def __init__(self,
model_name: Text,
ckpt_path: Text = None,
batch_size: int = 1,
only_network: bool = False,
model_params: Dict[Text, Any] = None):
"""Initialize the inference driver.
Args:
model_name: target model name, such as efficientdet-d0.
ckpt_path: checkpoint path, such as /tmp/efficientdet-d0/.
batch_size: batch size for inference.
only_network: only use the network without pre/post processing.
model_params: model parameters for overriding the config.
"""
super().__init__()
self.model_name = model_name
self.ckpt_path = ckpt_path
self.batch_size = batch_size
self.only_network = only_network
self.params = hparams_config.get_detection_config(model_name).as_dict()
if model_params:
self.params.update(model_params)
self.params.update(dict(is_training_bn=False))
self.label_map = self.params.get('label_map', None)
self._model = None
mixed_precision = self.params.get('mixed_precision', None)
precision = utils.get_precision(self.params.get('strategy', None),
mixed_precision)
policy = tf.keras.mixed_precision.Policy(precision)
tf.keras.mixed_precision.set_global_policy(policy)
def _build_model(self, grad_checkpoint=False):
tf.random.set_seed(1111)
config = hparams_config.get_detection_config('efficientdet-d0')
config.heads = ['object_detection', 'segmentation']
config.batch_size = 1
config.num_examples_per_epoch = 1
config.model_dir = tempfile.mkdtemp()
config.steps_per_epoch = 1
config.mixed_precision = True
config.grad_checkpoint = grad_checkpoint
x = tf.ones((1, 512, 512, 3))
labels = {
'box_targets_%d' % i: tf.ones((1, 512 // 2**i, 512 // 2**i, 36))
for i in range(3, 8)
}
labels.update({
'cls_targets_%d' % i: tf.ones((1, 512 // 2**i, 512 // 2**i, 9),
dtype=tf.int32)
for i in range(3, 8)
})
labels.update({'image_masks': tf.ones((1, 128, 128, 1))})
labels.update({'mean_num_positives': tf.constant([10.0])})
params = config.as_dict()
params['num_shards'] = 1
params['steps_per_execution'] = 100
params['model_dir'] = tempfile.mkdtemp()
params['profile'] = False
config.override(params, allow_new_keys=True)
model = train_lib.EfficientDetNetTrain(config=config)
model.build((1, 512, 512, 3))
model.compile(
optimizer=train_lib.get_optimizer(params),
loss={
train_lib.BoxLoss.__name__:
train_lib.BoxLoss(params['delta'],
reduction=tf.keras.losses.Reduction.NONE),
train_lib.BoxIouLoss.__name__:
train_lib.BoxIouLoss(params['iou_loss_type'],
params['min_level'],
params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'],
reduction=tf.keras.losses.Reduction.NONE),
train_lib.FocalLoss.__name__:
train_lib.FocalLoss(params['alpha'],
params['gamma'],
label_smoothing=params['label_smoothing'],
reduction=tf.keras.losses.Reduction.NONE),
tf.keras.losses.SparseCategoricalCrossentropy.__name__:
tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
})
return params, x, labels, model
def main(_):
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
# Parse image size in case it is in string format.
config.image_size = utils.parse_image_size(config.image_size)
try:
recordinspect = RecordInspect(config)
recordinspect.visualize()
except Exception as e: # pylint: disable=broad-except
logging.error(e)
else:
logging.info('Done, please find samples at %s', FLAGS.save_samples_dir)
def test_display_callback(self):
config = hparams_config.get_detection_config('efficientdet-d0')
config.batch_size = 1
config.num_examples_per_epoch = 1
config.model_dir = tempfile.mkdtemp()
fake_image = tf.ones([512, 512, 3], dtype=tf.uint8)
fake_jpeg = tf.image.encode_jpeg(fake_image)
sample_image = os.path.join(config.model_dir + 'fake_image.jpg')
tf.io.write_file(sample_image, fake_jpeg)
display_callback = train_lib.DisplayCallback(sample_image,
config.model_dir,
update_freq=1)
model = train_lib.EfficientDetNetTrain(config=config)
model.build((1, 512, 512, 3))
display_callback.set_model(model)
display_callback.on_train_batch_end(0)
def __init__(self,
model_name: Text,
logdir: Text,
tensorrt: Text = False,
use_xla: bool = False,
ckpt_path: Text = None,
export_ckpt: Text = None,
saved_model_dir: Text = None,
tflite_path: Text = None,
batch_size: int = 1,
hparams: Text = '',
**kwargs):
self.model_name = model_name
self.logdir = logdir
self.tensorrt = tensorrt
self.use_xla = use_xla
self.ckpt_path = ckpt_path
self.export_ckpt = export_ckpt
self.saved_model_dir = saved_model_dir
self.tflite_path = tflite_path
model_config = hparams_config.get_detection_config(model_name)
model_config.override(hparams) # Add custom overrides
model_config.is_training_bn = False
model_config.image_size = utils.parse_image_size(
model_config.image_size)
# If batch size is 0, then build a graph with dynamic batch size.
self.batch_size = batch_size or None
self.labels_shape = [batch_size, model_config.num_classes]
# A hack to make flag consistent with nms configs.
if kwargs.get('score_thresh', None):
model_config.nms_configs.score_thresh = kwargs['score_thresh']
if kwargs.get('nms_method', None):
model_config.nms_configs.method = kwargs['nms_method']
if kwargs.get('max_output_size', None):
model_config.nms_configs.max_output_size = kwargs[
'max_output_size']
height, width = model_config.image_size
if model_config.data_format == 'channels_first':
self.inputs_shape = [batch_size, 3, height, width]
else:
self.inputs_shape = [batch_size, height, width, 3]
self.model_config = model_config
def __init__(self,
model_name: Text,
ckpt_path: Text,
model_params: Dict[Text, Any] = None):
"""Initialize the inference driver.
Args:
model_name: target model name, such as efficientdet-d0.
ckpt_path: checkpoint path, such as /tmp/efficientdet-d0/.
model_params: model parameters for overriding the config.
"""
self.model_name = model_name
self.ckpt_path = ckpt_path
self.params = hparams_config.get_detection_config(model_name).as_dict()
if model_params:
self.params.update(model_params)
self.params.update(dict(is_training_bn=False))
self.label_map = self.params.get('label_map', None)
def test_parser(self):
tf.random.set_seed(111111)
params = hparams_config.get_detection_config(
'efficientdet-d0').as_dict()
input_anchors = anchors.Anchors(params['min_level'],
params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'])
anchor_labeler = anchors.AnchorLabeler(input_anchors,
params['num_classes'])
example_decoder = tf_example_decoder.TfExampleDecoder(
regenerate_source_id=params['regenerate_source_id'])
tfrecord_path = test_util.make_fake_tfrecord(self.get_temp_dir())
dataset = tf.data.TFRecordDataset([tfrecord_path])
value = next(iter(dataset))
reader = dataloader.InputReader(tfrecord_path, True)
result = reader.dataset_parser(value, example_decoder, anchor_labeler,
params)
self.assertEqual(len(result), 11)
def __init__(self,
model_name: Text,
ckpt_path: Text,
batch_size: int = 1,
use_xla: bool = False,
min_score_thresh: float = None,
max_boxes_to_draw: float = None,
line_thickness: int = None,
model_params: Dict[Text, Any] = None):
"""Initialize the inference driver.
Args:
model_name: target model name, such as efficientdet-d0.
ckpt_path: checkpoint path, such as /tmp/efficientdet-d0/.
batch_size: batch size for inference.
use_xla: Whether run with xla optimization.
min_score_thresh: minimal score threshold for filtering predictions.
max_boxes_to_draw: the maximum number of boxes per image.
line_thickness: the line thickness for drawing boxes.
model_params: model parameters for overriding the config.
"""
self.model_name = model_name
self.ckpt_path = ckpt_path
self.batch_size = batch_size
self.params = hparams_config.get_detection_config(model_name).as_dict()
if model_params:
self.params.update(model_params)
self.params.update(dict(is_training_bn=False))
self.label_map = self.params.get('label_map', None)
self.signitures = None
self.sess = None
self.use_xla = use_xla
self.min_score_thresh = min_score_thresh
self.max_boxes_to_draw = max_boxes_to_draw
self.line_thickness = line_thickness
def main(_):
tf.config.run_functions_eagerly(FLAGS.debug)
devices = tf.config.list_physical_devices('GPU')
for device in devices:
tf.config.experimental.set_memory_growth(device, True)
model_config = hparams_config.get_detection_config(FLAGS.model_name)
model_config.override(FLAGS.hparams) # Add custom overrides
model_config.is_training_bn = False
if FLAGS.image_size != -1:
model_config.image_size = FLAGS.image_size
model_config.image_size = utils.parse_image_size(model_config.image_size)
model_params = model_config.as_dict()
ckpt_path_or_file = FLAGS.model_dir
if tf.io.gfile.isdir(ckpt_path_or_file):
ckpt_path_or_file = tf.train.latest_checkpoint(ckpt_path_or_file)
driver = inference.ServingDriver(FLAGS.model_name, ckpt_path_or_file,
FLAGS.batch_size or None,
FLAGS.only_network, model_params)
if FLAGS.mode == 'export':
if not FLAGS.saved_model_dir:
raise ValueError('Please specify --saved_model_dir=')
model_dir = FLAGS.saved_model_dir
if tf.io.gfile.exists(model_dir):
tf.io.gfile.rmtree(model_dir)
driver.export(model_dir, FLAGS.tensorrt, FLAGS.tflite,
FLAGS.file_pattern, FLAGS.num_calibration_steps)
print('Model are exported to %s' % model_dir)
elif FLAGS.mode == 'infer':
image_file = tf.io.read_file(FLAGS.input_image)
image_arrays = tf.io.decode_image(image_file)
image_arrays.set_shape((None, None, 3))
image_arrays = tf.expand_dims(image_arrays, axis=0)
if FLAGS.saved_model_dir:
driver.load(FLAGS.saved_model_dir)
if FLAGS.saved_model_dir.endswith('.tflite'):
image_arrays = tf.image.resize_with_pad(
image_arrays, *model_config.image_size)
image_arrays = tf.cast(image_arrays, tf.uint8)
detections_bs = driver.serve(image_arrays)
boxes, scores, classes, _ = tf.nest.map_structure(
np.array, detections_bs)
raw_image = Image.fromarray(np.array(image_arrays)[0])
img = driver.visualize(
raw_image,
boxes[0],
classes[0],
scores[0],
min_score_thresh=model_config.nms_configs.score_thresh or 0.4,
max_boxes_to_draw=model_config.nms_configs.max_output_size)
output_image_path = os.path.join(FLAGS.output_image_dir, '0.jpg')
Image.fromarray(img).save(output_image_path)
print('writing file to %s' % output_image_path)
elif FLAGS.mode == 'benchmark':
if FLAGS.saved_model_dir:
driver.load(FLAGS.saved_model_dir)
batch_size = FLAGS.batch_size or 1
if FLAGS.input_image:
image_file = tf.io.read_file(FLAGS.input_image)
image_arrays = tf.image.decode_image(image_file)
image_arrays.set_shape((None, None, 3))
image_arrays = tf.expand_dims(image_arrays, 0)
if batch_size > 1:
image_arrays = tf.tile(image_arrays, [batch_size, 1, 1, 1])
else:
# use synthetic data if no image is provided.
image_arrays = tf.ones((batch_size, *model_config.image_size, 3),
dtype=tf.uint8)
if FLAGS.only_network:
image_arrays = tf.image.convert_image_dtype(
image_arrays, tf.float32)
image_arrays = tf.image.resize(image_arrays,
model_config.image_size)
driver.benchmark(image_arrays, FLAGS.bm_runs, FLAGS.trace_filename)
elif FLAGS.mode == 'dry':
# transfer to tf2 format ckpt
driver.build()
if FLAGS.export_ckpt:
driver.model.save_weights(FLAGS.export_ckpt)
elif FLAGS.mode == 'video':
import cv2 # pylint: disable=g-import-not-at-top
if FLAGS.saved_model_dir:
driver.load(FLAGS.saved_model_dir)
cap = cv2.VideoCapture(FLAGS.input_video)
if not cap.isOpened():
print('Error opening input video: {}'.format(FLAGS.input_video))
out_ptr = None
if FLAGS.output_video:
frame_width, frame_height = int(cap.get(3)), int(cap.get(4))
out_ptr = cv2.VideoWriter(
FLAGS.output_video, cv2.VideoWriter_fourcc('m', 'p', '4', 'v'),
cap.get(5), (frame_width, frame_height))
while cap.isOpened():
# Capture frame-by-frame
ret, frame = cap.read()
if not ret:
break
raw_frames = np.array([frame])
detections_bs = driver.serve(raw_frames)
boxes, scores, classes, _ = tf.nest.map_structure(
np.array, detections_bs)
new_frame = driver.visualize(
raw_frames[0],
boxes[0],
classes[0],
scores[0],
min_score_thresh=model_config.nms_configs.score_thresh or 0.4,
max_boxes_to_draw=model_config.nms_configs.max_output_size)
if out_ptr:
# write frame into output file.
out_ptr.write(new_frame)
else:
# show the frame online, mainly used for real-time speed test.
cv2.imshow('Frame', new_frame)
# Press Q on keyboard to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def main(_):
if FLAGS.strategy == 'tpu':
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
tpu_grpc_url = tpu_cluster_resolver.get_master()
tf.Session.reset(tpu_grpc_url)
else:
tpu_cluster_resolver = None
# Check data path
if FLAGS.mode in ('train', 'train_and_eval'):
if FLAGS.train_file_pattern is None:
raise RuntimeError('Must specify --train_file_pattern for train.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.val_file_pattern is None:
raise RuntimeError('Must specify --val_file_pattern for eval.')
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
if FLAGS.num_epochs: # NOTE: remove this flag after updating all docs.
config.num_epochs = FLAGS.num_epochs
# Parse image size in case it is in string format.
config.image_size = utils.parse_image_size(config.image_size)
# The following is for spatial partitioning. `features` has one tensor while
# `labels` had 4 + (`max_level` - `min_level` + 1) * 2 tensors. The input
# partition is performed on `features` and all partitionable tensors of
# `labels`, see the partition logic below.
# In the TPUEstimator context, the meaning of `shard` and `replica` is the
# same; follwing the API, here has mixed use of both.
if FLAGS.use_spatial_partition:
# Checks input_partition_dims agrees with num_cores_per_replica.
if FLAGS.num_cores_per_replica != np.prod(FLAGS.input_partition_dims):
raise RuntimeError('--num_cores_per_replica must be a product of array'
'elements in --input_partition_dims.')
labels_partition_dims = {
'mean_num_positives': None,
'source_ids': None,
'groundtruth_data': None,
'image_scales': None,
'image_masks': None,
}
# The Input Partition Logic: We partition only the partition-able tensors.
feat_sizes = utils.get_feat_sizes(
config.get('image_size'), config.get('max_level'))
for level in range(config.get('min_level'), config.get('max_level') + 1):
def _can_partition(spatial_dim):
partitionable_index = np.where(
spatial_dim % np.array(FLAGS.input_partition_dims) == 0)
return len(partitionable_index[0]) == len(FLAGS.input_partition_dims)
spatial_dim = feat_sizes[level]
if _can_partition(spatial_dim['height']) and _can_partition(
spatial_dim['width']):
labels_partition_dims['box_targets_%d' %
level] = FLAGS.input_partition_dims
labels_partition_dims['cls_targets_%d' %
level] = FLAGS.input_partition_dims
else:
labels_partition_dims['box_targets_%d' % level] = None
labels_partition_dims['cls_targets_%d' % level] = None
num_cores_per_replica = FLAGS.num_cores_per_replica
input_partition_dims = [FLAGS.input_partition_dims, labels_partition_dims]
num_shards = FLAGS.num_cores // num_cores_per_replica
else:
num_cores_per_replica = None
input_partition_dims = None
num_shards = FLAGS.num_cores
params = dict(
config.as_dict(),
model_name=FLAGS.model_name,
iterations_per_loop=FLAGS.iterations_per_loop,
model_dir=FLAGS.model_dir,
num_shards=num_shards,
num_examples_per_epoch=FLAGS.num_examples_per_epoch,
strategy=FLAGS.strategy,
backbone_ckpt=FLAGS.backbone_ckpt,
ckpt=FLAGS.ckpt,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
profile=FLAGS.profile,
mode=FLAGS.mode)
config_proto = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)
if FLAGS.strategy != 'tpu':
if FLAGS.use_xla:
config_proto.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
config_proto.gpu_options.allow_growth = True
model_dir = FLAGS.model_dir
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
max_instances_per_image = config.max_instances_per_image
if FLAGS.eval_samples:
eval_steps = int((FLAGS.eval_samples + FLAGS.eval_batch_size - 1) //
FLAGS.eval_batch_size)
else:
eval_steps = None
total_examples = int(config.num_epochs * FLAGS.num_examples_per_epoch)
train_steps = total_examples // FLAGS.train_batch_size
logging.info(params)
if not tf.io.gfile.exists(model_dir):
tf.io.gfile.makedirs(model_dir)
config_file = os.path.join(model_dir, 'config.yaml')
if not tf.io.gfile.exists(config_file):
tf.io.gfile.GFile(config_file, 'w').write(str(config))
train_input_fn = dataloader.InputReader(
FLAGS.train_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data,
max_instances_per_image=max_instances_per_image)
eval_input_fn = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
use_fake_data=FLAGS.use_fake_data,
max_instances_per_image=max_instances_per_image)
if FLAGS.strategy == 'tpu':
tpu_config = tf.estimator.tpu.TPUConfig(
FLAGS.iterations_per_loop if FLAGS.strategy == 'tpu' else 1,
num_cores_per_replica=num_cores_per_replica,
input_partition_dims=input_partition_dims,
per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig
.PER_HOST_V2)
run_config = tf.estimator.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=model_dir,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
tpu_config=tpu_config,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tf_random_seed=FLAGS.tf_random_seed,
)
# TPUEstimator can do both train and eval.
train_est = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=params)
eval_est = train_est
else:
strategy = None
if FLAGS.strategy == 'gpus':
strategy = tf.distribute.MirroredStrategy()
run_config = tf.estimator.RunConfig(
model_dir=model_dir,
train_distribute=strategy,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tf_random_seed=FLAGS.tf_random_seed,
)
def get_estimator(global_batch_size):
params['num_shards'] = getattr(strategy, 'num_replicas_in_sync', 1)
params['batch_size'] = global_batch_size // params['num_shards']
return tf.estimator.Estimator(
model_fn=model_fn_instance, config=run_config, params=params)
# train and eval need different estimator due to different batch size.
train_est = get_estimator(FLAGS.train_batch_size)
eval_est = get_estimator(FLAGS.eval_batch_size)
# start train/eval flow.
if FLAGS.mode == 'train':
train_est.train(input_fn=train_input_fn, max_steps=train_steps)
if FLAGS.eval_after_train:
eval_est.evaluate(input_fn=eval_input_fn, steps=eval_steps)
elif FLAGS.mode == 'eval':
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=FLAGS.min_eval_interval,
timeout=FLAGS.eval_timeout):
logging.info('Starting to evaluate.')
try:
eval_results = eval_est.evaluate(eval_input_fn, steps=eval_steps)
# Terminate eval job when final checkpoint is reached.
try:
current_step = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
logging.info('%s has no global step info: stop!', ckpt)
break
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
if current_step >= train_steps:
logging.info('Eval finished step %d/%d', current_step, train_steps)
break
except tf.errors.NotFoundError:
# Checkpoint might be not already deleted by the time eval finished.
# We simply skip ssuch case.
logging.info('Checkpoint %s no longer exists, skipping.', ckpt)
elif FLAGS.mode == 'train_and_eval':
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
try:
step = int(os.path.basename(ckpt).split('-')[1])
current_epoch = (
step * FLAGS.train_batch_size // FLAGS.num_examples_per_epoch)
logging.info('found ckpt at step %d (epoch %d)', step, current_epoch)
except (IndexError, TypeError):
logging.info('Folder %s has no ckpt with valid step.', FLAGS.model_dir)
current_epoch = 0
def run_train_and_eval(e):
print('\n =====> Starting training, epoch: %d.' % e)
train_est.train(
input_fn=train_input_fn,
max_steps=e * FLAGS.num_examples_per_epoch // FLAGS.train_batch_size)
print('\n =====> Starting evaluation, epoch: %d.' % e)
eval_results = eval_est.evaluate(input_fn=eval_input_fn, steps=eval_steps)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
epochs_per_cycle = 1 # higher number has less graph construction overhead.
for e in range(current_epoch + 1, config.num_epochs + 1, epochs_per_cycle):
if FLAGS.run_epoch_in_child_process:
p = multiprocessing.Process(target=run_train_and_eval, args=(e,))
p.start()
p.join()
if p.exitcode != 0:
return p.exitcode
else:
tf.reset_default_graph()
run_train_and_eval(e)
else:
logging.info('Invalid mode: %s', FLAGS.mode)
def __init__(self, model_name):
self.model_name = model_name
config = hparams_config.get_detection_config(model_name)
config.image_size = utils.parse_image_size(config.image_size)
config.update({'debug': False})
self.config = config
def main(_):
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
if FLAGS.num_epochs: # NOTE: remove this flag after updating all docs.
config.num_epochs = FLAGS.num_epochs
# Parse image size in case it is in string format.
config.image_size = utils.parse_image_size(config.image_size)
if FLAGS.use_xla and FLAGS.strategy != 'tpu':
tf.config.optimizer.set_jit(True)
for gpu in tf.config.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(gpu, True)
if FLAGS.debug:
tf.config.run_functions_eagerly(True)
tf.debugging.set_log_device_placement(True)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
tf.random.set_seed(FLAGS.tf_random_seed)
logging.set_verbosity(logging.DEBUG)
if FLAGS.strategy == 'tpu':
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)
ds_strategy = tf.distribute.TPUStrategy(tpu_cluster_resolver)
logging.info('All devices: %s', tf.config.list_logical_devices('TPU'))
elif FLAGS.strategy == 'gpus':
ds_strategy = tf.distribute.MirroredStrategy()
logging.info('All devices: %s', tf.config.list_physical_devices('GPU'))
else:
if tf.config.list_physical_devices('GPU'):
ds_strategy = tf.distribute.OneDeviceStrategy('device:GPU:0')
else:
ds_strategy = tf.distribute.OneDeviceStrategy('device:CPU:0')
steps_per_epoch = FLAGS.num_examples_per_epoch // FLAGS.batch_size
params = dict(profile=FLAGS.profile,
model_name=FLAGS.model_name,
steps_per_execution=FLAGS.steps_per_execution,
model_dir=FLAGS.model_dir,
steps_per_epoch=steps_per_epoch,
strategy=FLAGS.strategy,
batch_size=FLAGS.batch_size,
tf_random_seed=FLAGS.tf_random_seed,
debug=FLAGS.debug,
val_json_file=FLAGS.val_json_file,
eval_samples=FLAGS.eval_samples,
num_shards=ds_strategy.num_replicas_in_sync)
config.override(params, True)
# set mixed precision policy by keras api.
precision = utils.get_precision(config.strategy, config.mixed_precision)
policy = tf.keras.mixed_precision.Policy(precision)
tf.keras.mixed_precision.set_global_policy(policy)
def get_dataset(is_training, config):
file_pattern = (FLAGS.train_file_pattern
if is_training else FLAGS.val_file_pattern)
if not file_pattern:
raise ValueError('No matching files.')
return dataloader.InputReader(
file_pattern,
is_training=is_training,
use_fake_data=FLAGS.use_fake_data,
max_instances_per_image=config.max_instances_per_image,
debug=FLAGS.debug)(config.as_dict())
with ds_strategy.scope():
if config.model_optimizations:
tfmot.set_config(config.model_optimizations.as_dict())
if FLAGS.hub_module_url:
model = train_lib.EfficientDetNetTrainHub(
config=config, hub_module_url=FLAGS.hub_module_url)
else:
model = train_lib.EfficientDetNetTrain(config=config)
model = setup_model(model, config)
if FLAGS.pretrained_ckpt and not FLAGS.hub_module_url:
ckpt_path = tf.train.latest_checkpoint(FLAGS.pretrained_ckpt)
util_keras.restore_ckpt(model, ckpt_path,
config.moving_average_decay)
init_experimental(config)
if 'train' in FLAGS.mode:
val_dataset = get_dataset(False,
config) if 'eval' in FLAGS.mode else None
model.fit(
get_dataset(True, config),
epochs=config.num_epochs,
steps_per_epoch=steps_per_epoch,
callbacks=train_lib.get_callbacks(config.as_dict(),
val_dataset),
validation_data=val_dataset,
validation_steps=(FLAGS.eval_samples // FLAGS.batch_size))
else:
# Continuous eval.
for ckpt in tf.train.checkpoints_iterator(FLAGS.model_dir,
min_interval_secs=180):
logging.info('Starting to evaluate.')
# Terminate eval job when final checkpoint is reached.
try:
current_epoch = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
current_epoch = 0
val_dataset = get_dataset(False, config)
logging.info('start loading model.')
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
logging.info('finish loading model.')
coco_eval = train_lib.COCOCallback(val_dataset, 1)
coco_eval.set_model(model)
eval_results = coco_eval.on_epoch_end(current_epoch)
logging.info('eval results for %s: %s', ckpt, eval_results)
try:
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
except tf.errors.NotFoundError:
# Checkpoint might be not already deleted by the time eval finished.
logging.info('Checkpoint %s no longer exists, skipping.',
ckpt)
if current_epoch >= config.num_epochs or not current_epoch:
logging.info('Eval epoch %d / %d', current_epoch,
config.num_epochs)
break