def test(img_num):
with tf.Graph().as_default():
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \
next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE,
shortside_len=cfgs.SHORT_SIDE_LEN,
is_training=False)
gtboxes_and_label, head = get_head(
tf.squeeze(gtboxes_and_label_batch, 0))
gtboxes_and_label = tf.py_func(back_forward_convert,
inp=[gtboxes_and_label],
Tout=tf.float32)
gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6])
head_quadrant = tf.py_func(get_head_quadrant,
inp=[head, gtboxes_and_label],
Tout=tf.float32)
head_quadrant = tf.reshape(head_quadrant, [-1, 1])
gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(
gtboxes_and_label)
gtboxes_and_label_minAreaRectangle = tf.reshape(
gtboxes_and_label_minAreaRectangle, [-1, 5])
# ***********************************************************************************************
# * share net *
# ***********************************************************************************************
_, share_net = get_network_byname(net_name=cfgs.NET_NAME,
inputs=img_batch,
num_classes=None,
is_training=True,
output_stride=None,
global_pool=False,
spatial_squeeze=False)
# ***********************************************************************************************
# * RPN *
# ***********************************************************************************************
rpn = build_rpn.RPN(
net_name=cfgs.NET_NAME,
inputs=img_batch,
gtboxes_and_label=None,
is_training=False,
share_head=cfgs.SHARE_HEAD,
share_net=share_net,
stride=cfgs.STRIDE,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.
BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
top_k_nms=cfgs.RPN_TOP_K_NMS,
rpn_nms_iou_threshold=cfgs.RPN_NMS_IOU_THRESHOLD,
max_proposals_num=cfgs.MAX_PROPOSAL_NUM,
rpn_iou_positive_threshold=cfgs.RPN_IOU_POSITIVE_THRESHOLD,
rpn_iou_negative_threshold=cfgs.RPN_IOU_NEGATIVE_THRESHOLD,
rpn_mini_batch_size=cfgs.RPN_MINIBATCH_SIZE,
rpn_positives_ratio=cfgs.RPN_POSITIVE_RATE,
remove_outside_anchors=False, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME])
# rpn predict proposals
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals(
) # rpn_score shape: [300, ]
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
img_shape=tf.shape(img_batch),
img_batch=img_batch,
roi_size=cfgs.ROI_SIZE,
roi_pool_kernel_size=cfgs.ROI_POOL_KERNEL_SIZE,
scale_factors=cfgs.SCALE_FACTORS,
gtboxes_and_label=None,
gtboxes_and_label_minAreaRectangle=
gtboxes_and_label_minAreaRectangle,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
fast_rcnn_maximum_boxes_per_img=100,
fast_rcnn_nms_max_boxes_per_class=cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
show_detections_score_threshold=cfgs.FINAL_SCORE_THRESHOLD,
# show detections which score >= 0.6
num_classes=cfgs.CLASS_NUM,
fast_rcnn_minibatch_size=cfgs.FAST_RCNN_MINIBATCH_SIZE,
fast_rcnn_positives_ratio=cfgs.FAST_RCNN_POSITIVE_RATE,
fast_rcnn_positives_iou_threshold=cfgs.
FAST_RCNN_IOU_POSITIVE_THRESHOLD,
# iou>0.5 is positive, iou<0.5 is negative
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False,
level=cfgs.LEVEL,
head_quadrant=head_quadrant)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category, \
fast_rcnn_decode_boxes_rotate, fast_rcnn_score_rotate, fast_rcnn_head_quadrant, \
num_of_objects_rotate, detection_category_rotate = fast_rcnn.fast_rcnn_predict()
# train
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = restore_model.get_restorer()
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
for i in range(img_num):
start = time.time()
_img_name_batch, _img_batch, _gtboxes_and_label, _gtboxes_and_label_minAreaRectangle, _head_quadrant, \
_fast_rcnn_decode_boxes, _fast_rcnn_score, _detection_category, _fast_rcnn_decode_boxes_rotate, \
_fast_rcnn_score_rotate, _fast_rcnn_head_quadrant, _detection_category_rotate \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, gtboxes_and_label_minAreaRectangle, head_quadrant,
fast_rcnn_decode_boxes, fast_rcnn_score, detection_category, fast_rcnn_decode_boxes_rotate,
fast_rcnn_score_rotate, fast_rcnn_head_quadrant, detection_category_rotate])
end = time.time()
_img_batch = np.squeeze(_img_batch, axis=0)
_img_batch_fpn_horizonal = help_utils.draw_box_cv(
_img_batch,
boxes=_fast_rcnn_decode_boxes,
labels=_detection_category,
scores=_fast_rcnn_score)
_img_batch_fpn_rotate = help_utils.draw_rotate_box_cv(
_img_batch,
boxes=_fast_rcnn_decode_boxes_rotate,
labels=_detection_category_rotate,
scores=_fast_rcnn_score_rotate,
head=np.argmax(_fast_rcnn_head_quadrant, axis=1))
mkdir(cfgs.TEST_SAVE_PATH)
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_horizontal_fpn.jpg'.format(str(_img_name_batch[0])),
_img_batch_fpn_horizonal)
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_rotate_fpn.jpg'.format(str(_img_name_batch[0])),
_img_batch_fpn_rotate)
temp_label_horizontal = np.reshape(_gtboxes_and_label[:, -1:],
[
-1,
]).astype(np.int64)
temp_label_rotate = np.reshape(_gtboxes_and_label[:, -1:], [
-1,
]).astype(np.int64)
_img_batch_gt_horizontal = help_utils.draw_box_cv(
_img_batch,
boxes=_gtboxes_and_label_minAreaRectangle[:, :-1],
labels=temp_label_horizontal,
scores=None)
_img_batch_gt_rotate = help_utils.draw_rotate_box_cv(
_img_batch,
boxes=_gtboxes_and_label[:, :-1],
labels=temp_label_rotate,
scores=None,
head=np.reshape(_head_quadrant, [
-1,
]))
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_horizontal_gt.jpg'.format(str(_img_name_batch[0])),
_img_batch_gt_horizontal)
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_rotate_gt.jpg'.format(str(_img_name_batch[0])),
_img_batch_gt_rotate)
view_bar(
'{} image cost {}s'.format(str(_img_name_batch[0]),
(end - start)), i + 1, img_num)
coord.request_stop()
coord.join(threads)
def test_rotate():
with tf.Graph().as_default():
with tf.name_scope('get_batch'):
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \
next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE,
shortside_len=cfgs.SHORT_SIDE_LEN,
is_training=True)
gtboxes_and_label, head = get_head(
tf.squeeze(gtboxes_and_label_batch, 0))
gtboxes_and_label = tf.py_func(back_forward_convert,
inp=[gtboxes_and_label],
Tout=tf.float32)
gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6])
head_quadrant = tf.py_func(get_head_quadrant,
inp=[head, gtboxes_and_label],
Tout=tf.float32)
head_quadrant = tf.reshape(head_quadrant, [-1, 1])
gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(
gtboxes_and_label)
gtboxes_and_label_minAreaRectangle = tf.reshape(
gtboxes_and_label_minAreaRectangle, [-1, 5])
with tf.name_scope('draw_gtboxes'):
gtboxes_in_img = draw_box_with_color(
img_batch,
tf.reshape(gtboxes_and_label_minAreaRectangle,
[-1, 5])[:, :-1],
text=tf.shape(gtboxes_and_label_minAreaRectangle)[0])
gtboxes_rotate_in_img = draw_box_with_color_rotate(
img_batch,
tf.reshape(gtboxes_and_label, [-1, 6])[:, :-1],
text=tf.shape(gtboxes_and_label)[0],
head=head_quadrant)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
for i in range(650):
img_gtboxes, img_gtboxes_rotate, img_name = sess.run(
[gtboxes_in_img, gtboxes_rotate_in_img, img_name_batch])
img_gtboxes = np.squeeze(img_gtboxes, axis=0)
img_gtboxes_rotate = np.squeeze(img_gtboxes_rotate, axis=0)
print(i)
cv2.imwrite(
cfgs.INFERENCE_SAVE_PATH +
'/{}_horizontal_fpn.jpg'.format(str(img_name[0])),
img_gtboxes)
cv2.imwrite(
cfgs.INFERENCE_SAVE_PATH +
'/{}_rotate_fpn.jpg'.format(str(img_name[0])),
img_gtboxes_rotate)
coord.request_stop()
coord.join(threads)
def eval_ship(img_num):
with tf.Graph().as_default():
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \
next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE,
shortside_len=cfgs.SHORT_SIDE_LEN,
is_training=True)
gtboxes_and_label, head = get_head(
tf.squeeze(gtboxes_and_label_batch, 0))
gtboxes_and_label = tf.py_func(back_forward_convert,
inp=[gtboxes_and_label],
Tout=tf.float32)
gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6])
head_quadrant = tf.py_func(get_head_quadrant,
inp=[head, gtboxes_and_label],
Tout=tf.float32)
head_quadrant = tf.reshape(head_quadrant, [-1, 1])
gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(
gtboxes_and_label)
gtboxes_and_label_minAreaRectangle = tf.reshape(
gtboxes_and_label_minAreaRectangle, [-1, 5])
# ***********************************************************************************************
# * share net *
# ***********************************************************************************************
_, share_net = get_network_byname(net_name=cfgs.NET_NAME,
inputs=img_batch,
num_classes=None,
is_training=True,
output_stride=None,
global_pool=False,
spatial_squeeze=False)
# ***********************************************************************************************
# * RPN *
# ***********************************************************************************************
rpn = build_rpn.RPN(
net_name=cfgs.NET_NAME,
inputs=img_batch,
gtboxes_and_label=None,
is_training=False,
share_head=cfgs.SHARE_HEAD,
share_net=share_net,
stride=cfgs.STRIDE,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.
BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
top_k_nms=cfgs.RPN_TOP_K_NMS,
rpn_nms_iou_threshold=cfgs.RPN_NMS_IOU_THRESHOLD,
max_proposals_num=cfgs.MAX_PROPOSAL_NUM,
rpn_iou_positive_threshold=cfgs.RPN_IOU_POSITIVE_THRESHOLD,
rpn_iou_negative_threshold=cfgs.RPN_IOU_NEGATIVE_THRESHOLD,
rpn_mini_batch_size=cfgs.RPN_MINIBATCH_SIZE,
rpn_positives_ratio=cfgs.RPN_POSITIVE_RATE,
remove_outside_anchors=False, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME])
# rpn predict proposals
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals(
) # rpn_score shape: [300, ]
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
img_shape=tf.shape(img_batch),
img_batch=img_batch,
roi_size=cfgs.ROI_SIZE,
roi_pool_kernel_size=cfgs.ROI_POOL_KERNEL_SIZE,
scale_factors=cfgs.SCALE_FACTORS,
gtboxes_and_label=None,
gtboxes_and_label_minAreaRectangle=
gtboxes_and_label_minAreaRectangle,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
fast_rcnn_maximum_boxes_per_img=100,
fast_rcnn_nms_max_boxes_per_class=cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
show_detections_score_threshold=cfgs.FINAL_SCORE_THRESHOLD,
# show detections which score >= 0.6
num_classes=cfgs.CLASS_NUM,
fast_rcnn_minibatch_size=cfgs.FAST_RCNN_MINIBATCH_SIZE,
fast_rcnn_positives_ratio=cfgs.FAST_RCNN_POSITIVE_RATE,
fast_rcnn_positives_iou_threshold=cfgs.
FAST_RCNN_IOU_POSITIVE_THRESHOLD,
# iou>0.5 is positive, iou<0.5 is negative
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False,
level=cfgs.LEVEL,
head_quadrant=head_quadrant)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category, \
fast_rcnn_decode_boxes_rotate, fast_rcnn_score_rotate, fast_rcnn_head_quadrant, \
num_of_objects_rotate, detection_category_rotate = fast_rcnn.fast_rcnn_predict()
# train
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = restore_model.get_restorer()
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
gtboxes_horizontal_dict = {}
gtboxes_rotate_dict = {}
all_boxes_h = []
all_boxes_r = []
all_img_names = []
for i in range(img_num):
start = time.time()
_img_name_batch, _img_batch, _gtboxes_and_label, _gtboxes_and_label_minAreaRectangle, \
_fast_rcnn_decode_boxes, _fast_rcnn_score, _detection_category, _fast_rcnn_decode_boxes_rotate, \
_fast_rcnn_score_rotate, _detection_category_rotate \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, gtboxes_and_label_minAreaRectangle,
fast_rcnn_decode_boxes, fast_rcnn_score, detection_category, fast_rcnn_decode_boxes_rotate,
fast_rcnn_score_rotate, detection_category_rotate])
end = time.time()
# gtboxes convert dict
gtboxes_horizontal_dict[str(_img_name_batch[0])] = []
gtboxes_rotate_dict[str(_img_name_batch[0])] = []
gtbox_horizontal_list, gtbox_rotate_list = make_dict_packle(
_gtboxes_and_label, _gtboxes_and_label_minAreaRectangle)
xmin, ymin, xmax, ymax = _fast_rcnn_decode_boxes[:,
1], _fast_rcnn_decode_boxes[:,
0], _fast_rcnn_decode_boxes[:,
3], _fast_rcnn_decode_boxes[:,
2]
x_c, y_c, w, h, theta = _fast_rcnn_decode_boxes_rotate[:, 1], _fast_rcnn_decode_boxes_rotate[:, 0], _fast_rcnn_decode_boxes_rotate[:, 3], \
_fast_rcnn_decode_boxes_rotate[:, 2], _fast_rcnn_decode_boxes_rotate[:, 4]
boxes_h = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
boxes_r = np.transpose(np.stack([x_c, y_c, w, h, theta]))
dets_h = np.hstack((_detection_category.reshape(-1, 1),
_fast_rcnn_score.reshape(-1, 1), boxes_h))
dets_r = np.hstack(
(_detection_category_rotate.reshape(-1, 1),
_fast_rcnn_score_rotate.reshape(-1, 1), boxes_r))
all_boxes_h.append(dets_h)
all_boxes_r.append(dets_r)
all_img_names.append(str(_img_name_batch[0]))
gtboxes_horizontal_dict[str(
_img_name_batch[0])].extend(gtbox_horizontal_list)
gtboxes_rotate_dict[str(
_img_name_batch[0])].extend(gtbox_rotate_list)
print(str(_img_name_batch[0]))
view_bar(
'{} image cost {}s'.format(str(_img_name_batch[0]),
(end - start)), i + 1, img_num)
write_voc_results_file(all_boxes_h, all_img_names,
cfgs.EVALUATE_R_DIR, 0)
write_voc_results_file(all_boxes_r, all_img_names,
cfgs.EVALUATE_R_DIR, 1)
fw1 = open('gtboxes_horizontal_dict.pkl', 'wb')
fw2 = open('gtboxes_rotate_dict.pkl', 'wb')
pickle.dump(gtboxes_horizontal_dict, fw1)
pickle.dump(gtboxes_rotate_dict, fw2)
fw1.close()
fw2.close()
coord.request_stop()
coord.join(threads)
def train():
with tf.Graph().as_default():
with tf.name_scope('get_batch'):
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \
next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE,
shortside_len=cfgs.SHORT_SIDE_LEN,
is_training=True)
gtboxes_and_label, head = get_head(
tf.squeeze(gtboxes_and_label_batch, 0))
gtboxes_and_label = tf.py_func(back_forward_convert,
inp=[gtboxes_and_label],
Tout=tf.float32)
gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6])
head_quadrant = tf.py_func(get_head_quadrant,
inp=[head, gtboxes_and_label],
Tout=tf.float32)
head_quadrant = tf.reshape(head_quadrant, [-1, 1])
gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(
gtboxes_and_label)
gtboxes_and_label_minAreaRectangle = tf.reshape(
gtboxes_and_label_minAreaRectangle, [-1, 5])
with tf.name_scope('draw_gtboxes'):
gtboxes_in_img = draw_box_with_color(
img_batch,
tf.reshape(gtboxes_and_label_minAreaRectangle,
[-1, 5])[:, :-1],
text=tf.shape(gtboxes_and_label_minAreaRectangle)[0])
gtboxes_rotate_in_img = draw_box_with_color_rotate(
img_batch,
tf.reshape(gtboxes_and_label, [-1, 6])[:, :-1],
text=tf.shape(gtboxes_and_label)[0],
head=head_quadrant)
# ***********************************************************************************************
# * share net *
# ***********************************************************************************************
_, share_net = get_network_byname(net_name=cfgs.NET_NAME,
inputs=img_batch,
num_classes=None,
is_training=True,
output_stride=None,
global_pool=False,
spatial_squeeze=False)
# ***********************************************************************************************
# * rpn *
# ***********************************************************************************************
rpn = build_rpn.RPN(
net_name=cfgs.NET_NAME,
inputs=img_batch,
gtboxes_and_label=gtboxes_and_label_minAreaRectangle,
is_training=True,
share_head=cfgs.SHARE_HEAD,
share_net=share_net,
stride=cfgs.STRIDE,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.
BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
top_k_nms=cfgs.RPN_TOP_K_NMS,
rpn_nms_iou_threshold=cfgs.RPN_NMS_IOU_THRESHOLD,
max_proposals_num=cfgs.MAX_PROPOSAL_NUM,
rpn_iou_positive_threshold=cfgs.RPN_IOU_POSITIVE_THRESHOLD,
rpn_iou_negative_threshold=cfgs.
RPN_IOU_NEGATIVE_THRESHOLD, # iou>=0.7 is positive box, iou< 0.3 is negative
rpn_mini_batch_size=cfgs.RPN_MINIBATCH_SIZE,
rpn_positives_ratio=cfgs.RPN_POSITIVE_RATE,
remove_outside_anchors=False, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME])
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals(
) # rpn_score shape: [300, ]
rpn_location_loss, rpn_classification_loss = rpn.rpn_losses()
rpn_total_loss = rpn_classification_loss + rpn_location_loss
with tf.name_scope('draw_proposals'):
# score > 0.5 is object
rpn_object_boxes_indices = tf.reshape(
tf.where(tf.greater(rpn_proposals_scores, 0.5)), [-1])
rpn_object_boxes = tf.gather(rpn_proposals_boxes,
rpn_object_boxes_indices)
rpn_proposals_objcet_boxes_in_img = draw_box_with_color(
img_batch,
rpn_object_boxes,
text=tf.shape(rpn_object_boxes)[0])
rpn_proposals_boxes_in_img = draw_box_with_color(
img_batch,
rpn_proposals_boxes,
text=tf.shape(rpn_proposals_boxes)[0])
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
img_shape=tf.shape(img_batch),
img_batch=img_batch,
roi_size=cfgs.ROI_SIZE,
roi_pool_kernel_size=cfgs.ROI_POOL_KERNEL_SIZE,
scale_factors=cfgs.SCALE_FACTORS,
gtboxes_and_label=gtboxes_and_label,
gtboxes_and_label_minAreaRectangle=
gtboxes_and_label_minAreaRectangle,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
fast_rcnn_maximum_boxes_per_img=100,
fast_rcnn_nms_max_boxes_per_class=cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
show_detections_score_threshold=cfgs.
FINAL_SCORE_THRESHOLD, # show detections which score >= 0.6
num_classes=cfgs.CLASS_NUM,
fast_rcnn_minibatch_size=cfgs.FAST_RCNN_MINIBATCH_SIZE,
fast_rcnn_positives_ratio=cfgs.FAST_RCNN_POSITIVE_RATE,
fast_rcnn_positives_iou_threshold=cfgs.
FAST_RCNN_IOU_POSITIVE_THRESHOLD, # iou>0.5 is positive, iou<0.5 is negative
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=True,
level=cfgs.LEVEL,
head_quadrant=head_quadrant)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category, \
fast_rcnn_decode_boxes_rotate, fast_rcnn_score_rotate, fast_rcnn_head_quadrant, \
num_of_objects_rotate, detection_category_rotate = fast_rcnn.fast_rcnn_predict()
fast_rcnn_location_loss, fast_rcnn_classification_loss, \
fast_rcnn_location_rotate_loss, fast_rcnn_classification_rotate_loss, \
fast_rcnn_head_quadrant_loss = fast_rcnn.fast_rcnn_loss()
fast_rcnn_total_loss = fast_rcnn_location_loss + fast_rcnn_classification_loss + \
fast_rcnn_location_rotate_loss + fast_rcnn_classification_rotate_loss
with tf.name_scope('draw_boxes_with_categories'):
fast_rcnn_predict_boxes_in_imgs = draw_boxes_with_categories(
img_batch=img_batch,
boxes=fast_rcnn_decode_boxes,
labels=detection_category,
scores=fast_rcnn_score)
fast_rcnn_predict_rotate_boxes_in_imgs = draw_boxes_with_categories_rotate(
img_batch=img_batch,
boxes=fast_rcnn_decode_boxes_rotate,
labels=detection_category_rotate,
scores=fast_rcnn_score_rotate,
head=fast_rcnn_head_quadrant)
# train
total_loss = slim.losses.get_total_loss()
global_step = slim.get_or_create_global_step()
lr = tf.train.piecewise_constant(
global_step,
boundaries=[np.int64(20000), np.int64(40000)],
values=[0.001, 0.0001, 0.00001])
tf.summary.scalar('lr', lr)
optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM)
train_op = slim.learning.create_train_op(total_loss, optimizer,
global_step)
# ***********************************************************************************************
# * Summary *
# ***********************************************************************************************
# ground truth and predict
tf.summary.image('img/gtboxes', gtboxes_in_img)
tf.summary.image('img/gtboxes_rotate', gtboxes_rotate_in_img)
tf.summary.image('img/faster_rcnn_predict',
fast_rcnn_predict_boxes_in_imgs)
tf.summary.image('img/faster_rcnn_predict_rotate',
fast_rcnn_predict_rotate_boxes_in_imgs)
# rpn loss and image
tf.summary.scalar('rpn/rpn_location_loss', rpn_location_loss)
tf.summary.scalar('rpn/rpn_classification_loss',
rpn_classification_loss)
tf.summary.scalar('rpn/rpn_total_loss', rpn_total_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_location_loss',
fast_rcnn_location_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_classification_loss',
fast_rcnn_classification_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_location_rotate_loss',
fast_rcnn_location_rotate_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_classification_rotate_loss',
fast_rcnn_classification_rotate_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_head_quadrant_loss',
fast_rcnn_head_quadrant_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_total_loss',
fast_rcnn_total_loss)
tf.summary.scalar('loss/total_loss', total_loss)
tf.summary.image('rpn/rpn_all_boxes', rpn_proposals_boxes_in_img)
tf.summary.image('rpn/rpn_object_boxes',
rpn_proposals_objcet_boxes_in_img)
# learning_rate
tf.summary.scalar('learning_rate', lr)
summary_op = tf.summary.merge_all()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = restore_model.get_restorer()
saver = tf.train.Saver(max_to_keep=10)
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
summary_path = os.path.join(FLAGS.summary_path, cfgs.VERSION)
tools.mkdir(summary_path)
summary_writer = tf.summary.FileWriter(summary_path,
graph=sess.graph)
for step in range(cfgs.MAX_ITERATION):
training_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
start = time.time()
_global_step, _img_name_batch, _rpn_location_loss, _rpn_classification_loss, \
_rpn_total_loss, _fast_rcnn_location_loss, _fast_rcnn_classification_loss, \
_fast_rcnn_location_rotate_loss, _fast_rcnn_classification_rotate_loss, \
_fast_rcnn_total_loss, _total_loss, _ = \
sess.run([global_step, img_name_batch, rpn_location_loss, rpn_classification_loss,
rpn_total_loss, fast_rcnn_location_loss, fast_rcnn_classification_loss,
fast_rcnn_location_rotate_loss, fast_rcnn_classification_rotate_loss,
fast_rcnn_total_loss, total_loss, train_op])
end = time.time()
if step % 10 == 0:
print(""" {}: step{} image_name:{} |\t
rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t
rpn_total_loss:{} |
fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t
fast_rcnn_loc_rotate_loss:{} |\t fast_rcnn_cla_rotate_loss:{} |\t
fast_rcnn_total_loss:{} |\t
total_loss:{} |\t pre_cost_time:{}s""" \
.format(training_time, _global_step, str(_img_name_batch[0]), _rpn_location_loss,
_rpn_classification_loss, _rpn_total_loss, _fast_rcnn_location_loss,
_fast_rcnn_classification_loss, _fast_rcnn_location_rotate_loss,
_fast_rcnn_classification_rotate_loss, _fast_rcnn_total_loss, _total_loss,
(end - start)))
if step % 50 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, _global_step)
summary_writer.flush()
if (step > 0
and step % 5000 == 0) or (step
== cfgs.MAX_ITERATION - 1):
save_dir = os.path.join(FLAGS.trained_checkpoint,
cfgs.VERSION)
tools.mkdir(save_dir)
save_ckpt = os.path.join(
save_dir, 'voc_' + str(_global_step) + 'model.ckpt')
saver.save(sess, save_ckpt)
print(' weights had been saved')
coord.request_stop()
coord.join(threads)