PARALLEL_PATHS = PARALLEL_TILES * PATHS_PER_TILE_AXIS * PATHS_PER_TILE_AXIS
def epoch_to_learning_rate(epoch):
if epoch < 100:
return 1e-5
elif epoch < 200:
return 1e-6
elif epoch < 300:
return 1e-7
else:
return 1e-8
tiles = tileloader.Tiles(PATHS_PER_TILE_AXIS, SEGMENT_LENGTH, PARALLEL_TILES,
TILE_MODE)
tiles.prepare_training()
test_tile_data = tiles.get_test_tile_data()
# initialize model and session
print('initializing model')
m = model.Model(tiles.num_input_channels())
session = tf.Session()
model_path = MODEL_BASE + '/model_latest/model'
best_path = MODEL_BASE + '/model_best/model'
if os.path.isfile(model_path + '.meta'):
print('... loading existing model')
m.saver.restore(session, model_path)
else:
print('... initializing a new model')
session.run(m.init_op)
if args.t: tileloader.tile_dir = args.t
if args.g: tileloader.graph_dir = args.g
if args.j:
tileloader.pytiles_path = os.path.join(args.j, 'pytiles.json')
tileloader.startlocs_path = os.path.join(args.j, 'starting_locations.json')
MODEL_BASE = args.modelpath
WINDOW_SIZE = 512
NUM_TRAIN_TILES = 1024
TILE_SIZE = 4096
RECT_OVERRIDE = None
NUM_BUCKETS = 64
MASK_NEAR_ROADS = False
tiles = tileloader.Tiles(2, 20, NUM_TRAIN_TILES + 8, 'sat')
tiles.prepare_training()
train_tiles = tiles.train_tiles
# initialize model and session
print 'initializing model'
m = model.Model(bn=True, size=512)
session = tf.Session()
model_path = MODEL_BASE + '/model_latest/model'
best_path = MODEL_BASE + '/model_best/model'
if os.path.isfile(model_path + '.meta'):
print '... loading existing model'
m.saver.restore(session, model_path)
else:
print '... initializing a new model'
session.run(m.init_op)
if avg_prob < threshold:
bad_edges.update(rs.edges)
print('filtering {} edges'.format(len(bad_edges)))
ng = graph.Graph()
vertex_map = {}
for vertex in g.vertices:
vertex_map[vertex] = ng.add_vertex(vertex.point)
for edge in g.edges:
if edge not in bad_edges:
ng.add_edge(vertex_map[edge.src], vertex_map[edge.dst])
return ng
if __name__ == '__main__':
print('reading tiles')
tiles = tileloader.Tiles(2, SEGMENT_LENGTH, 16, TILE_MODE)
print('initializing model')
m = model.Model(bn=True, size=2048)
session = tf.Session()
m.saver.restore(session, MODEL_PATH)
def too_close(idx, p):
for edge in idx.search(p.bounds().add_tol(60)):
if edge.segment().distance(p) < 60:
return True
return False
for x, y in TILE_LIST:
EXISTING_GRAPH_FNAME = os.path.join(tile_graph_path,
'{}_{}.graph'.format(x, y))
TILE_START = geom.Point(1, -1).scale(TILE_SIZE) elif REGION == 'chicago': TILE_START = geom.Point(-1, -2).scale(TILE_SIZE) else: TILE_START = geom.Point(-1, -1).scale(TILE_SIZE) TILE_END = TILE_START.add(geom.Point(2, 2).scale(TILE_SIZE)) if args.t: tileloader.tile_dir = args.t if args.g: tileloader.graph_dir = args.g if args.j: tileloader.pytiles_path = os.path.join(args.j, 'pytiles.json') tileloader.startlocs_path = os.path.join(args.j, 'starting_locations.json') print 'reading tiles' #tileloader.use_vhr() tiles = tileloader.Tiles(PATHS_PER_TILE_AXIS, SEGMENT_LENGTH, 16, TILE_MODE) print 'initializing model' model.BATCH_SIZE = 1 m = model.Model(tiles.num_input_channels()) session = tf.Session() m.saver.restore(session, model_path) if EXISTING_GRAPH_FNAME is None: rect = geom.Rectangle(TILE_START, TILE_END) tile_data = tiles.get_tile_data(REGION, rect) if USE_TL_LOCATIONS: start_loc = random.choice(tile_data['starting_locations']) else: def match_point(p):
