125 lines
3.6 KiB
Python
125 lines
3.6 KiB
Python
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# Copyright (C) CVAT.ai Corporation
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#
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# SPDX-License-Identifier: MIT
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import pickle
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from copy import deepcopy
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from cvat_sdk import models
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from deepdiff import DeepDiff
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def test_models_do_not_change_input_values():
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# Nested containers may be modified during the model input data parsing.
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# This can lead to subtle memory errors, which are very hard to find.
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original_input_data = {
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"name": "test",
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"labels": [
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{
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"name": "cat",
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"attributes": [
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{
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"default_value": "yy",
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"input_type": "text",
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"mutable": False,
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"name": "x",
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"values": ["yy"],
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},
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{
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"default_value": "1",
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"input_type": "radio",
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"mutable": False,
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"name": "y",
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"values": ["1", "2"],
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},
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],
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}
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],
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}
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input_data = deepcopy(original_input_data)
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models.TaskWriteRequest(**input_data)
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assert DeepDiff(original_input_data, input_data) == {}
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def test_models_do_not_store_input_collections():
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# Avoid depending on input data for collection fields after the model is initialized.
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# This can lead to subtle memory errors and unexpected behavior
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# if the original input data is modified.
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input_data = {
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"name": "test",
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"labels": [
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{
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"name": "cat1",
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"attributes": [
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{
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"default_value": "yy",
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"input_type": "text",
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"mutable": False,
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"name": "x",
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"values": ["yy"],
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},
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{
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"default_value": "1",
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"input_type": "radio",
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"mutable": False,
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"name": "y",
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"values": ["1", "2"],
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},
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],
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},
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{"name": "cat2", "attributes": []},
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],
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}
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model = models.TaskWriteRequest(**input_data)
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model_data1 = model.to_dict()
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# Modify input value containers
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input_data["labels"][0]["attributes"].clear()
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input_data["labels"][1]["attributes"].append(
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{
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"default_value": "",
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"input_type": "text",
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"mutable": True,
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"name": "z",
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}
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)
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input_data["labels"].append({"name": "dog"})
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model_data2 = model.to_dict()
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assert DeepDiff(model_data1, model_data2) == {}
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def test_models_do_not_return_internal_collections():
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# Avoid returning internal data for mutable collection fields.
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# This can lead to subtle memory errors and unexpected behavior
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# if the returned data is modified.
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input_data = {
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"name": "test",
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"labels": [],
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}
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model = models.TaskWriteRequest(**input_data)
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model_data1 = model.to_dict()
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model_data1_original = deepcopy(model_data1)
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# Modify an output value container
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model_data1["labels"].append({"name": "dog"})
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model_data2 = model.to_dict()
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assert DeepDiff(model_data1_original, model_data2) == {}
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def test_models_are_pickleable():
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model = models.PatchedLabelRequest(id=5, name="person")
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pickled_model = pickle.dumps(model)
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unpickled_model = pickle.loads(pickled_model)
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assert unpickled_model.id == model.id
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assert unpickled_model.name == model.name
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