train_pipeline.py

import argparse
import numpy as np
import tensorflow as tf
import importlib
import requests
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'models'))
sys.path.append(os.path.join(BASE_DIR, 'utils'))
import provider
from config import Config
config = Config()

# Endpoint
pointnet_url = 'http://127.0.0.1:5000/api'
dgcnn_url = 'http://127.0.0.1:5001/api'
pointcnn_url = 'http://127.0.0.1:5002/api'

parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--dataset', default='modelnet', help='Dataset to train on: modelnet or shapenet [default: modelnet]')
parser.add_argument('--model', default='pointnet_pipeline', help='Model name: pointnet_cls or pointnet_cls_basic [default: pointnet_pipeline]')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log]')
parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--max_epoch', type=int, default=15, help='Epoch to run [default: 15]')
#parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]')
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 for modelnet, 850000 for shapenet]')
parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.8]')
FLAGS = parser.parse_args()

DATASET = FLAGS.dataset

#BATCH_SIZE = FLAGS.batch_size
#NUM_POINT = FLAGS.num_point

NUM_POINT = config.points_number
BATCH_SIZE = config.batch_size
NUM_FEATURES = 48

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


MODEL = importlib.import_module(FLAGS.model)  # import network module
MODEL_FILE = os.path.join(BASE_DIR, 'models', 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
os.system('cp train_pipeline.py %s' % LOG_DIR) # bkp of train procedure
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')

MAX_NUM_POINT = 2048
# Train/test on modelnet
if DATASET == 'modelnet':
    NUM_CLASSES = 40
    TRAIN_FILES = provider.get_data_files(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'))
    TEST_FILES = provider.get_data_files(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'))
# Train/test on shapenet
elif DATASET == 'shapenet':
    NUM_CLASSES = 55
    TRAIN_FILES = provider.get_data_files(os.path.join(BASE_DIR, 'data/shapenet_core55_1024/train_files.txt'))
    TEST_FILES = provider.get_data_files(os.path.join(BASE_DIR, 'data/shapenet_core55_1024/test_files.txt'))

BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99

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)
    learning_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('/gpu:'+str(GPU_INDEX)):
            features_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT, num_features=NUM_FEATURES)
            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.Variable(0)
            bn_decay = get_bn_decay(batch)
            tf.summary.scalar('bn_decay', bn_decay)

            # Get model and loss 
            pred, _, _ = MODEL.get_model(features_pl, is_training_pl, num_classes=NUM_CLASSES, bn_decay=bn_decay)
            loss = MODEL.get_loss(pred, labels_pl)
            tf.summary.scalar('loss', loss)

            correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl))
            accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE)
            tf.summary.scalar('accuracy', accuracy)

            # Get training operator
            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)
            train_op = optimizer.minimize(loss, global_step=batch)
            
            # Add ops to save and restore all the variables.
            saver = tf.train.Saver()
            
        # Create a session
        session_config = tf.ConfigProto()
        session_config.gpu_options.allow_growth = True
        session_config.allow_soft_placement = True
        session_config.log_device_placement = False
        sess = tf.Session(config=session_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'))

        # Init variables
        init = tf.global_variables_initializer()
        # To fix the bug introduced in TF 0.12.1 as in
        # http://stackoverflow.com/questions/41543774/invalidargumenterror-for-tensor-bool-tensorflow-0-12-1
        sess.run(init, {is_training_pl: True})

        ops = {'features_pl': features_pl,
               'labels_pl': labels_pl,
               'is_training_pl': is_training_pl,
               'pred': pred,
               'loss': loss,
               'train_op': train_op,
               'merged': merged,
               'step': batch}

        for epoch in range(MAX_EPOCH):
            log_string('**** EPOCH %03d ****' % (epoch))
            sys.stdout.flush()

            train_one_epoch(sess, ops, train_writer)
            eval_one_epoch(sess, ops, test_writer)

            
            # 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 train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train files
    train_file_idxs = np.arange(0, len(TRAIN_FILES))
    np.random.shuffle(train_file_idxs)
    
    for fn in range(len(TRAIN_FILES)):
        log_string('----' + str(fn) + '-----')
        current_data, current_label = provider.load_data_file(TRAIN_FILES[train_file_idxs[fn]], with_normals=True)
        current_data = current_data[:,0:NUM_POINT,:]
        current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))            
        current_label = np.squeeze(current_label)
        
        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        total_correct = 0
        total_seen = 0
        loss_sum = 0
       
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE
            
            # Augment batched point clouds by rotation and jittering
            # import pickle
            # from pypcd import pypcd
            # cloud_out = pypcd.make_xyz_normal_point_cloud(current_data[start_idx, :, :])
            # cloud_out.save_pcd('pc_orig.pcd')
            rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
            # cloud_out = pypcd.make_xyz_normal_point_cloud(rotated_data[0])
            # cloud_out.save_pcd('pc_rot.pcd')
            # exit()

            jittered_data = provider.jitter_point_cloud(rotated_data)
            jittered_data_json = {'point_clouds': jittered_data.tolist()}

            # Etract features pointcnn
            response_pointcnn = requests.post(pointcnn_url, json=jittered_data_json)
            pointcnn_features = np.array(response_pointcnn.json()['features'])

            # # Etract features pointnet
            # jittered_data_json = {'point_clouds': jittered_data[:, :, :3].tolist()}
            # response_pointnet = requests.post(pointnet_url, json=jittered_data_json)
            # pointnet_features = np.array(response_pointnet.json()['features'])

            # Etract features dgcnn
            # jittered_data_json = {'point_clouds': jittered_data[:, :, :3].tolist()}
            # response_dgcnn  = requests.post(dgcnn_url, json=jittered_data_json)
            # dgcnn_features = np.array(response_dgcnn.json()['features'])

            # Concatenate
            #point_features = np.concatenate((pointcnn_features, dgcnn_features), axis=-1)
            point_features = pointcnn_features

            # Train
            feed_dict = {ops['features_pl']: point_features,
                         ops['labels_pl']: current_label[start_idx:end_idx],
                         ops['is_training_pl']: 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)
            train_writer.add_summary(summary, step)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += loss_val
        
        log_string('mean loss: %f' % (loss_sum / float(num_batches)))
        log_string('accuracy: %f' % (total_correct / float(total_seen)))


def eval_one_epoch(sess, ops, test_writer):
    """ 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(NUM_CLASSES)]
    total_correct_class = [0 for _ in range(NUM_CLASSES)]
    
    for fn in range(len(TEST_FILES)):
        log_string('----' + str(fn) + '-----')
        current_data, current_label = provider.load_data_file(TEST_FILES[fn], with_normals=True)
        current_data = current_data[:,0:NUM_POINT,:]
        current_label = np.squeeze(current_label)
        
        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE

            # Current point_clouds & label
            current_point_clouds = current_data[start_idx:end_idx, :, :]
            current_labels = current_label[start_idx:end_idx]
            current_data_json = {'point_clouds': current_point_clouds.tolist()}

            # Etract features pointcnn
            response_pointcnn = requests.post(pointcnn_url, json=current_data_json)
            pointcnn_features = np.array(response_pointcnn.json()['features'])

            # # Etract features pointnet
            # current_data_json = {'point_clouds': current_point_clouds[:, :, :3].tolist()}
            # response_pointnet = requests.post(pointnet_url, json=current_data_json)
            # pointnet_features = np.array(response_pointnet.json()['features'])

            # # Etract features dgcnn
            # current_data_json = {'point_clouds': current_point_clouds[:, :, :3].tolist()}
            # response_dgcnn  = requests.post(dgcnn_url, json=current_data_json)
            # dgcnn_features = np.array(response_dgcnn.json()['features'])

            # Concatenate
            #point_features = np.concatenate((pointcnn_features, dgcnn_features), axis=-1)
            point_features = pointcnn_features

            feed_dict = {ops['features_pl']: point_features,
                         ops['labels_pl']: current_labels,
                         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)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += (loss_val*BATCH_SIZE)
            for i in range(start_idx, end_idx):
                l = current_label[i]
                total_seen_class[l] += 1
                total_correct_class[l] += (pred_val[i-start_idx] == l)
            
    log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
    log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
    log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))


if __name__ == "__main__":
    train()
    LOG_FOUT.close()