| import numpy as np |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| import copy |
| import math |
| from tqdm.auto import tqdm |
| import functools |
| from torch.utils.data import DataLoader |
| import os |
| import argparse |
|
|
| import pandas as pd |
|
|
| def process_dic(state_dict): |
| new_state_dict = {} |
| for k,v in state_dict.items(): |
| if 'module' in k: |
| new_state_dict[k[7:]] = v |
| else: |
| new_state_dict[k] = v |
| return new_state_dict |
|
|
|
|
| def calc_distogram(pos, min_bin, max_bin, num_bins): |
| dists_2d = torch.linalg.norm( |
| pos[:, :, None, :] - pos[:, None, :, :], axis=-1)[..., None] |
| lower = torch.linspace( |
| min_bin, |
| max_bin, |
| num_bins, |
| device=pos.device) |
| upper = torch.cat([lower[1:], lower.new_tensor([1e8])], dim=-1) |
| dgram = ((dists_2d > lower) * (dists_2d < upper)).type(pos.dtype) |
| return dgram |
|
|
|
|
| def get_index_embedding(indices, embed_size, max_len=2056): |
| """Creates sine / cosine positional embeddings from a prespecified indices. |
| |
| Args: |
| indices: offsets of size [..., N_edges] of type integer |
| max_len: maximum length. |
| embed_size: dimension of the embeddings to create |
| |
| Returns: |
| positional embedding of shape [N, embed_size] |
| """ |
| K = torch.arange(embed_size//2, device=indices.device) |
| pos_embedding_sin = torch.sin( |
| indices[..., None] * math.pi / (max_len**(2*K[None]/embed_size))).to(indices.device) |
| pos_embedding_cos = torch.cos( |
| indices[..., None] * math.pi / (max_len**(2*K[None]/embed_size))).to(indices.device) |
| pos_embedding = torch.cat([ |
| pos_embedding_sin, pos_embedding_cos], axis=-1) |
| return pos_embedding |
|
|
|
|
| def get_time_embedding(timesteps, embedding_dim, max_positions=2000): |
| |
| assert len(timesteps.shape) == 1 |
| timesteps = timesteps * max_positions |
| half_dim = embedding_dim // 2 |
| emb = math.log(max_positions) / (half_dim - 1) |
| emb = torch.exp(torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) * -emb) |
| emb = timesteps.float()[:, None] * emb[None, :] |
| emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) |
| if embedding_dim % 2 == 1: |
| emb = F.pad(emb, (0, 1), mode='constant') |
| assert emb.shape == (timesteps.shape[0], embedding_dim) |
| return emb |
