def main():
parser = create_parser()
args = parser.parse_args()
NAIP_STATE, NAIP_YEAR = args.naip_path
naiper = NAIPDownloader(args.number_of_naips, args.randomize_naips,
NAIP_STATE, NAIP_YEAR)
raster_data_paths = naiper.download_naips()
road_labels, naip_tiles, waymap = random_training_data(
raster_data_paths, args.extract_type, args.bands, args.tile_size,
args.pixels_to_fatten_roads, args.label_data_files, args.tile_overlap)
equal_count_way_list, equal_count_tile_list = equalize_data(
road_labels, naip_tiles, args.save_clippings)
test_labels, training_labels, test_images, training_images = split_train_test(
equal_count_tile_list, equal_count_way_list,
args.percent_for_training_data)
onehot_training_labels, onehot_test_labels = format_as_onehot_arrays(
waymap.extracter.types, training_labels, test_labels)
dump_data_to_disk(raster_data_paths, training_images, training_labels,
test_images, test_labels, waymap.extracter.types,
onehot_training_labels, onehot_test_labels)
def train_on_cached_data(raster_data_paths, neural_net_type, bands, tile_size,
number_of_epochs):
"""Load tiled/cached data, which was prepared for the NAIPs listed in raster_data_paths.
Read in each NAIP's images/labels, add to train/test data, run some epochs as each is added.
Keep the train and test sets to a max of 10K images by throwing out random data sometimes.
"""
training_images = []
onehot_training_labels = []
test_images = []
onehot_test_labels = []
model = None
for path in raster_data_paths:
# read in another NAIP worth of data
labels, images = load_training_tiles(path)
if len(labels) == 0 or len(images) == 0:
continue
equal_count_way_list, equal_count_tile_list = equalize_data(
labels, images, False)
new_test_labels, training_labels, new_test_images, new_training_images = \
split_train_test(equal_count_tile_list, equal_count_way_list, .9)
if len(training_labels) == 0:
print("WARNING: a naip image didn't have any road labels?")
continue
if len(new_test_labels) == 0:
print("WARNING: a naip image didn't have any road images?")
continue
# add it to the training and test lists
[training_images.append(i) for i in new_training_images]
[test_images.append(i) for i in new_test_images]
[
onehot_training_labels.append(l)
for l in format_as_onehot_arrays(training_labels)
]
[
onehot_test_labels.append(l)
for l in format_as_onehot_arrays(new_test_labels)
]
# once we have 100 test_images, maybe from more than one NAIP, train on a mini batch
if len(training_images) >= 100:
# continue training the model with the new data set
model = train_with_data(onehot_training_labels, onehot_test_labels,
test_images, training_images,
neural_net_type, bands, tile_size,
number_of_epochs, model)
training_images = []
onehot_training_labels = []
# keep test list to 10000 images, in case the machine doesn't have much memory
if len(test_images) > 10000:
# shuffle so when we chop off data, it's from many NAIPs, not just the last one
shuffle_in_unison(test_images, onehot_test_labels)
test_images = test_images[:9000]
onehot_test_labels = onehot_test_labels[:9000]
return test_images, model
def train_on_cached_data(raster_data_paths, neural_net_type, bands, tile_size):
"""Load tiled/cached data, which was prepared for the NAIPs listed in raster_data_paths.
Read in each NAIP's images/labels, add to train/test data, run some epochs as each is added.
Keep the train and test sets to a max of 10K images by throwing out random data sometimes.
"""
training_images = []
onehot_training_labels = []
test_images = []
onehot_test_labels = []
model = None
epoch = 0
for path in raster_data_paths:
# keep test list to 1000 images
if len(test_images) > 10000:
test_images = test_images[:9000]
onehot_test_labels = onehot_test_labels[:9000]
# keep train list to 10000 images
if len(training_images) > 10000:
training_images = training_images[:9000]
onehot_training_labels = onehot_training_labels[:9000]
# read in another NAIP worth of data
labels, images = load_training_tiles(path)
if len(labels) == 0 or len(images) == 0:
continue
equal_count_way_list, equal_count_tile_list = equalize_data(labels, images, False)
new_test_labels, training_labels, new_test_images, new_training_images = \
split_train_test(equal_count_tile_list, equal_count_way_list, .9)
if len(training_labels) == 0:
print("WARNING: a naip image didn't have any road labels?")
continue
if len(new_test_labels) == 0:
print("WARNING: a naip image didn't have any road images?")
continue
# add it to the training and test lists
[training_images.append(i) for i in new_training_images]
[test_images.append(i) for i in new_test_images]
[onehot_training_labels.append(l) for l in format_as_onehot_arrays(training_labels)]
[onehot_test_labels.append(l) for l in format_as_onehot_arrays(new_test_labels)]
# shuffle it so when we chop off data it's from many NAIPs, not just the last one
shuffle_in_unison(training_images, onehot_training_labels)
shuffle_in_unison(test_images, onehot_test_labels)
# continue training the model with the new data set
model = train_with_data(onehot_training_labels, onehot_test_labels, test_images,
training_images, neural_net_type, bands, tile_size,
epoch, model)
epoch += 1
return test_images, model
def train_on_cached_data(neural_net_type, number_of_epochs):
"""Load tiled/cached training data in batches, and train the neural net."""
with open(CACHE_PATH + METADATA_PATH, 'r') as infile:
training_info = pickle.load(infile)
bands = training_info['bands']
tile_size = training_info['tile_size']
training_images = []
onehot_training_labels = []
model = None
# there are usually 100+ images with road through the middle, out of every 10,000
# because we want half on, half off, and discard most images
EQUALIZATION_BATCH_SIZE = 10000
# the number of times to pull EQUALIZATION_BATCH_SIZE images from disk
NUMBER_OF_BATCHES = 50
for x in range(0, NUMBER_OF_BATCHES):
print("BATCH: {} of {}".format(str(x + 1), str(NUMBER_OF_BATCHES)))
new_label_paths = load_training_tiles(EQUALIZATION_BATCH_SIZE)
print("Got batch of {} labels".format(len(new_label_paths)))
new_training_images, new_onehot_training_labels = format_as_onehot_arrays(
new_label_paths)
equal_count_way_list, equal_count_tile_list = equalize_data(
new_onehot_training_labels, new_training_images, False)
[training_images.append(i) for i in equal_count_tile_list]
[onehot_training_labels.append(l) for l in equal_count_way_list]
# once we have 100 test_images, train on a mini batch
if len(training_images) >= 100:
# continue training the model with the new data set
model = train_with_data(onehot_training_labels, training_images,
neural_net_type, bands, tile_size,
number_of_epochs, model)
training_images = []
onehot_training_labels = []
save_model(model, neural_net_type, bands, tile_size)
return model
def train_on_cached_data(neural_net_type, number_of_epochs):
"""Load tiled/cached training data in batches, and train the neural net."""
with open(CACHE_PATH + METADATA_PATH, "r") as infile:
training_info = pickle.load(infile)
bands = training_info["bands"]
tile_size = training_info["tile_size"]
training_images = []
onehot_training_labels = []
model = None
# there are usually 100+ images with road through the middle, out of every 10,000
# because we want half on, half off, and discard most images
EQUALIZATION_BATCH_SIZE = 10000
# the number of times to pull EQUALIZATION_BATCH_SIZE images from disk
NUMBER_OF_BATCHES = 10
for x in range(0, NUMBER_OF_BATCHES):
new_label_paths = load_training_tiles(EQUALIZATION_BATCH_SIZE)
print("Got batch of {} labels".format(len(new_label_paths)))
new_training_images, new_onehot_training_labels = format_as_onehot_arrays(new_label_paths)
equal_count_way_list, equal_count_tile_list = equalize_data(
new_onehot_training_labels, new_training_images, False
)
[training_images.append(i) for i in equal_count_tile_list]
[onehot_training_labels.append(l) for l in equal_count_way_list]
# once we have 100 test_images, train on a mini batch
if len(training_images) >= 100:
# continue training the model with the new data set
model = train_with_data(
onehot_training_labels, training_images, neural_net_type, bands, tile_size, number_of_epochs, model
)
training_images = []
onehot_training_labels = []
save_model(model, neural_net_type, bands, tile_size)
return model
def main():
parser = create_parser()
args = parser.parse_args()
NAIP_STATE, NAIP_YEAR = args.naip_path
naiper = NAIPDownloader(args.number_of_naips, args.randomize_naips, NAIP_STATE, NAIP_YEAR)
raster_data_paths = naiper.download_naips()
road_labels, naip_tiles, waymap = random_training_data(
raster_data_paths, args.extract_type, args.bands, args.tile_size,
args.pixels_to_fatten_roads, args.label_data_files, args.tile_overlap)
equal_count_way_list, equal_count_tile_list = equalize_data(road_labels, naip_tiles,
args.save_clippings)
test_labels, training_labels, test_images, training_images = split_train_test(
equal_count_tile_list, equal_count_way_list, args.percent_for_training_data)
onehot_training_labels, onehot_test_labels = format_as_onehot_arrays(
waymap.extracter.types, training_labels, test_labels)
dump_data_to_disk(raster_data_paths, training_images, training_labels, test_images, test_labels,
waymap.extracter.types, onehot_training_labels, onehot_test_labels)