Python tensorflow.python.ops.array_ops.quantize_v2() Examples
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code examples of tensorflow.python.ops.array_ops.quantize_v2().
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Example #1
| Source File: quantize_graph.py From tensorflow-for-poets-2 with Apache License 2.0 | 4 votes |
|
def quantize_weight_eightbit(input_node, quantization_mode):
"""Returns replacement nodes for input_node using the Dequantize op."""
base_name = input_node.name + "_"
quint8_const_name = base_name + "quint8_const"
min_name = base_name + "min"
max_name = base_name + "max"
float_tensor = tensor_util.MakeNdarray(input_node.attr["value"].tensor)
min_value = np.min(float_tensor.flatten())
max_value = np.max(float_tensor.flatten())
# Make sure that the range includes zero.
if min_value > 0.0:
min_value = 0.0
# min_value == max_value is a tricky case. It can occur for general
# tensors, and of course for scalars. The quantized ops cannot deal
# with this case, so we set max_value to something else.
# It's a tricky question what is the numerically best solution to
# deal with this degeneracy.
# TODO(petewarden): Better use a tolerance than a hard comparison?
if min_value == max_value:
if abs(min_value) < 0.000001:
max_value = min_value + 1.0
elif min_value > 0:
max_value = 2 * min_value
else:
max_value = min_value / 2.0
sess = session.Session()
with sess.as_default():
quantize_op = array_ops.quantize_v2(
float_tensor,
min_value,
max_value,
dtypes.quint8,
mode=quantization_mode)
quint8_tensor = quantize_op[0].eval()
shape = tensor_util.TensorShapeProtoToList(input_node.attr["value"]
.tensor.tensor_shape)
quint8_const_node = create_constant_node(
quint8_const_name, quint8_tensor, dtypes.quint8, shape=shape)
min_node = create_constant_node(min_name, min_value, dtypes.float32)
max_node = create_constant_node(max_name, max_value, dtypes.float32)
dequantize_node = create_node("Dequantize", input_node.name,
[quint8_const_name, min_name, max_name])
set_attr_dtype(dequantize_node, "T", dtypes.quint8)
set_attr_string(dequantize_node, "mode", quantization_mode)
return [quint8_const_node, min_node, max_node, dequantize_node]
Example #2
| Source File: quantize_graph.py From MobileNet with Apache License 2.0 | 4 votes |
|
def quantize_weight_eightbit(input_node, quantization_mode):
"""Returns replacement nodes for input_node using the Dequantize op."""
base_name = input_node.name + "_"
quint8_const_name = base_name + "quint8_const"
min_name = base_name + "min"
max_name = base_name + "max"
float_tensor = tensor_util.MakeNdarray(input_node.attr["value"].tensor)
min_value = np.min(float_tensor.flatten())
max_value = np.max(float_tensor.flatten())
# Make sure that the range includes zero.
if min_value > 0.0:
min_value = 0.0
# min_value == max_value is a tricky case. It can occur for general
# tensors, and of course for scalars. The quantized ops cannot deal
# with this case, so we set max_value to something else.
# It's a tricky question what is the numerically best solution to
# deal with this degeneracy.
# TODO(petewarden): Better use a tolerance than a hard comparison?
if min_value == max_value:
if abs(min_value) < 0.000001:
max_value = min_value + 1.0
elif min_value > 0:
max_value = 2 * min_value
else:
max_value = min_value / 2.0
sess = session.Session()
with sess.as_default():
quantize_op = array_ops.quantize_v2(
float_tensor,
min_value,
max_value,
dtypes.quint8,
mode=quantization_mode)
quint8_tensor = quantize_op[0].eval()
shape = tensor_util.TensorShapeProtoToList(input_node.attr["value"]
.tensor.tensor_shape)
quint8_const_node = create_constant_node(
quint8_const_name, quint8_tensor, dtypes.quint8, shape=shape)
min_node = create_constant_node(min_name, min_value, dtypes.float32)
max_node = create_constant_node(max_name, max_value, dtypes.float32)
dequantize_node = create_node("Dequantize", input_node.name,
[quint8_const_name, min_name, max_name])
set_attr_dtype(dequantize_node, "T", dtypes.quint8)
set_attr_string(dequantize_node, "mode", quantization_mode)
return [quint8_const_node, min_node, max_node, dequantize_node]
Example #3
| Source File: quantize_graph.py From sketch-to-react-native with MIT License | 4 votes |
|
def quantize_weight_eightbit(input_node, quantization_mode):
"""Returns replacement nodes for input_node using the Dequantize op."""
base_name = input_node.name + "_"
quint8_const_name = base_name + "quint8_const"
min_name = base_name + "min"
max_name = base_name + "max"
float_tensor = tensor_util.MakeNdarray(input_node.attr["value"].tensor)
min_value = np.min(float_tensor.flatten())
max_value = np.max(float_tensor.flatten())
# Make sure that the range includes zero.
if min_value > 0.0:
min_value = 0.0
# min_value == max_value is a tricky case. It can occur for general
# tensors, and of course for scalars. The quantized ops cannot deal
# with this case, so we set max_value to something else.
# It's a tricky question what is the numerically best solution to
# deal with this degeneracy.
# TODO(petewarden): Better use a tolerance than a hard comparison?
if min_value == max_value:
if abs(min_value) < 0.000001:
max_value = min_value + 1.0
elif min_value > 0:
max_value = 2 * min_value
else:
max_value = min_value / 2.0
sess = session.Session()
with sess.as_default():
quantize_op = array_ops.quantize_v2(
float_tensor,
min_value,
max_value,
dtypes.quint8,
mode=quantization_mode)
quint8_tensor = quantize_op[0].eval()
shape = tensor_util.TensorShapeProtoToList(input_node.attr["value"]
.tensor.tensor_shape)
quint8_const_node = create_constant_node(
quint8_const_name, quint8_tensor, dtypes.quint8, shape=shape)
min_node = create_constant_node(min_name, min_value, dtypes.float32)
max_node = create_constant_node(max_name, max_value, dtypes.float32)
dequantize_node = create_node("Dequantize", input_node.name,
[quint8_const_name, min_name, max_name])
set_attr_dtype(dequantize_node, "T", dtypes.quint8)
set_attr_string(dequantize_node, "mode", quantization_mode)
return [quint8_const_node, min_node, max_node, dequantize_node]
Example #4
| Source File: quantize_graph.py From pokemon-mini with Apache License 2.0 | 4 votes |
|
def quantize_weight_eightbit(input_node, quantization_mode):
"""Returns replacement nodes for input_node using the Dequantize op."""
base_name = input_node.name + "_"
quint8_const_name = base_name + "quint8_const"
min_name = base_name + "min"
max_name = base_name + "max"
float_tensor = tensor_util.MakeNdarray(input_node.attr["value"].tensor)
min_value = np.min(float_tensor.flatten())
max_value = np.max(float_tensor.flatten())
# Make sure that the range includes zero.
if min_value > 0.0:
min_value = 0.0
# min_value == max_value is a tricky case. It can occur for general
# tensors, and of course for scalars. The quantized ops cannot deal
# with this case, so we set max_value to something else.
# It's a tricky question what is the numerically best solution to
# deal with this degeneracy.
# TODO(petewarden): Better use a tolerance than a hard comparison?
if min_value == max_value:
if abs(min_value) < 0.000001:
max_value = min_value + 1.0
elif min_value > 0:
max_value = 2 * min_value
else:
max_value = min_value / 2.0
sess = session.Session()
with sess.as_default():
quantize_op = array_ops.quantize_v2(
float_tensor,
min_value,
max_value,
dtypes.quint8,
mode=quantization_mode)
quint8_tensor = quantize_op[0].eval()
shape = tensor_util.TensorShapeProtoToList(input_node.attr["value"]
.tensor.tensor_shape)
quint8_const_node = create_constant_node(
quint8_const_name, quint8_tensor, dtypes.quint8, shape=shape)
min_node = create_constant_node(min_name, min_value, dtypes.float32)
max_node = create_constant_node(max_name, max_value, dtypes.float32)
dequantize_node = create_node("Dequantize", input_node.name,
[quint8_const_name, min_name, max_name])
set_attr_dtype(dequantize_node, "T", dtypes.quint8)
set_attr_string(dequantize_node, "mode", quantization_mode)
return [quint8_const_node, min_node, max_node, dequantize_node]
Example #5
| Source File: quantize_graph.py From AudioNet with MIT License | 4 votes |
|
def quantize_weight_eightbit(input_node, quantization_mode):
"""Returns replacement nodes for input_node using the Dequantize op."""
base_name = input_node.name + "_"
quint8_const_name = base_name + "quint8_const"
min_name = base_name + "min"
max_name = base_name + "max"
float_tensor = tensor_util.MakeNdarray(input_node.attr["value"].tensor)
min_value = np.min(float_tensor.flatten())
max_value = np.max(float_tensor.flatten())
# Make sure that the range includes zero.
if min_value > 0.0:
min_value = 0.0
# min_value == max_value is a tricky case. It can occur for general
# tensors, and of course for scalars. The quantized ops cannot deal
# with this case, so we set max_value to something else.
# It's a tricky question what is the numerically best solution to
# deal with this degeneracy.
# TODO(petewarden): Better use a tolerance than a hard comparison?
if min_value == max_value:
if abs(min_value) < 0.000001:
max_value = min_value + 1.0
elif min_value > 0:
max_value = 2 * min_value
else:
max_value = min_value / 2.0
sess = session.Session()
with sess.as_default():
quantize_op = array_ops.quantize_v2(
float_tensor,
min_value,
max_value,
dtypes.quint8,
mode=quantization_mode)
quint8_tensor = quantize_op[0].eval()
shape = tensor_util.TensorShapeProtoToList(input_node.attr["value"]
.tensor.tensor_shape)
quint8_const_node = create_constant_node(
quint8_const_name, quint8_tensor, dtypes.quint8, shape=shape)
min_node = create_constant_node(min_name, min_value, dtypes.float32)
max_node = create_constant_node(max_name, max_value, dtypes.float32)
dequantize_node = create_node("Dequantize", input_node.name,
[quint8_const_name, min_name, max_name])
set_attr_dtype(dequantize_node, "T", dtypes.quint8)
set_attr_string(dequantize_node, "mode", quantization_mode)
return [quint8_const_node, min_node, max_node, dequantize_node]
