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体验新版 GitCode,发现更多精彩内容 >>
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d28162b9
编写于
9月 18, 2020
作者:
Z
Zhen Wang
提交者:
GitHub
9月 18, 2020
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差异文件
Remove save_quantized_model in ImperativeQuantAware. (#27240)
上级
b6a4349d
变更
2
隐藏空白更改
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2 changed file
with
27 addition
and
92 deletion
+27
-92
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
.../paddle/fluid/contrib/slim/quantization/imperative/qat.py
+7
-76
python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
...on/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
+20
-16
未找到文件。
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
浏览文件 @
d28162b9
...
...
@@ -67,6 +67,7 @@ class ImperativeQuantAware(object):
Examples:
.. code-block:: python
import paddle
from paddle.fluid.contrib.slim.quantization
\
import ImperativeQuantAware
from paddle.vision.models
\
...
...
@@ -86,13 +87,12 @@ class ImperativeQuantAware(object):
# ...
# Save quant model for the inference.
imperative_qat.save_quantized_model(
dirname="./resnet50_qat",
model=model,
input_shape=[(3, 224, 224)],
input_dtype=['float32'],
feed=[0],
fetch=[0])
paddle.jit.save(
layer=model,
model_path="./resnet50_qat",
input_spec=[
paddle.static.InputSpec(
shape=[None, 3, 224, 224], dtype='float32')])
"""
super
(
ImperativeQuantAware
,
self
).
__init__
()
self
.
_weight_bits
=
weight_bits
...
...
@@ -148,75 +148,6 @@ class ImperativeQuantAware(object):
quant_layer
=
self
.
_get_quantized_counterpart
(
layer
)
setattr
(
obj
,
target
,
quant_layer
)
def
save_quantized_model
(
self
,
dirname
,
model
,
input_shape
,
input_dtype
,
feed
,
fetch
,
append_batch_size
=
True
):
"""
Save the quantized model for the inference.
Args:
dirname (str): the directory to save the quantized model.
model(fluid.dygraph.Layer): the quantized model to be saved.
input_shape(list[tuple(int)]): The shape value for each input,
e.g. [(3, 224, 224)].
input_dtype(list[str]): The dtype value for each input,
e.g. ['float32'].
feed(list[int]): the indices of the input variables of the
imperative functions which will be saved as input variables in
inference model.
fetch(list[int]): the indices of the returned variable of the
imperative functions which will be saved as output variables in
inference model.
append_batch_size(bool, optional):
If true, it prepends an extra axis to the input_shape, meanwhile,
the input_shape shouldn't contain the batch size dimension.
Otherwise, it just uses the input_shape. Default True.
Returns:
None
"""
assert
isinstance
(
input_shape
,
list
),
"The parameter `input_shape` shoubld be a list."
assert
isinstance
(
input_dtype
,
list
),
"The parameter `input_dtype` shoubld be a list."
assert
isinstance
(
feed
,
list
),
"The parameter `feed` shoubld be a list."
assert
isinstance
(
fetch
,
list
),
"The parameter `fetch` shoubld be a list."
assert
len
(
input_shape
)
==
len
(
input_dtype
),
"The length of input_shape should be equal to input_dtype's."
assert
len
(
input_dtype
)
==
len
(
feed
),
"The length of input_shape should be equal to feed's."
with
dygraph
.
guard
():
model
.
eval
()
input_vars
=
[]
for
i
,
(
shape
,
dtype
)
in
enumerate
(
zip
(
input_shape
,
input_dtype
)):
if
append_batch_size
:
shape
=
[
None
]
+
list
(
shape
)
# Note(Aurelius84): need a elegant way to name this.
in_spec
=
paddle
.
static
.
InputSpec
(
shape
,
dtype
,
'feed_%d'
%
i
)
input_vars
.
append
(
in_spec
)
# use `declarative` to convert dygraph into static program
model
.
forward
=
dygraph
.
jit
.
declarative
(
model
.
forward
,
input_spec
=
input_vars
)
outputs
=
model
.
forward
.
concrete_program
.
outputs
input_spec
=
[
input_vars
[
i
]
for
i
in
feed
]
configs
=
dygraph
.
jit
.
SaveLoadConfig
()
configs
.
separate_params
=
True
if
not
isinstance
(
outputs
,
(
tuple
,
list
)):
outputs
=
[
outputs
]
configs
.
output_spec
=
[
outputs
[
i
]
for
i
in
fetch
]
dygraph
.
jit
.
save
(
layer
=
model
,
model_path
=
dirname
,
input_spec
=
input_spec
,
configs
=
configs
)
def
_get_quantized_counterpart
(
self
,
layer
):
quant_layers
=
tuple
(
self
.
_quant_layers_map
.
values
())
quantized_counterpart
=
tuple
(
'Quantized'
+
k
...
...
python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
浏览文件 @
d28162b9
...
...
@@ -221,7 +221,7 @@ class TestImperativeQat(unittest.TestCase):
model_dict
=
lenet
.
state_dict
()
fluid
.
save_dygraph
(
model_dict
,
"save_temp"
)
# test the correctness of `
save_quantized_model
`
# test the correctness of `
paddle.jit.save
`
data
=
next
(
test_reader
())
test_data
=
np
.
array
([
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
...
...
@@ -231,13 +231,14 @@ class TestImperativeQat(unittest.TestCase):
# save inference quantized model
path
=
"./mnist_infer_model"
imperative_qat
.
save_quantized_model
(
dirname
=
path
,
model
=
lenet
,
input_shape
=
[(
1
,
28
,
28
)],
input_dtype
=
[
'float32'
],
feed
=
[
0
],
fetch
=
[
0
])
paddle
.
jit
.
save
(
layer
=
lenet
,
model_path
=
path
,
input_spec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
None
,
1
,
28
,
28
],
dtype
=
'float32'
)
])
if
core
.
is_compiled_with_cuda
():
place
=
core
.
CUDAPlace
(
0
)
else
:
...
...
@@ -245,7 +246,10 @@ class TestImperativeQat(unittest.TestCase):
exe
=
fluid
.
Executor
(
place
)
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
(
fluid
.
io
.
load_inference_model
(
dirname
=
path
,
executor
=
exe
))
dirname
=
path
,
executor
=
exe
,
model_filename
=
"__model__"
,
params_filename
=
"__variables__"
))
after_save
,
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
test_data
},
fetch_list
=
fetch_targets
)
...
...
@@ -332,13 +336,13 @@ class TestImperativeQat(unittest.TestCase):
if
batch_id
%
100
==
0
:
_logger
.
info
(
'{}: {}'
.
format
(
'loss'
,
avg_loss
.
numpy
()))
imperative_qat
.
save_quantized_model
(
dirname
=
"./dynamic_mnist"
,
model
=
lenet
,
input_s
hape
=
[(
1
,
28
,
28
)],
input_dtype
=
[
'float32'
],
feed
=
[
0
],
fetch
=
[
0
])
paddle
.
jit
.
save
(
layer
=
lenet
,
model
_path
=
"./dynamic_mnist"
,
input_s
pec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
None
,
1
,
28
,
28
],
dtype
=
'float32'
)
])
# static graph train
_logger
.
info
(
...
...
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