def run_model(project):
project.flash()
with project.transport() as transport:
aot_transport_init_wait(transport)
transport.write(b"infer%", timeout_sec=5)
result_line = aot_transport_find_message(transport, "result", timeout_sec=60)
result_line = result_line.strip("\n")
result_line = result_line.split(":")
result = int(result_line[1])
time = int(result_line[2])
_LOG.info(f"Result: {result}\ttime: {time} ms")
return result, time
def _run_model(temp_dir, board, west_cmd, lowered, build_config, sample,
output_shape):
project = _generate_project(temp_dir, board, west_cmd, lowered,
build_config, sample, output_shape)
project.flash()
with project.transport() as transport:
aot_transport_init_wait(transport)
transport.write(b"infer%", timeout_sec=5)
result_line = aot_transport_find_message(transport,
"result",
timeout_sec=60)
result_line = result_line.strip("\n")
result_line = result_line.split(":")
result = int(result_line[1])
time = int(result_line[2])
_LOG.info(f"Result: {result}\ttime: {time} ms")
return result, time
def test_tflite(temp_dir, board, west_cmd, tvm_debug):
"""Testing a TFLite model."""
model = test_utils.ZEPHYR_BOARDS[board]
input_shape = (1, 49, 10, 1)
output_shape = (1, 12)
build_config = {"debug": tvm_debug}
model_url = "https://github.com/tlc-pack/web-data/raw/25fe99fb00329a26bd37d3dca723da94316fd34c/testdata/microTVM/model/keyword_spotting_quant.tflite"
model_path = download_testdata(model_url, "keyword_spotting_quant.tflite", module="model")
# Import TFLite model
tflite_model_buf = open(model_path, "rb").read()
try:
import tflite
tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
import tflite.Model
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
# Load TFLite model and convert to Relay
relay_mod, params = relay.frontend.from_tflite(
tflite_model, shape_dict={"input_1": input_shape}, dtype_dict={"input_1 ": "int8"}
)
target = tvm.target.target.micro(model)
executor = Executor(
"aot", {"unpacked-api": True, "interface-api": "c", "workspace-byte-alignment": 4}
)
runtime = Runtime("crt")
with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
lowered = relay.build(relay_mod, target, params=params, runtime=runtime, executor=executor)
sample_url = "https://github.com/tlc-pack/web-data/raw/967fc387dadb272c5a7f8c3461d34c060100dbf1/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy"
sample_path = download_testdata(sample_url, "keyword_spotting_int8_6.pyc.npy", module="data")
sample = np.load(sample_path)
with tempfile.NamedTemporaryFile() as tar_temp_file:
with tarfile.open(tar_temp_file.name, "w:gz") as tf:
with tempfile.TemporaryDirectory() as tar_temp_dir:
model_files_path = os.path.join(tar_temp_dir, "include")
os.mkdir(model_files_path)
header_path = generate_c_interface_header(
lowered.libmod_name, ["input_1"], ["output"], [], 0, model_files_path
)
tf.add(header_path, arcname=os.path.relpath(header_path, tar_temp_dir))
test_utils.create_header_file("input_data", sample, "include", tf)
test_utils.create_header_file(
"output_data", np.zeros(shape=output_shape, dtype="int8"), "include", tf
)
project, _ = test_utils.build_project(
temp_dir,
board,
west_cmd,
lowered,
build_config,
extra_files_tar=tar_temp_file.name,
)
project.flash()
with project.transport() as transport:
aot_transport_init_wait(transport)
transport.write(b"infer%", timeout_sec=5)
result_line = aot_transport_find_message(transport, "result", timeout_sec=60)
result_line = result_line.strip("\n")
result_line = result_line.split(":")
result = int(result_line[1])
time = int(result_line[2])
logging.info(f"Result: {result}\ttime: {time} ms")
assert result == 6