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train_finetuning.py
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import tensorflow as tf
import numpy as np
np.set_printoptions(precision=2, linewidth=200)
import cv2
import os
import time
import sys
import tf_nndistance
import argparse
import glob
import PIL
import scipy.ndimage as ndimage
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils import *
from plane_utils import *
from modules import *
from planenet import PlaneNet
from RecordReader import *
from RecordReaderRGBD import *
from RecordReader3D import *
from RecordReaderAll import *
#from SegmentationRefinement import *
#training_flag: toggle dropout and batch normalization mode
#it's true for training and false for validation, testing, prediction
#it also controls which data batch to use (*_train or *_val)
def build_graph(img_inp_train, img_inp_val, training_flag, options):
with tf.device('/gpu:%d'%options.gpu_id):
img_inp = tf.cond(training_flag, lambda: img_inp_train, lambda: img_inp_val)
net = PlaneNet({'img_inp': img_inp}, is_training=training_flag, options=options)
#global predictions
plane_pred = net.layers['plane_pred']
segmentation_pred = net.layers['segmentation_pred']
non_plane_mask_pred = net.layers['non_plane_mask_pred']
non_plane_depth_pred = net.layers['non_plane_depth_pred']
non_plane_normal_pred = net.layers['non_plane_normal_pred']
non_plane_normal_pred = tf.nn.l2_normalize(non_plane_normal_pred, dim=-1)
if False:
plane_pred = gt_dict['plane']
non_plane_mask_pred = gt_dict['non_plane_mask'] * 10
non_plane_depth_pred = gt_dict['depth']
non_plane_normal_pred = gt_dict['normal']
segmentation_pred = gt_dict['segmentation'][:, :, :, :20] * 10
pass
global_pred_dict = {'plane': plane_pred, 'segmentation': segmentation_pred, 'non_plane_mask': non_plane_mask_pred, 'non_plane_depth': non_plane_depth_pred, 'non_plane_normal': non_plane_normal_pred}
if options.predictBoundary:
global_pred_dict['boundary'] = net.layers['boundary_pred']
pass
if options.predictConfidence:
global_pred_dict['confidence'] = net.layers['plane_confidence_pred']
pass
if options.predictSemantics:
global_pred_dict['semantics'] = net.layers['semantics_pred']
pass
#local predictions
if options.predictLocal:
local_pred_dict = {'score': net.layers['local_score_pred'], 'plane': net.layers['local_plane_pred'], 'mask': net.layers['local_mask_pred']}
else:
local_pred_dict = {}
pass
#deep supervision
deep_pred_dicts = []
for layer in options.deepSupervisionLayers:
pred_dict = {'plane': net.layers[layer+'_plane_pred'], 'segmentation': net.layers[layer+'_segmentation_pred'], 'non_plane_mask': net.layers[layer+'_non_plane_mask_pred']}
#if options.predictConfidence:
#pred_dict['confidence'] = net.layers[layer+'_plane_confidence_pred']
#pass
deep_pred_dicts.append(pred_dict)
continue
if options.anchorPlanes:
anchors_np = np.load('dump/anchors_' + options.hybrid + '.npy')
anchors = tf.reshape(tf.constant(anchors_np.reshape(-1)), anchors_np.shape)
anchors = tf.tile(tf.expand_dims(anchors, 0), [options.batchSize, 1, 1])
all_pred_dicts = deep_pred_dicts + [global_pred_dict]
for pred_index, pred_dict in enumerate(all_pred_dicts):
all_pred_dicts[pred_index]['plane'] += anchors
continue
pass
pass
return global_pred_dict, local_pred_dict, deep_pred_dicts
def build_loss(img_inp_train, img_inp_val, global_pred_dict, deep_pred_dicts, global_gt_dict_train, global_gt_dict_val, training_flag, options):
with tf.device('/gpu:%d'%options.gpu_id):
debug_dict = {}
img_inp = tf.cond(training_flag, lambda: img_inp_train, lambda: img_inp_val)
global_gt_dict = {}
for name in global_gt_dict_train.keys():
global_gt_dict[name] = tf.cond(training_flag, lambda: global_gt_dict_train[name], lambda: global_gt_dict_val[name])
continue
# local_gt_dict = {}
# for name in local_gt_dict_train.keys():
# local_gt_dict[name] = tf.cond(tf.equal(training_flag % 2, 0), lambda: local_gt_dict_train[name], lambda: local_gt_dict_val[name])
# continue
plane_parameters = tf.reshape(global_pred_dict['plane'], (-1, 3))
info = global_gt_dict['info'][0]
plane_depths = planeDepthsModule(plane_parameters, WIDTH, HEIGHT, info)
plane_depths = tf.transpose(tf.reshape(plane_depths, [HEIGHT, WIDTH, -1, options.numOutputPlanes]), [2, 0, 1, 3])
non_plane_depth = global_pred_dict['non_plane_depth']
all_depths = tf.concat([plane_depths, non_plane_depth], axis=3)
#if options.predictPixelwise == 1:
validDepthMask = tf.cast(tf.greater(global_gt_dict['depth'], 1e-4), tf.float32)
numValidPixels = tf.reduce_sum(validDepthMask, axis=[1, 2, 3])
depth_loss = tf.constant(0.0)
if True:
all_segmentations = tf.concat([global_pred_dict['segmentation'], global_pred_dict['non_plane_mask']], axis=3)
all_segmentations_softmax = tf.nn.softmax(all_segmentations)
depth_loss += tf.reduce_mean(tf.reduce_sum(tf.squared_difference(all_depths, global_gt_dict['depth']) * all_segmentations_softmax, axis=3, keep_dims=True) * validDepthMask) * 10000
else:
depth_diff = global_pred_dict['non_plane_depth'] - global_gt_dict['depth']
depth_diff_gx = depth_diff - tf.concat([tf.ones([options.batchSize, HEIGHT, 1, 1]), depth_diff[:, :, :WIDTH - 1]], axis=2)
depth_diff_gy = depth_diff - tf.concat([tf.ones([options.batchSize, 1, WIDTH, 1]), depth_diff[:, :HEIGHT - 1]], axis=1)
depth_loss += tf.reduce_mean(tf.reduce_sum(tf.pow(depth_diff * validDepthMask, 2), axis=[1, 2, 3]) / numValidPixels - 0.5 * tf.pow(tf.reduce_sum(depth_diff * validDepthMask, axis=[1, 2, 3]) / numValidPixels, 2) + tf.reduce_sum((tf.pow(depth_diff_gx, 2) + tf.pow(depth_diff_gy, 2)) * validDepthMask, axis=[1, 2, 3]) / numValidPixels) * 1000
pass
#depth_loss += tf.reduce_mean(tf.squared_difference(global_pred_dict['non_plane_depth'], global_gt_dict['depth']) * validDepthMask) * 10000
# valid_normal_mask = tf.squeeze(tf.cast(tf.less(tf.slice(global_gt_dict['info'], [0, 19], [options.batchSize, 1]), 2), tf.float32))
# normal_gt = tf.nn.l2_normalize(global_gt_dict['normal'], dim=-1)
# normal_loss = tf.reduce_mean(tf.reduce_sum(-global_pred_dict['non_plane_normal'] * normal_gt * validDepthMask, axis=[1, 2, 3]) / numValidPixels * valid_normal_mask) * 1000
# #normal_loss = tf.reduce_mean(tf.reduce_mean(tf.squared_difference(global_pred_dict['non_plane_normal'], global_gt_dict['normal']) * validDepthMask, axis=[1, 2, 3]) * valid_normal_mask) * 1000
# valid_semantics_mask = tf.squeeze(tf.cast(tf.not_equal(tf.slice(global_gt_dict['info'], [0, 19], [options.batchSize, 1]), 1), tf.float32))
# semantics_loss = tf.reduce_mean(tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=global_pred_dict['semantics'], labels=global_gt_dict['semantics']), axis=[1, 2]) * valid_semantics_mask) * 1000
normal_loss = tf.constant(0.0)
semantics_loss = tf.constant(0.0)
l2_losses = tf.add_n([options.l2Weight * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name])
loss = depth_loss + l2_losses
#if options.pixelwiseLoss:
#normal_loss = tf.reduce_mean(tf.squared_difference(global_pred_dict['non_plane_normal'], global_gt_dict['normal'])) * 1000
#depth_loss = tf.reduce_mean(tf.squared_difference(global_pred_dict['non_plane_depth'], global_gt_dict['depth']) * validDepthMask) * 1000
#pass
loss_dict = {'depth': depth_loss, 'normal': normal_loss, 'semantics': semantics_loss}
pass
return loss, loss_dict, debug_dict
def main(options):
if not os.path.exists(options.checkpoint_dir):
os.system("mkdir -p %s"%options.checkpoint_dir)
pass
if not os.path.exists(options.test_dir):
os.system("mkdir -p %s"%options.test_dir)
pass
min_after_dequeue = 1000
train_inputs = []
val_inputs = []
if '0' in options.hybrid:
train_inputs.append(options.rootFolder + '/planes_SUNCG_train.tfrecords')
val_inputs.append(options.rootFolder + '/planes_SUNCG_val.tfrecords')
pass
if '1' in options.hybrid:
for _ in xrange(10):
train_inputs.append(options.rootFolder + '/planes_nyu_rgbd_train.tfrecords')
train_inputs.append(options.rootFolder + '/planes_nyu_rgbd_labeled_train.tfrecords')
val_inputs.append(options.rootFolder + '/planes_nyu_rgbd_val.tfrecords')
continue
pass
if '2' in options.hybrid:
train_inputs.append(options.rootFolder + '/planes_matterport_train.tfrecords')
val_inputs.append(options.rootFolder + '/planes_matterport_val.tfrecords')
pass
if '3' in options.hybrid:
train_inputs.append(options.rootFolder + '/planes_scannet_train.tfrecords')
val_inputs.append(options.rootFolder + '/planes_scannet_val.tfrecords')
pass
reader_train = RecordReaderAll()
filename_queue_train = tf.train.string_input_producer(train_inputs, num_epochs=10000)
img_inp_train, global_gt_dict_train, local_gt_dict_train = reader_train.getBatch(filename_queue_train, numOutputPlanes=options.numOutputPlanes, batchSize=options.batchSize, min_after_dequeue=min_after_dequeue, getLocal=True)
reader_val = RecordReaderAll()
filename_queue_val = tf.train.string_input_producer(val_inputs, num_epochs=10000)
img_inp_val, global_gt_dict_val, local_gt_dict_val = reader_val.getBatch(filename_queue_val, numOutputPlanes=options.numOutputPlanes, batchSize=options.batchSize, min_after_dequeue=min_after_dequeue, getLocal=True)
training_flag = tf.placeholder(tf.bool, shape=[], name='training_flag')
#global_pred_dict, local_pred_dict, deep_pred_dicts = build_graph(img_inp_train, img_inp_val, img_inp_rgbd_train, img_inp_rgbd_val, img_inp_3d_train, img_inp_3d_val, training_flag, options)
global_pred_dict, local_pred_dict, deep_pred_dicts = build_graph(img_inp_train, img_inp_val, training_flag, options)
#loss, loss_dict, _ = build_loss(global_pred_dict, local_pred_dict, deep_pred_dicts, global_gt_dict_train, local_gt_dict_train, global_gt_dict_val, local_gt_dict_val, training_flag, options)
#loss_rgbd, loss_dict_rgbd, _ = build_loss_rgbd(global_pred_dict, deep_pred_dicts, global_gt_dict_rgbd_train, global_gt_dict_rgbd_val, training_flag, options)
loss, loss_dict, _ = build_loss(img_inp_train, img_inp_val, global_pred_dict, deep_pred_dicts, global_gt_dict_train, global_gt_dict_val, training_flag, options)
#loss = tf.cond(tf.less(training_flag, 2), lambda: loss, lambda: tf.cond(tf.less(training_flag, 4), lambda: loss_rgbd, lambda: loss_3d))
#train_writer = tf.summary.FileWriter(options.log_dir + '/train')
#val_writer = tf.summary.FileWriter(options.log_dir + '/val')
#train_writer_rgbd = tf.summary.FileWriter(options.log_dir + '/train_rgbd')
#val_writer_rgbd = tf.summary.FileWriter(options.log_dir + '/val_rgbd')
#writers = [train_writer, val_writer, train_writer_rgbd, val_writer_rgbd]
with tf.variable_scope('statistics'):
batchno = tf.Variable(0, dtype=tf.int32, trainable=False, name='batchno')
batchnoinc=batchno.assign(batchno+1)
pass
optimizer = tf.train.AdamOptimizer(options.LR)
train_op = optimizer.minimize(loss, global_step=batchno)
var_to_restore = [v for v in tf.global_variables()]
tf.summary.scalar('loss', loss)
summary_op = tf.summary.merge_all()
config=tf.ConfigProto()
config.allow_soft_placement=True
#config.gpu_options.allow_growth=True
config.gpu_options.per_process_gpu_memory_fraction=0.9
saver=tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session(config=config) as sess:
sess.run(init_op)
if options.restore == 0:
#fine-tune from DeepLab model
var_to_restore = [v for v in var_to_restore if 'res5d' not in v.name and 'segmentation' not in v.name and 'plane' not in v.name and 'deep_supervision' not in v.name and 'local' not in v.name and 'boundary' not in v.name and 'degridding' not in v.name and 'res2a_branch2a' not in v.name and 'res2a_branch1' not in v.name and 'Adam' not in v.name and 'beta' not in v.name and 'statistics' not in v.name and 'semantics' not in v.name]
pretrained_model_loader = tf.train.Saver(var_to_restore)
pretrained_model_loader.restore(sess,"../pretrained_models/deeplab_resnet.ckpt")
elif options.restore == 1:
#restore the same model from checkpoint
loader = tf.train.Saver(var_to_restore)
loader.restore(sess,"%s/checkpoint.ckpt"%(options.checkpoint_dir))
bno=sess.run(batchno)
print(bno)
elif options.restore == 2:
#restore the same model from checkpoint but reset batchno to 1
loader = tf.train.Saver(var_to_restore)
loader.restore(sess,"%s/checkpoint.ckpt"%(options.checkpoint_dir))
sess.run(batchno.assign(1))
elif options.restore == 3:
#restore the same model from standard training
# if options.predictBoundary == 1:
# var_to_restore = [v for v in var_to_restore if 'boundary' not in v.name]
# pass
# if options.predictConfidence == 1:
# var_to_restore = [v for v in var_to_restore if 'confidence' not in v.name]
# pass
if options.predictSemantics == 1:
var_to_restore = [v for v in var_to_restore if 'semantics' not in v.name]
pass
loader = tf.train.Saver(var_to_restore)
#loader.restore(sess, options.rootFolder + '/checkpoint/planenet_hybrid' + hybrid + '_bl0_ll1_bw0.5_pb_pp_ps_sm0/checkpoint.ckpt')
loader.restore(sess, options.rootFolder + '/checkpoint/planenet_hybrid3_bl0_dl0_ll1_pb_pp_sm0/checkpoint.ckpt')
#loader.restore(sess,"checkpoint/planenet/checkpoint.ckpt")
sess.run(batchno.assign(1))
elif options.restore == 4:
#fine-tune another model
#var_to_restore = [v for v in var_to_restore if 'res4b22_relu_non_plane' not in v.name]
loader = tf.train.Saver(var_to_restore)
loader.restore(sess, options.fineTuningCheckpoint)
sess.run(batchno.assign(1))
pass
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
MOVING_AVERAGE_DECAY = 0.99
ema = [0., 0., 0., 0.]
ema_acc = [1e-10, 1e-10, 1e-10, 1e-10]
last_snapshot_time = time.time()
bno=sess.run(batchno)
try:
while bno<300000:
t0 = time.time()
batchIndexPeriod = bno % 100
if batchIndexPeriod < len(options.hybrid):
#batchType = int(options.hybrid[batchIndexPeriod]) * 2 + 1
batchType = 1
_, total_loss, losses, summary_str, gt_dict = sess.run([batchnoinc, loss, loss_dict, summary_op, global_pred_dict], feed_dict = {training_flag: batchType == 0})
else:
batchType = 0
_, total_loss, losses, summary_str, gt_dict = sess.run([train_op, loss, loss_dict, summary_op, global_pred_dict], feed_dict = {training_flag: batchType == 0})
pass
# for batchIndex in xrange(options.batchSize):
# if np.isnan(global_gt['plane'][batchIndex]).any():
# #print(losses)
# #print(global_gt['plane'][batchIndex])
# print(global_gt['num_planes'][batchIndex])
# for planeIndex in xrange(global_gt['num_planes'][batchIndex]):
# cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(global_gt['segmentation'][batchIndex, :, :, planeIndex]))
# continue
# np.save('temp/plane.npy', global_gt['plane'][batchIndex])
# np.save('temp/depth.npy', global_gt['depth'][batchIndex])
# np.save('temp/segmentation.npy', global_gt['segmentation'][batchIndex])
# np.save('temp/info.npy', global_gt['info'][batchIndex])
# np.save('temp/num_planes.npy', global_gt['num_planes'][batchIndex])
# planes, segmentation, numPlanes = removeSmallSegments(global_gt['plane'][batchIndex], np.zeros((HEIGHT, WIDTH, 3)), global_gt['depth'][batchIndex].squeeze(), np.zeros((HEIGHT, WIDTH, 3)), np.argmax(global_gt['segmentation'][batchIndex], axis=-1), global_gt['semantics'][batchIndex], global_gt['info'][batchIndex], global_gt['num_planes'][batchIndex])
# print(planes)
# exit(1)
# pass
# continue
#writers[batchType].add_summary(summary_str, bno)
ema[batchType] = ema[batchType] * MOVING_AVERAGE_DECAY + total_loss
ema_acc[batchType] = ema_acc[batchType] * MOVING_AVERAGE_DECAY + 1
bno = sess.run(batchno)
if time.time()-last_snapshot_time > options.saveInterval:
print('save snapshot')
saver.save(sess,'%s/checkpoint.ckpt'%options.checkpoint_dir)
last_snapshot_time = time.time()
pass
print bno,'train', ema[0] / ema_acc[0], 'val', ema[1] / ema_acc[1], 'train rgbd', ema[2] / ema_acc[2], 'val rgbd', ema[3] / ema_acc[3], 'loss', total_loss, 'time', time.time()-t0
if np.random.random() < 0.01:
print(losses)
pass
continue
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
pass
# Wait for threads to finish.
coord.join(threads)
sess.close()
pass
return
def test(options):
if not os.path.exists(options.test_dir):
os.system("mkdir -p %s"%options.test_dir)
pass
if options.dataset == '':
assert(len(options.hybrid) == 1)
if options.hybrid == '0':
options.dataset = 'SUNCG'
elif options.hybrid == '1':
options.dataset = 'NYU_RGBD'
elif options.hybrid == '2':
options.dataset = 'matterport'
elif options.hybrid == '3':
options.dataset = 'ScanNet'
options.dataset
options.batchSize = 1
min_after_dequeue = 1000
reader = RecordReaderAll()
if options.dataset == 'SUNCG':
filename_queue = tf.train.string_input_producer([options.rootFolder + '/planes_SUNCG_val.tfrecords'], num_epochs=10000)
elif options.dataset == 'NYU_RGBD':
filename_queue = tf.train.string_input_producer([options.rootFolder + '/planes_nyu_rgbd_val.tfrecords'], num_epochs=1)
options.deepSupervision = 0
options.predictLocal = 0
elif options.dataset == 'matterport':
filename_queue = tf.train.string_input_producer([options.rootFolder + '/planes_matterport_val.tfrecords'], num_epochs=1)
else:
filename_queue = tf.train.string_input_producer([options.rootFolder + '/planes_scannet_train.tfrecords'], num_epochs=1)
pass
img_inp, global_gt_dict, local_gt_dict = reader.getBatch(filename_queue, numOutputPlanes=options.numOutputPlanes, batchSize=options.batchSize, min_after_dequeue=min_after_dequeue, getLocal=True, random=False)
training_flag = tf.constant(False, tf.bool)
global_pred_dict, local_pred_dict, deep_pred_dicts = build_graph(img_inp, img_inp, global_gt_dict, training_flag, options)
var_to_restore = tf.global_variables()
loss, loss_dict, debug_dict = build_loss(img_inp, img_inp, global_pred_dict, deep_pred_dicts, global_gt_dict, global_gt_dict, training_flag, options)
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
config.allow_soft_placement=True
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session(config=config) as sess:
sess.run(init_op)
#var_to_restore = [v for v in var_to_restore if 'res4b22_relu_non_plane' not in v.name]
loader = tf.train.Saver(var_to_restore)
loader.restore(sess, "%s/checkpoint.ckpt"%(options.checkpoint_dir))
#loader.restore(sess, "%s/checkpoint.ckpt"%('checkpoint/planenet_pb_pp_hybrid1'))
#loader.restore(sess, options.fineTuningCheckpoint)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
gtDepths = []
predDepths = []
planeMasks = []
#predMasks = []
gtPlanes = []
predPlanes = []
gtSegmentations = []
predSegmentations = []
gtNumPlanes = []
imageWidth = WIDTH
imageHeight = HEIGHT
focalLength = 517.97
urange = np.arange(imageWidth).reshape(1, -1).repeat(imageHeight, 0) - imageWidth * 0.5
vrange = np.arange(imageHeight).reshape(-1, 1).repeat(imageWidth, 1) - imageHeight * 0.5
ranges = np.array([urange / imageWidth * 640 / focalLength, np.ones(urange.shape), -vrange / imageHeight * 480 / focalLength]).transpose([1, 2, 0])
for index in xrange(10):
print(('image', index))
t0=time.time()
img, global_gt, local_gt, global_pred, local_pred, deep_preds, total_loss, losses, debug = sess.run([img_inp, global_gt_dict, local_gt_dict, global_pred_dict, local_pred_dict, deep_pred_dicts, loss, loss_dict, debug_dict])
# print(options.test_dir)
# cv2.imwrite(options.test_dir + '/depth.png', drawDepthImage(debug['depth'][0]))
# cv2.imwrite(options.test_dir + '/normal.png', drawNormalImage(debug['normal'][0]))
# boundary = debug['boundary'][0]
# boundary = np.concatenate([boundary, np.zeros((HEIGHT, WIDTH, 1))], axis=2)
# cv2.imwrite(options.test_dir + '/boundary.png', drawMaskImage(boundary))
# cv2.imwrite(options.test_dir + '/depth_gt.png', drawDepthImage(debug['depth_gt'][0].squeeze()))
# exit(1)
if 'pixelwise' in options.suffix or True:
image = ((img[0] + 0.5) * 255).astype(np.uint8)
gt_d = global_gt['depth'].squeeze()
pred_d = global_pred['non_plane_depth'].squeeze()
#depth = global_gt['depth'].squeeze()
if '_2' in options.suffix:
pred_p, pred_s, pred_d = fitPlanes(pred_d, numPlanes=20, planeAreaThreshold=3*4, numIterations=100, distanceThreshold=0.05, local=0.2)
elif '_3' in options.suffix:
pred_p, pred_s, pred_d = fitPlanes(gt_d, numPlanes=20, planeAreaThreshold=3*4, numIterations=100, distanceThreshold=0.05, local=0.2)
pass
# gt_p = global_gt['plane'][0]
# pred_p = planes
# valid_mask = (np.linalg.norm(gt_p, axis=1) > 0).astype(np.float32)
# diff = np.min(np.linalg.norm(np.expand_dims(gt_p, 1) - np.expand_dims(pred_p, 0), axis=2), 1)
# num += valid_mask.sum()
# lossSum += (diff * valid_mask).sum()
if options.dataset == 'SUNCG':
planeMask = np.squeeze(debug['segmentation']).sum(axis=2)
else:
planeMask = np.ones((HEIGHT, WIDTH))
if '_2' in options.suffix or '_3' in options.suffix:
planeMask *= (pred_s < 20).astype(np.float32)
pass
if index < 10:
cv2.imwrite(options.test_dir + '/' + str(index) + '_image.png', image)
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth.png', drawDepthImage(gt_d))
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth_pred.png', drawDepthImage(pred_d))
if '_2' in options.suffix or '_3' in options.suffix:
cv2.imwrite(options.test_dir + '/' + str(index) + '_segmentation_pred.png', drawSegmentationImage(pred_s))
pass
#cv2.imwrite(options.test_dir + '/' + str(index) + '_plane_mask.png', drawMaskImage(planeMask))
pass
gtDepths.append(gt_d)
predDepths.append(pred_d)
planeMasks.append(planeMask)
if options.dataset != 'NYU_RGBD' and ('_2' in options.suffix or '_3' in options.suffix):
gt_p = global_gt['plane'][0]
gt_s = global_gt['segmentation'][0]
gt_num_p = global_gt['num_planes'][0]
pred_s = (np.expand_dims(pred_s, -1) == np.reshape(np.arange(options.numOutputPlanes), [1, 1, -1])).astype(np.float32)
gtPlanes.append(gt_p)
predPlanes.append(pred_p)
gtSegmentations.append(gt_s)
gtNumPlanes.append(gt_num_p)
predSegmentations.append(pred_s)
pass
#planeMasks.append((planeSegmentation < 20).astype(np.float32))
continue
print(losses)
print(total_loss)
#print(losses)
#exit(1)
im = img[0]
image = ((im + 0.5) * 255).astype(np.uint8)
gt_d = global_gt['depth'].squeeze()
if options.predictLocal:
pred_local_s = 1 / (1 + np.exp(-local_pred['score'][0]))
pred_local_p = local_pred['plane'][0]
pred_local_m = local_pred['mask'][0]
gt_local_s = local_gt['score'][0]
gt_local_m = local_gt['mask'][0]
#visualize local plane prediction
stride = 8
boxSize = 64
xs = np.arange(WIDTH / stride) * stride + stride / 2
ys = np.arange(HEIGHT / stride) * stride + stride / 2
padding = boxSize / 2 + 1
maskImage = np.zeros((HEIGHT + padding * 2, WIDTH + padding * 2, 3), dtype=np.uint8)
maskImage[padding:padding + HEIGHT, padding:padding + WIDTH, :] = image / 2
for gridY, y in enumerate(ys):
for gridX, x in enumerate(xs):
score = pred_local_s[gridY][gridX]
if score < 0.5:
continue
mask = pred_local_m[gridY][gridX].reshape([16, 16])
mask = cv2.resize(mask, (boxSize, boxSize))
maskImage[y - boxSize / 2 + padding:y + boxSize / 2 + padding, x - boxSize / 2 + padding:x + boxSize / 2 + padding, 0][mask > 0.5] = 255
continue
continue
for gridY, y in enumerate(ys):
for gridX, x in enumerate(xs):
score = gt_local_s[gridY][gridX]
if score < 0.5:
continue
mask = gt_local_m[gridY][gridX].reshape([16, 16])
mask = cv2.resize(mask, (boxSize, boxSize))
maskImage[y - boxSize / 2 + padding:y + boxSize / 2 + padding, x - boxSize / 2 + padding:x + boxSize / 2 + padding, 2][mask > 0.5] = 255
continue
continue
pass
pred_p = global_pred['plane'][0]
pred_s = global_pred['segmentation'][0]
pred_np_m = global_pred['non_plane_mask'][0]
pred_np_d = global_pred['non_plane_depth'][0]
pred_np_n = global_pred['non_plane_normal'][0]
planeMask = 1 - global_gt['non_plane_mask'][0]
info = global_gt['info'][0]
all_segmentations = np.concatenate([pred_s, pred_np_m], axis=2)
all_segmentations_softmax = softmax(all_segmentations)
segmentation = np.argmax(all_segmentations, 2)
#pred_p, segmentation, numPlanes = mergePlanes(global_gt['plane'][0], np.concatenate([global_gt['segmentation'][0], global_gt['non_plane_mask'][0]], axis=2), global_gt['depth'][0].squeeze(), global_gt['info'][0], np.concatenate([pred_s, pred_np_m], axis=2))
plane_depths = calcPlaneDepths(pred_p, WIDTH, HEIGHT, info)
all_depths = np.concatenate([plane_depths, pred_np_d], axis=2)
pred_d = all_depths.reshape(-1, options.numOutputPlanes + 1)[np.arange(WIDTH * HEIGHT), segmentation.reshape(-1)].reshape(HEIGHT, WIDTH)
plane_normals = calcPlaneNormals(pred_p, WIDTH, HEIGHT)
all_normals = np.concatenate([plane_normals, np.expand_dims(pred_np_n, 2)], axis=2)
pred_n = np.sum(all_normals * np.expand_dims(one_hot(segmentation, options.numOutputPlanes+1), -1), 2)
#pred_n = all_normals.reshape(-1, options.numOutputPlanes + 1, 3)[np.arange(WIDTH * HEIGHT), segmentation.reshape(-1)].reshape((HEIGHT, WIDTH, 3))
if False:
gt_s = global_gt['segmentation'][0]
all_segmentations = np.concatenate([gt_s, 1 - planeMask], axis=2)
gt_p = global_gt['plane'][0]
# valid_mask = (np.linalg.norm(gt_p, axis=1) > 0).astype(np.float32)
# diff = np.min(np.linalg.norm(np.expand_dims(gt_p, 1) - np.expand_dims(pred_p, 0), axis=2), 1)
# num += valid_mask.sum()
# lossSum += (diff * valid_mask).sum()
#gt_p = np.stack([-gt_p[:, 0], -gt_p[:, 2], -gt_p[:, 1]], axis=1)
plane_depths = calcPlaneDepths(gt_p, WIDTH, HEIGHT, info)
all_depths = np.concatenate([plane_depths, pred_np_d], axis=2)
segmentation = np.argmax(all_segmentations, 2)
pred_d = all_depths.reshape(-1, options.numOutputPlanes + 1)[np.arange(WIDTH * HEIGHT), segmentation.reshape(-1)].reshape(HEIGHT, WIDTH)
# print(gt_p)
# for segmentIndex in xrange(options.numOutputPlanes):
# cv2.imwrite(options.test_dir + '/' + str(index) + '_mask_' + str(segmentIndex) + '.png', drawMaskImage(segmentation == segmentIndex))
# continue
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth_pred.png', drawDepthImage(pred_d))
#exit(1)
pass
gt_p = global_gt['plane'][0]
gt_s = global_gt['segmentation'][0]
gt_num_p = global_gt['num_planes'][0]
gtPlanes.append(gt_p)
predPlanes.append(pred_p)
gtSegmentations.append(gt_s)
gtNumPlanes.append(gt_num_p)
predSegmentations.append(pred_s)
gtDepths.append(gt_d)
planeMasks.append(planeMask.squeeze())
predDepths.append(pred_d)
evaluateDepths(predDepths[-1], gtDepths[-1], gtDepths[-1] > 0, planeMasks[-1])
if index >= 10:
continue
if options.predictSemantics:
cv2.imwrite(options.test_dir + '/' + str(index) + '_semantics_pred.png', drawSegmentationImage(global_pred['semantics'][0], blackIndex=0))
cv2.imwrite(options.test_dir + '/' + str(index) + '_semantics_gt.png', drawSegmentationImage(global_gt['semantics'][0], blackIndex=0))
pass
if 'cost_mask' in debug:
cv2.imwrite(options.test_dir + '/' + str(index) + '_cost_mask.png', drawMaskImage(np.sum(debug['cost_mask'][0], axis=-1)))
for planeIndex in xrange(options.numOutputPlanes + 1):
cv2.imwrite(options.test_dir + '/' + str(index) + '_cost_mask_' + str(planeIndex) + '.png', drawMaskImage(debug['cost_mask'][0, :, :, planeIndex]))
continue
all_segmentations = np.concatenate([pred_s, pred_np_m], axis=2)
for planeIndex in xrange(options.numOutputPlanes + 1):
cv2.imwrite(options.test_dir + '/' + str(index) + '_segmentation_pred_' + str(planeIndex) + '.png', drawMaskImage(all_segmentations[:, :, planeIndex]))
continue
exit(1)
pass
if 'normal' in global_gt:
gt_n = global_gt['normal'][0]
norm = np.linalg.norm(gt_n, axis=-1, keepdims=True)
gt_n /= np.maximum(norm, 1e-4)
#gt_n = np.stack([-gt_n[:, :, 0], -gt_n[:, :, 2], -gt_n[:, :, 1]], axis=2)
cv2.imwrite(options.test_dir + '/' + str(index) + '_normal_gt.png', drawNormalImage(gt_n))
#cv2.imwrite(options.test_dir + '/' + str(index) + '_normal_pred.png', drawNormalImage(pred_np_n))
cv2.imwrite(options.test_dir + '/' + str(index) + '_normal_pred.png', drawNormalImage(pred_n))
pass
if 'segmentation' in global_gt:
gt_s = global_gt['segmentation'][0]
gt_p = global_gt['plane'][0]
#for planeIndex in xrange(20):
#cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(gt_s[:, :, planeIndex]))
#continue
# print(gt_p)
# print(gt_n[109][129])
# print(gt_n[166][245])
# print(gt_s[109][129])
# print(gt_s[166][245])
# print(plane_normals[109][129])
# print(plane_normals[166][245])
# for planeIndex in xrange(20):
# cv2.imwrite('test/mask_' + str(planeIndex) + '.png', drawMaskImage(gt_s[:, :, planeIndex]))
# continue
# exit(1)
gt_s, gt_p = sortSegmentations(gt_s, gt_p, pred_p)
cv2.imwrite(options.test_dir + '/' + str(index) + '_plane_mask_gt.png', drawMaskImage(planeMask))
cv2.imwrite(options.test_dir + '/' + str(index) + '_segmentation_gt.png', drawSegmentationImage(np.concatenate([gt_s, 1 - planeMask], axis=2), blackIndex=options.numOutputPlanes)
)
#cv2.imwrite(options.test_dir + '/' + str(index) + '_test.png', drawMaskImage(np.sum(np.concatenate([gt_s, 1 - planeMask], axis=2), axis=2)))
#exit(1)
#exit(1)
pass
if options.predictConfidence == 1 and options.dataset == 'SUNCG':
assert(False)
pred_p_c = global_pred['confidence'][0]
pred_p_c = 1 / (1 + np.exp(-pred_p_c))
#print(pred_p_c)
# print(losses)
# print(debug['plane'][0])
# print(pred_p)
# exit(1)
numPlanes = global_gt['num_planes'][0]
print((numPlanes, (pred_p_c > 0.5).sum()))
pred_p_c = (pred_p_c > 0.5).astype(np.float32)
pred_p *= pred_p_c
pred_s -= (1 - pred_p_c.reshape([1, 1, options.numOutputPlanes])) * 10
pass
if False:
#dump results for post processing
if index >= 10:
break
np.save(options.dump_dir + '/planes_' + str(index) + '.npy', pred_p)
np.save(options.dump_dir + '/segmentations_' + str(index) + '.npy', pred_s)
np.save(options.dump_dir + '/segmentations_gt_' + str(index) + '.npy', gt_s)
np.save(options.dump_dir + '/non_plane_depth_' + str(index) + '.npy', pred_np_d)
np.save(options.dump_dir + '/non_plane_segmentation_' + str(index) + '.npy', pred_np_m)
boundary = pred_boundary[0]
boundary = 1 / (1 + np.exp(-boundary))
boundary = np.concatenate([boundary, np.zeros((HEIGHT, WIDTH, 1))], axis=2)
cv2.imwrite(options.dump_dir + '/boundary_' + str(index) + '.png', drawMaskImage(boundary))
cv2.imwrite(options.dump_dir + '/image_' + str(index) + '.png', cv2.resize(image, (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR))
np.save(options.dump_dir + '/depth_' + str(index) + '.npy', gt_d)
continue
cv2.imwrite(options.test_dir + '/' + str(index) + '_image.png', image)
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth.png', drawDepthImage(gt_d))
#cv2.imwrite(options.test_dir + '/' + str(index) + '_overlay.png', drawDepthImageOverlay(image, gt_d))
if options.predictBoundary:
pred_boundary = global_pred['boundary'][0]
pred_boundary = 1 / (1 + np.exp(-pred_boundary))
boundary = np.concatenate([pred_boundary, np.zeros((HEIGHT, WIDTH, 1))], axis=2)
cv2.imwrite(options.test_dir + '/' + str(index) + '_boundary_pred.png', drawMaskImage(boundary))
pass
if 'boundary' in global_gt:
gt_boundary = global_gt['boundary'][0]
boundary = np.concatenate([gt_boundary, np.zeros((HEIGHT, WIDTH, 1))], axis=2)
cv2.imwrite(options.test_dir + '/' + str(index) + '_boundary_gt.png', drawMaskImage(boundary))
pass
for layerIndex, layer in enumerate(options.deepSupervisionLayers):
segmentation_deep = np.argmax(deep_preds[layerIndex]['segmentation'][0], 2)
segmentation_deep[segmentation_deep == options.numOutputPlanes] = -1
segmentation_deep += 1
plane_depths_deep = calcPlaneDepths(deep_preds[layerIndex]['plane'][0], WIDTH, HEIGHT, info)
all_depths_deep = np.concatenate([pred_np_d, plane_depths_deep], axis=2)
pred_d_deep = all_depths_deep.reshape(-1, options.numOutputPlanes + 1)[np.arange(WIDTH * HEIGHT), segmentation_deep.reshape(-1)].reshape(HEIGHT, WIDTH)
cv2.imwrite(options.test_dir + '/' + str(index) + '_segmentation_pred_' + str(layerIndex) + '.png', drawSegmentationImage(deep_preds[layerIndex]['segmentation'][0]))
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth_pred_' + str(layerIndex) + '.png', drawDepthImage(pred_d_deep))
pass
#print(pred_np_m)
#print(pred_s)
#print(global_gt['plane'][0])
#print(pred_p)
#exit(1)
print('depth diff', np.abs(pred_d - gt_d).mean())
cv2.imwrite(options.test_dir + '/' + str(index) + '_segmentation_pred.png', drawSegmentationImage(all_segmentations, blackIndex=options.numOutputPlanes))
#exit(1)
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth_pred.png', drawDepthImage(pred_d))
cv2.imwrite(options.test_dir + '/' + str(index) + '_depth_diff.png', drawMaskImage(np.abs(pred_d - gt_d) * planeMask.squeeze() / 0.2))
#exit(1)
#cv2.imwrite(options.test_dir + '/' + str(index) + '_plane_mask.png', drawMaskImage(planeMask))
#print(np.concatenate([np.arange(options.numOutputPlanes).reshape(-1, 1), planes, pred_p, preds_p[0][0]], axis=1))
#print(np.concatenate([distance[0], preds_p[0][0], pred_p], axis=1))
#segmentation = np.argmax(pred_s, 2)
#writePLYFile(options.test_dir, index, image, pred_d, segmentation, np.zeros(pred_boundary[0].shape))
#writePLYFileParts(options.test_dir, index, image, pred_d, segmentation)
#gt_s_ori = gt_s_ori[0]
# if 'depth' in debug:
# cv2.imwrite(options.test_dir + '/' + str(index) + '_depth_pred_1.png', drawDepthImage(debug['depth'][0]))
# gt_s_ori = debug['segmentation'][0]
# cv2.imwrite(options.test_dir + '/' + str(index) + '_segmentation_gt.png', drawSegmentationImage(gt_s_ori, blackIndex=options.numOutputPlanes))
# total_loss = 0
# for planeIndex in xrange(gt_s_ori.shape[-1]):
# mask_pred = all_segmentations_softmax[:, :, planeIndex]
# loss = -(gt_s_ori[:, :, planeIndex] * np.log(np.maximum(all_segmentations_softmax[:, :, planeIndex], 1e-31))).mean() * 1000
# print(planeIndex, loss)
# total_loss += loss
# cv2.imwrite(options.test_dir + '/mask_pred_' + str(planeIndex) + '.png', drawMaskImage(mask_pred))
# cv2.imwrite(options.test_dir + '/mask_gt_' + str(planeIndex) + '.png', drawMaskImage(gt_s_ori[:, :, planeIndex]))
# continue
# print(total_loss)
# print(gt_s_ori.sum(2).max())
# exit(1)
# pass
if False:
#distance = distance[0]
print(distance)
diff = np.linalg.norm(np.expand_dims(planes, 1) - np.expand_dims(pred_p, 0), axis=2)
print(np.concatenate([planes, pred_p, pow(np.min(diff, axis=1, keepdims=True), 2), np.expand_dims(np.argmin(diff, axis=1), -1)], axis=1))
print(pred_p_c)
#print(distance[:, 6:8].sum(0))
#print(pow(np.linalg.norm(distance[:, :3] - distance[:, 3:6], 2, 1), 2) * 100)
#print(test)
segmentation = np.argmax(all_segmentations, 2) - 1
for planeIndex in xrange(options.numOutputPlanes):
cv2.imwrite(options.test_dir + '/segmentation_' + str(planeIndex) + '.png', drawMaskImage(segmentation == planeIndex))
cv2.imwrite(options.test_dir + '/segmentation_' + str(planeIndex) + '_gt.png', drawMaskImage(gt_s[:, :, planeIndex]))
cv2.imwrite(options.test_dir + '/segmentation_' + str(planeIndex) + '_gt_ori.png', drawMaskImage(gt_s_ori[:, :, planeIndex]))
continue
exit(1)
continue
# if options.dataset == 'SUNCG':
# if 'pixelwise' not in options.suffix:
# evaluatePlaneSegmentation(np.array(predPlanes), np.array(predSegmentations), np.array(gtPlanes), np.array(gtSegmentations), np.array(gtNumPlanes), planeDistanceThreshold = 0.3, IOUThreshold = 0.5, prefix='test/planenet_')
# elif '_2' in options.suffix:
# evaluatePlaneSegmentation(np.array(predPlanes), np.array(predSegmentations), np.array(gtPlanes), np.array(gtSegmentations), np.array(gtNumPlanes), planeDistanceThreshold = 0.3, IOUThreshold = 0.5, prefix='test/pixelwise_pred_')
# elif '_3' in options.suffix:
# evaluatePlaneSegmentation(np.array(predPlanes), np.array(predSegmentations), np.array(gtPlanes), np.array(gtSegmentations), np.array(gtNumPlanes), planeDistanceThreshold = 0.3, IOUThreshold = 0.5, prefix='test/pixelwise_gt_')
# pass
# pass
predDepths = np.array(predDepths)
gtDepths = np.array(gtDepths)
planeMasks = np.array(planeMasks)
#predMasks = np.array(predMasks)
evaluateDepths(predDepths, gtDepths, gtDepths > 0, planeMasks)
exit(1)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
pass
# Wait for threads to finish.
coord.join(threads)
sess.close()
pass
return
# def predict(options):
# options.test_dir += '_predict'
# if not os.path.exists(options.test_dir):
# os.system("mkdir -p %s"%options.test_dir)
# pass
# batchSize = 1
# img_inp = tf.placeholder(tf.float32,shape=(batchSize,HEIGHT,WIDTH,3),name='img_inp')
# plane_gt=tf.placeholder(tf.float32,shape=(batchSize,options.numOutputPlanes, 3),name='plane_inp')
# validating_inp = tf.constant(0, tf.int32)
# global_pred_dict, local_pred_dict, deep_pred_dicts = build_graph(img_inp_train, img_inp_val, img_inp_rgbd_train, img_inp_rgbd_val, training_flag, options)
# var_to_restore = tf.global_variables()
# config=tf.ConfigProto()
# config.gpu_options.allow_growth=True
# config.allow_soft_placement=True
# if dataset == 'SUNCG':
# image_list_file = os.path.join('../PythonScripts/SUNCG/image_list_100_tail_500000.txt')
# with open(image_list_file) as f:
# im_names = [{'image': im_name.strip().replace('plane_global.npy', 'mlt.png'), 'depth': im_name.strip().replace('plane_global.npy', 'depth.png'), 'normal': im_name.strip().replace('plane_global.npy', 'norm_camera.png'), 'valid': im_name.strip().replace('plane_global.npy', 'valid.png'), 'plane': im_name.strip()} for im_name in f.readlines()]
# pass
# else:
# im_names = glob.glob('../../Data/NYU_RGBD/*_color.png')
# im_names = [{'image': im_name, 'depth': im_name.replace('color.png', 'depth.png'), 'normal': im_name.replace('color.png', 'norm_camera.png'), 'invalid_mask': im_name.replace('color.png', 'valid.png')} for im_name in im_names]
# pass
# if numImages > 0:
# im_names = im_names[:numImages]
# pass
# #if args.imageIndex > 0:
# #im_names = im_names[args.imageIndex:args.imageIndex + 1]
# #pass
# init_op = tf.group(tf.global_variables_initializer(),
# tf.local_variables_initializer())
# with tf.Session(config=config) as sess:
# saver = tf.train.Saver()
# #sess.run(tf.global_variables_initializer())
# saver.restore(sess,"%s/%s.ckpt"%(options.checkpoint_dir,keyname))
# gtDepths = []
# predDepths = []
# segmentationDepths = []
# predDepthsOneHot = []
# planeMasks = []
# predMasks = []
# imageWidth = WIDTH
# imageHeight = HEIGHT
# focalLength = 517.97
# urange = np.arange(imageWidth).reshape(1, -1).repeat(imageHeight, 0) - imageWidth * 0.5
# vrange = np.arange(imageHeight).reshape(-1, 1).repeat(imageWidth, 1) - imageHeight * 0.5
# ranges = np.array([urange / focalLength, np.ones(urange.shape), -vrange / focalLength]).transpose([1, 2, 0])
# cv2.imwrite(options.test_dir + '/one.png', np.ones((HEIGHT, WIDTH), dtype=np.uint8) * 255)
# cv2.imwrite(options.test_dir + '/zero.png', np.zeros((HEIGHT, WIDTH), dtype=np.uint8) * 255)
# for index, im_name in enumerate(im_names):
# if index <= -1:
# continue
# print(im_name['image'])
# im = cv2.imread(im_name['image'])
# image = im.astype(np.float32, copy=False)
# image = image / 255 - 0.5
# image = cv2.resize(image, (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR)
# #planes = np.load(im_name['plane'])
# # numPlanes = planes.shape[0]
# # if numPlanes > options.numOutputPlanes:
# # planeAreas = planes[:, 3:].sum(1)
# # sortInds = np.argsort(planeAreas)[::-1]
# # planes = planes[sortInds[:options.numOutputPlanes]]
# # pass
# # gt_p = np.zeros((1, options.numOutputPlanes, 3))
# # gt_p[0, :numPlanes] = planes[:numPlanes, :3]
# normal = np.array(PIL.Image.open(im_name['normal'])).astype(np.float) / 255 * 2 - 1
# norm = np.linalg.norm(normal, 2, 2)
# for c in xrange(3):
# normal[:, :, c] /= norm
# continue
# normal = cv2.resize(normal, (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR)
# depth = np.array(PIL.Image.open(im_name['depth'])).astype(np.float) / 1000
# depth = cv2.resize(depth, (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR)
# invalid_mask = cv2.resize(cv2.imread(im_name['invalid_mask'], 0), (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR) > 128
# gtDepths.append(depth)
# pred_p, pred_d, pred_n, pred_s, pred_np_m, pred_np_d, pred_np_n, pred_boundary, pred_local_score, pred_local_p, pred_local_mask = sess.run([plane_pred, depth_pred, normal_pred, segmentation_pred, non_plane_mask_pred, non_plane_depth_pred, non_plane_normal_pred, boundary_pred, local_score_pred, local_p_pred, local_mask_pred], feed_dict = {img_inp:np.expand_dims(image, 0), plane_gt: np.zeros((batchSize, options.numOutputPlanes, 3))})
# pred_s = pred_s[0]
# pred_p = pred_p[0]
# pred_np_m = pred_np_m[0]
# pred_np_d = pred_np_d[0]
# pred_np_n = pred_np_n[0]
# #pred_s = 1 / (1 + np.exp(-pred_s))
# plane_depths = calcPlaneDepths(pred_p, WIDTH, HEIGHT)
# all_depths = np.concatenate([pred_np_d, plane_depths], axis=2)
# all_segmentations = np.concatenate([pred_np_m, pred_s], axis=2)
# segmentation = np.argmax(all_segmentations, 2)
# if suffix != 'pixelwise':
# pred_d = all_depths.reshape(-1, options.numOutputPlanes + 1)[np.arange(WIDTH * HEIGHT), segmentation.reshape(-1)].reshape(HEIGHT, WIDTH)
# else:
# pred_d = np.squeeze(pred_np_d)
# pass
# predDepths.append(pred_d)
# predMasks.append(segmentation != 0)
# planeMasks.append(invalid_mask)
# #depthError, normalError, occupancy, segmentationTest, reconstructedDepth, occupancyMask = evaluatePlanes(pred_p, im_name['image'])
# #reconstructedDepth = cv2.resize(reconstructedDepth, (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR)
# #evaluatePlanes(pred_p[0], im_name, options.test_dir, index)
# #print(pred_p)
# #print(gt_p)
# #print((pow(pred_d[0, :, :, 0] - depth, 2) * (gt_s.max(2) > 0.5)).mean())
# #print((depthError, normalError, occupancy))
# evaluateDepths(predDepths[index], gtDepths[index], np.ones(planeMasks[index].shape), planeMasks[index])