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  1. .gitattributes +3 -0
  2. Checkpoints/TRUMANS_mask_ind[0]_timesteps[100]_fixed_frame[2]_ac_type[last_add_first_token]_no_scene[False]_no_action[False]_batch_size[256]_epoch360.pth +3 -0
  3. Checkpoints/model_joints_to_smpl_wrist.pth +3 -0
  4. Data_blocks_motion_all/Object/basin.npy +3 -0
  5. Data_blocks_motion_all/Object/bed.npy +3 -0
  6. Data_blocks_motion_all/Object/flower.npy +3 -0
  7. Data_blocks_motion_all/Object/kitchen_chair_1.npy +3 -0
  8. Data_blocks_motion_all/Object/kitchen_chair_2.npy +3 -0
  9. Data_blocks_motion_all/Object/office_chair.npy +3 -0
  10. Data_blocks_motion_all/Object/sofa.npy +3 -0
  11. Data_blocks_motion_all/Object/table.npy +3 -0
  12. Data_blocks_motion_all/Object/wc.npy +3 -0
  13. Data_blocks_motion_all/Scene/2a8a1191-d4cc-46e4-b5ea-65e01954dbfa.npy +3 -0
  14. Data_blocks_motion_all/Scene/background.npy +3 -0
  15. Data_blocks_motion_all/meta.npy +3 -0
  16. Data_blocks_motion_all/norm.npy +3 -0
  17. Dockerfile +14 -0
  18. app.py +32 -0
  19. config/config_sample_synhsi.yaml +86 -0
  20. constants.py +93 -0
  21. datasets/__init__.py +1 -0
  22. datasets/__pycache__/__init__.cpython-39.pyc +0 -0
  23. datasets/__pycache__/trumans.cpython-39.pyc +0 -0
  24. datasets/trumans.py +228 -0
  25. models/__init__.py +2 -0
  26. models/__pycache__/__init__.cpython-39.pyc +0 -0
  27. models/__pycache__/joints_to_smplx.cpython-39.pyc +0 -0
  28. models/__pycache__/synhsi.cpython-39.pyc +0 -0
  29. models/joints_to_smplx.py +124 -0
  30. models/synhsi.py +444 -0
  31. not_used.py +4 -0
  32. objects_occ/Background.npy +3 -0
  33. objects_occ/background.blend +3 -0
  34. objects_occ/background.obj +3 -0
  35. objects_occ/basin.npy +3 -0
  36. objects_occ/basin.obj +0 -0
  37. objects_occ/bed.npy +3 -0
  38. objects_occ/bed.obj +0 -0
  39. objects_occ/flower.npy +3 -0
  40. objects_occ/flower.obj +4278 -0
  41. objects_occ/kitchen_chair_1.npy +3 -0
  42. objects_occ/kitchen_chair_1.obj +0 -0
  43. objects_occ/office_chair.npy +3 -0
  44. objects_occ/office_chair.obj +0 -0
  45. objects_occ/sofa.npy +3 -0
  46. objects_occ/sofa.obj +0 -0
  47. objects_occ/table.npy +3 -0
  48. objects_occ/table.obj +0 -0
  49. objects_occ/wc.npy +3 -0
  50. objects_occ/wc.obj +0 -0
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+ objects_occ/background.blend filter=lfs diff=lfs merge=lfs -text
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+ objects_occ/background.obj filter=lfs diff=lfs merge=lfs -text
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+ static/room.glb filter=lfs diff=lfs merge=lfs -text
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Dockerfile ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ FROM python:3.9
2
+
3
+ WORKDIR /app
4
+
5
+ COPY ./requirements.txt /app/requirements.txt
6
+ RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://download.pytorch.org/whl/cu113
7
+
8
+ COPY . .
9
+
10
+ ENV FLASK_APP=app.py
11
+
12
+ CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0"]
13
+
14
+
app.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # app.py
2
+ import os
3
+ # import numpy as np
4
+ from flask import Flask, jsonify, request, render_template
5
+ from sample_hsi import sample_wrapper
6
+ # from omegaconf import OmegaConf
7
+ # from hydra import compose, initialize
8
+
9
+
10
+ app = Flask(__name__)
11
+
12
+ @app.route('/')
13
+ def index():
14
+ return render_template('index.html')
15
+
16
+ @app.route('/move_cube', methods=['POST'])
17
+ def move_cube():
18
+ print(os.getcwd())
19
+ data = request.json
20
+ trajectory = data['trajectory']
21
+ print(data)
22
+ obj_locs = {obj_name.split('.')[0]: data[obj_name] for obj_name in data.keys() if 'trajectory' not in obj_name}
23
+
24
+ res = sample_wrapper(trajectory, obj_locs)
25
+
26
+ return jsonify(res)
27
+
28
+ if __name__ == '__main__':
29
+ # os.environ["HYDRA_FULL_ERROR"] = "1"
30
+ # initialize(version_base=None, config_path="./config")
31
+ # OmegaConf.register_new_resolver("times", lambda x, y: int(x) * int(y))
32
+ app.run(debug=True)
config/config_sample_synhsi.yaml ADDED
@@ -0,0 +1,86 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ exp_name: Test
3
+ train: false
4
+ batch_size: 1
5
+ device: cuda
6
+ interp_s: 3
7
+
8
+ len_pre: 4
9
+ len_act: 2
10
+ action_type: 'none'
11
+ scene_name: 'background'
12
+ action_id: 1
13
+ stay_and_act: false
14
+
15
+ method_name: Test
16
+ continue_last: false
17
+
18
+ #exp_dir: ${oc.env:ROOT_DIR}/Experiments/${exp_name}
19
+ ckpt_dir: ./Checkpoints
20
+ smpl_dir: ./smpl_models
21
+ #test_dir: ${oc.env:ROOT_DIR}/Test_settings
22
+
23
+ num_gpus: 1
24
+ num_workers: 0
25
+
26
+ dataset:
27
+ folder: ./Data_blocks_motion_all
28
+ device: cuda
29
+ batch_size: 1
30
+ seq_len: 16
31
+ step: 3
32
+ nb_voxels: 32
33
+ mesh_grid: [ -0.6, 0.6, 0, 1.2, -0.6, 0.6 ]
34
+ train: false
35
+ load_scene: true
36
+ load_action: true
37
+
38
+ joints_ind: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 40 ]
39
+ nb_joints: 24
40
+ nb_actions: 10
41
+
42
+ guidance:
43
+ pelvis:
44
+ seq_len: 16
45
+ step: 3
46
+ mask_ind: 0
47
+ fixed_frame: 2
48
+ mask_y: false
49
+ emb_f: -1
50
+ no_scene: false
51
+ no_action: false
52
+ fix_mode: true
53
+
54
+ model:
55
+ model_smplx:
56
+ input_dim: 72
57
+ output_dim: 132
58
+ hidden_dim: 64
59
+ ckpt: ./Checkpoints/model_joints_to_smpl_wrist.pth
60
+ synhsi_body:
61
+ dim_model: 512
62
+ num_heads: 16
63
+ num_layers: 8
64
+ dropout_p: 0.1
65
+ nb_voxels: 32
66
+ free_p: 0
67
+ ac_type: last_add_first_token
68
+ dim_input: 72
69
+ dim_output: 72
70
+ nb_actions: 10
71
+ no_scene: false
72
+ no_action: false
73
+ ckpt: ./Checkpoints/TRUMANS_mask_ind[0]_timesteps[100]_fixed_frame[2]_ac_type[last_add_first_token]_no_scene[False]_no_action[False]_batch_size[256]_epoch360.pth
74
+
75
+ sampler:
76
+ pelvis:
77
+ _target_: models.synhsi.Sampler
78
+ device: cuda
79
+ mask_ind: 0
80
+ emb_f: -1
81
+ batch_size: 1
82
+ seq_len: 16
83
+ channel: 72
84
+ fix_mode: true
85
+ timesteps: 100
86
+ fixed_frame: 2
constants.py ADDED
@@ -0,0 +1,93 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import os
3
+
4
+
5
+ try:
6
+ ROOT_DIR = os.environ['ROOT_DIR']
7
+ except:
8
+ ROOT_DIR = '/home/jiangnan/SyntheticHSI/'
9
+ DATA_DIR = os.path.join(ROOT_DIR, 'Data_augment', 'Data_blocks_motion_all')
10
+ CKPT_DIR = os.path.join(ROOT_DIR, 'HSIScripts', 'motion_gen_diffusion', 'checkpoints')
11
+ SMPL_DIR = os.path.join(ROOT_DIR, 'smpl_models')
12
+
13
+ OBJ_ACT_DICT = {
14
+ 'lie down': 0,
15
+ 'squat': 1,
16
+ 'mouse': 2,
17
+ 'keyboard': 3,
18
+ 'laptop': 4,
19
+ 'phone': 5,
20
+ 'book': 6,
21
+ 'bottle': 7,
22
+ 'pen': 8,
23
+ 'vase': 9,
24
+ }
25
+
26
+
27
+ CC_BONE_NAMES = ['CC_Base_Hip', 'CC_Base_Pelvis',
28
+ 'CC_Base_Waist', 'CC_Base_Spine01', 'CC_Base_Spine02',
29
+ 'CC_Base_NeckTwist01', 'CC_Base_NeckTwist02', 'CC_Base_Head',
30
+
31
+ 'CC_Base_R_Clavicle', 'CC_Base_R_Upperarm', 'CC_Base_R_Forearm', 'CC_Base_R_Hand',
32
+
33
+ 'CC_Base_R_Mid1', 'CC_Base_R_Mid2', 'CC_Base_R_Mid3', 'CC_Base_R_Ring1',
34
+ 'CC_Base_R_Ring2', 'CC_Base_R_Ring3', 'CC_Base_R_Pinky1', 'CC_Base_R_Pinky2',
35
+ 'CC_Base_R_Pinky3', 'CC_Base_R_Index1', 'CC_Base_R_Index2', 'CC_Base_R_Index3',
36
+ 'CC_Base_R_Thumb1', 'CC_Base_R_Thumb2', 'CC_Base_R_Thumb3',
37
+
38
+ 'CC_Base_L_Clavicle', 'CC_Base_L_Upperarm', 'CC_Base_L_Forearm', 'CC_Base_L_Hand',
39
+ 'CC_Base_L_Mid1', 'CC_Base_L_Mid2', 'CC_Base_L_Mid3',
40
+ 'CC_Base_L_Ring1', 'CC_Base_L_Ring2', 'CC_Base_L_Ring3',
41
+ 'CC_Base_L_Pinky1', 'CC_Base_L_Pinky2', 'CC_Base_L_Pinky3',
42
+ 'CC_Base_L_Index1', 'CC_Base_L_Index2', 'CC_Base_L_Index3', 'CC_Base_L_Thumb1',
43
+ 'CC_Base_L_Thumb2', 'CC_Base_L_Thumb3', 'CC_Base_R_Thigh',
44
+ 'CC_Base_R_Calf', 'CC_Base_R_Foot',
45
+ 'CC_Base_L_Thigh',
46
+ 'CC_Base_L_Calf', 'CC_Base_L_Foot',
47
+ 'CC_Base_R_ToeBase',
48
+ 'CC_Base_L_ToeBase',
49
+ ]
50
+
51
+ SMPLX_JOINT_NAMES = [
52
+ 'pelvis','left_hip','right_hip','spine1','left_knee','right_knee','spine2','left_ankle','right_ankle','spine3', 'left_foot','right_foot','neck','left_collar','right_collar','head','left_shoulder','right_shoulder','left_elbow', 'right_elbow','left_wrist','right_wrist',
53
+ 'jaw','left_eye_smplhf','right_eye_smplhf','left_index1','left_index2','left_index3','left_middle1','left_middle2','left_middle3','left_pinky1','left_pinky2','left_pinky3','left_ring1','left_ring2','left_ring3','left_thumb1','left_thumb2','left_thumb3','right_index1','right_index2','right_index3','right_middle1','right_middle2','right_middle3','right_pinky1','right_pinky2','right_pinky3','right_ring1','right_ring2','right_ring3','right_thumb1','right_thumb2','right_thumb3'
54
+ ]
55
+
56
+ # SMPL_MODEL_FOLDER = '/home/jiangnan/AHOI_cvpr/smpl_models'
57
+ SMPL_MODEL_FOLDER = '/home/jiangnan/SyntheticHSI/smpl_models'
58
+
59
+ rest_pelvis = np.matrix([[0.0000e+00, 0.0000e+00, 0.0000e+00],
60
+ [5.6144e-02, -9.4542e-02, -2.3475e-02],
61
+ [-5.7870e-02, -1.0517e-01, -1.6559e-02]])
62
+ pelvis_shift = [0.001144, -0.366919, 0.012666]
63
+
64
+ relaxed_hand_pose = np.array([0.11168, 0.04289, -0.41644,
65
+ 0.10881, -0.06599, -0.75622,
66
+ -0.09639, -0.09092, -0.18846,
67
+ -0.1181, 0.05094, -0.52958,
68
+ -0.1437, 0.05524, -0.70486,
69
+ -0.01918, -0.09234, -0.33791,
70
+ -0.45703, -0.19628, -0.62546,
71
+ -0.21465, -0.066, -0.50689,
72
+ -0.36972, -0.06034, -0.07949,
73
+ -0.14187, -0.08585, -0.63553,
74
+ -0.30334, -0.05788, -0.63139,
75
+ -0.17612, -0.13209, -0.37335,
76
+ 0.85096, 0.27692, -0.09155,
77
+ -0.49984, 0.02656, 0.05288,
78
+ 0.53556, 0.04596, -0.27736,
79
+ 0.11168, -0.04289, 0.41644,
80
+ 0.10881, 0.06599, 0.75622,
81
+ -0.09639, 0.09092, 0.18846,
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+ -0.1181, -0.05094, 0.52958,
83
+ -0.1437, -0.05524, 0.70486,
84
+ -0.01918, 0.09234, 0.33791,
85
+ -0.45703, 0.19628, 0.62546,
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+ -0.21465, 0.066, 0.50689,
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+ -0.36972, 0.06034, 0.07949,
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+ -0.14187, 0.08585, 0.63553,
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+ -0.30334, 0.05788, 0.63139,
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+ -0.17612, 0.13209, 0.37335,
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+ 0.85096, -0.27692, 0.09155,
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+ -0.49984, -0.02656, -0.05288,
93
+ 0.53556, -0.04596, 0.27736]).astype(np.float32)
datasets/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+ __all__ = ['trumans']
datasets/__pycache__/__init__.cpython-39.pyc ADDED
Binary file (172 Bytes). View file
 
datasets/__pycache__/trumans.cpython-39.pyc ADDED
Binary file (5.85 kB). View file
 
datasets/trumans.py ADDED
@@ -0,0 +1,228 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import torch
3
+ import numpy as np
4
+ from scipy.spatial.transform import Rotation as R
5
+ from torch.utils.data import Dataset, DataLoader, Subset
6
+
7
+
8
+ class TrumansDataset(Dataset):
9
+ def __init__(self, folder, device, mesh_grid, batch_size=1, seq_len=32, step=1, nb_voxels=32, train=True, load_scene=True, load_action=True, no_objects=False, **kwargs):
10
+ self.device = device
11
+ self.train = train
12
+ self.load_scene = load_scene
13
+ self.load_action = load_action
14
+
15
+ # self.body_pose = np.load(os.path.join(folder, 'human_pose.npy'))
16
+ # self.transl = np.load(os.path.join(folder, 'human_transl.npy'))
17
+ # self.global_orient = np.load(os.path.join(folder, 'human_orient.npy'))
18
+ # self.motion_ind = np.load(os.path.join(folder, 'idx_start.npy'))
19
+ # self.joints = np.load(os.path.join(folder, 'human_joints.npy'))
20
+ # self.file_blend = np.load(os.path.join(folder, 'file_blend.npy'))
21
+
22
+ self.seq_len=seq_len
23
+ self.step = step
24
+ self.batch_size = batch_size
25
+
26
+ # if self.load_action:
27
+ # self.action_label = np.load(os.path.join(folder, 'action_label.npy')).astype(np.float32)
28
+
29
+ if self.load_scene:
30
+ self.mesh_grid = mesh_grid
31
+ self.nb_voxels = nb_voxels
32
+ self.no_objects = no_objects
33
+ self.nb_voxels = nb_voxels
34
+ self.scene_occ = []
35
+ self.scene_dict = {}
36
+
37
+ self.scene_folder = os.path.join(folder, 'Scene')
38
+ # self.scene_flag = np.load(os.path.join(folder, 'scene_flag.npy'))
39
+ if not no_objects:
40
+ # self.object_flag = np.load(os.path.join(folder, 'object_flag.npy'))
41
+ # self.object_mat = np.load(os.path.join(folder, 'object_mat.npy'))
42
+ self.object_occ = {}
43
+ self.object_folder = os.path.join(folder, 'Object')
44
+ for file in sorted(os.listdir(self.object_folder)):
45
+ print(f"Loading object occupied coordinates {file}")
46
+ obj_name = file.replace('.npy', '')
47
+ self.object_occ[obj_name] = torch.from_numpy(np.load(os.path.join(self.object_folder, file))).to(device)
48
+
49
+ for sid, file in enumerate(sorted(os.listdir(self.scene_folder))):
50
+ # if scene_name != '' and scene_name not in file:
51
+ # continue
52
+ print(f"{sid} Loading Scene Mesh {file}")
53
+ scene_occ = np.load(os.path.join(self.scene_folder, file))
54
+ scene_occ = torch.from_numpy(scene_occ).to(device=device, dtype=bool)
55
+ self.scene_occ.append(scene_occ)
56
+ self.scene_dict[file] = sid
57
+ self.scene_occ = torch.stack(self.scene_occ)
58
+
59
+ self.scene_grid_np = np.array([-3, 0, -4, 3, 2, 4, 300, 100, 400])
60
+ self.scene_grid_torch = torch.tensor([-3, 0, -4, 3, 2, 4, 300, 100, 400]).to(device)
61
+ self.batch_id = torch.linspace(0, batch_size - 1, batch_size).tile((nb_voxels ** 3, 1)).T\
62
+ .reshape(-1, 1).to(device=device, dtype=torch.long)
63
+ self.batch_id_obj = torch.linspace(0, batch_size - 1, batch_size).tile((9000, 1)).T \
64
+ .reshape(-1, 1).to(device=device, dtype=torch.long)
65
+
66
+ # TODO CHANGE STEP
67
+ norm = np.load(os.path.join(folder, 'norm.npy'), allow_pickle=True).item()[f'{seq_len, 3}']
68
+ self.min = norm[0].astype(np.float32)
69
+ self.max = norm[1].astype(np.float32)
70
+ self.min_torch = torch.tensor(self.min).to(device)
71
+ self.max_torch = torch.tensor(self.max).to(device)
72
+
73
+
74
+ def add_object_points(self, points, occ):
75
+ points = points.reshape(-1, 3)
76
+ voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
77
+ voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
78
+ voxel = voxel.to(dtype=torch.long)
79
+ # voxel = rearrange(voxel, 'b p c -> (b p) c')
80
+ lb = torch.all(voxel >= 0, dim=-1)
81
+ ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
82
+ in_bound = torch.logical_and(lb, ub)
83
+ # voxel = torch.cat([self.batch_id_obj, voxel], dim=-1)
84
+ voxel = voxel[in_bound]
85
+ occ[0, voxel[:, 0], voxel[:, 1], voxel[:, 2]] = True
86
+
87
+ def get_occ_for_points(self, points, obj_locs, scene_flag):
88
+
89
+ #TODO
90
+
91
+ # points_new = points.reshape(-1, 3)
92
+ # center_xz = points_new[:, [0, 2]].mean(axis=0)
93
+ # if torch.norm(center_xz) > 0.:
94
+ # occ_for_points = torch.load('occ_for_points_at_clear_space.pt').to(points.device)
95
+ # return occ_for_points
96
+
97
+
98
+ if isinstance(scene_flag, str):
99
+ for k, v in self.scene_dict.items():
100
+ if scene_flag in k:
101
+ scene_flag = [v]
102
+ break
103
+ batch_size = points.shape[0]
104
+ seq_len = points.shape[1]
105
+ points = points.reshape(-1, 3)
106
+ voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
107
+ voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
108
+ voxel = voxel.to(dtype=torch.long)
109
+ # voxel = rearrange(voxel, 'b p c -> (b p) c')
110
+ lb = torch.all(voxel >= 0, dim=-1)
111
+ ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
112
+ in_bound = torch.logical_and(lb, ub)
113
+ voxel[torch.logical_not(in_bound)] = 0
114
+ voxel = torch.cat([self.batch_id, voxel], dim=1)
115
+ occ = self.scene_occ[scene_flag]
116
+
117
+ #TODO
118
+
119
+ # occ[:] = False
120
+ # occ[:, :, 0, :] = True
121
+
122
+ # import cv2
123
+ # img = occ[0, :, 10, :].detach().cpu().numpy()
124
+ # im = np.zeros((300, 400))
125
+ # im[img] = 255
126
+ # cv2.imwrite('gray.jpg', im.T)
127
+ if obj_locs:
128
+ for obj_name, obj_loc in obj_locs.items():
129
+ obj_points = self.object_occ[obj_name].clone()
130
+ obj_points[:, 0] += obj_loc['x']
131
+ obj_points[:, 2] += obj_loc['z']
132
+ # import pdb
133
+ # pdb.set_trace()
134
+ self.add_object_points(obj_points, occ)
135
+ occ_for_points = occ[voxel[:, 0], voxel[:, 1], voxel[:, 2], voxel[:, 3]]
136
+ occ_for_points[torch.logical_not(in_bound)] = True
137
+ occ_for_points = occ_for_points.reshape(batch_size, seq_len, -1)
138
+
139
+ # torch.save(occ_for_points, 'occ_for_points_at_clear_space.pt')
140
+
141
+ # occ_for_points = torch.ones(batch_size, seq_len, 22).to('cuda')
142
+
143
+
144
+ return occ_for_points
145
+
146
+ def create_meshgrid(self, batch_size=1):
147
+ bbox = self.mesh_grid
148
+ size = (self.nb_voxels, self.nb_voxels, self.nb_voxels)
149
+ x = torch.linspace(bbox[0], bbox[1], size[0])
150
+ y = torch.linspace(bbox[2], bbox[3], size[1])
151
+ z = torch.linspace(bbox[4], bbox[5], size[2])
152
+ xx, yy, zz = torch.meshgrid(x, y, z, indexing='ij')
153
+ grid = torch.stack([xx, yy, zz], dim=-1).reshape(-1, 3)
154
+ grid = grid.repeat(batch_size, 1, 1)
155
+
156
+ # aug_z = 0.75 + torch.rand(batch_size, 1) * 0.35
157
+ # grid[:, :, 2] = grid[:, :, 2] * aug_z
158
+
159
+ return grid
160
+
161
+
162
+ @staticmethod
163
+ def combine_mesh(vert_list, face_list):
164
+ assert len(vert_list) == len(face_list)
165
+ verts = None
166
+ faces = None
167
+ for v, f in zip(vert_list, face_list):
168
+ if verts is None:
169
+ verts = v
170
+ faces = f
171
+ else:
172
+ f = f + verts.shape[0]
173
+ verts = torch.cat([verts, v])
174
+ faces = torch.cat([faces, f])
175
+
176
+ return verts, faces
177
+
178
+ @staticmethod
179
+ def transform_mesh(vert_list, trans_mats):
180
+ assert len(vert_list) == len(trans_mats)
181
+ vert_list_new = []
182
+ for v, m in zip(vert_list, trans_mats):
183
+ v = v @ m[:3, :3].T + m[:3, 3]
184
+ vert_list_new.append(v)
185
+ vert_list_new = torch.stack(vert_list_new)
186
+
187
+ return vert_list_new
188
+
189
+ def __len__(self):
190
+ return len(self.motion_ind)
191
+
192
+
193
+ def normalize(self, data):
194
+ shape_orig = data.shape
195
+ data = data.reshape((-1, 3))
196
+ # data = (data - self.mean) / self.std
197
+ data = -1. + 2. * (data - self.min) / (self.max - self.min)
198
+ data = data.reshape(shape_orig)
199
+
200
+ return data
201
+
202
+ def normalize_torch(self, data):
203
+ shape_orig = data.shape
204
+ data = data.reshape((-1, 3))
205
+ # data = (data - self.mean) / self.std
206
+ data = -1. + 2. * (data - self.min_torch) / (self.max_torch - self.min_torch)
207
+ data = data.reshape(shape_orig)
208
+
209
+ return data
210
+
211
+ def denormalize(self, data):
212
+ shape_orig = data.shape
213
+ data = data.reshape((-1, 3))
214
+ # data = data * self.std + self.mean
215
+ data = (data + 1.) * (self.max - self.min) / 2. + self.min
216
+ data = data.reshape(shape_orig)
217
+
218
+ return data
219
+
220
+ def denormalize_torch(self, data):
221
+ shape_orig = data.shape
222
+ data = data.reshape((-1, 3))
223
+ # data = data * self.std + self.mean
224
+ import pdb
225
+ data = (data + 1.) * (self.max_torch - self.min_torch) / 2. + self.min_torch
226
+ data = data.reshape(shape_orig)
227
+
228
+ return data
models/__init__.py ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+
2
+ __all__ = ['synhsi', 'joints_to_smplx', 'goal_model', 'imos_model']
models/__pycache__/__init__.cpython-39.pyc ADDED
Binary file (214 Bytes). View file
 
models/__pycache__/joints_to_smplx.cpython-39.pyc ADDED
Binary file (3.64 kB). View file
 
models/__pycache__/synhsi.cpython-39.pyc ADDED
Binary file (10.1 kB). View file
 
models/joints_to_smplx.py ADDED
@@ -0,0 +1,124 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import smplx
3
+ from constants import *
4
+ from scipy.interpolate import interp1d
5
+ from torch import nn, einsum
6
+ import pytorch3d as T
7
+
8
+
9
+ class JointsToSMPLX(nn.Module):
10
+ def __init__(self, input_dim, output_dim, hidden_dim, **kwargs):
11
+ super().__init__()
12
+ self.layers = nn.Sequential(
13
+ nn.Linear(input_dim, hidden_dim),
14
+ nn.BatchNorm1d(hidden_dim),
15
+ nn.ReLU(),
16
+ # nn.Linear(hidden_dim, hidden_dim),
17
+ # nn.BatchNorm1d(hidden_dim),
18
+ # nn.ReLU(),
19
+ nn.Linear(hidden_dim, hidden_dim),
20
+ nn.BatchNorm1d(hidden_dim),
21
+ nn.ReLU(),
22
+ nn.Linear(hidden_dim, output_dim),
23
+ )
24
+
25
+ def forward(self, x):
26
+ return self.layers(x)
27
+
28
+
29
+ def optimize_smpl(pose_pred, joints, joints_ind, hand_pca=45):
30
+ device = joints.device
31
+ len = joints.shape[0]
32
+
33
+ smpl_model = smplx.create('./smpl_models', model_type='smplx',
34
+ gender='male', ext='npz',
35
+ num_betas=10,
36
+ use_pca=False,
37
+ create_global_orient=True,
38
+ create_body_pose=True,
39
+ create_betas=True,
40
+ create_left_hand_pose=True,
41
+ create_right_hand_pose=True,
42
+ create_expression=True,
43
+ create_jaw_pose=True,
44
+ create_leye_pose=True,
45
+ create_reye_pose=True,
46
+ create_transl=True,
47
+ batch_size=len,
48
+ ).to(device)
49
+ smpl_model.eval()
50
+
51
+ # weights = torch.tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 100, 100, 100, 100]).reshape(nb_joints, 1).repeat(1, 3).to(device)
52
+ joints = joints.reshape(len, -1, 3) + torch.tensor(pelvis_shift).to(device)
53
+ pose_input = torch.nn.Parameter(pose_pred.detach(), requires_grad=True)
54
+ transl = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], 3).to(device), requires_grad=True)
55
+ # left_hand = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], hand_pca).to(device), requires_grad=True)
56
+ # right_hand = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], hand_pca).to(device), requires_grad=True)
57
+ left_hand = torch.from_numpy(relaxed_hand_pose[:45].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device)
58
+ right_hand = torch.from_numpy(relaxed_hand_pose[45:].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device)
59
+ optimizer = torch.optim.Adam(params=[pose_input, transl], lr=0.05)
60
+ loss_fn = nn.MSELoss()
61
+ vertices_output = None
62
+ for step in range(100):
63
+ smpl_output = smpl_model(transl=transl, body_pose=pose_input[:, 3:], global_orient=pose_input[:, :3], return_verts=True,
64
+ left_hand_pose=left_hand,# @ left_hand_components[:hand_pca],
65
+ right_hand_pose=right_hand,# @ right_hand_components[:hand_pca],
66
+ )
67
+ joints_output = smpl_output.joints[:, joints_ind].reshape(len, -1, 3)
68
+ vertices_output = smpl_output.vertices[:, ::10].detach().cpu().numpy()
69
+ loss = loss_fn(joints[:, :], joints_output[:, :])
70
+ # loss = torch.mean((joints - joints_output) ** 2 * weights)
71
+ optimizer.zero_grad()
72
+ loss.backward()
73
+ optimizer.step()
74
+
75
+ print(loss.item())
76
+
77
+
78
+
79
+ #left_hand = left_hand @ left_hand_components[:hand_pca]
80
+ #right_hand = right_hand @ right_hand_components[:hand_pca]
81
+
82
+ return pose_input.detach().cpu().numpy(), transl.detach().cpu().numpy(), left_hand.detach().cpu().numpy(), right_hand.detach().cpu().numpy(), vertices_output
83
+
84
+
85
+ def joints_to_smpl(model, joints, joints_ind, interp_s):
86
+ joints = interpolate_joints(joints, scale=interp_s)
87
+ # joints = interpolate_joints(joints, scale=0.33)
88
+ # joints = interpolate_joints(joints, scale=interp_s * 3)
89
+ input_len = joints.shape[0]
90
+ joints = joints.reshape(input_len, -1, 3)
91
+ joints = joints.permute(1, 0, 2)
92
+ trans_np = joints[0].detach().cpu().numpy()
93
+ joints = joints - joints[0]
94
+ joints = joints.permute(1, 0, 2)
95
+ joints = joints.reshape(input_len, -1)
96
+ pose_pred = model(joints)
97
+ pose_pred = pose_pred.reshape(-1, 6)
98
+ pose_pred = T.matrix_to_axis_angle(T.rotation_6d_to_matrix(pose_pred)).reshape(input_len, -1)
99
+ # pose_pred = pose_pred[:seq_len]
100
+ pose_output, transl, left_hand, right_hand, vertices = optimize_smpl(pose_pred, joints, joints_ind)
101
+
102
+ transl = trans_np - np.array(pelvis_shift) + transl
103
+
104
+ vertices = vertices + transl.reshape(-1, 1, 3)
105
+
106
+
107
+ return pose_output, transl, left_hand, right_hand, vertices
108
+
109
+
110
+ def interpolate_joints(joints, scale):
111
+ if scale == 1:
112
+ return joints
113
+ device = joints.device
114
+ joints = joints.detach().cpu().numpy()
115
+ in_len = joints.shape[0]
116
+ out_len = int(in_len * scale)
117
+ joints = joints.reshape(in_len, -1)
118
+ x = np.array(range(in_len))
119
+ xnew = np.linspace(0, in_len - 1, out_len)
120
+ f = interp1d(x, joints, axis=0)
121
+ joints_new = f(xnew)
122
+ joints_new = torch.from_numpy(joints_new).to(device).float()
123
+
124
+ return joints_new
models/synhsi.py ADDED
@@ -0,0 +1,444 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import pdb
3
+
4
+ import torch
5
+ from torch import nn
6
+ import torch.nn.functional as F
7
+ from vit_pytorch import ViT
8
+ from tqdm import tqdm
9
+ from utils import *
10
+
11
+
12
+ class Sampler:
13
+ def __init__(self, device, mask_ind, emb_f, batch_size, seq_len, channel, fix_mode, timesteps, fixed_frame, **kwargs):
14
+ self.device = device
15
+ self.mask_ind = mask_ind
16
+ self.emb_f = emb_f
17
+ self.batch_size = batch_size
18
+ self.seq_len = seq_len
19
+ self.channel = channel
20
+ self.fix_mode = fix_mode
21
+ self.timesteps = timesteps
22
+ self.fixed_frame = fixed_frame
23
+ self.get_scheduler()
24
+
25
+ def set_dataset_and_model(self, dataset, model):
26
+ self.dataset = dataset
27
+ if dataset.load_scene:
28
+ self.grid = dataset.create_meshgrid(batch_size=self.batch_size).to(self.device)
29
+ self.model = model
30
+
31
+
32
+ def get_scheduler(self):
33
+ betas = linear_beta_schedule(timesteps=self.timesteps)
34
+
35
+ # define alphas
36
+ alphas = 1. - betas
37
+ alphas_cumprod = torch.cumprod(alphas, axis=0)
38
+ alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
39
+ self.sqrt_recip_alphas = torch.sqrt(1.0 / alphas)
40
+
41
+ # calculations for diffusion q(x_t | x_{t-1}) and others
42
+ self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
43
+ self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod)
44
+
45
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
46
+ self.posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
47
+ self.betas = betas
48
+
49
+ def q_sample(self, x_start, t, noise):
50
+ if noise is None:
51
+ noise = torch.randn_like(x_start)
52
+ sqrt_alphas_cumprod_t = extract(self.sqrt_alphas_cumprod, t, x_start.shape)
53
+ sqrt_one_minus_alphas_cumprod_t = extract(
54
+ self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
55
+ )
56
+ return sqrt_alphas_cumprod_t * x_start + sqrt_one_minus_alphas_cumprod_t * noise
57
+
58
+
59
+ def p_losses(self, x_start, obj_points, mat, scene_flag, mask, t, action_label, noise=None, loss_type='huber'):
60
+ if noise is None:
61
+ noise = torch.randn_like(x_start)
62
+
63
+ noise[mask] = 0.
64
+
65
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
66
+
67
+ if self.dataset.load_scene:
68
+ with torch.no_grad():
69
+ x_orig = transform_points(self.dataset.denormalize_torch(x_noisy), mat)
70
+ mat_for_query = mat.clone()
71
+ target_ind = self.mask_ind if self.mask_ind != -1 else 0
72
+ mat_for_query[:, :3, 3] = x_orig[:, self.emb_f, target_ind * 3: target_ind * 3 + 3]
73
+ mat_for_query[:, 1, 3] = 0
74
+ query_points = transform_points(self.grid, mat_for_query)
75
+
76
+ occ = self.dataset.get_occ_for_points(query_points, obj_points, scene_flag)
77
+ nb_voxels = self.dataset.nb_voxels
78
+ occ = occ.reshape(-1, nb_voxels, nb_voxels, nb_voxels).float()
79
+
80
+ # import trimesh
81
+ # print(mat[0])
82
+ # grid_np = self.grid[0].detach().cpu().numpy().reshape((-1, 3))
83
+ # occ_np = occ[0].detach().cpu().numpy().reshape((-1))
84
+ # points = grid_np[occ_np > 0.5]
85
+ # pcd_trimesh = trimesh.PointCloud(vertices=points)
86
+ # scene = trimesh.Scene([pcd_trimesh, trimesh.creation.axis(origin_color=[0, 0, 0])])
87
+ # scene.show()
88
+
89
+ occ = occ.permute(0, 2, 1, 3)
90
+ else:
91
+ occ = None
92
+
93
+ # x_noisy = torch.cat([x_noisy, occ], dim=-1).detach()
94
+
95
+ predicted_noise = self.model(x_noisy, occ, t, action_label, mask)
96
+
97
+ mask_inv = torch.logical_not(mask)
98
+
99
+ if loss_type == 'l1':
100
+ loss = F.l1_loss(noise[mask_inv], predicted_noise[mask_inv])
101
+ elif loss_type == 'l2':
102
+ loss = F.mse_loss(noise[mask_inv], predicted_noise[mask_inv])
103
+ elif loss_type == "huber":
104
+ loss = F.smooth_l1_loss(noise[mask_inv], predicted_noise[mask_inv])
105
+ else:
106
+ raise NotImplementedError()
107
+
108
+ return loss
109
+
110
+ @torch.no_grad()
111
+ def p_sample_loop(self, fixed_points, obj_locs, mat, scene, goal, action_label):
112
+ device = next(self.model.parameters()).device
113
+ shape = (self.batch_size, self.seq_len, self.channel)
114
+ points = torch.randn(shape, device=device) # + torch.tensor([0., 0.3, 0.] * 22, device=device)
115
+
116
+ if self.fix_mode:
117
+ self.set_fixed_points(points, goal, fixed_points, mat, joint_id=self.mask_ind, fix_mode=True, fix_goal=True)
118
+ imgs = []
119
+ occs = []
120
+
121
+ if self.dataset.load_scene:
122
+ x_orig = transform_points(self.dataset.denormalize_torch(points), mat)
123
+ mat_for_query = mat.clone()
124
+ target_ind = self.mask_ind if self.mask_ind != -1 else 0
125
+ mat_for_query[:, :3, 3] = x_orig[:, self.emb_f, target_ind * 3: target_ind * 3 + 3]
126
+ mat_for_query[:, 1, 3] = 0
127
+ query_points = transform_points(self.grid, mat_for_query)
128
+ occ = self.dataset.get_occ_for_points(query_points, obj_locs, scene)
129
+ nb_voxels = self.dataset.nb_voxels
130
+ occ = occ.reshape(-1, nb_voxels, nb_voxels, nb_voxels).float()
131
+
132
+ # import trimesh
133
+ # print('\n', mat[0])
134
+ # grid_np = self.grid[0].detach().cpu().numpy().reshape((-1, 3))
135
+ # occ_np = occ[0].detach().cpu().numpy().reshape((-1))
136
+ # pointcloud = grid_np[occ_np > 0.5]
137
+ # pcd_trimesh = trimesh.PointCloud(vertices=pointcloud)
138
+ # np.save('/home/jiangnan/SyntheticHSI/Paper/Teaser/occ.npy', pointcloud)
139
+ # scene = trimesh.Scene([pcd_trimesh, trimesh.creation.axis(origin_color=[0, 0, 0])])
140
+ # scene.show()
141
+
142
+ occ = occ.permute(0, 2, 1, 3)
143
+
144
+ else:
145
+ occ = None
146
+
147
+ for i in tqdm(reversed(range(0, self.timesteps)), desc='sampling loop time step', total=self.timesteps):
148
+ model_used = self.model
149
+ # if s < 3 or (s == 3 and i < 5) or i < 3:
150
+ # model_used = model_fix
151
+ # else:
152
+ # model_used = model
153
+ points, occ = self.p_sample(model_used, points, fixed_points, goal, None, mat, occ,
154
+ torch.full((self.batch_size,), i, device=device, dtype=torch.long), i, action_label, self.mask_ind,
155
+ self.emb_f, self.fix_mode)
156
+ if self.fix_mode:
157
+ self.set_fixed_points(points, goal, fixed_points, mat, joint_id=self.mask_ind, fix_mode=True, fix_goal=True)
158
+ # set_fixed_points(points, goal, mat, joint_id=mask_ind)
159
+ # # t = torch.ones(b, device=device, dtype=torch.int64) * i
160
+ # if fixed_points is not None:
161
+ # points[:, :fixed_points.shape[1], :] = fixed_points # q_sample(fixed_points, t, None, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod)
162
+
163
+ points_orig = transform_points(self.dataset.denormalize_torch(points), mat)
164
+ imgs.append(points_orig)
165
+ if occ is not None:
166
+ occs.append(occ.cpu().numpy())
167
+ return imgs, occs
168
+
169
+ @torch.no_grad()
170
+ def p_sample(self, model, x, fixed_points, goal, obj_points, mat, occ, t, t_index, action_label, mask_ind, emb_f,
171
+ fix_mode, no_scene=False):
172
+ betas_t = extract(self.betas, t, x.shape)
173
+ sqrt_one_minus_alphas_cumprod_t = extract(
174
+ self.sqrt_one_minus_alphas_cumprod, t, x.shape
175
+ )
176
+ sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape)
177
+
178
+ # Equation 11 in the paper
179
+ # Use our model (noise predictor) to predict the mean
180
+
181
+
182
+ # joints_orig = transform_points(synhsi_dataset.denormalize_torch(x), mat)
183
+ # occ = synhsi_dataset.get_occ_for_points(joints_orig, obj_points, scene)
184
+ # x_occ = torch.cat([x, occ], dim=-1).detach()
185
+
186
+ model_mean = sqrt_recip_alphas_t * (
187
+ x - betas_t * model(x, occ, t, action_label, mask=None) / sqrt_one_minus_alphas_cumprod_t
188
+ )
189
+ # model_mean_noact = sqrt_recip_alphas_t * (
190
+ # x - betas_t * model(x, occ, t, action_label, mask=None, no_action=True) / sqrt_one_minus_alphas_cumprod_t
191
+ # )
192
+ # model_mean = model_mean_noact + (model_mean - model_mean_noact) * 10
193
+ if not fix_mode:
194
+ self.set_fixed_points(model_mean, goal, fixed_points, mat, joint_id=mask_ind, fix_mode=True, fix_goal=False)
195
+
196
+ if t_index == 0:
197
+ return model_mean, occ
198
+ else:
199
+ posterior_variance_t = extract(self.posterior_variance, t, x.shape)
200
+ noise = torch.randn_like(x)
201
+ # Algorithm 2 line 4:
202
+ return model_mean + torch.sqrt(posterior_variance_t) * noise, occ
203
+
204
+ # Algorithm 2 (including returning all images)
205
+
206
+
207
+ def set_fixed_points(self, img, goal, fixed_points, mat, joint_id=0, fix_mode=False, fix_goal=True):
208
+ # if joint_id != 0:
209
+ # goal_len = 2
210
+ goal_len = goal.shape[1]
211
+ # goal_batch = goal.reshape(1, 1, 3).repeat(img.shape[0], 1, 1)
212
+ goal = self.dataset.normalize_torch(transform_points(goal, torch.inverse(mat)))
213
+ # img[:, -1, joint_id * 3: joint_id * 3 + 3] = goal_batch[:, 0]
214
+ if fix_goal:
215
+ img[:, -goal_len:, joint_id * 3] = goal[:, :, 0]
216
+ if joint_id != 0:
217
+ img[:, -goal_len:, joint_id * 3 + 1] = goal[:, :, 1]
218
+ img[:, -goal_len:, joint_id * 3 + 2] = goal[:, :, 2]
219
+
220
+ if fixed_points is not None and fix_mode:
221
+ img[:, :fixed_points.shape[1], :] = fixed_points
222
+
223
+
224
+ class Unet(nn.Module):
225
+ def __init__(
226
+ self,
227
+ dim_model,
228
+ num_heads,
229
+ num_layers,
230
+ dropout_p,
231
+ dim_input,
232
+ dim_output,
233
+ nb_voxels=None,
234
+ free_p=0.1,
235
+ nb_actions=0,
236
+ ac_type='',
237
+ no_scene=False,
238
+ no_action=False,
239
+ **kwargs
240
+ ):
241
+ super().__init__()
242
+
243
+ # INFO
244
+ self.model_type = "Transformer"
245
+ self.dim_model = dim_model
246
+ self.nb_actions = nb_actions
247
+ self.ac_type = ac_type
248
+ self.no_scene = no_scene
249
+ self.no_action = no_action
250
+
251
+ # LAYERS
252
+ if not no_scene:
253
+ self.scene_embedding = ViT(
254
+ image_size=nb_voxels,
255
+ patch_size=nb_voxels // 4,
256
+ channels=nb_voxels,
257
+ num_classes=dim_model,
258
+ dim=1024,
259
+ depth=6,
260
+ heads=16,
261
+ mlp_dim=2048,
262
+ dropout=0.1,
263
+ emb_dropout=0.1
264
+ )
265
+ self.free_p = free_p
266
+ self.positional_encoder = PositionalEncoding(
267
+ dim_model=dim_model, dropout_p=dropout_p, max_len=5000
268
+ )
269
+ self.embedding_input = nn.Linear(dim_input, dim_model)
270
+ self.embedding_output = nn.Linear(dim_output, dim_model)
271
+
272
+ # self.embedding_action = nn.Parameter(torch.randn(16, dim_model))
273
+
274
+ if not no_action and nb_actions > 0:
275
+ if self.ac_type in ['last_add_first_token', 'last_new_token']:
276
+ self.embedding_action = ActionTransformerEncoder(action_number=nb_actions,
277
+ dim_model=dim_model,
278
+ nhead=num_heads // 2,
279
+ num_layers=num_layers,
280
+ dim_feedforward=dim_model,
281
+ dropout_p=dropout_p,
282
+ activation="gelu")
283
+ elif self.ac_type in ['all_add_token']:
284
+ self.embedding_action = nn.Sequential(
285
+ nn.Linear(nb_actions, dim_model),
286
+ nn.SiLU(inplace=False),
287
+ nn.Linear(dim_model, dim_model),
288
+ )
289
+
290
+ encoder_layer = nn.TransformerEncoderLayer(d_model=dim_model,
291
+ nhead=num_heads,
292
+ dim_feedforward=dim_model,
293
+ dropout=dropout_p,
294
+ activation="gelu")
295
+
296
+ self.transformer = nn.TransformerEncoder(encoder_layer,
297
+ num_layers=num_layers
298
+ )
299
+ # self.out = nn.Linear(dim_model, dim_output)
300
+
301
+ self.out = nn.Linear(dim_model, dim_output)
302
+
303
+ self.embed_timestep = TimestepEmbedder(self.dim_model, self.positional_encoder)
304
+
305
+ def forward(self, x, cond, timesteps, action, mask, no_action=None):
306
+
307
+ #TODO ActionFlag
308
+ # action[action[:, 0] != 0., 0] = 1.
309
+
310
+ t_emb = self.embed_timestep(timesteps) # [1, b, d]
311
+
312
+ if self.no_scene:
313
+ scene_emb = torch.zeros_like(t_emb)
314
+ else:
315
+ scene_emb = self.scene_embedding(cond).reshape(-1, 1, self.dim_model)
316
+
317
+ if self.no_action or self.nb_actions == 0:
318
+ action_emb = torch.zeros_like(t_emb)
319
+ else:
320
+ if self.ac_type in ['all_add_token']:
321
+ action_emb = self.embedding_action(action)
322
+ elif self.ac_type in ['last_add_first_token', 'last_new_token']:
323
+ action_emb = self.embedding_action(action)
324
+ else:
325
+ raise NotImplementedError
326
+
327
+ t_emb = t_emb.permute(1, 0, 2)
328
+
329
+ free_ind = torch.rand(scene_emb.shape[0]).to(scene_emb.device) < self.free_p
330
+ scene_emb[free_ind] = 0.
331
+ # if mask is not None:
332
+ # x[free_ind][:, mask[0]] = 0.
333
+
334
+ if self.ac_type in ['last_add_first_token', 'last_new_token']:
335
+ action_emb[free_ind] = 0.
336
+ scene_emb = scene_emb.permute(1, 0, 2)
337
+ action_emb = action_emb.permute(1, 0, 2)
338
+
339
+ if self.ac_type in ['all_add_token', 'last_new_token']:
340
+ emb = t_emb + scene_emb
341
+ elif self.ac_type in ['last_add_first_token']:
342
+ emb = t_emb + scene_emb + action_emb
343
+
344
+ x = x.permute(1, 0, 2)
345
+ x = self.embedding_input(x) * math.sqrt(self.dim_model)
346
+ if self.ac_type in ['all_add_token', 'last_add_first_token']:
347
+ x = torch.cat((emb, x), dim=0)
348
+ elif self.ac_type in ['last_new_token']:
349
+ x = torch.cat((emb, action_emb, x), dim=0)
350
+
351
+ if self.ac_type in ['all_add_token']:
352
+ x[1:] = x[1:] + action_emb
353
+
354
+ x = self.positional_encoder(x)
355
+ x = self.transformer(x)
356
+ if self.ac_type in ['all_add_token', 'last_add_first_token']:
357
+ output = self.out(x)[1:]
358
+ elif self.ac_type in ['last_new_token']:
359
+ output = self.out(x)[2:]
360
+ output = output.permute(1, 0, 2)
361
+
362
+ return output
363
+
364
+
365
+ class PositionalEncoding(nn.Module):
366
+ def __init__(self, dim_model, dropout_p, max_len):
367
+ super().__init__()
368
+ # Modified version from: https://pytorch.org/tutorials/beginner/transformer_tutorial.html
369
+ # max_len determines how far the position can have an effect on a token (window)
370
+
371
+ # Info
372
+ self.dropout = nn.Dropout(dropout_p)
373
+
374
+ # Encoding - From formula
375
+ pos_encoding = torch.zeros(max_len, dim_model)
376
+ positions_list = torch.arange(0, max_len, dtype=torch.float).reshape(-1, 1) # 0, 1, 2, 3, 4, 5
377
+ division_term = torch.exp(
378
+ torch.arange(0, dim_model, 2).float() * (-math.log(10000.0)) / dim_model) # 1000^(2i/dim_model)
379
+
380
+ # PE(pos, 2i) = sin(pos/1000^(2i/dim_model))
381
+ pos_encoding[:, 0::2] = torch.sin(positions_list * division_term)
382
+
383
+ # PE(pos, 2i + 1) = cos(pos/1000^(2i/dim_model))
384
+ pos_encoding[:, 1::2] = torch.cos(positions_list * division_term)
385
+
386
+ # Saving buffer (same as parameter without gradients needed)
387
+ pos_encoding = pos_encoding.unsqueeze(0).transpose(0, 1)
388
+ self.register_buffer("pos_encoding", pos_encoding)
389
+
390
+ def forward(self, token_embedding: torch.tensor) -> torch.tensor:
391
+ # Residual connection + pos encoding
392
+ return self.dropout(token_embedding + self.pos_encoding[:token_embedding.size(0), :])
393
+
394
+
395
+ class TimestepEmbedder(nn.Module):
396
+ def __init__(self, latent_dim, sequence_pos_encoder):
397
+ super().__init__()
398
+ self.latent_dim = latent_dim
399
+ self.sequence_pos_encoder = sequence_pos_encoder
400
+
401
+ time_embed_dim = self.latent_dim
402
+ self.time_embed = nn.Sequential(
403
+ nn.Linear(self.latent_dim, time_embed_dim),
404
+ nn.SiLU(inplace=False),
405
+ nn.Linear(time_embed_dim, time_embed_dim),
406
+ )
407
+
408
+ def forward(self, timesteps):
409
+ return self.time_embed(self.sequence_pos_encoder.pos_encoding[timesteps])#.permute(1, 0, 2)
410
+
411
+
412
+ class ActionTransformerEncoder(nn.Module):
413
+ def __init__(self,
414
+ action_number,
415
+ dim_model,
416
+ nhead,
417
+ num_layers,
418
+ dim_feedforward,
419
+ dropout_p,
420
+ activation="gelu") -> None:
421
+ super().__init__()
422
+ self.positional_encoder = PositionalEncoding(
423
+ dim_model=dim_model, dropout_p=dropout_p, max_len=5000
424
+ )
425
+ self.input_embedder = nn.Linear(action_number, dim_model)
426
+ encoder_layer = nn.TransformerEncoderLayer(d_model=dim_model,
427
+ nhead=nhead,
428
+ dim_feedforward=dim_feedforward,
429
+ dropout=dropout_p,
430
+ activation=activation)
431
+ self.transformer_encoder = nn.TransformerEncoder(encoder_layer,
432
+ num_layers=num_layers
433
+ )
434
+
435
+ def forward(self, x):
436
+ x = x.permute(1, 0, 2)
437
+ x = self.input_embedder(x)
438
+ x = self.positional_encoder(x)
439
+ x = self.transformer_encoder(x)
440
+ x = x.permute(1, 0, 2)
441
+ x = torch.mean(x, dim=1, keepdim=True)
442
+ return x
443
+
444
+
not_used.py ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ import numpy as np
2
+
3
+ test = np.random.randn(3,4,5)
4
+ print(0)
objects_occ/Background.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:d3df6127677f16bc78d3193fe8a51fdde4e87e2651e1cb4e6935e348b2f28741
3
+ size 12000128
objects_occ/background.blend ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:dde8176ebca147248b6ff88d2aaa2d2de3522d84ad99133509195ea265b8f4c6
3
+ size 2747420
objects_occ/background.obj ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e8261b62db7767afa66e91c450af153c0eefc836d78b2a6b05dd1af74c6cee0b
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+ size 65882377
objects_occ/basin.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:aad0216f00c4162730cc572623d69a86e7473ab5d2d040cf33a48a4a1ae46b08
3
+ size 1605344
objects_occ/basin.obj ADDED
The diff for this file is too large to render. See raw diff
 
objects_occ/bed.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:aaae4ec071dca60fdc847add067a9fbd0f7f1194a1de42fa9896f57e294c704c
3
+ size 2243504
objects_occ/bed.obj ADDED
The diff for this file is too large to render. See raw diff
 
objects_occ/flower.npy ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:7aaaf632feec63231fb8dd8ff0bc5cf5c05a547c11901e966affd456d001ef7a
3
+ size 165428
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