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import os
from model.transformer import GPTConfig, GPT
import numpy as np
import networkx as nx
import argparse
import pickle
import re
import torch
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt_iter', type=int, default=10000)
parser.add_argument('--config', type=str, default='1_1_120')
parser.add_argument('--temperature', type=float, default=1)
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--num_nodes', type=int, default=100)
parser.add_argument('--num_of_paths', type=int, default=20)
return parser.parse_args()
args = parse_args()
dataset = 'simple_graph'
ckpt_iter = args.ckpt_iter
device = args.device
temperature = args.temperature
num_nodes = args.num_nodes
num_of_paths = args.num_of_paths
config = args.config
data_path = f'data/{dataset}/{num_nodes}'
meta_path = f'{data_path}/meta.pkl'
print(f"Loading meta from {meta_path}...")
with open(meta_path, 'rb') as f:
meta = pickle.load(f)
stoi, itos = meta['stoi'], meta['itos']
max_new_tokens = meta['block_size']
top_k = len(itos)
simple_format = meta['simple_format']
out_dir = f'out/{dataset}_{config}_{num_nodes}/'
if(num_of_paths == 0):
ckpt_path = os.path.join(out_dir, f'{ckpt_iter}_ckpt.pt')
else:
ckpt_path = os.path.join(out_dir, f'{ckpt_iter}_ckpt_{num_of_paths}.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
gptconf = GPTConfig(**checkpoint['model_args'])
model = GPT(gptconf)
state_dict = checkpoint['model']
unwanted_prefix = '_orig_mod.'
for k,v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
model.load_state_dict(state_dict)
model.eval()
model.to(device)
path_graph = f'{data_path}/path_graph.graphml'
path_graph = nx.read_graphml(path_graph)
def find_third_number_position(number_string):
numbers = number_string.split()
third_number_index = 2
position = sum(len(num) for num in numbers[:third_number_index]) + third_number_index-1
return position
def encode(s):
ss = s.split(" ")
encoded_string = [stoi[ch] for ch in ss]
return encoded_string
def decode(l):
dec = ""
for i in l:
dec = dec + itos[i] + " "
return dec[:-1]
def check_path(G, gen_str):
path = re.findall(r'\d+', gen_str)
if len(path) < 4:
return 'wrong syntax'
for node in path:
if int(node) > len(itos) or int(node) < 0:
return 'wrong syntax'
if path[2] != path[0] or path[-1] != path[1]:
return 'incorrect start/end'
for i in range(2, len(path) - 1):
if not G.has_edge(path[i], path[i + 1]):
return f'non-existence path {path[i], path[i + 1]}'
return ''
def check_path_unreachable(G, gen_str, gt):
path = re.findall(r'\d+|x', gen_str)
if 'x' in path and len(path) < 4:
return 0 if 'x' in gt else 1
if 'x' in gt and 'x' not in gen_str:
return 1
return check_path(G, gen_str)
typedata = 'test'
f = open(f'{data_path}/{typedata}.txt', encoding='gbk')
texts = []
encode_texts = []
ground_truth = []
for line in f:
if not simple_format:
texts.append(line.split(':')[0] + ':')
encode_texts.append(encode(line.split(':')[0] + ':'))
else:
pos = find_third_number_position(line)
if(line[:pos] != ''):
texts.append(line[:pos])
encode_texts.append(encode(line[:pos]))
ground_truth.append(line)
ground_truth = np.array(ground_truth)
encode_texts = torch.tensor(encode_texts, dtype=torch.long, device=device)
from tqdm import tqdm
batch_size = 1000
ix = torch.randint(len(encode_texts), (batch_size,))
with open(out_dir + f'pred_{typedata}_{ckpt_iter}.txt', 'w') as f:
pass
wrong = 0
for i in tqdm(range(10)):
x = encode_texts[ix]
x_gt = ground_truth[ix]
#x = (torch.tensor(text, dtype=torch.long, device=device))
y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
y_pred = [decode(y[t].tolist()).split('\n')[0] for t in range(batch_size)]
with open(out_dir + f'pred_{typedata}_{ckpt_iter}.txt', 'a') as f:
for t,item in enumerate(y_pred):
symbol = check_path(path_graph, item)
if(symbol != ""):
wrong = wrong + 1
f.write(item +" " + symbol + '\n')
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