未验证 提交 32d2ec39 编写于 作者: J Jason 提交者: GitHub

Merge pull request #413 from PaddlePaddle/develop_0925

update tensorflow module
...@@ -3,15 +3,3 @@ __version__ = "0.8.5" ...@@ -3,15 +3,3 @@ __version__ = "0.8.5"
from .core.program import PaddleGraph from .core.program import PaddleGraph
program = PaddleGraph() program = PaddleGraph()
name_counter = dict()
def gen_name(op_name, var_name):
name = "{}.{}".format(op_name, var_name)
if name not in name_counter:
name_counter[name] = 0
else:
name_counter[name] += 1
name = name + "." + str(name_counter[name])
return name
...@@ -98,7 +98,7 @@ def arg_parser(): ...@@ -98,7 +98,7 @@ def arg_parser():
def tf2paddle(model_path, def tf2paddle(model_path,
save_dir, save_dir,
without_data_format_optimization, without_data_format_optimization=False,
define_input_shape=False, define_input_shape=False,
params_merge=False): params_merge=False):
# check tensorflow installation and version # check tensorflow installation and version
...@@ -117,37 +117,24 @@ def tf2paddle(model_path, ...@@ -117,37 +117,24 @@ def tf2paddle(model_path,
"[ERROR] Tensorflow is not installed, use \"pip install tensorflow\"." "[ERROR] Tensorflow is not installed, use \"pip install tensorflow\"."
) )
return return
from x2paddle import program
from x2paddle.decoder.tf_decoder import TFDecoder from x2paddle.decoder.tf_decoder import TFDecoder
from x2paddle.op_mapper.tf_op_mapper import TFOpMapper from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
from x2paddle.op_mapper.tf_op_mapper_nhwc import TFOpMapperNHWC from x2paddle.optimizer.tensorflow.bias import BiasOpt
from x2paddle.optimizer.tf_optimizer import TFOptimizer from x2paddle.optimizer.tensorflow.transpose import TransposeOpt
from x2paddle.optimizer.tensorflow.batch_norm import BatchNormOpt
print("Now translating model from tensorflow to paddle.") print("Now translating model from tensorflow to paddle.")
model = TFDecoder(model_path, define_input_shape=define_input_shape) model = TFDecoder(model_path, define_input_shape=define_input_shape)
if not without_data_format_optimization:
mapper = TFOpMapper(model) mapper = TFOpMapper(model)
optimizer = TFOptimizer(mapper) program.build()
# neccesary optimization bias_opt = BiasOpt()
optimizer.delete_redundance_code() transpose_opt = TransposeOpt()
# optimizer below is experimental batch_norm_opt = BatchNormOpt()
optimizer.optimize_elementwise_op() bias_opt.run(program)
optimizer.merge_activation() batch_norm_opt.run(program)
optimizer.merge_bias() transpose_opt.run(program)
optimizer.optimize_sub_graph() program.gen_model(save_dir)
# optimizer.merge_batch_norm()
# optimizer.merge_prelu()
else:
mapper = TFOpMapperNHWC(model)
optimizer = TFOptimizer(mapper)
optimizer.delete_redundance_code()
optimizer.strip_graph()
optimizer.merge_activation()
optimizer.merge_bias()
optimizer.make_nchw_input_output()
optimizer.remove_transpose()
mapper.save_inference_model(save_dir, params_merge)
def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False): def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False):
......
...@@ -99,6 +99,53 @@ class PaddleGraph(object): ...@@ -99,6 +99,53 @@ class PaddleGraph(object):
self.layers[layer_id] = layer self.layers[layer_id] = layer
return layer_id return layer_id
def del_layer(self, layer_id):
layer = self.layers[layer_id]
outputs = self.edges_out.get(layer_id, [])
inputs = self.edges_in.get(layer_id, [])
assert len(
inputs) <= 1, "There should be 0 or 1 input for deleted layer."
if len(inputs) == 0:
for out in outputs:
while layer_id in self.edges_in[out]:
index = self.edges_in[out].index(layer_id)
del self.edges_in[out][index]
input_keys = list(self.layers[out].inputs.keys())
for k in input_keys:
if self.layers[out].inputs[k] == layer.outputs[0]:
del self.layers[out].inputs[k]
del self.layers[layer_id]
if layer_id in self.edges_in:
del self.edges_in[layer_id]
if layer_id in self.edges_out:
del self.edges_out[layer_id]
return
# 将所有输出layer的输入layer进行替换
for out in outputs:
for i in range(len(self.edges_in[out])):
if self.edges_in[out][i] == layer_id:
self.edges_in[out][i] = inputs[0]
# 将输出layer赋给输入layer的输出
replace_index = self.edges_out[inputs[0]].index(layer_id)
del self.edges_out[inputs[0]][replace_index]
for i, out in enumerate(outputs):
self.edges_out[inputs[0]].insert(replace_index + i, out)
for k, v in self.layers[out].inputs.items():
if v == layer.outputs[0]:
self.layers[out].inputs[k] = list(layer.inputs.values())[0]
del self.layers[layer_id]
if layer_id in self.edges_out:
del self.edges_out[layer_id]
if layer_id in self.edges_in:
del self.edges_in[layer_id]
def build(self, inputs=None, outputs=None): def build(self, inputs=None, outputs=None):
self.clear_edges() self.clear_edges()
outputs_from_nodes = dict() outputs_from_nodes = dict()
......
...@@ -89,6 +89,12 @@ class TFGraphNode(GraphNode): ...@@ -89,6 +89,12 @@ class TFGraphNode(GraphNode):
field = getattr(attr, attr.WhichOneof('value')) field = getattr(attr, attr.WhichOneof('value'))
return tensor_util.MakeNdarray(field) return tensor_util.MakeNdarray(field)
@property
def name(self):
if hasattr(self, 'index'):
return self.layer_name + "_p{}".format(self.index)
return self.layer_name
def get_attr(self, name): def get_attr(self, name):
if name not in self.layer.attr: if name not in self.layer.attr:
return None return None
......
此差异已折叠。
此差异已折叠。
import copy
from collections import OrderedDict
from x2paddle.core.program import PaddleLayer
class BatchNormOpt:
def __init__(self):
pass
def run(self, graph):
layers = copy.deepcopy(graph.layers)
for layer_id, layer in layers.items():
if layer.kernel != "fluid.layers.elementwise_add":
continue
axis = layer.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
input_ids0 = graph.edges_in[layer_id]
mul_layer0 = graph.layers[input_ids0[0]]
sub_layer0 = graph.layers[input_ids0[1]]
if mul_layer0.kernel != "fluid.layers.elementwise_mul":
continue
if sub_layer0.kernel != "fluid.layers.elementwise_sub":
continue
axis = mul_layer0.attrs.get('axis', -1)
if axis != -1 and axis != 3:
continue
axis = sub_layer0.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids0[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids0[1], [])) != 1:
continue
input_ids1 = graph.edges_in[input_ids0[0]]
nhwc_input = graph.layers[input_ids1[0]]
mul_layer1 = graph.layers[input_ids1[1]]
if mul_layer1.kernel != "fluid.layers.elementwise_mul":
continue
axis = mul_layer1.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids1[1], [])) != 2:
continue
input_ids2 = graph.edges_in[input_ids0[1]]
beta = graph.layers[input_ids2[0]]
mul_layer2 = graph.layers[input_ids2[1]]
if beta.kernel != "fluid.layers.create_parameter":
continue
axis = mul_layer2.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids2[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids2[1], [])) != 1:
continue
if beta.outputs[0] not in graph.parameters:
continue
beta_shape = graph.parameters[beta.outputs[0]].shape
if len(beta_shape) != 1:
continue
input_ids3 = graph.edges_in[input_ids2[1]]
mean = graph.layers[input_ids3[0]]
mul_layer3 = graph.layers[input_ids3[1]]
if mean.kernel != "fluid.layers.create_parameter":
continue
axis = mul_layer3.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids3[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids3[1], [])) != 2:
continue
if mul_layer3.id != mul_layer1.id:
continue
if mean.outputs[0] not in graph.parameters:
continue
mean_shape = graph.parameters[mean.outputs[0]].shape
if mean_shape != beta_shape:
continue
input_ids4 = graph.edges_in[input_ids3[1]]
rsqrt_layer = graph.layers[input_ids4[0]]
gamma = graph.layers[input_ids4[1]]
if rsqrt_layer.kernel != "fluid.layers.rsqrt":
continue
if gamma.kernel != "fluid.layers.create_parameter":
continue
if len(graph.edges_out.get(input_ids4[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids4[1], [])) != 1:
continue
if gamma.outputs[0] not in graph.parameters:
continue
gamma_shape = graph.parameters[gamma.outputs[0]].shape
if gamma_shape != beta_shape:
continue
input_ids5 = graph.edges_in[input_ids4[0]]
add_layer = graph.layers[input_ids5[0]]
if add_layer.kernel != "fluid.layers.elementwise_add":
continue
axis = add_layer.attrs.get('axis', -1)
if axis != -1 and axis != 0:
continue
if len(graph.edges_out.get(input_ids5[0], [])) != 1:
continue
input_ids6 = graph.edges_in[input_ids5[0]]
variance = graph.layers[input_ids6[0]]
other = graph.layers[input_ids6[1]]
if variance.kernel != "fluid.layers.create_parameter":
continue
if other.kernel != "fluid.layers.create_parameter":
continue
if len(graph.edges_out.get(input_ids6[0], [])) != 1:
continue
if len(graph.edges_out.get(input_ids6[1], [])) != 1:
continue
if variance.outputs[0] not in graph.parameters:
continue
variance_shape = graph.parameters[variance.outputs[0]].shape
if variance_shape != beta_shape:
continue
if other.outputs[0] not in graph.parameters:
continue
if graph.parameters[other.outputs[0]].size != 1:
continue
ids = set([
layer_id, mul_layer0.id, sub_layer0.id, mul_layer1.id, beta.id,
mul_layer2.id, mean.id, mul_layer2.id, rsqrt_layer.id, gamma.id,
add_layer.id, variance.id, other.id
])
for id in ids:
del graph.layers[id]
if id in graph.edges_in:
del graph.edges_in[id]
if id in graph.edges_out:
del graph.edges_out[id]
copy_layers = copy.deepcopy(graph.layers)
graph.layers = OrderedDict()
for k, v in copy_layers.items():
if k != nhwc_input.id:
graph.layers[k] = v
continue
graph.layers[k] = v
transpose0 = PaddleLayer(
id='{}_1'.format(k),
kernel="fluid.layers.transpose",
inputs={"x": v.outputs[0]},
outputs=["transpose_for_bn"],
perm=[0, 3, 1, 2])
bn = PaddleLayer(
id='{}_2'.format(k),
kernel="fluid.layers.batch_norm",
inputs={"input": "transpose_for_bn"},
outputs=layer.outputs,
epsilon=graph.parameters[other.outputs[0]],
param_attr="'{}'".format(gamma.outputs[0]),
bias_attr="'{}'".format(beta.outputs[0]),
moving_mean_name="'{}'".format(mean.outputs[0]),
moving_variance_name="'{}'".format(variance.outputs[0]))
transpose1 = PaddleLayer(
id=layer_id,
kernel="fluid.layers.transpose",
inputs={"x": layer.outputs[0]},
outputs=layer.outputs,
perm=[0, 2, 3, 1])
graph.layers[transpose0.id] = transpose0
graph.layers[bn.id] = bn
graph.layers[transpose1.id] = transpose1
graph.build()
import copy
class BiasOpt:
def __init__(self):
self.conv_layers = [
'fluid.layers.conv2d', 'fluid.layers.conv2d_transpose'
]
self.act_layers = [
'fluid.layers.relu', 'fluid.layers.relu6', 'fluid.layers.sigmoid',
'fluid.layers.exp', 'fluid.layers.tanh', 'fluid.layers.softplus',
'fluid.layers.leaky_relu'
]
def run(self, graph):
layers = copy.deepcopy(graph.layers)
for layer_id, layer in layers.items():
if layer.kernel in self.conv_layers or layer.kernel == "fluid.layers.transpose":
if len(graph.edges_out.get(layer_id, [])) > 1:
continue
if layer.outputs[0] in graph.outputs:
continue
out_layer_id = graph.edges_out[layer_id][0]
if graph.layers[
out_layer_id].kernel != "fluid.layers.elementwise_add":
continue
if graph.layers[out_layer_id].attrs.get('axis', -1) != -1:
continue
in_layer_id = graph.edges_in[out_layer_id]
bias_layer_id = in_layer_id[1 - in_layer_id.index(layer_id)]
if graph.layers[
bias_layer_id].kernel != "fluid.layers.create_parameter":
continue
bias_layer = graph.layers[bias_layer_id]
if len(bias_layer.attrs['shape']) != 1:
continue
if len(graph.edges_out[bias_layer_id]) != 1:
continue
if layer.kernel == "fluid.layers.transpose":
if layer.attrs['perm'] != [0, 2, 3, 1]:
continue
in_layer_id = graph.edges_in[layer_id][0]
if graph.layers[in_layer_id].kernel not in self.conv_layers:
continue
if graph.layers[in_layer_id].attrs['bias_attr'] != False:
continue
if graph.layers[in_layer_id].outputs[0] in graph.outputs:
continue
if len(graph.edges_out[in_layer_id]) != 1:
continue
graph.layers[in_layer_id].attrs[
'bias_attr'] = bias_layer.attrs['name']
else:
graph.layers[layer_id].attrs[
'bias_attr'] = bias_layer.attrs['name']
bias_add_outs = graph.edges_out.get(out_layer_id, [])
bias_add_output = graph.layers[out_layer_id].outputs[0]
graph.del_layer(bias_layer_id)
graph.del_layer(out_layer_id)
for out in bias_add_outs:
for k, v in graph.layers[out].inputs.items():
if v == layer.outputs[0]:
graph.layers[out].inputs[k] = bias_add_output
graph.layers[layer_id].outputs[0] = bias_add_output
if layer.kernel == "fluid.layers.transpose":
in_layer_id = graph.edges_in[layer_id][0]
graph.layers[in_layer_id].outputs[0] = bias_add_output
graph.layers[layer_id].inputs['x'] = bias_add_output
import copy
import sys
class TransposeOpt:
def __init__(self):
self.image_layers = [
'fluid.layers.conv2d', 'fluid.layers.batch_norm',
'fluid.layers.conv2d_transpose', 'fluid.layers.resize_nearest',
'fluid.layers.resize_bilinear', 'fluid.layers.pool2d',
'fluid.layers.pad2d'
]
self.direct_layers = [
'fluid.layers.relu', 'fluid.layers.relu6', 'fluid.layers.abs',
'fluid.layers.sigmoid', 'fluid.layers.exp', 'fluid.layers.rsqrt',
'fluid.layers.swish_f32', 'fluid.layers.tanh',
'fluid.layers.softplus', 'fluid.layers.leaky_relu',
'fluid.layers.floor', 'fluid.layers.erf', 'fluid.layers.swish'
]
self.elementwise_layers = [
'fluid.layers.elementwise_add', 'fluid.layers.elementwise_sub',
'fluid.layers.elementwise_mul', 'fluid.layers.elementwise_div'
]
# self.reduce_layers = []
self.reduce_layers = [
'fluid.layers.reduce_mean', 'fluid.layers.reduce_all',
'fluid.layers.reduce_max', 'fluid.layers.reduce_any',
'fluid.layers.reduce_sum', 'fluid.layers.reduce_prod'
]
def get_transpose_num(self, graph):
count = 0
for layer_id, layer in graph.layers.items():
if layer.kernel == "fluid.layers.transpose":
count += 1
return count
def run(self, graph):
total_layer_num = len(graph.layers)
scanned_layers = set()
optimized_transpose_layers = list()
optimized_reduce_layers = list()
optimized_concat_layers = list()
optimized_elementwise_layers = list()
def strip_transpose(_graph):
layers = copy.deepcopy(_graph.layers)
for layer_id, layer in layers.items():
if layer_id in scanned_layers:
continue
scanned_layers.add(layer_id)
percent = round(len(scanned_layers) / total_layer_num * 100, 2)
sys.stderr.write("\rOptimize Transpose Layers...{}%".format(
percent))
if layer.kernel != "fluid.layers.transpose":
continue
if layer.attrs["perm"] != [0, 2, 3, 1]:
continue
transpose_layers = list()
propagate_layers = list()
reduce_layers = list()
concat_layers = list()
# 此elementwise_layers专用于存储shape(4) + shape(1)的形式layer
elementwise_layers = list()
can_be_optimized = True
for out in _graph.edges_out.get(layer_id, []):
if _graph.layers[out].kernel == "fluid.layers.transpose":
if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
can_be_optimized = False
break
transpose_layers.append(out)
elif _graph.layers[out].kernel in self.elementwise_layers:
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.direct_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.reduce_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if not _graph.layers[out].attrs.get('keep_dim', False):
can_be_optimized = False
break
propagate_layers.append(out)
reduce_layers.append(out)
elif _graph.layers[out].kernel == "fluid.layers.concat":
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
propagate_layers.append(out)
concat_layers.append(out)
else:
can_be_optimized = False
break
visited_layers = set()
while len(propagate_layers) > 0 and can_be_optimized:
current_id = propagate_layers.pop(0)
visited_layers.add(current_id)
for out in _graph.edges_out.get(current_id, []):
if _graph.layers[
out].kernel == "fluid.layers.transpose":
if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
can_be_optimized = False
break
transpose_layers.append(out)
elif _graph.layers[
out].kernel in self.elementwise_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.direct_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
elif _graph.layers[out].kernel in self.reduce_layers:
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if not _graph.layers[out].attrs.get('keep_dim',
False):
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
reduce_layers.append(out)
elif _graph.layers[out].kernel == "fluid.layers.concat":
if _graph.layers[out].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if out not in visited_layers:
propagate_layers.append(out)
concat_layers.append(out)
else:
can_be_optimized = False
break
for ipt in _graph.edges_in.get(current_id, []):
if _graph.layers[
current_id].kernel in self.elementwise_layers:
try:
x_shape = _graph.layers[
current_id].input_shapes['x']
y_shape = _graph.layers[
current_id].input_shapes['y']
if _graph.layers[ipt].outputs[
0] == _graph.layers[current_id].inputs[
'x']:
if len(x_shape) <= 1:
elementwise_layers.append(current_id)
continue
elif _graph.layers[ipt].outputs[
0] == _graph.layers[current_id].inputs[
'y']:
if len(y_shape) <= 1:
elementwise_layers.append(current_id)
continue
else:
raise Exception(
"Unexcepted situation happend while optimizing transpose"
)
except Exception as e:
can_be_optimized = False
break
if _graph.layers[
ipt].kernel == "fluid.layers.transpose":
if _graph.layers[ipt].attrs["perm"] != [0, 2, 3, 1]:
can_be_optimized = False
break
if ipt not in visited_layers:
transpose_layers.append(ipt)
elif _graph.layers[
ipt].kernel in self.elementwise_layers:
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
elif _graph.layers[ipt].kernel in self.direct_layers:
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
elif _graph.layers[ipt].kernel in self.reduce_layers:
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if not _graph.layers[ipt].attrs.get('keep_dim',
False):
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
reduce_layers.append(ipt)
elif _graph.layers[ipt].kernel == "fluid.layers.concat":
if _graph.layers[ipt].outputs[0] in _graph.outputs:
can_be_optimized = False
break
if ipt not in visited_layers:
propagate_layers.append(ipt)
concat_layers.append(ipt)
else:
can_be_optimized = False
break
if not can_be_optimized:
break
if not can_be_optimized:
continue
transpose_layers.append(layer_id)
transpose_layers = list(set(transpose_layers))
for l in transpose_layers:
if graph.layers[l].outputs[0] in graph.outputs:
can_be_optimized = False
break
if not can_be_optimized:
continue
for l in transpose_layers:
_graph.del_layer(l)
optimized_transpose_layers.extend(transpose_layers)
optimized_reduce_layers.extend(reduce_layers)
optimized_concat_layers.extend(concat_layers)
optimized_elementwise_layers.extend(elementwise_layers)
return True
return False
before_transpose_num = self.get_transpose_num(graph)
opt_graph = copy.deepcopy(graph)
total_layer_num = len(opt_graph.layers)
while strip_transpose(opt_graph):
pass
for layer_id in list(set(optimized_transpose_layers)):
graph.del_layer(layer_id)
for layer_id in list(set(optimized_reduce_layers)):
dim = graph.layers[layer_id].attrs.get('dim', None)
if dim is not None:
for i in range(len(dim)):
dim[i] = [0, 2, 3, 1][dim[i]]
graph.layers[layer_id].attrs['dim'] = dim
for layer_id in list(set(optimized_concat_layers)):
axis = graph.layers[layer_id].attrs.get('axis', 0)
graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis]
for layer_id in list(set(optimized_elementwise_layers)):
axis = graph.layers[layer_id].attrs.get('axis', -1)
graph.layers[layer_id].attrs['axis'] = [0, 2, 3, 1][axis]
current_transpose_num = self.get_transpose_num(graph)
print(
"\nTranspose layers optimized, before: transpose_num={}, after: transpose_num={}".
format(before_transpose_num, current_transpose_num))
此差异已折叠。
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