Test Models Post

# initialize model
model = InferenceModel()
# load model weigths
model.model_load()
# process test sample
ret = model.process_sample(test_image)
# it should be ground truth instead of None
test_result = None

def test_process_sample_some_model_name():
    # initialize model
    model = InferenceModel()
    # load model weigths
    model.model_load()
    # process test sample
    ret = model.process_sample(test_image)
    # convert pydantic objects into dict
    ret = [pydantic.json.pydantic_encoder(item) for item in ret]
    # equal prediction with ground truth
    assert is_equal(ret, test_result)
# equal prediction with ground truth
assert is_equal(ret, test_result)
import pytest


def count_odd_numbers(a: list):
    return len(list(filter(lambda x: (x % 2 != 0), a)))


def test_count_odd_numbers():
    list_to_check = [1, 10, 5, 2, 8]
    print(f"The odd numbers amount is:{count_odd_numbers(list_to_check)}")
    assert count_odd_numbers(list_to_check) == 2
with open(input_file_path, "rb") as f:
        response = requests.post(
            f"{BASE_URL}/{API_VERSION}/process?model_id={model_id}",
            headers=headers,
            files={"upload_data": (osp.basename(input_file_path), f)},
        )

    assert response.status_code == 201

    task_id = json.loads(response.content)["task_id"]

    result = repeated_get_task(access_token, task_id, repeat_result)
    assert result is not None

    visualization = repeated_get_task(
        access_token, task_id, repeat_visualization, visualize=True,
    )
    assert bool(visualization)

    response = requests.get(visualization, headers=headers)
    assert response.status_code == 200

    if check_is_equal:
        assert is_equal(result, output_file, error=0.3)

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