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import json
import os.path as op
import sys
import numpy as np
import torch
import torch.nn as nn
import trimesh
from easydict import EasyDict
from scipy.spatial.distance import cdist
sys.path = [".."] + sys.path
import common.thing as thing
from common.rot import axis_angle_to_quaternion, quaternion_apply
from common.torch_utils import pad_tensor_list
from common.xdict import xdict
# objects to consider for training so far
OBJECTS = [
"capsulemachine",
"box",
"ketchup",
"laptop",
"microwave",
"mixer",
"notebook",
"espressomachine",
"waffleiron",
"scissors",
"phone",
]
class ObjectTensors(nn.Module):
def __init__(self):
super(ObjectTensors, self).__init__()
self.obj_tensors = thing.thing2dev(construct_obj_tensors(OBJECTS), "cpu")
self.dev = None
def forward_7d_batch(
self,
angles: (None, torch.Tensor),
global_orient: (None, torch.Tensor),
transl: (None, torch.Tensor),
query_names: list,
fwd_template: bool,
):
self._sanity_check(angles, global_orient, transl, query_names, fwd_template)
# store output
out = xdict()
# meta info
obj_idx = np.array(
[self.obj_tensors["names"].index(name) for name in query_names]
)
out["diameter"] = self.obj_tensors["diameter"][obj_idx]
out["f"] = self.obj_tensors["f"][obj_idx]
out["f_len"] = self.obj_tensors["f_len"][obj_idx]
out["v_len"] = self.obj_tensors["v_len"][obj_idx]
max_len = out["v_len"].max()
out["v"] = self.obj_tensors["v"][obj_idx][:, :max_len]
out["mask"] = self.obj_tensors["mask"][obj_idx][:, :max_len]
out["v_sub"] = self.obj_tensors["v_sub"][obj_idx]
out["parts_ids"] = self.obj_tensors["parts_ids"][obj_idx][:, :max_len]
out["parts_sub_ids"] = self.obj_tensors["parts_sub_ids"][obj_idx]
if fwd_template:
return out
# articulation + global rotation
quat_arti = axis_angle_to_quaternion(self.obj_tensors["z_axis"] * angles)
quat_global = axis_angle_to_quaternion(global_orient.view(-1, 3))
# mm
# collect entities to be transformed
tf_dict = xdict()
tf_dict["v_top"] = out["v"].clone()
tf_dict["v_sub_top"] = out["v_sub"].clone()
tf_dict["v_bottom"] = out["v"].clone()
tf_dict["v_sub_bottom"] = out["v_sub"].clone()
tf_dict["bbox_top"] = self.obj_tensors["bbox_top"][obj_idx]
tf_dict["bbox_bottom"] = self.obj_tensors["bbox_bottom"][obj_idx]
tf_dict["kp_top"] = self.obj_tensors["kp_top"][obj_idx]
tf_dict["kp_bottom"] = self.obj_tensors["kp_bottom"][obj_idx]
# articulate top parts
for key, val in tf_dict.items():
if "top" in key:
val_rot = quaternion_apply(quat_arti[:, None, :], val)
tf_dict.overwrite(key, val_rot)
# global rotation for all
for key, val in tf_dict.items():
val_rot = quaternion_apply(quat_global[:, None, :], val)
if transl is not None:
val_rot = val_rot + transl[:, None, :]
tf_dict.overwrite(key, val_rot)
# prep output
top_idx = out["parts_ids"] == 1
v_tensor = tf_dict["v_bottom"].clone()
v_tensor[top_idx, :] = tf_dict["v_top"][top_idx, :]
top_idx = out["parts_sub_ids"] == 1
v_sub_tensor = tf_dict["v_sub_bottom"].clone()
v_sub_tensor[top_idx, :] = tf_dict["v_sub_top"][top_idx, :]
bbox = torch.cat((tf_dict["bbox_top"], tf_dict["bbox_bottom"]), dim=1)
kp3d = torch.cat((tf_dict["kp_top"], tf_dict["kp_bottom"]), dim=1)
out.overwrite("v", v_tensor)
out.overwrite("v_sub", v_sub_tensor)
out.overwrite("bbox3d", bbox)
out.overwrite("kp3d", kp3d)
return out
def forward(self, angles, global_orient, transl, query_names):
out = self.forward_7d_batch(
angles, global_orient, transl, query_names, fwd_template=False
)
return out
def forward_template(self, query_names):
out = self.forward_7d_batch(
angles=None,
global_orient=None,
transl=None,
query_names=query_names,
fwd_template=True,
)
return out
def to(self, dev):
self.obj_tensors = thing.thing2dev(self.obj_tensors, dev)
self.dev = dev
def _sanity_check(self, angles, global_orient, transl, query_names, fwd_template):
# sanity check
if not fwd_template:
# assume transl is in meter
if transl is not None:
transl = transl * 1000 # mm
batch_size = angles.shape[0]
assert angles.shape == (batch_size, 1)
assert global_orient.shape == (batch_size, 3)
if transl is not None:
assert isinstance(transl, torch.Tensor)
assert transl.shape == (batch_size, 3)
assert len(query_names) == batch_size
def construct_obj(object_model_p):
# load vtemplate
mesh_p = op.join(object_model_p, "mesh.obj")
parts_p = op.join(object_model_p, f"parts.json")
json_p = op.join(object_model_p, "object_params.json")
obj_name = op.basename(object_model_p)
top_sub_p = f"./data/arctic_data/data/meta/object_vtemplates/{obj_name}/top_keypoints_300.json"
bottom_sub_p = top_sub_p.replace("top_", "bottom_")
with open(top_sub_p, "r") as f:
sub_top = np.array(json.load(f)["keypoints"])
with open(bottom_sub_p, "r") as f:
sub_bottom = np.array(json.load(f)["keypoints"])
sub_v = np.concatenate((sub_top, sub_bottom), axis=0)
with open(parts_p, "r") as f:
parts = np.array(json.load(f), dtype=np.bool)
assert op.exists(mesh_p), f"Not found: {mesh_p}"
mesh = trimesh.exchange.load.load_mesh(mesh_p, process=False)
mesh_v = mesh.vertices
mesh_f = torch.LongTensor(mesh.faces)
vidx = np.argmin(cdist(sub_v, mesh_v, metric="euclidean"), axis=1)
parts_sub = parts[vidx]
vsk = object_model_p.split("/")[-1]
with open(json_p, "r") as f:
params = json.load(f)
rest = EasyDict()
rest.top = np.array(params["mocap_top"])
rest.bottom = np.array(params["mocap_bottom"])
bbox_top = np.array(params["bbox_top"])
bbox_bottom = np.array(params["bbox_bottom"])
kp_top = np.array(params["keypoints_top"])
kp_bottom = np.array(params["keypoints_bottom"])
np.random.seed(1)
obj = EasyDict()
obj.name = vsk
obj.obj_name = "".join([i for i in vsk if not i.isdigit()])
obj.v = torch.FloatTensor(mesh_v)
obj.v_sub = torch.FloatTensor(sub_v)
obj.f = torch.LongTensor(mesh_f)
obj.parts = torch.LongTensor(parts)
obj.parts_sub = torch.LongTensor(parts_sub)
with open("./data/arctic_data/data/meta/object_meta.json", "r") as f:
object_meta = json.load(f)
obj.diameter = torch.FloatTensor(np.array(object_meta[obj.obj_name]["diameter"]))
obj.bbox_top = torch.FloatTensor(bbox_top)
obj.bbox_bottom = torch.FloatTensor(bbox_bottom)
obj.kp_top = torch.FloatTensor(kp_top)
obj.kp_bottom = torch.FloatTensor(kp_bottom)
obj.mocap_top = torch.FloatTensor(np.array(params["mocap_top"]))
obj.mocap_bottom = torch.FloatTensor(np.array(params["mocap_bottom"]))
return obj
def construct_obj_tensors(object_names):
obj_list = []
for k in object_names:
object_model_p = f"./data/arctic_data/data/meta/object_vtemplates/%s" % (k)
obj = construct_obj(object_model_p)
obj_list.append(obj)
bbox_top_list = []
bbox_bottom_list = []
mocap_top_list = []
mocap_bottom_list = []
kp_top_list = []
kp_bottom_list = []
v_list = []
v_sub_list = []
f_list = []
parts_list = []
parts_sub_list = []
diameter_list = []
for obj in obj_list:
v_list.append(obj.v)
v_sub_list.append(obj.v_sub)
f_list.append(obj.f)
# root_list.append(obj.root)
bbox_top_list.append(obj.bbox_top)
bbox_bottom_list.append(obj.bbox_bottom)
kp_top_list.append(obj.kp_top)
kp_bottom_list.append(obj.kp_bottom)
mocap_top_list.append(obj.mocap_top / 1000)
mocap_bottom_list.append(obj.mocap_bottom / 1000)
parts_list.append(obj.parts + 1)
parts_sub_list.append(obj.parts_sub + 1)
diameter_list.append(obj.diameter)
v_list, v_len_list = pad_tensor_list(v_list)
p_list, p_len_list = pad_tensor_list(parts_list)
ps_list = torch.stack(parts_sub_list, dim=0)
assert (p_len_list - v_len_list).sum() == 0
max_len = v_len_list.max()
mask = torch.zeros(len(obj_list), max_len)
for idx, vlen in enumerate(v_len_list):
mask[idx, :vlen] = 1.0
v_sub_list = torch.stack(v_sub_list, dim=0)
diameter_list = torch.stack(diameter_list, dim=0)
f_list, f_len_list = pad_tensor_list(f_list)
bbox_top_list = torch.stack(bbox_top_list, dim=0)
bbox_bottom_list = torch.stack(bbox_bottom_list, dim=0)
kp_top_list = torch.stack(kp_top_list, dim=0)
kp_bottom_list = torch.stack(kp_bottom_list, dim=0)
obj_tensors = {}
obj_tensors["names"] = object_names
obj_tensors["parts_ids"] = p_list
obj_tensors["parts_sub_ids"] = ps_list
obj_tensors["v"] = v_list.float() / 1000
obj_tensors["v_sub"] = v_sub_list.float() / 1000
obj_tensors["v_len"] = v_len_list
obj_tensors["f"] = f_list
obj_tensors["f_len"] = f_len_list
obj_tensors["diameter"] = diameter_list.float()
obj_tensors["mask"] = mask
obj_tensors["bbox_top"] = bbox_top_list.float() / 1000
obj_tensors["bbox_bottom"] = bbox_bottom_list.float() / 1000
obj_tensors["kp_top"] = kp_top_list.float() / 1000
obj_tensors["kp_bottom"] = kp_bottom_list.float() / 1000
obj_tensors["mocap_top"] = mocap_top_list
obj_tensors["mocap_bottom"] = mocap_bottom_list
obj_tensors["z_axis"] = torch.FloatTensor(np.array([0, 0, -1])).view(1, 3)
return obj_tensors
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