def test_invalid_inputs(self):
gradients = constant_op.constant(value=[[1.0], [2.0], [4.0]],
dtype=dtypes.float32)
inputs = constant_op.constant(value=[[1.0], [2.0], [4.0]],
dtype=dtypes.float32)
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
"must be equal rank|must be rank 0"):
self.evaluate(
array_ops.fake_quant_with_min_max_vars_gradient(
gradients=gradients,
inputs=inputs,
min=0.0,
max=[[1.0], [2.0], [4.0]]))
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
"must be rank 0"):
self.evaluate(
array_ops.fake_quant_with_min_max_vars_gradient(
gradients=gradients,
inputs=inputs,
min=[[1.0], [2.0], [4.0]],
max=[[1.0], [2.0], [4.0]]))
def _TestOp(self, input_min, input_max, num_bits, narrow_range,
expected_nudged_input_min, expected_nudged_input_max,
expected_step):
inputs = np.array([
expected_nudged_input_min - expected_step,
expected_nudged_input_min - 0.01, expected_nudged_input_min,
expected_nudged_input_min + 0.01, expected_nudged_input_min +
expected_step - 0.01, expected_nudged_input_min + expected_step,
expected_nudged_input_min + expected_step + 0.01,
expected_nudged_input_max - 0.01, expected_nudged_input_max,
expected_nudged_input_max + 0.01,
expected_nudged_input_max + expected_step
],
dtype=np.float32)
gradients = np.arange(1, len(inputs) + 1, dtype=np.float32)
expected_backprops_wrt_input = np.array(
[0.0, 0.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 0.0, 0.0],
dtype=np.float32)
expected_backprops_wrt_min = 1.0 + 2.0
expected_backprops_wrt_max = 10.0 + 11.0
with self.test_session() as session:
with self.test_scope():
gradient_placeholder = array_ops.placeholder(dtypes.float32,
gradients.shape,
name="gradients")
input_placeholder = array_ops.placeholder(dtypes.float32,
inputs.shape,
name="inputs")
min_placeholder = array_ops.placeholder(dtypes.float32, (),
name="min")
max_placeholder = array_ops.placeholder(dtypes.float32, (),
name="max")
outputs = array_ops.fake_quant_with_min_max_vars_gradient(
gradient_placeholder,
input_placeholder,
min_placeholder,
max_placeholder,
num_bits=num_bits,
narrow_range=narrow_range)
backprops_wrt_input, backprops_wrt_min, backprops_wrt_max = session.run(
outputs, {
gradient_placeholder: gradients,
input_placeholder: inputs,
min_placeholder: input_min,
max_placeholder: input_max
})
self.assertAllCloseAccordingToType(backprops_wrt_input,
expected_backprops_wrt_input,
rtol=1e-3,
atol=1e-5,
bfloat16_rtol=0.03)
self.assertAllCloseAccordingToType(backprops_wrt_min,
expected_backprops_wrt_min,
rtol=1e-3,
atol=1e-5,
bfloat16_rtol=0.03)
self.assertAllCloseAccordingToType(backprops_wrt_max,
expected_backprops_wrt_max,
rtol=1e-3,
atol=1e-5,
bfloat16_rtol=0.03)
def _TestOp(self, input_min, input_max, num_bits, narrow_range,
expected_nudged_input_min, expected_nudged_input_max,
expected_step):
inputs = np.array(
[
expected_nudged_input_min - expected_step,
expected_nudged_input_min - 0.01, expected_nudged_input_min,
expected_nudged_input_min + 0.01,
expected_nudged_input_min + expected_step - 0.01,
expected_nudged_input_min + expected_step,
expected_nudged_input_min + expected_step + 0.01,
expected_nudged_input_max - 0.01, expected_nudged_input_max,
expected_nudged_input_max + 0.01,
expected_nudged_input_max + expected_step
],
dtype=np.float32)
gradients = np.arange(1, len(inputs) + 1, dtype=np.float32)
expected_backprops_wrt_input = np.array(
[0.0, 0.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 0.0, 0.0],
dtype=np.float32)
expected_backprops_wrt_min = 1.0 + 2.0
expected_backprops_wrt_max = 10.0 + 11.0
with self.cached_session() as session:
with self.test_scope():
gradient_placeholder = array_ops.placeholder(
dtypes.float32, gradients.shape, name="gradients")
input_placeholder = array_ops.placeholder(
dtypes.float32, inputs.shape, name="inputs")
min_placeholder = array_ops.placeholder(dtypes.float32, (), name="min")
max_placeholder = array_ops.placeholder(dtypes.float32, (), name="max")
outputs = array_ops.fake_quant_with_min_max_vars_gradient(
gradient_placeholder,
input_placeholder,
min_placeholder,
max_placeholder,
num_bits=num_bits,
narrow_range=narrow_range)
backprops_wrt_input, backprops_wrt_min, backprops_wrt_max = session.run(
outputs, {
gradient_placeholder: gradients,
input_placeholder: inputs,
min_placeholder: input_min,
max_placeholder: input_max
})
self.assertAllCloseAccordingToType(
backprops_wrt_input,
expected_backprops_wrt_input,
rtol=1e-3,
atol=1e-5,
bfloat16_rtol=0.03)
self.assertAllCloseAccordingToType(
backprops_wrt_min,
expected_backprops_wrt_min,
rtol=1e-3,
atol=1e-5,
bfloat16_rtol=0.03)
self.assertAllCloseAccordingToType(
backprops_wrt_max,
expected_backprops_wrt_max,
rtol=1e-3,
atol=1e-5,
bfloat16_rtol=0.03)