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evaluate.py
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evaluate.py
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import tensorflow as tf
import numpy as np
import argparse
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 time
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='pointnet_cls_basic', help='Model name: pointnet_cls or pointnet_cls_basic [default: pointnet_cls]')
parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 1]')
parser.add_argument('--num_class', type=int, default=3, help='Number of Classes [default: 7]')
parser.add_argument('--num_point', type=int, default=2048, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--num_feature', type=int, default=256, help='Point Number [32-2048] [default: 128]')
parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
parser.add_argument('--visu', default=True,action='store_true', help='Whether to dump image for error case [default: False]')
FLAGS = parser.parse_args()
NAME_MODEL = ''
NUM_CLASSES = FLAGS.num_class
BATCH_SIZE = FLAGS.batch_size
NUM_POINT = FLAGS.num_point
NUM_FEATURE = FLAGS.num_feature
MODEL_PATH = 'log/' + FLAGS.model + NAME_MODEL +'_'+str(NUM_FEATURE)+'_'+'model.ckpt'
print(MODEL_PATH)
GPU_INDEX = FLAGS.gpu
MODEL = importlib.import_module(FLAGS.model) # import network module
DUMP_DIR = FLAGS.dump_dir
if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
SHAPE_NAMES = [line.rstrip() for line in \
open(os.path.join(BASE_DIR, 'data/3Classes/shape_names.txt'))]
HOSTNAME = socket.gethostname()
# ModelNet40 official train/test split
TRAIN_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, 'data/3Classes/train_files.txt'))
TEST_FILES = provider.getDataFiles(\
os.path.join(BASE_DIR, 'data/3Classes/test_files.txt'))
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def evaluate():
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=())
# simple model
pred, end_points,global_feature,global_matrix = MODEL.get_model(pointclouds_pl, is_training_pl,
num_feature = NUM_FEATURE, num_class = NUM_CLASSES)
loss = MODEL.get_loss(pred, labels_pl, end_points)
# 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 = True
sess = tf.Session(config=config)
# Restore variables from disk.
saver.restore(sess, MODEL_PATH)
log_string("Model restored.")
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss}
eval_one_epoch(sess, ops)
def eval_one_epoch(sess, ops):
""" ops: dict mapping from string to tf ops """
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_data = provider.offset_data(current_data)
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
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)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += (loss_val*BATCH_SIZE)
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)))
# acc = 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))))
# plotAcc(acc)
return total_correct / float(total_seen)
if __name__=='__main__':
with tf.Graph().as_default():
evaluate()
LOG_FOUT.close()