98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import pytest
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from openvino.inference_engine import PreProcessInfo, IECore, TensorDesc, Blob, PreProcessChannel,\
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MeanVariant, ResizeAlgorithm, ColorFormat
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from conftest import model_path
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test_net_xml, test_net_bin = model_path()
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def get_preprocess_info():
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ie_core = IECore()
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net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
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return net.input_info["data"].preprocess_info
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def test_preprocess_info():
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assert isinstance(get_preprocess_info(), PreProcessInfo)
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def test_color_format():
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preprocess_info = get_preprocess_info()
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assert preprocess_info.color_format == ColorFormat.RAW
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def test_color_format_setter():
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preprocess_info = get_preprocess_info()
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preprocess_info.color_format = ColorFormat.BGR
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assert preprocess_info.color_format == ColorFormat.BGR
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def test_resize_algorithm():
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preprocess_info = get_preprocess_info()
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assert preprocess_info.resize_algorithm == ResizeAlgorithm.NO_RESIZE
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def test_resize_algorithm_setter():
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preprocess_info = get_preprocess_info()
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preprocess_info.resize_algorithm = ResizeAlgorithm.RESIZE_BILINEAR
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assert preprocess_info.resize_algorithm == ResizeAlgorithm.RESIZE_BILINEAR
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def test_mean_variant():
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preprocess_info = get_preprocess_info()
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assert preprocess_info.mean_variant == MeanVariant.NONE
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def test_mean_variant_setter():
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preprocess_info = get_preprocess_info()
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preprocess_info.mean_variant = MeanVariant.MEAN_IMAGE
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assert preprocess_info.mean_variant == MeanVariant.MEAN_IMAGE
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def test_get_number_of_channels():
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ie_core = IECore()
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net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
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assert net.input_info["data"].preprocess_info.get_number_of_channels() == 0
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def test_init():
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ie_core = IECore()
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net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
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net.input_info['data'].preprocess_info.init(5)
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assert net.input_info["data"].preprocess_info.get_number_of_channels() == 5
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def test_set_mean_image():
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ie_core = IECore()
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net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
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tensor_desc = TensorDesc("FP32", [0, 127, 127], "CHW")
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mean_image_blob = Blob(tensor_desc)
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preprocess_info = net.input_info["data"].preprocess_info
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preprocess_info.set_mean_image(mean_image_blob)
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assert preprocess_info.mean_variant == MeanVariant.MEAN_IMAGE
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def test_get_pre_process_channel():
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ie_core = IECore()
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net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
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preprocess_info = net.input_info["data"].preprocess_info
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preprocess_info.init(1)
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pre_process_channel = preprocess_info[0]
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assert isinstance(pre_process_channel, PreProcessChannel)
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def test_set_mean_image_for_channel():
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ie_core = IECore()
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net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
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tensor_desc = TensorDesc("FP32", [127, 127], "HW")
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mean_image_blob = Blob(tensor_desc)
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preprocess_info = net.input_info["data"].preprocess_info
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preprocess_info.init(1)
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preprocess_info.set_mean_image_for_channel(mean_image_blob, 0)
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pre_process_channel = preprocess_info[0]
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assert isinstance(pre_process_channel.mean_data, Blob)
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assert pre_process_channel.mean_data.tensor_desc.dims == [127, 127]
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assert preprocess_info.mean_variant == MeanVariant.MEAN_IMAGE
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