def parse_fn(sequence_example):
"""Parses a clip classification example."""
context_features = {
ms.get_example_id_key():
ms.get_example_id_default_parser(),
ms.get_clip_label_index_key():
ms.get_clip_label_index_default_parser(),
ms.get_clip_label_string_key():
ms.get_clip_label_string_default_parser()
}
sequence_features = {
ms.get_image_encoded_key(): ms.get_image_encoded_default_parser(),
}
parsed_context, parsed_sequence = tf.io.parse_single_sequence_example(
sequence_example, context_features, sequence_features)
example_id = parsed_context[ms.get_example_id_key()]
classification_target = tf.one_hot(
tf.sparse_tensor_to_dense(
parsed_context[ms.get_clip_label_index_key()]), NUM_CLASSES)
images = tf.map_fn(
tf.image.decode_jpeg,
parsed_sequence[ms.get_image_encoded_key()],
back_prop=False,
dtype=tf.uint8)
return {
"id": example_id,
"labels": classification_target,
"images": images,
}
def parse_fn(sequence_example):
"""Parses a Kinetics example."""
context_features = {
ms.get_example_id_key(): ms.get_example_id_default_parser(),
}
if parse_labels:
context_features[
ms.get_clip_label_string_key()] = tf.FixedLenFeature(
(), tf.string)
context_features[
ms.get_clip_label_index_key()] = tf.FixedLenFeature(
(), tf.int64)
sequence_features = {
ms.get_image_encoded_key():
ms.get_image_encoded_default_parser(),
ms.get_forward_flow_encoded_key():
ms.get_forward_flow_encoded_default_parser(),
}
parsed_context, parsed_sequence = tf.io.parse_single_sequence_example(
sequence_example, context_features, sequence_features)
images = tf.image.convert_image_dtype(
tf.map_fn(tf.image.decode_jpeg,
parsed_sequence[ms.get_image_encoded_key()],
back_prop=False,
dtype=tf.uint8), tf.float32)
num_frames = tf.shape(images)[0]
flow = tf.image.convert_image_dtype(
tf.map_fn(tf.image.decode_jpeg,
parsed_sequence[ms.get_forward_flow_encoded_key()],
back_prop=False,
dtype=tf.uint8), tf.float32)
# The flow is quantized for storage in JPEGs by the FlowToImageCalculator.
# The quantization needs to be inverted.
flow = (flow[:, :, :, :2] - 0.5) * 2 * 20.
output_dict = {
"images": images,
"flow": flow,
"num_frames": num_frames,
}
if parse_labels:
target = tf.one_hot(
parsed_context[ms.get_clip_label_index_key()], 700)
output_dict["labels"] = target
return output_dict
def parse_fn(sequence_example):
"""Parses a Charades example."""
context_features = {
ms.get_example_id_key():
ms.get_example_id_default_parser(),
ms.get_segment_start_index_key():
(ms.get_segment_start_index_default_parser()),
ms.get_segment_end_index_key():
(ms.get_segment_end_index_default_parser()),
ms.get_segment_label_index_key():
(ms.get_segment_label_index_default_parser()),
ms.get_segment_label_string_key():
(ms.get_segment_label_string_default_parser()),
ms.get_segment_start_timestamp_key():
(ms.get_segment_start_timestamp_default_parser()),
ms.get_segment_end_timestamp_key():
(ms.get_segment_end_timestamp_default_parser()),
ms.get_image_frame_rate_key():
(ms.get_image_frame_rate_default_parser()),
}
sequence_features = {
ms.get_image_encoded_key():
ms.get_image_encoded_default_parser()
}
parsed_context, parsed_sequence = tf.io.parse_single_sequence_example(
sequence_example, context_features, sequence_features)
sequence_length = tf.shape(
parsed_sequence[ms.get_image_encoded_key()])[0]
num_segments = tf.shape(
parsed_context[ms.get_segment_label_index_key()])[0]
# segments matrix and targets for training.
segments_matrix, indicator = one_hot_segments(
tf.sparse_tensor_to_dense(
parsed_context[ms.get_segment_start_index_key()]),
tf.sparse_tensor_to_dense(
parsed_context[ms.get_segment_end_index_key()]),
sequence_length)
classification_target = timepoint_classification_target(
segments_matrix,
tf.sparse_tensor_to_dense(
parsed_context[ms.get_segment_label_index_key()]) +
CLASS_LABEL_OFFSET, NUM_CLASSES + CLASS_LABEL_OFFSET)
# [segments, 2] start and end time in seconds.
gt_segment_seconds = tf.to_float(
tf.concat([
tf.expand_dims(
tf.sparse_tensor_to_dense(parsed_context[
ms.get_segment_start_timestamp_key()]), 1),
tf.expand_dims(
tf.sparse_tensor_to_dense(parsed_context[
ms.get_segment_end_timestamp_key()]), 1)
], 1)) / float(SECONDS_TO_MICROSECONDS)
gt_segment_classes = tf.sparse_tensor_to_dense(parsed_context[
ms.get_segment_label_index_key()]) + CLASS_LABEL_OFFSET
example_id = parsed_context[ms.get_example_id_key()]
sampling_rate = parsed_context[ms.get_image_frame_rate_key()]
images = tf.map_fn(tf.image.decode_jpeg,
parsed_sequence[ms.get_image_encoded_key()],
back_prop=False,
dtype=tf.uint8)
output_dict = {
"segment_matrix": segments_matrix,
"indicator_matrix": indicator,
"classification_target": classification_target,
"example_id": example_id,
"sampling_rate": sampling_rate,
"gt_segment_seconds": gt_segment_seconds,
"gt_segment_classes": gt_segment_classes,
"num_segments": num_segments,
"num_timesteps": sequence_length,
"images": images,
}
return output_dict