def extract_batch(dataset, config):
with tf.device("/cpu:0"):
bboxer = PriorBoxGrid(config)
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=2,
common_queue_capacity=512,
common_queue_min=32)
if args.segment:
im, bbox, gt, seg = data_provider.get(
['image', 'object/bbox', 'object/label', 'image/segmentation'])
else:
im, bbox, gt = data_provider.get(
['image', 'object/bbox', 'object/label'])
seg = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
im = tf.to_float(im) / 255
bbox = yxyx_to_xywh(tf.clip_by_value(bbox, 0.0, 1.0))
im, bbox, gt, seg = data_augmentation(im, bbox, gt, seg, config)
inds, cats, refine = bboxer.encode_gt_tf(bbox, gt)
return tf.train.shuffle_batch([im, inds, refine, cats, seg],
args.batch_size,
2048,
64,
num_threads=4)
def extract_batch(dataset, config):
with tf.device("/cpu:0"):
bboxer = PriorBoxGrid(config)
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=2,
common_queue_capacity=512,
common_queue_min=32)
if args.instance and args.segment:
im, bbox, gt, seg, ins, im_h, im_w = \
data_provider.get(['image', 'object/bbox', 'object/label',
'image/segmentation',
'image/instance',
'image/height',
'image/width'])
print("ins: ", ins)
print("bbox: ", bbox)
print("gt: ", gt)
ins_shape = tf.stack([im_h, im_w, args.instance_num], axis=0)
ins = tf.reshape(ins, ins_shape)
elif args.segment:
im, bbox, gt, seg = data_provider.get(
['image', 'object/bbox', 'object/label', 'image/segmentation'])
elif args.instance:
im, bbox, gt, ins, im_h, im_w = \
data_provider.get(['image', 'object/bbox', 'object/label',
'image/instance',
'image/height',
'image/width'])
ins_shape = tf.stack([im_h, im_w, args.instance_num], axis=0)
ins = tf.reshape(ins, ins_shape)
else:
im, bbox, gt = data_provider.get(
['image', 'object/bbox', 'object/label'])
seg = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
ins = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
im = tf.to_float(im) / 255
bbox = yxyx_to_xywh(tf.clip_by_value(bbox, 0.0, 1.0))
im, bbox, gt, seg, ins = data_augmentation(im, bbox, gt, seg, ins,
config)
inds, cats, refine, gt_matches = bboxer.encode_gt_tf(bbox, gt)
bbox_pads = args.instance_num - tf.shape(bbox)[0]
paddings = [[0, bbox_pads], [0, 0]]
bbox = tf.pad(bbox, paddings)
bbox = tf.reshape(bbox, (args.instance_num, 4))
return tf.train.shuffle_batch(
[im, inds, refine, cats, seg, ins, gt_matches, bbox],
args.batch_size,
2048,
64,
num_threads=4)
def extract_batch(dataset, config):
with tf.device("/cpu:0"):
bboxer = PriorBoxGrid(config)
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, num_readers=2,
common_queue_capacity=512, common_queue_min=32)
if args.segment:
im, bbox, gt, seg = data_provider.get(['image', 'object/bbox', 'object/label',
'image/segmentation'])
else:
im, bbox, gt = data_provider.get(['image', 'object/bbox', 'object/label'])
seg = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
im = tf.to_float(im)/255
bbox = yxyx_to_xywh(tf.clip_by_value(bbox, 0.0, 1.0))
im, bbox, gt, seg = data_augmentation(im, bbox, gt, seg, config)
inds, cats, refine = bboxer.encode_gt_tf(bbox, gt)
return tf.train.shuffle_batch([im, inds, refine, cats, seg],
args.batch_size, 2048, 64, num_threads=4)
def extract_batch(dataset, config):
with tf.device("/cpu:0"):
bboxer = PriorBoxGrid(config)
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=2,
common_queue_capacity=512,
common_queue_min=32)
if args.instance and args.segment:
im, bbox, gt, seg, ins, im_h, im_w = \
data_provider.get(['image', 'object/bbox', 'object/label',
'image/segmentation',
'image/instance',
'image/height',
'image/width'])
ins_num = tf.cast(tf.shape(bbox)[0], tf.int64)
ins_shape = tf.stack([ins_num, im_h, im_w], axis=0)
ins = tf.reshape(ins, ins_shape)
ins = tf.transpose(ins, [1, 2, 0])
pads = args.instance_num - tf.shape(ins)[-1]
ins = tf.pad(ins, [[0, 0], [0, 0], [0, pads]])
# ins_summary = tf.image.resize_images(ins, (300, 300))
# ins_summary = tf.transpose(ins_summary, [2, 0, 1])
# ins_summary = ins_summary[0]
# ins_summary = tf.reshape(ins_summary, [300, 300])
elif args.segment:
im, bbox, gt, seg = data_provider.get(
['image', 'object/bbox', 'object/label', 'image/segmentation'])
elif args.instance:
im, bbox, gt, ins, im_h, im_w = \
data_provider.get(['image', 'object/bbox', 'object/label',
'image/instance',
'image/height',
'image/width'])
ins_num = tf.cast(tf.shape(bbox)[0], tf.int64)
ins_shape = tf.stack([ins_num, im_h, im_w], axis=0)
ins = tf.reshape(ins, ins_shape)
ins = tf.transpose(ins, [1, 2, 0])
pads = args.instance_num - tf.shape(ins)[-1]
ins = tf.pad(ins, [[0, 0], [0, 0], [0, pads]])
seg = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
else:
im, bbox, gt = data_provider.get(
['image', 'object/bbox', 'object/label'])
seg = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
ins = tf.expand_dims(tf.zeros(tf.shape(im)[:2]), 2)
im = tf.to_float(im) / 255
bbox = yxyx_to_xywh(tf.clip_by_value(bbox, 0.0, 1.0))
im, bbox, gt, seg, ins = data_augmentation(im, bbox, gt, seg, ins,
config)
inds, cats, refine, gt_matches = bboxer.encode_gt_tf(bbox, gt)
# ins_summary = ins
not_empty_gt_matches = tf.reduce_sum(gt_matches) > 0
gt_matches_idx = tf.reshape(tf.where(gt_matches > 0), (-1, ))
# gt_matches_idx, _ = tf.unique(gt_matches_idx)
match_roi_info = tf.cond(
not_empty_gt_matches,
true_fn=lambda: get_match_roi_info(gt_matches_idx, roi_info),
false_fn=lambda: tf.zeros((args.instance_num, 5)))
gt_matches = tf.gather(gt_matches, gt_matches_idx)
gt_matches = tf.cond(not_empty_gt_matches, lambda: gt_matches - 1,
lambda: gt_matches)
bbox_matches = tf.gather(bbox, gt_matches)
need_pad = args.instance_num > tf.shape(bbox_matches)[0]
pads = tf.cond(need_pad,
lambda: args.instance_num - tf.shape(bbox_matches)[0],
lambda: 0)
bbox_matches = tf.pad(bbox_matches, [[0, pads], [0, 0]])
bbox_matches = bbox_matches[:args.instance_num, :]
# ins = tf.gather(ins, gt_matches)
# ins = tf.pad(ins, [[0, pads], [0, 0], [0, 0]])
# ins = ins[:args.instance_num, ...]
# if tf.reduce_sum(gt_matches) > 0:
# gt_matches_idx = tf.where(gt_matches > 0)
# match_roi_info = tf.squeeze(tf.gather(roi_info, gt_matches_idx // 6)) # (?, 5)
# # print("gt_matches_idx: ", gt_matches_idx)
# # print("roi_info: ", roi_info)
# print("match roi info shape: ", match_roi_info.shape)
# roi_pads = args.instance_num - tf.shape(match_roi_info)[0]
# tf.pad(match_roi_info, [[0, roi_pads], [0, 0]],
# mode='CONSTANT', constant_values=0)
# match_roi_info = tf.reshape(match_roi_info, (args.instance_num, 5))
# else:
# match_roi_info = tf.zeros((args.instance_num, 5))
need_pad = args.instance_num > tf.shape(gt_matches)[0]
pads = tf.cond(need_pad,
lambda: args.instance_num - tf.shape(gt_matches)[0],
lambda: 0)
gt_matches = tf.pad(gt_matches, [[0, pads]])
gt_matches = gt_matches[:args.instance_num]
ins = extract_matched_gt_instance(bbox_matches, ins,
[args.ins_shape, args.ins_shape],
gt_matches)
ins = tf.reshape(ins,
(args.instance_num, args.ins_shape, args.ins_shape))
tf.summary.tensor_summary('tensor/bbox_matches', bbox_matches)
tf.summary.tensor_summary('tensor/gt_matches', gt_matches)
ins_cats = tf.gather(cats, gt_matches_idx)
need_pad = args.instance_num > tf.shape(ins_cats)[0]
pads = tf.cond(need_pad,
lambda: args.instance_num - tf.shape(ins_cats)[0],
lambda: 0)
ins_cats = tf.pad(ins_cats, [[0, pads]])
ins_cats = ins_cats[:args.instance_num]
ins_cats = tf.reshape(ins_cats, (args.instance_num, ))
return tf.train.shuffle_batch(
[im, inds, refine, cats, ins_cats, seg, ins, match_roi_info],
args.batch_size,
2048,
64,
num_threads=4)
