def create_dataset_batch_queue(dataset):
# 设置GPU
with tf.device('/cpu:0'):
# tf.name_scope可以让变量有相同的命名，只是限于tf.Variable的变量
with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
# 读取数据
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=50 * config.batch_size,
common_queue_min=30 * config.batch_size,
shuffle=True)
# Get for SSD network: image, labels, bboxes.
[image, gignored, gbboxes, x1, x2, x3, x4, y1, y2, y3, y4] = provider.get([
'image',
'object/ignored',
'object/bbox',
'object/oriented_bbox/x1',
'object/oriented_bbox/x2',
'object/oriented_bbox/x3',
'object/oriented_bbox/x4',
'object/oriented_bbox/y1',
'object/oriented_bbox/y2',
'object/oriented_bbox/y3',
'object/oriented_bbox/y4'
])
# tf.stack()矩阵拼接
# tf.transpos()转置
gxs = tf.transpose(tf.stack([x1, x2, x3, x4])) #shape = (N, 4)
gys = tf.transpose(tf.stack([y1, y2, y3, y4]))
image = tf.identity(image, 'input_image')
# Pre-processing image, labels and bboxes.
image, gignored, gbboxes, gxs, gys = ssd_vgg_preprocessing.preprocess_image(image, gignored, gbboxes, gxs, gys,
out_shape = config.image_shape,
data_format = config.data_format,
is_training = True)
image = tf.identity(image, 'processed_image')
# calculate ground truth
# 计算真实标签
seg_label, seg_loc, link_label = seglink.tf_get_all_seglink_gt(gxs, gys, gignored)
# batch them
# tf.train.batch():利用一个tensor的列表或字典来获取一个batch数据
b_image, b_seg_label, b_seg_loc, b_link_label = tf.train.batch(
[image, seg_label, seg_loc, link_label],
batch_size = config.batch_size_per_gpu,
num_threads= FLAGS.num_preprocessing_threads,
capacity = 50)
# prefetch_queue():从数据’Tensor‘ 中预取张量进入队列
batch_queue = slim.prefetch_queue.prefetch_queue(
[b_image, b_seg_label, b_seg_loc, b_link_label],
capacity = 50)
return batch_queue
def create_dataset_batch_queue(dataset):
batch_size = config.batch_size
with tf.device('/cpu:0'):
with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * batch_size,
common_queue_min=10 * batch_size,
shuffle=True)
# Get for SSD network: image, labels, bboxes.
[image, shape, gignored, gbboxes, x1, x2, x3, x4, y1, y2, y3, y4] = provider.get([
'image', 'shape',
'object/ignored',
'object/bbox',
'object/oriented_bbox/x1',
'object/oriented_bbox/x2',
'object/oriented_bbox/x3',
'object/oriented_bbox/x4',
'object/oriented_bbox/y1',
'object/oriented_bbox/y2',
'object/oriented_bbox/y3',
'object/oriented_bbox/y4'
])
gxs = tf.transpose(tf.stack([x1, x2, x3, x4])) #shape = (N, 4)
gys = tf.transpose(tf.stack([y1, y2, y3, y4]))
image = tf.identity(image, 'input_image')
# Pre-processing image, labels and bboxes.
image, gignored, gbboxes, gxs, gys = ssd_vgg_preprocessing.preprocess_image(
image, gignored, gbboxes, gxs, gys,
out_shape = config.image_shape,
data_format = config.data_format,
is_training = True)
image = tf.identity(image, 'processed_image')
# calculate ground truth
seg_label, seg_offsets, link_label = seglink.tf_get_all_seglink_gt(gxs, gys, gignored)
# batch them
b_image, b_seg_label, b_seg_offsets, b_link_label = tf.train.batch(
[image, seg_label, seg_offsets, link_label],
batch_size = config.batch_size_per_gpu,
num_threads=FLAGS.num_preprocessing_threads,
capacity = 50)
batch_queue = slim.prefetch_queue.prefetch_queue(
[b_image, b_seg_label, b_seg_offsets, b_link_label],
capacity = 50)
return batch_queue
def create_dataset_batch_queue(dataset):
batch_size = config.batch_size
with tf.device('/cpu:0'):
with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * batch_size,
common_queue_min=10 * batch_size,
shuffle=True)
# Get for SSD network: image, labels, bboxes.
[image, shape, gignored, gbboxes, x1, x2, x3, x4, y1, y2, y3,
y4] = provider.get([
'image', 'shape', 'object/ignored', 'object/bbox',
'object/oriented_bbox/x1', 'object/oriented_bbox/x2',
'object/oriented_bbox/x3', 'object/oriented_bbox/x4',
'object/oriented_bbox/y1', 'object/oriented_bbox/y2',
'object/oriented_bbox/y3', 'object/oriented_bbox/y4'
])
gxs = tf.transpose(tf.stack([x1, x2, x3, x4])) # shape = (N, 4)
gys = tf.transpose(tf.stack([y1, y2, y3, y4]))
image = tf.identity(image, 'input_image')
# Pre-processing image, labels and bboxes.
image, gignored, gbboxes, gxs, gys = ssd_vgg_preprocessing.preprocess_image(
image,
gignored,
gbboxes,
gxs,
gys,
out_shape=config.image_shape,
data_format=config.data_format,
is_training=True)
image = tf.identity(image, 'processed_image')
# calculate ground truth
seg_label, seg_offsets, link_label = seglink.tf_get_all_seglink_gt(
gxs, gys, gignored)
# batch them
b_image, b_seg_label, b_seg_offsets, b_link_label = tf.train.batch(
[image, seg_label, seg_offsets, link_label],
batch_size=batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=50)
batch_queue = slim.prefetch_queue.prefetch_queue(
[b_image, b_seg_label, b_seg_offsets, b_link_label], capacity=50)
return batch_queue
def read_dataset(dataset):
with tf.name_scope(FLAGS.dataset_name + '_' + FLAGS.dataset_split_name +
'_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, num_readers=FLAGS.num_readers, shuffle=False)
[
image, shape, filename, gignored, gbboxes, x1, x2, x3, x4, y1, y2, y3,
y4
] = provider.get([
'image', 'shape', 'filename', 'object/ignored', 'object/bbox',
'object/oriented_bbox/x1', 'object/oriented_bbox/x2',
'object/oriented_bbox/x3', 'object/oriented_bbox/x4',
'object/oriented_bbox/y1', 'object/oriented_bbox/y2',
'object/oriented_bbox/y3', 'object/oriented_bbox/y4'
])
gxs = tf.transpose(tf.stack([x1, x2, x3, x4])) #shape = (N, 4)
gys = tf.transpose(tf.stack([y1, y2, y3, y4]))
image = tf.identity(image, 'input_image')
# Pre-processing image, labels and bboxes.
image, gignored, gbboxes, gxs, gys = ssd_vgg_preprocessing.preprocess_image(
image,
gignored,
gbboxes,
gxs,
gys,
out_shape=config.image_shape,
data_format=config.data_format,
is_training=False)
image = tf.identity(image, 'processed_image')
# calculate ground truth
seg_label, seg_loc, link_gt = seglink.tf_get_all_seglink_gt(
gxs, gys, gignored)
return image, seg_label, seg_loc, link_gt, filename, shape, gignored, gxs, gys
def read_dataset(dataset):
with tf.name_scope(FLAGS.dataset_name +'_' + FLAGS.dataset_split_name + '_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
shuffle=False)
[image, shape, filename, gignored, gbboxes, x1, x2, x3, x4, y1, y2, y3, y4] = provider.get([
'image', 'shape', 'filename',
'object/ignored',
'object/bbox',
'object/oriented_bbox/x1',
'object/oriented_bbox/x2',
'object/oriented_bbox/x3',
'object/oriented_bbox/x4',
'object/oriented_bbox/y1',
'object/oriented_bbox/y2',
'object/oriented_bbox/y3',
'object/oriented_bbox/y4'
])
gxs = tf.transpose(tf.stack([x1, x2, x3, x4])) #shape = (N, 4)
gys = tf.transpose(tf.stack([y1, y2, y3, y4]))
image = tf.identity(image, 'input_image')
# Pre-processing image, labels and bboxes.
image, gignored, gbboxes, gxs, gys = ssd_vgg_preprocessing.preprocess_image(
image, gignored, gbboxes, gxs, gys,
out_shape = config.image_shape,
data_format = config.data_format,
is_training = False)
image = tf.identity(image, 'processed_image')
# calculate ground truth
seg_label, seg_loc, link_gt = seglink.tf_get_all_seglink_gt(gxs, gys, gignored)
return image, seg_label, seg_loc, link_gt, filename, shape, gignored, gxs, gys
