def export(self,
output_dir: Text = None,
tensorrt: Text = None,
tflite: Text = None):
"""Export a saved model, frozen graph, and potential tflite/tensorrt model.
Args:
output_dir: the output folder for saved model.
tensorrt: If not None, must be {'FP32', 'FP16', 'INT8'}.
tflite: If not None, must be {'FP32', 'FP16', 'INT8'}.
"""
if not self.model:
self.build()
export_model = ExportModel(self.model)
if output_dir:
tf.saved_model.save(
export_model,
output_dir,
signatures=export_model.__call__.get_concrete_function(
tf.TensorSpec(shape=[None, None, None, 3],
dtype=tf.uint8,
name='images')))
logging.info('Model saved at %s', output_dir)
# also save freeze pb file.
graphdef = self.freeze(
export_model.__call__.get_concrete_function(
tf.TensorSpec(shape=[None, None, None, 3],
dtype=tf.uint8,
name='images')))
proto_path = tf.io.write_graph(graphdef,
output_dir,
self.model_name + '_frozen.pb',
as_text=False)
logging.info('Frozen graph saved at %s', proto_path)
if tflite:
image_size = utils.parse_image_size(self.params['image_size'])
converter = tf.lite.TFLiteConverter.from_concrete_functions([
export_model.tflite_call.get_concrete_function(
tf.TensorSpec(shape=[1, *image_size, 3]))
])
if tflite == 'FP32':
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float32]
elif tflite == 'FP16':
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
elif tflite == 'INT8':
num_calibration_steps = 10
def representative_dataset_gen(
): # rewrite this for real data.
for _ in range(num_calibration_steps):
yield [np.ones((1, *image_size, 3), dtype=np.float32)]
converter.representative_dataset = representative_dataset_gen
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
else:
raise ValueError('tflite must be one of {FP32, FP16, INT8}.')
tflite_path = os.path.join(output_dir, tflite.lower() + '.tflite')
tflite_model = converter.convert()
tf.io.gfile.GFile(tflite_path, 'wb').write(tflite_model)
logging.info('TFLite is saved at %s', tflite_path)
if tensorrt:
trt_path = os.path.join(output_dir, 'tensorrt_' + tensorrt.lower())
conversion_params = tf.experimental.tensorrt.ConversionParams(
max_workspace_size_bytes=(2 << 20),
maximum_cached_engines=1,
precision_mode=tensorrt.upper())
converter = tf.experimental.tensorrt.Converter(
output_dir, conversion_params=conversion_params)
converter.convert()
converter.save(trt_path)
logging.info('TensorRT model is saved at %s', trt_path)
def main(argv):
del argv # Unused.
if FLAGS.use_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') and FLAGS.training_file_pattern is None:
raise RuntimeError(
'You must specify --training_file_pattern for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError('You must specify --validation_file_pattern '
'for evaluation.')
# 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,
}
# The Input Partition Logic: We partition only the partition-able tensors.
# Spatial partition requires that the to-be-partitioned tensors must have a
# dimension that is a multiple of `partition_dims`. Depending on the
# `partition_dims` and the `image_size` and the `max_level` in config, some
# high-level anchor labels (i.e., `cls_targets` and `box_targets`) cannot
# be partitioned. For example, when `partition_dims` is [1, 4, 2, 1], image
# size is 1536, `max_level` is 9, `cls_targets_8` has a shape of
# [batch_size, 6, 6, 9], which cannot be partitioned (6 % 4 != 0). In this
# case, the level-8 and level-9 target tensors are not partition-able, and
# the highest partition-able level is 7.
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,
use_tpu=FLAGS.use_tpu,
backbone_ckpt=FLAGS.backbone_ckpt,
ckpt=FLAGS.ckpt,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
mode=FLAGS.mode,
)
config_proto = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
if FLAGS.use_xla and not FLAGS.use_tpu:
config_proto.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
tpu_config = tf.estimator.tpu.TPUConfig(
FLAGS.iterations_per_loop,
num_shards=num_shards,
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,
evaluation_master=FLAGS.eval_master,
model_dir=FLAGS.model_dir,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
tpu_config=tpu_config,
tf_random_seed=FLAGS.tf_random_seed,
)
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
# TPU Estimator
logging.info(params)
if FLAGS.mode == 'train':
train_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
config=run_config,
params=params)
train_estimator.train(
input_fn=dataloader.InputReader(FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data),
max_steps=int((config.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size))
if FLAGS.eval_after_training:
# Run evaluation after training finishes.
eval_params = dict(
params,
use_tpu=FLAGS.use_tpu,
input_rand_hflip=False,
is_training_bn=False,
precision=None,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
logging.info('Eval results: %s', eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
elif FLAGS.mode == 'eval':
# Eval only runs on CPU or GPU host with batch_size = 1.
# Override the default options: disable randomization in the input pipeline
# and don't run on the TPU.
eval_params = dict(
params,
use_tpu=FLAGS.use_tpu,
input_rand_hflip=False,
is_training_bn=False,
precision=None,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
def terminate_eval():
logging.info('Terminating eval after %d seconds of no checkpoints',
FLAGS.eval_timeout)
return True
# 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,
timeout_fn=terminate_eval):
logging.info('Starting to evaluate.')
try:
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(
FLAGS.validation_file_pattern, is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
logging.info('Eval results: %s', eval_results)
# 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)
total_step = int(
(config.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size)
if current_step >= total_step:
logging.info('Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
elif FLAGS.mode == 'train_and_eval':
for cycle in range(config.num_epochs):
logging.info('Starting training cycle, epoch: %d.', cycle)
train_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
config=run_config,
params=params)
train_estimator.train(input_fn=dataloader.InputReader(
FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data),
steps=int(FLAGS.num_examples_per_epoch /
FLAGS.train_batch_size))
logging.info('Starting evaluation cycle, epoch: %d.', cycle)
# Run evaluation after every epoch.
eval_params = dict(
params,
use_tpu=FLAGS.use_tpu,
input_rand_hflip=False,
is_training_bn=False,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
logging.info('Evaluation results: %s', eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
else:
logging.info('Mode not found.')
def main(_):
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.override(FLAGS.hparams)
config.batch_size = FLAGS.batch_size
config.val_json_file = FLAGS.val_json_file
config.nms_configs.max_nms_inputs = anchors.MAX_DETECTION_POINTS
config.drop_remainder = False # eval all examples w/o drop.
config.image_size = utils.parse_image_size(config['image_size'])
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')
with ds_strategy.scope():
# Network
model = efficientdet_keras.EfficientDetNet(config=config)
model.build((config.batch_size, *config.image_size, 3))
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
@tf.function
def f(images, labels):
cls_outputs, box_outputs = model(images, training=False)
return postprocess.generate_detections(config, cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'])
# dataset
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
max_instances_per_image=config.max_instances_per_image)(config)
if FLAGS.eval_samples:
ds = ds.take(FLAGS.eval_samples // config.batch_size)
# create a evaluator for AP calculation.
label_map = label_util.get_label_map(config.label_map)
evaluator = coco_metric.EvaluationMetric(filename=config.val_json_file,
label_map=label_map)
# evaluate all images.
eval_samples = FLAGS.eval_samples or 5000
pbar = tf.keras.utils.Progbar(eval_samples // config.batch_size)
for i, (images, labels) in enumerate(ds):
detections = f(images, labels)
evaluator.update_state(
labels['groundtruth_data'].numpy(),
postprocess.transform_detections(detections).numpy())
pbar.update(i)
# compute the final eval results.
metrics = evaluator.result()
metric_dict = {}
for i, name in enumerate(evaluator.metric_names):
metric_dict[name] = metrics[i]
if label_map:
for i, cid in enumerate(sorted(label_map.keys())):
name = 'AP_/%s' % label_map[cid]
metric_dict[name] = metrics[i + len(evaluator.metric_names)]
print(FLAGS.model_name, metric_dict)
def export(self,
output_dir: Text,
tflite_path: Text = None,
tensorrt: Text = None):
"""Export a saved model, frozen graph, and potential tflite/tensorrt model.
Args:
output_dir: the output folder for saved model.
tflite_path: the path for saved tflite file.
tensorrt: If not None, must be {'FP32', 'FP16', 'INT8'}.
"""
signitures = self.signitures
signature_def_map = {
'serving_default':
tf.saved_model.predict_signature_def(
{signitures['image_arrays'].name: signitures['image_arrays']},
{signitures['prediction'].name: signitures['prediction']}),
}
b = tf.saved_model.Builder(output_dir)
b.add_meta_graph_and_variables(self.sess,
tags=['serve'],
signature_def_map=signature_def_map,
assets_collection=tf.get_collection(
tf.GraphKeys.ASSET_FILEPATHS),
clear_devices=True)
b.save()
logging.info('Model saved at %s', output_dir)
# also save freeze pb file.
graphdef = self.freeze()
pb_path = os.path.join(output_dir, self.model_name + '_frozen.pb')
tf.io.gfile.GFile(pb_path, 'wb').write(graphdef.SerializeToString())
logging.info('Frozen graph saved at %s', pb_path)
if tflite_path:
height, width = utils.parse_image_size(self.params['image_size'])
input_name = signitures['image_arrays'].op.name
input_shapes = {input_name: [None, height, width, 3]}
converter = tf.lite.TFLiteConverter.from_saved_model(
output_dir,
input_arrays=[input_name],
input_shapes=input_shapes,
output_arrays=[signitures['prediction'].op.name])
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS
]
tflite_model = converter.convert()
tf.io.gfile.GFile(tflite_path, 'wb').write(tflite_model)
logging.info('TFLite is saved at %s', tflite_path)
if tensorrt:
from tensorflow.python.compiler.tensorrt import trt # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
sess_config = tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))
trt_path = os.path.join(output_dir, 'tensorrt_' + tensorrt.lower())
trt.create_inference_graph(None,
None,
precision_mode=tensorrt,
input_saved_model_dir=output_dir,
output_saved_model_dir=trt_path,
session_config=sess_config)
logging.info('TensorRT model is saved at %s', trt_path)
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
def _generate_detections_tf(cls_outputs,
box_outputs,
anchor_boxes,
indices,
classes,
image_id,
image_scale,
image_size,
min_score_thresh=MIN_SCORE_THRESH,
max_boxes_to_draw=MAX_DETECTIONS_PER_IMAGE,
soft_nms_sigma=0.0,
iou_threshold=0.5,
use_native_nms=True):
"""Generates detections with model outputs and anchors.
Args:
cls_outputs: a numpy array with shape [N, 1], which has the highest class
scores on all feature levels. The N is the number of selected
top-K total anchors on all levels. (k being MAX_DETECTION_POINTS)
box_outputs: a numpy array with shape [N, 4], which stacks box regression
outputs on all feature levels. The N is the number of selected top-k
total anchors on all levels. (k being MAX_DETECTION_POINTS)
anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all
feature levels. The N is the number of selected top-k total anchors on
all levels.
indices: a numpy array with shape [N], which is the indices from top-k
selection.
classes: a numpy array with shape [N], which represents the class
prediction on all selected anchors from top-k selection.
image_id: an integer number to specify the image id.
image_scale: a float tensor representing the scale between original image
and input image for the detector. It is used to rescale detections for
evaluating with the original groundtruth annotations.
image_size: a tuple (height, width) or an integer for image size.
min_score_thresh: A float representing the threshold for deciding when to
remove boxes based on score.
max_boxes_to_draw: Max number of boxes to draw.
soft_nms_sigma: A scalar float representing the Soft NMS sigma parameter;
See Bodla et al, https://arxiv.org/abs/1704.04503). When
`soft_nms_sigma=0.0` (which is default), we fall back to standard (hard)
NMS.
iou_threshold: A float representing the threshold for deciding whether boxes
overlap too much with respect to IOU.
use_native_nms: a bool that indicates whether to use native nms.
Returns:
detections: detection results in a tensor with each row representing
[image_id, ymin, xmin, ymax, xmax, score, class]
"""
if not image_size:
raise ValueError(
'tf version generate_detection needs non-empty image_size')
logging.info('Using tf version of post-processing.')
anchor_boxes = tf.gather(anchor_boxes, indices)
scores = tf.math.sigmoid(cls_outputs)
# apply bounding box regression to anchors
boxes = decode_box_outputs_tf(box_outputs, anchor_boxes)
if use_native_nms:
logging.info('Using native nms.')
top_detection_idx, scores = tf.image.non_max_suppression_with_scores(
boxes,
scores,
max_boxes_to_draw,
iou_threshold=iou_threshold,
score_threshold=min_score_thresh,
soft_nms_sigma=soft_nms_sigma)
boxes = tf.gather(boxes, top_detection_idx)
else:
logging.info('Using customized nms.')
scores = tf.expand_dims(scores, axis=1)
all_detections = tf.concat([boxes, scores], axis=1)
top_detection_idx = nms_tf(all_detections, iou_threshold)
detections = tf.gather(all_detections, top_detection_idx)
scores = detections[:, 4]
boxes = detections[:, :4]
image_size = utils.parse_image_size(image_size)
detections = tf.stack([
tf.cast(tf.tile(image_id, tf.shape(top_detection_idx)), tf.float64),
tf.clip_by_value(boxes[:, 0], 0, image_size[0]) * image_scale,
tf.clip_by_value(boxes[:, 1], 0, image_size[1]) * image_scale,
tf.clip_by_value(boxes[:, 2], 0, image_size[0]) * image_scale,
tf.clip_by_value(boxes[:, 3], 0, image_size[1]) * image_scale, scores,
tf.cast(tf.gather(classes, top_detection_idx) + 1, tf.float64)
],
axis=1)
return detections
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.experimental_run_functions_eagerly(True)
tf.debugging.set_log_device_placement(True)
tf.random.set_seed(111111)
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')
# Check data path
if FLAGS.mode in (
'train', 'train_and_eval') and FLAGS.training_file_pattern is None:
raise RuntimeError(
'You must specify --training_file_pattern for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError('You must specify --validation_file_pattern '
'for evaluation.')
params = dict(config.as_dict(),
model_name=FLAGS.model_name,
iterations_per_loop=FLAGS.iterations_per_loop,
model_dir=FLAGS.model_dir,
num_examples_per_epoch=FLAGS.num_examples_per_epoch,
strategy=FLAGS.strategy,
batch_size=FLAGS.batch_size //
ds_strategy.num_replicas_in_sync,
num_shards=ds_strategy.num_replicas_in_sync,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
mode=FLAGS.mode)
# set mixed precision policy by keras api.
precision = utils.get_precision(params['strategy'],
params['mixed_precision'])
policy = tf.keras.mixed_precision.experimental.Policy(precision)
tf.keras.mixed_precision.experimental.set_policy(policy)
def get_dataset(is_training, params):
file_pattern = (FLAGS.training_file_pattern
if is_training else FLAGS.validation_file_pattern)
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)(params)
with ds_strategy.scope():
model = train_lib.EfficientDetNetTrain(params['model_name'], config)
height, width = utils.parse_image_size(params['image_size'])
model.build((params['batch_size'], height, width, 3))
model.compile(
optimizer=train_lib.get_optimizer(params),
loss={
'box_loss':
train_lib.BoxLoss(params['delta'],
reduction=tf.keras.losses.Reduction.NONE),
'box_iou_loss':
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),
'class_loss':
train_lib.FocalLoss(params['alpha'],
params['gamma'],
label_smoothing=params['label_smoothing'],
reduction=tf.keras.losses.Reduction.NONE)
})
ckpt_path = tf.train.latest_checkpoint(FLAGS.model_dir)
if ckpt_path:
model.load_weights(ckpt_path)
model.freeze_vars(params['var_freeze_expr'])
model.fit(get_dataset(True, params=params),
epochs=params['num_epochs'],
steps_per_epoch=FLAGS.num_examples_per_epoch,
callbacks=train_lib.get_callbacks(params, FLAGS.profile),
validation_data=get_dataset(False, params=params),
validation_steps=FLAGS.eval_samples)
model.save_weights(os.path.join(FLAGS.model_dir, 'model'))
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 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.experimental_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,
iterations_per_loop=FLAGS.iterations_per_loop,
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,
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.experimental.Policy(precision)
tf.keras.mixed_precision.experimental.set_policy(policy)
def get_dataset(is_training, config):
file_pattern = (FLAGS.training_file_pattern
if is_training else FLAGS.validation_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())
model = setup_model(config)
if FLAGS.pretrained_ckpt:
ckpt_path = tf.train.latest_checkpoint(FLAGS.pretrained_ckpt)
util_keras.restore_ckpt(model, ckpt_path,
config.moving_average_decay)
init_experimental(config)
model.fit(get_dataset(True, config),
epochs=config.num_epochs,
steps_per_epoch=steps_per_epoch,
callbacks=train_lib.get_callbacks(config.as_dict()),
validation_data=get_dataset(False, config).repeat(),
validation_steps=(FLAGS.eval_samples // FLAGS.batch_size))
model.save_weights(os.path.join(FLAGS.model_dir, 'ckpt-final'))
def main(_):
tf.config.run_functions_eagerly(FLAGS.debug)
tf.config.optimizer.set_jit(FLAGS.use_xla)
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()
if FLAGS.mode == 'export':
driver = infer_lib.KerasDriver(FLAGS.model_dir, FLAGS.debug,
FLAGS.model_name, FLAGS.batch_size
or None, FLAGS.only_network,
model_params)
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':
driver = infer_lib.ServingDriver.create(FLAGS.model_dir, FLAGS.debug,
FLAGS.saved_model_dir,
FLAGS.model_name,
FLAGS.batch_size or None,
FLAGS.only_network,
model_params)
image_file = tf.io.read_file(FLAGS.input_image)
image_arrays = tf.io.decode_image(image_file,
channels=3,
expand_animations=False)
image_arrays = tf.expand_dims(image_arrays, axis=0)
detections_bs = driver.serve(image_arrays)
boxes, scores, classes, _ = tf.nest.map_structure(
np.array, detections_bs)
img = driver.visualize(
np.array(image_arrays)[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)
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':
driver = infer_lib.ServingDriver.create(FLAGS.model_dir, FLAGS.debug,
FLAGS.saved_model_dir,
FLAGS.model_name,
FLAGS.batch_size or None,
FLAGS.only_network,
model_params)
batch_size = FLAGS.batch_size or 1
if FLAGS.input_image:
image_file = tf.io.read_file(FLAGS.input_image)
image_arrays = tf.io.decode_image(image_file,
channels=3,
expand_animations=False)
image_arrays = tf.image.resize_with_pad(image_arrays,
*model_config.image_size)
image_arrays = tf.cast(tf.expand_dims(image_arrays, 0), tf.uint8)
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, _ = driver._preprocess(image_arrays)
driver.benchmark(image_arrays, FLAGS.bm_runs, FLAGS.trace_filename)
elif FLAGS.mode == 'dry':
# transfer to tf2 format ckpt
driver = infer_lib.KerasDriver(FLAGS.model_dir, FLAGS.debug,
FLAGS.model_name, FLAGS.batch_size
or None, FLAGS.only_network,
model_params)
if FLAGS.export_ckpt:
driver.model.save_weights(FLAGS.export_ckpt)
elif FLAGS.mode == 'video':
import cv2 # pylint: disable=g-import-not-at-top
driver = infer_lib.ServingDriver.create(FLAGS.model_dir, FLAGS.debug,
FLAGS.saved_model_dir,
FLAGS.model_name,
FLAGS.batch_size or None,
FLAGS.only_network,
model_params)
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 clip_boxes(boxes: T, image_size: int) -> T:
"""Clip boxes to fit the image size."""
image_size = utils.parse_image_size(image_size) * 2
return tf.clip_by_value(boxes, [0], image_size)