"""This network has four convolution layers and three fully connected layers and a lot more

filters than the conv_4_fc_3 model.

Parameters:

dropout - list of dropout values for the 3 fully connected layers

Returns:

A model"""

# ensure the dropout list has enough values in it for the model

# augment the values of some are missing

if dropout == None or len(dropout) == 0:

dropout = [0.0, 0.0]

elif len(dropout) == 1:

dropout = dropout * 2

# this hack gets the current function name and sets it to the name of the model

model = Sequential(name=traceback.extract_stack(None, 2)[-1][2])

# crop top 56 rows and bottom 24 rows from the images

model.add(Cropping2D(cropping=((56, 24), (0, 0)), input_shape=(160, 320, 3), name='pp_crop'))

# mean center the pixels

model.add(Lambda(lambda x: (x / 255.0) - 0.5, name='pp_center'))

# layer 1: convolution + max pooling. Input 80x320x3. Output 40x160x32

model.add(Convolution2D(32, 5, 5, border_mode='same', name='conv1'))

model.add(MaxPooling2D((2, 2), name='pool1'))

model.add(Activation('relu', name='act1'))

# layer 2: convolution = max pooling. Input 40x160x32. Output 20x80x64

model.add(Convolution2D(64, 5, 5, border_mode='same', name='conv2'))

model.add(MaxPooling2D((2, 2), name='pool2'))

model.add(Activation('relu', name='act2'))

# layer 3: convolution = max pooling. Input 20x80x64. Output 10x40x128

model.add(Convolution2D(128, 3, 3, border_mode='same', name='conv3'))

model.add(MaxPooling2D((2, 2), name='pool3'))

model.add(Activation('relu', name='act3'))

# layer 4: convolution = max pooling. Input 10x40x128. Output 5x20x128

model.add(Convolution2D(128, 3, 3, border_mode='same', name='conv4'))

model.add(MaxPooling2D((2, 2), name='pool4'))

model.add(Activation('relu', name='act4'))

# flatten: Input 5x20x128. Output 12800

model.add(Flatten(name='flat'))

# layer 5: fully connected + dropout. Input 12800. Output 556

model.add(Dense(556, name='fc5'))

model.add(Dropout(dropout[0], name='drop5'))

model.add(Activation('relu', name='act5'))

# layer 6: fully connected + dropout. Input 556. Output 24

model.add(Dense(24, name='fc6'))

model.add(Dropout(dropout[1], name='drop6'))

model.add(Activation('relu', name='act6'))

# layer 7: fully connected. Input 24. Output 1.

model.add(Dense(1, name='out'))

"""This network has four convolution layers and three fully connected layers

Parameters:

dropout - list of dropout values for the 3 fully connected layers

Returns:

A model"""

# ensure the dropout list has enough values in it for the model

# augment the values of some are missing

if dropout == None or len(dropout) == 0:

dropout = [0.0, 0.0]

elif len(dropout) == 1:

dropout = dropout * 2

# this hack gets the current function name and sets it to the name of the model

model = Sequential(name=traceback.extract_stack(None, 2)[-1][2])

# crop top 56 rows and bottom 24 rows from the images

model.add(Cropping2D(cropping=((56, 24), (0, 0)), input_shape=(160, 320, 3), name='pp_crop'))

# mean center the pixels

model.add(Lambda(lambda x: (x / 255.0) - 0.5, name='pp_center'))

# layer 1: convolution + max pooling. Input 80x320x3. Output 40x160x8

model.add(Convolution2D(8, 5, 5, border_mode='same', name='conv1'))

model.add(MaxPooling2D((2, 2), name='pool1'))

model.add(Activation('relu', name='act1'))

# layer 2: convolution = max pooling. Input 40x160x8. Output 20x80x16

model.add(Convolution2D(16, 5, 5, border_mode='same', name='conv2'))

model.add(MaxPooling2D((2, 2), name='pool2'))

model.add(Activation('relu', name='act2'))

# layer 3: convolution = max pooling. Input 20x80x16. Output 10x40x32

model.add(Convolution2D(32, 3, 3, border_mode='same', name='conv3'))

model.add(MaxPooling2D((2, 2), name='pool3'))

model.add(Activation('relu', name='act3'))

# layer 4: convolution = max pooling. Input 10x40x32. Output 5x20x32

model.add(Convolution2D(32, 3, 3, border_mode='same', name='conv4'))

model.add(MaxPooling2D((2, 2), name='pool4'))

model.add(Activation('relu', name='act4'))

# flatten: Input 5x20x32. Output 3200

model.add(Flatten(name='flat'))

# layer 5: fully connected + dropout. Input 3200. Output 556

model.add(Dense(556, name='fc5'))

model.add(Dropout(dropout[0], name='drop5'))

model.add(Activation('relu', name='act5'))

# layer 6: fully connected + dropout. Input 556. Output 24

model.add(Dense(24, name='fc6'))

model.add(Dropout(dropout[1], name='drop6'))

model.add(Activation('relu', name='act6'))

# layer 7: fully connected. Input 24. Output 1.

model.add(Dense(1, name='out'))

"""This model attempts to use transfer learning on ResNet50

Parameters:

dropout - list of dropout values for the 2 fully connected layers

Returns:

A model with ResNet50 at it's core"""

# ensure the dropout list has enough values in it for the model

# augment the values of some are missing

if dropout == None or len(dropout) == 0:

dropout = [0.0, 0.0, 0.0]

elif len(dropout) == 1:

dropout = dropout * 3

elif len(dropout) == 2:

dropout.append(dropout[1])

# this hack gets the current function name and sets it to the name of the model

model = Sequential(name=traceback.extract_stack(None, 2)[-1][2])

# crop top 157 rows and bottom 67 rows from the images

model.add(Cropping2D(cropping=((157, 67), (0, 0)), input_shape=(448, 224, 3), name='pp_crop'))

# mean center the pixels

model.add(Lambda(lambda x: (x / 255.0) - 0.5, name='pp_center'))

# load the ResNet50 model with weights from imagenet. Do not include the top of the model

# as it will be replaced and trained for driving in the simulator.

resnet = ResNet50(weights='imagenet', include_top=False)

# freeze the ResNet50 layers to speed up training

for layer in resnet.layers:

layer.trainable = False

model.add(resnet)

# flatten

model.add(Flatten())

# layer 153. fully connected + dropout. Input 2048. Output 1000.

model.add(Dense(1000, name='fc153'))

model.add(Dropout(dropout[0], name='drop153'))

model.add(Activation('relu', name='act153'))

# layer 154. fully connected + dropout. Input 1000. Output 100.

model.add(Dense(100, name='fc154'))

model.add(Dropout(dropout[1], name='drop154'))

model.add(Activation('relu', name='act154'))

# layer 155: fully connected. Input 100. Output 1.

model.add(Dense(1, name='out'))

"""This model attempts to use transfer learning on VGG16

Parameters:

dropout - list of dropout values for the 2 fully connected layers

Returns:

A model with VGG16 at it's core"""

# ensure the dropout list has enough values in it for the model

# augment the values of some are missing

if dropout == None or len(dropout) == 0:

dropout = [0.0, 0.0, 0.0]

elif len(dropout) == 1:

dropout = dropout * 3

elif len(dropout) == 2:

dropout.append(dropout[1])

# this hack gets the current function name and sets it to the name of the model

model = Sequential(name=traceback.extract_stack(None, 2)[-1][2])

# crop top 157 rows and bottom 67 rows from the images

model.add(Cropping2D(cropping=((157, 67), (0, 0)), input_shape=(448, 224, 3), name='pp_crop'))

# mean center the pixels

model.add(Lambda(lambda x: (x / 255.0) - 0.5, name='pp_center'))

# load the VGG16 model with weights from imagenet. Do not include the top of the model

# as it will be replaced and trained for driving in the simulator.

vgg16 = VGG16(weights='imagenet', include_top=False)

# freeze the ResNet50 layers to speed up training

for layer in vgg16.layers:

layer.trainable = False

model.add(vgg16)

# flatten

model.add(Flatten())

# layer 17. fully connected + dropout. Input 25088. Output 1000.

model.add(Dense(1000, name='fc17'))

model.add(Dropout(dropout[0], name='drop17'))

model.add(Activation('relu', name='act17'))

# layer 18. fully connected + dropout. Input 1000. Output 100.

model.add(Dense(100, name='fc18'))

model.add(Dropout(dropout[1], name='drop18'))

model.add(Activation('relu', name='act18'))

# layer 19: fully connected. Input 100. Output 1.

model.add(Dense(1, name='out'))

"""This model attempts to mimic the model by NVIDIA in their paper End to End Learning for Self-Driving

Cars:

https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf

Parameters:

dropout - list of dropout values for the 3 fully connected layers

Returns:

A model based on the End to End NVIDIA model"""

# ensure the dropout list has enough values in it for the model

# augment the values of some are missing

if dropout == None or len(dropout) == 0:

dropout = [0.0, 0.0, 0.0]

elif len(dropout) == 1:

dropout = dropout * 3

elif len(dropout) == 2:

dropout.append(dropout[1])

# this hack gets the current function name and sets it to the name of the model

model = Sequential(name=traceback.extract_stack(None, 2)[-1][2])

# crop top 56 rows and bottom 24 rows from the images

model.add(Cropping2D(cropping=((56, 24), (0, 0)), input_shape=(160, 320, 3), name='pp_crop'))

# mean center the pixels

model.add(Lambda(lambda x: (x / 255.0) - 0.5, name='pp_center'))

# layer 1: convolution. Input 40x160x3. Output 36x156x24

model.add(Convolution2D(24, 5, 5, border_mode='valid', name='conv1'))

model.add(MaxPooling2D((2, 2), border_mode='valid', name='pool1'))

model.add(Activation('relu', name='act1'))

# layer 2: convolution + max pooling. Input 36x156x24. Output 16x76x36

model.add(Convolution2D(36, 5, 5, border_mode='valid', name='conv2'))

model.add(MaxPooling2D((2, 2), border_mode='valid', name='pool2'))

model.add(Activation('relu', name='act2'))

# layer 3: convolution + max pooling. Input 16x76x36. Output 6x36x48

model.add(Convolution2D(48, 5, 5, border_mode='valid', name='conv3'))

model.add(MaxPooling2D((2, 2), border_mode='valid', name='pool3'))

model.add(Activation('relu', name='act3'))

# layer 4: convolution. Input 6x36x48. Output 4x34x64

model.add(Convolution2D(64, 3, 3, border_mode='valid', name='conv4'))

model.add(Activation('relu', name='act4'))

# layer 5: convolution. Input 4x34x64. Output 1x16x64

model.add(Convolution2D(64, 3, 3, border_mode='valid', name='conv5'))

model.add(MaxPooling2D((2, 2), border_mode='valid', name='pool5'))

model.add(Activation('relu', name='act5'))

# flatten: Input 1x16x64. Output 1024

model.add(Flatten(name='flat'))

# layer 6: fully connected + dropout. Input 1024. Output 100

model.add(Dense(100, name='fc6'))

model.add(Dropout(dropout[0], name='drop6'))

model.add(Activation('relu', name='act6'))

# layer 7: fully connected + dropout. Input 100. Output 50

model.add(Dense(50, name='fc7'))

model.add(Dropout(dropout[1], name='drop7'))

model.add(Activation('relu', name='act7'))

# layer 8: fully connected + dropout. Input 50. Output 10

model.add(Dense(10, name='fc8'))

model.add(Dropout(dropout[2], name='drop8'))

model.add(Activation('relu', name='act8'))

# layer 9: fully connected. Input 10. Output 1.

model.add(Dense(1, name='out'))

"""A generator method to provide the model with data during training

Parameters:

samples - list of all samples to be used as inputs

resize - new image size to resize inputs to, if desired

batch_size - batch size to generate

Yields:

A shuffled batch of input samples and labels in groups of batch_size"""

num_samples = len(samples)

while 1: # Loop forever so the generator never terminates

np.random.shuffle(samples)

for offset in range(0, num_samples, batch_size):

batch_samples = samples[offset:offset+batch_size]

images = []

angles = []

for batch_sample in batch_samples:

name = batch_sample[0]

image = cv2.imread(name)

# resize the image if needed

if resize:

image = cv2.resize(image, resize)

# flip the image as determined by this boolean

if batch_sample[2]:

image = cv2.flip(image, 1)

angle = batch_sample[1]

images.append(image)

angles.append(angle)

# trim image to only see section with road

X_train = np.array(images)

y_train = np.array(angles)

"""Helper function to return the image relative to the base directory

Parameters:

- path: path to the image

Returns: string with the image file name realtive to the base directory"""

if path.lower().startswith("c:"):

path = path.split('\\')[-1]

"""Read the samples from CSV files

Parameters:

- base_dirs: list of directories containing the CSV files

- centers_only: only use center camera images

- zeros_to_ignore: percentage of samples with a steering angle of 0.0 to toss out

- steering_correction: steering correction for the left camera

Returns:

list of tuples containing the absolute path to the image, the steering angle, and whether the image should be flipped"""

samples = []

# helper function to help determine if an input sample should be ignored

def dont_ignore(steering, percent):

return (steering != 0.0) or (np.random.random() < (1 - percent))

for base_dir in base_dirs:

with open(os.path.join(base_dir, "driving_log.csv")) as f:

log = [l.split(',') for l in f.read().split('\n')[1:-1]]

for l in log:

center_steering = float(l[3])# / 25.0

left_steering = (float(l[3]) + steering_correction)# / 25.0

right_steering = (float(l[3]) - steering_correction)# / 25.0

# center image

if dont_ignore(center_steering, zeros_to_ignore):

samples.append((gen_rel_img(base_dir, l[0]), center_steering, False))

if not center_only:

# left image

if dont_ignore(left_steering, zeros_to_ignore):

samples.append((gen_rel_img(base_dir, l[1]), left_steering, False))

# right image

if dont_ignore(right_steering, zeros_to_ignore):

samples.append((gen_rel_img(base_dir, l[2]), right_steering, False))

# mirror images

if center_steering != 0.0:

samples.append((gen_rel_img(base_dir, l[0]), -center_steering, True))

if not center_only:

if left_steering != 0.0:

samples.append((gen_rel_img(base_dir, l[1]), -left_steering, True))

if right_steering != 0.0:

samples.append((gen_rel_img(base_dir, l[2]), -right_steering, True))