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Added code to load COCO validation data

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-04-17 12:46:23 +02:00
parent 97b1b8963f
commit 80d4861a4b
2 changed files with 116 additions and 30 deletions
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138
src/twomartens/masterthesis/data.py
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@ -18,19 +18,20 @@
Functionality to load data into Tensorflow data sets.
Functions:
load_coco(...): loads the COCO data into a Tensorflow data set
load_coco_train(...): loads the COCO training data into a Tensorflow data set
load_coco_val(...): loads the COCO validation data into a Tensorflow data set
load_scenenet(...): loads the SceneNet RGB-D data into a Tensorflow data set
"""
from typing import List
from typing import Callable, List, Mapping, Tuple
from typing import Sequence
import tensorflow as tf
from pycocotools import coco
def load_coco(data_path: str, category: int,
num_epochs: int, batch_size: int = 32,
resized_shape: Sequence[int] = (256, 256)) -> tf.data.Dataset:
def load_coco_train(data_path: str, category: int,
num_epochs: int, batch_size: int = 32,
resized_shape: Sequence[int] = (256, 256)) -> tf.data.Dataset:
"""
Loads the COCO trainval35k data and returns a data set.
@ -39,12 +40,14 @@ def load_coco(data_path: str, category: int,
category: id of the inlying class
num_epochs: number of epochs
batch_size: batch size (default: 32)
resized_shape: shape of images after resizing them (default: (300,300))
resized_shape: shape of images after resizing them (default: (256, 256))
Returns:
Tensorflow data set
"""
annotation_file_train = f"{data_path}/annotations/instances_train2014.json"
annotation_file_val = f"{data_path}/annotations/instances_valminusminival2014.json"
# load training images
coco_train = coco.COCO(annotation_file_train)
img_ids = coco_train.getImgIds(catIds=[category]) # return all image IDs belonging to given category
@ -60,17 +63,85 @@ def load_coco(data_path: str, category: int,
annotation_ids = coco_val.getAnnIds(img_ids, catIds=[category])
annotations_val = coco_val.loadAnns(annotation_ids) # load all image annotations
file_names_val = {image['id']: f"{data_path}/val2014/{image['file_name']}" for image in images_val}
images.extend(images_val)
annotations.extend(annotations_val)
file_names.update(file_names_val)
ids_to_images = {image['id']: image for image in images}
checked_file_names, checked_bboxes = _clean_dataset(annotations, file_names, ids_to_images)
length_dataset = len(checked_file_names)
# build image data set
path_dataset = tf.data.Dataset.from_tensor_slices(checked_file_names)
label_dataset = tf.data.Dataset.from_tensor_slices(checked_bboxes)
dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=length_dataset, count=num_epochs))
dataset = dataset.batch(batch_size=batch_size)
dataset = dataset.map(_load_images_callback(resized_shape))
return dataset
def load_coco_val(data_path: str, category: int,
num_epochs: int = 1, batch_size: int = 32,
resized_shape: Sequence[int] = (256, 256)) -> tf.data.Dataset:
"""
Loads the COCO minival2014/val2017 data and returns a data set.
Args:
data_path: path to the COCO data set
category: id of the inlying class
num_epochs: number of epochs (default: 1)
batch_size: batch size (default: 32)
resized_shape: shape of images after resizing them (default: (256, 256))
Returns:
Tensorflow data set
"""
annotation_file_minival = f"{data_path}/annotations/instances/val2017.json"
coco_val = coco.COCO(annotation_file_minival)
img_ids = coco_val.getImgIds(catIds=[category]) # return all image IDs belonging to given category
images = coco_val.loadImgs(img_ids) # load all images
annotation_ids = coco_val.getAnnIds(img_ids, catIds=[category])
annotations = coco_val.loadAnns(annotation_ids) # load all image annotations
file_names = {image['id']: f"{data_path}/val2014/{image['file_name']}" for image in images}
ids_to_images = {image['id']: image for image in images}
checked_file_names, checked_bboxes = _clean_dataset(annotations, file_names, ids_to_images)
length_dataset = len(checked_file_names)
# build image data set
path_dataset = tf.data.Dataset.from_tensor_slices(checked_file_names)
label_dataset = tf.data.Dataset.from_tensor_slices(checked_bboxes)
dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=length_dataset, count=num_epochs))
dataset = dataset.batch(batch_size=batch_size)
dataset = dataset.map(_load_images_callback(resized_shape))
return dataset
def _clean_dataset(annotations: Sequence[dict], file_names: Mapping[str, str],
ids_to_images: Mapping[str, dict]) -> Tuple[List[str], List[List[float]]]:
"""
Cleans a given data set from problematic cases and returns cleaned version.
Args:
annotations: list of annotation dictionaries
file_names: mapping of fileID -> file name
ids_to_images: mapping of imageID -> image dictionary
Returns:
cleaned file names, corresponding clean bounding boxes
"""
checked_file_names = []
checked_bboxes = []
for annotation in annotations:
img_id = annotation['image_id']
image = ids_to_images[img_id]
file_name = file_names[img_id] if img_id in file_names else file_names_val[img_id]
file_name = file_names[img_id]
bbox = annotation['bbox']
target_height = round(bbox[3])
target_width = round(bbox[2])
@ -97,15 +168,38 @@ def load_coco(data_path: str, category: int,
continue
bbox[2] = target_width
bbox[3] = target_height
checked_file_names.append(file_name)
checked_bboxes.append(bbox)
length_dataset = len(checked_file_names)
def _load_image(paths: Sequence[str], labels: Sequence[Sequence[float]]):
_images = tf.map_fn(lambda path: tf.read_file(path), paths)
return checked_file_names, checked_bboxes
def _load_images_callback(resized_shape: Sequence[int]) -> Callable[
[Sequence[str], Sequence[Sequence[float]]], tf.Tensor]:
"""
Returns the callback function to load images.
Args:
resized_shape: shape of resized image (height, width)
Returns:
callback function
"""
def _load_images(paths: Sequence[str], labels: Sequence[Sequence[float]]) -> tf.Tensor:
"""
Callback function to load images.
Args:
paths: list of file paths
labels: list of bounding boxes
Returns:
loaded images
"""
_images = tf.map_fn(lambda path: tf.read_file(path), paths)
def _get_images(image_data: Sequence[tf.Tensor]) -> List[tf.Tensor]:
image = tf.image.decode_image(image_data[0], channels=3, dtype=tf.float32)
image_shape = tf.shape(image)
@ -116,24 +210,16 @@ def load_coco(data_path: str, category: int,
tf.cast(tf.floor(label[3]), dtype=tf.int32),
tf.cast(tf.floor(label[2]), dtype=tf.int32))
image_resized = tf.image.resize_image_with_pad(image_cut, resized_shape[0], resized_shape[1])
return [image_resized, label]
processed = tf.map_fn(_get_images, [_images, labels], dtype=[tf.float32, tf.float32])
processed_images = processed[0]
processed_images = tf.reshape(processed_images, [-1, resized_shape[0], resized_shape[1], 3])
return processed_images
# build image data set
path_dataset = tf.data.Dataset.from_tensor_slices(checked_file_names)
label_dataset = tf.data.Dataset.from_tensor_slices(checked_bboxes)
dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
dataset = dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=length_dataset, count=num_epochs))
dataset = dataset.batch(batch_size=batch_size)
dataset = dataset.map(_load_image)
return dataset
return _load_images
def load_scenenet(data_path: str, num_epochs: int, batch_size: int = 32) -> tf.data.Dataset:

8
src/twomartens/masterthesis/main.py
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@ -122,8 +122,8 @@ def _use(args: argparse.Namespace) -> None:
category = args.category
category_trained = args.category_trained
batch_size = 16
coco_data = data.load_coco(coco_path, category, num_epochs=1,
batch_size=batch_size, resized_shape=(256, 256))
coco_data = data.load_coco_train(coco_path, category, num_epochs=1,
batch_size=batch_size, resized_shape=(256, 256))
use_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/use/category-{category}/{args.iteration}"
)
@ -143,8 +143,8 @@ def _auto_encoder_train(args: argparse.Namespace) -> None:
coco_path = args.coco_path
category = args.category
batch_size = 16
coco_data = data.load_coco(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size,
resized_shape=(256, 256))
coco_data = data.load_coco_train(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size,
resized_shape=(256, 256))
train_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/train/category-{category}/{args.iteration}"
)