exe1.arg_dict["pose_src"][:] = mx.ndarray.array(v_pose_src, ctx=ctx, dtype="float32")
exe1.forward(is_train=is_train)
simple_forward(exe1, v_point_cloud, v_rotation, v_translation, v_pose_src, ctx=ctx, is_train=True)
y1_raw = exe1.outputs[0]
y1 = y1_raw.asnumpy()
sum_y1 = get_output(y1)
# forward check
for iter in range(batch_size):
pc = np.squeeze(v_point_cloud[iter])
rot_q_delta = v_rotation[iter]
trans_delta = v_translation[iter]
pose_src = v_pose_src[iter]
rot_m_delta = quat2mat(rot_q_delta)
pose_est = RT_transform(pose_src, rot_q_delta, trans_delta, T_means, T_stds, rot_coord)
rot_m_tgt = pose_est[:3, :3]
trans_tgt = pose_est[:, 3]
pc_gt = np.matmul(rot_m_tgt, pc) + trans_tgt.reshape((3, 1))
pc_est = y1[iter]
assert (np.abs(pc_est - pc_gt).max(1) < 1e-4).all(), "forward check failed, {} > {}".format(
np.abs(pc_est - pc_gt).sum(), thresh
)
print("forward check succeed")
top_grad = mx.nd.zeros_like(y1_raw)
set_diff(top_grad)
exe1.backward(top_grad)
grad_rot = exe1.grad_dict["rotation"].asnumpy()
rot_shape = grad_rot.shape
def pred_eval(config,
predictor,
test_data,
imdb_test,
vis=False,
ignore_cache=None,
logger=None,
pairdb=None):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb_test: image database
:param vis: controls visualization
:param ignore_cache: ignore the saved cache file
:param logger: the logger instance
:return:
"""
logger.info(imdb_test.result_path)
logger.info("test iter size: {}".format(config.TEST.test_iter))
pose_err_file = os.path.join(
imdb_test.result_path,
imdb_test.name + "_pose_iter{}.pkl".format(config.TEST.test_iter))
if os.path.exists(pose_err_file) and not ignore_cache and not vis:
with open(pose_err_file, "rb") as fid:
if six.PY3:
[all_rot_err, all_trans_err, all_poses_est,
all_poses_gt] = cPickle.load(fid, encoding="latin1")
else:
[all_rot_err, all_trans_err, all_poses_est,
all_poses_gt] = cPickle.load(fid)
imdb_test.evaluate_pose(config, all_poses_est, all_poses_gt)
pose_add_plots_dir = os.path.join(imdb_test.result_path, "add_plots")
mkdir_p(pose_add_plots_dir)
imdb_test.evaluate_pose_add(config,
all_poses_est,
all_poses_gt,
output_dir=pose_add_plots_dir)
pose_arp2d_plots_dir = os.path.join(imdb_test.result_path,
"arp_2d_plots")
mkdir_p(pose_arp2d_plots_dir)
imdb_test.evaluate_pose_arp_2d(config,
all_poses_est,
all_poses_gt,
output_dir=pose_arp2d_plots_dir)
return
assert vis or not test_data.shuffle
assert config.TEST.BATCH_PAIRS == 1
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
num_pairs = len(pairdb)
height = 480
width = 640
data_time, net_time, post_time = 0.0, 0.0, 0.0
sum_EPE_all = 0.0
num_inst_all = 0.0
sum_EPE_viz = 0.0
num_inst_viz = 0.0
sum_EPE_vizbg = 0.0
num_inst_vizbg = 0.0
sum_PoseErr = [
np.zeros((len(imdb_test.classes) + 1, 2))
for batch_idx in range(config.TEST.test_iter)
]
all_rot_err = [[[] for j in range(config.TEST.test_iter)]
for batch_idx in range(len(imdb_test.classes))
] # num_cls x test_iter
all_trans_err = [[[] for j in range(config.TEST.test_iter)]
for batch_idx in range(len(imdb_test.classes))]
all_poses_est = [[[] for j in range(config.TEST.test_iter)]
for batch_idx in range(len(imdb_test.classes))]
all_poses_gt = [[[] for j in range(config.TEST.test_iter)]
for batch_idx in range(len(imdb_test.classes))]
num_inst = np.zeros(len(imdb_test.classes) + 1)
K = config.dataset.INTRINSIC_MATRIX
if (config.TEST.test_iter > 1 or config.TEST.VISUALIZE) and True:
print(
"************* start setup render_glumpy environment... ******************"
)
if config.dataset.dataset.startswith("ModelNet"):
from lib.render_glumpy.render_py_light_modelnet_multi import Render_Py_Light_ModelNet_Multi
modelnet_root = config.modelnet_root
texture_path = os.path.join(modelnet_root, "gray_texture.png")
model_path_list = [
os.path.join(config.dataset.model_dir,
"{}.obj".format(model_name))
for model_name in config.dataset.class_name
]
render_machine = Render_Py_Light_ModelNet_Multi(
model_path_list,
texture_path,
K,
width,
height,
config.dataset.ZNEAR,
config.dataset.ZFAR,
brightness_ratios=[0.7],
)
else:
render_machine = Render_Py(
config.dataset.model_dir,
config.dataset.class_name,
K,
width,
height,
config.dataset.ZNEAR,
config.dataset.ZFAR,
)
def render(render_machine, pose, cls_idx, K=None):
if config.dataset.dataset.startswith("ModelNet"):
idx = 2
# generate random light_position
if idx % 6 == 0:
light_position = [1, 0, 1]
elif idx % 6 == 1:
light_position = [1, 1, 1]
elif idx % 6 == 2:
light_position = [0, 1, 1]
elif idx % 6 == 3:
light_position = [-1, 1, 1]
elif idx % 6 == 4:
light_position = [-1, 0, 1]
elif idx % 6 == 5:
light_position = [0, 0, 1]
else:
raise Exception("???")
light_position = np.array(light_position) * 0.5
# inverse yz
light_position[0] += pose[0, 3]
light_position[1] -= pose[1, 3]
light_position[2] -= pose[2, 3]
colors = np.array([1, 1, 1]) # white light
intensity = np.random.uniform(0.9, 1.1, size=(3, ))
colors_randk = 0
light_intensity = colors[colors_randk] * intensity
# randomly choose a render machine
rm_randk = 0 # random.randint(0, len(brightness_ratios) - 1)
rgb_gl, depth_gl = render_machine.render(
cls_idx,
pose[:3, :3],
pose[:3, 3],
light_position,
light_intensity,
brightness_k=rm_randk,
r_type="mat",
)
rgb_gl = rgb_gl.astype("uint8")
else:
rgb_gl, depth_gl = render_machine.render(cls_idx,
pose[:3, :3],
pose[:, 3],
r_type="mat",
K=K)
rgb_gl = rgb_gl.astype("uint8")
return rgb_gl, depth_gl
print(
"***************setup render_glumpy environment succeed ******************"
)
if config.TEST.PRECOMPUTED_ICP:
print("precomputed_ICP")
config.TEST.test_iter = 1
all_rot_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
all_trans_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
all_poses_est = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
all_poses_gt = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
xy_trans_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
z_trans_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
for idx in range(len(pairdb)):
pose_path = pairdb[idx]["depth_rendered"][:-10] + "-pose_icp.txt"
pose_rendered_update = np.loadtxt(pose_path, skiprows=1)
pose_observed = pairdb[idx]["pose_observed"]
r_dist_est, t_dist_est = calc_rt_dist_m(pose_rendered_update,
pose_observed)
xy_dist = np.linalg.norm(pose_rendered_update[:2, -1] -
pose_observed[:2, -1])
z_dist = np.linalg.norm(pose_rendered_update[-1, -1] -
pose_observed[-1, -1])
print(
"{}: r_dist_est: {}, t_dist_est: {}, xy_dist: {}, z_dist: {}".
format(idx, r_dist_est, t_dist_est, xy_dist, z_dist))
class_id = imdb_test.classes.index(pairdb[idx]["gt_class"])
# store poses estimation and gt
all_poses_est[class_id][0].append(pose_rendered_update)
all_poses_gt[class_id][0].append(pairdb[idx]["pose_observed"])
all_rot_err[class_id][0].append(r_dist_est)
all_trans_err[class_id][0].append(t_dist_est)
xy_trans_err[class_id][0].append(xy_dist)
z_trans_err[class_id][0].append(z_dist)
all_rot_err = np.array(all_rot_err)
all_trans_err = np.array(all_trans_err)
print("rot = {} +/- {}".format(np.mean(all_rot_err[class_id][0]),
np.std(all_rot_err[class_id][0])))
print("trans = {} +/- {}".format(np.mean(all_trans_err[class_id][0]),
np.std(all_trans_err[class_id][0])))
num_list = all_trans_err[class_id][0]
print("xyz: {:.2f} +/- {:.2f}".format(
np.mean(num_list) * 100,
np.std(num_list) * 100))
num_list = xy_trans_err[class_id][0]
print("xy: {:.2f} +/- {:.2f}".format(
np.mean(num_list) * 100,
np.std(num_list) * 100))
num_list = z_trans_err[class_id][0]
print("z: {:.2f} +/- {:.2f}".format(
np.mean(num_list) * 100,
np.std(num_list) * 100))
imdb_test.evaluate_pose(config, all_poses_est, all_poses_gt)
pose_add_plots_dir = os.path.join(imdb_test.result_path,
"add_plots_precomputed_ICP")
mkdir_p(pose_add_plots_dir)
imdb_test.evaluate_pose_add(config,
all_poses_est,
all_poses_gt,
output_dir=pose_add_plots_dir)
pose_arp2d_plots_dir = os.path.join(imdb_test.result_path,
"arp_2d_plots_precomputed_ICP")
mkdir_p(pose_arp2d_plots_dir)
imdb_test.evaluate_pose_arp_2d(config,
all_poses_est,
all_poses_gt,
output_dir=pose_arp2d_plots_dir)
return
if config.TEST.BEFORE_ICP:
print("before_ICP")
config.TEST.test_iter = 1
all_rot_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
all_trans_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
all_poses_est = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
all_poses_gt = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
xy_trans_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
z_trans_err = [[[] for j in range(1)]
for batch_idx in range(len(imdb_test.classes))]
for idx in range(len(pairdb)):
pose_path = pairdb[idx]["depth_rendered"][:-10] + "-pose.txt"
pose_rendered_update = np.loadtxt(pose_path, skiprows=1)
pose_observed = pairdb[idx]["pose_observed"]
r_dist_est, t_dist_est = calc_rt_dist_m(pose_rendered_update,
pose_observed)
xy_dist = np.linalg.norm(pose_rendered_update[:2, -1] -
pose_observed[:2, -1])
z_dist = np.linalg.norm(pose_rendered_update[-1, -1] -
pose_observed[-1, -1])
class_id = imdb_test.classes.index(pairdb[idx]["gt_class"])
# store poses estimation and gt
all_poses_est[class_id][0].append(pose_rendered_update)
all_poses_gt[class_id][0].append(pairdb[idx]["pose_observed"])
all_rot_err[class_id][0].append(r_dist_est)
all_trans_err[class_id][0].append(t_dist_est)
xy_trans_err[class_id][0].append(xy_dist)
z_trans_err[class_id][0].append(z_dist)
all_trans_err = np.array(all_trans_err)
imdb_test.evaluate_pose(config, all_poses_est, all_poses_gt)
pose_add_plots_dir = os.path.join(imdb_test.result_path,
"add_plots_before_ICP")
mkdir_p(pose_add_plots_dir)
imdb_test.evaluate_pose_add(config,
all_poses_est,
all_poses_gt,
output_dir=pose_add_plots_dir)
pose_arp2d_plots_dir = os.path.join(imdb_test.result_path,
"arp_2d_plots_before_ICP")
mkdir_p(pose_arp2d_plots_dir)
imdb_test.evaluate_pose_arp_2d(config,
all_poses_est,
all_poses_gt,
output_dir=pose_arp2d_plots_dir)
return
# ------------------------------------------------------------------------------
t_start = time.time()
t = time.time()
for idx, data_batch in enumerate(test_data):
if np.sum(pairdb[idx]
["pose_rendered"]) == -12: # NO POINT VALID IN INIT POSE
print(idx)
class_id = imdb_test.classes.index(pairdb[idx]["gt_class"])
for pose_iter_idx in range(config.TEST.test_iter):
all_poses_est[class_id][pose_iter_idx].append(
pairdb[idx]["pose_rendered"])
all_poses_gt[class_id][pose_iter_idx].append(
pairdb[idx]["pose_observed"])
r_dist = 1000
t_dist = 1000
all_rot_err[class_id][pose_iter_idx].append(r_dist)
all_trans_err[class_id][pose_iter_idx].append(t_dist)
sum_PoseErr[pose_iter_idx][class_id, :] += np.array(
[r_dist, t_dist])
sum_PoseErr[pose_iter_idx][-1, :] += np.array([r_dist, t_dist])
# post process
if idx % 50 == 0:
logger.info(
"testing {}/{} data {:.4f}s net {:.4f}s calc_gt {:.4f}s".
format(
(idx + 1),
num_pairs,
data_time / ((idx + 1) * test_data.batch_size),
net_time / ((idx + 1) * test_data.batch_size),
post_time / ((idx + 1) * test_data.batch_size),
))
print("in test: NO POINT_VALID IN rendered")
continue
data_time += time.time() - t
t = time.time()
pose_rendered = pairdb[idx]["pose_rendered"]
if np.sum(pose_rendered) == -12:
print(idx)
class_id = imdb_test.classes.index(pairdb[idx]["gt_class"])
num_inst[class_id] += 1
num_inst[-1] += 1
for pose_iter_idx in range(config.TEST.test_iter):
all_poses_est[class_id][pose_iter_idx].append(pose_rendered)
all_poses_gt[class_id][pose_iter_idx].append(
pairdb[idx]["pose_observed"])
# post process
if idx % 50 == 0:
logger.info(
"testing {}/{} data {:.4f}s net {:.4f}s calc_gt {:.4f}s".
format(
(idx + 1),
num_pairs,
data_time / ((idx + 1) * test_data.batch_size),
net_time / ((idx + 1) * test_data.batch_size),
post_time / ((idx + 1) * test_data.batch_size),
))
t = time.time()
continue
output_all = predictor.predict(data_batch)
net_time += time.time() - t
t = time.time()
rst_iter = []
for output in output_all:
cur_rst = {}
cur_rst["se3"] = np.squeeze(
output["se3_output"].asnumpy()).astype("float32")
if not config.TEST.FAST_TEST and config.network.PRED_FLOW:
cur_rst["flow"] = np.squeeze(
output["flow_est_crop_output"].asnumpy().transpose(
(2, 3, 1, 0))).astype("float16")
else:
cur_rst["flow"] = None
if config.network.PRED_MASK and config.TEST.UPDATE_MASK not in [
"init", "box_rendered"
]:
mask_pred = np.squeeze(
output["mask_observed_pred_output"].asnumpy()).astype(
"float32")
cur_rst["mask_pred"] = mask_pred
rst_iter.append(cur_rst)
post_time += time.time() - t
# sample_ratio = 1 # 0.01
for batch_idx in range(0, test_data.batch_size):
# if config.TEST.VISUALIZE and not (r_dist>15 and t_dist>0.05):
# continue # 3388, 5326
# calculate the flow error --------------------------------------------
t = time.time()
if config.network.PRED_FLOW and not config.TEST.FAST_TEST:
# evaluate optical flow
flow_gt = par_generate_gt(config, pairdb[idx])
if config.network.PRED_FLOW:
all_diff = calc_EPE_one_pair(rst_iter[batch_idx], flow_gt,
"flow")
sum_EPE_all += all_diff["epe_all"]
num_inst_all += all_diff["num_all"]
sum_EPE_viz += all_diff["epe_viz"]
num_inst_viz += all_diff["num_viz"]
sum_EPE_vizbg += all_diff["epe_vizbg"]
num_inst_vizbg += all_diff["num_vizbg"]
# calculate the se3 error ---------------------------------------------
# evaluate se3 estimation
pose_rendered = pairdb[idx]["pose_rendered"]
class_id = imdb_test.classes.index(pairdb[idx]["gt_class"])
num_inst[class_id] += 1
num_inst[-1] += 1
post_time += time.time() - t
# iterative refine se3 estimation --------------------------------------------------
for pose_iter_idx in range(config.TEST.test_iter):
t = time.time()
pose_rendered_update = RT_transform(
pose_rendered,
rst_iter[0]["se3"][:-3],
rst_iter[0]["se3"][-3:],
config.dataset.trans_means,
config.dataset.trans_stds,
config.network.ROT_COORD,
)
# calculate error
r_dist, t_dist = calc_rt_dist_m(pose_rendered_update,
pairdb[idx]["pose_observed"])
# store poses estimation and gt
all_poses_est[class_id][pose_iter_idx].append(
pose_rendered_update)
all_poses_gt[class_id][pose_iter_idx].append(
pairdb[idx]["pose_observed"])
all_rot_err[class_id][pose_iter_idx].append(r_dist)
all_trans_err[class_id][pose_iter_idx].append(t_dist)
sum_PoseErr[pose_iter_idx][class_id, :] += np.array(
[r_dist, t_dist])
sum_PoseErr[pose_iter_idx][-1, :] += np.array([r_dist, t_dist])
if config.TEST.VISUALIZE:
print("idx {}, iter {}: rError: {}, tError: {}".format(
idx + batch_idx, pose_iter_idx + 1, r_dist, t_dist))
post_time += time.time() - t
# # if more than one iteration
if pose_iter_idx < (config.TEST.test_iter -
1) or config.TEST.VISUALIZE:
t = time.time()
# get refined image
K_path = pairdb[idx]["image_observed"][:-10] + "-K.txt"
if os.path.exists(K_path):
K = np.loadtxt(K_path)
image_refined, depth_refined = render(
render_machine,
pose_rendered_update,
config.dataset.class_name.index(
pairdb[idx]["gt_class"]),
K=K,
)
image_refined = image_refined[:, :, :3]
# update minibatch
update_package = [{
"image_rendered": image_refined,
"src_pose": pose_rendered_update
}]
if config.network.INPUT_DEPTH:
update_package[0]["depth_rendered"] = depth_refined
if config.network.INPUT_MASK:
mask_rendered_refined = np.zeros(depth_refined.shape)
mask_rendered_refined[depth_refined > 0.2] = 1
update_package[0][
"mask_rendered"] = mask_rendered_refined
if config.network.PRED_MASK:
# init, box_rendered, mask_rendered, box_observed, mask_observed
if config.TEST.UPDATE_MASK == "box_rendered":
input_names = [
blob_name[0]
for blob_name in data_batch.provide_data[0]
]
update_package[0][
"mask_observed"] = np.squeeze(
data_batch.data[0][input_names.index(
"mask_rendered")].asnumpy()
[batch_idx]) # noqa
elif config.TEST.UPDATE_MASK == "init":
pass
else:
raise Exception(
"Unknown UPDATE_MASK type: {}".format(
config.network.UPDATE_MASK))
pose_rendered = pose_rendered_update
data_batch = update_data_batch(config, data_batch,
update_package)
data_time += time.time() - t
# forward and get rst
if pose_iter_idx < config.TEST.test_iter - 1:
t = time.time()
output_all = predictor.predict(data_batch)
net_time += time.time() - t
t = time.time()
rst_iter = []
for output in output_all:
cur_rst = {}
if config.network.REGRESSOR_NUM == 1:
cur_rst["se3"] = np.squeeze(
output["se3_output"].asnumpy()).astype(
"float32")
if not config.TEST.FAST_TEST and config.network.PRED_FLOW:
cur_rst["flow"] = np.squeeze(
output["flow_est_crop_output"].asnumpy().
transpose((2, 3, 1, 0))).astype("float16")
else:
cur_rst["flow"] = None
if config.network.PRED_MASK and config.TEST.UPDATE_MASK not in [
"init", "box_rendered"
]:
mask_pred = np.squeeze(
output["mask_observed_pred_output"].
asnumpy()).astype("float32")
cur_rst["mask_pred"] = mask_pred
rst_iter.append(cur_rst)
post_time += time.time() - t
# post process
if idx % 50 == 0:
logger.info(
"testing {}/{} data {:.4f}s net {:.4f}s calc_gt {:.4f}s".
format(
(idx + 1),
num_pairs,
data_time / ((idx + 1) * test_data.batch_size),
net_time / ((idx + 1) * test_data.batch_size),
post_time / ((idx + 1) * test_data.batch_size),
))
t = time.time()
all_rot_err = np.array(all_rot_err)
all_trans_err = np.array(all_trans_err)
# save inference results
if not config.TEST.VISUALIZE:
with open(pose_err_file, "wb") as f:
logger.info("saving result cache to {}".format(pose_err_file))
cPickle.dump(
[all_rot_err, all_trans_err, all_poses_est, all_poses_gt],
f,
protocol=2)
logger.info("done")
if config.network.PRED_FLOW:
logger.info("evaluate flow:")
logger.info("EPE all: {}".format(sum_EPE_all / max(num_inst_all, 1.0)))
logger.info("EPE ignore unvisible: {}".format(
sum_EPE_vizbg / max(num_inst_vizbg, 1.0)))
logger.info("EPE visible: {}".format(sum_EPE_viz /
max(num_inst_viz, 1.0)))
logger.info("evaluate pose:")
imdb_test.evaluate_pose(config, all_poses_est, all_poses_gt)
# evaluate pose add
pose_add_plots_dir = os.path.join(imdb_test.result_path, "add_plots")
mkdir_p(pose_add_plots_dir)
imdb_test.evaluate_pose_add(config,
all_poses_est,
all_poses_gt,
output_dir=pose_add_plots_dir)
pose_arp2d_plots_dir = os.path.join(imdb_test.result_path, "arp_2d_plots")
mkdir_p(pose_arp2d_plots_dir)
imdb_test.evaluate_pose_arp_2d(config,
all_poses_est,
all_poses_gt,
output_dir=pose_arp2d_plots_dir)
logger.info("using {} seconds in total".format(time.time() - t_start))