evaluate_pipeline.py

from timeit import default_timer as timer
import tensorflow as tf
import numpy as np
import argparse
import importlib
import os
import scipy.misc
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
import requests
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_cls]')
parser.add_argument('--batch_size', type=int, default=4, help='Batch Size during training [default: 4]')
parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
parser.add_argument('--visu', action='store_true', help='Whether to dump image for error case [default: False]')
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

GPU_INDEX = FLAGS.gpu
MODEL_PATH = FLAGS.model_path
GPU_INDEX = FLAGS.gpu
MODEL = importlib.import_module(FLAGS.model) # import network module
DUMP_DIR = FLAGS.dump_dir
if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.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'))
    SHAPE_NAMES = [line.rstrip() for line in open(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.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'))

def log_string(out_str):
    LOG_FOUT.write(out_str+'\n')
    LOG_FOUT.flush()
    print(out_str)

def evaluate(num_votes):
    is_training = False
     
    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=())

        # simple model
        pred, _, _ = MODEL.get_model(features_pl, is_training_pl, num_classes=NUM_CLASSES)
        loss = MODEL.get_loss(pred, labels_pl)
        
        # 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 = True
    sess = tf.Session(config=session_config)

    # Restore variables from disk.
    saver.restore(sess, MODEL_PATH)
    log_string("Model restored.")

    ops = {'features_pl': features_pl,
           'labels_pl': labels_pl,
           'is_training_pl': is_training_pl,
           'pred': pred,
           'loss': loss}

    eval_one_epoch(sess, ops, num_votes)

   
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
    error_cnt = 0
    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)]
    total_time = 0.
    total_batches = 0.
    fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w')
    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)
        print(current_data.shape)
        
        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        print(file_size)
        
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE
            cur_batch_size = end_idx - start_idx
            
            # Aggregating BEG
            batch_loss_sum = 0 # sum of losses for the batch
            batch_pred_sum = np.zeros((cur_batch_size, NUM_CLASSES)) # score for classes
            batch_pred_classes = np.zeros((cur_batch_size, NUM_CLASSES)) # 0/1 for classes
            for vote_idx in range(num_votes):
                rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :],
                                                  vote_idx/float(num_votes) * np.pi * 2)
                rotated_data_json = {'point_clouds': rotated_data.tolist()}

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

                # # Etract features pointnet
                # rotated_data_json = {'point_clouds': rotated_data[:, :, :3].tolist()}
                # response_pointnet = requests.post(pointnet_url, json=rotated_data_json)
                # pointnet_features = np.array(response_pointnet.json()['features'])
                #
                # # Etract features dgcnn
                # rotated_data_json = {'point_clouds': rotated_data[:, :, :3].tolist()}
                # response_dgcnn  = requests.post(dgcnn_url, json=rotated_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_label[start_idx:end_idx],
                             ops['is_training_pl']: is_training}
                # Time measurement
                start = timer()
                loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
                                          feed_dict=feed_dict)
                end = timer()
                if batch_idx != 0:
                    total_time += (end-start)
                    total_batches += 1
                batch_pred_sum += pred_val
                batch_pred_val = np.argmax(pred_val, 1)
                for el_idx in range(cur_batch_size):
                    batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
                batch_loss_sum += (loss_val * cur_batch_size / float(num_votes))
            # pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1]
            # pred_val = np.argmax(batch_pred_classes, 1)
            pred_val = np.argmax(batch_pred_sum, 1)
            # Aggregating END
            
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            # correct = np.sum(pred_val_topk[:,0:topk] == label_val)
            total_correct += correct
            total_seen += cur_batch_size
            loss_sum += batch_loss_sum

            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)
                fout.write('%d, %d\n' % (pred_val[i-start_idx], l))
                
                if pred_val[i-start_idx] != l and FLAGS.visu: # ERROR CASE, DUMP!
                    img_filename = '%d_label_%s_pred_%s.jpg' % (error_cnt, SHAPE_NAMES[l],
                                                           SHAPE_NAMES[pred_val[i-start_idx]])
                    img_filename = os.path.join(DUMP_DIR, img_filename)
                    output_img = pc_util.point_cloud_three_views(np.squeeze(current_data[i, :, :]))
                    scipy.misc.imsave(img_filename, output_img)
                    error_cnt += 1
                
    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))))
    log_string('mean evaluation time of one batch: %f' % (total_time / total_batches))
    parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
    log_string('parameter number: %f' % (parameter_num))

    class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)
    for i, name in enumerate(SHAPE_NAMES):
        log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))


if __name__=='__main__':
    with tf.Graph().as_default():
        evaluate(num_votes=1)
    LOG_FOUT.close()