def build(self, input_shape):
super().build(input_shape)
if self.depthwise_quantizer:
if "flip_ratio" in self._custom_metrics:
self.depthwise_flip_ratio = lq_metrics.FlipRatio(
name=f"flip_ratio/{self.name}_depthwise",
)
if self.pointwise_quantizer:
if "flip_ratio" in self._custom_metrics:
self.pointwise_flip_ratio = lq_metrics.FlipRatio(
name=f"flip_ratio/{self.name}_pointwise",
)
def build(self, input_shape):
super().build(input_shape)
if self.kernel_quantizer:
if "flip_ratio" in self._custom_metrics:
self.flip_ratio = lq_metrics.FlipRatio(
values_shape=self.kernel.shape, name=f"flip_ratio/{self.name}"
)
def test_metric(self):
mcv = metrics.FlipRatio([2])
self.evaluate(tf.compat.v1.variables_initializer(mcv.variables))
self.assertAllClose(0, mcv.result())
self.assertAllClose(0, self.evaluate(mcv.total))
self.assertAllClose(0, self.evaluate(mcv.count))
self.evaluate(mcv.update_state([1, 1]))
self.assertAllClose([1, 1], self.evaluate(mcv._previous_values))
self.assertAllClose(0, self.evaluate(mcv.total))
self.assertAllClose(1, self.evaluate(mcv.count))
self.assertAllClose(0, mcv.result())
self.evaluate(mcv.update_state([2, 2]))
self.assertAllClose([2, 2], self.evaluate(mcv._previous_values))
self.assertAllClose(1, self.evaluate(mcv.total))
self.assertAllClose(2, self.evaluate(mcv.count))
self.assertAllClose(1, mcv.result())
self.evaluate(mcv.update_state([1, 2]))
self.assertAllClose([1, 2], self.evaluate(mcv._previous_values))
self.assertAllClose(1.5, self.evaluate(mcv.total))
self.assertAllClose(3, self.evaluate(mcv.count))
self.assertAllClose(1.5 / 2, mcv.result())
def build(self, input_shape):
super().build(input_shape)
if self.kernel_quantizer:
self.quantized_latent_weights.append(self.kernel)
self.quantizers.append(self.kernel_quantizer)
if "flip_ratio" in self._custom_metrics and utils.supports_metrics():
self.flip_ratio = metrics.FlipRatio(
values_shape=self.kernel.shape, name=f"{self.name}/flip_ratio"
)
def build(self, input_shape):
super().build(input_shape)
if self.depthwise_quantizer:
self.quantized_latent_weights.append(self.depthwise_kernel)
self.quantizers.append(self.depthwise_quantizer)
if "flip_ratio" in self._custom_metrics:
self.depthwise_flip_ratio = metrics.FlipRatio(
values_shape=self.depthwise_kernel.shape,
name=f"{self.name}/depthwise_flip_ratio",
)
if self.pointwise_quantizer:
self.quantized_latent_weights.append(self.pointwise_kernel)
self.quantizers.append(self.pointwise_quantizer)
if "flip_ratio" in self._custom_metrics:
self.pointwise_flip_ratio = metrics.FlipRatio(
values_shape=self.pointwise_kernel.shape,
name=f"{self.name}/pointwise_flip_ratio",
)
def build(self, input_shape):
super().build(input_shape)
if self.depthwise_quantizer:
self.quantized_latent_weights.append(self.depthwise_kernel)
self.quantizers.append(self.depthwise_quantizer)
if "flip_ratio" in self._custom_metrics and utils.supports_metrics(
):
self.depthwise_flip_ratio = lq_metrics.FlipRatio(
values_shape=self.depthwise_kernel.shape,
name=f"flip_ratio/{self.name}_depthwise",
)
if self.pointwise_quantizer:
self.quantized_latent_weights.append(self.pointwise_kernel)
self.quantizers.append(self.pointwise_quantizer)
if "flip_ratio" in self._custom_metrics and utils.supports_metrics(
):
self.pointwise_flip_ratio = lq_metrics.FlipRatio(
values_shape=self.pointwise_kernel.shape,
name=f"flip_ratio/{self.name}_pointwise",
)
def test_config():
mcv = metrics.FlipRatio(values_dtype="int16", name="mcv", dtype=tf.float16)
assert mcv.name == "mcv"
assert mcv.stateful
assert mcv.dtype == tf.float16
assert mcv.values_dtype == tf.int16
mcv2 = metrics.FlipRatio.from_config(mcv.get_config())
assert mcv2.name == "mcv"
assert mcv2.stateful
assert mcv2.dtype == tf.float16
assert mcv2.values_dtype == tf.int16
def test_config(self):
mcv = metrics.FlipRatio(
values_shape=[3, 3], values_dtype="int16", name="mcv", dtype=tf.float16
)
self.assertEqual(mcv.name, "mcv")
self.assertTrue(mcv.stateful)
self.assertEqual(mcv.dtype, tf.float16)
self.assertEqual(mcv.values_dtype, tf.int16)
self.assertEqual(mcv.values_shape, [3, 3])
mcv2 = metrics.FlipRatio.from_config(mcv.get_config())
self.assertEqual(mcv2.name, "mcv")
self.assertTrue(mcv2.stateful)
self.assertEqual(mcv2.dtype, tf.float16)
self.assertEqual(mcv2.values_dtype, tf.int16)
self.assertEqual(mcv2.values_shape, [3, 3])
def test_metric_implicit_build(eager_mode):
mcv = metrics.FlipRatio()
mcv.update_state(np.array([1, 1]))
assert all([1, 1] == mcv._previous_values.numpy())
assert 0 == mcv.total.numpy()
assert 1 == mcv.count.numpy()
assert 0 == mcv.result().numpy()
mcv.update_state(np.array([2, 2]))
assert all([2, 2] == mcv._previous_values.numpy())
assert 1 == mcv.total.numpy()
assert 2 == mcv.count.numpy()
assert 1 == mcv.result().numpy()
mcv.update_state(np.array([1, 2]))
assert all([1, 2] == mcv._previous_values.numpy())
assert 1.5 == mcv.total.numpy()
assert 3 == mcv.count.numpy()
assert 1.5 / 2 == mcv.result().numpy()
def test_metric_in_graph_mode(graph_mode):
mcv = metrics.FlipRatio()
mcv.build((2, ))
new_state = tf.compat.v1.placeholder(dtype=tf.float32, shape=[2])
update_state_op = mcv.update_state(new_state)
metric_value = mcv.result()
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.variables_initializer(mcv.variables))
sess.run(update_state_op, feed_dict={new_state: [1, 1]})
sess.run(update_state_op, feed_dict={new_state: [2, 2]})
sess.run(update_state_op, feed_dict={new_state: [1, 2]})
previous, total, count, result = sess.run(
[mcv._previous_values, mcv.total, mcv.count, metric_value])
assert all([1, 2] == previous)
assert 1.5 == total
assert 3 == count
assert 1.5 / 2 == result
def test_metric_in_graph_mode(self):
mcv = metrics.FlipRatio([2])
new_state = tf.compat.v1.placeholder(dtype=tf.float32, shape=[2])
update_state_op = mcv.update_state(new_state)
metric_value = mcv.result()
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.variables_initializer(mcv.variables))
sess.run(update_state_op, feed_dict={new_state: [1, 1]})
sess.run(update_state_op, feed_dict={new_state: [2, 2]})
sess.run(update_state_op, feed_dict={new_state: [1, 2]})
previous, total, count, result = sess.run(
[mcv._previous_values, mcv.total, mcv.count, metric_value]
)
self.assertAllClose([1, 2], previous)
self.assertAllClose(1.5, total)
self.assertAllClose(3, count)
self.assertAllClose(1.5 / 2, result)
def test_metric_wrong_shape(eager_mode):
mcv = metrics.FlipRatio()
mcv.build((3, ))
with pytest.raises((ValueError, tf.errors.InvalidArgumentError)):
mcv.update_state(np.array([1, 1]))
def build(self, input_shape):
if self._custom_metrics and "flip_ratio" in self._custom_metrics:
self.flip_ratio = lq_metrics.FlipRatio(
name=f"flip_ratio/{self.name}")
