tawnkramer

#!/usr/bin/env python3
"""
Scripts to train a keras model using tensorflow.
Uses the data written by the donkey v2.2 tub writer,
but faster training with proper sampling of distribution over tubs. 
Has settings for continuous training that will look for new files as it trains. 
Modify send_model_to_pi is you wish continuous training to update your pi as it builds.
You can drop this in your ~/d2 dir.
Basic usage should feel familiar: python train.py --model models/mypilot
You might need to do a: pip install scikit-learn

tawnkramer

def imu_rnn_lstm(seq_length, num_outputs, imu_vec_size=6):
    from keras.layers import Input, Dense
    from keras.models import Model
    from keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization
    from keras.layers import Activation, Dropout, Flatten, Cropping2D, Lambda
    from keras.layers.merge import concatenate
    from keras.layers import LSTM
    
    img_in = Input(batch_shape=(seq_length, 120,160,3), name='img_in')
    imu_in = Input(batch_shape=(seq_length, imu_vec_size), name="imu_in")

tawnkramer

import os
import numpy as np
import keras
import donkeycar as dk
from donkeycar.parts.keras import KerasPilot

class KerasIMU(KerasPilot):
    def __init__(self, model=None, num_outputs=None, *args, **kwargs):
        super(KerasIMU, self).__init__(*args, **kwargs)
        self.model = default_imu(2)

tawnkramer

import os
import sys
import glob
import json
import shutil

def go(src_path, dest_path):
    print('adding tub', src_path, 'to', dest_path)
    src_files = glob.glob(os.path.join(src_path, '*.jpg'))
    dest_files = glob.glob(os.path.join(dest_path, '*.jpg'))

tawnkramer

class ImgFIFO:
    """
    Stack N previous images into a single N channel image, after converting each to grayscale.
    The most recent image is the last channel, and pushes previous images towards the front.
    """
    def __init__(self, num_channels=3):
        self.img_arr = None
        self.num_channels = num_channels
        
    def run(self, img_arr):

tawnkramer

on your pi

wget https://raw.githubusercontent.com/tawnkramer/donkey/dev-mainline/donkeycar/parts/controllers/joystick.py
cp joystick.py ~/d2/

now edit ~/d2/manage.py

near the top, add a

tawnkramer

'''
from the CarND-Alexnet-Feature-Extraction project
https://github.com/udacity/CarND-Alexnet-Feature-Extraction

This is modified to set the number of classes dynamically from the loaded data.
'''
import pickle
import time
import tensorflow as tf
from sklearn.model_selection import train_test_split

tawnkramer

'''
Author: Tawn Kramer
Date: June 22, 2017
Brief:

This will crop the set of images in a LISA sign data set, resize to a constant dimension,
and save out a pickle file. You can download the data here:
http://cvrr.ucsd.edu/LISA/lisa-traffic-sign-dataset.html

The output of this data set is a pickle file. Consideering the output is 15Mb while the input is 7GB,