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pc_motion_train.py
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pc_motion_train.py
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import os
import sys
import numpy as np
import math
import tensorflow as tf
from tensorflow.python import debug as tf_debug
from utils.ops import *
from utils.pc_config import cfg
from utils.data_generator_utils import DataGenerator, load_npy_filenames, jitter_point_cloud
from tqdm import tqdm
from sklearn.utils import shuffle
from pc_motion_model import build_graph, get_loss
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
MODEL_NAME = cfg.model_name
LOGDIR = cfg.logdir+MODEL_NAME+"_"+str(cfg.num_frames)+"_"+str(cfg.num_points)
if not os.path.exists(LOGDIR):
os.mkdir(LOGDIR)
log_screen_file = LOGDIR + '/log_screen.txt'
if os.path.exists(log_screen_file):
os.remove(log_screen_file)
fout = open(log_screen_file,'w')
def log_string(out_str):
fout.write(out_str+'\n')
fout.flush()
print(out_str)
def get_learning_rate(step):
learning_rate = tf.train.exponential_decay(
cfg.init_learning_rate, # Base learning rate.
step * cfg.batch_size, # Current index into the dataset.
cfg.decay_step, # Decay step.
cfg.decay_rate, # Minimum learning rate * init_lr.
staircase=True)
learning_rate = tf.maximum(learning_rate, 1e-6) # CLIP THE LEARNING RATE!
return learning_rate
def get_bn_decay(step):
bn_momentum = tf.train.exponential_decay(
0.5,
step * cfg.batch_size,
float(cfg.decay_step),
0.5,
staircase=True)
bn_decay = tf.minimum(0.99, 1 - bn_momentum)
return bn_decay
def F1_score(precision, recall):
f1_score = 2*((precision*recall)/(precision+recall))
return f1_score
def MCC(TP, TN, FP, FN):
mcc = ((TP*TN)-(FP*FN))/tf.sqrt(((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)))
return mcc
def placeholder_inputs(batch_size, num_frames):
# batch size should be Nnone
inputs_pl = tf.placeholder(tf.float32, shape=(
cfg.batch_size, cfg.num_points, 3, num_frames))
labels_pl = tf.placeholder(tf.int32, shape=(None))
return inputs_pl, labels_pl
def train():
log_string('***** Config *****')
log_string('***** Building Point {}...'.format(MODEL_NAME))
log_string('** num_frames: {}'.format(cfg.num_frames))
log_string('** num_classes: {}'.format(cfg.num_classes))
log_string('** batch_size: {}'.format(cfg.batch_size))
log_string('** epoch: {}'.format(cfg.epoch))
log_string('** init_learning_rate: {}'.format(cfg.init_learning_rate))
log_string('** decay_step: {}'.format(cfg.decay_step))
log_string('** decay_rate: {}'.format(cfg.decay_rate))
log_string('** weight_decay: {}'.format(cfg.weight_decay))
with tf.Graph().as_default():
inputs, labels = placeholder_inputs(cfg.batch_size, cfg.num_frames)
is_training_pl = tf.placeholder(tf.bool, shape=())
keep_prob_pl = tf.placeholder(tf.float32)
global_step = tf.Variable(0, dtype=tf.int64)
bn_decay = get_bn_decay(global_step)
tf.summary.scalar('bn_decay', bn_decay)
pred = build_graph(inputs, is_training_pl, weight_decay=cfg.weight_decay,
keep_prob=keep_prob_pl, bn_decay=bn_decay)
loss = get_loss(pred, labels)
# raise
tf.summary.scalar('total_loss', loss)
correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels))
correct = tf.reduce_sum(tf.cast(correct, tf.float32))
accuracy = correct / float(cfg.batch_size)
tf.summary.scalar('accuracy', accuracy)
# Get training operator
learning_rate = get_learning_rate(global_step)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
# optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step=global_step)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# config.log_device_placement = True
# config.gpu_options.allocator_type = "BFC"
sess = tf.Session(config=config)
# # restore model #################
load_model_path = LOGDIR+'/model_epoch_{}'.format(cfg.load_model_epoch)
try:
saver = tf.train.Saver()
saver.restore(sess, load_model_path)
print("\nPrevious model restored... ", load_model_path)
except Exception as e:
print("\nCannot find the requested model... {}".format(e))
sess.run(tf.global_variables_initializer())
# %% create a saver object
saver = tf.train.Saver()
print("\nCreating new model...", load_model_path)
if cfg.debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# init = tf.global_variables_initializer()
# sess.run(init, {is_training_pl: True})
# saver = tf.train.Saver()
# Plot Variable Histogram
t_vars = tf.trainable_variables()
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(LOGDIR+'/train')
train_writer.add_graph(tf.get_default_graph())
test_writer = tf.summary.FileWriter(LOGDIR+'/test')
test_writer.add_graph(tf.get_default_graph())
# running_vars = tf.get_collection('metric_vars')
# running_vars = tf.get_collection(tf.GraphKeys.METRIC_VARIABLES)
# running_vars = [ var for var in running_vars if isinstance(var, tf.Variable)]
# print(running_vars)
# running_vars_initializer = tf.variables_initializer(var_list=running_vars)
# Count number of trainable parameters
num_params = np.sum([np.prod(v.get_shape().as_list()) for v in t_vars])
log_string('************ The Number of Trainable Parameters: {} ************'.format(num_params))
num_g_params = np.sum([np.prod(v.get_shape().as_list()) for v in tf.global_variables()])
log_string('************ The Number of Global Parameters: {} ************'.format(num_g_params))
ops = {'inputs_pl': inputs,
'labels_pl': labels,
'is_training_pl': is_training_pl,
'keep_prob_pl':keep_prob_pl,
'pred': pred,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': global_step}
training_dataset = np.load(
'/media/tjosh/vault/MSRAction3D/new_pc_npy_5_training.npy')
validation_dataset = np.load(
'/media/tjosh/vault/MSRAction3D/new_pc_npy_5_validation.npy')
# set_size = len(dataset)
# dataset = shuffle(dataset)
# training_dataset = dataset[:int(set_size*0.67)]
# validation_dataset = dataset[int(set_size*0.67):]
validation_dataset = validation_dataset
train_data_gen = DataGenerator(training_dataset, batch_size=cfg.batch_size)
validation_data_gen = DataGenerator(validation_dataset, batch_size=cfg.batch_size, augment=False)
for epoch in range(1, cfg.epoch+1):
log_string('\n******** Training:---Epoch_{}/{} *********'.format(epoch, cfg.epoch))
log_string('Training ...')
train_one_epoch(sess, train_data_gen, ops, train_writer)
log_string('Validating ...')
val_one_epoch(sess, validation_data_gen, ops, test_writer)
if epoch % cfg.save_model_freq == 0:
saver.save(sess, LOGDIR+'/model_epoch_{}'.format(epoch))
log_string('Model saved at epoch {}'.format(epoch))
def test_model():
with tf.Graph().as_default():
validation_dataset = np.load('path/to/dataset')
validation_data_gen = DataGenerator(
validation_dataset, batch_size=cfg.batch_size)
inputs, labels = placeholder_inputs(
cfg.batch_size, cfg.num_frames)
is_training_pl = tf.placeholder(tf.bool, shape=())
keep_prob_pl = tf.placeholder(tf.float32)
global_step = tf.Variable(0, dtype=tf.int64)
bn_decay = get_bn_decay(global_step)
tf.summary.scalar('bn_decay', bn_decay)
# Get model and loss
pred = build_graph(inputs, is_training_pl, weight_decay=cfg.weight_decay,
keep_prob=keep_prob_pl, bn_decay=bn_decay)
loss = get_loss(pred, labels)
tf.summary.scalar('total_loss', loss)
# raise
correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels))
correct = tf.reduce_sum(tf.cast(correct, tf.float32))
accuracy = correct / float(cfg.batch_size)
tf.summary.scalar('accuracy', accuracy)
# Get training operator
learning_rate = get_learning_rate(global_step)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
# optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step=global_step)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# config.log_device_placement = True
# config.gpu_options.allocator_type = "BFC"
sess = tf.Session(config=config)
# # restore model #################
# load_model_path = LOGDIR+'/model_epoch_{}'.format(cfg.load_model_epoch)
load_model_path = LOGDIR+'/model_epoch_{}'.format(cfg.load_model_epoch)
try:
saver = tf.train.Saver()
saver.restore(sess, load_model_path)
print("\nLoaded previous model... ", load_model_path)
except Exception as e:
raise
if cfg.debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
# Plot Variable Histogram
t_vars = tf.trainable_variables()
# for var in t_vars:
# tf.summary.histogram(var.op.name, var)
# saver = tf.train.Saver()
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(LOGDIR+'/train')
train_writer.add_graph(tf.get_default_graph())
test_writer = tf.summary.FileWriter(LOGDIR+'/test')
test_writer.add_graph(tf.get_default_graph())
# Count number of trainable parameters
num_params = np.sum([np.prod(v.get_shape().as_list()) for v in t_vars])
print('************ The Number of Trainable Parameters: {} ************'.format(num_params))
num_g_params = np.sum([np.prod(v.get_shape().as_list())
for v in tf.global_variables()])
print('************ The Number of Global Parameters: {} ************'.format(num_g_params))
ops = {'inputs_pl': inputs,
'labels_pl': labels,
'keep_prob_pl': keep_prob_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': global_step}
# validation_data_gen = DataGenerator(validation_dataset, batch_size=cfg.batch_size)
print('Validating ...')
val_one_epoch(sess, validation_data_gen, ops,
test_writer, logging=False)
# log_string('learning rate at epoch {}: {}'.format(epoch, sess.run(model.learning_rate)))
def train_one_epoch(sess, train_data_gen, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
iters_per_epoch = train_data_gen.iters_per_epoch
total_correct = 0
total_seen = 0
loss_sum = 0
pbar = tqdm(range(iters_per_epoch))
# for batch_idx in range(num_batches):
for iteration in pbar:
current_data, current_label = next(train_data_gen.generator)
# # Augment batched point clouds by jittering
# if np.random.rand()>0.3:
# current_data = jitter_point_cloud(current_data, sigma=200)
feed_dict = {ops['inputs_pl']: current_data,
ops['labels_pl']: current_label,
ops['keep_prob_pl']: 0.4,
ops['is_training_pl']: is_training}
_, summary, step, loss_val, pred_val = sess.run([ops['train_op'], ops['merged'],
ops['step'], ops['loss'], ops['pred']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
# print("\npredicted: ", pred_val)
# print("labels: ", current_label)
# print("predicted: ", pred_val)
pred_val = np.argmax(pred_val, 1)
# print("predicted: ", pred_val)
# print("correct: ", current_label)
correct = np.sum(pred_val == current_label)
total_correct += correct
total_seen += cfg.batch_size
loss_sum += loss_val
pbar.set_description("Training Accuracry: %.6f, Training Loss: %.6f" % (
(total_correct/(total_seen)), loss_sum/(iteration+1)))
# print("correct: ", correct)
mean_loss = loss_sum / float(iters_per_epoch)
mean_acc = total_correct / float(total_seen)
# precision, recall, TP, TN, FP, FN = sess.run([ops['precision'], ops['recall'], ops['TP'],
# ops['TN'], ops['FP'], ops['FN']])
# f1_score = F1_score(precision, recall)
# mcc = MCC(float(TP),float(TN),float(FP),float(FN))
# precision, recall, f1_score = sess.run([ops['precision'], ops['recall'], ops['f1_score']])
log_string('mean loss: %f' % (mean_loss))
log_string('accuracy: %f' % (mean_acc))
# tf.saved_model.simple_save(sess, LOGDIR+'/saved_model_simple', inputs={"input_node": ops['inputs_pl']}, outputs={"output":ops['pred']})
# log_string('precision: %f' % (precision))
# log_string('recall: %f' % (recall))
# log_string('f1_score: %f' % (f1_score))
def val_one_epoch(sess, validation_data_gen, ops, test_writer, logging=True):
""" ops: dict mapping from string to tf ops """
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(cfg.num_classes)]
total_correct_class = [0 for _ in range(cfg.num_classes)]
iters_per_epoch = validation_data_gen.iters_per_epoch
# for batch_idx in range(num_batches):
for iterations in range(iters_per_epoch):
current_data, current_labels = next(validation_data_gen.generator)
feed_dict = {ops['inputs_pl']: current_data,
ops['labels_pl']: current_labels,
ops['keep_prob_pl']: 1.0,
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)
# print("pred_vals", pred_val)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_labels)
total_correct += correct
total_seen += cfg.batch_size
loss_sum += loss_val
# print("correct: ", correct)
# print("labels_shape: ", np.shape(current_labels))
# for i in range(cfg.batch_size):
# l = current_labels[i]
# total_seen_class[l] += 1
# total_correct_class[l] += (pred_val[i] == l)
total_avg_loss = loss_sum / float(iters_per_epoch)
total_avg_acc = total_correct / float(total_seen)
if logging:
log_string('eval mean loss: %f' % (total_avg_loss))
log_string('eval accuracy: %f' % (total_avg_acc))
else:
print('eval mean loss: %f' % (total_avg_loss))
print('eval accuracy: %f' % (total_avg_acc))
# log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class, dtype=np.float))))
def save_net():
model_path = LOGDIR + "/model_epoch_" + cfg.load_model_epoch
save_name = LOGDIR + "/saved_model_pb"
#Step 1
#import the model metagraph
saver = tf.train.import_meta_graph(model_path+'.meta', clear_devices=True)
# make that as the default graph
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
sess = tf.Session()
#now restore the variables
saver.restore(sess, model_path)
# #Step 2, if output node name is not known
# # Find the output name
# graph = tf.get_default_graph()
# for op in graph.get_operations():
# print(op.name)
#Step 3
from tensorflow.python.platform import gfile
from tensorflow.python.framework import graph_util
output_node_names = "output_node"
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session
input_graph_def, # input_graph_def is useful for retrieving the nodes
output_node_names.split(","))
#Step 4
#output folder
output_fld = ''
#output pb file name
output_model_file = save_name+'.pb'
from tensorflow.python.framework import graph_io
#write the graph
graph_io.write_graph(output_graph_def, output_fld,
output_model_file, as_text=False)
def main(_):
if cfg.is_training:
print('Start Training ...')
train()
print('Finished Training')
fout.close()
# test_model()
else:
save_net()
if __name__ == "__main__":
tf.app.run()