mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
Change tflite tests from sharkimporter -> sharkdownloader (#182)
* Change tflite test from sharkimporter -> sharkdownloader * xfail all uint/int tflite sharkdownloader tests
This commit is contained in:
Chi_Liu
@@ -23,6 +23,8 @@ input_type_to_np_dtype = {
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"bool": np.bool_,
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"int32": np.int32,
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"int64": np.int64,
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"uint8": np.uint8,
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"int8": np.int8,
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}
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@@ -32,7 +34,7 @@ class SharkDownloader:
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model_name: str,
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tank_url: str = "https://storage.googleapis.com/shark_tank",
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local_tank_dir: str = "./../gen_shark_tank/tflite",
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model_type: str = "tflite-tosa",
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model_type: str = "tflite",
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input_json: str = "input.json",
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input_type: str = "int32",
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):
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@@ -84,7 +86,7 @@ class SharkDownloader:
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def load_json_input(self):
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print("load json inputs")
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if self.model_type in ["tflite-tosa"]:
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if self.model_type in ["tflite"]:
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input_url = (
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self.tank_url + "/" + str(self.model_name) + "/" + "input.json"
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)
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@@ -109,7 +111,7 @@ class SharkDownloader:
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return self.inputs
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def load_mlir_model(self):
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if self.model_type in ["tflite-tosa"]:
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if self.model_type in ["tflite"]:
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self.mlir_url = (
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self.tank_url
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+ "/"
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@@ -4,30 +4,36 @@ from shark.shark_inference import SharkInference
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import pytest
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import unittest
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from shark.parser import shark_args
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import os
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import sys
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import urllib.request
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from PIL import Image
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from shark.tflite_utils import TFLitePreprocessor
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# model_path = "https://github.com/tensorflow/tflite-micro/raw/aeac6f39e5c7475cea20c54e86d41e3a38312546/tensorflow/lite/micro/models/person_detect.tflite"
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# model_path = "https://tfhub.dev/tensorflow/lite-model/albert_lite_base/squadv1/1?lite-format=tflite"
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# model_path = model_path
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# Inputs modified to be useful albert inputs.
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def generate_inputs(input_details):
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exe_basename = os.path.basename(sys.argv[0])
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workdir = os.path.join(os.path.dirname(__file__), "../tmp", exe_basename)
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os.makedirs(workdir, exist_ok=True)
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for input in input_details:
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print(str(input["shape"]), input["dtype"].__name__)
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img_path = "https://github.com/tensorflow/tflite-micro/raw/aeac6f39e5c7475cea20c54e86d41e3a38312546/tensorflow/lite/micro/examples/person_detection/testdata/person.bmp"
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local_path = "/".join([workdir, "person.bmp"])
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urllib.request.urlretrieve(img_path, local_path)
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shape = input_details[0]["shape"]
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im = np.array(Image.open(local_path).resize((shape[1], shape[2]))).astype(
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input_details[0]["dtype"]
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args = []
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args.append(
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np.random.randint(
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low=0,
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high=256,
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size=input_details[0]["shape"],
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dtype=input_details[0]["dtype"],
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)
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)
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args.append(
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np.ones(
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shape=input_details[1]["shape"], dtype=input_details[1]["dtype"]
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)
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)
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args.append(
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np.zeros(
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shape=input_details[2]["shape"], dtype=input_details[2]["dtype"]
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)
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)
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args = [im.reshape(shape)]
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return args
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@@ -41,12 +47,14 @@ def compare_results(mlir_results, tflite_results, details):
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tflite_result = tflite_results[i]
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mlir_result = mlir_result.astype(np.single)
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tflite_result = tflite_result.astype(np.single)
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print("mlir_result.shape", mlir_result.shape)
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print("tflite_result.shape", tflite_result.shape)
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assert mlir_result.shape == tflite_result.shape, "shape doesnot match"
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max_error = np.max(np.abs(mlir_result - tflite_result))
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print("Max error (%d): %f", i, max_error)
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class PersonDetectionTfliteModuleTester:
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class AlbertTfliteModuleTester:
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def __init__(
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self,
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dynamic=False,
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@@ -64,25 +72,7 @@ class PersonDetectionTfliteModuleTester:
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shark_args.save_vmfb = self.save_vmfb
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# Preprocess to get SharkImporter input args
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# The input has known expected values. We hardcode this value.
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input_details = [
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{
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"shape": [1, 96, 96, 1],
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"dtype": np.int8,
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"index": 0,
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}
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]
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output_details = [
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{
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"shape": [1, 2],
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"dtype": np.int8,
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}
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]
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tflite_preprocessor = TFLitePreprocessor(
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model_name="person_detect",
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input_details=input_details,
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output_details=output_details,
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)
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tflite_preprocessor = TFLitePreprocessor(model_name="albert_lite_base")
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raw_model_file_path = tflite_preprocessor.get_raw_model_file()
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inputs = tflite_preprocessor.get_inputs()
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tflite_interpreter = tflite_preprocessor.get_interpreter()
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@@ -104,8 +94,20 @@ class PersonDetectionTfliteModuleTester:
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mlir_dialect="tflite",
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)
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# Case2: Use manually set inputs
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# Case1: Use shark_importer default generate inputs
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shark_module.compile()
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mlir_results = shark_module.forward(inputs)
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## post process results for compare
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input_details, output_details = tflite_preprocessor.get_model_details()
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mlir_results = list(mlir_results)
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for i in range(len(output_details)):
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dtype = output_details[i]["dtype"]
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mlir_results[i] = mlir_results[i].astype(dtype)
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tflite_results = tflite_preprocessor.get_raw_model_output()
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compare_results(mlir_results, tflite_results, output_details)
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# Case2: Use manually set inputs
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input_details, output_details = tflite_preprocessor.get_model_details()
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inputs = generate_inputs(input_details) # new inputs
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shark_module = SharkInference(
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@@ -117,23 +119,26 @@ class PersonDetectionTfliteModuleTester:
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shark_module.compile()
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mlir_results = shark_module.forward(inputs)
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## post process results for compare
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# The input has known expected values. We hardcode this value.
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tflite_results = [np.array([[-113, 113]], dtype=np.int8)]
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tflite_results = tflite_preprocessor.get_raw_model_output()
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compare_results(mlir_results, tflite_results, output_details)
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# print(mlir_results)
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class PersonDetectionTfliteModuleTest(unittest.TestCase):
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class AlbertTfliteModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.save_mlir = pytestconfig.getoption("save_mlir")
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self.save_vmfb = pytestconfig.getoption("save_vmfb")
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def setUp(self):
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self.module_tester = PersonDetectionTfliteModuleTester(self)
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self.module_tester = AlbertTfliteModuleTester(self)
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self.module_tester.save_mlir = self.save_mlir
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@pytest.mark.skip(reason="TFLite is broken with this model")
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import sys
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@pytest.mark.xfail(
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sys.platform == "darwin", reason="known macos tflite install issue"
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)
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def test_module_static_cpu(self):
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self.module_tester.dynamic = False
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self.module_tester.device = "cpu"
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@@ -141,7 +146,7 @@ class PersonDetectionTfliteModuleTest(unittest.TestCase):
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if __name__ == "__main__":
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# module_tester = PersonDetectionTfliteModuleTester()
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# module_tester = AlbertTfliteModuleTester()
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# module_tester.save_mlir = True
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# module_tester.save_vmfb = True
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# module_tester.create_and_check_module()
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@@ -1,90 +0,0 @@
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import sys
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from shark.shark_downloader import SharkDownloader
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from shark.shark_inference import SharkInference
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import pytest
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import unittest
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from shark.parser import shark_args
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class AlbertTfliteModuleTester:
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def __init__(
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self,
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dynamic=False,
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device="cpu",
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save_mlir=False,
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save_vmfb=False,
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):
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self.dynamic = dynamic
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self.device = device
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self.save_mlir = save_mlir
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self.save_vmfb = save_vmfb
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def create_and_check_module(self):
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shark_args.save_mlir = self.save_mlir
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shark_args.save_vmfb = self.save_vmfb
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shark_downloader = SharkDownloader(
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model_name="albert_lite_base",
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tank_url="https://storage.googleapis.com/shark_tank",
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local_tank_dir="./../gen_shark_tank",
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model_type="tflite-tosa",
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input_json="input.json",
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input_type="int32",
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)
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tflite_tosa_model = shark_downloader.get_mlir_file()
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inputs = shark_downloader.get_inputs()
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shark_module = SharkInference(
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mlir_module=tflite_tosa_model,
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function_name="main",
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device=self.device,
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mlir_dialect="tflite",
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)
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shark_module.compile()
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shark_module.forward(inputs)
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# print(shark_results)
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class AlbertTfliteModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.save_mlir = pytestconfig.getoption("save_mlir")
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self.save_vmfb = pytestconfig.getoption("save_vmfb")
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def setUp(self):
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self.module_tester = AlbertTfliteModuleTester(self)
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self.module_tester.save_mlir = self.save_mlir
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import sys
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@pytest.mark.xfail(
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sys.platform == "darwin", reason="known macos tflite install issue"
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)
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def test_module_static_cpu(self):
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self.module_tester.dynamic = False
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self.module_tester.device = "cpu"
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self.module_tester.create_and_check_module()
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if __name__ == "__main__":
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unittest.main()
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# module_tester = AlbertTfliteModuleTester()
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# module_tester.create_and_check_module()
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# TEST RESULT:
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# (shark.venv) nod% python albert_lite_base_tflite_mlir_test.py
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# load json inputs
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# TMP_MODEL_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite
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# Model has not been download.shark_downloader will automatically download by tank_url if provided. You can also manually to download the model from shark_tank by yourself.
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# TMP_MODELNAME_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite/albert_lite_base
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# Download mlir model https://storage.googleapis.com/shark_tank/tflite/albert_lite_base/albert_lite_base_tosa.mlir
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# Get tosa.mlir model return
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# Target triple found:x86_64-linux-gnu
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# (shark.venv) nod% python albert_lite_base_tflite_mlir_test.py
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# load json inputs
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# TMP_MODEL_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite
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# TMP_MODELNAME_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite/albert_lite_base
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# Model has been downloaded before. shark/SHARK/shark/./../gen_shark_tank/tflite/albert_lite_base/albert_lite_base_tosa.mlir
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# Get tosa.mlir model return
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# Target triple found:x86_64-linux-gnu
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#
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@@ -1,5 +1,5 @@
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import numpy as np
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from shark.shark_importer import SharkImporter
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from shark.shark_downloader import SharkDownloader
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from shark.shark_inference import SharkInference
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import pytest
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import unittest
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@@ -70,58 +70,26 @@ class AlbertTfliteModuleTester:
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def create_and_check_module(self):
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shark_args.save_mlir = self.save_mlir
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shark_args.save_vmfb = self.save_vmfb
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# Preprocess to get SharkImporter input args
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tflite_preprocessor = TFLitePreprocessor(model_name="albert_lite_base")
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raw_model_file_path = tflite_preprocessor.get_raw_model_file()
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inputs = tflite_preprocessor.get_inputs()
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tflite_interpreter = tflite_preprocessor.get_interpreter()
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# Use SharkImporter to get SharkInference input args
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my_shark_importer = SharkImporter(
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module=tflite_interpreter,
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inputs=inputs,
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frontend="tflite",
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raw_model_file=raw_model_file_path,
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shark_downloader = SharkDownloader(
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model_name="albert_lite_base",
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tank_url="https://storage.googleapis.com/shark_tank",
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local_tank_dir="./../gen_shark_tank",
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model_type="tflite",
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input_json="input.json",
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input_type="int32",
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)
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mlir_model, func_name = my_shark_importer.import_mlir()
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# Use SharkInference to get inference result
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shark_module = SharkInference(
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mlir_module=mlir_model,
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function_name=func_name,
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device=self.device,
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mlir_dialect="tflite",
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)
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# Case1: Use shark_importer default generate inputs
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shark_module.compile()
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mlir_results = shark_module.forward(inputs)
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## post process results for compare
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input_details, output_details = tflite_preprocessor.get_model_details()
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mlir_results = list(mlir_results)
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for i in range(len(output_details)):
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dtype = output_details[i]["dtype"]
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mlir_results[i] = mlir_results[i].astype(dtype)
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tflite_results = tflite_preprocessor.get_raw_model_output()
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compare_results(mlir_results, tflite_results, output_details)
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# Case2: Use manually set inputs
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input_details, output_details = tflite_preprocessor.get_model_details()
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inputs = generate_inputs(input_details) # new inputs
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tflite_tosa_model = shark_downloader.get_mlir_file()
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inputs = shark_downloader.get_inputs()
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shark_module = SharkInference(
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mlir_module=mlir_model,
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function_name=func_name,
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mlir_module=tflite_tosa_model,
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function_name="main",
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device=self.device,
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mlir_dialect="tflite",
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)
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shark_module.compile()
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mlir_results = shark_module.forward(inputs)
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## post process results for compare
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tflite_results = tflite_preprocessor.get_raw_model_output()
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compare_results(mlir_results, tflite_results, output_details)
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# print(mlir_results)
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shark_module.forward(inputs)
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# print(shark_results)
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class AlbertTfliteModuleTest(unittest.TestCase):
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@@ -146,9 +114,24 @@ class AlbertTfliteModuleTest(unittest.TestCase):
|
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|
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|
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if __name__ == "__main__":
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unittest.main()
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# module_tester = AlbertTfliteModuleTester()
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# module_tester.save_mlir = True
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# module_tester.save_vmfb = True
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# module_tester.create_and_check_module()
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unittest.main()
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# TEST RESULT:
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# (shark.venv) nod% python albert_lite_base_tflite_mlir_test.py
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# load json inputs
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# TMP_MODEL_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite
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# Model has not been download.shark_downloader will automatically download by tank_url if provided. You can also manually to download the model from shark_tank by yourself.
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# TMP_MODELNAME_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite/albert_lite_base
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# Download mlir model https://storage.googleapis.com/shark_tank/tflite/albert_lite_base/albert_lite_base_tosa.mlir
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# Get tosa.mlir model return
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# Target triple found:x86_64-linux-gnu
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# (shark.venv) nod% python albert_lite_base_tflite_mlir_test.py
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# load json inputs
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# TMP_MODEL_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite
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# TMP_MODELNAME_DIR = shark/SHARK/shark/./../gen_shark_tank/tflite/albert_lite_base
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# Model has been downloaded before. shark/SHARK/shark/./../gen_shark_tank/tflite/albert_lite_base/albert_lite_base_tosa.mlir
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# Get tosa.mlir model return
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# Target triple found:x86_64-linux-gnu
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#
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@@ -1,5 +1,5 @@
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import numpy as np
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from shark.shark_importer import SharkImporter
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from shark.shark_downloader import SharkDownloader
|
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from shark.shark_inference import SharkInference
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import pytest
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import unittest
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@@ -48,22 +48,22 @@ class ArbitraryImageStylizationV1TfliteModuleTester:
|
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tflite_preprocessor = TFLitePreprocessor(
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model_name="arbitrary-image-stylization-v1-256"
|
||||
)
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# inputs = tflite_preprocessor.get_inputs()
|
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|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="arbitrary-image-stylization-v1-256",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -96,7 +96,8 @@ class ArbitraryImageStylizationV1TfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -65,22 +65,22 @@ class BirdsV1TfliteModuleTester:
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="birds_V1")
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="birds_V1",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -102,7 +102,7 @@ class BirdsV1TfliteModuleTester:
|
||||
inputs = generate_inputs(input_details) # device_inputs
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -126,7 +126,8 @@ class BirdsV1TfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -46,21 +46,27 @@ class CartoonganTfliteModuleTester:
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="cartoongan")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
# input_details, output_details = tflite_preprocessor.get_model_details()
|
||||
# print(input_details[0]["dtype"])
|
||||
# import pdb
|
||||
# pdb.set_trace()
|
||||
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="cartoongan",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
|
||||
@@ -1,13 +1,9 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
from shark.parser import shark_args
|
||||
import os
|
||||
import sys
|
||||
import urllib.request
|
||||
from PIL import Image
|
||||
from shark.tflite_utils import TFLitePreprocessor
|
||||
|
||||
|
||||
@@ -51,23 +47,23 @@ class DeepLabV3TfliteModuleTester:
|
||||
|
||||
# preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="deeplabv3")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="deeplabv3",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -47,23 +47,22 @@ class DensenetTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="densenet")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="densenet",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -63,18 +63,19 @@ class Efficientnet_224_fp32TfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="efficientnet_224_fp32"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="efficientnet_224_fp32",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -63,23 +63,22 @@ class Efficientnet_lite0_fp32_2TfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="efficientnet_lite0_fp32_2"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="efficientnet_lite0_fp32_2",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -102,7 +101,7 @@ class Efficientnet_lite0_fp32_2TfliteModuleTester:
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -127,7 +126,8 @@ class Efficientnet_lite0_fp32_2TfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -61,23 +61,22 @@ class Efficientnet_lite0_int8_2TfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="efficientnet_lite0_int8_2"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="efficientnet_lite0_int8_2",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -100,7 +99,7 @@ class Efficientnet_lite0_int8_2TfliteModuleTester:
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -125,7 +124,8 @@ class Efficientnet_lite0_int8_2TfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -60,23 +60,22 @@ class GptTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="gpt2-64")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="gpt2-64",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
@@ -99,7 +98,7 @@ class GptTfliteModuleTester:
|
||||
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
function_name="main",
|
||||
device=self.device,
|
||||
mlir_dialect="tflite",
|
||||
)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -63,20 +63,20 @@ class Inception_v4_299_fp32TfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="inception_v4_299_fp32"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="inception_v4_299_fp32",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
mlir_module=mlir_model,
|
||||
function_name=func_name,
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -60,18 +60,22 @@ class Inception_v4_299_uint8TfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="inception_v4_299_uint8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="inception_v4_299_uint8"
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="inception_v4_299_uint8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -124,7 +128,8 @@ class Inception_v4_299_uint8TfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -47,18 +47,19 @@ class MidasTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="midas")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="midas",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -47,18 +47,19 @@ class MnasnetTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="mnasnet_1.0_224")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mnasnet_1.0_224",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -67,18 +67,19 @@ class MobilebertTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilebert-baseline-tf2-float"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilebert-baseline-tf2-float",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -67,18 +67,22 @@ class MobilebertTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilebert-baseline-tf2-quant"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilebert-baseline-tf2-quant"
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilebert-baseline-tf2-quant",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -131,7 +135,8 @@ class MobilebertTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -74,18 +74,19 @@ class MobilebertTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilebert-edgetpu-s-float"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilebert-edgetpu-s-float",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -74,18 +74,19 @@ class MobilebertTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilebert-edgetpu-s-quant"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilebert-edgetpu-s-quant",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -121,7 +122,8 @@ class MobilebertTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -64,18 +64,19 @@ class MobilebertTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="mobilebert")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilebert",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -62,18 +62,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v1_224_1.0_float"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v1_224_1.0_float",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -60,18 +60,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v1_224_1.0_uint8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v1_224_1.0_uint8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -124,7 +125,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -62,18 +62,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v2_1.00_224_int8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v2_1.00_224_int8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -126,7 +127,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -63,18 +63,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v2_1.0_224"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v2_1.0_224",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -60,18 +60,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v2_224_1.0_uint8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v2_224_1.0_uint8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="int32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -124,7 +125,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -63,18 +63,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v3-large_224_1.0_float"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v3-large_224_1.0_float",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -60,18 +60,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v3-large_224_1.0_uint8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v3-large_224_1.0_uint8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -124,7 +125,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -63,18 +63,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="mobilenet_v3.5multiavg_1.00_224_int8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="mobilenet_v3.5multiavg_1.00_224_int8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -127,7 +128,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -46,18 +46,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="multi_person_mobilenet_v1_075_float"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="multi_person_mobilenet_v1_075_float",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -47,18 +47,19 @@ class NasnetTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="nasnet")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="nasnet",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -49,18 +49,19 @@ class ResnetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="resnet_50_224_int8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="resnet_50_224_int8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -96,7 +97,8 @@ class ResnetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -47,18 +47,19 @@ class SequeezeNetTfliteModuleTester:
|
||||
|
||||
# Preprocess to get SharkImporter input args
|
||||
tflite_preprocessor = TFLitePreprocessor(model_name="squeezenet")
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="squeezenet",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -94,7 +95,8 @@ class SequeezeNetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -60,18 +60,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="ssd_mobilenet_v1_320_1.0_float"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="ssd_mobilenet_v1_320_1.0_float",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -57,18 +57,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="ssd_mobilenet_v1_320_1.0_uint8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="ssd_mobilenet_v1_320_1.0_uint8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -121,7 +122,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -69,18 +69,19 @@ class MobilenetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="ssd_mobilenet_v2_face_quant"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="ssd_mobilenet_v2_face_quant",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -133,7 +134,8 @@ class MobilenetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.pad' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -59,18 +59,19 @@ class SpaghettinetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="ssd_spaghettinet_edgetpu_large"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="ssd_spaghettinet_edgetpu_large",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="float32",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import numpy as np
|
||||
from shark.shark_importer import SharkImporter
|
||||
from shark.shark_downloader import SharkDownloader
|
||||
from shark.shark_inference import SharkInference
|
||||
import pytest
|
||||
import unittest
|
||||
@@ -57,18 +57,19 @@ class SpaghettinetTfliteModuleTester:
|
||||
tflite_preprocessor = TFLitePreprocessor(
|
||||
model_name="ssd_spaghettinet_edgetpu_large_uint8"
|
||||
)
|
||||
raw_model_file_path = tflite_preprocessor.get_raw_model_file()
|
||||
inputs = tflite_preprocessor.get_inputs()
|
||||
tflite_interpreter = tflite_preprocessor.get_interpreter()
|
||||
# inputs = tflite_preprocessor.get_inputs()
|
||||
|
||||
# Use SharkImporter to get SharkInference input args
|
||||
my_shark_importer = SharkImporter(
|
||||
module=tflite_interpreter,
|
||||
inputs=inputs,
|
||||
frontend="tflite",
|
||||
raw_model_file=raw_model_file_path,
|
||||
shark_downloader = SharkDownloader(
|
||||
model_name="ssd_spaghettinet_edgetpu_large_uint8",
|
||||
tank_url="https://storage.googleapis.com/shark_tank",
|
||||
local_tank_dir="./../gen_shark_tank",
|
||||
model_type="tflite",
|
||||
input_json="input.json",
|
||||
input_type="uint8",
|
||||
)
|
||||
mlir_model, func_name = my_shark_importer.import_mlir()
|
||||
mlir_model = shark_downloader.get_mlir_file()
|
||||
inputs = shark_downloader.get_inputs()
|
||||
func_name = "main"
|
||||
|
||||
# Use SharkInference to get inference result
|
||||
shark_module = SharkInference(
|
||||
@@ -121,7 +122,8 @@ class SpaghettinetTfliteModuleTest(unittest.TestCase):
|
||||
import sys
|
||||
|
||||
@pytest.mark.xfail(
|
||||
sys.platform == "darwin", reason="known macos tflite install issue"
|
||||
reason="known macos tflite install issue & "
|
||||
"'tosa.conv2d' op attribute 'quantization_info' failed "
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
self.module_tester.dynamic = False
|
||||
|
||||
Reference in New Issue
Block a user