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inf_cvae.py
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inf_cvae.py
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import argparse
import json
import math
import os.path
import time
import sys
import shutil
import trimesh.sample
import torch
import plotly.graph_objects as go
from utils.visualize_plotly import plot_point_cloud, plot_point_cloud_cmap, plot_mesh_from_name
from utils.set_seed import set_global_seed
from torch.utils.tensorboard import SummaryWriter
import trimesh as tm
import torch.nn as nn
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--pre_process', default='sharp_lift', type=str)
parser.add_argument('--s_model', default='PointNetCVAE_SqrtUnseenShadowhand', type=str)
parser.add_argument('--num_per_seen_object', default=4, type=int)
parser.add_argument('--num_per_unseen_object', default=16, type=int)
parser.add_argument('--comment', default='debug', type=str)
args_ = parser.parse_args()
tag = str(time.time())
return args_, tag
def pre_process_sharp_clamp(contact_map):
gap_th = 0.5 # delta_th = (1 - gap_th)
gap_th = min(contact_map.max().item(), gap_th)
delta_th = (1 - gap_th)
contact_map[contact_map > 0.4] += delta_th
# contact_map += delta_th
contact_map = torch.clamp_max(contact_map, 1.)
return contact_map
def identity_map(contact_map):
return contact_map
if __name__ == '__main__':
set_global_seed(seed=42)
args, time_tag = get_parser()
pre_process_map = {'sharp_lift': pre_process_sharp_clamp,
'identity': identity_map}
pre_process_contact_map_goal = pre_process_map[args.pre_process]
logs_basedir = os.path.join('logs_inf_cvae', f'{args.s_model}', f'{args.pre_process}', f'{args.comment}-{time_tag}')
vis_id_dir = os.path.join(logs_basedir, 'vis_id_dir')
vis_ood_dir = os.path.join(logs_basedir, 'vis_ood_dir')
cmap_path_id = os.path.join(logs_basedir, 'cmap_id.pt')
cmap_path_ood = os.path.join(logs_basedir, 'cmap_ood.pt')
os.makedirs(logs_basedir, exist_ok=False)
os.makedirs(vis_id_dir, exist_ok=False)
os.makedirs(vis_ood_dir, exist_ok=False)
device = "cuda"
if args.s_model == 'PointNetCVAE_SqrtUnseenShadowhand':
model_basedir = 'ckpts/SqrtUnseenShadowhand'
from ckpts.SqrtUnseenShadowhand.src.utils_model.PointNetCVAE import PointNetCVAE
model: nn.Module
model = PointNetCVAE(latent_size=128,
encoder_layers_size=[4, 64, 128, 512],
decoder_global_feat_size=512,
decoder_pointwise_layers_size=[3, 64, 64],
decoder_global_layers_size=[64, 128, 512],
decoder_decoder_layers_size=[64 + 512 + 128, 512, 64, 64, 1])
model.load_state_dict(torch.load(os.path.join(model_basedir, 'weights', 'pointnet_cvae_model.pth')))
model = model.to(device)
model.eval()
seen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['train']
unseen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['validate']
elif args.s_model == 'PointNetCVAE_SqrtUnseenBarrett':
model_basedir = 'models/SqrtUnseenBarrett'
from ckpts.SqrtUnseenBarrett.src.utils_model.PointNetCVAE import PointNetCVAE
model: nn.Module
model = PointNetCVAE(latent_size=128,
encoder_layers_size=[4, 64, 128, 512],
decoder_global_feat_size=512,
decoder_pointwise_layers_size=[3, 64, 64],
decoder_global_layers_size=[64, 128, 512],
decoder_decoder_layers_size=[64 + 512 + 128, 512, 64, 64, 1])
model.load_state_dict(torch.load(os.path.join(model_basedir, 'weights', 'pointnet_cvae_model.pth')))
model = model.to(device)
model.eval()
seen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['train']
unseen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['validate']
elif args.s_model == 'PointNetCVAE_SqrtUnseenEzgripper':
model_basedir = 'models/SqrtUnseenEzgripper'
from ckpts.SqrtUnseenEzgripper.src.utils_model.PointNetCVAE import PointNetCVAE
model: nn.Module
model = PointNetCVAE(latent_size=128,
encoder_layers_size=[4, 64, 128, 512],
decoder_global_feat_size=512,
decoder_pointwise_layers_size=[3, 64, 64],
decoder_global_layers_size=[64, 128, 512],
decoder_decoder_layers_size=[64 + 512 + 128, 512, 64, 64, 1])
model.load_state_dict(torch.load(os.path.join(model_basedir, 'weights', 'pointnet_cvae_model.pth')))
model = model.to(device)
model.eval()
seen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['train']
unseen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['validate']
elif args.s_model == 'PointNetCVAE_SqrtFullRobots':
model_basedir = 'models/SqrtFullRobots'
from ckpts.SqrtFullRobots.src.utils_model.PointNetCVAE import PointNetCVAE
model: nn.Module
model = PointNetCVAE(latent_size=128,
encoder_layers_size=[4, 64, 128, 512],
decoder_global_feat_size=512,
decoder_pointwise_layers_size=[3, 64, 64],
decoder_global_layers_size=[64, 128, 512],
decoder_decoder_layers_size=[64 + 512 + 128, 512, 64, 64, 1])
model.load_state_dict(torch.load(os.path.join(model_basedir, 'weights', 'pointnet_cvae_model.pth')))
model = model.to(device)
model.eval()
seen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['train']
unseen_object_list = json.load(open(os.path.join(model_basedir, "split_train_validate_objects.json"), 'rb'))['validate']
else:
raise NotImplementedError("Occur when load model...")
cmap_ood = []
for object_name in unseen_object_list:
print(f'unseen object name: {object_name}')
object_mesh: tm.Trimesh
object_mesh = tm.load(os.path.join('data/object', object_name.split('+')[0], object_name.split("+")[1],
f'{object_name.split("+")[1]}.stl'))
for i_sample in range(args.num_per_unseen_object):
cmap_ood_sample = {'object_name': object_name,
'i_sample': i_sample,
'object_point_cloud': None,
'contact_map_value': None}
print(f'[{i_sample}/{args.num_per_unseen_object}] | {object_name}')
object_point_cloud, faces_indices = trimesh.sample.sample_surface(mesh=object_mesh, count=2048)
contact_points_normal = torch.tensor([object_mesh.face_normals[x] for x in faces_indices]).float()
object_point_cloud = torch.Tensor(object_point_cloud).float()
object_point_cloud = torch.cat([object_point_cloud, contact_points_normal], dim=1).to(device)
z_latent_code = torch.randn(1, model.latent_size, device=device).float()
contact_map_value = model.inference(object_point_cloud[:, :3].unsqueeze(0), z_latent_code).squeeze(0)
# process the contact map value
contact_map_value = contact_map_value.detach().cpu().unsqueeze(1)
contact_map_value = pre_process_contact_map_goal(contact_map_value).to(device)
contact_map_goal = torch.cat([object_point_cloud, contact_map_value], dim=1)
cmap_ood_sample['object_point_cloud'] = object_point_cloud
cmap_ood_sample['contact_map_value'] = contact_map_value
cmap_ood.append(cmap_ood_sample)
vis_data = []
vis_data += [plot_point_cloud_cmap(contact_map_goal[:, :3].cpu().detach().numpy(),
contact_map_goal[:, 6].cpu().detach().numpy())]
vis_data += [plot_mesh_from_name(f'{object_name}')]
fig = go.Figure(data=vis_data)
fig.write_html(os.path.join(vis_ood_dir, f'unseen-{object_name}-{i_sample}.html'))
torch.save(cmap_ood, cmap_path_ood)
cmap_id = []
for object_name in seen_object_list:
print(f'seen object name: {object_name}')
object_mesh: tm.Trimesh
object_mesh = tm.load(os.path.join('data/object', object_name.split('+')[0], object_name.split("+")[1],
f'{object_name.split("+")[1]}.stl'))
for i_sample in range(args.num_per_seen_object):
cmap_id_sample = {'object_name': object_name,
'i_sample': i_sample,
'object_point_cloud': None,
'contact_map_value': None}
print(f'[{i_sample}/{args.num_per_seen_object}] | {object_name}')
object_point_cloud, faces_indices = trimesh.sample.sample_surface(mesh=object_mesh, count=2048)
contact_points_normal = torch.tensor([object_mesh.face_normals[x] for x in faces_indices]).float()
object_point_cloud = torch.Tensor(object_point_cloud).float()
object_point_cloud = torch.cat([object_point_cloud, contact_points_normal], dim=1).to(device)
z_latent_code = torch.randn(1, model.latent_size, device=device).float()
contact_map_value = model.inference(object_point_cloud[:, :3].unsqueeze(0), z_latent_code).squeeze(0)
# process the contact map value
contact_map_value = contact_map_value.detach().cpu().unsqueeze(1)
contact_map_value = pre_process_contact_map_goal(contact_map_value).to(device)
contact_map_goal = torch.cat([object_point_cloud, contact_map_value], dim=1)
cmap_id_sample['object_point_cloud'] = object_point_cloud
cmap_id_sample['contact_map_value'] = contact_map_value
cmap_id.append(cmap_id_sample)
vis_data = []
vis_data += [plot_point_cloud_cmap(contact_map_goal[:, :3].cpu().detach().numpy(),
contact_map_goal[:, 6].cpu().detach().numpy())]
vis_data += [plot_mesh_from_name(f'{object_name}')]
fig = go.Figure(data=vis_data)
fig.write_html(os.path.join(vis_id_dir, f'seen-{object_name}-{i_sample}.html'))
torch.save(cmap_id, cmap_path_id)