ViTacFormer/utils.py at main · RoboVerseOrg/ViTacFormer · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
import numpy as np
import torch
import os
import h5py
import json
from torch.utils.data import TensorDataset, DataLoader
from PIL import Image

import IPython
e = IPython.embed

def get_norm_stats(dataset_dir, num_episodes):
    all_qpos_data = []
    all_action_data = []
    max_episode_len = 0
    for episode_idx in range(num_episodes):
        dataset_path = os.path.join(dataset_dir, "robot-franka", 'demo_{:04d}'.format(episode_idx))
        metadata_path = os.path.join(dataset_path, 'metadata.json')
        with open(metadata_path, 'r') as f:
            metadata = json.load(f)
        ori_qpos = metadata.get('joint_qpos', [])
        qpos = np.array(ori_qpos[:-1])
        action = np.array(ori_qpos[1:]) # require slice off all elements' last element
        max_episode_len = max(max_episode_len, qpos.shape[0])
        # dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5')
        # with h5py.File(dataset_path, 'r') as root:
        #     qpos = root['/observations/qpos'][()]
        #     qvel = root['/observations/qvel'][()]
        #     action = root['/action'][()]

        all_qpos_data.append(torch.from_numpy(qpos))
        all_action_data.append(torch.from_numpy(action))
    all_qpos_data = torch.vstack(all_qpos_data)
    all_action_data = torch.vstack(all_action_data)
    all_action_data = all_action_data

    # normalize action data
    action_mean = all_action_data.mean(dim=[0, 1], keepdim=True)
    action_std = all_action_data.std(dim=[0, 1], keepdim=True)
    action_std = torch.clip(action_std, 1e-2, np.inf) # clipping

    # normalize qpos data
    qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True)
    qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True)
    qpos_std = torch.clip(qpos_std, 1e-2, np.inf) # clipping

    stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(),
             "qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(),
             "example_qpos": qpos, "max_episode_len": max_episode_len}

    return stats


def compute_dict_mean(epoch_dicts):
    result = {k: None for k in epoch_dicts[0]}
    num_items = len(epoch_dicts)
    for k in result:
        value_sum = 0
        for epoch_dict in epoch_dicts:
            value_sum += epoch_dict[k]
        result[k] = value_sum / num_items
    return result

def detach_dict(d):
    new_d = dict()
    for k, v in d.items():
        new_d[k] = v.detach()
    return new_d

def set_seed(seed):
    torch.manual_seed(seed)
    np.random.seed(seed)

def unnormalize_image(img_tensor, mean, std):
    img = img_tensor.clone().cpu().numpy()
    for c in range(3):
        img[c] = img[c] * std[c] + mean[c]
    img = img.transpose(1, 2, 0)  # [H, W, C]
    img = np.clip(img * 255.0, 0, 255).astype(np.uint8)
    return img

def normalize_action(action_tensor, normalizer):
        splits = [7, 21, 7, 21, 2]
        keys = [
            "/action/right_arm/joint_angle/rel",
            "/action/right_hand/joint_angle/rel",
            "/action/left_arm/joint_angle/rel",
            "/action/left_hand/joint_angle/rel",
            "/action/neck/joint_angle/rel",
        ]

        chunks = torch.split(action_tensor, splits, dim=-1)
        norm_chunks = []
        for key, chunk in zip(keys, chunks):
            norm_chunks.append(normalizer[key].normalize(chunk))
        return torch.cat(norm_chunks, dim=-1)

def denormalize_action(action_tensor, normalizer):
        splits = [7, 21, 7, 21, 2]
        keys = [
            "/action/right_arm/joint_angle/rel",
            "/action/right_hand/joint_angle/rel",
            "/action/left_arm/joint_angle/rel",
            "/action/left_hand/joint_angle/rel",
            "/action/neck/joint_angle/rel",
        ]

        chunks = torch.split(action_tensor, splits, dim=-1)
        denorm_chunks = []
        for key, chunk in zip(keys, chunks):
            denorm_chunks.append(normalizer[key].unnormalize(chunk))
        return torch.cat(denorm_chunks, dim=-1)

def normalize_obs_lowdim(lowdim_tensor, normalizer):
        splits = [7, 21, 7, 21, 2]
        keys = [
            "/state/right_arm/joint_angle",
            "/state/right_hand/joint_angle",
            "/state/left_arm/joint_angle",
            "/state/left_hand/joint_angle",
            "/state/neck/joint_angle",
        ]
        chunks = torch.split(lowdim_tensor, splits, dim=-1)
        norm_chunks = [
            normalizer[key].normalize(chunk)
            for key, chunk in zip(keys, chunks)
        ]
        return torch.cat(norm_chunks, dim=-1)

def denormalize_obs_lowdim(lowdim_tensor, normalizer):
        splits = [7, 21, 7, 21, 2]
        keys = [
            "/state/right_arm/joint_angle",
            "/state/right_hand/joint_angle",
            "/state/left_arm/joint_angle",
            "/state/left_hand/joint_angle",
            "/state/neck/joint_angle",
        ]
        chunks = torch.split(lowdim_tensor, splits, dim=-1)
        denorm_chunks = [
            normalizer[key].unnormalize(chunk)
            for key, chunk in zip(keys, chunks)
        ]
        return torch.cat(denorm_chunks, dim=-1)

def normalize_tactile(tactile_tensor, normalizer):
    return normalizer["/observe/tactile/total_force"].normalize(tactile_tensor)

def denormalize_tactile(tactile_tensor, normalizer):
    return normalizer["/observe/tactile/total_force"].unnormalize(tactile_tensor)

def normalize_tactile_next(tactile_tensor, normalizer):
    return normalizer["/observe/tactile/total_force/next"].normalize(tactile_tensor)

def denormalize_tactile_next(tactile_tensor, normalizer):
    return normalizer["/observe/tactile/total_force/next"].unnormalize(tactile_tensor)

def apply_joint_mask(qpos_tensor, mask, start_index):
    B, T, D = qpos_tensor.shape
    mask_tensor = torch.tensor(mask, dtype=qpos_tensor.dtype, device=qpos_tensor.device).view(1, 1, -1)
    qpos_tensor[..., start_index:start_index+len(mask)] *= mask_tensor
    return qpos_tensor