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evaluate_samplenet.py
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evaluate_samplenet.py
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from __future__ import print_function
from builtins import str
from builtins import range
import tensorflow as tf
import numpy as np
import argparse
import socket
import importlib
import os
import sys
from soft_projection import SoftProjection
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
# command line arguments
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--classifier_model', default='pointnet_cls', help='Classifier model name [pointnet_cls/pointnet_cls_basic] [default:pointnet_cls]')
parser.add_argument('--sampler_model', default='samplenet_model', help='Sampler model name: [default: samplenet_model]')
parser.add_argument('--sampler_model_path', default='log/SampleNet32/model.ckpt', help='Path to model.ckpt file of SampleNet')
parser.add_argument('--use_restore_epoch', action='store_true', help='Add restore_epoch to sampler_model_path')
parser.add_argument('--restore_epoch', type=int, default=500, help='Epoch for model restore [default: 500]')
parser.add_argument('--infer_set', default='test', help='Data set for inference (train or test) [default: test]')
parser.add_argument('--num_in_points', type=int, default=1024, help='Number of input Points [default: 1024]')
parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during evaluation [default: 1]')
parser.add_argument('--bottleneck_size', type=int, default=128, help='bottleneck size [default: 128]')
parser.add_argument('--match_output', type=int, default=1, help='Matching flag: 1 - match, 0 - do not match [default:1]')
parser.add_argument('--dump_dir', default='log/SampleNet32/eval', help='dump folder path')
parser.add_argument('--num_out_points', type=int, default=32, help='Number of output points [2,4,...,1024] [default: 32]')
# projection arguments
parser.add_argument("--projection_group_size", type=int, default=7, help='Neighborhood size in Soft Projection [default: 7]')
FLAGS = parser.parse_args()
# fmt: on
GPU_INDEX = FLAGS.gpu
CLASSIFIER_MODEL = importlib.import_module(
FLAGS.classifier_model
) # import network module
SAMPLER_MODEL = importlib.import_module(FLAGS.sampler_model) # import network module
SAMPLER_MODEL_PATH = FLAGS.sampler_model_path
USE_RESTORE_EPOCH = FLAGS.use_restore_epoch
INFER_SET = FLAGS.infer_set
RESTORE_EPOCH = FLAGS.restore_epoch
NUM_IN_POINTS = FLAGS.num_in_points
BATCH_SIZE = FLAGS.batch_size
BOTTLENECK_SIZE = FLAGS.bottleneck_size
MATCH_OUTPUT = FLAGS.match_output
DUMP_DIR = FLAGS.dump_dir
NUM_OUT_POINTS = FLAGS.num_out_points
# projection configuration
PROJECTION_GROUP_SIZE = FLAGS.projection_group_size
if USE_RESTORE_EPOCH:
SAMPLER_MODEL_PATH += "-" + str(int(RESTORE_EPOCH))
if not os.path.exists(DUMP_DIR):
os.makedirs(DUMP_DIR)
LOG_FOUT = open(os.path.join(DUMP_DIR, "log_evaluate.txt"), "w")
LOG_FOUT.write(str(FLAGS) + "\n")
NUM_CLASSES = 40
SHAPE_NAMES = [
line.rstrip()
for line in open(
os.path.join(BASE_DIR, "data/modelnet40_ply_hdf5_2048/shape_names.txt")
)
]
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")
)
if INFER_SET == "train":
INFER_FILES = TRAIN_FILES
else:
INFER_FILES = TEST_FILES
def log_string(out_str):
LOG_FOUT.write(out_str + "\n")
LOG_FOUT.flush()
print(out_str)
def evaluate():
with tf.device("/gpu:" + str(GPU_INDEX)):
pointclouds_pl, labels_pl = CLASSIFIER_MODEL.placeholder_inputs(
BATCH_SIZE, NUM_IN_POINTS
)
is_training_pl = tf.placeholder(tf.bool, shape=())
with tf.variable_scope("sampler"):
simplified_points = SAMPLER_MODEL.get_model(
pointclouds_pl, is_training_pl, NUM_OUT_POINTS, BOTTLENECK_SIZE
)
projector = SoftProjection(PROJECTION_GROUP_SIZE)
hard_projected_points, _, _ = projector.project(
pointclouds_pl, simplified_points, hard=True
)
soft_projected_points, _, _ = projector.project(
pointclouds_pl, simplified_points, hard=False
)
idx = SAMPLER_MODEL.get_nn_indices(pointclouds_pl, simplified_points)
outcloud = simplified_points
pred, end_points = CLASSIFIER_MODEL.get_model(outcloud, is_training_pl)
loss = CLASSIFIER_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, SAMPLER_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,
"simplified_points": simplified_points,
"soft_projected_points": soft_projected_points,
"hard_projected_points": hard_projected_points,
"idx": idx,
"outcloud": outcloud,
}
eval_one_epoch(sess, ops)
def eval_one_epoch(sess, ops):
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
num_unique_idx = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
fout = open(os.path.join(DUMP_DIR, "pred_label.txt"), "w")
for fn in range(len(INFER_FILES)):
log_string(
"---- file number "
+ str(fn + 1)
+ " out of "
+ str(len(INFER_FILES))
+ " files ----"
)
current_data, current_label = provider.loadDataFile(INFER_FILES[fn])
current_data = current_data[:, 0:NUM_IN_POINTS, :]
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
curr_data = current_data[start_idx:end_idx, :, :]
feed_dict = {
ops["pointclouds_pl"]: curr_data,
ops["labels_pl"]: current_label[start_idx:end_idx],
ops["is_training_pl"]: is_training,
}
(
simplified_points,
soft_projected_points,
hard_projected_points,
nn_indices,
) = sess.run(
[
ops["simplified_points"],
ops["soft_projected_points"],
ops["hard_projected_points"],
ops["idx"],
],
feed_dict=feed_dict,
)
sampled_points = SAMPLER_MODEL.nn_matching(
curr_data, nn_indices, NUM_OUT_POINTS
)
if MATCH_OUTPUT:
outcloud = sampled_points
else:
outcloud = simplified_points
for ii in range(0, BATCH_SIZE):
num_unique_idx += np.size(np.unique(nn_indices[ii]))
feed_dict = {
ops["pointclouds_pl"]: curr_data,
ops["outcloud"]: outcloud,
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
pred_val = np.argmax(batch_pred_sum, 1)
# Aggregating END
correct = np.sum(pred_val == current_label[start_idx:end_idx])
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
fout.write("%d, %d\n" % (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)
)
)
)
log_string("total_seen: %f" % (total_seen))
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__":
evaluate()
LOG_FOUT.close()