def _TestSkipReshapeQuantization(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
conv = conv2d(input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=nn_ops.relu6,
scope='test/test')
reshape = array_ops.reshape(
conv, (int(10), int(height / 2), int(width / 2), int(16)))
# Insert a fake quant node after the reshape. We will check that one isn't
# insert before.
array_ops.fake_quant_with_min_max_vars(reshape, -1, 1)
quantize.Quantize(graph,
is_training,
weight_bits=8,
activation_bits=8)
# Ensure that there isn't a FakeQuant added before the reshape.
self.assertFalse('FakeQuantWithMinMaxVars' in
[i.op.type for i in reshape.op.inputs])
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
conv = conv2d(input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=nn_ops.relu6,
scope='test/test')
reshape = array_ops.reshape(
conv, (int(10), int(height / 2), int(width / 2), int(16)))
# If no fake quant is added after the reshape, a FakeQuant should be added
# before the reshape.
quantize.Quantize(graph,
is_training,
weight_bits=8,
activation_bits=8)
# Ensure that there isn't a FakeQuant added before the reshape.
self.assertTrue('FakeQuantWithMinMaxVars' in
[i.op.type for i in reshape.op.inputs])
def call(self, inputs):
x = array_ops.fake_quant_with_min_max_vars(
inputs, self.min_var, self.max_var)
w_fq = array_ops.fake_quant_with_min_max_vars(
self.w, self.min_var, self.max_var)
x = math_ops.matmul(x, w_fq)
x = array_ops.fake_quant_with_min_max_vars(
x, self.min_var, self.max_var)
return x
def _TestSkipReshapeQuantization(self, is_training):
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
conv = conv2d(
input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=nn_ops.relu6,
scope='test/test')
reshape = array_ops.reshape(
conv, (int(10), int(height / 2), int(width / 2), int(16)))
# Insert a fake quant node after the reshape. We will check that one isn't
# insert before.
array_ops.fake_quant_with_min_max_vars(reshape, -1, 1)
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
# Ensure that there isn't a FakeQuant added before the reshape.
self.assertFalse(
'FakeQuantWithMinMaxVars' in [i.op.type for i in reshape.op.inputs])
graph = ops.Graph()
with graph.as_default():
batch_size, height, width, depth = 5, 128, 128, 3
input1 = array_ops.zeros((batch_size, height, width, depth))
conv = conv2d(
input1,
32, [5, 5],
stride=2,
padding='SAME',
weights_initializer=self._WeightInit(0.09),
activation_fn=nn_ops.relu6,
scope='test/test')
reshape = array_ops.reshape(
conv, (int(10), int(height / 2), int(width / 2), int(16)))
# If no fake quant is added after the reshape, a FakeQuant should be added
# before the reshape.
quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
# Ensure that there isn't a FakeQuant added before the reshape.
self.assertTrue(
'FakeQuantWithMinMaxVars' in [i.op.type for i in reshape.op.inputs])
def _FakeQuantWithMinMaxVars(inputs, min_var, max_var, per_channel, num_bits,
narrow_range):
"""Adds a fake quantization operation.
Depending on value of per_channel, this operation may do global quantization
or per channel quantization. min_var and max_var should have corresponding
shapes: [1] when per_channel == False and [d] when per_channel == True.
Args:
inputs: a tensor containing values to be quantized.
min_var: a variable containing quantization range lower end(s).
max_var: a variable containing quantization range upper end(s).
per_channel: a boolean specifying whether to use per-channel quantization.
num_bits: Number of bits to use for quantization, must be between 2 and 8.
narrow_range: Whether to use the narrow quantization range
[1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
Returns:
a tensor containing quantized values.
"""
if per_channel:
assert len(min_var.get_shape()) == 1
assert len(max_var.get_shape()) == 1
return array_ops.fake_quant_with_min_max_vars_per_channel(
inputs, min_var, max_var, num_bits=num_bits, narrow_range=narrow_range)
else:
assert min_var.get_shape() == [] # pylint: disable=g-explicit-bool-comparison
assert max_var.get_shape() == [] # pylint: disable=g-explicit-bool-comparison
return array_ops.fake_quant_with_min_max_vars(
inputs, min_var, max_var, num_bits=num_bits, narrow_range=narrow_range)
def _FakeQuantWithMinMaxVars(inputs, min_var, max_var, per_channel, num_bits,
narrow_range):
"""Adds a fake quantization operation.
Depending on value of per_channel, this operation may do global quantization
or per channel quantization. min_var and max_var should have corresponding
shapes: [1] when per_channel == False and [d] when per_channel == True.
Args:
inputs: a tensor containing values to be quantized.
min_var: a variable containing quantization range lower end(s).
max_var: a variable containing quantization range upper end(s).
per_channel: a boolean specifying whether to use per-channel quantization.
num_bits: Number of bits to use for quantization, must be between 2 and 8.
narrow_range: Whether to use the narrow quantization range
[1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
Returns:
a tensor containing quantized values.
"""
if per_channel:
assert len(min_var.get_shape()) == 1
assert len(max_var.get_shape()) == 1
return array_ops.fake_quant_with_min_max_vars_per_channel(
inputs, min_var, max_var, num_bits=num_bits, narrow_range=narrow_range)
else:
assert min_var.get_shape() == [] # pylint: disable=g-explicit-bool-comparison
assert max_var.get_shape() == [] # pylint: disable=g-explicit-bool-comparison
return array_ops.fake_quant_with_min_max_vars(
inputs, min_var, max_var, num_bits=num_bits, narrow_range=narrow_range)
def test_invalid_inputs(self):
inputs = constant_op.constant(value=[[1.0], [2.0], [4.0]],
dtype=dtypes.float32)
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
"must be rank 0"):
self.evaluate(
array_ops.fake_quant_with_min_max_vars(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(inputs=inputs,
min=[[1.0], [2.0],
[4.0]],
max=1.0))
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)
expected = np.array([
expected_nudged_input_min, expected_nudged_input_min,
expected_nudged_input_min, expected_nudged_input_min,
expected_nudged_input_min + expected_step,
expected_nudged_input_min + expected_step,
expected_nudged_input_min + expected_step,
expected_nudged_input_max, expected_nudged_input_max,
expected_nudged_input_max, expected_nudged_input_max
],
dtype=np.float32)
with self.test_session() as session:
with self.test_scope():
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(
input_placeholder,
min_placeholder,
max_placeholder,
num_bits=num_bits,
narrow_range=narrow_range)
result = session.run(
outputs, {
input_placeholder: inputs,
min_placeholder: input_min,
max_placeholder: input_max
})
self.assertAllCloseAccordingToType(result,
expected,
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)
expected = np.array(
[
expected_nudged_input_min, expected_nudged_input_min,
expected_nudged_input_min, expected_nudged_input_min,
expected_nudged_input_min + expected_step,
expected_nudged_input_min + expected_step,
expected_nudged_input_min + expected_step,
expected_nudged_input_max, expected_nudged_input_max,
expected_nudged_input_max, expected_nudged_input_max
],
dtype=np.float32)
with self.cached_session() as session:
with self.test_scope():
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(
input_placeholder,
min_placeholder,
max_placeholder,
num_bits=num_bits,
narrow_range=narrow_range)
result = session.run(
outputs, {
input_placeholder: inputs,
min_placeholder: input_min,
max_placeholder: input_max
})
self.assertAllCloseAccordingToType(
result, expected, rtol=1e-3, atol=1e-5, bfloat16_rtol=0.03)
