def test_pascalvoc_2007_2012_no_skip_difficult():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc20072012(
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size),
skip_difficult=False,
)
dataset = DatasetIterator(dataset)
num_max_boxes = 56
num_train_val_2007 = 2501 + 2510
num_train_val_2012 = 5717 + 5823
num_test_2007 = 4952
assert dataset.num_max_boxes == num_max_boxes
assert Pascalvoc20072012.count_max_boxes(
skip_difficult=False) == num_max_boxes
assert dataset.num_per_epoch == num_train_val_2007 + num_train_val_2012
val_dataset = Pascalvoc20072012(
subset="validation",
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size),
skip_difficult=False)
val_dataset = DatasetIterator(val_dataset)
assert val_dataset.num_per_epoch == num_test_2007
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = val_dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def test_bdd100k():
batch_size = 3
image_size = [320, 320]
num_max_boxes = 100
num_train = 70000
num_val = 10000
dataset = BDD100K(batch_size=batch_size,
max_boxes=num_max_boxes,
pre_processor=ResizeWithGtBoxes(image_size))
dataset = DatasetIterator(dataset)
assert dataset.num_max_boxes == num_max_boxes
assert dataset.num_per_epoch == num_train
val_dataset = BDD100K(subset="validation",
batch_size=batch_size,
max_boxes=num_max_boxes,
pre_processor=ResizeWithGtBoxes(image_size))
val_dataset = DatasetIterator(val_dataset)
assert val_dataset.num_per_epoch == num_val
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = val_dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def test_open_images_v4_object_detection():
batch_size = 1
image_size = [256, 256]
dataset = OpenImagesV4BoundingBox(batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size))
dataset = DatasetIterator(dataset)
num_max_boxes = dataset.num_max_boxes
assert dataset.num_max_boxes == OpenImagesV4BoundingBox.count_max_boxes()
for _ in range(5):
images, labels = dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def test_cifar10():
batch_size = 3
image_size = [256, 512]
dataset = Cifar10(batch_size=batch_size, pre_processor=Resize(image_size))
dataset = DatasetIterator(dataset)
val_dataset = Cifar10(subset="validation",
batch_size=batch_size,
pre_processor=Resize(image_size))
val_dataset = DatasetIterator(val_dataset)
assert dataset.num_classes == 10
assert dataset.num_per_epoch == 50000
assert val_dataset.num_per_epoch == 10000
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == dataset.num_classes
def test_pascalvoc_2012():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2012(batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size))
num_max_boxes = 39
dataset = DatasetIterator(dataset)
assert dataset.num_max_boxes == num_max_boxes
assert Pascalvoc2012.count_max_boxes() == num_max_boxes
assert dataset.num_per_epoch == 5717
val_dataset = Pascalvoc2012(subset="validation",
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size))
val_dataset = DatasetIterator(val_dataset)
assert val_dataset.num_per_epoch == 5823
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = val_dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def test_hue():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=Hue((-10, 10)),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_filp_top_bottom():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=FlipTopBottom(),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_ilsvrc_2012():
batch_size = 3
image_size = [256, 512]
dataset = Ilsvrc2012(batch_size=batch_size,
pre_processor=Resize(image_size))
dataset = DatasetIterator(dataset)
val_dataset = Ilsvrc2012(subset="validation",
batch_size=batch_size,
pre_processor=Resize(image_size))
val_dataset = DatasetIterator(val_dataset)
num_train = 1281167
num_validation = 50000
assert dataset.num_per_epoch == num_train
assert val_dataset.num_per_epoch == num_validation
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == dataset.num_classes
for _ in range(STEP_SIZE):
images, labels = val_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == dataset.num_classes
def test_ssd_random_crop():
batch_size = 3
image_size = [256, 512]
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=SSDRandomCrop(),
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
assert np.all(labels[:, :, 2] <= 512)
assert np.all(labels[:, :, 3] <= 256)
def test_sequence():
batch_size = 3
image_size = [256, 512]
augmentor = Sequence([
FlipLeftRight(),
FlipTopBottom(),
SSDRandomCrop(),
])
dataset = Pascalvoc2007(
batch_size=batch_size, pre_processor=ResizeWithGtBoxes(image_size),
augmentor=augmentor,
)
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
_show_images_with_boxes(images, labels)
def test_can_iterate(set_test_environment, subset):
batch_size = 1
image_size = (100, 100)
dataset = Div2k(subset, batch_size=batch_size)
iterator = DatasetIterator(dataset)
for _ in range(len(dataset)):
images, labels = iterator.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels[0] is None
def test_open_images_v4_classification():
batch_size = 1
image_size = [256, 256]
dataset = OpenImagesV4Classification(batch_size=batch_size,
pre_processor=Resize(image_size))
dataset = DatasetIterator(dataset)
for _ in range(5):
images, labels = dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == dataset.num_classes
def test_caltech101():
batch_size = 3
image_size = [256, 512]
dataset = Caltech101(batch_size=batch_size,
pre_processor=Resize(image_size))
dataset = DatasetIterator(dataset)
assert dataset.num_classes == 101
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == dataset.num_classes
def test_pascalvoc_2007_with_target_classes():
batch_size = 3
image_size = [256, 512]
num_max_boxes = 12
num_train = 240
num_validation = 254
num_test = 433
assert TargetClassesPascalvoc2007.count_max_boxes() == num_max_boxes
dataset = TargetClassesPascalvoc2007(
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size),
)
dataset = DatasetIterator(dataset)
assert dataset.num_per_epoch == num_train
val_dataset = TargetClassesPascalvoc2007(
subset="validation",
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size))
val_dataset = DatasetIterator(val_dataset)
assert val_dataset.num_per_epoch == num_validation
test_dataset = TargetClassesPascalvoc2007(
subset="test",
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size))
test_dataset = DatasetIterator(test_dataset)
assert test_dataset.num_per_epoch == num_test
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = val_dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = test_dataset.feed()
# _show_images_with_boxes(images, labels)
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def test_pascalvoc_2007_not_skip_difficult():
batch_size = 3
image_size = [256, 512]
num_max_boxes = 42
num_train = 2501
num_validation = 2510
num_test = 4952
assert Pascalvoc2007.count_max_boxes(skip_difficult=False) == num_max_boxes
dataset = Pascalvoc2007(batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size),
skip_difficult=False)
dataset = DatasetIterator(dataset)
assert dataset.num_per_epoch == num_train
val_dataset = Pascalvoc2007(subset="validation",
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size),
skip_difficult=False)
val_dataset = DatasetIterator(val_dataset)
assert val_dataset.num_per_epoch == num_validation
test_dataset = Pascalvoc2007(subset="test",
batch_size=batch_size,
pre_processor=ResizeWithGtBoxes(image_size),
skip_difficult=False)
test_dataset = DatasetIterator(test_dataset)
assert test_dataset.num_per_epoch == num_test
for _ in range(STEP_SIZE):
images, labels = dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = val_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
for _ in range(STEP_SIZE):
images, labels = test_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == batch_size
assert images.shape[1] == image_size[0]
assert images.shape[2] == image_size[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == batch_size
assert labels.shape[1] == num_max_boxes
assert labels.shape[2] == 5
def evaluate(config, restore_path):
if restore_path is None:
restore_file = executor.search_restore_filename(
environment.CHECKPOINTS_DIR)
restore_path = os.path.join(environment.CHECKPOINTS_DIR, restore_file)
if not os.path.exists("{}.index".format(restore_path)):
raise Exception("restore file {} dont exists.".format(restore_path))
print("restore_path:", restore_path)
DatasetClass = config.DATASET_CLASS
ModelClass = config.NETWORK_CLASS
network_kwargs = dict(
(key.lower(), val) for key, val in config.NETWORK.items())
dataset_kwargs = dict(
(key.lower(), val) for key, val in config.DATASET.items())
if "test" in DatasetClass.available_subsets:
subset = "test"
else:
subset = "validation"
validation_dataset = DatasetIterator(DatasetClass(subset=subset,
**dataset_kwargs),
seed=0)
graph = tf.Graph()
with graph.as_default():
if ModelClass.__module__.startswith("lmnet.networks.object_detection"):
model = ModelClass(
classes=validation_dataset.classes,
num_max_boxes=validation_dataset.num_max_boxes,
is_debug=config.IS_DEBUG,
**network_kwargs,
)
else:
model = ModelClass(
classes=validation_dataset.classes,
is_debug=config.IS_DEBUG,
**network_kwargs,
)
global_step = tf.Variable(0, name="global_step", trainable=False)
is_training = tf.constant(False, name="is_training")
images_placeholder, labels_placeholder = model.placeholderes()
output = model.inference(images_placeholder, is_training)
metrics_ops_dict, metrics_update_op = model.metrics(
output, labels_placeholder)
model.summary(output, labels_placeholder)
summary_op = tf.summary.merge_all()
metrics_summary_op, metrics_placeholders = executor.prepare_metrics(
metrics_ops_dict)
init_op = tf.global_variables_initializer()
reset_metrics_op = tf.local_variables_initializer()
saver = tf.train.Saver(max_to_keep=None)
session_config = None # tf.ConfigProto(log_device_placement=True)
sess = tf.Session(graph=graph, config=session_config)
sess.run([init_op, reset_metrics_op])
validation_writer = tf.summary.FileWriter(environment.TENSORBOARD_DIR +
"/evaluate")
saver.restore(sess, restore_path)
last_step = sess.run(global_step)
# init metrics values
test_step_size = int(
math.ceil(validation_dataset.num_per_epoch / config.BATCH_SIZE))
print("test_step_size", test_step_size)
for test_step in range(test_step_size):
print("test_step", test_step)
images, labels = validation_dataset.feed()
feed_dict = {
images_placeholder: images,
labels_placeholder: labels,
}
# Summarize at only last step.
if test_step == test_step_size - 1:
summary, _ = sess.run([summary_op, metrics_update_op],
feed_dict=feed_dict)
validation_writer.add_summary(summary, last_step)
else:
sess.run([metrics_update_op], feed_dict=feed_dict)
metrics_values = sess.run(list(metrics_ops_dict.values()))
metrics_feed_dict = {
placeholder: value
for placeholder, value in zip(metrics_placeholders, metrics_values)
}
metrics_summary, = sess.run(
[metrics_summary_op],
feed_dict=metrics_feed_dict,
)
validation_writer.add_summary(metrics_summary, last_step)
