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Functions60 in github.com/ZibinDong/AlignDiff-ICLR2024

⨍ Functions 60◇ Types & classes 14
↓ 9 callersMethodnormalize
(self, x: torch.Tensor)
utils/dataset_utils.py:15
↓ 6 callersMethodpredict_attr
Input: - traj: (batch_size, traj_len, o_dim) - ensemble_idx: int Output: - attr_stre
utils/attrfunc_utils.py:61
↓ 4 callersMethod__init__
(self, x_dim: int, attr_dim: int, d_model: int = 384, n_heads: int = 6, depth: int = 12, dropo
utils/dit_utils.py:95
↓ 4 callersMethodupdate
(self, x, condition)
diffusion/ode.py:60
↓ 3 callersMethodload
(self, file_name)
diffusion/ode.py:100
↓ 3 callersFunctionmodulate
(x, shift, scale)
utils/dit_utils.py:35
↓ 3 callersFunctionset_seed
(seed: int)
utils/common_utils.py:6
↓ 3 callersMethodstep
(self, a)
environments/hopper.py:18
↓ 2 callersMethodD
(self, x, sigma, condition = None, mask = None, use_ema = False)
diffusion/ode.py:55
↓ 2 callersMethod_get_obs
(self)
environments/hopper.py:34
↓ 2 callersMethod_predict_attr_ensemble
Input: - traj: (batch_size, traj_len, o_dim) - ensemble_idx: int Output: - attr_stre
utils/attrfunc_utils.py:49
↓ 2 callersMethodforward
Input: - x: (batch, horizon, x_dim) - t: (batch, 1) - attr: (batch, attr_dim) - mask: (
utils/dit_utils.py:134
↓ 2 callersFunctionget_grid_indices
(x, x_min, x_max, num_bins)
utils/common_utils.py:13
↓ 2 callersMethodpredict_pref_prob
Compute P[t_0 > t_1] = exp[sum(r(t_0))]/{exp[sum(r(t_0))]+exp[sum(r(t_1))]}= 1 /{1+exp[sum(r(t_1) - r(t_0))]} ---- Input:
utils/attrfunc_utils.py:76
↓ 2 callersMethodsave
(self, file_name)
diffusion/ode.py:96
↓ 2 callersMethodset_N
(self, N)
diffusion/ode.py:34
↓ 1 callersMethod__init__
(self, o_dim: int, attr_dim: int, attr_clip: float = 20., ensemble_size: int = 3, lr:
utils/attrfunc_utils.py:32
↓ 1 callersMethodc_in
(self, sigma)
diffusion/ode.py:48
↓ 1 callersMethodc_noise
(self, sigma)
diffusion/ode.py:49
↓ 1 callersMethodc_out
(self, sigma)
diffusion/ode.py:47
↓ 1 callersMethodc_skip
(self, sigma)
diffusion/ode.py:46
↓ 1 callersFunctioncount_parameters
(model)
utils/common_utils.py:23
↓ 1 callersMethodema_update
(self, decay=0.999)
diffusion/ode.py:30
↓ 1 callersFunctiongenerate_random_mask
(n, p)
eval.py:40
↓ 1 callersMethodinitialize_weights
(self)
utils/dit_utils.py:110
↓ 1 callersMethodloss_weighting
(self, sigma)
diffusion/ode.py:50
↓ 1 callersMethodplan
(self, o, traj_len, attr, mask, n_samples = 64, w = 1.5, sample_steps = 5)
diffusion/planner.py:17
↓ 1 callersMethodsample
(self, o, attr, attr_mask, traj_len, n_samples: int, w: float = 1.5, N: int = None)
diffusion/ode.py:78
↓ 1 callersMethodsample_noise_distribution
(self, N)
diffusion/ode.py:51
↓ 1 callersFunctionset_mujoco_state
(env, qpos, qvel)
utils/env_utils.py:4
Method__init__
(self)
environments/humanoid.py:19
Method__init__
(self)
environments/hopper.py:12
Method__init__
(self, ode: ODE, attr_func: AttrFunc, normalizer, max_attr, min_attr)
diffusion/planner.py:6
Method__init__
(self, o_dim: int, a_dim, attr_dim: int, sigma_data: float, sigma_min: float = 0.002, sigma_m
diffusion/ode.py:11
Method__init__
(self, attr_dim: int, hidden_size: int, num_bins: int = 100)
utils/dit_utils.py:15
Method__init__
(self, dim: int)
utils/dit_utils.py:54
Method__init__
(self, hidden_size: int, n_heads: int, dropout: float = 0.1)
utils/dit_utils.py:63
Method__init__
(self, hidden_size: int, out_dim: int)
utils/dit_utils.py:83
Method__init__
(self, x: torch.Tensor)
utils/dataset_utils.py:12
Method__init__
(self, x: torch.Tensor)
utils/dataset_utils.py:21
Method__init__
(self, o_dim: int, attr_dim: int, d_model: int, nhead: int, num_layers: int)
utils/attrfunc_utils.py:10
Method__init__
(self, dim)
utils/network_utils.py:6
Method_basic_init
(module)
utils/dit_utils.py:112
Method_get_obs
(self)
environments/humanoid.py:25
Methodforward
attr: (batch_size, attr_dim) mask: (batch_size, attr_dim) 0 or 1, 0 means ignoring
utils/dit_utils.py:23
Methodforward
(self, x: torch.Tensor)
utils/dit_utils.py:58
Methodforward
(self, x: torch.Tensor, t: torch.Tensor)
utils/dit_utils.py:75
Methodforward
(self, x: torch.Tensor, t: torch.Tensor)
utils/dit_utils.py:89
Methodforward
(self, traj: torch.Tensor)
utils/attrfunc_utils.py:24
Methodforward
(self, x)
utils/network_utils.py:9
Methodload
(self, filename: str, map_location = None)
utils/attrfunc_utils.py:124
Functionmass_center
(model, sim)
environments/humanoid.py:13
Methodnormalize
(self, x: torch.Tensor)
utils/dataset_utils.py:23
Methodreset_model
(self)
environments/hopper.py:40
Methodsave
(self, filename: str)
utils/attrfunc_utils.py:119
Methodstep
(self, a)
environments/humanoid.py:29
Methodunnormalize
(self, x: torch.Tensor)
utils/dataset_utils.py:17
Methodunnormalize
(self, x: torch.Tensor)
utils/dataset_utils.py:25
Methodupdate
Update the parameters of the attribute function by minimizing the negative log-likelihood loss ---- Input: - traj
utils/attrfunc_utils.py:95
Methodviewer_setup
(self)
environments/hopper.py:48