generate_random_indices.py

import os
import collections
import json
import logging
import argparse
import numpy as np
import pandas as pd
import torch
from time import time
from torch import optim
from tqdm import tqdm
import torch.utils.data as data
from torch.utils.data import DataLoader
from index.models.rqvae import RQVAE
# from rq_llama import *
# from index.datasets import EmbDataset
import random

class NpyDataset(data.Dataset):
    def __init__(self, data_path):
        self.data_path = data_path
        self.embeddings = np.load(data_path)
        self.dim = self.embeddings.shape[-1]

    def __getitem__(self, index):
        emb = self.embeddings[index]
        tensor_emb = torch.FloatTensor(emb)
        return tensor_emb

    def __len__(self):
        return len(self.embeddings)

def if_collided(all_indices_str):
    tot_item = len(all_indices_str)
    tot_indice = len(set(all_indices_str.tolist()))
    return tot_item == tot_indice

def get_indices_count(all_indices_str):
    indices_count = collections.defaultdict(int)
    for index in all_indices_str:
        indices_count[index] += 1
    return indices_count

def get_collision_item(all_indices_str):
    index2id = {}
    for i, index in enumerate(all_indices_str):
        if index not in index2id:
            index2id[index] = []
        index2id[index].append(i)
    collision_item_groups = []
    for index in index2id:
        if len(index2id[index]) > 1:
            collision_item_groups.append(index2id[index])
    return collision_item_groups

def parse_args():
    parser = argparse.ArgumentParser(description = "Index")
    parser.add_argument("--item_model_path", type = str, default = "", help = "")
    parser.add_argument("--item_data_path", type = str, default = "", help = "")
    parser.add_argument("--user_model_path", type = str, default = "", help = "")
    parser.add_argument("--user_data_path", type = str, default = "", help = "")
    # parser.add_argument("--save_path", type = str, default = "", help = "")
    parser.add_argument("--device", type = str, default = "cuda:0", help = "gpu or cpu")
    return parser.parse_args()

generate_args = parse_args()
print(generate_args)

device = torch.device(generate_args.device)

# generate item index
ckpt = torch.load(os.path.join(generate_args.item_model_path, 'best_collision_model.pth'), map_location = torch.device('cpu'))
args = ckpt['args']
state_dict = ckpt['state_dict']

data = NpyDataset(generate_args.item_data_path)
data_loader = DataLoader(data, num_workers = args.num_workers, batch_size = 64, shuffle = False, pin_memory = True)
# model = RQVAE(
#     in_dim = data.dim,
#     num_emb_list = args.num_emb_list,
#     e_dim = args.e_dim,
#     layers = args.layers,
#     dropout_prob = args.dropout_prob,
#     bn = args.bn,
#     loss_type = args.loss_type,
#     quant_loss_weight = args.quant_loss_weight,
#     kmeans_init = args.kmeans_init,
#     kmeans_iters = args.kmeans_iters,
#     sk_epsilons = args.sk_epsilons,
#     sk_iters = args.sk_iters,
# )
# model.load_state_dict(state_dict)
# model = model.to(device)
# model.eval()
# print(model)

prefix = ["<a_{}>","<b_{}>","<c_{}>","<d_{}>","<e_{}>"]
postfix = "<p_{}>"

index_table = {}
all_indices = []
all_indices_str = []
with torch.no_grad():
    for x in tqdm(data_loader):
        # indices = model.get_indices(x.to(device), False)
        # indices = indices.view(-1, indices.shape[-1]).cpu().numpy()

        indices = np.random.randint(0, 256, size = (64, 4), dtype = int)
        for index in indices:
            code = []
            for i, ind in enumerate(index):
                code.append(prefix[i].format(int(ind)))

            if str(code) in index_table:
                index_table[str(code)] += 1
            else:
                index_table[str(code)] = 0
            code.append(postfix.format(index_table[str(code)]))

            all_indices.append(code)
            all_indices_str.append(str(code))

all_indices = np.array(all_indices)
all_indices_str = np.array(all_indices_str)

print("All indices number: ", len(all_indices))
print("Max number of conflicts: ", max(get_indices_count(all_indices_str).values()))
print('Re-index number:', max(index_table.values()))

all_indices_dict = {}
for item, indices in enumerate(all_indices.tolist()):
    all_indices_dict[item] = list(indices)
reindex_dict = {'reindex': max(index_table.values())}

item_index_path = os.path.join(generate_args.item_model_path, 'indices.random.item.json')
with open(item_index_path, 'w', encoding = 'utf-8') as f:
    json.dump(all_indices_dict, f)

item_reindex_path = os.path.join(generate_args.item_model_path, 'reindex.random.item.json')
with open(item_reindex_path, 'w', encoding = 'utf-8') as f:
    json.dump(reindex_dict, f)

# generate user index
ckpt = torch.load(os.path.join(generate_args.user_model_path, 'best_collision_model.pth'), map_location = torch.device('cpu'))
args = ckpt['args']
state_dict = ckpt['state_dict']

data = NpyDataset(generate_args.user_data_path)
data_loader = DataLoader(data, num_workers = args.num_workers, batch_size = 64, shuffle = False, pin_memory = True)
# model = RQVAE(
#     in_dim = data.dim,
#     num_emb_list = args.num_emb_list,
#     e_dim = args.e_dim,
#     layers = args.layers,
#     dropout_prob = args.dropout_prob,
#     bn = args.bn,
#     loss_type = args.loss_type,
#     quant_loss_weight = args.quant_loss_weight,
#     kmeans_init = args.kmeans_init,
#     kmeans_iters = args.kmeans_iters,
#     sk_epsilons = args.sk_epsilons,
#     sk_iters = args.sk_iters,
# )
# model.load_state_dict(state_dict)
# model = model.to(device)
# model.eval()
# print(model)

prefix = ['<z-{}>','<y-{}>','<x-{}>','<w-{}>','<v-{}>']

all_indices = []
all_indices_str = []
with torch.no_grad():
    for x in tqdm(data_loader):
        # indices = rqvae.get_indices(x.to(device), False)
        # indices = indices.view(-1, indices.shape[-1]).cpu().numpy()
        indices = np.random.randint(0, 256, size = (64, 4), dtype = int)
        for index in indices:
            code = []
            for i, ind in enumerate(index):
                code.append(prefix[i].format(int(ind)))

            all_indices.append(code)
            all_indices_str.append(str(code))

all_indices = np.array(all_indices)
all_indices_str = np.array(all_indices_str)

print("All indices number: ", len(all_indices))
print("Max number of conflicts: ", max(get_indices_count(all_indices_str).values()))

all_indices_dict = {}
for item, indices in enumerate(all_indices.tolist()):
    all_indices_dict[item] = list(indices)

json_path = os.path.join(generate_args.user_model_path, 'indices.random.user.json')
with open(json_path, 'w', encoding = 'utf-8') as f:
    json.dump(all_indices_dict, f)