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@salehjg
Created March 24, 2018 16:09
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# Modified evaluation script of PointNet for DGCNN
# (unofficial)
import argparse
import math
import h5py
import numpy as np
import tensorflow as tf
import socket
import importlib
import os
import sys
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'))
import provider
import tf_util
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='dgcnn', help='Model name: dgcnn')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--max_epoch', type=int, default=250, help='Epoch to run [default: 250]')
parser.add_argument('--batch_size', type=int, default=24, help='Batch Size during training [default: 32]')
parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
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]')
parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
FLAGS = parser.parse_args()
BATCH_SIZE = FLAGS.batch_size
NUM_POINT = FLAGS.num_point
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
DUMP_DIR = FLAGS.dump_dir
MODEL_PATH = FLAGS.model_path
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = os.path.join(BASE_DIR, 'models', FLAGS.model+'.py')
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
MAX_NUM_POINT = 2048
NUM_CLASSES = 40
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
# ModelNet40 official train/test split
TRAIN_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'))
TEST_FILES = provider.getDataFiles(\
os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'))
SHAPE_NAMES = [line.rstrip() for line in \
open(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))]
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
return learning_rate
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
def evaluate(num_votes):
is_training = False
with tf.device('/gpu:'+str(GPU_INDEX)):
pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
is_training_pl = tf.placeholder(tf.bool, shape=())
print(is_training_pl)
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0)
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
# Get model and loss
pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)
loss = MODEL.get_loss(pred, labels_pl, end_points)
tf.summary.scalar('loss', loss)
correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE)
tf.summary.scalar('accuracy', accuracy)
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# Restore variables from disk.
saver.restore(sess, MODEL_PATH)
log_string("Model restored.")
# Add summary writers
#merged = tf.merge_all_summaries()
merged = tf.summary.merge_all()
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': batch}
eval_one_epoch(sess, ops, num_votes)
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
error_cnt = 0
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
for fn in range(len(TEST_FILES)):
log_string('----'+str(fn)+'----')
current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
current_data = current_data[:,0:NUM_POINT,:]
current_label = np.squeeze(current_label)
print(current_data.shape)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
print(file_size)
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
cur_batch_size = end_idx - start_idx
# Aggregating BEG
batch_loss_sum = 0 # sum of losses for the batch
batch_pred_sum = np.zeros((cur_batch_size, NUM_CLASSES)) # score for classes
batch_pred_classes = np.zeros((cur_batch_size, NUM_CLASSES)) # 0/1 for classes
for vote_idx in range(num_votes):
orig_data = current_data[start_idx:end_idx, :, :]
rotated_data = provider.rotate_point_cloud_by_angle(orig_data,
vote_idx/float(num_votes) * np.pi * 2)
feed_dict = {ops['pointclouds_pl']: rotated_data, #orig_data or rotated_data
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
feed_dict=feed_dict)
batch_pred_sum += pred_val
batch_pred_val = np.argmax(pred_val, 1)
for el_idx in range(cur_batch_size):
batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
batch_loss_sum += (loss_val * cur_batch_size / float(num_votes))
# pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1]
# pred_val = np.argmax(batch_pred_classes, 1)
pred_val = np.argmax(batch_pred_sum, 1)
# Aggregating END
correct = np.sum(pred_val == current_label[start_idx:end_idx])
# correct = np.sum(pred_val_topk[:,0:topk] == label_val)
total_correct += correct
total_seen += cur_batch_size
loss_sum += batch_loss_sum
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i-start_idx] == l)
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f' % (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)
for i, name in enumerate(SHAPE_NAMES):
log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))
if __name__=='__main__':
with tf.Graph().as_default():
evaluate(num_votes=1)
LOG_FOUT.close()
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