def test_run_tflite_module__with_profile__valid_input(
use_vm, tflite_mobilenet_v1_1_quant, tflite_compile_model,
imagenet_cat):
# some CI environments wont offer TFLite, so skip in case it is not present
pytest.importorskip("tflite")
inputs = np.load(imagenet_cat)
input_dict = {"input": inputs["input"].astype("uint8")}
tflite_compiled_model = tflite_compile_model(tflite_mobilenet_v1_1_quant,
use_vm=use_vm)
result = tvmc.run(
tflite_compiled_model,
inputs=input_dict,
hostname=None,
device="cpu",
profile=True,
)
# collect the top 5 results
top_5_results = get_top_results(result, 5)
top_5_ids = top_5_results[0]
# IDs were collected from this reference:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/
# java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt
tiger_cat_mobilenet_id = 283
assert (
tiger_cat_mobilenet_id
in top_5_ids), "tiger cat is expected in the top-5 for mobilenet v1"
assert type(result.outputs) is dict
assert type(result.times) is BenchmarkResult
assert "output_0" in result.outputs.keys()
def test_run_tflite_module_with_rpc(tflite_mobilenet_v1_1_quant,
tflite_compile_model, imagenet_cat):
"""
Test to check that TVMC run is functional when it is being used in
conjunction with an RPC server.
"""
pytest.importorskip("tflite")
inputs = np.load(imagenet_cat)
input_dict = {"input": inputs["input"].astype("uint8")}
tflite_compiled_model = tflite_compile_model(tflite_mobilenet_v1_1_quant)
server = rpc.Server("127.0.0.1", 9099)
result = tvmc.run(
tflite_compiled_model,
inputs=input_dict,
hostname=server.host,
port=server.port,
device="cpu",
)
top_5_results = get_top_results(result, 5)
top_5_ids = top_5_results[0]
# IDs were collected from this reference:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/
# java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt
tiger_cat_mobilenet_id = 283
assert (
tiger_cat_mobilenet_id
in top_5_ids), "tiger cat is expected in the top-5 for mobilenet v1"
assert isinstance(result.outputs, dict)
assert "output_0" in result.outputs.keys()
def test_get_top_results_keep_results():
fake_outputs = {"output_0": np.array([[1, 2, 3, 4], [5, 6, 7, 8]])}
fake_result = TVMCResult(outputs=fake_outputs, times=None)
number_of_results_wanted = 3
sut = get_top_results(fake_result, number_of_results_wanted)
expected_number_of_lines = 2
assert len(sut) == expected_number_of_lines
expected_number_of_results_per_line = 3
assert len(sut[0]) == expected_number_of_results_per_line
assert len(sut[1]) == expected_number_of_results_per_line
