def sample_mini_batch(self, anchor_box_list_gt, anchor_box_list,
img_box_list_gt, img_box_list, class_labels):
with tf.variable_scope('mlod_create_mb_mask'):
# Get IoU for every anchor
all_bev_ious = box_list_ops.iou(anchor_box_list_gt,
anchor_box_list)
max_bev_ious = tf.reduce_max(all_bev_ious, axis=0)
max_bev_iou_indices = tf.argmax(all_bev_ious, axis=0)
all_img_ious = box_list_ops.iou(img_box_list_gt, img_box_list)
max_img_ious = tf.reduce_max(all_img_ious, axis=0)
max_img_iou_indices = tf.argmax(all_img_ious, axis=0)
# Sample a pos/neg mini-batch from anchors with highest IoU match
mini_batch_utils = self.dataset.kitti_utils.mini_batch_utils
#bev mask
mb_bev_mask, mb_pos_bev_mask = mini_batch_utils.sample_mlod_mini_batch(
max_bev_ious)
mb_mask, mb_pos_img_mask = mini_batch_utils.double_sample_img_mini_batch(
max_img_ious, mb_bev_mask)
mb_bev_class_label_indices = mini_batch_utils.mask_class_label_indices(
mb_pos_bev_mask, mb_mask, max_bev_iou_indices, class_labels)
mb_img_class_label_indices = mini_batch_utils.mask_class_label_indices(
mb_pos_img_mask, mb_mask, max_img_iou_indices, class_labels)
mb_bev_gt_indices = tf.boolean_mask(max_bev_iou_indices, mb_mask)
mb_img_gt_indices = tf.boolean_mask(max_img_iou_indices, mb_mask)
return mb_mask, mb_bev_class_label_indices, mb_img_class_label_indices, mb_bev_gt_indices, mb_img_gt_indices
def test_iouworks_on_empty_inputs(self):
corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]])
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
boxes_empty = box_list.BoxList(tf.zeros((0, 4)))
iou_empty_1 = box_list_ops.iou(boxes1, boxes_empty)
iou_empty_2 = box_list_ops.iou(boxes_empty, boxes2)
iou_empty_3 = box_list_ops.iou(boxes_empty, boxes_empty)
with self.test_session() as sess:
iou_output_1, iou_output_2, iou_output_3 = sess.run(
[iou_empty_1, iou_empty_2, iou_empty_3])
self.assertAllEqual(iou_output_1.shape, (2, 0))
self.assertAllEqual(iou_output_2.shape, (0, 3))
self.assertAllEqual(iou_output_3.shape, (0, 0))
def test_iou(self):
corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]])
exp_output = [
[2.0 / 16.0, 0, 6.0 / 400.0],
[1.0 / 16.0, 0.0, 5.0 / 400.0]]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
iou = box_list_ops.iou(boxes1, boxes2)
with self.test_session() as sess:
iou_output = sess.run(iou)
self.assertAllClose(iou_output, exp_output)
def test_iou_mask_ops(self):
# corners are in [y1, x1, y2, x2] format
corners_pred = tf.constant(
[[4.0, 3.0, 7.0, 5.0],
[14.0, 14.0, 16.0, 16.0],
[0.0, 0.0, 21.0, 19.0],
[3.0, 4.0, 5.0, 7.0]])
corners_gt = tf.constant(
[[4.0, 3.0, 7.0, 6.0],
[14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]])
# 3 classes
class_indices = tf.constant([1., 2., 3.])
exp_ious = [[0.66666669, 0., 0.02255639, 0.15384616],
[0., 0.25, 0.00250627, 0.],
[0.015, 0.01, 0.90692127, 0.015]]
exp_max_ious = np.array([0.66666669, 0.25, 0.90692127, 0.15384616])
exp_max_indices = np.array([0, 1, 2, 0])
exp_pos_mask = np.array([True, False, True, False])
exp_class_and_background_indices = np.array([1, 0, 3, 0])
# Convert to box_list format
boxes_pred = box_list.BoxList(corners_pred)
boxes_gt = box_list.BoxList(corners_gt)
# Calculate IoU
iou = box_list_ops.iou(boxes_gt,
boxes_pred)
# Get max IoU, the dimension should match the anchors we are
# evaluating
max_ious = tf.reduce_max(iou, axis=0)
max_iou_indices = tf.argmax(iou, axis=0)
# Sample a mini-batch from anchors with highest IoU match
mini_batch_size = 4
# Custom positive/negative iou ranges
neg_2d_iou_range = [0.0, 0.3]
pos_2d_iou_range = [0.6, 0.7]
mb_mask, mb_pos_mask = \
self.mb_utils.sample_mini_batch(max_ious,
mini_batch_size,
neg_2d_iou_range,
pos_2d_iou_range)
mb_class_indices = self.mb_utils.mask_class_label_indices(
mb_pos_mask, mb_mask, max_iou_indices, class_indices)
with self.test_session() as sess:
iou_out = sess.run(iou)
max_ious_out, max_iou_indices_out = sess.run([max_ious,
max_iou_indices])
mb_mask_out, mb_pos_mask_out = sess.run([mb_mask,
mb_pos_mask])
class_indices_out = sess.run(mb_class_indices)
self.assertAllClose(iou_out, exp_ious)
self.assertAllClose(max_ious_out, exp_max_ious)
self.assertAllEqual(max_iou_indices_out, exp_max_indices)
self.assertAllEqual(exp_pos_mask, mb_pos_mask_out)
self.assertAllEqual(class_indices_out,
exp_class_and_background_indices)
def main():
"""
This demo shows example mini batch info for full MlodModel training.
This includes ground truth, ortho rotated ground truth,
negative proposal anchors, positive proposal anchors, and a sampled
mini batch.
The 2D iou can be modified to show the effect of changing the iou
threshold for mini batch sampling.
In order to let this demo run without training an RPN, the proposals
shown are being read from a text file.
Keys:
F1: Toggle ground truth
F2: Toggle ortho rotated ground truth
F3: Toggle negative proposal anchors
F4: Toggle positive proposal anchors
F5: Toggle mini batch anchors
"""
##############################
# Options
##############################
# Config file folder, default (<mlod_root>/data/outputs/<checkpoint_name>)
config_dir = None
# checkpoint_name = None
checkpoint_name = 'mlod_exp_example'
data_split = 'val_half'
# global_step = None
global_step = 100000
# # # Cars # # #
# sample_name = "000050"
sample_name = "000104"
# sample_name = "000764"
# # # People # # #
# val_half
# sample_name = '000001' # Hard, 1 far cyc
# sample_name = '000005' # Easy, 1 ped
# sample_name = '000122' # Easy, 1 cyc
# sample_name = '000134' # Hard, lots of people
# sample_name = '000167' # Medium, 1 ped, 2 cycs
# sample_name = '000187' # Medium, 1 ped on left
# sample_name = '000381' # Easy, 1 ped
# sample_name = '000398' # Easy, 1 ped
# sample_name = '000401' # Hard, obscured peds
# sample_name = '000407' # Easy, 1 ped
# sample_name = '000448' # Hard, several far people
# sample_name = '000486' # Hard 2 obscured peds
# sample_name = '000509' # Easy, 1 ped
# sample_name = '000718' # Hard, lots of people
# sample_name = '002216' # Easy, 1 cyc
mini_batch_size = 512
neg_proposal_2d_iou_hi = 0.6
pos_proposal_2d_iou_lo = 0.65
bkg_proposals_line_width = 0.5
neg_proposals_line_width = 0.5
mid_proposals_line_width = 0.5
pos_proposals_line_width = 1.0
##############################
# End of Options
##############################
img_idx = int(sample_name)
print("Showing mini batch for sample {}".format(sample_name))
# Read proposals from file
if checkpoint_name is None:
# Use VAL Dataset
dataset = DatasetBuilder.build_kitti_dataset(DatasetBuilder.KITTI_VAL)
# Load demo proposals
proposals_and_scores_dir = mlod.top_dir() + \
'/demos/data/predictions/' + checkpoint_name + \
'/proposals_and_scores/' + dataset.data_split
else:
if config_dir is None:
config_dir = mlod.root_dir() + '/data/outputs/' + checkpoint_name
# Parse experiment config
pipeline_config_file = \
config_dir + '/' + checkpoint_name + '.config'
_, _, _, dataset_config = \
config_builder_util.get_configs_from_pipeline_file(
pipeline_config_file, is_training=False)
dataset_config.data_split = data_split
dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
use_defaults=False)
# Overwrite
mini_batch_utils = dataset.kitti_utils.mini_batch_utils
mini_batch_utils.mlod_neg_iou_range[1] = neg_proposal_2d_iou_hi
mini_batch_utils.mlod_pos_iou_range[0] = pos_proposal_2d_iou_lo
# Load proposals from outputs folder
proposals_and_scores_dir = mlod.root_dir() + \
'/data/outputs/' + checkpoint_name + \
'/predictions/proposals_and_scores/' + dataset.data_split
# Get checkpoint step
steps = os.listdir(proposals_and_scores_dir)
steps.sort(key=int)
print('Available steps: {}'.format(steps))
# Use latest checkpoint if no index provided
if global_step is None:
global_step = steps[-1]
proposals_and_scores = np.loadtxt(
proposals_and_scores_dir +
"/{}/{}.txt".format(global_step, sample_name))
proposal_boxes_3d = proposals_and_scores[:, 0:7]
proposal_anchors = box_3d_encoder.box_3d_to_anchor(proposal_boxes_3d)
# Get filtered ground truth
obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)
filtered_objs = dataset.kitti_utils.filter_labels(obj_labels)
# Convert ground truth to anchors
gt_boxes_3d = np.asarray([
box_3d_encoder.object_label_to_box_3d(obj_label)
for obj_label in filtered_objs
])
gt_anchors = box_3d_encoder.box_3d_to_anchor(gt_boxes_3d,
ortho_rotate=True)
# Ortho rotate ground truth
gt_ortho_boxes_3d = box_3d_encoder.anchors_to_box_3d(gt_anchors)
gt_ortho_objs = [
box_3d_encoder.box_3d_to_object_label(box_3d, obj_type='OrthoGt')
for box_3d in gt_ortho_boxes_3d
]
# Project gt and anchors into BEV
gt_bev_anchors, _ = \
anchor_projector.project_to_bev(gt_anchors,
dataset.kitti_utils.bev_extents)
bev_anchors, _ = \
anchor_projector.project_to_bev(proposal_anchors,
dataset.kitti_utils.bev_extents)
# Reorder boxes into (y1, x1, y2, x2) order
gt_bev_anchors_tf_order = anchor_projector.reorder_projected_boxes(
gt_bev_anchors)
bev_anchors_tf_order = anchor_projector.reorder_projected_boxes(
bev_anchors)
# Convert to box_list format for iou calculation
gt_anchor_box_list = box_list.BoxList(
tf.cast(gt_bev_anchors_tf_order, tf.float32))
anchor_box_list = box_list.BoxList(
tf.cast(bev_anchors_tf_order, tf.float32))
# Get IoU for every anchor
tf_all_ious = box_list_ops.iou(gt_anchor_box_list, anchor_box_list)
valid_ious = True
# Make sure the calculated IoUs contain values. Since its a [N, M]
# tensor, if there are no gt's for instance, that entry will be zero.
if tf_all_ious.shape[0] == 0 or tf_all_ious.shape[1] == 0:
print('#################################################')
print('Warning: This sample does not contain valid IoUs')
print('#################################################')
valid_ious = False
if valid_ious:
tf_max_ious = tf.reduce_max(tf_all_ious, axis=0)
tf_max_iou_indices = tf.argmax(tf_all_ious, axis=0)
# Sample an RPN mini batch from the non empty anchors
mini_batch_utils = dataset.kitti_utils.mini_batch_utils
# Overwrite mini batch size and sample a mini batch
mini_batch_utils.mlod_mini_batch_size = mini_batch_size
mb_mask_tf, _ = mini_batch_utils.sample_mlod_mini_batch(tf_max_ious)
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Run the graph to calculate ious for every proposal and
# to get the mini batch mask
all_ious, max_ious, max_iou_indices = sess.run(
[tf_all_ious, tf_max_ious, tf_max_iou_indices])
mb_mask = sess.run(mb_mask_tf)
mb_anchors = proposal_anchors[mb_mask]
mb_anchor_boxes_3d = box_3d_encoder.anchors_to_box_3d(mb_anchors)
mb_anchor_ious = max_ious[mb_mask]
else:
# We have no valid IoU's, so assume all IoUs are zeros
# and the mini-batch contains all the anchors since we cannot
# mask without IoUs.
max_ious = np.zeros(proposal_boxes_3d.shape[0])
mb_anchor_ious = max_ious
mb_anchors = proposal_anchors
mb_anchor_boxes_3d = box_3d_encoder.anchors_to_box_3d(mb_anchors)
# Create list of positive/negative proposals based on iou
pos_proposal_objs = []
mid_proposal_objs = []
neg_proposal_objs = []
bkg_proposal_objs = []
for i in range(len(proposal_boxes_3d)):
box_3d = proposal_boxes_3d[i]
if max_ious[i] == 0.0:
# Background proposals
bkg_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='BackgroundProposal'))
elif max_ious[i] < neg_proposal_2d_iou_hi:
# Negative proposals
neg_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='NegativeProposal'))
elif max_ious[i] < pos_proposal_2d_iou_lo:
# Middle proposals (in between negative and positive)
mid_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='MiddleProposal'))
elif max_ious[i] <= 1.0:
# Positive proposals
pos_proposal_objs.append(
box_3d_encoder.box_3d_to_object_label(
box_3d, obj_type='PositiveProposal'))
else:
raise ValueError('Invalid IoU > 1.0')
print('{} bkg, {} neg, {} mid, {} pos proposals:'.format(
len(bkg_proposal_objs), len(neg_proposal_objs), len(mid_proposal_objs),
len(pos_proposal_objs)))
# Convert the mini_batch anchors to object list
mb_obj_list = []
for i in range(len(mb_anchor_ious)):
if valid_ious and (mb_anchor_ious[i] >
mini_batch_utils.mlod_pos_iou_range[0]):
obj_type = "Positive"
else:
obj_type = "Negative"
obj = box_3d_encoder.box_3d_to_object_label(mb_anchor_boxes_3d[i],
obj_type)
mb_obj_list.append(obj)
# Point cloud
image = cv2.imread(dataset.get_rgb_image_path(sample_name))
points, point_colours = demo_utils.get_filtered_pc_and_colours(
dataset, image, img_idx)
# Visualize from here
vis_utils.visualization(dataset.rgb_image_dir, img_idx)
plt.show(block=False)
# VtkPointCloud
vtk_point_cloud = VtkPointCloud()
vtk_point_cloud.set_points(points, point_colours)
# VtkAxes
axes = vtk.vtkAxesActor()
axes.SetTotalLength(5, 5, 5)
# VtkBoxes for ground truth
vtk_gt_boxes = VtkBoxes()
vtk_gt_boxes.set_objects(filtered_objs, COLOUR_SCHEME)
# VtkBoxes for ortho ground truth
vtk_gt_ortho_boxes = VtkBoxes()
vtk_gt_ortho_boxes.set_objects(gt_ortho_objs, COLOUR_SCHEME)
# VtkBoxes for background proposals
vtk_bkg_proposal_boxes = VtkBoxes()
vtk_bkg_proposal_boxes.set_objects(bkg_proposal_objs, COLOUR_SCHEME)
vtk_bkg_proposal_boxes.set_line_width(bkg_proposals_line_width)
# VtkBoxes for negative proposals
vtk_neg_proposal_boxes = VtkBoxes()
vtk_neg_proposal_boxes.set_objects(neg_proposal_objs, COLOUR_SCHEME)
vtk_neg_proposal_boxes.set_line_width(neg_proposals_line_width)
# VtkBoxes for middle proposals
vtk_mid_proposal_boxes = VtkBoxes()
vtk_mid_proposal_boxes.set_objects(mid_proposal_objs, COLOUR_SCHEME)
vtk_mid_proposal_boxes.set_line_width(mid_proposals_line_width)
# VtkBoxes for positive proposals
vtk_pos_proposal_boxes = VtkBoxes()
vtk_pos_proposal_boxes.set_objects(pos_proposal_objs, COLOUR_SCHEME)
vtk_pos_proposal_boxes.set_line_width(pos_proposals_line_width)
# Create VtkBoxes for mini batch anchors
vtk_mb_boxes = VtkBoxes()
vtk_mb_boxes.set_objects(mb_obj_list, COLOUR_SCHEME)
# Create Voxel Grid Renderer in bottom half
vtk_renderer = vtk.vtkRenderer()
vtk_renderer.SetBackground(0.2, 0.3, 0.4)
# Add actors
vtk_renderer.AddActor(axes)
vtk_renderer.AddActor(vtk_point_cloud.vtk_actor)
vtk_renderer.AddActor(vtk_gt_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_gt_ortho_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_bkg_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_neg_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_mid_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_pos_proposal_boxes.vtk_actor)
vtk_renderer.AddActor(vtk_mb_boxes.vtk_actor)
# Setup Camera
current_cam = vtk_renderer.GetActiveCamera()
current_cam.Pitch(160.0)
current_cam.Roll(180.0)
# Zooms out to fit all points on screen
vtk_renderer.ResetCamera()
# Zoom in slightly
current_cam.Zoom(2.5)
# Reset the clipping range to show all points
vtk_renderer.ResetCameraClippingRange()
# Setup Render Window
vtk_render_window = vtk.vtkRenderWindow()
vtk_render_window.SetWindowName("MLOD Mini Batch")
vtk_render_window.SetSize(900, 500)
vtk_render_window.AddRenderer(vtk_renderer)
# Setup custom interactor style, which handles mouse and key events
vtk_render_window_interactor = vtk.vtkRenderWindowInteractor()
vtk_render_window_interactor.SetRenderWindow(vtk_render_window)
vtk_render_window_interactor.SetInteractorStyle(
vis_utils.ToggleActorsInteractorStyle([
vtk_gt_boxes.vtk_actor,
vtk_gt_ortho_boxes.vtk_actor,
vtk_bkg_proposal_boxes.vtk_actor,
vtk_neg_proposal_boxes.vtk_actor,
vtk_mid_proposal_boxes.vtk_actor,
vtk_pos_proposal_boxes.vtk_actor,
vtk_mb_boxes.vtk_actor,
]))
# Render in VTK
vtk_render_window.Render()
vtk_render_window_interactor.Start()