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inference.py
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# Author: Chuanzhe Suo ([email protected]) 10/26/2018
# Thanks to Mikaela Angelina Uy, modified from PointNetVLAD
# Reference: LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis, ICCV 2019
import argparse
import os
import sys
import importlib
import tensorflow as tf
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
from loading_pointclouds import *
from sklearn.neighbors import KDTree
#params
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 1]')
parser.add_argument('--log_dir', default='log/', help='Log dir [default: log]')
parser.add_argument('--positives_per_query', type=int, default=0, help='Number of potential positives in each training tuple [default: 2]')
parser.add_argument('--negatives_per_query', type=int, default=0, help='Number of definite negatives in each training tuple [default: 20]')
parser.add_argument('--batch_num_queries', type=int, default=25, help='Batch Size during training [default: 1]')
parser.add_argument('--dimension', type=int, default=256)
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.8]')
FLAGS = parser.parse_args()
#BATCH_SIZE = FLAGS.batch_size
BATCH_NUM_QUERIES = FLAGS.batch_num_queries
EVAL_BATCH_SIZE = 1
NUM_POINTS = 4096
POSITIVES_PER_QUERY= FLAGS.positives_per_query
NEGATIVES_PER_QUERY= FLAGS.negatives_per_query
GPU_INDEX = FLAGS.gpu
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
INFERENCE_FILE= 'generating_queries/oxford_inference_database.pickle'
LOG_DIR = FLAGS.log_dir
model = LOG_DIR.split('/')[1]
model = model.split('-')[0]
print(LOG_DIR)
MODEL = importlib.import_module(model)
model_file= "model.ckpt"
INFERENCE_SETS= get_sets_dict(INFERENCE_FILE)
global INFERENCE_VECTORS
INFERENCE_VECTORS=[]
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_NUM_QUERIES,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
def inference():
global INFERENCE_VECTORS
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
print("In Graph")
query= MODEL.placeholder_inputs(BATCH_NUM_QUERIES, 1, NUM_POINTS)
positives= MODEL.placeholder_inputs(BATCH_NUM_QUERIES, POSITIVES_PER_QUERY, NUM_POINTS)
negatives= MODEL.placeholder_inputs(BATCH_NUM_QUERIES, NEGATIVES_PER_QUERY, NUM_POINTS)
eval_queries= MODEL.placeholder_inputs(EVAL_BATCH_SIZE, 1, NUM_POINTS)
is_training_pl = tf.placeholder(tf.bool, shape=())
print(is_training_pl)
batch = tf.Variable(0)
bn_decay = get_bn_decay(batch)
with tf.variable_scope("query_triplets") as scope:
vecs= tf.concat([query, positives, negatives],1)
print(vecs)
out_vecs= MODEL.forward(vecs, is_training_pl, bn_decay=bn_decay)
q_vec, pos_vecs, neg_vecs= tf.split(out_vecs, [1,POSITIVES_PER_QUERY,NEGATIVES_PER_QUERY],1)
print(q_vec)
print(pos_vecs)
print(neg_vecs)
saver = tf.train.Saver()
# Create a session
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
saver.restore(sess, os.path.join(LOG_DIR, model_file))
print("Model restored:{}".format(os.path.join(LOG_DIR, model_file)))
ops = {'query': query,
'positives': positives,
'negatives': negatives,
'is_training_pl': is_training_pl,
'eval_queries': eval_queries,
'q_vec':q_vec,
'pos_vecs': pos_vecs,
'neg_vecs': neg_vecs}
recall= np.zeros(25)
count=0
similarity=[]
one_percent_recall=[]
for i in range(len(INFERENCE_SETS)):
INFERENCE_VECTORS.append(get_latent_vectors(sess, ops, INFERENCE_SETS[i]))
INFERENCE_VECTORS = np.asarray(INFERENCE_VECTORS)
INFERENCE_VECTORS.tofile('inference_vectors.bin')
print("feature saved:inference_vectors.bin")
def get_latent_vectors(sess, ops, dict_to_process):
is_training=False
train_file_idxs = np.arange(0, len(dict_to_process.keys()))
#print(len(train_file_idxs))
batch_num= BATCH_NUM_QUERIES*(1+POSITIVES_PER_QUERY+NEGATIVES_PER_QUERY)
q_output = []
for q_index in range(len(train_file_idxs)//batch_num):
file_indices=train_file_idxs[q_index*batch_num:(q_index+1)*(batch_num)]
file_names=[]
for index in file_indices:
file_names.append(dict_to_process[index]["query"])
queries=load_pc_files(file_names)
# queries= np.expand_dims(queries,axis=1)
q1=queries[0:BATCH_NUM_QUERIES]
q1=np.expand_dims(q1,axis=1)
#print(q1.shape)
q2=queries[BATCH_NUM_QUERIES:BATCH_NUM_QUERIES*(POSITIVES_PER_QUERY+1)]
q2=np.reshape(q2,(BATCH_NUM_QUERIES,POSITIVES_PER_QUERY,NUM_POINTS,13))
q3=queries[BATCH_NUM_QUERIES*(POSITIVES_PER_QUERY+1):BATCH_NUM_QUERIES*(NEGATIVES_PER_QUERY+POSITIVES_PER_QUERY+1)]
q3=np.reshape(q3,(BATCH_NUM_QUERIES,NEGATIVES_PER_QUERY,NUM_POINTS,13))
feed_dict={ops['query']:q1, ops['positives']:q2, ops['negatives']:q3, ops['is_training_pl']:is_training}
o1, o2, o3=sess.run([ops['q_vec'], ops['pos_vecs'], ops['neg_vecs']], feed_dict=feed_dict)
o1=np.reshape(o1,(-1,o1.shape[-1]))
o2=np.reshape(o2,(-1,o2.shape[-1]))
o3=np.reshape(o3,(-1,o3.shape[-1]))
out=np.vstack((o1,o2,o3))
q_output.append(out)
q_output=np.array(q_output)
if(len(q_output)!=0):
q_output=q_output.reshape(-1,q_output.shape[-1])
#print(q_output.shape)
#handle edge case
for q_index in range((len(train_file_idxs)//batch_num*batch_num),len(dict_to_process.keys())):
index=train_file_idxs[q_index]
queries=load_pc_files([dict_to_process[index]["query"]])
queries= np.expand_dims(queries,axis=1)
#print(query.shape)
#exit()
fake_queries=np.zeros((BATCH_NUM_QUERIES-1,1,NUM_POINTS,13))
fake_pos=np.zeros((BATCH_NUM_QUERIES,POSITIVES_PER_QUERY,NUM_POINTS,13))
fake_neg=np.zeros((BATCH_NUM_QUERIES,NEGATIVES_PER_QUERY,NUM_POINTS,13))
q=np.vstack((queries,fake_queries))
#print(q.shape)
feed_dict={ops['query']:q, ops['positives']:fake_pos, ops['negatives']:fake_neg, ops['is_training_pl']:is_training}
output=sess.run(ops['q_vec'], feed_dict=feed_dict)
#print(output.shape)
output=output[0]
output=np.squeeze(output)
if (q_output.shape[0]!=0):
q_output=np.vstack((q_output,output))
else:
q_output=output
#q_output=np.array(q_output)
#q_output=q_output.reshape(-1,q_output.shape[-1])
print(q_output.shape)
return q_output
if __name__ == "__main__":
inference()