提交 f45818e7 编写于 作者: L Luo Tao

create new varible in scope

上级 6e735e1e
......@@ -67,6 +67,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + [
'clip',
'SimpleDistributeTranspiler',
'DistributeTranspiler',
'InferenceTranspiler',
'memory_optimize',
'release_memory',
'profiler',
......
......@@ -21,7 +21,20 @@ from . import core
class InferenceTranspiler:
def transpile(self, program, scope, place):
'''
Transpile the program to a inference program by fused batch normalization.
Transpile the program. Support only fuse batch normalization now.
:param program: program to transpile
:type program: Program
:param scope: inference scope
:type scope: Scope
:param place: inference place
:type place: Place
'''
self.fuse_batch_norm(program, scope, place)
def fuse_batch_norm(self, program, scope, place):
'''
Transpile the program by fused batch normalization.
The batch normalization followed the convolution or fully connected layer
can be integrated with them. Doing so will give us a forward acceleration,
......@@ -57,8 +70,6 @@ class InferenceTranspiler:
:type scope: Scope
:param place: inference place
:type place: Place
:return: program by fused batch normalization
:rtype: Program
'''
self.scope = scope
self.place = place
......@@ -96,7 +107,7 @@ class InferenceTranspiler:
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
return program.clone()
program = program.clone()
# ====================== private transpiler functions =====================
def _insert_bias_op(self, index, current_op, bn_op):
......@@ -142,11 +153,25 @@ class InferenceTranspiler:
:type with_bias: Int
'''
def _load_tensor(param_name):
return self.scope.find_var(param_name[0]).get_tensor()
def _update_param(op, old_param_name, new_param):
# For the sake of remaining the original variables the same as before,
# create new variables in scope to store the new parameters.
old_param_name = old_param_name[0]
old_var = self.block.vars[old_param_name]
new_param_name = old_param_name + '_fuse_bn'
new_var = self.block.create_parameter(
name=new_param_name.encode('ascii'),
type=old_var.type,
dtype=old_var.dtype,
shape=old_var.shape)
op.rename_input(old_param_name, new_param_name)
self.scope.var(new_param_name)
tensor = self.scope.find_var(new_param_name).get_tensor()
tensor.set(np.array(new_param), self.place)
def _load_param(param_name):
return np.array(_load_tensor(param_name))
return np.array(self.scope.find_var(param_name[0]).get_tensor())
bias_bn = _load_param(bn_op.input("Bias")) #Bias
scale_bn = _load_param(bn_op.input("Scale")) #Scale
......@@ -155,8 +180,6 @@ class InferenceTranspiler:
# TODO(luotao1): consider only conv2d now. fc would be delt later.
current_param = _load_param(current_op.input("Filter"))
current_tensor = _load_tensor(current_op.input("Filter"))
std_bn = np.float32(np.sqrt(np.add(var_bn, 1e-5)))
tmp = np.float32(np.divide(scale_bn, std_bn))
......@@ -167,8 +190,6 @@ class InferenceTranspiler:
bias = np.zeros(bias_bn.shape)
bias = np.float32(
np.add(np.multiply(np.subtract(bias, mean_bn), tmp), bias_bn))
bias_tensor = _load_tensor(bias_op.input("Y"))
bias_tensor.set(bias, self.place)
# re-compute weight of conv2d
tmp = tmp.reshape(tmp.shape[0], -1)
......@@ -176,8 +197,9 @@ class InferenceTranspiler:
dst_param = np.float32(np.multiply(dst_param, tmp))
dst_param = dst_param.reshape(current_param.shape)
# set the updated parameters
current_tensor.set(np.array(dst_param), self.place)
# update parameters
_update_param(current_op, current_op.input("Filter"), dst_param)
_update_param(bias_op, bias_op.input("Y"), bias)
# collect the renamed input
self.input_map[bn_op.output("Y")[0]] = bias_op.output("Out")[0]
......
......@@ -226,16 +226,17 @@ def infer(use_cuda, save_dirname=None):
batch_size = 1
tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
# Use inference_transpiler to speedup
inference_transpiler_program = inference_program.clone()
t = fluid.InferenceTranspiler()
t.transpile(inference_transpiler_program, inference_scope, place)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
# Use inference_transpiler to speedup
t = fluid.InferenceTranspiler()
inference_transpiler_program = t.transpile(inference_program,
inference_scope, place)
transpiler_results = exe.run(inference_transpiler_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
......
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