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s3dis_test.py
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s3dis_test.py
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import tensorflow as tf
import numpy as np
import time
from os import makedirs
from os.path import exists, join
from sklearn.metrics import confusion_matrix
from helper_ply import write_ply
from helper_dp import DataProcessing as DP
def log_out(out_str, log_f_out):
log_f_out.write(out_str + '\n')
log_f_out.flush()
print(out_str)
class ModelTester:
def __init__(self, model, dataset, restore_snap=None):
my_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.saver = tf.train.Saver(my_vars, max_to_keep=100)
self.Log_file = open('log_test_' + str(dataset.val_split) + '.txt', 'a')
# Create a session for running Ops on the Graph.
on_cpu = False
if on_cpu:
c_proto = tf.ConfigProto(device_count={'GPU': 0})
else:
c_proto = tf.ConfigProto()
c_proto.gpu_options.allow_growth = True
self.sess = tf.Session(config=c_proto)
self.sess.run(tf.global_variables_initializer())
# Load trained model
if restore_snap is not None:
self.saver.restore(self.sess, restore_snap)
print("Model restored from " + restore_snap)
self.prob_logits = tf.nn.softmax(model.logits)
# Initiate global prediction over all test clouds
self.test_probs = [np.zeros(shape=[l.shape[0], model.config.num_classes], dtype=np.float32)
for l in dataset.input_labels['validation']]
def test(self, model, dataset, num_votes=100):
# Smoothing parameter for votes
test_smooth = 0.95
# Initialise iterator with validation/test data
self.sess.run(dataset.val_init_op)
# Number of points per class in validation set
val_proportions = np.zeros(model.config.num_classes, dtype=np.float32)
i = 0
for label_val in dataset.label_values:
if label_val not in dataset.ignored_labels:
val_proportions[i] = np.sum([np.sum(labels == label_val) for labels in dataset.val_labels])
i += 1
# Test saving path
saving_path = time.strftime('results/Log_%Y-%m-%d_%H-%M-%S', time.gmtime())
test_path = join('test') # Save all results in the same path for the convinience of 6fold evaluation
makedirs(test_path) if not exists(test_path) else None
makedirs(join(test_path, 's3dis_preds')) if not exists(join(test_path, 's3dis_preds')) else None
step_id = 0
epoch_id = 0
last_min = -0.5
while last_min < num_votes:
try:
ops = (self.prob_logits,
model.labels,
model.inputs['input_inds'],
model.inputs['cloud_inds'],
)
stacked_probs, stacked_labels, point_idx, cloud_idx = self.sess.run(ops, {model.is_training: False})
correct = np.sum(np.argmax(stacked_probs, axis=1) == stacked_labels)
acc = correct / float(np.prod(np.shape(stacked_labels)))
print('step' + str(step_id) + ' acc:' + str(acc))
stacked_probs = np.reshape(stacked_probs, [model.config.val_batch_size, model.config.num_points,
model.config.num_classes])
for j in range(np.shape(stacked_probs)[0]):
probs = stacked_probs[j, :, :]
p_idx = point_idx[j, :]
c_i = cloud_idx[j][0]
self.test_probs[c_i][p_idx] = test_smooth * self.test_probs[c_i][p_idx] + (1 - test_smooth) * probs
step_id += 1
except tf.errors.OutOfRangeError:
new_min = np.min(dataset.min_possibility['validation'])
log_out('Epoch {:3d}, end. Min possibility = {:.1f}'.format(epoch_id, new_min), self.Log_file)
if last_min + 1 < new_min:
# Update last_min
last_min += 1
# Show vote results (On subcloud so it is not the good values here)
log_out('\nConfusion on sub clouds', self.Log_file)
confusion_list = []
num_val = len(dataset.input_labels['validation'])
for i_test in range(num_val):
probs = self.test_probs[i_test]
preds = dataset.label_values[np.argmax(probs, axis=1)].astype(np.int32)
labels = dataset.input_labels['validation'][i_test]
# Confs
confusion_list += [confusion_matrix(labels, preds, dataset.label_values)]
# Regroup confusions
C = np.sum(np.stack(confusion_list), axis=0).astype(np.float32)
# Rescale with the right number of point per class
C *= np.expand_dims(val_proportions / (np.sum(C, axis=1) + 1e-6), 1)
# Compute IoUs
IoUs = DP.IoU_from_confusions(C)
m_IoU = np.mean(IoUs)
s = '{:5.2f} | '.format(100 * m_IoU)
for IoU in IoUs:
s += '{:5.2f} '.format(100 * IoU)
log_out(s + '\n', self.Log_file)
if int(np.ceil(new_min)) % 1 == 0:
# Project predictions
log_out('\nReproject Vote #{:d}'.format(int(np.floor(new_min))), self.Log_file)
proj_probs_list = []
for i_val in range(num_val):
# Reproject probs back to the evaluations points
proj_idx = dataset.val_proj[i_val]
probs = self.test_probs[i_val][proj_idx, :]
proj_probs_list += [probs]
# Show vote results
log_out('Confusion on full clouds', self.Log_file)
confusion_list = []
for i_test in range(num_val):
# Get the predicted labels
preds = dataset.label_values[np.argmax(proj_probs_list[i_test], axis=1)].astype(np.uint8)
# Confusion
labels = dataset.val_labels[i_test]
acc = np.sum(preds == labels) / len(labels)
log_out(dataset.input_names['validation'][i_test] + ' Acc:' + str(acc), self.Log_file)
confusion_list += [confusion_matrix(labels, preds, dataset.label_values)]
name = dataset.input_names['validation'][i_test] + '.ply'
write_ply(join(test_path, 's3dis_preds', name), [preds, labels], ['pred', 'label'])
# Regroup confusions
C = np.sum(np.stack(confusion_list), axis=0)
IoUs = DP.IoU_from_confusions(C)
m_IoU = np.mean(IoUs)
s = '{:5.2f} | '.format(100 * m_IoU)
for IoU in IoUs:
s += '{:5.2f} '.format(100 * IoU)
log_out('-' * len(s), self.Log_file)
log_out(s, self.Log_file)
log_out('-' * len(s) + '\n', self.Log_file)
print('finished \n')
self.sess.close()
return
self.sess.run(dataset.val_init_op)
epoch_id += 1
step_id = 0
continue
return