mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-08-10 21:01:10 +03:00
Migrate to pydantic TypeAdapter
https://pydantic.dev/docs/validation/2.12/get-started/migration/#introduction-of-typeadapter
This commit is contained in:
@@ -2,7 +2,7 @@ import unittest
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from unittest.mock import Mock, patch
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from unittest.mock import Mock, patch
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import numpy as np
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import numpy as np
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from pydantic import parse_obj_as
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from pydantic import TypeAdapter
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import frigate.detectors as detectors
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import frigate.detectors as detectors
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import frigate.object_detection.base
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import frigate.object_detection.base
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@@ -19,8 +19,8 @@ class TestLocalObjectDetector(unittest.TestCase):
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"frigate.detectors.api_types",
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"frigate.detectors.api_types",
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{det_type: Mock() for det_type in DetectorTypeEnum},
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{det_type: Mock() for det_type in DetectorTypeEnum},
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):
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):
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test_cfg = parse_obj_as(
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test_cfg = TypeAdapter(DetectorConfig).validate_python(
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DetectorConfig, ({"type": det_type, "model": {}})
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{"type": det_type, "model": {}}
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)
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)
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test_cfg.model.path = "/test/modelpath"
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test_cfg.model.path = "/test/modelpath"
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test_obj = frigate.object_detection.base.LocalObjectDetector(
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test_obj = frigate.object_detection.base.LocalObjectDetector(
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@@ -44,7 +44,9 @@ class TestLocalObjectDetector(unittest.TestCase):
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TEST_DATA = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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TEST_DATA = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32])
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TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32])
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test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
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test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
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detector_config=parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}})
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detector_config=TypeAdapter(DetectorConfig).validate_python(
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{"type": "cpu", "model": {}}
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)
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)
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)
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mock_det_api = mock_cputfl.return_value
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mock_det_api = mock_cputfl.return_value
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@@ -67,7 +69,9 @@ class TestLocalObjectDetector(unittest.TestCase):
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TEST_DATA = np.zeros((1, 32, 32, 3), np.uint8)
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TEST_DATA = np.zeros((1, 32, 32, 3), np.uint8)
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TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32])
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TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32])
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test_cfg = parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}})
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test_cfg = TypeAdapter(DetectorConfig).validate_python(
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{"type": "cpu", "model": {}}
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)
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test_cfg.model.input_tensor = InputTensorEnum.nchw
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test_cfg.model.input_tensor = InputTensorEnum.nchw
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test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
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test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
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@@ -116,7 +120,9 @@ class TestLocalObjectDetector(unittest.TestCase):
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"label-5",
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"label-5",
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]
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]
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test_cfg = parse_obj_as(DetectorConfig, {"type": "cpu", "model": {}})
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test_cfg = TypeAdapter(DetectorConfig).validate_python(
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{"type": "cpu", "model": {}}
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)
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test_cfg.model = ModelConfig()
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test_cfg.model = ModelConfig()
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test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
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test_obj_detect = frigate.object_detection.base.LocalObjectDetector(
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detector_config=test_cfg,
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detector_config=test_cfg,
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