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import collections
import json
import logging
import argparse
import numpy as np
import torch
from time import time
from torch import optim
from tqdm import tqdm
from torch.utils.data import DataLoader
from datasets import EmbDataset
from models.rqvae import RQVAE
import os
def check_collision(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("--ckpt_path", type = str, default = "", help = "")
parser.add_argument("--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()
infer_args = parse_args()
print(infer_args)
# dataset = "Games"
# ckpt_path = "/zhengbowen/rqvae_ckpt/xxxx"
# output_dir = f"/zhengbowen/data/{dataset}/"
# output_file = f"{dataset}.index.json"
# output_file = os.path.join(output_dir,output_file)
# device = torch.device("cuda:1")
device = torch.device(infer_args.device)
ckpt = torch.load(infer_args.ckpt_path, map_location = torch.device('cpu'))
args = ckpt["args"]
state_dict = ckpt["state_dict"]
data = EmbDataset(infer_args.data_path)
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)
data_loader = DataLoader(data,num_workers=args.num_workers,
batch_size=64, shuffle=False,
pin_memory=True)
all_indices = []
all_indices_str = []
prefix = ["<a_{}>","<b_{}>","<c_{}>","<d_{}>","<e_{}>"]
for d in tqdm(data_loader):
d = d.to(device)
indices = model.get_indices(d,use_sk=False)
indices = indices.view(-1, indices.shape[-1]).cpu().numpy()
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))
# break
all_indices = np.array(all_indices)
all_indices_str = np.array(all_indices_str)
for vq in model.rq.vq_layers[:-1]:
vq.sk_epsilon=0.0
if model.rq.vq_layers[-1].sk_epsilon == 0.0:
model.rq.vq_layers[-1].sk_epsilon = 0.003
tt = 0
#There are often duplicate items in the dataset, and we no longer differentiate them
while True:
if tt >= 10 or check_collision(all_indices_str):
break
collision_item_groups = get_collision_item(all_indices_str)
# print(collision_item_groups)
print(len(collision_item_groups))
for collision_items in collision_item_groups:
d = data[collision_items].to(device)
indices = model.get_indices(d, use_sk=True)
indices = indices.view(-1, indices.shape[-1]).cpu().numpy()
for item, index in zip(collision_items, indices):
code = []
for i, ind in enumerate(index):
code.append(prefix[i].format(int(ind)))
all_indices[item] = code
all_indices_str[item] = str(code)
tt += 1
print("All indices number: ",len(all_indices))
print("Max number of conflicts: ", max(get_indices_count(all_indices_str).values()))
tot_item = len(all_indices_str)
tot_indice = len(set(all_indices_str.tolist()))
print("Collision Rate",(tot_item-tot_indice)/tot_item)
all_indices_dict = {}
for item, indices in enumerate(all_indices.tolist()):
all_indices_dict[item] = list(indices)
with open(infer_args.save_path, 'w') as fp:
json.dump(all_indices_dict, fp)
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