| | 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 |
