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Remove save_quantized_model in ImperativeQuantAware. #27240

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Merged
juncaipeng merged 1 commit into from
Sep 18, 2020
Merged

Remove save_quantized_model in ImperativeQuantAware. #27240

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83 changes: 7 additions & 76 deletions python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ def __init__(self,
Examples:
.. code-block:: python

import paddle
from paddle.fluid.contrib.slim.quantization \
import ImperativeQuantAware
from paddle.vision.models \
Expand All @@ -86,13 +87,12 @@ def __init__(self,
# ...

# 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
Expand Down Expand Up @@ -148,75 +148,6 @@ def quantize(self, model):
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
Expand Down
36 changes: 20 additions & 16 deletions python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,7 +221,7 @@ def test_qat_save(self):
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')
Expand All @@ -231,21 +231,25 @@ def test_qat_save(self):

# 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:
place = core.CPUPlace()
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)
Expand Down Expand Up @@ -332,13 +336,13 @@ def _build_static_lenet(main, startup, is_test=False, seed=1000):
if batch_id % 100 == 0:
_logger.info('{}: {}'.format('loss', avg_loss.numpy()))

imperative_qat.save_quantized_model(
dirname="./dynamic_mnist",
model=lenet,
input_shape=[(1, 28, 28)],
input_dtype=['float32'],
feed=[0],
fetch=[0])
paddle.jit.save(
layer=lenet,
model_path="./dynamic_mnist",
input_spec=[
paddle.static.InputSpec(
shape=[None, 1, 28, 28], dtype='float32')
])

# static graph train
_logger.info(
Expand Down