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import os
import sys
import torch
from torch.utils.data import Dataset
import json
import numpy as np
from torch.utils.data.dataloader import default_collate
import time
class ESMDataset(Dataset):
def __init__(self, pdb_root, seq_root, ann_paths, dataset_description, chain="A"):
"""
pdb_root (string): Root directory of protein pdb embeddings (e.g. xyz/pdb/)
seq_root (string): Root directory of sequences embeddings (e.g. xyz/seq/)
ann_root (string): directory to store the annotation file
dataset_description (string): json file that describes what data are used for training/testing
"""
data_describe = json.load(open(dataset_description, "r"))
train_set = set(data_describe["train"])
self.pdb_root = pdb_root
self.seq_root = seq_root
self.annotation = json.load(open(ann_paths, "r"))
keep = []
for i in range(0, len(self.annotation)):
if (self.annotation[i]["pdb_id"] in train_set):
keep.append(self.annotation[i])
self.annotation = keep
self.pdb_ids = {}
self.chain = chain
def __len__(self):
return len(self.annotation)
def __getitem__(self, index):
ann = self.annotation[index]
pdb_embedding = '{}.pt'.format(ann["pdb_id"])
pdb_embedding_path = os.path.join(self.pdb_root, pdb_embedding)
pdb_embedding = torch.load(
pdb_embedding_path, map_location=torch.device('cpu'))
# pdb_embedding_path, map_location=torch.device('cuda'))
pdb_embedding.requires_grad = False
seq_embedding = '{}.pt'.format(ann["pdb_id"])
seq_embedding_path = os.path.join(self.seq_root, seq_embedding)
seq_embedding = torch.load(
seq_embedding_path, map_location=torch.device('cpu'))
# seq_embedding_path, map_location=torch.device('cuda'))
seq_embedding.requires_grad = False
caption = ann["caption"]
return {
"text_input": caption,
"pdb_encoder_out": pdb_embedding,
"seq_encoder_out": seq_embedding,
"chain": self.chain,
"pdb_id": ann["pdb_id"]
}
# Yijia please check :)
# def collater(self, samples):
# # print(samples)
# max_len_pdb_dim0 = -1
# max_len_seq_dim0 = -1
# for pdb_json in samples:
# pdb_embeddings = pdb_json["pdb_encoder_out"]
# shape_dim0 = pdb_embeddings.shape[0]
# max_len_pdb_dim0 = max(max_len_pdb_dim0, shape_dim0)
# seq_embeddings = pdb_json["seq_encoder_out"]
# shape_dim0 = seq_embeddings.shape[0]
# max_len_seq_dim0 = max(max_len_seq_dim0, shape_dim0)
# for pdb_json in samples:
# pdb_embeddings = pdb_json["pdb_encoder_out"]
# shape_dim0 = pdb_embeddings.shape[0]
# pad1 = ((0, max_len_pdb_dim0 - shape_dim0), (0, 0), (0, 0))
# arr1_padded = np.pad(pdb_embeddings, pad1, mode='constant', )
# pdb_json["pdb_encoder_out"] = arr1_padded
# seq_embeddings = pdb_json["seq_encoder_out"]
# shape_dim0 = seq_embeddings.shape[0]
# pad1 = ((0, max_len_seq_dim0 - shape_dim0), (0, 0), (0, 0))
# arr1_padded = np.pad(seq_embeddings, pad1, mode='constant', )
# pdb_json["seq_encoder_out"] = arr1_padded
# print(samples[0].keys())
# return default_collate(samples)
def collater(self, samples):
max_len_pdb_dim0 = max(pdb_json["pdb_encoder_out"].shape[0] for pdb_json in samples)
max_len_seq_dim0 = max(pdb_json["seq_encoder_out"].shape[0] for pdb_json in samples)
for pdb_json in samples:
pdb_embeddings = pdb_json["pdb_encoder_out"]
pad_pdb = ((0, max_len_pdb_dim0 - pdb_embeddings.shape[0]), (0, 0), (0, 0))
pdb_json["pdb_encoder_out"] = torch.tensor(np.pad(pdb_embeddings, pad_pdb, mode='constant'))
seq_embeddings = pdb_json["seq_encoder_out"]
pad_seq = ((0, max_len_seq_dim0 - seq_embeddings.shape[0]), (0, 0), (0, 0))
pdb_json["seq_encoder_out"] = torch.tensor(np.pad(seq_embeddings, pad_seq, mode='constant'))
return default_collate(samples)
# import os
# import sys
# import torch
# from torch.utils.data import Dataset
# import json
# import numpy as np
# from torch.utils.data.dataloader import default_collate
# import time
# class ESMDataset(Dataset):
# def __init__(self, pdb_root, ann_paths, chain="A"):
# """
# protein (string): Root directory of protein (e.g. coco/images/)
# ann_root (string): directory to store the annotation file
# """
# self.pdb_root = pdb_root
# self.annotation = json.load(open(ann_paths, "r"))
# self.pdb_ids = {}
# self.chain = chain
# def __len__(self):
# return len(self.annotation)
# def __getitem__(self, index):
# ann = self.annotation[index]
# protein_embedding = '{}.pt'.format(ann["pdb_id"])
# protein_embedding_path = os.path.join(self.pdb_root, protein_embedding)
# protein_embedding = torch.load(protein_embedding_path, map_location=torch.device('cpu'))
# protein_embedding.requires_grad = False
# caption = ann["caption"]
# return {
# "text_input": caption,
# "encoder_out": protein_embedding,
# "chain": self.chain,
# "pdb_id": ann["pdb_id"]
# }
# def collater(self, samples):
# max_len_protein_dim0 = -1
# for pdb_json in samples:
# pdb_embeddings = pdb_json["encoder_out"]
# shape_dim0 = pdb_embeddings.shape[0]
# max_len_protein_dim0 = max(max_len_protein_dim0, shape_dim0)
# for pdb_json in samples:
# pdb_embeddings = pdb_json["encoder_out"]
# shape_dim0 = pdb_embeddings.shape[0]
# pad1 = ((0, max_len_protein_dim0 - shape_dim0), (0, 0), (0, 0))
# arr1_padded = np.pad(pdb_embeddings, pad1, mode='constant', )
# pdb_json["encoder_out"] = arr1_padded
# return default_collate(samples) |