mirror of
https://github.com/wassname/Flex-Convolution.git
synced 2026-09-09 11:14:10 +08:00
Mike Clark
aae13f0126
Tried building a model with these ops, but with multiple layers of pooling I get NaN value after a while. I think there is a problem with an unstable gradient in the this layer.
1010 lines
40 KiB
Plaintext
1010 lines
40 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There seems to be a error when using more than one layer of pooling of conv_transpose. Perhaps it will be fixed in a later flex-conv commit. currently at commit e2fdad61d93a2ec830f68b9cf130b4688a4c8040 (20181016)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:28.366086Z",
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"start_time": "2018-10-16T00:03:28.342706Z"
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}
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},
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"outputs": [],
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"source": [
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"%reload_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:28.404059Z",
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"start_time": "2018-10-16T00:03:28.387011Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # see issue #152\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:38.756642Z",
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"start_time": "2018-10-16T00:03:28.406461Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/wassname/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
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" from ._conv import register_converters as _register_converters\n",
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"\n",
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"from keras import backend as K\n",
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"K.set_image_data_format('channels_first')\n",
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"import tensorflow as tf\n",
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"\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tabulate import tabulate\n",
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"from layers import flex_convolution, flex_convolution_transpose, flex_pooling"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:38.821985Z",
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"start_time": "2018-10-16T00:03:38.760136Z"
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}
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},
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"outputs": [],
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"source": [
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"###### img_rows = 512\n",
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"img_cols = 512\n",
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"img_channels = 3\n",
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"batch_size = 1\n",
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"num_epochs = 100\n",
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"input_positions = 3\n",
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"k = 5\n",
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"network_channel_sizes = (8, 16, 16, 32, 32, 64, 64, 128, 128, 256, 256, 512, 512)\n",
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"model_name = '3dflex_2_t_minst'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-03T08:03:19.091280Z",
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"start_time": "2018-10-03T08:03:19.080019Z"
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}
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},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:38.888021Z",
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"start_time": "2018-10-16T00:03:38.824873Z"
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}
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},
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"outputs": [],
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"source": [
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"# https://github.com/Harry-Zhi/3DMNIST/blob/master/3DMNIST.ipynb\n",
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"def img_to_point_cloud(input_image, voxel):\n",
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"\n",
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" non_zero_coord = np.transpose(np.nonzero(input_image))\n",
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" \n",
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" # dict for fast looking of neighboor ocupancy\n",
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" non_zero_dict = {}\n",
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" for i in range(input_image.shape[0]):\n",
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" for j in range(input_image.shape[1]):\n",
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" non_zero_dict[str([i,j])] = any(np.all([i,j] == non_zero_coord, axis=1))\n",
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"\n",
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" cloud = []\n",
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" \n",
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" for n in range(len(non_zero_coord)):\n",
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" x = non_zero_coord[n][0]\n",
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" y = non_zero_coord[n][1]\n",
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" \n",
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" components = [0,1]\n",
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" \n",
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" # top\n",
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" if not non_zero_dict[str([x-1, y])]:\n",
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" components.append(2)\n",
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" \n",
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" # bottom\n",
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" if not non_zero_dict[str([x+1, y])]:\n",
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" components.append(3)\n",
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" \n",
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" # left\n",
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" if not non_zero_dict[str([x, y-1])]:\n",
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" components.append(4)\n",
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" \n",
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" # right\n",
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" if not non_zero_dict[str([x, y+1])]:\n",
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" components.append(5)\n",
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" \n",
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" pixel_cloud = np.concatenate(voxel[components])\n",
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" \n",
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" # move the voxel to its position\n",
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" pixel_cloud[:,0] +=x\n",
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" pixel_cloud[:,1] += y\n",
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" \n",
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" cloud.append(pixel_cloud)\n",
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" \n",
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" cloud = np.concatenate(cloud)\n",
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" \n",
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" # make max range 0-1\n",
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" xyzmin = np.min(cloud[:,:3], axis=0)\n",
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" xyzmax = np.max(cloud[:,:3], axis=0)\n",
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" diff = xyzmax - xyzmin\n",
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" cloud[:,:3] = ((cloud[:,:3] - xyzmin[np.argmax(diff)]) / diff[np.argmax(diff)])\n",
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" \n",
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" # 0 mean\n",
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" cloud[:,:3] -= np.mean(cloud[:,:3], axis=0)\n",
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"\n",
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" return cloud"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:38.948978Z",
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"start_time": "2018-10-16T00:03:38.890743Z"
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}
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},
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"outputs": [],
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"source": [
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"MIN_X, MAX_X = (-0.5, 0.5)\n",
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"MIN_Y, MAX_Y = (-0.5, 0.5)\n",
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"MIN_Z, MAX_Z = (-3, 3)\n",
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"\n",
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"N_X = 5\n",
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"N_Y = 5\n",
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"N_Z = 30"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-16T00:03:39.016126Z",
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"start_time": "2018-10-16T00:03:38.951042Z"
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}
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},
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"outputs": [],
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"source": [
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"# VOXEL CREATION\n",
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"# with normals\n",
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"\n",
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"front = np.array(np.meshgrid(np.linspace(MIN_X, MAX_X, N_X),\n",
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" np.linspace(MIN_Y, MAX_Y, N_Y),\n",
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" MAX_Z )).T.reshape(-1,3) \n",
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"front = np.concatenate((front, [[1,0,0]] * len(front)), axis=1)\n",
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"\n",
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"\n",
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"back = np.array(np.meshgrid(np.linspace(MIN_X, MAX_X, N_X), \n",
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" np.linspace(MIN_Y, MAX_Y, N_Y), \n",
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" MIN_Z )).T.reshape(-1,3) \n",
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"back = np.concatenate((back, [[-1,0,0]] * len(back)), axis=1)\n",
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"\n",
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"\n",
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"top = np.array(np.meshgrid(MIN_X,\n",
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" np.linspace(MIN_Y, MAX_Y, N_Y), \n",
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" np.linspace(MIN_Z, MAX_Z, N_Z))).T.reshape(-1,3)\n",
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"top = np.concatenate((top, [[0,0,1]] * len(top)), axis=1)\n",
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"\n",
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"\n",
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"bottom = np.array(np.meshgrid(MAX_X, \n",
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" np.linspace(MIN_Y, MAX_Y, N_Y), \n",
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" np.linspace(MIN_Z, MAX_Z, N_Z))).T.reshape(-1,3) \n",
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"bottom = np.concatenate((bottom, [[0,0,-1]] * len(bottom)), axis=1)\n",
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"\n",
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"\n",
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"left = np.array(np.meshgrid(np.linspace(MIN_X, MAX_X, N_X), \n",
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" MIN_Y,\n",
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" np.linspace(MIN_Z, MAX_Z, N_Z))).T.reshape(-1,3)\n",
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"left = np.concatenate((left, [[0,-1,0]] * len(left)), axis=1)\n",
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"\n",
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"\n",
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"right = np.array(np.meshgrid(np.linspace(MIN_X, MAX_X, N_X), \n",
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" MAX_Y, \n",
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" np.linspace(MIN_Z, MAX_Z, N_Z))).T.reshape(-1,3) \n",
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"right = np.concatenate((right, [[0,1,0]] * len(right)), axis=1)\n",
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"\n",
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"\n",
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"voxel = np.array([front, back, top, bottom, left, right])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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|
"ExecuteTime": {
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|
"end_time": "2018-10-16T00:03:39.370688Z",
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"start_time": "2018-10-16T00:03:39.018380Z"
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}
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},
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"outputs": [],
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"source": [
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"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"ExecuteTime": {
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|
"end_time": "2018-10-16T00:03:39.428144Z",
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"start_time": "2018-10-16T00:03:39.372881Z"
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}
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},
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"outputs": [],
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"source": [
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"from scipy.spatial import cKDTree\n",
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"\n",
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"def cloud_to_tftree(xv, K=5):\n",
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" positions = xv[:, :3]\n",
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" features = xv[:, 3:]\n",
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" tree = cKDTree(positions)\n",
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" neighbors = tree.query(positions, k=K)[1]\n",
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"\n",
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" positions = positions.transpose(1, 0).astype(np.float32)\n",
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" features = features.transpose(1, 0).astype(np.float32)\n",
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" neighbors = neighbors.transpose(1, 0).astype(np.int32)\n",
|
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" return features, positions, neighbors"
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]
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},
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{
|
|
"cell_type": "code",
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|
"execution_count": 11,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:39.663879Z",
|
|
"start_time": "2018-10-16T00:03:39.430990Z"
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}
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|
},
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|
"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([[1., 1., 1., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 1., 1., 1.],\n",
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" [0., 0., 0., ..., 0., 0., 0.]], dtype=float32),\n",
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" array([[-0.47305447, -0.47305447, -0.47305447, ..., 0.5019455 ,\n",
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" 0.51444554, 0.52694553],\n",
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" [-0.1094358 , -0.0969358 , -0.0844358 , ..., -0.1094358 ,\n",
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" -0.1094358 , -0.1094358 ],\n",
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" [ 0.15 , 0.15 , 0.15 , ..., 0.15 ,\n",
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" 0.15 , 0.15 ]], dtype=float32),\n",
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" array([[ 345, 196, 197, ..., 25364, 25369, 25699],\n",
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" [ 0, 1, 2, ..., 25697, 25698, 25374],\n",
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" [ 195, 191, 192, ..., 25692, 25693, 25549],\n",
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" [ 340, 345, 7, ..., 25696, 25699, 25694],\n",
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" [ 190, 195, 3, ..., 25359, 25549, 25544]], dtype=int32))"
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]
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},
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"execution_count": 11,
|
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"metadata": {},
|
|
"output_type": "execute_result"
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}
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|
],
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"source": [
|
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"cloud_to_tftree(img_to_point_cloud(x_train[0], voxel))"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 66,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:10:03.566867Z",
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"start_time": "2018-10-16T00:10:03.494666Z"
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}
|
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},
|
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"outputs": [
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{
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|
"data": {
|
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"text/plain": [
|
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"<__main__.DataLoader at 0x7f02759a12e8>"
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]
|
|
},
|
|
"execution_count": 66,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"class DataLoader(tf.keras.utils.Sequence):\n",
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" \n",
|
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" def __init__(self, x_train, y_train, batch_size=4):\n",
|
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" super().__init__()\n",
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" self.batch_size = batch_size\n",
|
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" self.x_train = x_train\n",
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" self.y_train = y_train\n",
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" \n",
|
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" def __getitem__(self, index):\n",
|
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" start = index * self.batch_size\n",
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" end = min(start + self.batch_size, len(self.x_train))\n",
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"\n",
|
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" xx = [self.x_train[i] for i in range(start, end)]\n",
|
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" yy = [self.y_train[i] for i in range(start, end)]\n",
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" \n",
|
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" yy = np.stack(yy, 0)\n",
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" \n",
|
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" x2 = []\n",
|
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" for x in xx:\n",
|
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" try:\n",
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" x = img_to_point_cloud(x, voxel)\n",
|
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" except KeyError as e:\n",
|
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" print('error', index, e) \n",
|
|
" x = np.zeros((512, 6))\n",
|
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" idx = np.random.choice(range(len(x)), size=img_cols, replace=False)\n",
|
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" x = cloud_to_tftree(x[idx])\n",
|
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" x2.append(x)\n",
|
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"# x2 = np.stack(x2, 0)\n",
|
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" x2 = [np.stack(x) for x in zip(*x2)]\n",
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" return x2, yy\n",
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" \n",
|
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" def __len__(self):\n",
|
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" return len(self.x_train)//self.batch_size-1\n",
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"\n",
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"\n",
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"train_gen = DataLoader(x_train, y_train, batch_size=batch_size)\n",
|
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"test_gen = DataLoader(x_test, y_test, batch_size=batch_size)\n",
|
|
"train_gen"
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:10:03.811773Z",
|
|
"start_time": "2018-10-16T00:10:03.715292Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-03T08:04:08.385788Z",
|
|
"start_time": "2018-10-03T08:04:08.374907Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-08T09:02:51.764390Z",
|
|
"start_time": "2018-10-08T09:02:51.712010Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:39.789715Z",
|
|
"start_time": "2018-10-16T00:03:39.732871Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# # model\n",
|
|
"# # from https://github.com/mikelane/SegNet/blob/master/network.py\n",
|
|
"from typing import Tuple, TypeVar\n",
|
|
"\n",
|
|
"from tensorflow.python.keras.layers import concatenate, Conv2D, Conv2DTranspose, MaxPooling2D, BatchNormalization, Lambda, Input, ReLU, Conv1D, Dense, Reshape\n",
|
|
"from tensorflow.python.keras.layers.advanced_activations import LeakyReLU\n",
|
|
"from tensorflow.python.keras.models import Model, Sequential\n",
|
|
"from tensorflow.python.keras.optimizers import Adam\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:39.862238Z",
|
|
"start_time": "2018-10-16T00:03:39.792646Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from layers import FlexConvolution, FlexPooling\n",
|
|
"\n",
|
|
"class DownsampleGroup(tf.keras.Model):\n",
|
|
" def __init__(\n",
|
|
" self, num_filters: int, pool_strides=2, leaky_alpha=0.3):\n",
|
|
" \"\"\"\n",
|
|
" A Unet downsampling group using flex-net operations\n",
|
|
" \"\"\"\n",
|
|
" super().__init__()\n",
|
|
" self.num_filters = num_filters\n",
|
|
" self.leaky_alpha = leaky_alpha\n",
|
|
" self.pool_strides = pool_strides\n",
|
|
" \n",
|
|
" def build(self, input_shape):\n",
|
|
" \n",
|
|
"\n",
|
|
" input_features_shape, input_positions_shape, input_neighbors_shape = input_shape\n",
|
|
" \n",
|
|
" input_features = Input(shape=[s.value for s in input_features_shape[1:]])\n",
|
|
" input_positions = Input(shape=[s.value for s in input_positions_shape[1:]])\n",
|
|
" input_neighbors = Input(shape=[s.value for s in input_neighbors_shape[1:]], dtype='int32')\n",
|
|
" \n",
|
|
" self.flex0 = FlexConvolution(\n",
|
|
" input_features,\n",
|
|
" input_positions,\n",
|
|
" input_neighbors,\n",
|
|
" filters=self.num_filters,\n",
|
|
" activation=None,\n",
|
|
" kernel_initializer=None,\n",
|
|
" position_bias_initializer=tf.zeros_initializer(),\n",
|
|
" features_bias_initializer=tf.zeros_initializer(),\n",
|
|
" use_feature_bias=True,\n",
|
|
" data_format='simple',\n",
|
|
" trainable=True,\n",
|
|
" name=None)\n",
|
|
"# self.bn = BatchNormalization(axis=1)\n",
|
|
" self.act = LeakyReLU(alpha=self.leaky_alpha)\n",
|
|
" self.pool = FlexPooling(\n",
|
|
" input_features,\n",
|
|
" input_neighbors,\n",
|
|
" data_format='simple',\n",
|
|
" name=None)\n",
|
|
"\n",
|
|
"# conv_trans_features = Input(shape=[self.num_filters, input_features_shape[2]])\n",
|
|
"# self.conv_trans = FlexConvolutionTranspose(\n",
|
|
"# conv_trans_features,\n",
|
|
"# input_positions,\n",
|
|
"# input_neighbors,\n",
|
|
"# filters=self.num_filters,\n",
|
|
"# activation=None,\n",
|
|
"# kernel_initializer=None,\n",
|
|
"# position_bias_initializer=tf.zeros_initializer(),\n",
|
|
"# features_bias_initializer=tf.zeros_initializer(),\n",
|
|
"# use_feature_bias=True,\n",
|
|
"# data_format=\"simple\",\n",
|
|
"# trainable=True,\n",
|
|
"# name=None,\n",
|
|
"# )\n",
|
|
" \n",
|
|
" self.subsample_f = Lambda(lambda x: x[:, :, :input_features.shape[2]//self.pool_strides], output_shape=(self.num_filters, input_features.shape[2]//self.pool_strides))\n",
|
|
" self.subsample_p = Lambda(lambda x: x[:, :, :input_positions.shape[2]//self.pool_strides], output_shape=(input_positions.shape[1], input_positions.shape[2]//self.pool_strides))\n",
|
|
" self.subsample_n = Lambda(lambda x: x[:, :, :input_neighbors.shape[2]//self.pool_strides], output_shape=(input_neighbors.shape[1], input_neighbors.shape[2]//self.pool_strides))\n",
|
|
" \n",
|
|
" \n",
|
|
" def call(self, inputs, training=False):\n",
|
|
" \n",
|
|
" input_to_group, positions, neighborhoods = inputs\n",
|
|
" \n",
|
|
" conv_1 = self.flex0.apply([input_to_group, positions, neighborhoods])\n",
|
|
"# conv_1 = self.bn(conv_1, training=training)\n",
|
|
" conv_1 = self.act(conv_1)\n",
|
|
" \n",
|
|
" # FIXME the pooling op is unstable and more than 1 downsample module will result in NaN's after a few steps of training\n",
|
|
" # you can see this clearly by running this notebook without cuda\n",
|
|
" output = self.pool.apply([conv_1, neighborhoods])\n",
|
|
"# output = self.conv_trans.apply([conv_1, positions, neighborhoods]) \n",
|
|
" output = self.subsample_f(conv_1)\n",
|
|
" \n",
|
|
" positions = self.subsample_p(positions)\n",
|
|
" \n",
|
|
" neighborhoods = self.subsample_n(neighborhoods)\n",
|
|
" \n",
|
|
" return output, positions, neighborhoods"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-10T01:55:09.396077Z",
|
|
"start_time": "2018-10-10T01:55:09.330451Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:39.934678Z",
|
|
"start_time": "2018-10-16T00:03:39.864702Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from typing import Tuple\n",
|
|
"from layers import FlexConvolutionTranspose, FlexConvolution, FlexPooling\n",
|
|
"\n",
|
|
"\n",
|
|
"class Network2:\n",
|
|
" def __init__(self, \n",
|
|
" input_height: int,\n",
|
|
" input_channels: int,\n",
|
|
" network_channel_sizes: Tuple[int, ...],\n",
|
|
" channels_last: bool = True,\n",
|
|
" conv_padding: str = 'same',\n",
|
|
" down_conv_kernel: Tuple[int, int] = (3, 3),\n",
|
|
" up_conv_kernel: Tuple[int, int] = (2, 2),\n",
|
|
" up_conv_kernel_strides: int = 2,\n",
|
|
" leaky_alpha=0.3,\n",
|
|
" pool_size: Tuple[int, int] = (2, 2),\n",
|
|
" pool_strides: int = 2,\n",
|
|
" input_positions: int = 3,\n",
|
|
" output_dim: int = 128,\n",
|
|
" k: int = 5,\n",
|
|
" downsample_layers = 2,\n",
|
|
" ):\n",
|
|
" self.input_features = Input(shape=(input_channels, input_height))\n",
|
|
" self.input_positions = Input(shape=(input_positions, input_height))\n",
|
|
" self.input_neighbors = Input(shape=(k, input_height), dtype='int32')\n",
|
|
" self.inputs1 = [self.input_features, self.input_positions, self.input_neighbors]\n",
|
|
" \n",
|
|
" self.output_dim = output_dim\n",
|
|
" self.leaky_alpha = leaky_alpha\n",
|
|
" self.downsample_layers = downsample_layers\n",
|
|
" \n",
|
|
" self.inputs = [self.input_features, self.input_positions, self.input_neighbors]\n",
|
|
" \n",
|
|
" self.seq = []\n",
|
|
" for i in range(self.downsample_layers):\n",
|
|
" self.seq.append(DownsampleGroup(network_channel_sizes[i], leaky_alpha=leaky_alpha))\n",
|
|
"\n",
|
|
" self.subtract = Lambda(lambda x: x[0]-x[1], output_shape=(network_channel_sizes[i], 1))\n",
|
|
" \n",
|
|
" self.reshape = Reshape((network_channel_sizes[i] * input_height//(2**self.downsample_layers),))\n",
|
|
"\n",
|
|
" def get_model(self):\n",
|
|
" \n",
|
|
" inputs1 = self.inputs1\n",
|
|
" \n",
|
|
" for i in range(self.downsample_layers):\n",
|
|
" inputs1 = self.seq[i].apply(inputs1)\n",
|
|
" \n",
|
|
" \n",
|
|
" outputs = inputs1[0]\n",
|
|
" outputs = self.reshape(outputs)\n",
|
|
" \n",
|
|
"# outputs = self.reshape(inputs1[0])\n",
|
|
" \n",
|
|
"# outputs = Dense(128)(outputs)\n",
|
|
"# outputs = BatchNormalization(axis=1)(outputs)\n",
|
|
"# outputs = LeakyReLU(alpha=self.leaky_alpha)(outputs)\n",
|
|
" \n",
|
|
"# outputs = Dense(64)(outputs)\n",
|
|
"# outputs = BatchNormalization(axis=1)(outputs)\n",
|
|
"# outputs = LeakyReLU(alpha=self.leaky_alpha)(outputs)\n",
|
|
" \n",
|
|
" outputs = Dense(1)(outputs)\n",
|
|
" \n",
|
|
" self.model = Model(inputs=self.inputs, outputs=outputs)\n",
|
|
"\n",
|
|
" return self.model\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-15T23:38:13.156468Z",
|
|
"start_time": "2018-10-15T23:38:13.094567Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Init"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:40.192919Z",
|
|
"start_time": "2018-10-16T00:03:39.936896Z"
|
|
},
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"__________________________________________________________________________________________________\n",
|
|
"Layer (type) Output Shape Param # Connected to \n",
|
|
"==================================================================================================\n",
|
|
"input_1 (InputLayer) (None, 3, 512) 0 \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"input_2 (InputLayer) (None, 3, 512) 0 \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"input_3 (InputLayer) (None, 5, 512) 0 \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"downsample_group (DownsampleGro [(None, 8, 256), (No 104 input_1[0][0] \n",
|
|
" input_2[0][0] \n",
|
|
" input_3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"downsample_group_1 (DownsampleG [(None, 16, 128), (N 528 downsample_group[0][0] \n",
|
|
" downsample_group[0][1] \n",
|
|
" downsample_group[0][2] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"reshape (Reshape) (None, 2048) 0 downsample_group_1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"dense (Dense) (None, 1) 2049 reshape[0][0] \n",
|
|
"==================================================================================================\n",
|
|
"Total params: 2,681\n",
|
|
"Trainable params: 2,681\n",
|
|
"Non-trainable params: 0\n",
|
|
"__________________________________________________________________________________________________\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"net1 = Network2(\n",
|
|
" input_height=img_cols,\n",
|
|
" input_channels=img_channels,\n",
|
|
" network_channel_sizes=network_channel_sizes,\n",
|
|
" leaky_alpha=0.1, k=k)\n",
|
|
"net = net1.get_model()\n",
|
|
"\n",
|
|
"net.compile(\n",
|
|
" optimizer=Adam(lr=1e-7),\n",
|
|
" loss='mean_absolute_error',\n",
|
|
" metrics=['mean_absolute_error'])\n",
|
|
"\n",
|
|
"net.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-10T02:04:10.811276Z",
|
|
"start_time": "2018-10-10T02:04:10.725334Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:40.262776Z",
|
|
"start_time": "2018-10-16T00:03:40.195822Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"_________________________________________________________________\n",
|
|
"Layer (type) Output Shape Param # \n",
|
|
"=================================================================\n",
|
|
"flex_convolution (FlexConvol (None, 8, 512) 104 \n",
|
|
"_________________________________________________________________\n",
|
|
"leaky_re_lu (LeakyReLU) (None, 8, 512) 0 \n",
|
|
"_________________________________________________________________\n",
|
|
"flex_pooling (FlexPooling) (None, 8, 512) 0 \n",
|
|
"_________________________________________________________________\n",
|
|
"lambda_1 (Lambda) (None, 8, 256) 0 \n",
|
|
"_________________________________________________________________\n",
|
|
"lambda_2 (Lambda) (None, 3, 256) 0 \n",
|
|
"_________________________________________________________________\n",
|
|
"lambda_3 (Lambda) (None, 5, 256) 0 \n",
|
|
"=================================================================\n",
|
|
"Total params: 104\n",
|
|
"Trainable params: 104\n",
|
|
"Non-trainable params: 0\n",
|
|
"_________________________________________________________________\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"d = net1.seq[0]\n",
|
|
"d.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-08T05:09:34.498114Z",
|
|
"start_time": "2018-10-08T05:09:34.302103Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Run"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:40.387720Z",
|
|
"start_time": "2018-10-16T00:03:40.266718Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"([array([[[-1., -1., 1., ..., 0., 0., 0.],\n",
|
|
" [ 0., 0., 0., ..., -1., -1., 0.],\n",
|
|
" [ 0., 0., 0., ..., 0., 0., -1.]]], dtype=float32),\n",
|
|
" array([[[-0.32888693, 0.07111307, 0.37111306, ..., -0.14138693,\n",
|
|
" -0.16638693, -0.17888692],\n",
|
|
" [-0.27791518, -0.12791519, -0.2904152 , ..., -0.12791519,\n",
|
|
" -0.12791519, 0.3720848 ],\n",
|
|
" [-0.15 , -0.15 , 0.15 , ..., -0.11896551,\n",
|
|
" -0.08793104, -0.04655172]]], dtype=float32),\n",
|
|
" array([[[ 0, 1, 2, ..., 509, 510, 511],\n",
|
|
" [ 74, 162, 503, ..., 510, 322, 297],\n",
|
|
" [265, 216, 255, ..., 322, 509, 228],\n",
|
|
" [414, 358, 337, ..., 481, 181, 277],\n",
|
|
" [ 49, 106, 230, ..., 181, 394, 67]]], dtype=int32)],\n",
|
|
" array([3], dtype=uint8))"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(test_gen)\n",
|
|
"test_gen[200]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:03:40.487172Z",
|
|
"start_time": "2018-10-16T00:03:40.391319Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"([array([[[-1., 0., 0., ..., 0., 0., 0.],\n",
|
|
" [ 0., -1., 1., ..., 0., 0., 0.],\n",
|
|
" [ 0., 0., 0., ..., -1., -1., 1.]]], dtype=float32),\n",
|
|
" array([[[ 0.33352602, 0.258526 , 0.471026 , ..., -0.24147399,\n",
|
|
" -0.341474 , -0.441474 ],\n",
|
|
" [ 0.0800578 , -0.0199422 , 0.2300578 , ..., -0.0199422 ,\n",
|
|
" 0.2675578 , -0.0074422 ],\n",
|
|
" [-0.15 , 0.05689655, -0.0775862 , ..., 0.00517241,\n",
|
|
" -0.09827586, 0.06724138]]], dtype=float32),\n",
|
|
" array([[[ 0, 1, 2, ..., 509, 510, 511],\n",
|
|
" [418, 450, 322, ..., 434, 43, 461],\n",
|
|
" [327, 15, 36, ..., 416, 278, 309],\n",
|
|
" [ 66, 49, 430, ..., 323, 361, 424],\n",
|
|
" [ 99, 456, 353, ..., 388, 291, 265]]], dtype=int32)],\n",
|
|
" array([5], dtype=uint8))"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(train_gen)\n",
|
|
"train_gen[100]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-10-16T00:10:33.992327Z",
|
|
"start_time": "2018-10-16T00:10:18.921204Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "6801deb33dc64833b1555e2bb5c32288",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"HBox(children=(IntProgress(value=0, description='Training', max=1), HTML(value='')))"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "af99a934c85948aba139e22e98b03979",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(IntProgress(value=0, description='Epoch 0', max=59999), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"error 53214 '[28, 14]'\n",
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"error 27485 '[28, 16]'\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-69-5612a99b03c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m callbacks=[\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mTQDMNotebookCallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;31m# TensorBoard(log_dir='logs/'),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# ModelCheckpoint(epoch_model_path,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1777\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1778\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1779\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1781\u001b[0m def evaluate_generator(self,\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m outs = model.train_on_batch(\n\u001b[0;32m--> 204\u001b[0;31m x, y, sample_weight=sample_weight, class_weight=class_weight)\n\u001b[0m\u001b[1;32m 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1552\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1554\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2912\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_arrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_symbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2914\u001b[0;31m \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2915\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2916\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1380\u001b[0m ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m 1381\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 1383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1384\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"from keras.callbacks import TensorBoard, ModelCheckpoint\n",
|
|
"from keras_tqdm import TQDMNotebookCallback\n",
|
|
"\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
" epoch_model_path = 'outputs/{model_name:}_epoch_{epoch:}.h5'.format(model_name=model_name, epoch=epoch)\n",
|
|
" net.fit_generator(generator=train_gen,\n",
|
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" validation_data=test_gen,\n",
|
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" verbose=0,\n",
|
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" shuffle=True,\n",
|
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" callbacks=[\n",
|
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" TQDMNotebookCallback()\n",
|
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"# TensorBoard(log_dir='logs/'),\n",
|
|
"# ModelCheckpoint(epoch_model_path,\n",
|
|
"# monitor='loss',\n",
|
|
"# save_best_only=True,\n",
|
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"# save_weights_only=True,\n",
|
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"# mode='auto',\n",
|
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"# period=1)\n",
|
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" ]\n",
|
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" )\n"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
|
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"kernelspec": {
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"display_name": "jupyter3",
|
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"language": "python",
|
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"name": "jupyter3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
|
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"version": "3.5.3"
|
|
},
|
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"toc": {
|
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"colors": {
|
|
"hover_highlight": "#DAA520",
|
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"navigate_num": "#000000",
|
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"navigate_text": "#333333",
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"running_highlight": "#FF0000",
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"selected_highlight": "#FFD700",
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"sidebar_border": "#EEEEEE",
|
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"wrapper_background": "#FFFFFF"
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},
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"moveMenuLeft": true,
|
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"nav_menu": {
|
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"height": "12px",
|
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"width": "252px"
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},
|
|
"navigate_menu": true,
|
|
"number_sections": false,
|
|
"sideBar": true,
|
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"threshold": 4,
|
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"toc_cell": false,
|
|
"toc_position": {
|
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"height": "857px",
|
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"left": "0px",
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"right": "1714px",
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"top": "152px",
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"width": "191px"
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},
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"toc_section_display": "block",
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"toc_window_display": true,
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"widenNotebook": false
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}
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 2
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}
|