def mode_test():
"""The main testing mode entrypoint"""
start_time = time.time()
io = get_io()
logline("test")
enter_group()
logline("reconstructing model")
model = create_model(1)
logline("applying learned weights")
model = apply_weights(model, io)
logline("reading testing files")
test_files = read_test_files(io)
logline("running testing data")
enter_group()
run_tests(io, model, test_files)
exit_group()
exit_group()
logline("done training, runtime is {}".format(
Timer.stringify_time(Timer.format_time(time.time() - start_time))))
def mode_train():
"""The main training mode entrypoint"""
start_time = time.time()
io = get_io()
logline("using GPU?", tf.test.is_gpu_available())
logline("train")
enter_group()
logline("loading preprocessed data")
preprocessed = load_preprocessed(io)
logline("creating models")
train_model = create_model(batch_size=io.get("batch_size"))
logline("fitting model")
enter_group()
fit_model(io, train_model, preprocessed)
exit_group()
logline("exporting model")
export_model(train_model, io)
exit_group()
logline("done training, runtime is {}".format(
Timer.stringify_time(Timer.format_time(time.time() - start_time))))
def start_server(io: IO):
global interval
interval = io.get("interval")
port = io.get("port")
httpd = HTTPServer(("", port),
partial(WebServer,
directory=os.path.join(CUR_DIR, "public")))
logline("listening at port", port)
enter_group()
try:
httpd.serve_forever()
except KeyboardInterrupt:
pass
httpd.server_close()
exit_group()
logline("stopped listening")
def mode_preprocess():
"""The main preprocessing entrypoint"""
start_time = time.time()
preprocessed = []
io = get_io()
logline("preprocessing")
enter_group()
logline("reading input paths")
enter_group()
input_paths = collect_input_paths(io)
for input_path in input_paths:
logline('found path: "{}"'.format(input_path))
exit_group()
logline("iterating files")
enter_group()
for file in get_files(input_paths):
if not file:
error("no files")
return None
features = gen_features(file)
outputs = gen_outputs(file, io)
feature_arr = list(map(lambda x: x.to_arr(), features))
output_arr = list(map(lambda x: x.to_arr(), outputs))
assert np.array(feature_arr).shape[1] == Features.length()
assert np.array(output_arr).shape[1] == OUT_VEC_SIZE
preprocessed.append({
"file_name": file.name,
"features": feature_arr,
"outputs": output_arr
})
logline('done with file: "{}"'.format(file.name))
file.close()
exit_group()
logline("done iterating files")
with open(io.get("output_file"), "wb+") as file:
pickle.dump(preprocessed, file)
logline("wrote output to file: {}".format(io.get("output_file")))
exit_group()
logline("done preprocessing, runtime is {}".format(
Timer.stringify_time(Timer.format_time(time.time() - start_time))))