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Compatibility with tensorflow 2, python3.8 #72

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2 changes: 1 addition & 1 deletion tensorflow/provider.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,7 +140,7 @@ def getDataFiles(list_filename):
return [line.rstrip() for line in open(list_filename)]

def load_h5(h5_filename):
f = h5py.File(h5_filename)
f = h5py.File(h5_filename, 'r')
data = f['data'][:]
label = f['label'][:]
return (data, label)
Expand Down
16 changes: 9 additions & 7 deletions tensorflow/sem_seg/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,20 +11,22 @@
import tf_util

def placeholder_inputs(batch_size, num_point):
pointclouds_pl = tf.placeholder(tf.float32,
pointclouds_pl = tf.compat.v1.placeholder(tf.float32,
shape=(batch_size, num_point, 9))
labels_pl = tf.placeholder(tf.int32,
labels_pl = tf.compat.v1.placeholder(tf.int32,
shape=(batch_size, num_point))
return pointclouds_pl, labels_pl

def get_model(point_cloud, is_training, bn_decay=None):
""" ConvNet baseline, input is BxNx9 gray image """
batch_size = point_cloud.get_shape()[0].value
num_point = point_cloud.get_shape()[1].value
batch_size = point_cloud.get_shape()[0]
num_point = point_cloud.get_shape()[1]
input_image = tf.expand_dims(point_cloud, -1)

k = 20

weight_decay = 0.0

adj = tf_util.pairwise_distance(point_cloud[:, :, 6:])
nn_idx = tf_util.knn(adj, k=k) # (batch, num_points, k)
edge_feature = tf_util.get_edge_feature(input_image, nn_idx=nn_idx, k=k)
Expand All @@ -39,7 +41,7 @@ def get_model(point_cloud, is_training, bn_decay=None):
bn=True, is_training=is_training, weight_decay=weight_decay,
scope='adj_conv2', bn_decay=bn_decay, is_dist=True)

net_1 = tf.reduce_max(out2, axis=-2, keep_dims=True)
net_1 = tf.compat.v1.reduce_max(out2, axis=-2, keep_dims=True)



Expand All @@ -57,7 +59,7 @@ def get_model(point_cloud, is_training, bn_decay=None):
bn=True, is_training=is_training, weight_decay=weight_decay,
scope='adj_conv4', bn_decay=bn_decay, is_dist=True)

net_2 = tf.reduce_max(out4, axis=-2, keep_dims=True)
net_2 = tf.compat.v1.reduce_max(out4, axis=-2, keep_dims=True)



Expand All @@ -75,7 +77,7 @@ def get_model(point_cloud, is_training, bn_decay=None):
# bn=True, is_training=is_training, weight_decay=weight_decay,
# scope='adj_conv6', bn_decay=bn_decay, is_dist=True)

net_3 = tf.reduce_max(out5, axis=-2, keep_dims=True)
net_3 = tf.compat.v1.reduce_max(out5, axis=-2, keep_dims=True)



Expand Down
64 changes: 33 additions & 31 deletions tensorflow/sem_seg/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@
import math
import h5py
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import socket

Expand All @@ -17,11 +19,11 @@
from model import *

parser = argparse.ArgumentParser()
parser.add_argument('--num_gpu', type=int, default=2, help='the number of GPUs to use [default: 2]')
parser.add_argument('--num_gpu', type=int, default=1, help='the number of GPUs to use [default: 2]')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
parser.add_argument('--num_point', type=int, default=4096, help='Point number [default: 4096]')
parser.add_argument('--max_epoch', type=int, default=101, help='Epoch to run [default: 50]')
parser.add_argument('--batch_size', type=int, default=12, help='Batch Size during training for each GPU [default: 24]')
parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training for each GPU [default: 24]')
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]')
Expand Down Expand Up @@ -61,7 +63,7 @@

ALL_FILES = provider.getDataFiles('indoor3d_sem_seg_hdf5_data/all_files.txt')
room_filelist = [line.rstrip() for line in open('indoor3d_sem_seg_hdf5_data/room_filelist.txt')]
print len(room_filelist)
print(len(room_filelist))

# Load ALL data
data_batch_list = []
Expand Down Expand Up @@ -99,7 +101,7 @@ def log_string(out_str):


def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
learning_rate = tf.compat.v1.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
Expand All @@ -109,7 +111,7 @@ def get_learning_rate(batch):
return learning_rate

def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
bn_momentum = tf.compat.v1.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
Expand Down Expand Up @@ -162,20 +164,21 @@ def train():
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)

trainer = tf.train.AdamOptimizer(learning_rate)
trainer = tf.compat.v1.train.AdamOptimizer(learning_rate)
#trainer = tf.keras.optimizers.Adam(learning_rate)

tower_grads = []
pointclouds_phs = []
labels_phs = []
is_training_phs =[]

with tf.variable_scope(tf.get_variable_scope()):
for i in xrange(FLAGS.num_gpu):
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
for i in range(FLAGS.num_gpu):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope:

pointclouds_pl, labels_pl = placeholder_inputs(BATCH_SIZE, NUM_POINT)
is_training_pl = tf.placeholder(tf.bool, shape=())
is_training_pl = tf.compat.v1.placeholder(tf.bool, shape=())

pointclouds_phs.append(pointclouds_pl)
labels_phs.append(labels_pl)
Expand All @@ -185,11 +188,11 @@ def train():
loss = get_loss(pred, labels_phs[-1])
tf.summary.scalar('loss', loss)

correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_phs[-1]))
correct = tf.equal(tf.argmax(pred, 2), tf.compat.v1.to_int64(labels_phs[-1]))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT)
tf.summary.scalar('accuracy', accuracy)

tf.get_variable_scope().reuse_variables()
tf.compat.v1.get_variable_scope().reuse_variables()

grads = trainer.compute_gradients(loss)

Expand All @@ -199,23 +202,23 @@ def train():

train_op = trainer.apply_gradients(grads, global_step=batch)

saver = tf.train.Saver(tf.global_variables(), sharded=True, max_to_keep=10)
saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables(), sharded=True, max_to_keep=10)

# Create a session
config = tf.ConfigProto()
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
sess = tf.Session(config=config)
sess = tf.compat.v1.Session(config=config)

# Add summary writers
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'),
merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter(os.path.join(LOG_DIR, 'train'),
sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'))
test_writer = tf.compat.v1.summary.FileWriter(os.path.join(LOG_DIR, 'test'))

# Init variables for two GPUs
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Init variables for one GPU
init = tf.group(tf.compat.v1.global_variables_initializer(),
tf.compat.v1.local_variables_initializer())
sess.run(init)

ops = {'pointclouds_phs': pointclouds_phs,
Expand Down Expand Up @@ -259,21 +262,20 @@ def train_one_epoch(sess, ops, train_writer):
print('Current batch/total batch num: %d/%d'%(batch_idx,num_batches))
start_idx_0 = batch_idx * BATCH_SIZE
end_idx_0 = (batch_idx+1) * BATCH_SIZE
start_idx_1 = (batch_idx+1) * BATCH_SIZE
end_idx_1 = (batch_idx+2) * BATCH_SIZE



feed_dict = {ops['pointclouds_phs'][0]: current_data[start_idx_0:end_idx_0, :, :],
ops['pointclouds_phs'][1]: current_data[start_idx_1:end_idx_1, :, :],
ops['labels_phs'][0]: current_label[start_idx_0:end_idx_0],
ops['labels_phs'][1]: current_label[start_idx_1:end_idx_1],
ops['is_training_phs'][0]: is_training,
ops['is_training_phs'][1]: is_training}
summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']],
ops['is_training_phs'][0]: is_training}

# summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']],
# feed_dict=feed_dict)

step, _, loss_val, pred_val = sess.run([ops['step'], ops['train_op'], ops['loss'], ops['pred']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)

# train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 2)
correct = np.sum(pred_val == current_label[start_idx_1:end_idx_1])
correct = np.sum(pred_val == current_label[start_idx_0:end_idx_0])
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
Expand Down
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