must supply the path to save to with --output_file')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default() as graph:
if FLAGS.input_format == 'NCHW':
input_shape = [
FLAGS.batch_size, 3, FLAGS.image_size, FLAGS.image_size
]
else:
input_shape = [
FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3
]
input_images = tf.placeholder(name='input',
dtype=tf.float32,
shape=input_shape)
resnet50_config = resnet.model_architectures[FLAGS.model_name]
network = resnet.ResnetModel(FLAGS.model_name, FLAGS.num_classes,
resnet50_config['layers'],
resnet50_config['widths'],
resnet50_config['expansions'],
FLAGS.compute_format, FLAGS.input_format)
probs, logits = network.build_model(
input_images,
training=False,
reuse=False,
use_final_conv=FLAGS.use_final_conv)
if FLAGS.quantize:
tf.contrib.quantize.experimental_create_eval_graph(
symmetric=FLAGS.symmetric, use_qdq=FLAGS.use_qdq)
# Define the saver and restore the checkpoint
saver = tf.train.Saver()
with tf.Session() as sess:
if FLAGS.checkpoint:
saver.restore(sess, FLAGS.checkpoint)
else:
sess.run(tf.global_variables_initializer())
graph_def = graph.as_graph_def()
frozen_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph_def, [probs.op.name])
# Write out the frozen graph
tf.io.write_graph(frozen_graph_def,
os.path.dirname(FLAGS.output_file),
os.path.basename(FLAGS.output_file),
as_text=FLAGS.write_text_graphdef)
def __init__(
self,
# ========= Model HParams ========= #
n_classes=1001,
architecture='resnet50',
input_format='NHWC', # NCHW or NHWC
compute_format='NCHW', # NCHW or NHWC
dtype=tf.float32, # tf.float32 or tf.float16
n_channels=3,
height=224,
width=224,
distort_colors=False,
model_dir=None,
log_dir=None,
data_dir=None,
data_idx_dir=None,
weight_init="fan_out",
# ======= Optimization HParams ======== #
use_xla=False,
use_tf_amp=False,
use_dali=False,
gpu_memory_fraction=1.0,
gpu_id=0,
# ======== Debug Flags ======== #
debug_verbosity=0,
seed=None):
if dtype not in [tf.float32, tf.float16]:
raise ValueError(
"Unknown dtype received: %s (allowed: `tf.float32` and `tf.float16`)"
% dtype)
if compute_format not in ["NHWC", 'NCHW']:
raise ValueError(
"Unknown `compute_format` received: %s (allowed: ['NHWC', 'NCHW'])"
% compute_format)
if input_format not in ["NHWC", 'NCHW']:
raise ValueError(
"Unknown `input_format` received: %s (allowed: ['NHWC', 'NCHW'])"
% input_format)
if n_channels not in [1, 3]:
raise ValueError(
"Unsupported number of channels: %d (allowed: 1 (grayscale) and 3 (color))"
% n_channels)
tf_seed = 2 * (seed + hvd.rank()) if seed is not None else None
# ============================================
# Optimsation Flags - Do not remove
# ============================================
os.environ['CUDA_CACHE_DISABLE'] = '0'
os.environ['HOROVOD_GPU_ALLREDUCE'] = 'NCCL'
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
os.environ['TF_GPU_THREAD_COUNT'] = '1' if not hvd_utils.is_using_hvd(
) else str(hvd.size())
os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
os.environ['TF_ADJUST_HUE_FUSED'] = '1'
os.environ['TF_ADJUST_SATURATION_FUSED'] = '1'
os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
os.environ['TF_SYNC_ON_FINISH'] = '0'
os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'
os.environ['TF_DISABLE_NVTX_RANGES'] = '1'
os.environ["TF_XLA_FLAGS"] = (
os.environ.get("TF_XLA_FLAGS", "") +
" --tf_xla_enable_lazy_compilation=false")
# ============================================
# TF-AMP Setup - Do not remove
# ============================================
if dtype == tf.float16:
if use_tf_amp:
raise RuntimeError(
"TF AMP can not be activated for FP16 precision")
elif use_tf_amp:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
else:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "0"
# =================================================
model_hparams = tf.contrib.training.HParams(
width=height,
height=width,
n_channels=n_channels,
n_classes=n_classes,
dtype=dtype,
input_format=input_format,
compute_format=compute_format,
distort_colors=distort_colors,
seed=tf_seed)
num_preprocessing_threads = 10 if not use_dali else 4
run_config_performance = tf.contrib.training.HParams(
num_preprocessing_threads=num_preprocessing_threads,
use_tf_amp=use_tf_amp,
use_xla=use_xla,
use_dali=use_dali,
gpu_memory_fraction=gpu_memory_fraction,
gpu_id=gpu_id)
run_config_additional = tf.contrib.training.HParams(
model_dir=model_dir
if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
log_dir=log_dir
if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
data_dir=data_dir,
data_idx_dir=data_idx_dir,
num_preprocessing_threads=num_preprocessing_threads)
self.run_hparams = Runner._build_hparams(model_hparams,
run_config_additional,
run_config_performance)
model_name = architecture
architecture = resnet.model_architectures[architecture]
self._model = resnet.ResnetModel(
model_name=model_name,
n_classes=model_hparams.n_classes,
layers_count=architecture["layers"],
layers_depth=architecture["widths"],
expansions=architecture["expansions"],
input_format=model_hparams.input_format,
compute_format=model_hparams.compute_format,
dtype=model_hparams.dtype,
weight_init=weight_init,
use_dali=use_dali,
cardinality=architecture['cardinality']
if 'cardinality' in architecture else 1,
use_se=architecture['use_se']
if 'use_se' in architecture else False,
se_ratio=architecture['se_ratio']
if 'se_ratio' in architecture else 1)
if self.run_hparams.seed is not None:
np.random.seed(self.run_hparams.seed)
tf.set_random_seed(self.run_hparams.seed)
self.training_logging_hook = None
self.eval_logging_hook = None