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visualizeCriticalPoints.py
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visualizeCriticalPoints.py
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# This file is used to construct and visualize critical point sets
# and upper-bound sets based on a trained model and a sample.
# This file only support batch_size = 1 and is modified base on file
# "evalutate.py"
import tensorflow as tf
import numpy as np
import argparse
import socket
import importlib
import time
import os
import errno
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 pc_util
import math
import matplotlib.pyplot as plt
# changed by wind:
# set batch_size = 1
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='pointnet_cls_basic', help='Model name: pointnet_cls or pointnet_cls_basic [default: pointnet_cls]')
parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 1]')
parser.add_argument('--num_class', type=int, default=40, help='Number of Classes [default: 7]')
parser.add_argument('--num_point', type=int, default=2048, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--num_feature', type=int, default=256, help='Point Number [32-2048] [default: 128]')
parser.add_argument('--dump_dir', default='dump_visual', 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()
NUM_CLASSES = FLAGS.num_class
BATCH_SIZE = FLAGS.batch_size
NUM_POINT = FLAGS.num_point
NUM_FEATURE = FLAGS.num_feature
MODEL_PATH = 'log/' + FLAGS.model+'_'+str(NUM_FEATURE)+'_'+'model.ckpt'
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')
SHAPE_NAMES = [line.rstrip() for line in \
open(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))]
HOSTNAME = socket.gethostname()
# ModelNet40 official train/test split
TRAIN_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'))
TEST_FILES = provider.getDataFiles(\
os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/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)):
pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
is_training_pl = tf.placeholder(tf.bool, shape=())
# simple model
pred, end_points, global_feature,global_matrix = MODEL.get_model(
pointclouds_pl, is_training_pl,
num_feature = NUM_FEATURE, num_class = NUM_CLASSES)
loss = MODEL.get_loss(pred, labels_pl, end_points)
# 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 = True
sess = tf.Session(config=config)
# Restore variables from disk.
saver.restore(sess, MODEL_PATH)
log_string("Model restored.")
ops = {'pointclouds_pl': pointclouds_pl,
'labels_pl': labels_pl,
'is_training_pl': is_training_pl,
'pred': pred,
'loss': loss,
'global_matrix': global_matrix,
'global_feature': global_feature}
run_meta = tf.RunMetadata()
opts = tf.profiler.ProfileOptionBuilder.float_operation()
flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)
opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter()
params = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)
print("{:,} --- {:,}".format(flops.total_float_ops, params.total_parameters))
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)]
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.loadDataFile(TEST_FILES[fn])
current_data = current_data[:,0:NUM_POINT,:]
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
print(file_size)
# set by wind:
# my code is based on batch_size = 1
# set batch_size = 1 for this file
for batch_idx in range(file_size):
# for batch_idx in np.array([1,710,1805,385,703,1501])-1:
print(batch_idx)
start_idx = batch_idx
end_idx = batch_idx + 1
cur_batch_size = 1
no_influence_position = current_data[start_idx,0,:].copy()
global_feature_list = []
orgin_data = current_data[start_idx,:,:].copy()
#-------------------------------------------------------------------
# save origin data
#-------------------------------------------------------------------
fileName = 'dataAnalysis/figures/%d_orgin_points' % (start_idx)
img_filename = fileName + '.jpg'
# plyFileName = fileName + '.ply'
# pc_util.write_ply(np.squeeze(orgin_data),plyFileName)
# pc_util.pyplot_draw_point_cloud(np.squeeze( orgin_data ),'')
output_img = pc_util.draw_point_cloud(np.squeeze(orgin_data))
scipy.misc.imsave(img_filename, output_img)
#-------------------------------------------------------------------
# get global matrix
#-------------------------------------------------------------------
feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :] ,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training}
global_matrix = np.array(sess.run(ops['global_matrix'],
feed_dict=feed_dict))
global_matrix = global_matrix[global_matrix[:,0].argsort()]
# print(global_matrix.shape)
# plt.imshow(global_matrix, interpolation='nearest')
# plt.show()
fileName = 'dataAnalysis/figures/%d/%d_global_matrix' % (current_label[start_idx], batch_idx)
img_filename = fileName + '.png'
if not os.path.exists(os.path.dirname(img_filename)):
try:
os.makedirs(os.path.dirname(img_filename))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
scipy.misc.imsave(img_filename, global_matrix)
# #-------------------------------------------------------------------
# # get critical points
# #-------------------------------------------------------------------
# for change_point in range(NUM_POINT):
# current_data[start_idx, change_point, :] = no_influence_position.copy()
#
# for change_point in range(NUM_POINT):
# current_data[start_idx, change_point, :] = orgin_data[change_point, :].copy()
# # Aggregating BEG
# for vote_idx in range(num_votes):
# if FLAGS.model == 'pointnet_cls_basic':
# rotated_data = current_data[start_idx:end_idx, :, :] # directional pointNet
# else:
# rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :],
# vote_idx/float(num_votes) * np.pi * 2) # pointNet
# feed_dict = {ops['pointclouds_pl']: rotated_data,
# ops['labels_pl']: current_label[start_idx:end_idx],
# ops['is_training_pl']: is_training}
#
# global_feature_val = sess.run(ops['global_feature'],
# feed_dict=feed_dict)
#
# global_feature_list.append(global_feature_val)
#
# critical_points = []
# max_feature = np.zeros(global_feature_list[0].size) - 10
# feature_points = np.zeros(global_feature_list[0].size)
# for index in range(len(global_feature_list)):
# #distance = math.sqrt(((global_feature_list[index] - global_feature_list[-1]) ** 2).sum())
# #distance_list.append(distance)
# top = global_feature_list[index]
# feature_points = np.where(top > max_feature, index, feature_points)
# max_feature = np.where(top > max_feature, top, max_feature)
#
# for index in feature_points[0]:
# critical_points.append(orgin_data[int(index), :])
# critical_points = list(set([tuple(t) for t in critical_points]))
#
# fileName = 'dataAnalysis/figures/%d_critical_points' % (start_idx)
# img_filename = fileName + '.jpg'
# plyFileName = fileName + '.ply'
# pc_util.write_ply(np.squeeze( critical_points),plyFileName)
## pc_util.pyplot_draw_point_cloud(np.squeeze( critical_points ),'')
# output_img = pc_util.draw_point_cloud(np.squeeze( critical_points))
# scipy.misc.imsave(img_filename, output_img)
# #-------------------------------------------------------------------
# # get upper-bound points
# #-------------------------------------------------------------------
# upper_bound_points = np.empty_like(orgin_data.shape)
# upper_bound_points = orgin_data.copy()
# current_data[start_idx,:,:] = orgin_data.copy()
#
# search_step = 0.05
# stand_feature = np.empty_like(global_feature_list[-1].shape)
# max_position = [-0.5,-0.5,-0.5]
# min_position = [0.5, 0.5, 0.5]
#
# for point_index in range(NUM_POINT):
# max_position = np.maximum(max_position, current_data[start_idx,point_index,:])
# min_position = np.minimum(min_position, current_data[start_idx,point_index,:])
#
# print(max_position)
# print(min_position)
# for vote_idx in range(num_votes):
# if FLAGS.model == 'pointnet_cls_basic':
# rotated_data = current_data[start_idx:end_idx, :, :] # directional pointNet
# else:
# rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :],
# vote_idx/float(num_votes) * np.pi * 2) # pointNet
#
# feed_dict = {ops['pointclouds_pl']: rotated_data,
# ops['labels_pl']: current_label[start_idx:end_idx],
# ops['is_training_pl']: is_training}
#
#
# global_feature_val = sess.run(ops['global_feature'],feed_dict=feed_dict)
# stand_feature = global_feature_val.copy()
#
# change_point = 0
# current_data[start_idx,:,:] = orgin_data.copy()
# for point_index in range(NUM_POINT):
# if not (point_index in feature_points[0]):
# change_point = point_index
# break
#
# for x in np.linspace(min_position[0], max_position[0], (max_position[0]-min_position[0])//search_step +1):
# for y in np.linspace(min_position[1], max_position[1], (max_position[1]-min_position[1])//search_step +1):
# for z in np.linspace(min_position[2], max_position[2], (max_position[2]-min_position[2])//search_step +1):
# current_data[start_idx,change_point,:] = (x,y,z) #+ orgin_position
#
# # Aggregating BEG
# for vote_idx in range(num_votes):
#
# if FLAGS.model == 'pointnet_cls_basic':
# rotated_data = current_data[start_idx:end_idx, :, :] # directional pointNet
# else:
# rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :],
# vote_idx/float(num_votes) * np.pi * 2) # pointNet
#
# feed_dict = {ops['pointclouds_pl']: rotated_data,
# ops['labels_pl']: current_label[start_idx:end_idx],
# ops['is_training_pl']: is_training}
#
# global_feature_val = sess.run(ops['global_feature'],feed_dict=feed_dict)
#
# if (global_feature_val <= stand_feature).all():
# upper_bound_points = np.append(upper_bound_points, np.array([[x,y,z]]),axis = 0)
#
# fileName = 'dataAnalysis/figures/%d_upper_bound_points' % (start_idx)
# img_filename = fileName + '.jpg'
# plyFileName = fileName + '.ply'
# pc_util.write_ply(np.squeeze(upper_bound_points),plyFileName)
## pc_util.pyplot_draw_point_cloud(np.squeeze( upper_bound_points ),'')
# output_img = pc_util.draw_point_cloud(np.squeeze(upper_bound_points))
# scipy.misc.imsave(img_filename, output_img)
current_data[start_idx,:,:] = orgin_data.copy()
print('------Finished!---------\n')
if __name__=='__main__':
with tf.Graph().as_default():
evaluate(num_votes=1)
LOG_FOUT.close()