def set_model(self, model: tf.keras.Model):
self.model = model
config = model.config
self.config = config
label_map = label_util.get_label_map(config.label_map)
log_dir = os.path.join(config.model_dir, 'coco')
self.file_writer = tf.summary.create_file_writer(log_dir)
self.evaluator = coco_metric.EvaluationMetric(
filename=config.val_json_file, label_map=label_map)
def main(_):
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.override(FLAGS.hparams)
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'])
# 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)
# dataset
batch_size = 1
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
max_instances_per_image=config.max_instances_per_image)(
config, batch_size=batch_size)
eval_samples = FLAGS.eval_samples
if eval_samples:
ds = ds.take((eval_samples + batch_size - 1) // batch_size)
# Network
lite_runner = LiteRunner(FLAGS.tflite_path)
eval_samples = FLAGS.eval_samples or 5000
pbar = tf.keras.utils.Progbar((eval_samples + batch_size - 1) // batch_size)
for i, (images, labels) in enumerate(ds):
cls_outputs, box_outputs = lite_runner.run(images)
detections = postprocess.generate_detections(config, cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'])
detections = postprocess.transform_detections(detections)
evaluator.update_state(labels['groundtruth_data'].numpy(),
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 estimator_metric_fn(self,
detections,
groundtruth_data,
label_map=None):
"""Constructs the metric function for tf.TPUEstimator.
For each metric, we return the evaluation op and an update op; the update op
is shared across all metrics and simply appends the set of detections to the
`self.detections` list. The metric op is invoked after all examples have
been seen and computes the aggregate COCO metrics. Please find details API
in: https://www.tensorflow.org/api_docs/python/tf/contrib/learn/MetricSpec
Args:
detections: Detection results in a tensor with each row representing
[image_id, x, y, width, height, score, class]
groundtruth_data: Groundtruth annotations in a tensor with each row
representing [y1, x1, y2, x2, is_crowd, area, class].
label_map: optional, a map from class id to name.
Returns:
metrics_dict: A dictionary mapping from evaluation name to a tuple of
operations (`metric_op`, `update_op`). `update_op` appends the
detections for the metric to the `self.detections` list.
"""
with tf.name_scope('coco_metric'):
if self.testdev_dir:
update_op = tf.numpy_function(self.update_state,
[groundtruth_data, detections],
[])
metrics = tf.numpy_function(self.result, [], tf.float32)
metrics_dict = {'AP': (metrics, update_op)}
return metrics_dict
else:
update_op = tf.numpy_function(self.update_state,
[groundtruth_data, detections],
[])
metrics = tf.numpy_function(self.result, [], tf.float32)
metrics_dict = {}
for i, name in enumerate(self.metric_names):
metrics_dict[name] = (metrics[i], update_op)
if label_map:
label_map = label_util.get_label_map(label_map)
for i, cid in enumerate(sorted(label_map.keys())):
name = 'AP_/%s' % label_map[cid]
metrics_dict[name] = (metrics[i -
len(self.metric_names)],
update_op)
return metrics_dict
def visualize_image(image,
boxes,
classes,
scores,
label_map=None,
min_score_thresh=0.01,
max_boxes_to_draw=1000,
line_thickness=2,
**kwargs):
"""Visualizes a given image.
Args:
image: a image with shape [H, W, C].
boxes: a box prediction with shape [N, 4] ordered [ymin, xmin, ymax, xmax].
classes: a class prediction with shape [N].
scores: A list of float value with shape [N].
label_map: a dictionary from class id to name.
min_score_thresh: minimal score for showing. If claass probability is below
this threshold, then the object will not show up.
max_boxes_to_draw: maximum bounding box to draw.
line_thickness: how thick is the bounding box line.
**kwargs: extra parameters.
Returns:
output_image: an output image with annotated boxes and classes.
"""
label_map = label_util.get_label_map(label_map or 'coco')
category_index = {k: {'id': k, 'name': label_map[k]} for k in label_map}
img = np.array(image)
vis_utils.visualize_boxes_and_labels_on_image_array(
img,
boxes,
classes,
scores,
category_index,
min_score_thresh=min_score_thresh,
max_boxes_to_draw=max_boxes_to_draw,
line_thickness=line_thickness,
**kwargs)
return img
def to_json(self, prediction, **kwargs):
"""predictions to json"""
boxes = prediction[:, 1:5]
classes = prediction[:, 6].astype(int)
scores = prediction[:, 5]
label_map = label_util.get_label_map(self.label_map or 'coco')
category_index = {
k: {
'id': k,
'name': label_map[k]
}
for k in label_map
}
min_score_thresh = kwargs.get('min_score_thresh', 0.01)
# first check for scores higher than given threshold
scores_idx = scores > min_score_thresh
scores_abv_thres = scores[scores_idx]
boxes_abv_thres = boxes[scores_idx]
classes_abv_thres = classes[scores_idx]
class_names_abv_thresh = []
for class_abv in classes_abv_thres:
if class_abv in six.viewkeys(category_index):
class_name = category_index[class_abv]['name']
class_names_abv_thresh.append(class_name)
else:
class_names_abv_thresh.append('UNKNOWN')
json_obj = {}
json_obj['boxes'] = boxes_abv_thres.tolist()
json_obj['classes_idx'] = classes_abv_thres.tolist()
json_obj['classes_name'] = class_names_abv_thresh
json_obj['scores'] = scores_abv_thres.tolist()
json_str = json.dumps(json_obj, indent=4)
return json_str
def main(_):
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.override(FLAGS.hparams)
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 config.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 config.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((None, *config.image_size, 3))
util_keras.restore_ckpt(model,
tf.train.latest_checkpoint(FLAGS.model_dir),
config.moving_average_decay,
skip_mismatch=False)
@tf.function
def model_fn(images, labels):
cls_outputs, box_outputs = model(images, training=False)
detections = postprocess.generate_detections(config,
cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'])
tf.numpy_function(evaluator.update_state,
[labels['groundtruth_data'],
postprocess.transform_detections(detections)], [])
# 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)
# dataset
batch_size = FLAGS.batch_size # global batch size.
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
max_instances_per_image=config.max_instances_per_image)(
config, batch_size=batch_size)
if FLAGS.eval_samples:
ds = ds.take((FLAGS.eval_samples + batch_size - 1) // batch_size)
ds = ds_strategy.experimental_distribute_dataset(ds)
# evaluate all images.
eval_samples = FLAGS.eval_samples or 5000
pbar = tf.keras.utils.Progbar((eval_samples + batch_size - 1) // batch_size)
for i, (images, labels) in enumerate(ds):
ds_strategy.run(model_fn, (images, labels))
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 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
base_height, base_width = 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')
# in format (height, width, flip)
augmentations = []
if FLAGS.enable_tta:
for size_offset in (0, 128, 256):
for flip in (False, True):
augmentations.append((base_height + size_offset,
base_width + size_offset, flip))
else:
augmentations.append((base_height, base_width, False))
all_detections = []
all_labels = []
with ds_strategy.scope():
# Network
model = efficientdet_keras.EfficientDetNet(config=config)
model.build((config.batch_size, base_height, base_width, 3))
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
first_loop = True
for height, width, flip in augmentations:
config.image_size = (height, width)
# dataset
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
use_fake_data=False,
max_instances_per_image=config.max_instances_per_image)(config)
# create the function once per augmentation, since it closes over the
# value of config, which gets updated with the new image size
@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'],
flip)
# inference
for images, labels in ds:
if flip:
images = tf.image.flip_left_right(images)
detections = f(images, labels)
all_detections.append(detections)
if first_loop:
all_labels.append(labels)
first_loop = False
# collect the giant list of detections into a map from image id to
# detections
detections_per_source = dict()
for batch in all_detections:
for d in batch:
img_id = d[0][0]
if img_id.numpy() in detections_per_source:
detections_per_source[img_id.numpy()] = tf.concat(
[d, detections_per_source[img_id.numpy()]], 0)
else:
detections_per_source[img_id.numpy()] = d
# collect the groundtruth per image id
groundtruth_per_source = dict()
for batch in all_labels:
for img_id, groundtruth in zip(batch['source_ids'],
batch['groundtruth_data']):
groundtruth_per_source[img_id.numpy()] = groundtruth
# calucate the AP scores for all the images
evaluator = coco_metric.EvaluationMetric(filename=config.val_json_file)
for img_id, d in detections_per_source.items():
if FLAGS.enable_tta:
d = wbf.ensemble_detections(config, d, len(augmentations))
evaluator.update_state(
tf.stack([groundtruth_per_source[img_id]]).numpy(),
postprocess.transform_detections(tf.stack([d])).numpy())
# compute the final eval results.
if evaluator:
metrics = evaluator.result()
metric_dict = {}
for i, name in enumerate(evaluator.metric_names):
metric_dict[name] = metrics[i]
label_map = label_util.get_label_map(config.label_map)
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(metric_dict)
def main(_):
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.override(FLAGS.hparams)
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'])
# 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)
# dataset
batch_size = 1
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
max_instances_per_image=config.max_instances_per_image)(
config, batch_size=batch_size)
eval_samples = FLAGS.eval_samples
if eval_samples:
ds = ds.take((eval_samples + batch_size - 1) // batch_size)
# Network
lite_runner = LiteRunner(FLAGS.tflite_path, FLAGS.only_network)
eval_samples = FLAGS.eval_samples or 5000
pbar = tf.keras.utils.Progbar(
(eval_samples + batch_size - 1) // batch_size)
for i, (images, labels) in enumerate(ds):
if not FLAGS.only_network:
nms_boxes_bs, nms_classes_bs, nms_scores_bs, _ = lite_runner.run(
images)
nms_classes_bs += postprocess.CLASS_OFFSET
height, width = utils.parse_image_size(config.image_size)
normalize_factor = tf.constant([height, width, height, width],
dtype=tf.float32)
nms_boxes_bs *= normalize_factor
if labels['image_scales'] is not None:
scales = tf.expand_dims(
tf.expand_dims(labels['image_scales'], -1), -1)
nms_boxes_bs = nms_boxes_bs * tf.cast(scales,
nms_boxes_bs.dtype)
detections = postprocess.generate_detections_from_nms_output(
nms_boxes_bs, nms_classes_bs, nms_scores_bs,
labels['source_ids'])
else:
cls_outputs, box_outputs = lite_runner.run(images)
detections = postprocess.generate_detections(
config,
cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'],
pre_class_nms=FLAGS.pre_class_nms)
detections = postprocess.transform_detections(detections)
evaluator.update_state(labels['groundtruth_data'].numpy(),
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 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
base_height, base_width = utils.parse_image_size(config['image_size'])
# Network
model = efficientdet_keras.EfficientDetNet(config=config)
model.build((config.batch_size, base_height, base_width, 3))
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
@tf.function
def f(imgs, labels, flip):
cls_outputs, box_outputs = model(imgs, training=False)
return postprocess.generate_detections(config, cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'], flip)
# in format (height, width, flip)
augmentations = []
if FLAGS.enable_tta:
for size_offset in (0, 128, 256):
for flip in (False, True):
augmentations.append((base_height + size_offset,
base_width + size_offset, flip))
else:
augmentations.append((base_height, base_width, False))
evaluator = None
detections_per_source = dict()
for height, width, flip in augmentations:
config.image_size = (height, width)
# dataset
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
use_fake_data=False,
max_instances_per_image=config.max_instances_per_image)(config)
# compute stats for all batches.
total_steps = FLAGS.eval_samples // FLAGS.batch_size
progress = tf.keras.utils.Progbar(total_steps)
for i, (images, labels) in enumerate(ds):
progress.update(i, values=None)
if i > total_steps:
break
if flip:
images = tf.image.flip_left_right(images)
detections = f(images, labels, flip)
for img_id, d in zip(labels['source_ids'], detections):
if img_id.numpy() in detections_per_source:
detections_per_source[img_id.numpy()] = tf.concat(
[d, detections_per_source[img_id.numpy()]], 0)
else:
detections_per_source[img_id.numpy()] = d
evaluator = coco_metric.EvaluationMetric(
filename=config.val_json_file)
for d in detections_per_source.values():
if FLAGS.enable_tta:
d = wbf.ensemble_detections(config, d, len(augmentations))
evaluator.update_state(
labels['groundtruth_data'].numpy(),
postprocess.transform_detections(tf.stack([d])).numpy())
# compute the final eval results.
if evaluator:
metrics = evaluator.result()
metric_dict = {}
for i, name in enumerate(evaluator.metric_names):
metric_dict[name] = metrics[i]
label_map = label_util.get_label_map(config.label_map)
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(metric_dict)
