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train_voxel_joint_multi_v1.py
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train_voxel_joint_multi_v1.py
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import argparse
import math
from datetime import datetime
import numpy as np
import tensorflow as tf
import socket
import importlib
import os
import sys
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(ROOT_DIR) # provider
import provider
import tf_util
import pc_util
import scannet_dataset
import suncg_dataset_multi
import math
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='pointnet2_sem_seg_voxel', help='Model name [default: model]')
parser.add_argument('--log_dir', default='log_vol32_joint/', help='Log dir [default: log]')
### Start with isolated trained model
parser.add_argument('--restore_dir', default='models/VOLI/best_model.ckpt', help='Restore dir [default: log]')
parser.add_argument('--num_point', type=int, default=12288, help='Point Number [default: 8192]')
parser.add_argument('--max_epoch', type=int, default=201*3, help='Epoch to run [default: 201]')
parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 16]')
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: 0.7]')
FLAGS = parser.parse_args()
EPOCH_CNT = 0
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
restore_dir = FLAGS.restore_dir
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = 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
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
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()
NUM_CLASSES = 21
V_SIZE = 32
# Shapenet official train/test split
DATA_PATH = os.path.join(ROOT_DIR,'data','scannet_data_pointnet2')
TRAIN_DATASET = scannet_dataset.ScannetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train')
TEST_DATASET = scannet_dataset.ScannetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test')
TEST_DATASET_WHOLE_SCENE = scannet_dataset.ScannetDatasetWholeScene(root=DATA_PATH, npoints=NUM_POINT, split='test')
SUNCG_DATASET = suncg_dataset_multi.SuncgDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', rep='voxel1')
def pc_normalize_batch(pc):
bsize = pc.shape[0]
newpc = []
for i in range(bsize):
curpc = pc[i]
centroid = np.mean(curpc, axis=0)
curpc = curpc - centroid
m = np.max(np.sqrt(np.sum(curpc**2, axis=1)))
curpc = curpc / m
newpc.append(curpc)
return np.array(newpc)
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)
learing_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 train():
with tf.Graph().as_default():
with tf.device('/cpu:0'):
pointclouds_pl, labels_pl, smpws_pl = MODEL.placeholder_inputs(BATCH_SIZE, V_SIZE)
is_training_pl = tf.placeholder(tf.bool, shape=())
# 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.get_variable('batch', [],
initializer=tf.constant_initializer(0), trainable=False)
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
# Set learning rate and optimizer
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)
# -------------------------------------------
# Get model and loss on multiple GPU devices
# -------------------------------------------
# Allocating variables on CPU first will greatly accelerate multi-gpu training.
# Ref: https://github.com/kuza55/keras-extras/issues/21
print "--- Get model and loss"
# Get model and loss
pred = MODEL.get_model(pointclouds_pl, NUM_CLASSES, is_training_pl, bn_decay=bn_decay)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
with tf.device('/gpu:%d'%(0)), tf.name_scope('gpu_%d'%(0)) as scope:
pred = MODEL.get_model(pointclouds_pl, NUM_CLASSES, is_training_pl, bn_decay=bn_decay)
_, loss1, loss2 = MODEL.get_loss(pred, labels_pl, smpws_pl)
losses = tf.get_collection('losses', scope)
total_loss = tf.add_n(losses, name='total_loss')
grads = optimizer.compute_gradients(total_loss)
# Get training operator
train_op = optimizer.apply_gradients(grads, global_step=batch)
correct = tf.equal(tf.argmax(pred, 4), tf.to_int64(labels_pl))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT)
tf.summary.scalar('accuracy', accuracy)
# 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)
# Add summary writers
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)
# Init variables
init = tf.global_variables_initializer()
sess.run(init)
if restore_dir != 'None':
saver.restore(sess, restore_dir)
else:
print ("issue here! Must have a pretrained model")
sys.exit(0)
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'smpws_pl': smpws_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': total_loss,
'loss1': loss1,
'loss2': loss2,
'train_op': train_op,
'merged': merged,
'step': batch}
### Evaluate first
best_acc = eval_whole_scene_one_epoch(sess, ops, test_writer)
for epoch in range(MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush()
train_one_epoch(sess, ops, train_writer)
if (epoch+1)%5==0:
acc = eval_whole_scene_one_epoch(sess, ops, test_writer)
if acc > best_acc:
best_acc = acc
save_path = saver.save(sess, os.path.join(LOG_DIR, "best_model_epoch_%03d.ckpt"%(epoch)))
log_string("Model saved in file: %s" % save_path)
# Save the variables to disk.
if epoch % 10 == 0:
save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"))
log_string("Model saved in file: %s" % save_path)
def get_batch_wdp(dataset, idxs, start_idx, end_idx):
bsize = end_idx-start_idx
batch_data = np.zeros((bsize, NUM_POINT, 3))
batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
batch_smpw = np.zeros((bsize, NUM_POINT), dtype=np.float32)
for i in range(bsize):
ps,seg,smpw = dataset[idxs[i+start_idx]]
batch_data[i,...] = ps
batch_label[i,:] = seg
batch_smpw[i,:] = smpw
dropout_ratio = np.random.random()*0.875 # 0-0.875
drop_idx = np.where(np.random.random((ps.shape[0]))<=dropout_ratio)[0]
batch_data[i,drop_idx,:] = batch_data[i,0,:]
batch_label[i,drop_idx] = batch_label[i,0]
batch_smpw[i,drop_idx] *= 0
return batch_data, batch_label, batch_smpw
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET)/(BATCH_SIZE // 2)
log_string(str(datetime.now()))
total_correct = 0
total_seen = 0
loss_sum = 0
loss_sum1 = 0
loss_sum2 = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * (BATCH_SIZE // 2)
end_idx = (batch_idx+1) * (BATCH_SIZE // 2)
### Get input from other process
batch_data, batch_smpw = SUNCG_DATASET.wait_other()
###Convert it to voxel
batch_data_norm = pc_normalize_batch(batch_data)
batch_data_temp = pc_util.point_cloud_label_to_volume_batch_exact(batch_data_norm, vsize=V_SIZE, flatten=True)
batch_data_vol = np.zeros((BATCH_SIZE, V_SIZE, V_SIZE, V_SIZE, 1))
batch_data_vol[0:BATCH_SIZE//2,:,:,:,:] = batch_data_temp
feed_dict = {ops['pointclouds_pl']: batch_data_vol,
ops['is_training_pl']: False}
pred_val = sess.run(ops['pred'], feed_dict=feed_dict)
pred_val = np.expand_dims(np.argmax(pred_val, 4), -1)
pred_val = pred_val[0:BATCH_SIZE//2,:,:,:,:]
###Convert it back to pc
pred_val = np.clip(pc_util.volume_topc_batch_exact(pred_val, batch_data_norm) - 1, a_min=0, a_max=None) ### Clip the label in case of Nan in training
batch_data_extra, batch_label_extra, batch_smpw_extra = SUNCG_DATASET.ready(batch_data, np.squeeze(pred_val), batch_smpw, TRAIN_DATASET.labelweights)
batch_data, batch_label, batch_smpw = get_batch_wdp(TRAIN_DATASET, train_idxs, start_idx, end_idx)
batch_data = np.concatenate([batch_data, batch_data_extra], 0)
batch_label = np.concatenate([batch_label, batch_label_extra], 0)
batch_smpw = np.concatenate([batch_smpw, batch_smpw_extra], 0)
# Augment batched point clouds by rotation
aug_data = provider.rotate_point_cloud_z(batch_data)
###Convert it to voxel
aug_data_vol, batch_label_vol, batch_smpw_vol = pc_util.point_cloud_label_to_volume_batch(pc_normalize_batch(aug_data), batch_label+1, batch_smpw, vsize=V_SIZE, flatten=True)
feed_dict = {ops['pointclouds_pl']: aug_data_vol,
ops['labels_pl']: batch_label_vol,
ops['smpws_pl']:batch_smpw_vol,
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, loss_val1, loss_val2, pred_val = sess.run([ops['merged'], ops['step'],
ops['train_op'], ops['loss'], ops['loss1'], ops['loss2'], ops['pred']], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
### Change the voxel back to pc
pred_val = np.argmax(pred_val, 4)
pred_val, batch_label, batch_smpw, _, _ = pc_util.volume_topc_batch(pred_val, batch_label_vol, batch_smpw_vol)
for i in range(len(pred_val)):
pred_val[i] -= 1
for i in range(len(batch_label)):
batch_label[i] -= 1
for i in range(len(pred_val)):
correct = np.sum(pred_val[i] == batch_label[i])
total_correct += correct
total_seen += pred_val[i].shape[0]
loss_sum += loss_val
loss_sum1 += loss_val1
loss_sum2 += loss_val2
if (batch_idx+1)%10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('mean loss1: %f' % (loss_sum1 / 10))
log_string('mean loss2: %f' % (loss_sum2 / 10))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
total_correct = 0
total_seen = 0
loss_sum = 0
# evaluate on whole scenes to generate numbers provided in the paper
# For consistency, convert it back to pointcloud and evaluated with the code provided in pointnet2
def eval_whole_scene_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
global EPOCH_CNT
is_training = False
test_idxs = np.arange(0, len(TEST_DATASET_WHOLE_SCENE))
num_batches = len(TEST_DATASET_WHOLE_SCENE)
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)]
total_correct_vox = 0
total_seen_vox = 0
total_seen_class_vox = [0 for _ in range(NUM_CLASSES)]
total_correct_class_vox = [0 for _ in range(NUM_CLASSES)]
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION WHOLE SCENE----'%(EPOCH_CNT))
labelweights = np.zeros(21)
labelweights_vox = np.zeros(21)
is_continue_batch = False
extra_batch_data = np.zeros((0,NUM_POINT,3))
extra_batch_label = np.zeros((0,NUM_POINT))
extra_batch_smpw = np.zeros((0,NUM_POINT))
for batch_idx in range(num_batches):
if not is_continue_batch:
batch_data, batch_label, batch_smpw = TEST_DATASET_WHOLE_SCENE[batch_idx]
batch_data = np.concatenate((batch_data,extra_batch_data),axis=0)
batch_label = np.concatenate((batch_label,extra_batch_label),axis=0)
batch_smpw = np.concatenate((batch_smpw,extra_batch_smpw),axis=0)
else:
batch_data_tmp, batch_label_tmp, batch_smpw_tmp = TEST_DATASET_WHOLE_SCENE[batch_idx]
batch_data = np.concatenate((batch_data,batch_data_tmp),axis=0)
batch_label = np.concatenate((batch_label,batch_label_tmp),axis=0)
batch_smpw = np.concatenate((batch_smpw,batch_smpw_tmp),axis=0)
if batch_data.shape[0]<BATCH_SIZE:
is_continue_batch = True
continue
elif batch_data.shape[0]==BATCH_SIZE:
is_continue_batch = False
extra_batch_data = np.zeros((0,NUM_POINT,3))
extra_batch_label = np.zeros((0,NUM_POINT))
extra_batch_smpw = np.zeros((0,NUM_POINT))
else:
is_continue_batch = False
extra_batch_data = batch_data[BATCH_SIZE:,:,:]
extra_batch_label = batch_label[BATCH_SIZE:,:]
extra_batch_smpw = batch_smpw[BATCH_SIZE:,:]
batch_data = batch_data[:BATCH_SIZE,:,:]
batch_label = batch_label[:BATCH_SIZE,:]
batch_smpw = batch_smpw[:BATCH_SIZE,:]
aug_data = batch_data
aug_data_vol, batch_label_vol, batch_smpw_vol = pc_util.point_cloud_label_to_volume_batch(pc_normalize_batch(aug_data), batch_label+1, batch_smpw, vsize=V_SIZE, flatten=True)
feed_dict = {ops['pointclouds_pl']: aug_data_vol,
ops['labels_pl']: batch_label_vol,
ops['smpws_pl']: batch_smpw_vol,
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
test_writer.add_summary(summary, step)
### Change the voxel back to pc
pred_val = np.argmax(pred_val, 4)
pred_val, batch_label, batch_smpw, _, aug_data = pc_util.volume_topc_batch(pred_val, batch_label_vol, batch_smpw_vol)
for i in range(len(pred_val)):
pred_val[i] -= 1
for i in range(len(batch_label)):
batch_label[i] -= 1
for i in range(len(batch_label)):
correct = np.sum((pred_val[i] == batch_label[i]) & (batch_label[i]>0) & (batch_smpw[i]>0)) # evaluate only on 20 categories but not unknown
total_correct += correct
total_seen += np.sum((batch_label[i]>0) & (batch_smpw[i]>0))
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum((batch_label[i]==l) & (batch_smpw[i]>0))
total_correct_class[l] += np.sum((pred_val[i]==l) & (batch_label[i]==l) & (batch_smpw[i]>0))
for b in range(len(batch_label)):
if (aug_data[b][batch_smpw[b]>0,:].shape)[0] == 0:
continue
_, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(aug_data[b][batch_smpw[b]>0,:], np.concatenate((np.expand_dims(batch_label[b][batch_smpw[b]>0],1),np.expand_dims(pred_val[b][batch_smpw[b]>0],1)),axis=1), res=0.02)
total_correct_vox += np.sum((uvlabel[:,0]==uvlabel[:,1])&(uvlabel[:,0]>0))
total_seen_vox += np.sum(uvlabel[:,0]>0)
tmp,_ = np.histogram(uvlabel[:,0],range(22))
labelweights_vox += tmp
for l in range(NUM_CLASSES):
total_seen_class_vox[l] += np.sum(uvlabel[:,0]==l)
total_correct_class_vox[l] += np.sum((uvlabel[:,0]==l) & (uvlabel[:,1]==l))
log_string('eval whole scene mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval whole scene point accuracy vox: %f'% (total_correct_vox / float(total_seen_vox)))
log_string('eval whole scene point avg class acc vox: %f' % (np.mean(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6))))
log_string('eval whole scene point accuracy: %f'% (total_correct / float(total_seen)))
log_string('eval whole scene point avg class acc: %f' % (np.mean(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6))))
labelweights = labelweights[1:].astype(np.float32)/np.sum(labelweights[1:].astype(np.float32))
labelweights_vox = labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32))
caliweights = np.array([0.388,0.357,0.038,0.033,0.017,0.02,0.016,0.025,0.002,0.002,0.002,0.007,0.006,0.022,0.004,0.0004,0.003,0.002,0.024,0.029])
caliacc = np.average(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6),weights=caliweights)
log_string('eval whole scene point calibrated average acc vox: %f' % caliacc)
per_class_str = 'vox based --------'
for l in range(1,NUM_CLASSES):
per_class_str += 'class %d weight: %f, acc: %f; ' % (l,labelweights_vox[l-1],total_correct_class_vox[l]/float(total_seen_class_vox[l]))
log_string(per_class_str)
EPOCH_CNT += 1
return caliacc
if __name__ == "__main__":
log_string('pid: %s'%(str(os.getpid())))
train()
LOG_FOUT.close()