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- .gitattributes +3 -0
- 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
- Checkpoints/model_joints_to_smpl_wrist.pth +3 -0
- Data_blocks_motion_all/Object/basin.npy +3 -0
- Data_blocks_motion_all/Object/bed.npy +3 -0
- Data_blocks_motion_all/Object/flower.npy +3 -0
- Data_blocks_motion_all/Object/kitchen_chair_1.npy +3 -0
- Data_blocks_motion_all/Object/kitchen_chair_2.npy +3 -0
- Data_blocks_motion_all/Object/office_chair.npy +3 -0
- Data_blocks_motion_all/Object/sofa.npy +3 -0
- Data_blocks_motion_all/Object/table.npy +3 -0
- Data_blocks_motion_all/Object/wc.npy +3 -0
- Data_blocks_motion_all/Scene/2a8a1191-d4cc-46e4-b5ea-65e01954dbfa.npy +3 -0
- Data_blocks_motion_all/Scene/background.npy +3 -0
- Data_blocks_motion_all/meta.npy +3 -0
- Data_blocks_motion_all/norm.npy +3 -0
- Dockerfile +14 -0
- app.py +32 -0
- config/config_sample_synhsi.yaml +86 -0
- constants.py +93 -0
- datasets/__init__.py +1 -0
- datasets/__pycache__/__init__.cpython-39.pyc +0 -0
- datasets/__pycache__/trumans.cpython-39.pyc +0 -0
- datasets/trumans.py +228 -0
- models/__init__.py +2 -0
- models/__pycache__/__init__.cpython-39.pyc +0 -0
- models/__pycache__/joints_to_smplx.cpython-39.pyc +0 -0
- models/__pycache__/synhsi.cpython-39.pyc +0 -0
- models/joints_to_smplx.py +124 -0
- models/synhsi.py +444 -0
- not_used.py +4 -0
- objects_occ/Background.npy +3 -0
- objects_occ/background.blend +3 -0
- objects_occ/background.obj +3 -0
- objects_occ/basin.npy +3 -0
- objects_occ/basin.obj +0 -0
- objects_occ/bed.npy +3 -0
- objects_occ/bed.obj +0 -0
- objects_occ/flower.npy +3 -0
- objects_occ/flower.obj +4278 -0
- objects_occ/kitchen_chair_1.npy +3 -0
- objects_occ/kitchen_chair_1.obj +0 -0
- objects_occ/office_chair.npy +3 -0
- objects_occ/office_chair.obj +0 -0
- objects_occ/sofa.npy +3 -0
- objects_occ/sofa.obj +0 -0
- objects_occ/table.npy +3 -0
- objects_occ/table.obj +0 -0
- objects_occ/wc.npy +3 -0
- objects_occ/wc.obj +0 -0
.gitattributes
CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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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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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
ADDED
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Checkpoints/model_joints_to_smpl_wrist.pth
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Data_blocks_motion_all/Object/basin.npy
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Data_blocks_motion_all/Object/bed.npy
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Data_blocks_motion_all/Object/flower.npy
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Data_blocks_motion_all/Object/kitchen_chair_1.npy
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Data_blocks_motion_all/Object/kitchen_chair_2.npy
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Data_blocks_motion_all/Object/office_chair.npy
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version https://git-lfs.github.com/spec/v1
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Data_blocks_motion_all/Object/sofa.npy
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version https://git-lfs.github.com/spec/v1
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Data_blocks_motion_all/Object/table.npy
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Data_blocks_motion_all/Object/wc.npy
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version https://git-lfs.github.com/spec/v1
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Data_blocks_motion_all/Scene/2a8a1191-d4cc-46e4-b5ea-65e01954dbfa.npy
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version https://git-lfs.github.com/spec/v1
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Data_blocks_motion_all/Scene/background.npy
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version https://git-lfs.github.com/spec/v1
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Data_blocks_motion_all/meta.npy
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version https://git-lfs.github.com/spec/v1
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size 503
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Data_blocks_motion_all/norm.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:027b38e3ce51684de16d6de2c44bca9aaa89e910ab3c2242cfb7f5a839c08c40
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size 1706
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Dockerfile
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FROM python:3.9
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WORKDIR /app
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COPY ./requirements.txt /app/requirements.txt
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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
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COPY . .
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ENV FLASK_APP=app.py
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CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0"]
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app.py
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# app.py
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import os
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# import numpy as np
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from flask import Flask, jsonify, request, render_template
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from sample_hsi import sample_wrapper
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# from omegaconf import OmegaConf
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# from hydra import compose, initialize
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app = Flask(__name__)
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/move_cube', methods=['POST'])
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def move_cube():
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print(os.getcwd())
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data = request.json
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trajectory = data['trajectory']
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print(data)
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obj_locs = {obj_name.split('.')[0]: data[obj_name] for obj_name in data.keys() if 'trajectory' not in obj_name}
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res = sample_wrapper(trajectory, obj_locs)
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return jsonify(res)
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if __name__ == '__main__':
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# os.environ["HYDRA_FULL_ERROR"] = "1"
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# initialize(version_base=None, config_path="./config")
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# OmegaConf.register_new_resolver("times", lambda x, y: int(x) * int(y))
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app.run(debug=True)
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config/config_sample_synhsi.yaml
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exp_name: Test
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train: false
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batch_size: 1
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device: cuda
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interp_s: 3
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len_pre: 4
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len_act: 2
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action_type: 'none'
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scene_name: 'background'
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action_id: 1
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stay_and_act: false
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method_name: Test
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continue_last: false
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#exp_dir: ${oc.env:ROOT_DIR}/Experiments/${exp_name}
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ckpt_dir: ./Checkpoints
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smpl_dir: ./smpl_models
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#test_dir: ${oc.env:ROOT_DIR}/Test_settings
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num_gpus: 1
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num_workers: 0
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dataset:
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folder: ./Data_blocks_motion_all
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device: cuda
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batch_size: 1
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seq_len: 16
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step: 3
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nb_voxels: 32
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mesh_grid: [ -0.6, 0.6, 0, 1.2, -0.6, 0.6 ]
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train: false
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load_scene: true
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load_action: true
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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 ]
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nb_joints: 24
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nb_actions: 10
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guidance:
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pelvis:
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seq_len: 16
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step: 3
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mask_ind: 0
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fixed_frame: 2
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mask_y: false
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emb_f: -1
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no_scene: false
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no_action: false
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fix_mode: true
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model:
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model_smplx:
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input_dim: 72
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output_dim: 132
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hidden_dim: 64
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ckpt: ./Checkpoints/model_joints_to_smpl_wrist.pth
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synhsi_body:
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dim_model: 512
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num_heads: 16
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num_layers: 8
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dropout_p: 0.1
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nb_voxels: 32
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free_p: 0
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ac_type: last_add_first_token
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dim_input: 72
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dim_output: 72
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nb_actions: 10
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+
no_scene: false
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no_action: false
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+
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
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sampler:
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pelvis:
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_target_: models.synhsi.Sampler
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device: cuda
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+
mask_ind: 0
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emb_f: -1
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+
batch_size: 1
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seq_len: 16
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+
channel: 72
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fix_mode: true
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timesteps: 100
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fixed_frame: 2
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constants.py
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import numpy as np
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import os
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try:
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ROOT_DIR = os.environ['ROOT_DIR']
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except:
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ROOT_DIR = '/home/jiangnan/SyntheticHSI/'
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DATA_DIR = os.path.join(ROOT_DIR, 'Data_augment', 'Data_blocks_motion_all')
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CKPT_DIR = os.path.join(ROOT_DIR, 'HSIScripts', 'motion_gen_diffusion', 'checkpoints')
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SMPL_DIR = os.path.join(ROOT_DIR, 'smpl_models')
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OBJ_ACT_DICT = {
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'lie down': 0,
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'squat': 1,
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'mouse': 2,
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'keyboard': 3,
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'laptop': 4,
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'phone': 5,
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'book': 6,
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'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,
|
82 |
+
-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,
|
86 |
+
-0.21465, 0.066, 0.50689,
|
87 |
+
-0.36972, 0.06034, 0.07949,
|
88 |
+
-0.14187, 0.08585, 0.63553,
|
89 |
+
-0.30334, 0.05788, 0.63139,
|
90 |
+
-0.17612, 0.13209, 0.37335,
|
91 |
+
0.85096, -0.27692, 0.09155,
|
92 |
+
-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 @@
|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 @@
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
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 @@
|
|
|
|
|
|
|
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|
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|
|
|
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
|
2 |
+
oid sha256:d3df6127677f16bc78d3193fe8a51fdde4e87e2651e1cb4e6935e348b2f28741
|
3 |
+
size 12000128
|
objects_occ/background.blend
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:dde8176ebca147248b6ff88d2aaa2d2de3522d84ad99133509195ea265b8f4c6
|
3 |
+
size 2747420
|
objects_occ/background.obj
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e8261b62db7767afa66e91c450af153c0eefc836d78b2a6b05dd1af74c6cee0b
|
3 |
+
size 65882377
|
objects_occ/basin.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
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
|
2 |
+
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
|
2 |
+
oid sha256:7aaaf632feec63231fb8dd8ff0bc5cf5c05a547c11901e966affd456d001ef7a
|
3 |
+
size 165428
|
objects_occ/flower.obj
ADDED
@@ -0,0 +1,4278 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
1 |
+
# Blender 3.3.0
|
2 |
+
# www.blender.org
|
3 |
+
o flower
|
4 |
+
v 0.072182 0.241364 0.155908
|
5 |
+
v 0.074947 0.201392 0.151069
|
6 |
+
v 0.096334 0.218628 0.144440
|
7 |
+
v 0.097632 0.241364 0.145513
|
8 |
+
v 0.072182 0.285249 0.160051
|
9 |
+
v 0.100138 0.285249 0.147585
|
10 |
+
v 0.072182 0.329133 0.164194
|
11 |
+
v 0.102643 0.329133 0.149656
|
12 |
+
v 0.024061 0.065827 0.154991
|
13 |
+
v 0.024061 0.028971 0.148670
|
14 |
+
v 0.049943 0.037191 0.145081
|
15 |
+
v 0.053519 0.065827 0.146474
|
16 |
+
v 0.024061 0.109711 0.159019
|
17 |
+
v 0.058888 0.109711 0.148546
|
18 |
+
v 0.024061 0.153596 0.163048
|
19 |
+
v 0.064328 0.153596 0.150617
|
20 |
+
v 0.024061 0.197480 0.167076
|
21 |
+
v 0.024061 0.241364 0.171104
|
22 |
+
v 0.024061 0.285249 0.175132
|
23 |
+
v 0.024061 0.329133 0.179161
|
24 |
+
v 0.118633 0.241364 0.120304
|
25 |
+
v 0.111110 0.201392 0.123958
|
26 |
+
v 0.127505 0.287277 0.115823
|
27 |
+
v 0.126704 0.329133 0.122670
|
28 |
+
v 0.144807 0.303257 0.096795
|
29 |
+
v 0.146266 0.329133 0.098609
|
30 |
+
v 0.077580 0.065827 0.133724
|
31 |
+
v 0.068463 0.023452 0.120245
|
32 |
+
v 0.102788 0.030660 0.104165
|
33 |
+
v 0.107303 0.065827 0.109663
|
34 |
+
v 0.082949 0.109711 0.136808
|
35 |
+
v 0.109788 0.109711 0.112748
|
36 |
+
v 0.088389 0.153596 0.139913
|
37 |
+
v 0.112274 0.153596 0.115852
|
38 |
+
v 0.092831 0.189372 0.142434
|
39 |
+
v 0.024061 0.007765 0.120304
|
40 |
+
v 0.158039 0.329133 0.072182
|
41 |
+
v 0.152482 0.287277 0.075653
|
42 |
+
v 0.133583 0.065700 0.068314
|
43 |
+
v 0.112705 0.023373 0.072182
|
44 |
+
v 0.133849 0.109711 0.076835
|
45 |
+
v 0.145036 0.080244 0.049695
|
46 |
+
v 0.146350 0.109711 0.052775
|
47 |
+
v 0.136334 0.153596 0.081422
|
48 |
+
v 0.148308 0.153596 0.057362
|
49 |
+
v 0.138820 0.197480 0.086009
|
50 |
+
v 0.150265 0.197480 0.061948
|
51 |
+
v 0.141305 0.241364 0.090596
|
52 |
+
v 0.152222 0.241364 0.066535
|
53 |
+
v 0.143153 0.274001 0.094007
|
54 |
+
v 0.072182 0.002168 0.072182
|
55 |
+
v 0.024061 0.001474 0.072182
|
56 |
+
v 0.152138 0.109711 0.024061
|
57 |
+
v 0.147832 0.065700 0.026467
|
58 |
+
v 0.156053 0.153596 0.024061
|
59 |
+
v 0.159968 0.197480 0.024061
|
60 |
+
v 0.163883 0.241364 0.024061
|
61 |
+
v 0.167798 0.285249 0.024061
|
62 |
+
v 0.171712 0.329133 0.024061
|
63 |
+
v 0.143729 0.050988 0.044112
|
64 |
+
v 0.124735 0.020112 0.026467
|
65 |
+
v 0.146266 0.032526 0.012031
|
66 |
+
v 0.072182 0.001483 0.024061
|
67 |
+
v 0.024061 0.001474 0.024061
|
68 |
+
v 0.072182 0.373018 0.167523
|
69 |
+
v 0.104656 0.373018 0.151321
|
70 |
+
v 0.072182 0.416902 0.168418
|
71 |
+
v 0.105262 0.416902 0.151822
|
72 |
+
v 0.072182 0.460787 0.167921
|
73 |
+
v 0.104952 0.460787 0.151566
|
74 |
+
v 0.072182 0.504671 0.164371
|
75 |
+
v 0.104365 0.504671 0.151061
|
76 |
+
v 0.072182 0.531799 0.152659
|
77 |
+
v 0.101369 0.530099 0.148577
|
78 |
+
v 0.024061 0.373018 0.182398
|
79 |
+
v 0.024061 0.416902 0.183086
|
80 |
+
v 0.024061 0.460787 0.182542
|
81 |
+
v 0.024061 0.504671 0.175948
|
82 |
+
v 0.024061 0.531039 0.156863
|
83 |
+
v 0.128717 0.373018 0.125143
|
84 |
+
v 0.148259 0.373018 0.101082
|
85 |
+
v 0.129323 0.416902 0.125887
|
86 |
+
v 0.148859 0.416902 0.101827
|
87 |
+
v 0.129013 0.460787 0.125507
|
88 |
+
v 0.148552 0.460787 0.101446
|
89 |
+
v 0.122964 0.505867 0.117497
|
90 |
+
v 0.147054 0.492036 0.099585
|
91 |
+
v 0.101306 0.529348 0.120304
|
92 |
+
v 0.072182 0.536961 0.120304
|
93 |
+
v 0.024061 0.543140 0.120304
|
94 |
+
v 0.161604 0.373018 0.072182
|
95 |
+
v 0.162679 0.416902 0.072182
|
96 |
+
v 0.160972 0.460787 0.072182
|
97 |
+
v 0.151953 0.500636 0.072182
|
98 |
+
v 0.116466 0.518564 0.076801
|
99 |
+
v 0.097868 0.526774 0.095920
|
100 |
+
v 0.086893 0.518578 0.063839
|
101 |
+
v 0.071385 0.537097 0.076800
|
102 |
+
v 0.025004 0.560201 0.069696
|
103 |
+
v 0.174859 0.373018 0.024061
|
104 |
+
v 0.175578 0.416902 0.024061
|
105 |
+
v 0.173097 0.460787 0.024061
|
106 |
+
v 0.157726 0.502899 0.024061
|
107 |
+
v 0.120304 0.509748 0.024061
|
108 |
+
v 0.077518 0.511521 0.024061
|
109 |
+
v 0.053457 0.535243 0.024061
|
110 |
+
v 0.037076 0.557186 0.024061
|
111 |
+
v 0.019802 0.592440 0.024061
|
112 |
+
v 0.013626 0.592440 0.055118
|
113 |
+
v 0.009237 0.632344 0.025254
|
114 |
+
v 0.011191 0.636324 0.056940
|
115 |
+
v 0.008917 0.680209 0.039975
|
116 |
+
v 0.008917 0.680209 0.057374
|
117 |
+
v 0.009315 0.724093 0.038862
|
118 |
+
v 0.009315 0.724093 0.056894
|
119 |
+
v 0.009838 0.767978 0.035045
|
120 |
+
v 0.009838 0.767978 0.055419
|
121 |
+
v 0.009920 0.809559 0.022921
|
122 |
+
v 0.007835 0.811862 0.052846
|
123 |
+
v 0.013141 0.855747 0.024061
|
124 |
+
v 0.004234 0.855747 0.050338
|
125 |
+
v 0.009008 0.899631 0.024061
|
126 |
+
v 0.001140 0.899631 0.048697
|
127 |
+
v 0.003313 0.939832 0.024547
|
128 |
+
v 0.000082 0.922748 0.048168
|
129 |
+
v 0.072182 1.149307 0.195860
|
130 |
+
v 0.071382 1.123089 0.201452
|
131 |
+
v 0.096972 1.140407 0.192934
|
132 |
+
v 0.096972 1.141724 0.192934
|
133 |
+
v 0.024061 1.119053 0.217147
|
134 |
+
v 0.024061 1.084237 0.208449
|
135 |
+
v 0.054808 1.088320 0.202931
|
136 |
+
v 0.022866 1.164761 0.199816
|
137 |
+
v 0.101708 1.156486 0.170765
|
138 |
+
v 0.096972 1.140407 0.187414
|
139 |
+
v 0.074169 1.121857 0.168407
|
140 |
+
v 0.054808 1.088320 0.181481
|
141 |
+
v 0.093530 1.141992 0.155333
|
142 |
+
v 0.072182 1.171249 0.186855
|
143 |
+
v 0.072182 1.206822 0.178483
|
144 |
+
v 0.109051 1.206822 0.160207
|
145 |
+
v 0.072182 1.250707 0.169524
|
146 |
+
v 0.103387 1.250707 0.154442
|
147 |
+
v 0.072182 1.294591 0.163184
|
148 |
+
v 0.106731 1.294591 0.154268
|
149 |
+
v 0.073416 1.334442 0.157927
|
150 |
+
v 0.114284 1.338476 0.160326
|
151 |
+
v 0.090279 1.374577 0.154505
|
152 |
+
v 0.108963 1.374577 0.154505
|
153 |
+
v 0.026827 1.082518 0.166634
|
154 |
+
v 0.044712 1.103307 0.149400
|
155 |
+
v 0.038110 1.180605 0.191268
|
156 |
+
v 0.022866 1.203040 0.182180
|
157 |
+
v 0.006029 1.185890 0.193105
|
158 |
+
v 0.024061 1.250707 0.169375
|
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objects_occ/kitchen_chair_1.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f3c3bff6e28a359fb3126b1f76f5ecf15da4ddecfe886c2b0e147fd2415284ef
|
3 |
+
size 60884
|
objects_occ/kitchen_chair_1.obj
ADDED
The diff for this file is too large to render.
See raw diff
|
|
objects_occ/office_chair.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ecd0ff556c3cf5bb8690b5e5fb4b617e9d22e3d5c3b34bddab759e1a4b1bd48c
|
3 |
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size 60800
|
objects_occ/office_chair.obj
ADDED
The diff for this file is too large to render.
See raw diff
|
|
objects_occ/sofa.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
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oid sha256:4d26ab04c8a18c379d1d042c589d9cf5519e80a12162c2f3b00413ed6151344d
|
3 |
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size 1620836
|
objects_occ/sofa.obj
ADDED
The diff for this file is too large to render.
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|
|
objects_occ/table.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:314ed22ea632e624d3b32a0be9b507ed26051b58b17a1f18d08cb8a93c6d08f6
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3 |
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size 327836
|
objects_occ/table.obj
ADDED
The diff for this file is too large to render.
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|
|
objects_occ/wc.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:406643187fca05716b183bd9bc84f66759903ffde58bed5c211f06ee96f8752e
|
3 |
+
size 17240
|
objects_occ/wc.obj
ADDED
The diff for this file is too large to render.
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|
|