diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..dedfb4819e22e82f4b5a16b3bd4aa707c7f95f60 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +objects_occ/background.blend filter=lfs diff=lfs merge=lfs -text +objects_occ/background.obj filter=lfs diff=lfs merge=lfs -text +static/room.glb filter=lfs diff=lfs merge=lfs -text diff --git a/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 b/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 new file mode 100644 index 0000000000000000000000000000000000000000..312a6dd2e9634f142b6adb5169ccae06c9328af3 --- /dev/null +++ b/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 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:310b10bdfeb2f4bafff082154026fba5df9444ee923866b6a53bf44ef2a64272 +size 336277321 diff --git a/Checkpoints/model_joints_to_smpl_wrist.pth b/Checkpoints/model_joints_to_smpl_wrist.pth new file mode 100644 index 0000000000000000000000000000000000000000..4dccaea9200172b1f864ee8bb524ccdc05557440 --- /dev/null +++ b/Checkpoints/model_joints_to_smpl_wrist.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1aae2dc839a901dc41cd74ac19aa04aa4996ebf8dab91f65803d6b3478cce +size 76341 diff --git a/Data_blocks_motion_all/Object/basin.npy b/Data_blocks_motion_all/Object/basin.npy new file mode 100644 index 0000000000000000000000000000000000000000..a72d30e9852d931164c27de81b002f67129f6a1a --- /dev/null +++ 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a/Data_blocks_motion_all/Object/office_chair.npy b/Data_blocks_motion_all/Object/office_chair.npy new file mode 100644 index 0000000000000000000000000000000000000000..c0ae2d8365b887db4566d7fddfc71fd710bbdf0d --- /dev/null +++ b/Data_blocks_motion_all/Object/office_chair.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ecd0ff556c3cf5bb8690b5e5fb4b617e9d22e3d5c3b34bddab759e1a4b1bd48c +size 60800 diff --git a/Data_blocks_motion_all/Object/sofa.npy b/Data_blocks_motion_all/Object/sofa.npy new file mode 100644 index 0000000000000000000000000000000000000000..b2c8e2384d3af1b1d88e7b1c14c33fe7490c39c9 --- /dev/null +++ b/Data_blocks_motion_all/Object/sofa.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d26ab04c8a18c379d1d042c589d9cf5519e80a12162c2f3b00413ed6151344d +size 1620836 diff --git a/Data_blocks_motion_all/Object/table.npy b/Data_blocks_motion_all/Object/table.npy new file mode 100644 index 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b/Data_blocks_motion_all/Scene/2a8a1191-d4cc-46e4-b5ea-65e01954dbfa.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8469bc60961ea5d83960da904f2b51f9c4e986f02c8540d50613f6ef0f9868da +size 12000128 diff --git a/Data_blocks_motion_all/Scene/background.npy b/Data_blocks_motion_all/Scene/background.npy new file mode 100644 index 0000000000000000000000000000000000000000..7b6603dcc27f753e82586ba6aab6400b3d87bc5c --- /dev/null +++ b/Data_blocks_motion_all/Scene/background.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3df6127677f16bc78d3193fe8a51fdde4e87e2651e1cb4e6935e348b2f28741 +size 12000128 diff --git a/Data_blocks_motion_all/meta.npy b/Data_blocks_motion_all/meta.npy new file mode 100644 index 0000000000000000000000000000000000000000..abb8289903722883e035f58c7e701bf5df53b718 --- /dev/null +++ b/Data_blocks_motion_all/meta.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:25e7501ebb452d583a8756a531690204fc1a1a75b0b819d1b584b96faacd36e7 +size 503 diff --git a/Data_blocks_motion_all/norm.npy b/Data_blocks_motion_all/norm.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa3ca942f454f31ae33c92c110088dc48ad06882 --- /dev/null +++ b/Data_blocks_motion_all/norm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:027b38e3ce51684de16d6de2c44bca9aaa89e910ab3c2242cfb7f5a839c08c40 +size 1706 diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..732cf8f5eabb4dc63e1e9c64429c8bb4a256e300 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,14 @@ +FROM python:3.9 + +WORKDIR /app + +COPY ./requirements.txt /app/requirements.txt +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 + +COPY . . + +ENV FLASK_APP=app.py + +CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0"] + + diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..33ff896da6b2b1aebfa3fd7be8a6e7040418aa5d --- /dev/null +++ b/app.py @@ -0,0 +1,32 @@ +# app.py +import os +# import numpy as np +from flask import Flask, jsonify, request, render_template +from sample_hsi import sample_wrapper +# from omegaconf import OmegaConf +# from hydra import compose, initialize + + +app = Flask(__name__) + +@app.route('/') +def index(): + return render_template('index.html') + +@app.route('/move_cube', methods=['POST']) +def move_cube(): + print(os.getcwd()) + data = request.json + trajectory = data['trajectory'] + print(data) + obj_locs = {obj_name.split('.')[0]: data[obj_name] for obj_name in data.keys() if 'trajectory' not in obj_name} + + res = sample_wrapper(trajectory, obj_locs) + + return jsonify(res) + +if __name__ == '__main__': + # os.environ["HYDRA_FULL_ERROR"] = "1" + # initialize(version_base=None, config_path="./config") + # OmegaConf.register_new_resolver("times", lambda x, y: int(x) * int(y)) + app.run(debug=True) diff --git a/config/config_sample_synhsi.yaml b/config/config_sample_synhsi.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80a3a3e5ad79f4cbba5a9d75393377ebb22f1e71 --- /dev/null +++ b/config/config_sample_synhsi.yaml @@ -0,0 +1,86 @@ + +exp_name: Test +train: false +batch_size: 1 +device: cuda +interp_s: 3 + +len_pre: 4 +len_act: 2 +action_type: 'none' +scene_name: 'background' +action_id: 1 +stay_and_act: false + +method_name: Test +continue_last: false + +#exp_dir: ${oc.env:ROOT_DIR}/Experiments/${exp_name} +ckpt_dir: ./Checkpoints +smpl_dir: ./smpl_models +#test_dir: ${oc.env:ROOT_DIR}/Test_settings + +num_gpus: 1 +num_workers: 0 + +dataset: + folder: ./Data_blocks_motion_all + device: cuda + batch_size: 1 + seq_len: 16 + step: 3 + nb_voxels: 32 + mesh_grid: [ -0.6, 0.6, 0, 1.2, -0.6, 0.6 ] + train: false + load_scene: true + load_action: true + + 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 ] + nb_joints: 24 + nb_actions: 10 + +guidance: + pelvis: + seq_len: 16 + step: 3 + mask_ind: 0 + fixed_frame: 2 + mask_y: false + emb_f: -1 + no_scene: false + no_action: false + fix_mode: true + +model: + model_smplx: + input_dim: 72 + output_dim: 132 + hidden_dim: 64 + ckpt: ./Checkpoints/model_joints_to_smpl_wrist.pth + synhsi_body: + dim_model: 512 + num_heads: 16 + num_layers: 8 + dropout_p: 0.1 + nb_voxels: 32 + free_p: 0 + ac_type: last_add_first_token + dim_input: 72 + dim_output: 72 + nb_actions: 10 + no_scene: false + no_action: false + 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 + +sampler: + pelvis: + _target_: models.synhsi.Sampler + device: cuda + mask_ind: 0 + emb_f: -1 + batch_size: 1 + seq_len: 16 + channel: 72 + fix_mode: true + timesteps: 100 + fixed_frame: 2 \ No newline at end of file diff --git a/constants.py b/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..e97b01dfbc60c054199e82746ad4451ae7d790d4 --- /dev/null +++ b/constants.py @@ -0,0 +1,93 @@ +import numpy as np +import os + + +try: + ROOT_DIR = os.environ['ROOT_DIR'] +except: + ROOT_DIR = '/home/jiangnan/SyntheticHSI/' +DATA_DIR = os.path.join(ROOT_DIR, 'Data_augment', 'Data_blocks_motion_all') +CKPT_DIR = os.path.join(ROOT_DIR, 'HSIScripts', 'motion_gen_diffusion', 'checkpoints') +SMPL_DIR = os.path.join(ROOT_DIR, 'smpl_models') + +OBJ_ACT_DICT = { + 'lie down': 0, + 'squat': 1, + 'mouse': 2, + 'keyboard': 3, + 'laptop': 4, + 'phone': 5, + 'book': 6, + 'bottle': 7, + 'pen': 8, + 'vase': 9, +} + + +CC_BONE_NAMES = ['CC_Base_Hip', 'CC_Base_Pelvis', + 'CC_Base_Waist', 'CC_Base_Spine01', 'CC_Base_Spine02', + 'CC_Base_NeckTwist01', 'CC_Base_NeckTwist02', 'CC_Base_Head', + + 'CC_Base_R_Clavicle', 'CC_Base_R_Upperarm', 'CC_Base_R_Forearm', 'CC_Base_R_Hand', + + 'CC_Base_R_Mid1', 'CC_Base_R_Mid2', 'CC_Base_R_Mid3', 'CC_Base_R_Ring1', + 'CC_Base_R_Ring2', 'CC_Base_R_Ring3', 'CC_Base_R_Pinky1', 'CC_Base_R_Pinky2', + 'CC_Base_R_Pinky3', 'CC_Base_R_Index1', 'CC_Base_R_Index2', 'CC_Base_R_Index3', + 'CC_Base_R_Thumb1', 'CC_Base_R_Thumb2', 'CC_Base_R_Thumb3', + + 'CC_Base_L_Clavicle', 'CC_Base_L_Upperarm', 'CC_Base_L_Forearm', 'CC_Base_L_Hand', + 'CC_Base_L_Mid1', 'CC_Base_L_Mid2', 'CC_Base_L_Mid3', + 'CC_Base_L_Ring1', 'CC_Base_L_Ring2', 'CC_Base_L_Ring3', + 'CC_Base_L_Pinky1', 'CC_Base_L_Pinky2', 'CC_Base_L_Pinky3', + 'CC_Base_L_Index1', 'CC_Base_L_Index2', 'CC_Base_L_Index3', 'CC_Base_L_Thumb1', + 'CC_Base_L_Thumb2', 'CC_Base_L_Thumb3', 'CC_Base_R_Thigh', + 'CC_Base_R_Calf', 'CC_Base_R_Foot', + 'CC_Base_L_Thigh', + 'CC_Base_L_Calf', 'CC_Base_L_Foot', + 'CC_Base_R_ToeBase', + 'CC_Base_L_ToeBase', + ] + +SMPLX_JOINT_NAMES = [ + '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', + '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' +] + +# SMPL_MODEL_FOLDER = '/home/jiangnan/AHOI_cvpr/smpl_models' +SMPL_MODEL_FOLDER = '/home/jiangnan/SyntheticHSI/smpl_models' + +rest_pelvis = np.matrix([[0.0000e+00, 0.0000e+00, 0.0000e+00], + [5.6144e-02, -9.4542e-02, -2.3475e-02], + [-5.7870e-02, -1.0517e-01, -1.6559e-02]]) +pelvis_shift = [0.001144, -0.366919, 0.012666] + +relaxed_hand_pose = np.array([0.11168, 0.04289, -0.41644, + 0.10881, -0.06599, -0.75622, + -0.09639, -0.09092, -0.18846, + -0.1181, 0.05094, -0.52958, + -0.1437, 0.05524, -0.70486, + -0.01918, -0.09234, -0.33791, + -0.45703, -0.19628, -0.62546, + -0.21465, -0.066, -0.50689, + -0.36972, -0.06034, -0.07949, + -0.14187, -0.08585, -0.63553, + -0.30334, -0.05788, -0.63139, + -0.17612, -0.13209, -0.37335, + 0.85096, 0.27692, -0.09155, + -0.49984, 0.02656, 0.05288, + 0.53556, 0.04596, -0.27736, + 0.11168, -0.04289, 0.41644, + 0.10881, 0.06599, 0.75622, + -0.09639, 0.09092, 0.18846, + -0.1181, -0.05094, 0.52958, + -0.1437, -0.05524, 0.70486, + -0.01918, 0.09234, 0.33791, + -0.45703, 0.19628, 0.62546, + -0.21465, 0.066, 0.50689, + -0.36972, 0.06034, 0.07949, + -0.14187, 0.08585, 0.63553, + -0.30334, 0.05788, 0.63139, + -0.17612, 0.13209, 0.37335, + 0.85096, -0.27692, 0.09155, + -0.49984, -0.02656, -0.05288, + 0.53556, -0.04596, 0.27736]).astype(np.float32) \ No newline at end of file diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d9566179540abb09860b64b169734469f711155a --- /dev/null +++ b/datasets/__init__.py @@ -0,0 +1 @@ +__all__ = ['trumans'] \ No newline at end of file diff --git a/datasets/__pycache__/__init__.cpython-39.pyc b/datasets/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..340243ed1cba716dcd294607cd6a203f5f8dbde8 Binary files /dev/null and b/datasets/__pycache__/__init__.cpython-39.pyc differ diff --git a/datasets/__pycache__/trumans.cpython-39.pyc b/datasets/__pycache__/trumans.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..86e06ff80ef2cf4be3ad7e3f6d97d05dba05f9c6 Binary files /dev/null and b/datasets/__pycache__/trumans.cpython-39.pyc differ diff --git a/datasets/trumans.py b/datasets/trumans.py new file mode 100644 index 0000000000000000000000000000000000000000..a77677b619b50d4339f781f14233e05065dadbe1 --- /dev/null +++ b/datasets/trumans.py @@ -0,0 +1,228 @@ +import os +import torch +import numpy as np +from scipy.spatial.transform import Rotation as R +from torch.utils.data import Dataset, DataLoader, Subset + + +class TrumansDataset(Dataset): + 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): + self.device = device + self.train = train + self.load_scene = load_scene + self.load_action = load_action + + # self.body_pose = np.load(os.path.join(folder, 'human_pose.npy')) + # self.transl = np.load(os.path.join(folder, 'human_transl.npy')) + # self.global_orient = np.load(os.path.join(folder, 'human_orient.npy')) + # self.motion_ind = np.load(os.path.join(folder, 'idx_start.npy')) + # self.joints = np.load(os.path.join(folder, 'human_joints.npy')) + # self.file_blend = np.load(os.path.join(folder, 'file_blend.npy')) + + self.seq_len=seq_len + self.step = step + self.batch_size = batch_size + + # if self.load_action: + # self.action_label = np.load(os.path.join(folder, 'action_label.npy')).astype(np.float32) + + if self.load_scene: + self.mesh_grid = mesh_grid + self.nb_voxels = nb_voxels + self.no_objects = no_objects + self.nb_voxels = nb_voxels + self.scene_occ = [] + self.scene_dict = {} + + self.scene_folder = os.path.join(folder, 'Scene') + # self.scene_flag = np.load(os.path.join(folder, 'scene_flag.npy')) + if not no_objects: + # self.object_flag = np.load(os.path.join(folder, 'object_flag.npy')) + # self.object_mat = np.load(os.path.join(folder, 'object_mat.npy')) + self.object_occ = {} + self.object_folder = os.path.join(folder, 'Object') + for file in sorted(os.listdir(self.object_folder)): + print(f"Loading object occupied coordinates {file}") + obj_name = file.replace('.npy', '') + self.object_occ[obj_name] = torch.from_numpy(np.load(os.path.join(self.object_folder, file))).to(device) + + for sid, file in enumerate(sorted(os.listdir(self.scene_folder))): + # if scene_name != '' and scene_name not in file: + # continue + print(f"{sid} Loading Scene Mesh {file}") + scene_occ = np.load(os.path.join(self.scene_folder, file)) + scene_occ = torch.from_numpy(scene_occ).to(device=device, dtype=bool) + self.scene_occ.append(scene_occ) + self.scene_dict[file] = sid + self.scene_occ = torch.stack(self.scene_occ) + + self.scene_grid_np = np.array([-3, 0, -4, 3, 2, 4, 300, 100, 400]) + self.scene_grid_torch = torch.tensor([-3, 0, -4, 3, 2, 4, 300, 100, 400]).to(device) + self.batch_id = torch.linspace(0, batch_size - 1, batch_size).tile((nb_voxels ** 3, 1)).T\ + .reshape(-1, 1).to(device=device, dtype=torch.long) + self.batch_id_obj = torch.linspace(0, batch_size - 1, batch_size).tile((9000, 1)).T \ + .reshape(-1, 1).to(device=device, dtype=torch.long) + + # TODO CHANGE STEP + norm = np.load(os.path.join(folder, 'norm.npy'), allow_pickle=True).item()[f'{seq_len, 3}'] + self.min = norm[0].astype(np.float32) + self.max = norm[1].astype(np.float32) + self.min_torch = torch.tensor(self.min).to(device) + self.max_torch = torch.tensor(self.max).to(device) + + + def add_object_points(self, points, occ): + points = points.reshape(-1, 3) + voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:]) + voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size) + voxel = voxel.to(dtype=torch.long) + # voxel = rearrange(voxel, 'b p c -> (b p) c') + lb = torch.all(voxel >= 0, dim=-1) + ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1) + in_bound = torch.logical_and(lb, ub) + # voxel = torch.cat([self.batch_id_obj, voxel], dim=-1) + voxel = voxel[in_bound] + occ[0, voxel[:, 0], voxel[:, 1], voxel[:, 2]] = True + + def get_occ_for_points(self, points, obj_locs, scene_flag): + + #TODO + + # points_new = points.reshape(-1, 3) + # center_xz = points_new[:, [0, 2]].mean(axis=0) + # if torch.norm(center_xz) > 0.: + # occ_for_points = torch.load('occ_for_points_at_clear_space.pt').to(points.device) + # return occ_for_points + + + if isinstance(scene_flag, str): + for k, v in self.scene_dict.items(): + if scene_flag in k: + scene_flag = [v] + break + batch_size = points.shape[0] + seq_len = points.shape[1] + points = points.reshape(-1, 3) + voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:]) + voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size) + voxel = voxel.to(dtype=torch.long) + # voxel = rearrange(voxel, 'b p c -> (b p) c') + lb = torch.all(voxel >= 0, dim=-1) + ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1) + in_bound = torch.logical_and(lb, ub) + voxel[torch.logical_not(in_bound)] = 0 + voxel = torch.cat([self.batch_id, voxel], dim=1) + occ = self.scene_occ[scene_flag] + + #TODO + + # occ[:] = False + # occ[:, :, 0, :] = True + + # import cv2 + # img = occ[0, :, 10, :].detach().cpu().numpy() + # im = np.zeros((300, 400)) + # im[img] = 255 + # cv2.imwrite('gray.jpg', im.T) + if obj_locs: + for obj_name, obj_loc in obj_locs.items(): + obj_points = self.object_occ[obj_name].clone() + obj_points[:, 0] += obj_loc['x'] + obj_points[:, 2] += obj_loc['z'] + # import pdb + # pdb.set_trace() + self.add_object_points(obj_points, occ) + occ_for_points = occ[voxel[:, 0], voxel[:, 1], voxel[:, 2], voxel[:, 3]] + occ_for_points[torch.logical_not(in_bound)] = True + occ_for_points = occ_for_points.reshape(batch_size, seq_len, -1) + + # torch.save(occ_for_points, 'occ_for_points_at_clear_space.pt') + + # occ_for_points = torch.ones(batch_size, seq_len, 22).to('cuda') + + + return occ_for_points + + def create_meshgrid(self, batch_size=1): + bbox = self.mesh_grid + size = (self.nb_voxels, self.nb_voxels, self.nb_voxels) + x = torch.linspace(bbox[0], bbox[1], size[0]) + y = torch.linspace(bbox[2], bbox[3], size[1]) + z = torch.linspace(bbox[4], bbox[5], size[2]) + xx, yy, zz = torch.meshgrid(x, y, z, indexing='ij') + grid = torch.stack([xx, yy, zz], dim=-1).reshape(-1, 3) + grid = grid.repeat(batch_size, 1, 1) + + # aug_z = 0.75 + torch.rand(batch_size, 1) * 0.35 + # grid[:, :, 2] = grid[:, :, 2] * aug_z + + return grid + + + @staticmethod + def combine_mesh(vert_list, face_list): + assert len(vert_list) == len(face_list) + verts = None + faces = None + for v, f in zip(vert_list, face_list): + if verts is None: + verts = v + faces = f + else: + f = f + verts.shape[0] + verts = torch.cat([verts, v]) + faces = torch.cat([faces, f]) + + return verts, faces + + @staticmethod + def transform_mesh(vert_list, trans_mats): + assert len(vert_list) == len(trans_mats) + vert_list_new = [] + for v, m in zip(vert_list, trans_mats): + v = v @ m[:3, :3].T + m[:3, 3] + vert_list_new.append(v) + vert_list_new = torch.stack(vert_list_new) + + return vert_list_new + + def __len__(self): + return len(self.motion_ind) + + + def normalize(self, data): + shape_orig = data.shape + data = data.reshape((-1, 3)) + # data = (data - self.mean) / self.std + data = -1. + 2. * (data - self.min) / (self.max - self.min) + data = data.reshape(shape_orig) + + return data + + def normalize_torch(self, data): + shape_orig = data.shape + data = data.reshape((-1, 3)) + # data = (data - self.mean) / self.std + data = -1. + 2. * (data - self.min_torch) / (self.max_torch - self.min_torch) + data = data.reshape(shape_orig) + + return data + + def denormalize(self, data): + shape_orig = data.shape + data = data.reshape((-1, 3)) + # data = data * self.std + self.mean + data = (data + 1.) * (self.max - self.min) / 2. + self.min + data = data.reshape(shape_orig) + + return data + + def denormalize_torch(self, data): + shape_orig = data.shape + data = data.reshape((-1, 3)) + # data = data * self.std + self.mean + import pdb + data = (data + 1.) * (self.max_torch - self.min_torch) / 2. + self.min_torch + data = data.reshape(shape_orig) + + return data \ No newline at end of file diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ab5495451781859b35cec91529943df441a69b85 --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,2 @@ + +__all__ = ['synhsi', 'joints_to_smplx', 'goal_model', 'imos_model'] \ No newline at end of file diff --git a/models/__pycache__/__init__.cpython-39.pyc b/models/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a7e33ba31fa7a14e50895f9432e29acf7dba2a65 Binary files /dev/null and b/models/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/__pycache__/joints_to_smplx.cpython-39.pyc b/models/__pycache__/joints_to_smplx.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..018478f5ce20e4143515004fb395cebddf65c6b5 Binary files /dev/null and b/models/__pycache__/joints_to_smplx.cpython-39.pyc differ diff --git a/models/__pycache__/synhsi.cpython-39.pyc b/models/__pycache__/synhsi.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..977ad1b8b949dee7f343adbd46f7df616bc80ff5 Binary files /dev/null and b/models/__pycache__/synhsi.cpython-39.pyc differ diff --git a/models/joints_to_smplx.py b/models/joints_to_smplx.py new file mode 100644 index 0000000000000000000000000000000000000000..91d1cfb64a0a040892bfd84a617ad6a4b523a22b --- /dev/null +++ b/models/joints_to_smplx.py @@ -0,0 +1,124 @@ +import torch +import smplx +from constants import * +from scipy.interpolate import interp1d +from torch import nn, einsum +import pytorch3d as T + + +class JointsToSMPLX(nn.Module): + def __init__(self, input_dim, output_dim, hidden_dim, **kwargs): + super().__init__() + self.layers = nn.Sequential( + nn.Linear(input_dim, hidden_dim), + nn.BatchNorm1d(hidden_dim), + nn.ReLU(), + # nn.Linear(hidden_dim, hidden_dim), + # nn.BatchNorm1d(hidden_dim), + # nn.ReLU(), + nn.Linear(hidden_dim, hidden_dim), + nn.BatchNorm1d(hidden_dim), + nn.ReLU(), + nn.Linear(hidden_dim, output_dim), + ) + + def forward(self, x): + return self.layers(x) + + +def optimize_smpl(pose_pred, joints, joints_ind, hand_pca=45): + device = joints.device + len = joints.shape[0] + + smpl_model = smplx.create('./smpl_models', model_type='smplx', + gender='male', ext='npz', + num_betas=10, + use_pca=False, + create_global_orient=True, + create_body_pose=True, + create_betas=True, + create_left_hand_pose=True, + create_right_hand_pose=True, + create_expression=True, + create_jaw_pose=True, + create_leye_pose=True, + create_reye_pose=True, + create_transl=True, + batch_size=len, + ).to(device) + smpl_model.eval() + + # 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) + joints = joints.reshape(len, -1, 3) + torch.tensor(pelvis_shift).to(device) + pose_input = torch.nn.Parameter(pose_pred.detach(), requires_grad=True) + transl = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], 3).to(device), requires_grad=True) + # left_hand = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], hand_pca).to(device), requires_grad=True) + # right_hand = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], hand_pca).to(device), requires_grad=True) + left_hand = torch.from_numpy(relaxed_hand_pose[:45].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device) + right_hand = torch.from_numpy(relaxed_hand_pose[45:].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device) + optimizer = torch.optim.Adam(params=[pose_input, transl], lr=0.05) + loss_fn = nn.MSELoss() + vertices_output = None + for step in range(100): + smpl_output = smpl_model(transl=transl, body_pose=pose_input[:, 3:], global_orient=pose_input[:, :3], return_verts=True, + left_hand_pose=left_hand,# @ left_hand_components[:hand_pca], + right_hand_pose=right_hand,# @ right_hand_components[:hand_pca], + ) + joints_output = smpl_output.joints[:, joints_ind].reshape(len, -1, 3) + vertices_output = smpl_output.vertices[:, ::10].detach().cpu().numpy() + loss = loss_fn(joints[:, :], joints_output[:, :]) + # loss = torch.mean((joints - joints_output) ** 2 * weights) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + print(loss.item()) + + + + #left_hand = left_hand @ left_hand_components[:hand_pca] + #right_hand = right_hand @ right_hand_components[:hand_pca] + + return pose_input.detach().cpu().numpy(), transl.detach().cpu().numpy(), left_hand.detach().cpu().numpy(), right_hand.detach().cpu().numpy(), vertices_output + + +def joints_to_smpl(model, joints, joints_ind, interp_s): + joints = interpolate_joints(joints, scale=interp_s) + # joints = interpolate_joints(joints, scale=0.33) + # joints = interpolate_joints(joints, scale=interp_s * 3) + input_len = joints.shape[0] + joints = joints.reshape(input_len, -1, 3) + joints = joints.permute(1, 0, 2) + trans_np = joints[0].detach().cpu().numpy() + joints = joints - joints[0] + joints = joints.permute(1, 0, 2) + joints = joints.reshape(input_len, -1) + pose_pred = model(joints) + pose_pred = pose_pred.reshape(-1, 6) + pose_pred = T.matrix_to_axis_angle(T.rotation_6d_to_matrix(pose_pred)).reshape(input_len, -1) + # pose_pred = pose_pred[:seq_len] + pose_output, transl, left_hand, right_hand, vertices = optimize_smpl(pose_pred, joints, joints_ind) + + transl = trans_np - np.array(pelvis_shift) + transl + + vertices = vertices + transl.reshape(-1, 1, 3) + + + return pose_output, transl, left_hand, right_hand, vertices + + +def interpolate_joints(joints, scale): + if scale == 1: + return joints + device = joints.device + joints = joints.detach().cpu().numpy() + in_len = joints.shape[0] + out_len = int(in_len * scale) + joints = joints.reshape(in_len, -1) + x = np.array(range(in_len)) + xnew = np.linspace(0, in_len - 1, out_len) + f = interp1d(x, joints, axis=0) + joints_new = f(xnew) + joints_new = torch.from_numpy(joints_new).to(device).float() + + return joints_new \ No newline at end of file diff --git a/models/synhsi.py b/models/synhsi.py new file mode 100644 index 0000000000000000000000000000000000000000..3c74622ca8d69363108446b51aa5cc2634bfb2a0 --- /dev/null +++ b/models/synhsi.py @@ -0,0 +1,444 @@ +import math +import pdb + +import torch +from torch import nn +import torch.nn.functional as F +from vit_pytorch import ViT +from tqdm import tqdm +from utils import * + + +class Sampler: + def __init__(self, device, mask_ind, emb_f, batch_size, seq_len, channel, fix_mode, timesteps, fixed_frame, **kwargs): + self.device = device + self.mask_ind = mask_ind + self.emb_f = emb_f + self.batch_size = batch_size + self.seq_len = seq_len + self.channel = channel + self.fix_mode = fix_mode + self.timesteps = timesteps + self.fixed_frame = fixed_frame + self.get_scheduler() + + def set_dataset_and_model(self, dataset, model): + self.dataset = dataset + if dataset.load_scene: + self.grid = dataset.create_meshgrid(batch_size=self.batch_size).to(self.device) + self.model = model + + + def get_scheduler(self): + betas = linear_beta_schedule(timesteps=self.timesteps) + + # define alphas + alphas = 1. - betas + alphas_cumprod = torch.cumprod(alphas, axis=0) + alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0) + self.sqrt_recip_alphas = torch.sqrt(1.0 / alphas) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod) + self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + self.posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + self.betas = betas + + def q_sample(self, x_start, t, noise): + if noise is None: + noise = torch.randn_like(x_start) + sqrt_alphas_cumprod_t = extract(self.sqrt_alphas_cumprod, t, x_start.shape) + sqrt_one_minus_alphas_cumprod_t = extract( + self.sqrt_one_minus_alphas_cumprod, t, x_start.shape + ) + return sqrt_alphas_cumprod_t * x_start + sqrt_one_minus_alphas_cumprod_t * noise + + + def p_losses(self, x_start, obj_points, mat, scene_flag, mask, t, action_label, noise=None, loss_type='huber'): + if noise is None: + noise = torch.randn_like(x_start) + + noise[mask] = 0. + + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + + if self.dataset.load_scene: + with torch.no_grad(): + x_orig = transform_points(self.dataset.denormalize_torch(x_noisy), mat) + mat_for_query = mat.clone() + target_ind = self.mask_ind if self.mask_ind != -1 else 0 + mat_for_query[:, :3, 3] = x_orig[:, self.emb_f, target_ind * 3: target_ind * 3 + 3] + mat_for_query[:, 1, 3] = 0 + query_points = transform_points(self.grid, mat_for_query) + + occ = self.dataset.get_occ_for_points(query_points, obj_points, scene_flag) + nb_voxels = self.dataset.nb_voxels + occ = occ.reshape(-1, nb_voxels, nb_voxels, nb_voxels).float() + + # import trimesh + # print(mat[0]) + # grid_np = self.grid[0].detach().cpu().numpy().reshape((-1, 3)) + # occ_np = occ[0].detach().cpu().numpy().reshape((-1)) + # points = grid_np[occ_np > 0.5] + # pcd_trimesh = trimesh.PointCloud(vertices=points) + # scene = trimesh.Scene([pcd_trimesh, trimesh.creation.axis(origin_color=[0, 0, 0])]) + # scene.show() + + occ = occ.permute(0, 2, 1, 3) + else: + occ = None + + # x_noisy = torch.cat([x_noisy, occ], dim=-1).detach() + + predicted_noise = self.model(x_noisy, occ, t, action_label, mask) + + mask_inv = torch.logical_not(mask) + + if loss_type == 'l1': + loss = F.l1_loss(noise[mask_inv], predicted_noise[mask_inv]) + elif loss_type == 'l2': + loss = F.mse_loss(noise[mask_inv], predicted_noise[mask_inv]) + elif loss_type == "huber": + loss = F.smooth_l1_loss(noise[mask_inv], predicted_noise[mask_inv]) + else: + raise NotImplementedError() + + return loss + + @torch.no_grad() + def p_sample_loop(self, fixed_points, obj_locs, mat, scene, goal, action_label): + device = next(self.model.parameters()).device + shape = (self.batch_size, self.seq_len, self.channel) + points = torch.randn(shape, device=device) # + torch.tensor([0., 0.3, 0.] * 22, device=device) + + if self.fix_mode: + self.set_fixed_points(points, goal, fixed_points, mat, joint_id=self.mask_ind, fix_mode=True, fix_goal=True) + imgs = [] + occs = [] + + if self.dataset.load_scene: + x_orig = transform_points(self.dataset.denormalize_torch(points), mat) + mat_for_query = mat.clone() + target_ind = self.mask_ind if self.mask_ind != -1 else 0 + mat_for_query[:, :3, 3] = x_orig[:, self.emb_f, target_ind * 3: target_ind * 3 + 3] + mat_for_query[:, 1, 3] = 0 + query_points = transform_points(self.grid, mat_for_query) + occ = self.dataset.get_occ_for_points(query_points, obj_locs, scene) + nb_voxels = self.dataset.nb_voxels + occ = occ.reshape(-1, nb_voxels, nb_voxels, nb_voxels).float() + + # import trimesh + # print('\n', mat[0]) + # grid_np = self.grid[0].detach().cpu().numpy().reshape((-1, 3)) + # occ_np = occ[0].detach().cpu().numpy().reshape((-1)) + # pointcloud = grid_np[occ_np > 0.5] + # pcd_trimesh = trimesh.PointCloud(vertices=pointcloud) + # np.save('/home/jiangnan/SyntheticHSI/Paper/Teaser/occ.npy', pointcloud) + # scene = trimesh.Scene([pcd_trimesh, trimesh.creation.axis(origin_color=[0, 0, 0])]) + # scene.show() + + occ = occ.permute(0, 2, 1, 3) + + else: + occ = None + + for i in tqdm(reversed(range(0, self.timesteps)), desc='sampling loop time step', total=self.timesteps): + model_used = self.model + # if s < 3 or (s == 3 and i < 5) or i < 3: + # model_used = model_fix + # else: + # model_used = model + points, occ = self.p_sample(model_used, points, fixed_points, goal, None, mat, occ, + torch.full((self.batch_size,), i, device=device, dtype=torch.long), i, action_label, self.mask_ind, + self.emb_f, self.fix_mode) + if self.fix_mode: + self.set_fixed_points(points, goal, fixed_points, mat, joint_id=self.mask_ind, fix_mode=True, fix_goal=True) + # set_fixed_points(points, goal, mat, joint_id=mask_ind) + # # t = torch.ones(b, device=device, dtype=torch.int64) * i + # if fixed_points is not None: + # points[:, :fixed_points.shape[1], :] = fixed_points # q_sample(fixed_points, t, None, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod) + + points_orig = transform_points(self.dataset.denormalize_torch(points), mat) + imgs.append(points_orig) + if occ is not None: + occs.append(occ.cpu().numpy()) + return imgs, occs + + @torch.no_grad() + def p_sample(self, model, x, fixed_points, goal, obj_points, mat, occ, t, t_index, action_label, mask_ind, emb_f, + fix_mode, no_scene=False): + betas_t = extract(self.betas, t, x.shape) + sqrt_one_minus_alphas_cumprod_t = extract( + self.sqrt_one_minus_alphas_cumprod, t, x.shape + ) + sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape) + + # Equation 11 in the paper + # Use our model (noise predictor) to predict the mean + + + # joints_orig = transform_points(synhsi_dataset.denormalize_torch(x), mat) + # occ = synhsi_dataset.get_occ_for_points(joints_orig, obj_points, scene) + # x_occ = torch.cat([x, occ], dim=-1).detach() + + model_mean = sqrt_recip_alphas_t * ( + x - betas_t * model(x, occ, t, action_label, mask=None) / sqrt_one_minus_alphas_cumprod_t + ) + # model_mean_noact = sqrt_recip_alphas_t * ( + # x - betas_t * model(x, occ, t, action_label, mask=None, no_action=True) / sqrt_one_minus_alphas_cumprod_t + # ) + # model_mean = model_mean_noact + (model_mean - model_mean_noact) * 10 + if not fix_mode: + self.set_fixed_points(model_mean, goal, fixed_points, mat, joint_id=mask_ind, fix_mode=True, fix_goal=False) + + if t_index == 0: + return model_mean, occ + else: + posterior_variance_t = extract(self.posterior_variance, t, x.shape) + noise = torch.randn_like(x) + # Algorithm 2 line 4: + return model_mean + torch.sqrt(posterior_variance_t) * noise, occ + + # Algorithm 2 (including returning all images) + + + def set_fixed_points(self, img, goal, fixed_points, mat, joint_id=0, fix_mode=False, fix_goal=True): + # if joint_id != 0: + # goal_len = 2 + goal_len = goal.shape[1] + # goal_batch = goal.reshape(1, 1, 3).repeat(img.shape[0], 1, 1) + goal = self.dataset.normalize_torch(transform_points(goal, torch.inverse(mat))) + # img[:, -1, joint_id * 3: joint_id * 3 + 3] = goal_batch[:, 0] + if fix_goal: + img[:, -goal_len:, joint_id * 3] = goal[:, :, 0] + if joint_id != 0: + img[:, -goal_len:, joint_id * 3 + 1] = goal[:, :, 1] + img[:, -goal_len:, joint_id * 3 + 2] = goal[:, :, 2] + + if fixed_points is not None and fix_mode: + img[:, :fixed_points.shape[1], :] = fixed_points + + +class Unet(nn.Module): + def __init__( + self, + dim_model, + num_heads, + num_layers, + dropout_p, + dim_input, + dim_output, + nb_voxels=None, + free_p=0.1, + nb_actions=0, + ac_type='', + no_scene=False, + no_action=False, + **kwargs + ): + super().__init__() + + # INFO + self.model_type = "Transformer" + self.dim_model = dim_model + self.nb_actions = nb_actions + self.ac_type = ac_type + self.no_scene = no_scene + self.no_action = no_action + + # LAYERS + if not no_scene: + self.scene_embedding = ViT( + image_size=nb_voxels, + patch_size=nb_voxels // 4, + channels=nb_voxels, + num_classes=dim_model, + dim=1024, + depth=6, + heads=16, + mlp_dim=2048, + dropout=0.1, + emb_dropout=0.1 + ) + self.free_p = free_p + self.positional_encoder = PositionalEncoding( + dim_model=dim_model, dropout_p=dropout_p, max_len=5000 + ) + self.embedding_input = nn.Linear(dim_input, dim_model) + self.embedding_output = nn.Linear(dim_output, dim_model) + + # self.embedding_action = nn.Parameter(torch.randn(16, dim_model)) + + if not no_action and nb_actions > 0: + if self.ac_type in ['last_add_first_token', 'last_new_token']: + self.embedding_action = ActionTransformerEncoder(action_number=nb_actions, + dim_model=dim_model, + nhead=num_heads // 2, + num_layers=num_layers, + dim_feedforward=dim_model, + dropout_p=dropout_p, + activation="gelu") + elif self.ac_type in ['all_add_token']: + self.embedding_action = nn.Sequential( + nn.Linear(nb_actions, dim_model), + nn.SiLU(inplace=False), + nn.Linear(dim_model, dim_model), + ) + + encoder_layer = nn.TransformerEncoderLayer(d_model=dim_model, + nhead=num_heads, + dim_feedforward=dim_model, + dropout=dropout_p, + activation="gelu") + + self.transformer = nn.TransformerEncoder(encoder_layer, + num_layers=num_layers + ) + # self.out = nn.Linear(dim_model, dim_output) + + self.out = nn.Linear(dim_model, dim_output) + + self.embed_timestep = TimestepEmbedder(self.dim_model, self.positional_encoder) + + def forward(self, x, cond, timesteps, action, mask, no_action=None): + + #TODO ActionFlag + # action[action[:, 0] != 0., 0] = 1. + + t_emb = self.embed_timestep(timesteps) # [1, b, d] + + if self.no_scene: + scene_emb = torch.zeros_like(t_emb) + else: + scene_emb = self.scene_embedding(cond).reshape(-1, 1, self.dim_model) + + if self.no_action or self.nb_actions == 0: + action_emb = torch.zeros_like(t_emb) + else: + if self.ac_type in ['all_add_token']: + action_emb = self.embedding_action(action) + elif self.ac_type in ['last_add_first_token', 'last_new_token']: + action_emb = self.embedding_action(action) + else: + raise NotImplementedError + + t_emb = t_emb.permute(1, 0, 2) + + free_ind = torch.rand(scene_emb.shape[0]).to(scene_emb.device) < self.free_p + scene_emb[free_ind] = 0. + # if mask is not None: + # x[free_ind][:, mask[0]] = 0. + + if self.ac_type in ['last_add_first_token', 'last_new_token']: + action_emb[free_ind] = 0. + scene_emb = scene_emb.permute(1, 0, 2) + action_emb = action_emb.permute(1, 0, 2) + + if self.ac_type in ['all_add_token', 'last_new_token']: + emb = t_emb + scene_emb + elif self.ac_type in ['last_add_first_token']: + emb = t_emb + scene_emb + action_emb + + x = x.permute(1, 0, 2) + x = self.embedding_input(x) * math.sqrt(self.dim_model) + if self.ac_type in ['all_add_token', 'last_add_first_token']: + x = torch.cat((emb, x), dim=0) + elif self.ac_type in ['last_new_token']: + x = torch.cat((emb, action_emb, x), dim=0) + + if self.ac_type in ['all_add_token']: + x[1:] = x[1:] + action_emb + + x = self.positional_encoder(x) + x = self.transformer(x) + if self.ac_type in ['all_add_token', 'last_add_first_token']: + output = self.out(x)[1:] + elif self.ac_type in ['last_new_token']: + output = self.out(x)[2:] + output = output.permute(1, 0, 2) + + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, dim_model, dropout_p, max_len): + super().__init__() + # Modified version from: https://pytorch.org/tutorials/beginner/transformer_tutorial.html + # max_len determines how far the position can have an effect on a token (window) + + # Info + self.dropout = nn.Dropout(dropout_p) + + # Encoding - From formula + pos_encoding = torch.zeros(max_len, dim_model) + positions_list = torch.arange(0, max_len, dtype=torch.float).reshape(-1, 1) # 0, 1, 2, 3, 4, 5 + division_term = torch.exp( + torch.arange(0, dim_model, 2).float() * (-math.log(10000.0)) / dim_model) # 1000^(2i/dim_model) + + # PE(pos, 2i) = sin(pos/1000^(2i/dim_model)) + pos_encoding[:, 0::2] = torch.sin(positions_list * division_term) + + # PE(pos, 2i + 1) = cos(pos/1000^(2i/dim_model)) + pos_encoding[:, 1::2] = torch.cos(positions_list * division_term) + + # Saving buffer (same as parameter without gradients needed) + pos_encoding = pos_encoding.unsqueeze(0).transpose(0, 1) + self.register_buffer("pos_encoding", pos_encoding) + + def forward(self, token_embedding: torch.tensor) -> torch.tensor: + # Residual connection + pos encoding + return self.dropout(token_embedding + self.pos_encoding[:token_embedding.size(0), :]) + + +class TimestepEmbedder(nn.Module): + def __init__(self, latent_dim, sequence_pos_encoder): + super().__init__() + self.latent_dim = latent_dim + self.sequence_pos_encoder = sequence_pos_encoder + + time_embed_dim = self.latent_dim + self.time_embed = nn.Sequential( + nn.Linear(self.latent_dim, time_embed_dim), + nn.SiLU(inplace=False), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + def forward(self, timesteps): + return self.time_embed(self.sequence_pos_encoder.pos_encoding[timesteps])#.permute(1, 0, 2) + + +class ActionTransformerEncoder(nn.Module): + def __init__(self, + action_number, + dim_model, + nhead, + num_layers, + dim_feedforward, + dropout_p, + activation="gelu") -> None: + super().__init__() + self.positional_encoder = PositionalEncoding( + dim_model=dim_model, dropout_p=dropout_p, max_len=5000 + ) + self.input_embedder = nn.Linear(action_number, dim_model) + encoder_layer = nn.TransformerEncoderLayer(d_model=dim_model, + nhead=nhead, + dim_feedforward=dim_feedforward, + dropout=dropout_p, + activation=activation) + self.transformer_encoder = nn.TransformerEncoder(encoder_layer, + num_layers=num_layers + ) + + def forward(self, x): + x = x.permute(1, 0, 2) + x = self.input_embedder(x) + x = self.positional_encoder(x) + x = self.transformer_encoder(x) + x = x.permute(1, 0, 2) + x = torch.mean(x, dim=1, keepdim=True) + return x + + diff --git a/not_used.py b/not_used.py new file mode 100644 index 0000000000000000000000000000000000000000..259c1897922b728ef717ced22f6ca0c8f562b01e --- /dev/null +++ b/not_used.py @@ -0,0 +1,4 @@ +import numpy as np + +test = np.random.randn(3,4,5) +print(0) \ No newline at end of file diff --git a/objects_occ/Background.npy b/objects_occ/Background.npy new file mode 100644 index 0000000000000000000000000000000000000000..7b6603dcc27f753e82586ba6aab6400b3d87bc5c --- /dev/null +++ b/objects_occ/Background.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3df6127677f16bc78d3193fe8a51fdde4e87e2651e1cb4e6935e348b2f28741 +size 12000128 diff --git a/objects_occ/background.blend b/objects_occ/background.blend new file mode 100644 index 0000000000000000000000000000000000000000..0348ccaaa7f5f3f0ef118b965a04e9cb0fe16f81 --- /dev/null +++ b/objects_occ/background.blend @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dde8176ebca147248b6ff88d2aaa2d2de3522d84ad99133509195ea265b8f4c6 +size 2747420 diff --git a/objects_occ/background.obj b/objects_occ/background.obj new file mode 100644 index 0000000000000000000000000000000000000000..c1d8217939b7c84c3af51fa7a5abe8b092423c4a --- /dev/null +++ b/objects_occ/background.obj @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8261b62db7767afa66e91c450af153c0eefc836d78b2a6b05dd1af74c6cee0b +size 65882377 diff --git a/objects_occ/basin.npy b/objects_occ/basin.npy new file mode 100644 index 0000000000000000000000000000000000000000..a72d30e9852d931164c27de81b002f67129f6a1a --- /dev/null +++ b/objects_occ/basin.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aad0216f00c4162730cc572623d69a86e7473ab5d2d040cf33a48a4a1ae46b08 +size 1605344 diff --git a/objects_occ/basin.obj b/objects_occ/basin.obj new 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24743 24742 24728 +f 24743 24729 24725 +f 24734 24744 24717 +f 24744 24745 24718 +f 24746 24719 24718 +f 24747 24720 24719 +f 24735 24712 24720 +f 24748 24744 24734 +f 24748 24749 24745 +f 24750 24746 24745 +f 24751 24747 24746 +f 24751 24750 24738 +f 24751 24739 24735 +f 24752 24748 24737 +f 24752 24753 24749 +f 24741 24753 24752 +f 24754 24750 24749 +f 24754 24753 24741 +f 24754 24742 24738 diff --git a/pytorch3d.py b/pytorch3d.py new file mode 100644 index 0000000000000000000000000000000000000000..51668b7c3b4183b13f0d8674393229410ae71ab2 --- /dev/null +++ b/pytorch3d.py @@ -0,0 +1,254 @@ +import functools + +import torch +import torch.nn.functional as F + + +def quaternion_to_matrix(quaternions): + r, i, j, k = torch.unbind(quaternions, -1) + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + +def _copysign(a, b): + signs_differ = (a < 0) != (b < 0) + return torch.where(signs_differ, -a, a) + + +def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + + +def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor: + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.") + + batch_dim = matrix.shape[:-2] + m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind( + matrix.reshape(*batch_dim, 9), dim=-1 + ) + + q_abs = _sqrt_positive_part( + torch.stack( + [ + 1.0 + m00 + m11 + m22, + 1.0 + m00 - m11 - m22, + 1.0 - m00 + m11 - m22, + 1.0 - m00 - m11 + m22, + ], + dim=-1, + ) + ) + + quat_by_rijk = torch.stack( + [ + torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), + torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), + torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), + torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), + ], + dim=-2, + ) + + quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1))) + + return quat_candidates[ + F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : + ].reshape(*batch_dim, 4) + + +def _axis_angle_rotation(axis: str, angle): + cos = torch.cos(angle) + sin = torch.sin(angle) + one = torch.ones_like(angle) + zero = torch.zeros_like(angle) + + if axis == "X": + R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) + if axis == "Y": + R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) + if axis == "Z": + R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) + + return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) + + +def euler_angles_to_matrix(euler_angles, convention: str): + if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: + raise ValueError("Invalid input euler angles.") + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1)) + return functools.reduce(torch.matmul, matrices) + + +def _angle_from_tan( + axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool +): + i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] + if horizontal: + i2, i1 = i1, i2 + even = (axis + other_axis) in ["XY", "YZ", "ZX"] + if horizontal == even: + return torch.atan2(data[..., i1], data[..., i2]) + if tait_bryan: + return torch.atan2(-data[..., i2], data[..., i1]) + return torch.atan2(data[..., i2], -data[..., i1]) + + +def _index_from_letter(letter: str): + if letter == "X": + return 0 + if letter == "Y": + return 1 + if letter == "Z": + return 2 + + +def matrix_to_euler_angles(matrix, convention: str): + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape f{matrix.shape}.") + i0 = _index_from_letter(convention[0]) + i2 = _index_from_letter(convention[2]) + tait_bryan = i0 != i2 + if tait_bryan: + central_angle = torch.asin( + matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) + ) + else: + central_angle = torch.acos(matrix[..., i0, i0]) + + o = ( + _angle_from_tan( + convention[0], convention[1], matrix[..., i2], False, tait_bryan + ), + central_angle, + _angle_from_tan( + convention[2], convention[1], matrix[..., i0, :], True, tait_bryan + ), + ) + return torch.stack(o, -1) + + +def standardize_quaternion(quaternions): + return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions) + + +def quaternion_raw_multiply(a, b): + aw, ax, ay, az = torch.unbind(a, -1) + bw, bx, by, bz = torch.unbind(b, -1) + ow = aw * bw - ax * bx - ay * by - az * bz + ox = aw * bx + ax * bw + ay * bz - az * by + oy = aw * by - ax * bz + ay * bw + az * bx + oz = aw * bz + ax * by - ay * bx + az * bw + return torch.stack((ow, ox, oy, oz), -1) + + +def quaternion_multiply(a, b): + ab = quaternion_raw_multiply(a, b) + return standardize_quaternion(ab) + + +def quaternion_invert(quaternion): + return quaternion * quaternion.new_tensor([1, -1, -1, -1]) + + +def quaternion_apply(quaternion, point): + if point.size(-1) != 3: + raise ValueError(f"Points are not in 3D, f{point.shape}.") + real_parts = point.new_zeros(point.shape[:-1] + (1,)) + point_as_quaternion = torch.cat((real_parts, point), -1) + out = quaternion_raw_multiply( + quaternion_raw_multiply(quaternion, point_as_quaternion), + quaternion_invert(quaternion), + ) + return out[..., 1:] + + +def axis_angle_to_matrix(axis_angle): + return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) + + +def matrix_to_axis_angle(matrix): + return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) + + +def axis_angle_to_quaternion(axis_angle): + angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) + half_angles = 0.5 * angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + quaternions = torch.cat( + [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 + ) + return quaternions + + +def quaternion_to_axis_angle(quaternions): + norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) + half_angles = torch.atan2(norms, quaternions[..., :1]) + angles = 2 * half_angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + return quaternions[..., 1:] / sin_half_angles_over_angles + + +def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: + a1, a2 = d6[..., :3], d6[..., 3:] + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + return torch.stack((b1, b2, b3), dim=-2) + + +def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: + return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c238cae9872ffc11d7f9204277eb343b1c9df7bd --- /dev/null +++ b/requirements.txt @@ -0,0 +1,52 @@ +antlr4-python3-runtime==4.9.3 +blinker==1.7.0 +certifi==2023.11.17 +charset-normalizer==3.3.2 +click==8.1.7 +einops==0.7.0 +filelock +Flask==3.0.0 +fsspec==2023.12.2 +future==1.0.0 +gmpy2 +hydra-core==1.3.2 +idna==3.6 +importlib-metadata==7.0.1 +itsdangerous==2.1.2 +Jinja2 +MarkupSafe +mpmath +networkx +numpy==1.26.3 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==8.9.2.26 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu12==2.18.1 +nvidia-nvjitlink-cu12==12.3.101 +nvidia-nvtx-cu12==12.1.105 +omegaconf==2.3.0 +packaging==23.2 +pillow==10.2.0 +pyglet==1.5.0 +PyYAML==6.0.1 +requests==2.31.0 +scipy==1.12.0 +smplx==0.1.28 +sympy +torch==1.11.0+cu113 +torchaudio==0.11.0+cu113 +torchvision==0.12.0+cu113 +tqdm==4.66.1 +trimesh==4.1.8 +triton==2.1.0 +typing_extensions +urllib3==2.1.0 +vit-pytorch==1.4.4 +Werkzeug==3.0.1 +zipp==3.17.0 diff --git a/sample_hsi.py b/sample_hsi.py new file mode 100644 index 0000000000000000000000000000000000000000..c7b70def0ecb9031597e16e323aa14b3409d01f1 --- /dev/null +++ b/sample_hsi.py @@ -0,0 +1,307 @@ +import os +import pdb +import pickle as pkl +from torch.utils.data import Dataset, DataLoader +# from omegaconf import DictConfig, OmegaConf +from scipy.spatial.transform import Rotation as R +from models.joints_to_smplx import joints_to_smpl, JointsToSMPLX +from utils import * +from constants import * +from datasets.trumans import TrumansDataset +from models.synhsi import Unet +# from hydra import compose, initialize +import yaml + + +ACT_TYPE = ['scene', 'grasp', 'artic', 'none'] + + +def convert_trajectory(trajectory): + trajectory_new = [[t['x'], t['y'], t['z']] for t in trajectory] + trajectory_new = np.array(trajectory_new) + return trajectory_new + + +def get_base_speed(cfg, trajectory, is_zup=True): + trajectory_layer = trajectory + if is_zup: + trajectory_layer = zup_to_yup(trajectory_layer) + trajectory2D = trajectory_layer[:, [0, 2]] + distance = np.sum(np.linalg.norm(trajectory2D[1:] - trajectory2D[:-1], axis=1)) + speed = trajectory_layer.shape[0] // distance + print('Base Speed:', speed, flush=True) + + return speed + + +def get_guidance(cfg, trajectory, samplers, act_type='none', speed=35): + trajectory_layer = trajectory + # trajectory_layer = zup_to_yup(trajectory_layer) + print(trajectory_layer.shape) + if cfg.action_type != 'pure_inter': + #TODO + midpoints = trajectory_layer[[0] * cfg.len_pre + list(range(0, len(trajectory_layer), speed)) + [-1] * (cfg.len_act + (1 if cfg.stay_and_act else 0))] + # midpoints[0, 0] = 1.4164 + # midpoints[0, 2] = 2.2544 + # midpoints = trajectory_layer[[0] * cfg.len_pre + [25, 50] + [50] + [70, 90]] + + else: + midpoints = trajectory_layer[[0] * cfg.len_pre + [0] + [0] * (cfg.len_act + 1 if cfg.stay_and_act else 0)] + midpoints = torch.tensor(midpoints).float().to(cfg.device) + max_step = midpoints.shape[0] - 1 + + mat_init = cfg.batch_size * [np.eye(4)] + mat_init = torch.from_numpy(np.stack(mat_init, axis=0)).float().to(cfg.device) + print(midpoints) + mat_init[:, 0, 3] = midpoints[0, 0] + mat_init[:, 2, 3] = midpoints[0, 2] + + dx = midpoints[cfg.len_pre + 1, 0] - midpoints[0, 0] + dz = midpoints[cfg.len_pre + 1, 2] - midpoints[0, 2] + + print(-np.arctan2(dx.item(), dz.item()), dx, dz) + + mat_rot_y = R.from_rotvec(np.array([0, np.arctan2(dx.item(), dz.item()), 0])).as_matrix() + # mat_rot_y = R.from_rotvec(np.array([0, np.arctan2(-1, -2), 0])).as_matrix() + mat_init[:, :3, :3] = torch.from_numpy(mat_rot_y).float().to(cfg.device) + + + # goal_list = torch.zeros((max_step, cfg.batch_size, cfg.dataset.seq_len, 3)).float().to(cfg.device) + goal_list = [] + action_label_list = [] + # action_label_list = torch.zeros((max_step, cfg.batch_size, cfg.dataset.seq_len, cfg.dataset.nb_actions)).float().to(cfg.device) + sampler_list = [] + + if act_type == 'none': + for s in range(max_step): + goal = torch.zeros((cfg.batch_size, 1, 3)).float().to(cfg.device) + goal[:, :] = midpoints[s + 1] + goal_list.append(goal) + if cfg.dataset.nb_actions > 0: + action_label = torch.zeros((cfg.batch_size, cfg.dataset.seq_len, cfg.dataset.nb_actions)).float().to(cfg.device) + action_label_list.append(action_label) + else: + action_label_list.append(None) + sampler_list.append(samplers['body']) + elif act_type == 'write': + midpoints = torch.from_numpy(trajectory_layer[::4]).float().to(cfg.device) + for s in range(midpoints.shape[0] // 16): + goal = torch.zeros((cfg.batch_size, 16, 3)).float().to(cfg.device) + goal[:, :] = midpoints[s * 16: (s + 1) * 16] + goal_list.append(goal) + if cfg.dataset.nb_actions > 0: + action_label = torch.zeros((cfg.batch_size, cfg.dataset.seq_len, cfg.dataset.nb_actions)).float().to(cfg.device) + action_label_list.append(action_label) + else: + action_label_list.append(None) + sampler_list.append(samplers['hand']) + elif act_type == 'scene': + for s in range(max_step): + goal = torch.zeros((cfg.batch_size, 1, 3)).float().to(cfg.device) + goal[:, :] = midpoints[s + 1] + goal_list.append(goal) + sampler_list.append(samplers['body']) + action_label = torch.zeros((cfg.batch_size, cfg.dataset.seq_len, cfg.dataset.nb_actions)).float().to(cfg.device) + if s > max_step - cfg.len_act: + action_label[:, :, cfg.action_id] = 1. + action_label_list.append(action_label) + elif act_type == 'grasp': + for s in range(max_step): + if s != max_step - cfg.len_act: + goal = torch.zeros((cfg.batch_size, 1, 3)).float().to(cfg.device) + goal[:, :] = midpoints[s + 1] + goal_list.append(goal) + else: + # grasp_goal = zup_to_yup(np.array([[-0.32, -3.36, 0.395], + # [-0.3, -3.2, 0.395], + # [-0.25, -3, 0.394], + # [-0.147, -3.0, 0.395]])).reshape((cfg.batch_size, -1, 3)) + + grasp_goal = zup_to_yup(np.array(trajectory['Object'])).reshape((cfg.batch_size, 1, 3)) + goal = torch.zeros((cfg.batch_size, 3, 3)).float().to(cfg.device) + goal[:, :] = torch.from_numpy(grasp_goal).float().to(cfg.device) + goal_list.append(goal) + + action_label = torch.zeros((cfg.batch_size, cfg.dataset.seq_len, cfg.dataset.nb_actions)).float().to(cfg.device) + if s < max_step - cfg.len_act: + sampler_list.append(samplers['body']) + elif s == max_step - cfg.len_act: + sampler_list.append(samplers['hand']) + else: + sampler_list.append(samplers['body']) + if cfg.action_id != -1: + action_label[:, :, cfg.action_id] = 1. + action_label_list.append(action_label) + elif act_type == 'pure_inter': + sampler_list += [samplers['body']] * (cfg.len_pre + cfg.len_act) + goal = torch.zeros((cfg.batch_size, 1, 3)).float().to(cfg.device) + goal[:, :] = midpoints[0] + goal_list += [goal] * (cfg.len_pre + + cfg.len_act) + action_label = torch.zeros((cfg.batch_size, cfg.dataset.seq_len, cfg.dataset.nb_actions)).float().to(cfg.device) + action_label_list += [action_label.clone()] * cfg.len_pre + action_label[:, :, cfg.action_id] = 2. + action_label_list += [action_label.clone()] * cfg.len_act + + + return mat_init, goal_list, action_label_list, sampler_list + + +def sample_step(cfg, mat, obj_locs, goal_list, action_label_list, sampler_list): + max_step = len(goal_list) + fixed_points = None + fixed_frame = 2 + points_all = [] + cnt_fixed_frame = 0 + cnt_seq_len = 0 + + + for s in range(max_step): + print('step', s) + + sampler = sampler_list[s] + if s != 0: + fixed_points = sampler.dataset.normalize_torch(transform_points(fixed_points, torch.inverse(mat))) + else: + if cfg.continue_last: + method_id = cfg.method_name.split('_')[-1] + method_name_last = cfg.method_name[:-1] + str(int(method_id) - 1) + mat = torch.from_numpy(np.load(os.path.join(cfg.exp_dir, f'{method_name_last}_mat.npy'))).to(sampler.device) + fixed_points = torch.from_numpy(np.load(os.path.join(cfg.exp_dir, f'{method_name_last}_fixed_points.npy'))).to(sampler.device) + fixed_points = sampler.dataset.normalize_torch(transform_points(fixed_points, torch.inverse(mat))) + + samples, occs = sampler.p_sample_loop(fixed_points, obj_locs, mat, cfg.scene_name, goal_list[s], action_label_list[s]) + + if 0 <= s < cfg.len_pre: + cnt_fixed_frame += sampler.fixed_frame + if 0 <= s < cfg.len_pre: + cnt_seq_len += cfg.dataset.seq_len + + points_gene = samples[-1] + points_gene_np = points_gene.reshape(cfg.batch_size, cfg.dataset.seq_len, -1, 3).cpu().numpy() + + if s == 0 or fixed_frame == 0: + #TODO + points_all.append(points_gene_np[:, fixed_frame - 1:]) + elif fixed_frame > 0: + points_all.append(points_gene_np[:, fixed_frame:]) + + # fixed_frame = 0 if s == max_step - 1 else sampler_list[s + 1].fixed_frame + fixed_frame = sampler_list[s].fixed_frame if s == max_step - 1 else sampler_list[s + 1].fixed_frame + + pelvis_new = points_gene[:, -fixed_frame, :9].cpu().numpy().reshape(cfg.batch_size, 3, 3) + trans_mats = np.repeat(np.eye(4)[np.newaxis, :, :], cfg.batch_size, axis=0) + for ip, pn in enumerate(pelvis_new): + _, ret_R, ret_t = rigid_transform_3D(np.matrix(pn), rest_pelvis, False) + ret_t[1] = 0.0 + rot_euler = R.from_matrix(ret_R).as_euler('zxy') + shift_euler = np.array([0, 0, rot_euler[2]]) + shift_rot_matrix2 = R.from_euler('zxy', shift_euler).as_matrix() + trans_mats[ip, :3, :3] = shift_rot_matrix2 + trans_mats[ip, :3, 3] = ret_t.reshape(-1) + mat = torch.from_numpy(trans_mats).to(device=cfg.device, dtype=torch.float32) + + if fixed_frame > 0: + fixed_points = points_gene[:, -fixed_frame:] + if s == max_step - 1: + print('Saved Mat and Fixed Points', flush=True) + # np.save(os.path.join(cfg.exp_dir, f'{cfg.method_name}_mat.npy'), mat.cpu().numpy()) + # np.save(os.path.join(cfg.exp_dir, f'{cfg.method_name}_fixed_points.npy'), fixed_points.cpu().numpy()) + + points_all = np.concatenate(points_all, axis=1) + points_all = points_all[:, cnt_seq_len - cnt_fixed_frame:] + + return points_all + + +def sample_wrapper(trajectory, obj_locs): + trajectory = convert_trajectory(trajectory) + # obj_locs = {key: [data[key]['x'], data['key']['z']] for key in data.keys() if 'trajectory' not in key} + + # cfg = compose(config_name="config_sample_synhsi") + with open('config/config_sample_synhsi.yaml') as f: + cfg = yaml.safe_load(f) + cfg = dotDict(cfg) + + + # @hydra.main(version_base=None, config_path="../config", config_name="config_sample_synhsi") + # def sample(cfg) -> None: + print(cfg) + + # seed_everything(100) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # model_joints_to_smplx = init_model(cfg.model.model_smplx, device=device, eval=True) + model_joints_to_smplx = JointsToSMPLX(**cfg.model.model_smplx) + model_joints_to_smplx.load_state_dict(torch.load(cfg.model.model_smplx.ckpt)) + model_joints_to_smplx.to(device) + model_joints_to_smplx.eval() + + # model_body = init_model(cfg.model.synhsi_body, device=device, eval=True) + model_body = Unet(**cfg.model.synhsi_body) + model_body.load_state_dict(torch.load(cfg.model.synhsi_body.ckpt)) + model_body.to(device) + model_body.eval() + # model_hand = init_model(cfg.model.synhsi_hand, device=device, eval=True) + + # synhsi_dataset = hydra.utils.instantiate(cfg.dataset) + synhsi_dataset = TrumansDataset(**cfg.dataset) + + sampler_body = hydra.utils.instantiate(cfg.sampler.pelvis) + # sampler_hand = hydra.utils.instantiate(cfg.sampler.right_hand) + sampler_body.set_dataset_and_model(synhsi_dataset, model_body) + # sampler_hand.set_dataset_and_model(None, model_hand) + + samplers = {'body': sampler_body, 'hand': None} + + # for scene_name in ['N3OpenArea']: + + # trajectory = np.load(os.path.join(cfg.test_dir, cfg.exp_name, f'trajectories.npy'), allow_pickle=True).item() + # cfg.scene_name = scene_name + # cfg.action_type = trajectory['action_type'] + # if 'action_id' in trajectory.keys(): + # cfg.action_id = trajectory['action_id'] + + # GP_LAYERS = ['GP_Layer'] + method_name = cfg.method_name + lid = 0 + + base_speed = get_base_speed(cfg, trajectory, is_zup=False) + + mat, goal_list, action_label_list, sampler_list = get_guidance(cfg, trajectory, samplers, act_type=cfg.action_type, + speed=int(0.6 * base_speed)) + points_all = sample_step(cfg, mat, obj_locs, goal_list, action_label_list, sampler_list) + + # os.makedirs(cfg.exp_dir, exist_ok=True) + vertices = None + for i in range(cfg.batch_size): + keypoint_gene_torch = torch.from_numpy(points_all[i]).reshape(-1, cfg.dataset.nb_joints * 3).to(device) + pose, transl, left_hand, right_hand, vertices = joints_to_smpl(model_joints_to_smplx, keypoint_gene_torch, cfg.dataset.joints_ind, cfg.interp_s) + # output_data = {'transl': transl, 'body_pose': pose[:, 3:], 'global_orient': pose[:, :3], + # 'id': 0} + # print(output_data) + # with open(os.path.join(cfg.exp_dir, f'{method_name}_{lid}_{i}.pkl'), 'wb') as f: + # pkl.dump(output_data, f) + + # vertices = np.load('/home/jiangnan/SyntheticHSI/Gradio_demo/vertices.npy', allow_pickle=True) + # np.save('/home/jiangnan/SyntheticHSI/Gradio_demo/vertices.npy', vertices) + + return vertices.tolist() + + + + # v = sample() + # + # + # return v + + + + +# def load_dataset_meta(cfg): +# metas = np.load(cfg.) + +# if __name__ == '__main__': + # OmegaConf.register_resolver("times_three", times_three) + # OmegaConf.register_new_resolver("times", lambda x, y: int(x) * int(y)) + # sample() diff --git a/smpl_models/smplx/SMPLX_MALE.npz b/smpl_models/smplx/SMPLX_MALE.npz new file mode 100644 index 0000000000000000000000000000000000000000..41fdef3ff2784eb06bb479ebf5fb6887aafbc183 --- /dev/null +++ b/smpl_models/smplx/SMPLX_MALE.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab318e3f37d2bfaae26abf4e6fab445c2a610e1d63714794d60379cc263bc2a5 +size 108753445 diff --git a/smpl_models/smplx/version.txt b/smpl_models/smplx/version.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac6ab56466944d45e30add02cd7607b768a66b5a --- /dev/null +++ b/smpl_models/smplx/version.txt @@ -0,0 +1 @@ +This is "Version 1.0" of SMPL-X. diff --git a/static/background.glb b/static/background.glb new file mode 100644 index 0000000000000000000000000000000000000000..d983b633d9fcf1c02609901a0d6609fb5e66e09f Binary files /dev/null and b/static/background.glb differ diff --git a/static/basin.glb b/static/basin.glb new file mode 100644 index 0000000000000000000000000000000000000000..bb3a73af9451e55bfa0118f4c010c1644bbf5d15 Binary files /dev/null and b/static/basin.glb differ diff --git a/static/bed.glb b/static/bed.glb new file mode 100644 index 0000000000000000000000000000000000000000..ad6e22dd666a7299369bfac97bc85e00a8a7d7be Binary files /dev/null and b/static/bed.glb differ diff --git a/static/flower.glb b/static/flower.glb new file mode 100644 index 0000000000000000000000000000000000000000..374a05ffb68901f12b71086985f86377772ec395 Binary files /dev/null and b/static/flower.glb differ diff --git a/static/index.js b/static/index.js new file mode 100644 index 0000000000000000000000000000000000000000..0f1c4f176e64974cf13baffdb414881f4fc8eec6 --- /dev/null +++ b/static/index.js @@ -0,0 +1,272 @@ +import * as THREE from 'https://unpkg.com/three@0.127.0/build/three.module.js'; +import {OrbitControls} from 'https://unpkg.com/three@0.127.0/examples/jsm/controls/OrbitControls.js'; +import {GLTFLoader} from 'https://unpkg.com/three@0.127.0/examples/jsm/loaders/GLTFLoader.js'; +import {DragControls} from 'https://unpkg.com/three@0.127.0/examples/jsm/controls/DragControls.js' + +let scene, camera, renderer, cube, isDrawing, pointerDown; +let frameIndex = 0; +let pointCloudData; +let showAnimation = false; +let pointCloud +const allModelsUrl = ['background.glb', 'basin.glb', 'bed.glb', 'flower.glb', 'kitchen_chair_1.glb', 'kitchen_chair_2.glb', 'office_chair.glb', 'sofa.glb', 'table.glb', 'wc.glb']; +// const allModelsUrl = ['table.glb'] +const allModels_dict = {} +const allModels_list = [] + +function init() { + scene = new THREE.Scene(); + camera = new THREE.PerspectiveCamera(45, window.innerWidth / window.innerHeight, 0.1, 100); + camera.position.set(9, 6, -9); + scene.add(camera) + + renderer = new THREE.WebGLRenderer({alpha: true, antialias: true}); + renderer.setSize(window.innerWidth, window.innerHeight); + renderer.shadowMap.enabled = true; + renderer.shadowMap.type = THREE.PCFSoftShadowMap; // default THREE.PCFShadowMap + document.body.appendChild(renderer.domElement); + + const planeGeometry = new THREE.PlaneGeometry(1000, 1000); + const plane = new THREE.Mesh(planeGeometry, new THREE.MeshBasicMaterial({ color: 0x000000, opacity: 0.25, transparent: true })); + plane.rotation.x = -Math.PI / 2; + plane.visible = false; // Make the plane invisible + scene.add(plane); + + let points = []; + const line_geometry = new THREE.BufferGeometry().setFromPoints(points); + const line_material = new THREE.LineBasicMaterial({ color: 0x0000ff }); + const line = new THREE.Line(line_geometry, line_material); + scene.add(line); + + + const controls = new OrbitControls(camera, renderer.domElement); + controls.addEventListener('change', render); + + // const light = new THREE.HemisphereLight(0xffffbb, 0x080820, 1); + // scene.add(light); + + const ambientLight = new THREE.AmbientLight(0xffffff, 0.6); // soft white light + scene.add(ambientLight); + + const directionalLight = new THREE.DirectionalLight(0xffffff, 0.8); + directionalLight.position.set(0, 10, 5 ); + directionalLight.castShadow = true; + scene.add(directionalLight); + + const grid = new THREE.GridHelper(30, 30); + scene.add(grid); + + // Handle dynamic point cloud + const geometry = new THREE.BufferGeometry(); + const points_frame = new Float32Array(1048 * 3); + geometry.setAttribute('position', new THREE.BufferAttribute(points_frame, 3)); + const material = new THREE.PointsMaterial({ size: 0.04, color: 0x00ffff }); + pointCloud = new THREE.Points(geometry, material); + scene.add(pointCloud); + + + for (let i = 0; i < allModelsUrl.length; i++) { + const assetLoader = new GLTFLoader(); + assetLoader.load('/static/'.concat(allModelsUrl[i]), function (gltf) { + const model = gltf.scene; + if (i !== 0) {allModels_dict[allModelsUrl[i]] = model; + allModels_list.push(model);} + + scene.add(model); + }, undefined, function (error) { + console.error(error); + }); + + } + + const dragControls = new DragControls(allModels_list, camera, renderer.domElement); + + dragControls.addEventListener('dragstart', function (event) { + if (isDrawing) {return} + controls.enabled = false; + raycaster.setFromCamera(mouse, camera); + + const intersects = raycaster.intersectObject(plane); + if (intersects.length > 0) { + const point = intersects[0].point; + event.object.position.set(point.x, 0, point.z); + } + }); + + dragControls.addEventListener('dragend', function (event) { + if (isDrawing) {return} + controls.enabled = true; + }); + + dragControls.addEventListener('drag', function (event) { + if (isDrawing) {return} + raycaster.setFromCamera(mouse, camera); + + const intersects = raycaster.intersectObject(plane); + if (intersects.length > 0) { + const point = intersects[0].point; + event.object.position.set(point.x, 0, point.z); + } + }); + + // Raycaster for constraining movement to the plane + const raycaster = new THREE.Raycaster(); + + // Mouse vector for the raycaster + const mouse = new THREE.Vector2(); + // Update the mouse vector with the current mouse position + document.addEventListener('pointermove', function (event) { + mouse.x = (event.x / renderer.domElement.clientWidth) * 2 - 1; + mouse.y = -(event.y / renderer.domElement.clientHeight) * 2 + 1; + + if (isDrawing && pointerDown) { + raycaster.setFromCamera(mouse, camera); + const intersects = raycaster.intersectObject(plane); + const point = intersects[0].point; + point.y = 0.1 + points.push(point); + line.geometry.setFromPoints(points); + }}, false); + + // pointerDown = false; + document.addEventListener('pointerdown', function (event) { + pointerDown = true; + }, false); + document.addEventListener('pointerup', function (event) { + pointerDown = false; + isDrawing = false; + controls.enabled = true + dragControls.enabled = true + + }, false); + + document.getElementById('buttonOverlaySecond').addEventListener('click', toggleDrawing); + isDrawing = false; + + + function toggleDrawing() { + points = [] + isDrawing = true; + controls.enabled = false + dragControls.enabled = false + } + + document.getElementById('buttonOverlay').addEventListener('click', runGeneration); + function runGeneration() { + const userData = {} + for (let [key, value] of Object.entries(allModels_dict)) { + userData[key] = value.children[0].position + } + userData['trajectory'] = points + console.log(userData) + + fetch('/move_cube', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(userData), + }) + .then(response => response.json()) + .then(data => { + pointCloudData = data + }) + .catch((error) => { + console.error('Error:', error); + }); + + } + + + + document.addEventListener('keypress', function(event) { + const key = event.key; // "a", "1", "Shift", etc. + if (key === 'q') {if (isDrawing) {isDrawing = false;} + else {isDrawing = true;}} + }); + + // Resize Listener + window.addEventListener('resize', onWindowResize, false); + + animate(); +} + +function updatePointCloud() { + if (!pointCloudData) {return} + + // const geometry = new THREE.BufferGeometry(); + // const material = new THREE.PointsMaterial({ size: 0.1, color: 0x00ff00 }); + // const pointsCloud = new THREE.Points(geometry, material); + // scene.add(pointsCloud); + const positions = pointCloud.geometry.attributes.position.array; + const frameData = pointCloudData[frameIndex]; + + for (let i = 0, j = 0; i < frameData.length; i++, j += 3) { + positions[j] = frameData[i][0]; + positions[j + 1] = frameData[i][1]; + positions[j + 2] = frameData[i][2]; + } + pointCloud.geometry.attributes.position.needsUpdate = true; + + frameIndex += 1; + if (frameIndex >= pointCloudData.length) frameIndex = 0; // Loop back to the start +} + +function animate() { + requestAnimationFrame(animate); + updatePointCloud() + renderer.render(scene, camera); +} + +function onWindowResize() { + camera.aspect = window.innerWidth / window.innerHeight; + camera.updateProjectionMatrix(); + renderer.setSize(window.innerWidth, window.innerHeight); + + render(); +} + + +// function onDocumentKeyDown(event) { +// var keyCode = event.which; +// // Move the cube with arrow keys +// if (keyCode == 87) { cube.position.y += 0.1; } // W key +// if (keyCode == 83) { cube.position.y -= 0.1; } // S key +// if (keyCode == 65) { cube.position.x -= 0.1; } // A key +// if (keyCode == 68) { cube.position.x += 0.1; } // D key +// sendCubePosition(); +// } + +// function runGeneration() { +// const position = { +// x: cube.position.x, +// y: cube.position.y, +// z: cube.position.z +// }; +// +// fetch('/move_cube', { +// method: 'POST', +// headers: { +// 'Content-Type': 'application/json', +// }, +// body: JSON.stringify(position), +// }) +// .then(response => response.json()) +// .then(data => { +// // Use the data to create a new cube +// const newGeometry = new THREE.BoxGeometry(); +// const newMaterial = new THREE.MeshBasicMaterial({ color: 0xff0000 }); +// const newCube = new THREE.Mesh(newGeometry, newMaterial); +// newCube.position.set(data.x, data.y, data.z); +// scene.add(newCube); +// }) +// .catch((error) => { +// console.error('Error:', error); +// }); +// } + +function render() { + renderer.render(scene, camera); +} + +// document.addEventListener("keydown", onDocumentKeyDown, false); +init(); \ No newline at end of file diff --git a/static/kitchen_chair_1.glb b/static/kitchen_chair_1.glb new file mode 100644 index 0000000000000000000000000000000000000000..2b8549ba5a713b02ac6c95ffae049b80cff58d0a Binary files /dev/null and b/static/kitchen_chair_1.glb differ diff --git a/static/kitchen_chair_2.glb b/static/kitchen_chair_2.glb new file mode 100644 index 0000000000000000000000000000000000000000..74545fd87113b60a0e327b8191e355eeaa0f3c83 Binary files /dev/null and b/static/kitchen_chair_2.glb differ diff --git a/static/office_chair.glb b/static/office_chair.glb new file mode 100644 index 0000000000000000000000000000000000000000..5f0c2b074e9663277a63bb7fecc978b2034e406c Binary files /dev/null and b/static/office_chair.glb differ diff --git a/static/room.glb b/static/room.glb new file mode 100644 index 0000000000000000000000000000000000000000..4602f5856217cff6045f76145e30a575a4f29725 --- /dev/null +++ b/static/room.glb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:caeb9b1ca3ce50bc35c56d80a63a9446068e38959d100d6ba9e424969f255847 +size 1078512 diff --git a/static/sofa.glb b/static/sofa.glb new file mode 100644 index 0000000000000000000000000000000000000000..68939aaa92fbaf023803f55c5fb987e05253e08f Binary files /dev/null and b/static/sofa.glb differ diff --git a/static/table.glb b/static/table.glb new file mode 100644 index 0000000000000000000000000000000000000000..3f4e777aa4f560c6766b375f88fbe93ef5b5fbff Binary files /dev/null and b/static/table.glb differ diff --git a/static/wc.glb b/static/wc.glb new file mode 100644 index 0000000000000000000000000000000000000000..3ef4e1a155ea34ade6e0592f6a127341faed493a Binary files /dev/null and b/static/wc.glb differ diff --git a/templates/index.html b/templates/index.html new file mode 100644 index 0000000000000000000000000000000000000000..5db331d0d1a1fbecb3deec47a4b347820c457adf --- /dev/null +++ b/templates/index.html @@ -0,0 +1,41 @@ + + + + + + + Three.js Cube + + + + + + + + + + + + + \ No newline at end of file diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7f25e28cef6c8765fd36b0e016f3c5d4d6eaf0c6 --- /dev/null +++ b/utils.py @@ -0,0 +1,151 @@ +import torch +import hydra +import numpy as np +from einops import rearrange +import random +import os + + +def seed_everything(seed: int): + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + + +def transform_points(x, mat): + shape = x.shape + x = rearrange(x, 'b t (j c) -> b (t j) c', c=3) # B x N x 3 + x = torch.einsum('bpc,bck->bpk', mat[:, :3, :3], x.permute(0, 2, 1)) # B x 3 x N N x B x 3 + x = x.permute(2, 0, 1) + mat[:, :3, 3] + x = x.permute(1, 0, 2) + x = x.reshape(shape) + + return x + + +def create_meshgrid(bbox, size, batch_size=1): + x = torch.linspace(bbox[0], bbox[1], size[0]) + y = torch.linspace(bbox[2], bbox[3], size[1]) + z = torch.linspace(bbox[4], bbox[5], size[2]) + xx, yy, zz = torch.meshgrid(x, y, z, indexing='ij') + grid = torch.stack([xx, yy, zz], dim=-1).reshape(-1, 3) + grid = grid.repeat(batch_size, 1, 1) + + # aug_z = 0.75 + torch.rand(batch_size, 1) * 0.35 + # grid[:, :, 2] = grid[:, :, 2] * aug_z + + return grid + + +def zup_to_yup(coord): + # change the coordinate from yup to zup + if len(coord.shape) > 1: + coord = coord[..., [0, 2, 1]] + coord[..., 2] *= -1 + else: + coord = coord[[0, 2, 1]] + coord[2] *= -1 + + return coord + + +def rigid_transform_3D(A, B, scale=False): + assert len(A) == len(B) + + N = A.shape[0] # total points + + centroid_A = np.mean(A, axis=0) + centroid_B = np.mean(B, axis=0) + + # center the points + AA = A - np.tile(centroid_A, (N, 1)) + BB = B - np.tile(centroid_B, (N, 1)) + + # dot is matrix multiplication for array + if scale: + H = np.transpose(BB) * AA / N + else: + H = np.transpose(BB) * AA + + U, S, Vt = np.linalg.svd(H) + + R = Vt.T * U.T + + # special reflection case + if np.linalg.det(R) < 0: + print("Reflection detected") + # return None, None, None + Vt[2, :] *= -1 + R = Vt.T * U.T + + if scale: + varA = np.var(A, axis=0).sum() + c = 1 / (1 / varA * np.sum(S)) # scale factor + t = -R * (centroid_B.T * c) + centroid_A.T + else: + c = 1 + t = -R * centroid_B.T + centroid_A.T + + return c, R, t + + +def find_free_port(): + from contextlib import closing + import socket + + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: + s.bind(('', 0)) + s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + return str(s.getsockname()[1]) + + +def extract(a, t, x_shape): + batch_size = t.shape[0] + out = a.gather(-1, t.cpu()) + return out.reshape(batch_size, *((1,) * (len(x_shape) - 1))).to(t.device) + + +def linear_beta_schedule(timesteps): + beta_start = 0.0001 + beta_end = 0.02 + return torch.linspace(beta_start, beta_end, timesteps) + + +def init_model(model_cfg, device, eval, load_state_dict=False): + model = hydra.utils.instantiate(model_cfg) + if eval: + load_state_dict_eval(model, model_cfg.ckpt, device=device) + else: + model = model.to(device) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device], broadcast_buffers=False, + find_unused_parameters=True) + if load_state_dict: + model.module.load_state_dict(torch.load(model_cfg.ckpt)) + model.train() + + return model + + +def load_state_dict_eval(model, state_dict_path, map_location='cuda:0', device='cuda'): + state_dict = torch.load(state_dict_path, map_location=map_location) + key_list = [key for key in state_dict.keys()] + for old_key in key_list: + new_key = old_key.replace('module.', '') + state_dict[new_key] = state_dict.pop(old_key) + model.load_state_dict(state_dict) + model.to(device) + model.eval() + + +class dotDict(dict): + """dot.notation access to dictionary attributes""" + def __getattr__(*args): + val = dict.get(*args) + return dotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ \ No newline at end of file