Created
April 17, 2017 06:04
-
-
Save daerduoCarey/d91a0983140acbb8bc6455f58cbf45cb to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import os | |
import sys | |
import numpy as np | |
from data_util import * | |
import datetime | |
ply_filelist = 'scripts/modelnet40_ply_filelist_shuffled.txt' | |
H5_BATCH_SIZE = 2000 | |
shape_names = get_category_names() | |
shape_name_dict = {shape_names[i]: i for i in range(len(shape_names))} | |
def filename_to_class_label(filename): | |
shape_name = filename.split('/')[-2] | |
return shape_name_dict[shape_name] | |
def main(): | |
ply_filenames = [line.rstrip() for line in open(ply_filelist)] | |
labels = [filename_to_class_label(fn) for fn in ply_filenames] | |
N = len(labels) | |
data_dim = [SAMPLING_POINT_NUM, 3] | |
label_dim = [1] | |
data_dtype = 'float32' | |
label_dtype = 'uint8' | |
output_dir = os.path.join(BASE_DIR, 'data', 'modelnet40_ply_0930', 'ply_data_hdf5') | |
if not os.path.exists(output_dir): | |
os.mkdir(output_dir) | |
output_filename_prefix = os.path.join(output_dir, 'ply_data') | |
batch_data_dim = [min(H5_BATCH_SIZE, N)] + data_dim | |
batch_label_dim = [min(H5_BATCH_SIZE, N)] + label_dim | |
h5_batch_data = np.zeros(batch_data_dim, dtype = np.float32) | |
h5_batch_label = np.zeros(batch_label_dim, dtype = np.uint8) | |
print h5_batch_data.shape | |
print h5_batch_label.shape | |
def unit_test(batch_num): | |
h5_filename = output_filename_prefix + '_' + str(batch_num) + '.h5' | |
print 'Performing unit test for hdf5 file %s' % h5_filename | |
d, l = load_h5(h5_filename) | |
print 'data shape: ', d.shape | |
print 'label shape: ', l.shape | |
print 'label diff: %f' % np.linalg.norm(l.T - labels[batch_num*H5_BATCH_SIZE: min((batch_num+1)*H5_BATCH_SIZE, N)]) | |
for k in range(N): | |
if k % 100 == 0: | |
print 'Iteration %d/%d' % (k, N) | |
d = load_ply_data(ply_filenames[k]) | |
d = pad_arr_rows(d, row=SAMPLING_POINT_NUM) | |
l = labels[k] | |
h5_batch_data[k%H5_BATCH_SIZE, ...] = d | |
h5_batch_label[k%H5_BATCH_SIZE, ...] = l | |
if (k+1)%H5_BATCH_SIZE == 0 or k == N - 1: | |
print '[%s] %d/%d' % (datetime.datetime.now(), k+1, N) | |
h5_filename = output_filename_prefix + '_' + str(k/H5_BATCH_SIZE) + '.h5' | |
begidx = 0 | |
endidx = k % H5_BATCH_SIZE + 1 | |
save_h5(h5_filename, h5_batch_data[begidx:endidx, ...], | |
h5_batch_label[begidx:endidx, ...], | |
data_dtype, label_dtype) | |
print 'Stored %d objects' % (endidx - begidx) | |
unit_test(k/H5_BATCH_SIZE) | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
I am facing the same issue. Any help would be much appreciated.
Thank you!!!