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@@ 282-323 (lines=42) @@
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expected = tf.constant(expected) |
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assert is_equal_tf(got[0], expected) |
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@pytest.mark.parametrize("binary", [True, False]) |
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@pytest.mark.parametrize("background_weight", [0.0, 0.1, 0.5, 1.0]) |
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@pytest.mark.parametrize("shape", [(1,), (10,), (100,), (2, 3), (2, 3, 4)]) |
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def test_exact_value(self, binary: bool, background_weight: float, shape: Tuple): |
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""" |
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Test Jaccard index by comparing at ground truth values. |
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:param binary: if project labels to binary values. |
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:param background_weight: the weight of background class. |
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:param shape: shape of input. |
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""" |
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# init |
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shape = (1,) + shape # add batch axis |
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foreground_weight = 1 - background_weight |
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tf.random.set_seed(0) |
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y_true = tf.random.uniform(shape=shape) |
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y_pred = tf.random.uniform(shape=shape) |
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# obtained value |
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got = label.JaccardIndex( |
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binary=binary, |
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background_weight=background_weight, |
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).call(y_true=y_true, y_pred=y_pred) |
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# expected value |
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flatten = tf.keras.layers.Flatten() |
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y_true = flatten(y_true) |
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y_pred = flatten(y_pred) |
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if binary: |
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y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype) |
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y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype) |
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num = foreground_weight * tf.reduce_sum( |
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y_true * y_pred, axis=1 |
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) + background_weight * tf.reduce_sum((1 - y_true) * (1 - y_pred), axis=1) |
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denom = foreground_weight * tf.reduce_sum( |
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y_true + y_pred, axis=1 |
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) + background_weight * tf.reduce_sum((1 - y_true) + (1 - y_pred), axis=1) |
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denom = denom - num |
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expected = (num + EPS) / (denom + EPS) |
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assert is_equal_tf(got, expected) |
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def test_get_config(self): |
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got = label.JaccardIndex().get_config() |
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@@ 79-120 (lines=42) @@
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expected = tf.constant(expected) |
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assert is_equal_tf(got[0], expected) |
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@pytest.mark.parametrize("binary", [True, False]) |
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@pytest.mark.parametrize("background_weight", [0.0, 0.1, 0.5, 1.0]) |
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@pytest.mark.parametrize("shape", [(1,), (10,), (100,), (2, 3), (2, 3, 4)]) |
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def test_exact_value(self, binary: bool, background_weight: float, shape: Tuple): |
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""" |
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Test dice score by comparing at ground truth values. |
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:param binary: if project labels to binary values. |
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:param background_weight: the weight of background class. |
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:param shape: shape of input. |
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""" |
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# init |
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shape = (1,) + shape # add batch axis |
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foreground_weight = 1 - background_weight |
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tf.random.set_seed(0) |
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y_true = tf.random.uniform(shape=shape) |
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y_pred = tf.random.uniform(shape=shape) |
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# obtained value |
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got = label.DiceScore( |
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binary=binary, |
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background_weight=background_weight, |
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).call(y_true=y_true, y_pred=y_pred) |
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|
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# expected value |
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flatten = tf.keras.layers.Flatten() |
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y_true = flatten(y_true) |
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y_pred = flatten(y_pred) |
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if binary: |
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y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype) |
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y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype) |
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num = foreground_weight * tf.reduce_sum( |
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y_true * y_pred, axis=1 |
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) + background_weight * tf.reduce_sum((1 - y_true) * (1 - y_pred), axis=1) |
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num *= 2 |
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denom = foreground_weight * tf.reduce_sum( |
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y_true + y_pred, axis=1 |
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) + background_weight * tf.reduce_sum((1 - y_true) + (1 - y_pred), axis=1) |
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expected = (num + EPS) / (denom + EPS) |
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assert is_equal_tf(got, expected) |
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def test_get_config(self): |
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got = label.DiceScore().get_config() |
DeepRegNet /
DeepReg
