test/models/segmentation_model_test.py

from unittest.mock import Mock

import numpy as np
import tensorflow as tf

from deep_learning.models.segmentation import SegmentationModelBuilder


def test_segmentation_model_builder_builds_pixel_classifier():
    """验证分割模型保持输入分辨率，并为每个像素输出类别概率。"""
    artifact = SegmentationModelBuilder(
        image_size=(32, 32),
        num_classes=3,
        model_filters=(8,)
    ).build_training_artifact()
    model = artifact.model
    images = tf.zeros((2, 32, 32, 3), dtype=tf.float32)

    outputs = model(images)

    assert outputs.shape == (2, 32, 32, 3)
    np.testing.assert_allclose(
        tf.reduce_sum(outputs, axis=-1).numpy(),
        np.ones((2, 32, 32)),
        atol=1e-5
    )


def test_segmentation_model_builder_compiles_training_model():
    """验证分割模型构建器会使用稀疏多分类和前景 IoU 编译模型。"""
    model = Mock()
    builder = SegmentationModelBuilder(
        image_size=(32, 32),
        num_classes=3,
        model_filters=(8,)
    )

    builder.compile_training_model(model)

    model.compile.assert_called_once()
    _, kwargs = model.compile.call_args
    assert kwargs["optimizer"] == "adam"
    assert kwargs["loss"] == "sparse_categorical_crossentropy"
    assert kwargs["metrics"][0].name == "foreground_iou"