def _get_session_config(mode, use_xla):
if mode not in ["train", 'validation', 'benchmark']:
raise ValueError(
"Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark')"
% mode)
config = tf.ConfigProto()
config.allow_soft_placement = True
config.log_device_placement = False
config.gpu_options.allow_growth = True
if hvd_utils.is_using_hvd():
config.gpu_options.visible_device_list = str(hvd.local_rank())
if use_xla:
LOGGER.log("XLA is activated - Experimental Feature")
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
config.gpu_options.force_gpu_compatible = True # Force pinned memory
if mode == 'train':
config.intra_op_parallelism_threads = 1 # Avoid pool of Eigen threads
if hvd_utils.is_using_hvd():
config.inter_op_parallelism_threads = max(
2, (multiprocessing.cpu_count() // hvd.size()) - 2)
else:
config.inter_op_parallelism_threads = 4
return config
def get_tfrecords_input_fn(filenames, batch_size, height, width, training,
distort_color, num_threads, deterministic):
shuffle_buffer_size = 4096
if deterministic:
if hvd_utils.is_using_hvd():
seed = 13 * (1 + hvd.rank())
else:
seed = 13
else:
seed = None
ds = tf.data.Dataset.from_tensor_slices(filenames)
if hvd_utils.is_using_hvd() and training:
ds = ds.shard(hvd.size(), hvd.rank())
ds = ds.interleave(tf.data.TFRecordDataset,
cycle_length=10,
block_length=8)
def preproc_func(record):
return image_processing.preprocess_image_record(
record, height, width, _NUM_CHANNELS, training)
if training:
ds = ds.shuffle(buffer_size=shuffle_buffer_size, seed=seed)
ds = ds.repeat().map(preproc_func, num_parallel_calls=num_threads)
ds = ds.batch(batch_size=batch_size, drop_remainder=True)
ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return ds
def _get_run_config(mode,
model_dir,
use_xla,
use_dali,
use_cpu,
gpu_memory_fraction,
gpu_id=0,
seed=None):
if mode not in ["train", 'validation', 'benchmark', 'inference']:
raise ValueError(
"Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark', 'inference')"
% mode)
if seed is not None:
if hvd_utils.is_using_hvd():
tf_random_seed = 2 * (seed + hvd.rank())
else:
tf_random_seed = 2 * seed
else:
tf_random_seed = None
config = tf.estimator.RunConfig(
model_dir=model_dir,
tf_random_seed=tf_random_seed,
save_summary_steps=100 if mode in ['train', 'validation'] else
1e9, # disabled in benchmark mode
save_checkpoints_steps=None,
save_checkpoints_secs=None,
session_config=Runner._get_session_config(
mode=mode,
use_xla=use_xla,
use_dali=use_dali,
use_cpu=use_cpu,
gpu_memory_fraction=gpu_memory_fraction,
gpu_id=gpu_id),
keep_checkpoint_max=5,
keep_checkpoint_every_n_hours=1e6, # disabled
log_step_count_steps=1e9,
train_distribute=None,
device_fn=None,
protocol=None,
eval_distribute=None,
experimental_distribute=None)
if mode == 'train':
if hvd_utils.is_using_hvd():
config = config.replace(
save_checkpoints_steps=1000 if hvd.rank() == 0 else None,
keep_checkpoint_every_n_hours=3)
else:
config = config.replace(save_checkpoints_steps=1000,
keep_checkpoint_every_n_hours=3)
return config
def get_tfrecords_input_fn(filenames, batch_size, height, width, training,
distort_color, num_threads, deterministic):
shuffle_buffer_size = 4096
if deterministic:
if hvd_utils.is_using_hvd():
seed = 13 * (1 + hvd.rank())
else:
seed = 13
else:
seed = None
ds = tf.data.Dataset.from_tensor_slices(filenames)
if hvd_utils.is_using_hvd() and training:
ds = ds.shard(hvd.size(), hvd.rank())
ds = ds.apply(
tf.data.experimental.parallel_interleave(tf.data.TFRecordDataset,
cycle_length=10,
block_length=8,
sloppy=not deterministic,
prefetch_input_elements=16))
counter = tf.data.Dataset.range(sys.maxsize)
ds = tf.data.Dataset.zip((ds, counter))
def preproc_func(record, counter_):
return image_processing.preprocess_image_record(
record, height, width, _NUM_CHANNELS, training)
ds = ds.cache()
if training:
ds = ds.apply(
tf.data.experimental.shuffle_and_repeat(
buffer_size=shuffle_buffer_size, seed=seed))
else:
ds = ds.repeat()
ds = ds.apply(
tf.data.experimental.map_and_batch(
map_func=preproc_func,
num_parallel_calls=num_threads,
batch_size=batch_size,
drop_remainder=True,
))
ds = ds.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
return ds
def _get_session_config(mode,
use_xla,
use_dali,
gpu_memory_fraction,
gpu_id=0):
if mode not in ["train", 'validation', 'benchmark', 'inference']:
raise ValueError(
"Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark', 'inference')"
% mode)
# Limit available GPU memory (tune the size)
if use_dali:
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = False
else:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
config.gpu_options.visible_device_list = str(gpu_id)
if hvd_utils.is_using_hvd():
config.gpu_options.visible_device_list = str(hvd.local_rank())
if use_xla:
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
config.gpu_options.force_gpu_compatible = True # Force pinned memory
# Bug - disable bn+relu fusion
from tensorflow.core.protobuf import rewriter_config_pb2
config.graph_options.rewrite_options.remapping = (
rewriter_config_pb2.RewriterConfig.OFF)
if mode == 'train':
config.intra_op_parallelism_threads = 1 # Avoid pool of Eigen threads
if hvd_utils.is_using_hvd():
config.inter_op_parallelism_threads = max(
2, (multiprocessing.cpu_count() // hvd.size()) - 2)
else:
config.inter_op_parallelism_threads = 4
return config
def _get_session_config(mode,
use_xla,
use_dali,
gpu_memory_fraction,
gpu_id=0):
if mode not in ["train", 'validation', 'benchmark', 'inference']:
raise ValueError(
"Unknown mode received: %s (allowed: 'train', 'validation', 'benchmark', 'inference')"
% mode)
# Limit available GPU memory (tune the size)
if use_dali:
LOGGER.log(
"DALI is activated, GPU memory fraction used for training is limited to",
gpu_memory_fraction)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = False
else:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
config.gpu_options.visible_device_list = str(gpu_id)
if hvd_utils.is_using_hvd():
config.gpu_options.visible_device_list = str(hvd.local_rank())
if use_xla:
LOGGER.log("XLA is activated - Experimental Feature")
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
config.gpu_options.force_gpu_compatible = True # Force pinned memory
if mode == 'train':
config.intra_op_parallelism_threads = 1 # Avoid pool of Eigen threads
if hvd_utils.is_using_hvd():
config.inter_op_parallelism_threads = max(
2, (multiprocessing.cpu_count() // hvd.size()) - 2)
else:
config.inter_op_parallelism_threads = 4
return config
def _augment_data(input_image, mask_image, label):
if augment_data:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print("Using data augmentation ...")
#input_image = tf.image.per_image_standardization(input_image)
horizontal_flip = tf.random_uniform(shape=(), seed=seed) > 0.5
input_image = tf.cond(
horizontal_flip,
lambda: tf.image.flip_left_right(input_image),
lambda: input_image)
mask_image = tf.cond(
horizontal_flip,
lambda: tf.image.flip_left_right(mask_image),
lambda: mask_image)
n_rots = tf.random_uniform(shape=(),
dtype=tf.int32,
minval=0,
maxval=3,
seed=seed)
input_image = tf.image.rot90(input_image, k=n_rots)
mask_image = tf.image.rot90(mask_image, k=n_rots)
return (input_image, mask_image), label
def training_data_fn():
if not is_benchmark or self.run_hparams.data_dir is not None:
return self.dataset.dataset_fn(
batch_size=batch_size,
training=True,
only_defective_images=True,
augment_data=augment_data,
input_shape=list(self.run_hparams.input_shape) +
[self.run_hparams.n_channels],
mask_shape=list(self.run_hparams.mask_shape) +
[self.run_hparams.n_channels],
num_threads=64,
use_gpu_prefetch=True,
normalize_data_method="zero_centered",
seed=self.run_hparams.seed)
else:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print("Using Synthetic Data ...")
return self.dataset.synth_dataset_fn(
batch_size=batch_size,
training=True,
input_shape=list(self.run_hparams.input_shape) +
[self.run_hparams.n_channels],
mask_shape=list(self.run_hparams.mask_shape) +
[self.run_hparams.n_channels],
num_threads=64,
use_gpu_prefetch=True,
normalize_data_method="zero_centered",
only_defective_images=True,
augment_data=augment_data,
seed=self.run_hparams.seed)
def after_run(self, run_context, run_values):
self.global_step = run_values.results[0] + 1
if is_using_hvd() and len(run_values.results) == 2:
if run_values.results[1] > 0:
run_context.request_stop()
elif self.signal_recieved:
run_context.request_stop()
def begin(self):
if is_using_hvd():
with tf.device("/cpu:0"):
self.input_op = tf.placeholder(tf.int32, shape=())
self.allreduce_op = hvd.allreduce(
self.input_op,
op=hvd.Sum,
name="signal_handler_all_reduce")
def before_run(self, run_context):
fetches = [tf.train.get_global_step()]
feed_dict = None
if is_using_hvd() and (self.global_step % self.sync_freq) == 0:
fetches += [self.allreduce_op]
feed_dict = {self.input_op: int(self.signal_recieved)}
return tf.train.SessionRunArgs(fetches, feed_dict=feed_dict)
def _log_hparams(classname, layername, **kwargs):
log_msg = "%s: `%s`" % (classname, layername)
for arg, val in sorted(kwargs.items()):
log_msg += "\n\t[*] {}: {}".format(arg, val)
log_msg += "\n"
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print(log_msg)
def after_run(self, run_context, run_values):
self.global_step = run_values.results[0]
if self.should_exit:
run_context.request_stop()
return
if is_using_hvd() and len(run_values.results) == 3:
self.should_exit = (run_values.results[2][0] == hvd.size())
else:
self.should_exit = self.signal_recieved
def before_run(self, run_context):
fetches = [tf.train.get_global_step()]
if is_using_hvd():
fetches.append("signal_handler_var_set:0" if self.
signal_recieved else "signal_handler_var:0")
if self.should_exit:
fetches.append("signal_handler_var_reset:0")
elif self.signal_recieved:
fetches.append("signal_handler_var_set:0")
else:
fetches.append("signal_handler_var:0")
if ((self.global_step % self.sync_freq)
== 0) and not self.should_exit:
fetches.append("signal_handler_all_reduce:0")
run_args = tf.train.SessionRunArgs(fetches)
return run_args
def __init__(
self,
# ========= Model HParams ========= #
n_classes=1001,
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,
# ======= Optimization HParams ======== #
use_xla=False,
use_tf_amp=False,
use_dali=False,
gpu_memory_fraction=1.0,
# ======== 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)
if data_dir is not None and not os.path.exists(data_dir):
raise ValueError("The `data_dir` received does not exists: %s" % data_dir)
hvd.init()
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'
# ============================================
# 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:
if hvd.rank() == 0:
LOGGER.log("TF AMP is activated - Experimental Feature")
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
# =================================================
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
)
if use_dali:
num_preprocessing_threads=4
else:
num_preprocessing_threads=10
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
)
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)
self._model = resnet_v1_5.ResnetModel(
model_name="resnet50_v1.5",
n_classes=model_hparams.n_classes,
input_format=model_hparams.input_format,
compute_format=model_hparams.compute_format,
dtype=model_hparams.dtype,
use_dali=use_dali
)
if self.run_hparams.seed is not None:
if hvd.rank() == 0:
LOGGER.log("Deterministic Run - Seed: %d" % seed)
tf.set_random_seed(self.run_hparams.seed)
def train(
self,
iter_unit,
num_iter,
batch_size,
warmup_steps=50,
weight_decay=1e-4,
lr_init=0.1,
lr_warmup_epochs=5,
momentum=0.9,
log_every_n_steps=1,
loss_scale=256,
label_smoothing=0.0,
use_cosine_lr=False,
use_static_loss_scaling=False,
is_benchmark=False
):
if iter_unit not in ["epoch", "batch"]:
raise ValueError('`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])' % iter_unit)
if self.run_hparams.data_dir is None and not is_benchmark:
raise ValueError('`data_dir` must be specified for training!')
if self.run_hparams.use_tf_amp or self.run_hparams.dtype == tf.float16:
if use_static_loss_scaling:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "0"
else:
LOGGER.log("TF Loss Auto Scaling is activated")
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1"
else:
use_static_loss_scaling = False # Make sure it hasn't been set to True on FP32 training
num_gpus = 1 if not hvd_utils.is_using_hvd() else hvd.size()
global_batch_size = batch_size * num_gpus
if self.run_hparams.data_dir is not None:
filenames,num_samples, num_steps, num_epochs, num_decay_steps = runner_utils.parse_tfrecords_dataset(
data_dir=self.run_hparams.data_dir,
mode="train",
iter_unit=iter_unit,
num_iter=num_iter,
global_batch_size=global_batch_size,
)
steps_per_epoch = num_steps / num_epochs
else:
num_epochs = 1
num_steps = num_iter
steps_per_epoch = num_steps
num_decay_steps = num_steps
num_samples = num_steps * batch_size
if self.run_hparams.data_idx_dir is not None:
idx_filenames = runner_utils.parse_dali_idx_dataset(
data_idx_dir=self.run_hparams.data_idx_dir,
mode="train"
)
training_hooks = []
if hvd.rank() == 0:
LOGGER.log('Starting Model Training...')
LOGGER.log("Training Epochs", num_epochs)
LOGGER.log("Total Steps", num_steps)
LOGGER.log("Steps per Epoch", steps_per_epoch)
LOGGER.log("Decay Steps", num_decay_steps)
LOGGER.log("Weight Decay Factor", weight_decay)
LOGGER.log("Init Learning Rate", lr_init)
LOGGER.log("Momentum", momentum)
LOGGER.log("Num GPUs", num_gpus)
LOGGER.log("Per-GPU Batch Size", batch_size)
if is_benchmark:
benchmark_logging_hook = hooks.BenchmarkLoggingHook(
log_file_path=os.path.join(self.run_hparams.log_dir, "training_benchmark.json"),
global_batch_size=global_batch_size,
log_every=log_every_n_steps,
warmup_steps=warmup_steps
)
training_hooks.append(benchmark_logging_hook)
else:
training_logging_hook = hooks.TrainingLoggingHook(
log_file_path=os.path.join(self.run_hparams.log_dir, "training.json"),
global_batch_size=global_batch_size,
num_steps=num_steps,
num_samples=num_samples,
num_epochs=num_epochs,
log_every=log_every_n_steps
)
training_hooks.append(training_logging_hook)
if hvd_utils.is_using_hvd():
bcast_hook = hvd.BroadcastGlobalVariablesHook(0)
training_hooks.append(bcast_hook)
training_hooks.append(hooks.PrefillStagingAreasHook())
# NVTX
nvtx_callback = NVTXHook(skip_n_steps=1, name='Train')
training_hooks.append(nvtx_callback)
estimator_params = {
'batch_size': batch_size,
'steps_per_epoch': steps_per_epoch,
'num_gpus': num_gpus,
'momentum': momentum,
'lr_init': lr_init,
'lr_warmup_epochs': lr_warmup_epochs,
'weight_decay': weight_decay,
'loss_scale': loss_scale,
'apply_loss_scaling': use_static_loss_scaling,
'label_smoothing': label_smoothing,
'num_decay_steps': num_decay_steps,
'use_cosine_lr': use_cosine_lr
}
image_classifier = self._get_estimator(
mode='train',
run_params=estimator_params,
use_xla=self.run_hparams.use_xla,
use_dali=self.run_hparams.use_dali,
gpu_memory_fraction=self.run_hparams.gpu_memory_fraction
)
def training_data_fn():
if self.run_hparams.use_dali and self.run_hparams.data_idx_dir is not None:
if hvd.rank() == 0:
LOGGER.log("Using DALI input... ")
return data_utils.get_dali_input_fn(
filenames=filenames,
idx_filenames=idx_filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=True,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False if self.run_hparams.seed is None else True
)
elif self.run_hparams.data_dir is not None:
return data_utils.get_tfrecords_input_fn(
filenames=filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=True,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False if self.run_hparams.seed is None else True
)
else:
if hvd.rank() == 0:
LOGGER.log("Using Synthetic Data ...")
return data_utils.get_synth_input_fn(
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
num_channels=self.run_hparams.n_channels,
data_format=self.run_hparams.input_format,
num_classes=self.run_hparams.n_classes,
dtype=self.run_hparams.dtype,
)
try:
image_classifier.train(
input_fn=training_data_fn,
steps=num_steps,
hooks=training_hooks,
)
except KeyboardInterrupt:
print("Keyboard interrupt")
if hvd.rank() == 0:
LOGGER.log('Ending Model Training ...')
def __call__(self, features, labels, mode, params):
if mode == tf.estimator.ModeKeys.TRAIN:
mandatory_params = ["batch_size", "lr_init", "num_gpus", "steps_per_epoch",
"momentum", "weight_decay", "loss_scale", "label_smoothing"]
for p in mandatory_params:
if p not in params:
raise RuntimeError("Parameter {} is missing.".format(p))
if mode == tf.estimator.ModeKeys.TRAIN and not self.model_hparams.use_dali:
with tf.device('/cpu:0'):
# Stage inputs on the host
cpu_prefetch_op, (features, labels) = self._stage([features, labels])
with tf.device('/gpu:0'):
# Stage inputs to the device
gpu_prefetch_op, (features, labels) = self._stage([features, labels])
with tf.device("/gpu:0"):
if features.dtype != self.model_hparams.dtype:
features = tf.cast(features, self.model_hparams.dtype)
# Subtract mean per channel
# and enforce values between [-1, 1]
if not self.model_hparams.use_dali:
features = normalized_inputs(features)
mixup = 0
eta = 0
if mode == tf.estimator.ModeKeys.TRAIN:
eta = params['label_smoothing']
mixup = params['mixup']
if mode != tf.estimator.ModeKeys.PREDICT:
one_hot_smoothed_labels = tf.one_hot(labels, 1001,
on_value = 1 - eta + eta/1001,
off_value = eta/1001)
if mixup != 0:
print("Using mixup training with beta=", params['mixup'])
beta_distribution = tf.distributions.Beta(params['mixup'], params['mixup'])
feature_coefficients = beta_distribution.sample(sample_shape=[params['batch_size'], 1, 1, 1])
reversed_feature_coefficients = tf.subtract(tf.ones(shape=feature_coefficients.shape), feature_coefficients)
rotated_features = tf.reverse(features, axis=[0])
features = feature_coefficients * features + reversed_feature_coefficients * rotated_features
label_coefficients = tf.squeeze(feature_coefficients, axis=[2, 3])
rotated_labels = tf.reverse(one_hot_smoothed_labels, axis=[0])
reversed_label_coefficients = tf.subtract(tf.ones(shape=label_coefficients.shape), label_coefficients)
one_hot_smoothed_labels = label_coefficients * one_hot_smoothed_labels + reversed_label_coefficients * rotated_labels
# Update Global Step
global_step = tf.train.get_or_create_global_step()
tf.identity(global_step, name="global_step_ref")
tf.identity(features, name="features_ref")
if mode == tf.estimator.ModeKeys.TRAIN:
tf.identity(labels, name="labels_ref")
probs, logits = self.build_model(
features,
training=mode == tf.estimator.ModeKeys.TRAIN,
reuse=False
)
y_preds = tf.argmax(logits, axis=1, output_type=tf.int32)
# Check the output dtype, shall be FP32 in training
assert (probs.dtype == tf.float32)
assert (logits.dtype == tf.float32)
assert (y_preds.dtype == tf.int32)
tf.identity(logits, name="logits_ref")
tf.identity(probs, name="probs_ref")
tf.identity(y_preds, name="y_preds_ref")
#if mode == tf.estimator.ModeKeys.TRAIN:
#
# assert (len(tf.trainable_variables()) == 161)
#
#else:
#
# assert (len(tf.trainable_variables()) == 0)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {'classes': y_preds, 'probabilities': probs}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={'predict': tf.estimator.export.PredictOutput(predictions)}
)
else:
with tf.device("/gpu:0"):
if mode == tf.estimator.ModeKeys.TRAIN:
acc_top1 = tf.nn.in_top_k(predictions=logits, targets=labels, k=1)
acc_top5 = tf.nn.in_top_k(predictions=logits, targets=labels, k=5)
else:
acc_top1, acc_top1_update_op = tf.metrics.mean(tf.nn.in_top_k(predictions=logits, targets=labels, k=1))
acc_top5, acc_top5_update_op = tf.metrics.mean(tf.nn.in_top_k(predictions=logits, targets=labels, k=5))
tf.identity(acc_top1, name="acc_top1_ref")
tf.identity(acc_top5, name="acc_top5_ref")
predictions = {
'classes': y_preds,
'probabilities': probs,
'accuracy_top1': acc_top1,
'accuracy_top5': acc_top5
}
cross_entropy = tf.losses.softmax_cross_entropy(
logits=logits, onehot_labels=one_hot_smoothed_labels)
assert (cross_entropy.dtype == tf.float32)
tf.identity(cross_entropy, name='cross_entropy_loss_ref')
def loss_filter_fn(name):
"""we don't need to compute L2 loss for BN and bias (eq. to add a cste)"""
return all([
tensor_name not in name.lower()
# for tensor_name in ["batchnorm", "batch_norm", "batch_normalization", "bias"]
for tensor_name in ["batchnorm", "batch_norm", "batch_normalization"]
])
filtered_params = [tf.cast(v, tf.float32) for v in tf.trainable_variables() if loss_filter_fn(v.name)]
if len(filtered_params) != 0:
l2_loss_per_vars = [tf.nn.l2_loss(v) for v in filtered_params]
l2_loss = tf.multiply(tf.add_n(l2_loss_per_vars), params["weight_decay"])
else:
l2_loss = tf.zeros(shape=(), dtype=tf.float32)
assert (l2_loss.dtype == tf.float32)
tf.identity(l2_loss, name='l2_loss_ref')
total_loss = tf.add(cross_entropy, l2_loss, name="total_loss")
assert (total_loss.dtype == tf.float32)
tf.identity(total_loss, name='total_loss_ref')
tf.summary.scalar('cross_entropy', cross_entropy)
tf.summary.scalar('l2_loss', l2_loss)
tf.summary.scalar('total_loss', total_loss)
if mode == tf.estimator.ModeKeys.TRAIN:
with tf.device("/cpu:0"):
learning_rate = learning_rate_scheduler(
lr_init=params["lr_init"],
lr_warmup_epochs=params["lr_warmup_epochs"],
global_step=global_step,
batch_size=params["batch_size"],
num_batches_per_epoch=params["steps_per_epoch"],
num_decay_steps=params["num_decay_steps"],
num_gpus=params["num_gpus"],
use_cosine_lr=params["use_cosine_lr"]
)
tf.identity(learning_rate, name='learning_rate_ref')
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=params["momentum"])
if params["apply_loss_scaling"]:
optimizer = FixedLossScalerOptimizer(optimizer, scale=params["loss_scale"])
if hvd_utils.is_using_hvd():
optimizer = hvd.DistributedOptimizer(optimizer)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if mode != tf.estimator.ModeKeys.TRAIN:
update_ops += [acc_top1_update_op, acc_top5_update_op]
deterministic = True
gate_gradients = (tf.train.Optimizer.GATE_OP if deterministic else tf.train.Optimizer.GATE_NONE)
backprop_op = optimizer.minimize(total_loss, gate_gradients=gate_gradients, global_step=global_step)
if self.model_hparams.use_dali:
train_ops = tf.group(backprop_op, update_ops, name='train_ops')
else:
train_ops = tf.group(backprop_op, cpu_prefetch_op, gpu_prefetch_op, update_ops, name='train_ops')
return tf.estimator.EstimatorSpec(mode=mode, loss=total_loss, train_op=train_ops)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = {
"top1_accuracy": (acc_top1, acc_top1_update_op),
"top5_accuracy": (acc_top5, acc_top5_update_op)
}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=total_loss,
eval_metric_ops=eval_metrics
)
else:
raise NotImplementedError('Unknown mode {}'.format(mode))
runner.train(
iter_unit=RUNNING_CONFIG.iter_unit,
num_iter=RUNNING_CONFIG.num_iter,
batch_size=RUNNING_CONFIG.batch_size,
warmup_steps=RUNNING_CONFIG.warmup_steps,
log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,
weight_decay=RUNNING_CONFIG.weight_decay,
learning_rate_init=RUNNING_CONFIG.learning_rate_init,
momentum=RUNNING_CONFIG.momentum,
loss_scale=RUNNING_CONFIG.loss_scale,
use_static_loss_scaling=FLAGS.use_static_loss_scaling,
is_benchmark=RUNNING_CONFIG.mode == 'training_benchmark',
)
if RUNNING_CONFIG.mode in ["train_and_evaluate", 'evaluate', 'inference_benchmark']:
if RUNNING_CONFIG.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
raise NotImplementedError("Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.evaluate(
iter_unit=RUNNING_CONFIG.iter_unit if RUNNING_CONFIG.mode != "train_and_evaluate" else "epoch",
num_iter=RUNNING_CONFIG.num_iter if RUNNING_CONFIG.mode != "train_and_evaluate" else 1,
warmup_steps=RUNNING_CONFIG.warmup_steps,
batch_size=RUNNING_CONFIG.batch_size,
log_every_n_steps=RUNNING_CONFIG.log_every_n_steps,
is_benchmark=RUNNING_CONFIG.mode == 'inference_benchmark'
)
def train(self,
iter_unit,
num_iter,
batch_size,
weight_decay,
learning_rate,
learning_rate_decay_factor,
learning_rate_decay_steps,
rmsprop_decay,
rmsprop_momentum,
use_auto_loss_scaling,
augment_data,
warmup_steps=50,
is_benchmark=False):
if iter_unit not in ["epoch", "batch"]:
raise ValueError(
'`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])'
% iter_unit)
if self.run_hparams.data_dir is None and not is_benchmark:
raise ValueError('`data_dir` must be specified for training!')
if self.run_hparams.amp:
if use_auto_loss_scaling:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print(
"TF Loss Auto Scaling is activated - Experimental Feature"
)
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1"
apply_manual_loss_scaling = False
else:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "0"
apply_manual_loss_scaling = True
else:
apply_manual_loss_scaling = False
global_batch_size = batch_size * self.num_gpus
if self.run_hparams.data_dir is not None:
filenames, num_samples, num_steps, num_epochs = self.dataset.get_dataset_runtime_specs(
training=True,
iter_unit=iter_unit,
num_iter=num_iter,
global_batch_size=global_batch_size)
steps_per_epoch = int(num_steps / num_epochs)
else:
num_epochs = 1
num_steps = num_iter
steps_per_epoch = 625
training_hooks = []
if hvd_utils.is_using_hvd():
training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
training_hooks.append(
ProfilerHook(global_batch_size=global_batch_size,
log_every=self.run_hparams.log_every_n_steps,
warmup_steps=warmup_steps,
is_training=True,
sample_dir=self.run_hparams.sample_dir))
print("Starting Model Training ...")
Logger.log(step=('PARAMETER'),
data={"Epochs": num_epochs},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"Total Steps": num_steps},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"Steps per Epoch": steps_per_epoch},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"Weight Decay Factor": weight_decay},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"Learning Rate": learning_rate},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={
"Learning Rate Decay Factor":
learning_rate_decay_factor
},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(
step=('PARAMETER'),
data={"Learning Rate Decay Steps": learning_rate_decay_steps},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"RMSProp - Decay": rmsprop_decay},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"RMSProp - Momentum": rmsprop_momentum},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(
step=('PARAMETER'),
data={"Loss Function Name": self.run_hparams.loss_fn_name},
verbosity=Logger.Verbosity.DEFAULT)
if self.run_hparams.amp:
Logger.log(
step=('PARAMETER'),
data={"Use Auto Loss Scaling": use_auto_loss_scaling},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"# GPUs": self.num_gpus},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"GPU Batch Size": batch_size},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={"Global Batch Size": global_batch_size},
verbosity=Logger.Verbosity.DEFAULT)
Logger.log(step=('PARAMETER'),
data={
"Total Files to be Processed":
num_steps * global_batch_size
},
verbosity=Logger.Verbosity.DEFAULT)
print() # visual spacing
estimator_params = {
'batch_size': batch_size,
'steps_per_epoch': steps_per_epoch,
'learning_rate': learning_rate,
'learning_rate_decay_steps': learning_rate_decay_steps,
'learning_rate_decay_factor': learning_rate_decay_factor,
'rmsprop_decay': rmsprop_decay,
'rmsprop_momentum': rmsprop_momentum,
'weight_decay': weight_decay,
'apply_manual_loss_scaling': apply_manual_loss_scaling,
'loss_fn_name': self.run_hparams.loss_fn_name,
'debug_verbosity': self.run_hparams.debug_verbosity,
}
def training_data_fn():
if not is_benchmark or self.run_hparams.data_dir is not None:
return self.dataset.dataset_fn(
batch_size=batch_size,
training=True,
only_defective_images=True,
augment_data=augment_data,
input_shape=list(self.run_hparams.input_shape) +
[self.run_hparams.n_channels],
mask_shape=list(self.run_hparams.mask_shape) +
[self.run_hparams.n_channels],
num_threads=64,
use_gpu_prefetch=True,
normalize_data_method="zero_centered",
seed=self.run_hparams.seed)
else:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print("Using Synthetic Data ...")
return self.dataset.synth_dataset_fn(
batch_size=batch_size,
training=True,
input_shape=list(self.run_hparams.input_shape) +
[self.run_hparams.n_channels],
mask_shape=list(self.run_hparams.mask_shape) +
[self.run_hparams.n_channels],
num_threads=64,
use_gpu_prefetch=True,
normalize_data_method="zero_centered",
only_defective_images=True,
augment_data=augment_data,
seed=self.run_hparams.seed)
model = self._get_estimator(mode='train',
run_params=estimator_params,
xla=self.xla)
try:
model.train(
input_fn=training_data_fn,
steps=num_steps,
hooks=training_hooks,
)
except KeyboardInterrupt:
print("Keyboard interrupt")
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print('Ending Model Training ...')
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
def build_stats(history,
validation_output,
train_callbacks,
eval_callbacks,
logger,
comment=''):
stats = {}
stats['comment'] = comment
if validation_output:
stats['eval_loss'] = float(validation_output[0])
stats['eval_accuracy_top_1'] = float(validation_output[1])
stats['eval_accuracy_top_5'] = float(validation_output[2])
#This part is train loss on GPU_0
if history and history.history:
train_hist = history.history
#Gets final loss from training.
stats['training_loss'] = float(
hvd.allreduce(tf.constant(train_hist['loss'][-1],
dtype=tf.float32),
average=True))
# Gets top_1 training accuracy.
if 'categorical_accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(
hvd.allreduce(tf.constant(
train_hist['categorical_accuracy'][-1], dtype=tf.float32),
average=True))
elif 'sparse_categorical_accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(
hvd.allreduce(tf.constant(
train_hist['sparse_categorical_accuracy'][-1],
dtype=tf.float32),
average=True))
elif 'accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(
hvd.allreduce(tf.constant(train_hist['accuracy'][-1],
dtype=tf.float32),
average=True))
stats['training_accuracy_top_5'] = float(
hvd.allreduce(tf.constant(train_hist['top_5_accuracy'][-1],
dtype=tf.float32),
average=True))
# Look for the time history callback which was used during keras.fit
if train_callbacks:
for callback in train_callbacks:
if isinstance(callback, callbacks.TimeHistory):
if callback.epoch_runtime_log:
stats[
'avg_exp_per_second_training'] = callback.average_examples_per_second
stats[
'avg_exp_per_second_training_per_GPU'] = callback.average_examples_per_second / hvd.size(
)
if eval_callbacks:
for eval_callback in eval_callbacks:
if not isinstance(eval_callback, callbacks.EvalTimeHistory):
continue
stats['avg_exp_per_second_eval'] = float(
eval_callback.average_examples_per_second
) # * hvd.size(), performing one-gpu evluation now
stats['avg_exp_per_second_eval_per_GPU'] = float(
eval_callback.average_examples_per_second)
stats['avg_time_per_exp_eval'] = 1000. / stats[
'avg_exp_per_second_eval']
batch_time = eval_callback.batch_time
batch_time.sort()
latency_pct_per_batch = sum(
batch_time[:-1]) / int(len(batch_time) - 1)
stats['latency_pct'] = 1000.0 * latency_pct_per_batch
latency_90pct_per_batch = sum(
batch_time[:int(0.9 * len(batch_time))]) / int(
0.9 * len(batch_time))
stats['latency_90pct'] = 1000.0 * latency_90pct_per_batch
latency_95pct_per_batch = sum(
batch_time[:int(0.95 * len(batch_time))]) / int(
0.95 * len(batch_time))
stats['latency_95pct'] = 1000.0 * latency_95pct_per_batch
latency_99pct_per_batch = sum(
batch_time[:int(0.99 * len(batch_time))]) / int(
0.99 * len(batch_time))
stats['latency_99pct'] = 1000.0 * latency_99pct_per_batch
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
logger.log(step=(), data=stats)
loss_scale=FLAGS.static_loss_scale,
label_smoothing=FLAGS.label_smoothing,
mixup=FLAGS.mixup,
use_static_loss_scaling=(FLAGS.static_loss_scale != -1),
use_cosine_lr=FLAGS.cosine_lr,
is_benchmark=FLAGS.mode == 'training_benchmark',
use_final_conv=FLAGS.use_final_conv,
quantize=FLAGS.quantize,
symmetric=FLAGS.symmetric,
quant_delay=FLAGS.quant_delay,
use_qdq=FLAGS.use_qdq,
finetune_checkpoint=FLAGS.finetune_checkpoint)
if FLAGS.mode in ["train_and_evaluate", 'evaluate', 'inference_benchmark']:
if FLAGS.mode == 'inference_benchmark' and hvd_utils.is_using_hvd():
raise NotImplementedError(
"Only single GPU inference is implemented.")
elif not hvd_utils.is_using_hvd() or hvd.rank() == 0:
runner.evaluate(iter_unit=FLAGS.iter_unit
if FLAGS.mode != "train_and_evaluate" else "epoch",
num_iter=FLAGS.num_iter
if FLAGS.mode != "train_and_evaluate" else 1,
warmup_steps=FLAGS.warmup_steps,
batch_size=FLAGS.batch_size,
log_every_n_steps=FLAGS.display_every,
is_benchmark=FLAGS.mode == 'inference_benchmark',
export_dir=FLAGS.export_dir,
quantize=FLAGS.quantize,
def __call__(self, features, labels, mode, params):
# print(params)
if mode == tf.estimator.ModeKeys.TRAIN:
if "batch_size" not in params.keys():
raise RuntimeError("Parameter `batch_size` is missing...")
if "learning_rate_init" not in params.keys():
raise RuntimeError("Parameter `learning_rate` is missing...")
if "num_gpus" not in params.keys():
raise RuntimeError("Parameter `num_gpus` is missing...")
if "steps_per_epoch" not in params.keys():
raise RuntimeError("Parameter `steps_per_epoch` is missing...")
if "momentum" not in params.keys():
raise RuntimeError("Parameter `momentum` is missing...")
if "weight_decay" not in params.keys():
raise RuntimeError("Parameter `weight_decay` is missing...")
if "loss_scale" not in params.keys():
raise RuntimeError("Parameter `loss_scale` is missing...")
if mode == tf.estimator.ModeKeys.TRAIN:
with tf.device('/cpu:0'):
# Stage inputs on the host
cpu_prefetch_op, (features, labels) = ResnetModel._stage(
[features, labels])
with tf.device('/gpu:0'):
# Stage inputs to the device
gpu_prefetch_op, (features, labels) = ResnetModel._stage(
[features, labels])
with tf.device("/gpu:0"):
if True: # not params['use_trt']:
if features.dtype != self.model_hparams.dtype:
features = tf.cast(features, self.model_hparams.dtype)
# Subtract mean per channel
# and enforce values between [-1, 1]
# features = normalized_inputs(features)
# Update Global Step
global_step = tf.train.get_or_create_global_step()
tf.identity(global_step, name="global_step_ref")
# tf.identity(features, name="features_ref")
# tf.identity(labels, name="labels_ref")
probs, logits = self.build_model(
features,
training=mode == tf.estimator.ModeKeys.TRAIN,
reuse=False)
else:
trt_graph = trt.create_inference_graph(
input_graph_def=None,
outputs=None,
input_saved_model_dir=os.path.join(
self.model_hparams.model_dir, '1554216247'),
input_saved_model_tags=['serve'],
max_batch_size=params["batch_size"],
max_workspace_size_bytes=1 << 20,
precision_mode="FP32")
for node in trt_graph.node:
print(node.name)
y_preds = tf.import_graph_def(
trt_graph,
return_elements=['resnet50_v1.5/output/softmax:0'])
predictions = {'classes': y_preds[0]}
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions)
y_preds = tf.argmax(logits, axis=1, output_type=tf.int32)
# Check the output dtype, shall be FP32 in training
assert (probs.dtype == tf.float32)
assert (logits.dtype == tf.float32)
assert (y_preds.dtype == tf.int32)
tf.identity(logits, name="logits_ref")
tf.identity(probs, name="probs_ref")
tf.identity(y_preds, name="y_preds_ref")
if mode == tf.estimator.ModeKeys.TRAIN:
assert (len(tf.trainable_variables()) == 161)
else:
assert (len(tf.trainable_variables()) == 0)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {'classes': y_preds, 'probabilities': probs}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
'predict': tf.estimator.export.PredictOutput(predictions)
})
else:
with tf.device("/gpu:0"):
if mode == tf.estimator.ModeKeys.TRAIN:
acc_top1 = tf.nn.in_top_k(predictions=logits,
targets=labels,
k=1)
acc_top5 = tf.nn.in_top_k(predictions=logits,
targets=labels,
k=5)
else:
acc_top1, acc_top1_update_op = tf.metrics.mean(
tf.nn.in_top_k(predictions=logits, targets=labels,
k=1))
acc_top5, acc_top5_update_op = tf.metrics.mean(
tf.nn.in_top_k(predictions=logits, targets=labels,
k=5))
tf.identity(acc_top1, name="acc_top1_ref")
tf.identity(acc_top5, name="acc_top5_ref")
predictions = {
'classes': y_preds,
'probabilities': probs,
'accuracy_top1': acc_top1,
'accuracy_top5': acc_top5
}
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
logits=logits, labels=labels)
assert (cross_entropy.dtype == tf.float32)
tf.identity(cross_entropy, name='cross_entropy_loss_ref')
def loss_filter_fn(name):
"""we don't need to compute L2 loss for BN and bias (eq. to add a cste)"""
return all([
tensor_name not in name.lower()
# for tensor_name in ["batchnorm", "batch_norm", "batch_normalization", "bias"]
for tensor_name in
["batchnorm", "batch_norm", "batch_normalization"]
])
filtered_params = [
tf.cast(v, tf.float32) for v in tf.trainable_variables()
if loss_filter_fn(v.name)
]
if len(filtered_params) != 0:
l2_loss_per_vars = [
tf.nn.l2_loss(v) for v in filtered_params
]
l2_loss = tf.multiply(tf.add_n(l2_loss_per_vars),
params["weight_decay"])
else:
l2_loss = tf.zeros(shape=(), dtype=tf.float32)
assert (l2_loss.dtype == tf.float32)
tf.identity(l2_loss, name='l2_loss_ref')
total_loss = tf.add(cross_entropy, l2_loss, name="total_loss")
assert (total_loss.dtype == tf.float32)
tf.identity(total_loss, name='total_loss_ref')
tf.summary.scalar('cross_entropy', cross_entropy)
tf.summary.scalar('l2_loss', l2_loss)
tf.summary.scalar('total_loss', total_loss)
if mode == tf.estimator.ModeKeys.TRAIN:
with tf.device("/cpu:0"):
learning_rate = learning_rate_scheduler(
learning_rate_init=params["learning_rate_init"],
global_step=global_step,
batch_size=params["batch_size"],
num_batches_per_epoch=params["steps_per_epoch"],
num_gpus=params["num_gpus"])
tf.identity(learning_rate, name='learning_rate_ref')
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.MomentumOptimizer(
learning_rate=learning_rate,
momentum=params["momentum"])
if params["apply_loss_scaling"]:
optimizer = FixedLossScalerOptimizer(
optimizer, scale=params["loss_scale"])
if hvd_utils.is_using_hvd():
optimizer = hvd.DistributedOptimizer(optimizer)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if mode != tf.estimator.ModeKeys.TRAIN:
update_ops += [acc_top1_update_op, acc_top5_update_op]
deterministic = True
gate_gradients = (tf.train.Optimizer.GATE_OP
if deterministic else
tf.train.Optimizer.GATE_NONE)
backprop_op = optimizer.minimize(
total_loss,
gate_gradients=gate_gradients,
global_step=global_step)
train_ops = tf.group(backprop_op,
cpu_prefetch_op,
gpu_prefetch_op,
update_ops,
name='train_ops')
return tf.estimator.EstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_ops)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = {
"top1_accuracy": (acc_top1, acc_top1_update_op),
"top5_accuracy": (acc_top5, acc_top5_update_op)
}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=total_loss,
eval_metric_ops=eval_metrics)
else:
raise NotImplementedError('Unknown mode {}'.format(mode))
def evaluate(
self,
iter_unit,
num_iter,
batch_size,
warmup_steps=50,
log_every_n_steps=1,
is_benchmark=False
):
if iter_unit not in ["epoch", "batch"]:
raise ValueError('`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])' % iter_unit)
if self.run_hparams.data_dir is None and not is_benchmark:
raise ValueError('`data_dir` must be specified for evaluation!')
if hvd_utils.is_using_hvd() and hvd.rank() != 0:
raise RuntimeError('Multi-GPU inference is not supported')
estimator_params = {}
image_classifier = self._get_estimator(
mode='validation',
run_params=estimator_params,
use_xla=self.run_hparams.use_xla,
use_dali=self.run_hparams.use_dali,
gpu_memory_fraction=self.run_hparams.gpu_memory_fraction
)
if self.run_hparams.data_dir is not None:
filenames, num_samples, num_steps, num_epochs, num_decay_steps = runner_utils.parse_tfrecords_dataset(
data_dir=self.run_hparams.data_dir,
mode="validation",
iter_unit=iter_unit,
num_iter=num_iter,
global_batch_size=batch_size,
)
else:
num_epochs = 1
num_decay_steps = -1
num_steps = num_iter
if self.run_hparams.data_idx_dir is not None:
idx_filenames = runner_utils.parse_dali_idx_dataset(
data_idx_dir=self.run_hparams.data_idx_dir,
mode="validation"
)
eval_hooks = []
if hvd.rank() == 0:
if is_benchmark:
benchmark_logging_hook = hooks.BenchmarkLoggingHook(
log_file_path=os.path.join(self.run_hparams.log_dir, "eval_benchmark.json"),
global_batch_size=batch_size,
log_every=log_every_n_steps,
warmup_steps=warmup_steps
)
eval_hooks.append(benchmark_logging_hook)
LOGGER.log('Starting Model Evaluation...')
LOGGER.log("Evaluation Epochs", num_epochs)
LOGGER.log("Evaluation Steps", num_steps)
LOGGER.log("Decay Steps", num_decay_steps)
LOGGER.log("Global Batch Size", batch_size)
def evaluation_data_fn():
if self.run_hparams.use_dali and self.run_hparams.data_idx_dir is not None:
if hvd.rank() == 0:
LOGGER.log("Using DALI input... ")
return data_utils.get_dali_input_fn(
filenames=filenames,
idx_filenames=idx_filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=False,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False if self.run_hparams.seed is None else True
)
elif self.run_hparams.data_dir is not None:
return data_utils.get_tfrecords_input_fn(
filenames=filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=False,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False if self.run_hparams.seed is None else True
)
else:
LOGGER.log("Using Synthetic Data ...\n")
return data_utils.get_synth_input_fn(
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
num_channels=self.run_hparams.n_channels,
data_format=self.run_hparams.input_format,
num_classes=self.run_hparams.n_classes,
dtype=self.run_hparams.dtype,
)
try:
eval_results = image_classifier.evaluate(
input_fn=evaluation_data_fn,
steps=num_steps,
hooks=eval_hooks,
)
LOGGER.log('Top-1 Accuracy: %.3f' % float(eval_results['top1_accuracy'] * 100))
LOGGER.log('Top-5 Accuracy: %.3f' % float(eval_results['top5_accuracy'] * 100))
except KeyboardInterrupt:
print("Keyboard interrupt")
LOGGER.log('Ending Model Evaluation ...')
def _get_global_batch_size(worker_batch_size):
if hvd_utils.is_using_hvd():
return worker_batch_size * hvd.size()
else:
return worker_batch_size
def __call__(self, features, labels, mode, params):
if "debug_verbosity" not in params.keys():
raise RuntimeError("Parameter `debug_verbosity` is missing...")
if mode == tf.estimator.ModeKeys.TRAIN:
if "rmsprop_decay" not in params.keys():
raise RuntimeError("Parameter `rmsprop_decay` is missing...")
if "rmsprop_momentum" not in params.keys():
raise RuntimeError(
"Parameter `rmsprop_momentum` is missing...")
if "learning_rate" not in params.keys():
raise RuntimeError("Parameter `learning_rate` is missing...")
if "learning_rate_decay_steps" not in params.keys():
raise RuntimeError("Parameter `learning_rate` is missing...")
if "learning_rate_decay_factor" not in params.keys():
raise RuntimeError("Parameter `learning_rate` is missing...")
if "weight_decay" not in params.keys():
raise RuntimeError("Parameter `weight_decay` is missing...")
if "loss_fn_name" not in params.keys():
raise RuntimeError("Parameter `loss_fn_name` is missing...")
if mode == tf.estimator.ModeKeys.PREDICT:
y_pred, y_pred_logits = self.build_model(
features,
training=False,
reuse=False,
debug_verbosity=params["debug_verbosity"])
predictions = {'logits': y_pred}
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions)
input_image, mask_image = features
with tf.device("/gpu:0"):
tf.identity(input_image, name="input_image_ref")
tf.identity(mask_image, name="mask_image_ref")
tf.identity(labels, name="labels_ref")
y_pred, y_pred_logits = self.build_model(
input_image,
training=mode == tf.estimator.ModeKeys.TRAIN,
reuse=False,
debug_verbosity=params["debug_verbosity"])
all_trainable_vars = tf.reduce_sum(
[tf.reduce_prod(v.shape) for v in tf.trainable_variables()])
tf.identity(all_trainable_vars,
name='trainable_parameters_count_ref')
if mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = dict()
# ==================== Samples ==================== #
image_uint8 = tf.cast((input_image + 1) * 127.5, dtype=tf.uint8)
input_image_jpeg = tf.image.encode_jpeg(image_uint8[0],
format='grayscale',
quality=100)
tf.identity(input_image_jpeg, name="input_image_jpeg_ref")
for threshold in [
None, 0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99
]:
binarize_img, binarize_img_jpeg = image_processing.binarize_output(
y_pred[0], threshold=threshold)
tf.identity(binarize_img_jpeg,
name="output_sample_ths_%s_ref" % threshold)
tf.summary.image('output_sample_ths_%s' % threshold,
binarize_img, 10)
# ==============+ Evaluation Metrics ==================== #
with tf.name_scope("IoU_Metrics"):
for threshold in [
0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99
]:
iou_score = metrics.iou_score(y_pred=y_pred,
y_true=mask_image,
threshold=threshold)
tf.identity(iou_score,
name='iou_score_ths_%s_ref' % threshold)
tf.summary.scalar('iou_score_ths_%s' % threshold,
iou_score)
if mode == tf.estimator.ModeKeys.EVAL:
eval_metrics["IoU_THS_%s" %
threshold] = tf.metrics.mean(iou_score)
labels = tf.cast(labels, tf.float32)
labels_preds = tf.reduce_max(y_pred, axis=(1, 2, 3))
assert (abs(labels_preds - tf.clip_by_value(labels_preds, 0, 1)) <
0.00001,
"Clipping labels_preds introduces non-trivial loss.")
labels_preds = tf.clip_by_value(labels_preds, 0, 1)
with tf.variable_scope("Confusion_Matrix") as scope:
tp, update_tp = tf.metrics.true_positives_at_thresholds(
labels=labels,
predictions=labels_preds,
thresholds=[
0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99
],
)
tn, update_tn = tf.metrics.true_negatives_at_thresholds(
labels=labels,
predictions=labels_preds,
thresholds=[
0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99
],
)
fp, update_fp = tf.metrics.false_positives_at_thresholds(
labels=labels,
predictions=labels_preds,
thresholds=[
0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99
],
)
fn, update_fn = tf.metrics.false_negatives_at_thresholds(
labels=labels,
predictions=labels_preds,
thresholds=[
0.05, 0.125, 0.25, 0.5, 0.75, 0.85, 0.95, 0.99
],
)
if mode == tf.estimator.ModeKeys.TRAIN:
local_vars = tf.get_collection(
tf.GraphKeys.LOCAL_VARIABLES, scope=scope.name)
confusion_matrix_reset_op = tf.initializers.variables(
local_vars, name='reset_op')
with tf.control_dependencies([confusion_matrix_reset_op]):
with tf.control_dependencies(
[update_tp, update_tn, update_fp, update_fn]):
tp = tf.identity(tp)
tn = tf.identity(tn)
fp = tf.identity(fp)
fn = tf.identity(fn)
else:
eval_metrics["Confusion_Matrix_TP"] = tp, update_tp
eval_metrics["Confusion_Matrix_TN"] = tn, update_tn
eval_metrics["Confusion_Matrix_FP"] = fp, update_fp
eval_metrics["Confusion_Matrix_FN"] = fn, update_fn
tf.identity(tp, name='true_positives_ref'
) # Confusion_Matrix/true_positives_ref:0
tf.identity(tn, name='true_negatives_ref'
) # Confusion_Matrix/true_negatives_ref:0
tf.identity(fp, name='false_positives_ref'
) # Confusion_Matrix/false_positives_ref:0
tf.identity(fn, name='false_negatives_ref'
) # Confusion_Matrix/false_negatives_ref:0
tf.summary.scalar('true_positives', tp[3]) # For Ths = 0.5
tf.summary.scalar('true_negatives', tn[3]) # For Ths = 0.5
tf.summary.scalar('false_positives', fp[3]) # For Ths = 0.5
tf.summary.scalar('false_negatives', fn[3]) # For Ths = 0.5
binarized_mask, binarized_mask_jpeg = image_processing.binarize_output(
mask_image[0], threshold=0.5)
tf.identity(binarized_mask_jpeg, name="mask_sample_ref")
tf.summary.image('sample_mask', binarized_mask, 10)
##########################
mask_max_val = tf.reduce_max(mask_image)
tf.identity(mask_max_val, name='mask_max_val_ref')
mask_min_val = tf.reduce_min(mask_image)
tf.identity(mask_min_val, name='mask_min_val_ref')
mask_mean_val = tf.reduce_mean(mask_image)
tf.identity(mask_mean_val, name='mask_mean_val_ref')
mask_std_val = tf.math.reduce_std(mask_image)
tf.identity(mask_std_val, name='mask_std_val_ref')
##########################
output_max_val = tf.reduce_max(y_pred)
tf.identity(output_max_val, name='output_max_val_ref')
output_min_val = tf.reduce_min(y_pred)
tf.identity(output_min_val, name='output_min_val_ref')
output_mean_val = tf.reduce_mean(y_pred)
tf.identity(output_mean_val, name='output_mean_val_ref')
output_std_val = tf.math.reduce_std(y_pred)
tf.identity(output_std_val, name='output_std_val_ref')
with tf.variable_scope("losses"):
# ==============+ Reconstruction Loss ==================== #
if params["loss_fn_name"] == "x-entropy":
reconstruction_loss = losses.reconstruction_x_entropy(
y_pred=y_pred, y_true=mask_image)
elif params["loss_fn_name"] == "l2_loss":
reconstruction_loss = losses.reconstruction_l2loss(
y_pred=y_pred, y_true=mask_image)
elif params["loss_fn_name"] == "dice_sorensen":
reconstruction_loss = 1 - losses.dice_coe(
y_pred=y_pred, y_true=mask_image, loss_type='sorensen')
elif params["loss_fn_name"] == "dice_jaccard":
reconstruction_loss = 1 - losses.dice_coe(
y_pred=y_pred, y_true=mask_image, loss_type='jaccard')
elif params["loss_fn_name"] == "adaptive_loss":
reconstruction_loss = losses.adaptive_loss(
y_pred=y_pred,
y_pred_logits=y_pred_logits,
y_true=mask_image,
switch_at_threshold=0.3,
loss_type='sorensen')
else:
raise ValueError("Unknown loss function received: %s" %
params["loss_fn_name"])
tf.identity(reconstruction_loss,
name='reconstruction_loss_ref')
tf.summary.scalar('reconstruction_loss', reconstruction_loss)
if mode == tf.estimator.ModeKeys.TRAIN:
# ============== Regularization Loss ==================== #
l2_loss = losses.regularization_l2loss(
weight_decay=params["weight_decay"])
tf.identity(l2_loss, name='l2_loss_ref')
tf.summary.scalar('l2_loss', l2_loss)
total_loss = tf.add(reconstruction_loss,
l2_loss,
name="total_loss")
else:
total_loss = reconstruction_loss
tf.identity(total_loss, name='total_loss_ref')
tf.summary.scalar('total_loss', total_loss)
if mode == tf.estimator.ModeKeys.TRAIN:
with tf.variable_scope("optimizers"):
# Update Global Step
global_step = tf.train.get_or_create_global_step()
tf.identity(global_step, name="global_step_ref")
learning_rate = tf.train.exponential_decay(
learning_rate=params["learning_rate"],
decay_steps=params["learning_rate_decay_steps"],
decay_rate=params["learning_rate_decay_factor"],
global_step=global_step,
staircase=True)
tf.identity(learning_rate, name="learning_rate_ref")
tf.summary.scalar('learning_rate_ref', learning_rate)
opt = tf.train.RMSPropOptimizer(
learning_rate=learning_rate,
use_locking=False,
centered=True,
decay=params["rmsprop_decay"],
momentum=params["rmsprop_momentum"],
)
if hvd_utils.is_using_hvd():
# Apply gradient compression using GRACE.
from grace_dl.tensorflow.communicator.allgather import Allgather
from grace_dl.tensorflow.compressor.topk import TopKCompressor
from grace_dl.tensorflow.memory.residual import ResidualMemory
world_size = hvd.size()
grc = Allgather(TopKCompressor(0.3), ResidualMemory(),
world_size)
opt = hvd.DistributedOptimizer(opt,
grace=grc,
device_dense='/gpu:0')
if params["apply_manual_loss_scaling"]:
# if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
# Logger.log("Applying manual Loss Scaling ...")
loss_scale_manager = tf.contrib.mixed_precision.ExponentialUpdateLossScaleManager(
init_loss_scale=2**32, # 4,294,967,296
incr_every_n_steps=1000)
opt = tf.contrib.mixed_precision.LossScaleOptimizer(
opt, loss_scale_manager)
deterministic = True
gate_gradients = (tf.train.Optimizer.GATE_OP
if deterministic else
tf.train.Optimizer.GATE_NONE)
backprop_op = opt.minimize(total_loss,
gate_gradients=gate_gradients,
global_step=global_step)
train_op = tf.group(
backprop_op,
tf.get_collection(tf.GraphKeys.UPDATE_OPS))
return tf.estimator.EstimatorSpec(
mode,
loss=total_loss,
train_op=train_op,
)
elif mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode,
loss=total_loss,
eval_metric_ops=eval_metrics,
predictions={"output": y_pred})
else:
raise NotImplementedError('Unknown mode {}'.format(mode))
def train(self,
iter_unit,
num_iter,
run_iter,
batch_size,
warmup_steps=50,
weight_decay=1e-4,
lr_init=0.1,
lr_warmup_epochs=5,
momentum=0.9,
log_every_n_steps=1,
loss_scale=256,
label_smoothing=0.0,
mixup=0.0,
use_cosine_lr=False,
use_static_loss_scaling=False,
is_benchmark=False,
quantize=False,
symmetric=False,
quant_delay=0,
finetune_checkpoint=None,
use_final_conv=False,
use_qdq=False):
if iter_unit not in ["epoch", "batch"]:
raise ValueError(
'`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])'
% iter_unit)
if self.run_hparams.data_dir is None and not is_benchmark:
raise ValueError('`data_dir` must be specified for training!')
if self.run_hparams.use_tf_amp or self.run_hparams.dtype == tf.float16:
if use_static_loss_scaling:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "0"
else:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1"
else:
use_static_loss_scaling = False # Make sure it hasn't been set to True on FP32 training
num_gpus = 1 if not hvd_utils.is_using_hvd() else hvd.size()
global_batch_size = batch_size * num_gpus
if self.run_hparams.data_dir is not None:
filenames, num_samples, num_steps, num_epochs, num_decay_steps = runner_utils.parse_tfrecords_dataset(
data_dir=self.run_hparams.data_dir,
mode="train",
iter_unit=iter_unit,
num_iter=num_iter,
global_batch_size=global_batch_size,
)
steps_per_epoch = num_steps / num_epochs
else:
num_epochs = 1
num_steps = num_iter
steps_per_epoch = num_steps
num_decay_steps = num_steps
num_samples = num_steps * batch_size
if run_iter == -1:
run_iter = num_steps
else:
run_iter = steps_per_epoch * run_iter if iter_unit == "epoch" else run_iter
if self.run_hparams.use_dali and self.run_hparams.data_idx_dir is not None:
idx_filenames = runner_utils.parse_dali_idx_dataset(
data_idx_dir=self.run_hparams.data_idx_dir, mode="train")
training_hooks = []
if hvd.rank() == 0:
print('Starting Model Training...')
print("Training Epochs", num_epochs)
print("Total Steps", num_steps)
print("Steps per Epoch", steps_per_epoch)
print("Decay Steps", num_decay_steps)
print("Weight Decay Factor", weight_decay)
print("Init Learning Rate", lr_init)
print("Momentum", momentum)
print("Num GPUs", num_gpus)
print("Per-GPU Batch Size", batch_size)
if is_benchmark:
self.training_logging_hook = hooks.BenchmarkLoggingHook(
global_batch_size=global_batch_size,
warmup_steps=warmup_steps)
else:
self.training_logging_hook = hooks.TrainingLoggingHook(
global_batch_size=global_batch_size,
num_steps=num_steps,
num_samples=num_samples,
num_epochs=num_epochs,
steps_per_epoch=steps_per_epoch)
training_hooks.append(self.training_logging_hook)
if hvd_utils.is_using_hvd():
bcast_hook = hvd.BroadcastGlobalVariablesHook(0)
training_hooks.append(bcast_hook)
training_hooks.append(hooks.PrefillStagingAreasHook())
training_hooks.append(hooks.TrainingPartitionHook())
estimator_params = {
'batch_size': batch_size,
'steps_per_epoch': steps_per_epoch,
'num_gpus': num_gpus,
'momentum': momentum,
'lr_init': lr_init,
'lr_warmup_epochs': lr_warmup_epochs,
'weight_decay': weight_decay,
'loss_scale': loss_scale,
'apply_loss_scaling': use_static_loss_scaling,
'label_smoothing': label_smoothing,
'mixup': mixup,
'num_decay_steps': num_decay_steps,
'use_cosine_lr': use_cosine_lr,
'use_final_conv': use_final_conv,
'quantize': quantize,
'use_qdq': use_qdq,
'symmetric': symmetric,
'quant_delay': quant_delay
}
if finetune_checkpoint:
estimator_params['finetune_checkpoint'] = finetune_checkpoint
image_classifier = self._get_estimator(
mode='train',
run_params=estimator_params,
use_xla=self.run_hparams.use_xla,
use_dali=self.run_hparams.use_dali,
gpu_memory_fraction=self.run_hparams.gpu_memory_fraction,
gpu_id=self.run_hparams.gpu_id)
def training_data_fn():
if self.run_hparams.use_dali and self.run_hparams.data_idx_dir is not None:
if hvd.rank() == 0:
print("Using DALI input... ")
return data_utils.get_dali_input_fn(
filenames=filenames,
idx_filenames=idx_filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=True,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False
if self.run_hparams.seed is None else True)
elif self.run_hparams.data_dir is not None:
return data_utils.get_tfrecords_input_fn(
filenames=filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=True,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False
if self.run_hparams.seed is None else True)
else:
if hvd.rank() == 0:
print("Using Synthetic Data ...")
return data_utils.get_synth_input_fn(
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
num_channels=self.run_hparams.n_channels,
data_format=self.run_hparams.input_format,
num_classes=self.run_hparams.n_classes,
dtype=self.run_hparams.dtype,
)
try:
current_step = image_classifier.get_variable_value("global_step")
except ValueError:
current_step = 0
run_iter = max(0, min(run_iter, num_steps - current_step))
print("Current step:", current_step)
if run_iter > 0:
try:
image_classifier.train(
input_fn=training_data_fn,
steps=run_iter,
hooks=training_hooks,
)
except KeyboardInterrupt:
print("Keyboard interrupt")
if hvd.rank() == 0:
if run_iter > 0:
print('Ending Model Training ...')
train_throughput = self.training_logging_hook.mean_throughput.value(
)
train_time = self.training_logging_hook.train_time
dllogger.log(data={'train_throughput': train_throughput},
step=tuple())
dllogger.log(data={'Total Training time': train_time},
step=tuple())
else:
print(
'Model already trained required number of steps. Skipped')
def dataset_fn(self,
batch_size,
training,
input_shape,
mask_shape,
num_threads,
use_gpu_prefetch,
normalize_data_method,
only_defective_images,
augment_data,
seed=None):
super(DAGM2007_Dataset, self).dataset_fn(
batch_size=batch_size,
training=training,
input_shape=input_shape,
mask_shape=mask_shape,
num_threads=num_threads,
use_gpu_prefetch=use_gpu_prefetch,
normalize_data_method=
normalize_data_method, # [None, "zero_centered", "zero_one"]
only_defective_images=only_defective_images,
augment_data=augment_data,
seed=seed)
shuffle_buffer_size = 10000
image_dir, csv_file = self._get_data_dirs(training=training)
mask_image_dir = os.path.join(image_dir, "Label")
dataset = tf.data.TextLineDataset(csv_file)
dataset = dataset.skip(1) # Skip CSV Header
if only_defective_images:
dataset = dataset.filter(
lambda line: tf.not_equal(tf.strings.substr(line, -1, 1), "0"))
if hvd_utils.is_using_hvd() and training:
dataset = dataset.shard(hvd.size(), hvd.rank())
def _load_dagm_data(line):
input_image_name, image_mask_name, label = tf.decode_csv(
line, record_defaults=[[""], [""], [0]], field_delim=',')
def decode_image(filepath, resize_shape, normalize_data_method):
image_content = tf.read_file(filepath)
# image = tf.image.decode_image(image_content, channels=resize_shape[-1])
image = tf.image.decode_png(contents=image_content,
channels=resize_shape[-1],
dtype=tf.uint8)
image = tf.image.resize_images(
image,
size=resize_shape[:2],
method=tf.image.ResizeMethod.
BILINEAR, # [BILINEAR, NEAREST_NEIGHBOR, BICUBIC, AREA]
align_corners=False,
preserve_aspect_ratio=True)
image.set_shape(resize_shape)
image = tf.cast(image, tf.float32)
if normalize_data_method == "zero_centered":
image = tf.divide(image, 127.5) - 1
elif normalize_data_method == "zero_one":
image = tf.divide(image, 255.0)
return image
input_image = decode_image(
filepath=tf.strings.join([image_dir, input_image_name],
separator='/'),
resize_shape=input_shape,
normalize_data_method=normalize_data_method,
)
mask_image = tf.cond(
tf.equal(image_mask_name, ""),
true_fn=lambda: tf.zeros(mask_shape, dtype=tf.float32),
false_fn=lambda: decode_image(
filepath=tf.strings.join([mask_image_dir, image_mask_name],
separator='/'),
resize_shape=mask_shape,
normalize_data_method="zero_one",
),
)
label = tf.cast(label, tf.int32)
return tf.data.Dataset.from_tensor_slices(
([input_image], [mask_image], [label]))
dataset = dataset.apply(
tf.data.experimental.parallel_interleave(
_load_dagm_data,
cycle_length=batch_size * 8,
block_length=4,
buffer_output_elements=batch_size * 8))
dataset = dataset.cache()
if training:
dataset = dataset.apply(
tf.data.experimental.shuffle_and_repeat(
buffer_size=shuffle_buffer_size, seed=seed))
else:
dataset = dataset.repeat()
def _augment_data(input_image, mask_image, label):
if augment_data:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print("Using data augmentation ...")
#input_image = tf.image.per_image_standardization(input_image)
horizontal_flip = tf.random_uniform(shape=(), seed=seed) > 0.5
input_image = tf.cond(
horizontal_flip,
lambda: tf.image.flip_left_right(input_image),
lambda: input_image)
mask_image = tf.cond(
horizontal_flip,
lambda: tf.image.flip_left_right(mask_image),
lambda: mask_image)
n_rots = tf.random_uniform(shape=(),
dtype=tf.int32,
minval=0,
maxval=3,
seed=seed)
input_image = tf.image.rot90(input_image, k=n_rots)
mask_image = tf.image.rot90(mask_image, k=n_rots)
return (input_image, mask_image), label
dataset = dataset.apply(
tf.data.experimental.map_and_batch(
map_func=_augment_data,
num_parallel_calls=num_threads,
batch_size=batch_size,
drop_remainder=True,
))
dataset = dataset.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
if use_gpu_prefetch:
dataset.apply(
tf.data.experimental.prefetch_to_device(device="/gpu:0",
buffer_size=4))
return dataset
def evaluate(
self,
iter_unit,
num_iter,
batch_size,
warmup_steps=50,
log_every_n_steps=1,
is_benchmark=False,
export_dir=None,
quantize=False,
symmetric=False,
use_qdq=False,
use_final_conv=False,
):
if iter_unit not in ["epoch", "batch"]:
raise ValueError(
'`iter_unit` value is unknown: %s (allowed: ["epoch", "batch"])'
% iter_unit)
if self.run_hparams.data_dir is None and not is_benchmark:
raise ValueError('`data_dir` must be specified for evaluation!')
if hvd_utils.is_using_hvd() and hvd.rank() != 0:
raise RuntimeError('Multi-GPU inference is not supported')
estimator_params = {
'quantize': quantize,
'symmetric': symmetric,
'use_qdq': use_qdq,
'use_final_conv': use_final_conv
}
image_classifier = self._get_estimator(
mode='validation',
run_params=estimator_params,
use_xla=self.run_hparams.use_xla,
use_dali=self.run_hparams.use_dali,
gpu_memory_fraction=self.run_hparams.gpu_memory_fraction,
gpu_id=self.run_hparams.gpu_id)
if self.run_hparams.data_dir is not None:
filenames, num_samples, num_steps, num_epochs, num_decay_steps = runner_utils.parse_tfrecords_dataset(
data_dir=self.run_hparams.data_dir,
mode="validation",
iter_unit=iter_unit,
num_iter=num_iter,
global_batch_size=batch_size,
)
else:
num_epochs = 1
num_decay_steps = -1
num_steps = num_iter
if self.run_hparams.use_dali and self.run_hparams.data_idx_dir is not None:
idx_filenames = runner_utils.parse_dali_idx_dataset(
data_idx_dir=self.run_hparams.data_idx_dir, mode="validation")
eval_hooks = []
if hvd.rank() == 0:
self.eval_logging_hook = hooks.BenchmarkLoggingHook(
global_batch_size=batch_size, warmup_steps=warmup_steps)
eval_hooks.append(self.eval_logging_hook)
print('Starting Model Evaluation...')
print("Evaluation Epochs", num_epochs)
print("Evaluation Steps", num_steps)
print("Decay Steps", num_decay_steps)
print("Global Batch Size", batch_size)
def evaluation_data_fn():
if self.run_hparams.use_dali and self.run_hparams.data_idx_dir is not None:
if hvd.rank() == 0:
print("Using DALI input... ")
return data_utils.get_dali_input_fn(
filenames=filenames,
idx_filenames=idx_filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=False,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False
if self.run_hparams.seed is None else True)
elif self.run_hparams.data_dir is not None:
return data_utils.get_tfrecords_input_fn(
filenames=filenames,
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
training=False,
distort_color=self.run_hparams.distort_colors,
num_threads=self.run_hparams.num_preprocessing_threads,
deterministic=False
if self.run_hparams.seed is None else True)
else:
print("Using Synthetic Data ...\n")
return data_utils.get_synth_input_fn(
batch_size=batch_size,
height=self.run_hparams.height,
width=self.run_hparams.width,
num_channels=self.run_hparams.n_channels,
data_format=self.run_hparams.input_format,
num_classes=self.run_hparams.n_classes,
dtype=self.run_hparams.dtype,
)
try:
eval_results = image_classifier.evaluate(
input_fn=evaluation_data_fn,
steps=num_steps,
hooks=eval_hooks,
)
eval_throughput = self.eval_logging_hook.mean_throughput.value()
eval_latencies = np.array(self.eval_logging_hook.latencies) * 1000
eval_latencies_q = np.quantile(eval_latencies, q=[0.9, 0.95, 0.99])
eval_latencies_mean = np.mean(eval_latencies)
dllogger.log(data={
'top1_accuracy': float(eval_results['top1_accuracy']),
'top5_accuracy': float(eval_results['top5_accuracy']),
'eval_throughput': eval_throughput,
'eval_latency_avg': eval_latencies_mean,
'eval_latency_p90': eval_latencies_q[0],
'eval_latency_p95': eval_latencies_q[1],
'eval_latency_p99': eval_latencies_q[2],
},
step=tuple())
if export_dir is not None:
dllogger.log(data={'export_dir': export_dir}, step=tuple())
input_receiver_fn = data_utils.get_serving_input_receiver_fn(
batch_size=None,
height=self.run_hparams.height,
width=self.run_hparams.width,
num_channels=self.run_hparams.n_channels,
data_format=self.run_hparams.input_format,
dtype=self.run_hparams.dtype)
image_classifier.export_savedmodel(export_dir,
input_receiver_fn)
except KeyboardInterrupt:
print("Keyboard interrupt")
print('Model evaluation finished')
def __init__(
self,
# Model Params
input_format, # NCHW or NHWC
compute_format, # NCHW or NHWC
n_channels,
activation_fn,
weight_init_method,
model_variant,
input_shape,
mask_shape,
input_normalization_method,
# Training HParams
augment_data,
loss_fn_name,
# Runtime HParams
amp,
xla,
# Directory Params
model_dir=None,
log_dir=None,
sample_dir=None,
data_dir=None,
dataset_name=None,
dataset_hparams=None,
# Debug Params
log_every_n_steps=1,
debug_verbosity=0,
seed=None):
if dataset_hparams is None:
dataset_hparams = dict()
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)
if data_dir is not None and not os.path.exists(data_dir):
raise ValueError("The `data_dir` received does not exists: %s" %
data_dir)
if hvd_utils.is_using_hvd():
hvd.init()
if hvd.rank() == 0:
print("Horovod successfully initialized ...")
tf_seed = 2 * (seed + hvd.rank()) if seed is not None else None
else:
tf_seed = 2 * seed if seed is not None else None
# ============================================
# Optimisation Flags - Do not remove
# ============================================
os.environ['CUDA_CACHE_DISABLE'] = '0'
os.environ['HOROVOD_GPU_ALLREDUCE'] = 'NCCL'
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())
print("WORLD_SIZE", 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'
# =================================================
self.xla = xla
if amp:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print("TF AMP is activated - Experimental Feature")
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
# =================================================
model_hparams = tf.contrib.training.HParams(
# Model Params
input_format=input_format,
compute_format=compute_format,
input_shape=input_shape,
mask_shape=mask_shape,
n_channels=n_channels,
activation_fn=activation_fn,
weight_init_method=weight_init_method,
model_variant=model_variant,
input_normalization_method=input_normalization_method,
# Training HParams
augment_data=augment_data,
loss_fn_name=loss_fn_name,
# Runtime Params
amp=amp,
# Debug Params
log_every_n_steps=log_every_n_steps,
debug_verbosity=debug_verbosity,
seed=tf_seed)
run_config_additional = tf.contrib.training.HParams(
dataset_hparams=dataset_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,
sample_dir=sample_dir
if not hvd_utils.is_using_hvd() or hvd.rank() == 0 else None,
data_dir=data_dir,
num_preprocessing_threads=32,
)
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
try:
os.makedirs(sample_dir)
except FileExistsError:
pass
self.run_hparams = Runner._build_hparams(model_hparams,
run_config_additional)
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print('Defining Model Estimator ...\n')
self._model = UNet_v1(
model_name="UNet_v1",
input_format=self.run_hparams.input_format,
compute_format=self.run_hparams.compute_format,
n_output_channels=1,
unet_variant=self.run_hparams.model_variant,
weight_init_method=self.run_hparams.weight_init_method,
activation_fn=self.run_hparams.activation_fn)
if self.run_hparams.seed is not None:
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
print("Deterministic Run - Seed: %d\n" % seed)
tf.set_random_seed(self.run_hparams.seed)
np.random.seed(self.run_hparams.seed)
random.seed(self.run_hparams.seed)
if dataset_name not in known_datasets.keys():
raise RuntimeError(
"The dataset `%s` is unknown, allowed values: %s ..." %
(dataset_name, list(known_datasets.keys())))
self.dataset = known_datasets[dataset_name](
data_dir=data_dir, **self.run_hparams.dataset_hparams)
self.num_gpus = 1 if not hvd_utils.is_using_hvd() else hvd.size()
