# need [img, label, pose(camera meta data), dataID]
img_all_view, labels, poses, data_id, mesh = data.fetch()
feed_dict.update({placeholders['features']: mesh})
feed_dict.update({placeholders['img_inp']: img_all_view})
feed_dict.update({placeholders['labels']: labels})
feed_dict.update({placeholders['cameras']: poses})
# ---------------------------------------------------------------
_, dists, summaries, out1l, out2l = sess.run([model.opt_op, model.loss, model.merged_summary_op, model.output1l, model.output2l], feed_dict=feed_dict)
# ---------------------------------------------------------------
all_loss[iters] = dists
mean_loss = np.mean(all_loss[np.where(all_loss)])
print('Epoch {}, Iteration {}, Mean loss = {}, iter loss = {}, {}, data id {}'.format(current_epoch, iters + 1, mean_loss, dists, data.queue.qsize(), data_id))
train_writer.add_summary(summaries, step)
if (iters + 1) % 1000 == 0:
plot_scatter(pt=out2l, data_name=data_id, plt_path=epoch_plt_dir)
# ---------------------------------------------------------------
# Save model
model.save(sess=sess, ckpt_path=model_dir, step=current_epoch)
train_loss.write('Epoch {}, loss {}\n'.format(current_epoch, mean_loss))
train_loss.flush()
# ---------------------------------------------------------------
data.shutdown()
print('CNN-GCN Optimization Finished!')
if __name__ == '__main__':
print('=> set config')
args = execute()
pprint.pprint(vars(args))
main(args)
def main():
args = execute()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
pred_file_list = os.path.join(args.test_results_path,
args.p2mpp_experiment_name, '*.xyz')
xyz_list_path = glob.glob(pred_file_list)
xyz1 = tf.placeholder(tf.float32, shape=(None, 3))
xyz2 = tf.placeholder(tf.float32, shape=(None, 3))
dist1, idx1, dist2, idx2 = nn_distance(xyz1, xyz2)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
sess = tf.Session(config=config)
#A f1-score will be calculated for each threshold.
threshold = [0.00005, 0.00010, 0.00015, 0.00020]
name = {
'02828884': 'bench',
'03001627': 'chair',
'03636649': 'lamp',
'03691459': 'speaker',
'04090263': 'firearm',
'04379243': 'table',
'04530566': 'watercraft',
'02691156': 'plane',
'02933112': 'cabinet',
'02958343': 'car',
'03211117': 'monitor',
'04256520': 'couch',
'04401088': 'cellphone'
}
length = {
'02828884': 0,
'03001627': 0,
'03636649': 0,
'03691459': 0,
'04090263': 0,
'04379243': 0,
'04530566': 0,
'02691156': 0,
'02933112': 0,
'02958343': 0,
'03211117': 0,
'04256520': 0,
'04401088': 0
}
sum_pred = {
'02828884': np.zeros(4),
'03001627': np.zeros(4),
'03636649': np.zeros(4),
'03691459': np.zeros(4),
'04090263': np.zeros(4),
'04379243': np.zeros(4),
'04530566': np.zeros(4),
'02691156': np.zeros(4),
'02933112': np.zeros(4),
'02958343': np.zeros(4),
'03211117': np.zeros(4),
'04256520': np.zeros(4),
'04401088': np.zeros(4)
}
index = 0
for pred_path in xyz_list_path:
filename = os.path.basename(pred_path)
#Ground_thruth contains an image of the reconstructed object in [0] and the point cloud and normals on [1]
ground_truth_path = os.path.join(args.test_models_path,
filename.replace(".xyz", ".dat"))
ground = pickle.load(open(ground_truth_path, 'rb'),
encoding='bytes')[1][:, :3]
predict = np.loadtxt(pred_path)
class_id = pred_path.split('/')[-1].split('_')[0]
length[class_id] += 1.0
d1, i1, d2, i2 = sess.run([dist1, idx1, dist2, idx2],
feed_dict={
xyz1: predict,
xyz2: ground
})
sum_pred[class_id] += f_score(predict, ground, d1, i1, d2, i2,
threshold)
index += 1
log_dir = os.path.join(args.misc_results_path, args.p2mpp_experiment_name)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log_path = os.path.join(log_dir, 'f1_score.log')
with open(log_path, 'a+') as log:
for item in length:
number = length[item] + 1e-6
score = sum_pred[item] / number
log.write(", ".join(
[item, name[item],
str(length[item]),
str(score), "\n"]))
sess.close()