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X2Paddle
提交 6da21ebe 编写于 作者: J jiangjiajun
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temporay support

上级 23ba3b50
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Showing with 582 addition and 31 deletion
x2paddle/convert.py
浏览文件 @ 6da21ebe
......@@ -118,13 +118,21 @@ def tf2paddle(model_path,
from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
from x2paddle.op_mapper.tf_op_mapper_nhwc import TFOpMapperNHWC
from x2paddle.optimizer.tf_optimizer import TFOptimizer
from x2paddle.optimizer.transpose import TransposeOpt
from x2paddle.optimizer.bias import BiasOpt
print("Now translating model from tensorflow to paddle.")
model = TFDecoder(model_path, define_input_shape=define_input_shape)
mapper = TFOpMapperNHWC(model)
program.build()
opt = BiasOpt()
opt.run(program)
opt = TransposeOpt()
opt.run(program)
program.gen_model(save_dir)
program.visualize(save_dir)
def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False):
......
x2paddle/core/program.py
浏览文件 @ 6da21ebe
......@@ -15,8 +15,11 @@
from __future__ import print_function
from __future__ import division
import paddle.fluid as fluid
from paddle.fluid.initializer import Constant
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.proto import framework_pb2
from collections import OrderedDict
import copy
import numpy
import time
import collections
......@@ -57,6 +60,29 @@ class PaddleLayer(object):
block.father_layer = self
self.blocks.append(block)
def get_code(self, with_outputs=True):
code = ""
# if len(self.outputs) == 1:
# code = self.outputs[0]
# else:
# for output in self.outputs:
# code += "{}, ".format(output)
# code = code.strip(", ")
# code += " = "
code += "{}(".format(self.kernel)
for k, v in self.inputs.items():
if isinstance(v, list):
code += "{}=[{}], ".format(k, ", ".join(v))
else:
code += "{}={}, ".format(k, v)
for k, v in self.attrs.items():
code += "{}={}, ".format(k, v)
code = code.strip(", ")
code += ")"
return code
class PaddleProgram(object):
def __init__(self):
......@@ -80,10 +106,59 @@ class PaddleProgram(object):
layer = PaddleLayer(kernel, inputs, outputs, **kwargs)
layer_id = str(len(self.layers))
if self.father_layer is not None:
layer_id = "{}.{}.{}".format(layer_id, len(self.father_layer.blocks()), self.father_layer.id)
layer_id = "{}.{}.{}".format(layer_id,
len(self.father_layer.blocks()),
self.father_layer.id)
self.layers[layer_id] = layer
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):
outputs_from_nodes = dict()
for layer_id, layer in self.layers.items():
......@@ -105,6 +180,12 @@ class PaddleProgram(object):
for output in layer.outputs:
outputs_from_nodes[output] = layer_id
layer_ids = copy.deepcopy(list(self.layers.keys()))
for layer_id in layer_ids:
if len(self.edges_in.get(layer_id, [])) == 0 and len(
self.edges_out.get(layer_id, [])) == 0:
del self.layers[layer_id]
def gen_code(self, code_dir):
def write_code(f, code_list, indent=0):
indent_blank = " " * indent
......@@ -193,6 +274,13 @@ class PaddleProgram(object):
feeded_var_names=[i.name for i in inputs],
target_vars=outputs,
executor=exe)
print("Model has been converted, saved in {}".format(save_dir))
print("=====Model inputs info=====")
for ipt in self.inputs:
print("Tensor: {}".format(ipt))
print("=====Model outputs info====")
for out in self.outputs:
print("Tensor: {}".format(out))
def dump_parameter(self, param_name, param, save_dir):
if not os.path.exists(save_dir):
......@@ -227,3 +315,19 @@ class PaddleProgram(object):
fp.write(tensor_desc.SerializeToString())
param.tofile(fp)
fp.close()
def visualize(self, save_dir):
from graphviz import Digraph
dot = Digraph("PaddleGraph", "Generated by X2Paddle")
for layer_id, layer in self.layers.items():
dot.node(layer_id, layer.kernel)
for layer_id, outputs in self.edges_out.items():
for out in outputs:
dot.edge(layer_id, out)
with open(os.path.join(save_dir, 'graph.dot'), 'w') as f:
f.write(dot.source)
dot.format = 'svg'
dot.render(filename='graph', directory=save_dir)
x2paddle/decoder/tf_decoder.py
浏览文件 @ 6da21ebe
......@@ -60,7 +60,7 @@ class TFGraphNode(GraphNode):
@property
def dtype(self):
keys = ['dtype', 'Tidx', 'T', 'DstT']
keys = ['dtype', 'T', 'DstT', 'Tidx']
for k in keys:
dtype = self.layer.attr[k].type
if dtype > 0:
......@@ -74,7 +74,7 @@ class TFGraphNode(GraphNode):
@property
def raw_dtype(self):
keys = ['dtype', 'Tidx', 'T', 'DstT']
keys = ['dtype', 'T', 'DstT', 'Tidx']
for k in keys:
dtype = self.layer.attr[k].type
if dtype > 0:
......@@ -121,7 +121,7 @@ class TFGraph(Graph):
def __init__(self, model, data_format="NHWC"):
super(TFGraph, self).__init__(model)
self.identity_map = dict()
self.multi_out_ops = ['Split', 'SplitV', 'IteratorV2']
self.multi_out_ops = ['Split', 'SplitV', 'IteratorV2', 'Unpack']
self.tf_data_format = data_format
def build(self):
......@@ -159,6 +159,7 @@ class TFGraph(Graph):
del self.output_nodes[idx]
# tensorflow graph optimize
self._get_inputs_outputs()
self._remove_isolated_node()
self._optimize_dialiation_conv()
self._remove_identity_node()
......@@ -167,8 +168,10 @@ class TFGraph(Graph):
def get_node(self, node_name, copy=False):
items = node_name.strip().split(':')
items[0] = items[0].replace('/', '_').replace('-', '_')
if items[0] in self.identity_map:
items[0] = self.identity_map[items[0]]
new_node_name = self.identity_map[items[0]]
else:
new_node_name = ":".join(items)
node = super(TFGraph, self).get_node(new_node_name, copy)
if node is None:
......@@ -200,6 +203,27 @@ class TFGraph(Graph):
idx = self.topo_sort.index(node_name)
del self.topo_sort[idx]
def _get_inputs_outputs(self):
node_inputs_info = dict()
node_outputs_info = dict()
self.input_nodes = list()
self.output_nodes = list()
for node in self.model.node:
inputs = [ipt.split(':')[0].replace('^', '') for ipt in node.input]
node_inputs_info[node.name] = inputs
for ipt in inputs:
if ipt not in node_outputs_info:
node_outputs_info[ipt] = list()
node_outputs_info[ipt].append(node.name)
for node in self.model.node:
if node.op == "Placeholder":
self.input_nodes.append(
node.name.replace('/', '_').replace('-', '_'))
if len(node_inputs_info.get(node.name, [])) > 0 and len(
node_outputs_info.get(node.name, [])) == 0:
self.output_nodes.append(
node.name.replace('/', '_').replace('-', '_'))
def _optimize_dialiation_conv(self):
for name in list(self.node_map.keys()):
node = self.node_map[name]
......@@ -268,6 +292,14 @@ class TFGraph(Graph):
idx = self.output_nodes.index(node_name)
self.output_nodes[idx] = input_node.layer_name
for i, out in enumerate(cp.deepcopy(self.output_nodes)):
if out not in self.node_map:
index = self.output_nodes.index(out)
del self.output_nodes[index]
elif len(self.node_map[out].layer.input) == 0:
index = self.output_nodes.index(out)
del self.output_nodes[index]
def _remove_cast_node(self):
cast_node = list()
for node_name, node in self.node_map.items():
......@@ -289,16 +321,6 @@ class TFGraph(Graph):
idx = self.output_nodes.index(node_name)
self.output_nodes[idx] = input_node.layer_name
def data_format_propagation(self, node):
current_node = self.node_map[node.layer_name]
outputs = current_node.outputs
if len(outputs) == 0:
return
for out in outputs:
next_node = self.node_map[out]
next_node.tf_data_format = node.tf_data_format
self.data_format_propagation(next_node)
class TFDecoder(object):
def __init__(self, pb_model, data_format="NHWC", define_input_shape=False):
......
x2paddle/op_mapper/tf_op_mapper_nhwc.py
浏览文件 @ 6da21ebe
......@@ -51,7 +51,8 @@ class TFOpMapperNHWC(OpMapper):
'alpha': 'alpha'
}],
'Floor': ['floor'],
'Erf': ['erf']
'Erf': ['erf'],
'Square': ['square']
}
elementwise_ops = {
'Add': 'elementwise_add',
......@@ -145,12 +146,23 @@ class TFOpMapperNHWC(OpMapper):
op_type = self.elementwise_ops[node.layer_type]
x = self.graph.get_node(node.layer.input[0])
y = self.graph.get_node(node.layer.input[1])
program.add_layer(
kernel="fluid.layers.{}".format(op_type),
inputs={"x": x.name,
"y": y.name},
outputs=[node.name])
def NotEqual(self, node):
x = self.graph.get_node(node.layer.input[0])
y = self.graph.get_node(node.layer.input[1])
program.add_layer(
kernel="fluid.layers.not_equal",
inputs={"x": x.name,
"y": y.name},
outputs=[node.name])
def Placeholder(self, node):
shape = node.out_shapes[0]
assert len(shape) != 0, "Unknown shape of input nodes[{}].".format(
......@@ -172,6 +184,8 @@ class TFOpMapperNHWC(OpMapper):
if len(shape) == 0:
assert value.size == 1, "Unexpected situation happend"
shape = [1]
if value == float('inf'):
value = "float('inf')"
initializer = "Constant({})".format(value)
program.parameters[node.name] = node.value
......@@ -441,17 +455,28 @@ class TFOpMapperNHWC(OpMapper):
def Reshape(self, node):
input = self.graph.get_node(node.layer.input[0])
param = self.graph.get_node(node.layer.input[1])
input_name = input.name
if input.dtype == 'bool':
cast_name = gen_name('reshape', 'cast')
program.add_layer(
kernel="fluid.layers.cast",
inputs={"x": input_name},
outputs=[cast_name],
dtype="'int32'")
input_name = cast_name
if param.layer_type == "Const":
shape = param.value.tolist()
program.add_layer(
kernel="fluid.layers.reshape",
inputs={"x": input.name},
inputs={"x": input_name},
outputs=[node.name],
shape=shape)
else:
program.add_layer(
kernel="fluid.layers.reshape",
inputs={"x": input.name,
inputs={"x": input_name,
"shape": param.name},
outputs=[node.name])
if param.layer_type != "Const":
......@@ -464,6 +489,13 @@ class TFOpMapperNHWC(OpMapper):
outputs=[node.name],
shape=out_shape.tolist())
if input.dtype == 'bool':
program.add_layer(
kernel="fluid.layers.cast",
inputs={"x": node.name},
outputs=[node.name],
dtype="'bool'")
def Pad(self, node):
input = self.graph.get_node(node.layer.input[0])
paddings = self.graph.get_node(node.layer.input[1])
......@@ -517,9 +549,18 @@ class TFOpMapperNHWC(OpMapper):
def Shape(self, node):
input = self.graph.get_node(node.layer.input[0])
input_name = input.name
if input.dtype == 'bool':
cast_name = gen_name('shape', 'cast')
program.add_layer(
kernel="fluid.layers.cast",
inputs={"x": input.name},
outputs=[cast_name],
dtype="'int32'")
input_name = cast_name
program.add_layer(
kernel="fluid.layers.shape",
inputs={"input": input.name},
inputs={"input": input_name},
outputs=[node.name])
def ArgMax(self, node):
......@@ -642,12 +683,43 @@ class TFOpMapperNHWC(OpMapper):
def Pack(self, node):
inputs = [self.graph.get_node(name) for name in node.layer.input]
input_names = [i.name for i in inputs]
axis = node.get_attr("axis")
program.add_layer(
kernel="fluid.layers.stack",
inputs={"x": [i.name for i in inputs]},
inputs={"x": input_names},
outputs=[node.name],
axis=axis)
if len(node.out_shapes[0]) == 1:
program.add_layer(
kernel="fluid.layers.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=[-1])
def Unpack(self, node):
input = self.graph.get_node(node.layer.input[0])
axis = node.get_attr("axis")
num = node.get_attr("num")
shape = input.out_shapes[0]
input_name = input.name
if len(shape) == 1:
if shape[0] > 0 and num == shape[0]:
program.add_layer(
kernel="fluid.layers.unsqueeze",
inputs={"input": input.name},
outputs=[node.name],
axes=[0])
input_name = node.name
axis = 1
else:
raise Exception("Unexpected situation happend in Unpack OP")
program.add_layer(
kernel="fluid.layers.unstack",
inputs={"x": input_name},
outputs=["{}_p{}".format(node.layer_name, i) for i in range(num)],
axis=axis,
num=num)
def ConcatV2(self, node):
inputs = [self.graph.get_node(name) for name in node.layer.input[:-1]]
......@@ -656,27 +728,55 @@ class TFOpMapperNHWC(OpMapper):
axis = axis.value
if axis < 0:
axis += len(inputs[0].out_shapes[0])
input_names = [i.name for i in inputs]
for i, ipt in enumerate(inputs):
if node.dtype == 'bool':
cast_name = gen_name('concat', 'cast')
program.add_layer(
kernel="fluid.layers.cast",
inputs={"x": ipt.name},
outputs=[cast_name],
dtype="'int32'")
input_names[i] = cast_name
program.add_layer(
kernel="fluid.layers.concat",
inputs={"input": [i.name for i in inputs]},
inputs={"input": input_names},
outputs=[node.name],
axis=axis)
if node.dtype == 'bool':
program.add_layer(
kernel="fluid.layers.cast",
inputs={"x": node.name},
outputs=[node.name],
dtype="'bool'")
def StridedSlice(self, node):
input = self.graph.get_node(node.layer.input[0])
begin = self.graph.get_node(node.layer.input[1])
end = self.graph.get_node(node.layer.input[2])
strides = self.graph.get_node(node.layer.input[3])
assert begin.layer_type == "Const"
assert end.layer_type == "Const"
assert strides.layer_type == "Const"
strides = strides.value.tolist()
assert len(set(strides)) == 1 and strides[
0] == 1, "Only support strides be 1 in StridedSlice OP"
if strides.layer_type == "Const":
strides = strides.value.tolist()
else:
strides = self.decoder.infer_shape_tensor(strides)
if begin.layer_type == "Const":
begin = begin.value.tolist()
else:
begin = self.decoder.infer_shape_tensor(begin)
if end.layer_type == "Const":
end = end.value.tolist()
else:
end = self.decoder.infer_shape_tensor(end)
assert len(set(strides)) == 1 and strides[
0] == 1, "Only support strides be 1 in StridedSlice OP"
if len(begin) < len(input.out_shapes[0]):
begin = begin + [0] * (len(input.out_shapes[0]) - len(begin))
if len(end) < len(input.out_shapes[0]):
end = end + [0] * (len(input.out_shapes[0]) - len(end))
for i in range(len(end)):
if end[i] == 0:
end[i] = 999999
......@@ -736,10 +836,10 @@ class TFOpMapperNHWC(OpMapper):
pass
else:
program.add_layer(
kernel="fluid.layers.unsqueeze",
kernel="fluid.layers.squeeze",
inputs={"input": node.name},
outputs=[node.name],
axes=new_axes)
axes=shrink_axes)
def Split(self, node):
dim = self.graph.get_node(node.layer.input[0])
......@@ -1099,6 +1199,8 @@ class TFOpMapperNHWC(OpMapper):
outputs=[node.name],
**attr)
node.layer.attr['dtype'].type = 10
def GatherV2(self, node):
embeddings = self.graph.get_node(node.layer.input[0])
index = self.graph.get_node(node.layer.input[1])
......@@ -1121,6 +1223,13 @@ class TFOpMapperNHWC(OpMapper):
inputs=inputs,
outputs=[node.name],
overwrite=False)
if len(index.out_shapes[0]) != 1:
out_shape = node.out_shapes[0]
program.add_layer(
kernel="fluid.layers.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=out_shape)
def ExpandDims(self, node):
x = self.graph.get_node(node.layer.input[0], copy=True)
......
x2paddle/optimizer/batch_norm.py 0 → 100644
浏览文件 @ 6da21ebe
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():
can_be_optimized = True
if layer.kernel != "fluid.layers.elemenwise_mul":
can_be_optimized = False
continue
input_ids = graph.edges_in[layer_id]
x2paddle/optimizer/bias.py 0 → 100644
浏览文件 @ 6da21ebe
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[layer_id]) != 1:
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 bias_layer.outputs[0] in graph.outputs:
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 len(graph.edges_out[in_layer_id]) != 1:
continue
graph.layers[in_layer_id].attrs[
'bias_attr'] = bias_layer.attrs['name']
graph.del_layer(bias_layer_id)
graph.del_layer(out_layer_id)
else:
graph.layers[layer_id].attrs[
'bias_attr'] = bias_layer.attrs['name']
graph.del_layer(bias_layer_id)
graph.del_layer(out_layer_id)
x2paddle/optimizer/transpose.py 0 → 100644
浏览文件 @ 6da21ebe
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'
]
self.elementwise_layers = [
'fluid.layers.elementwise_add', 'fluid.layers.elementwise_sub',
'fluid.layers.elementwise_mul', 'fluid.layers.elementwise_div'
]
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 strip_direct_layers(self, graph):
# 构建opt_graph
# 删除所有direct_layers， 便于对transpose进行优化
opt_graph = copy.deepcopy(graph)
remove_layer_ids = set()
for layer_id, layer in opt_graph.layers.items():
if layer.kernel in self.direct_layers:
layer_out = opt_graph.edges_out[layer_id]
layer_in = opt_graph.edges_in[layer_id]
if len(layer_out) == 0 or len(layer_in) == 0:
continue
assert len(
layer_in
) == 1, "There should be only 1 input for direct layers."
remove_layer_ids.add(layer_id)
for layer_id in remove_layer_ids:
opt_graph.del_layer(layer_id)
return opt_graph
def run(self, graph):
optimized_transpose_layers = list()
modified_layer_attrs = dict()
modified_parameters = dict()
scanned_layers = set()
total_layer_num = len(graph.layers)
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_layer_ids = list()
elementwise_layer_ids = list()
concat_layer_ids = list()
can_be_optimized = True
modified_attrs = dict()
parameter_layers = list()
parameters = dict()
for out in _graph.edges_out[layer_id]:
if _graph.layers[out].kernel == "fluid.layers.transpose":
if _graph.layers[out].attrs["perm"] != [0, 3, 1, 2]:
can_be_optimized = False
continue
transpose_layer_ids.append(out)
elif _graph.layers[out].kernel in self.elementwise_layers:
elementwise_layer_ids.append(out)
elif _graph.layers[out].kernel == "fluid.layers.concat":
elementwise_layer_ids.append(out)
concat_layer_ids.append(out)
else:
can_be_optimized = False
break
visited_layers = set()
while len(elementwise_layer_ids) > 0 and can_be_optimized:
current_id = elementwise_layer_ids.pop(0)
visited_layers.add(current_id)
for out in _graph.edges_out[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
if out not in visited_layers:
transpose_layer_ids.append(out)
elif _graph.layers[
out].kernel in self.elementwise_layers:
if out not in visited_layers:
elementwise_layer_ids.append(out)
elif _graph.layers[out].kernel == "fluid.layers.concat":
if out not in visited_layers:
elementwise_layer_ids.append(out)
concat_layer_ids.append(out)
else:
can_be_optimized = False
break
all_create_parameter = True
for ipt in _graph.edges_in.get(current_id, []):
if _graph.layers[
ipt].kernel == "fluid.layers.transpose":
all_creater_parameter = False
if _graph.layers[ipt].attrs["perm"] != [0, 2, 3, 1]:
can_be_optimized = False
break
if ipt not in visited_layers:
transpose_layer_ids.append(ipt)
elif _graph.layers[
ipt].kernel in self.elementwise_layers:
all_creater_parameter = False
if ipt not in visited_layers:
elementwise_layer_ids.append(ipt)
elif _graph.layers[ipt].kernel == "fluid.layers.concat":
all_creater_parameter = False
if ipt not in visited_layers:
elementwise_layer_ids.append(ipt)
concat_layer_ids.append(ipt)
elif _graph.layers[
ipt].kernel == "fluid.layers.create_parameter":
if ipt not in visited_layers:
elementwise_layer_ids.append(ipt)
parameter_layers.append(ipt)
else:
can_be_optimized = False
break
if all_create_parameter:
can_be_optimized = False
break
if not can_be_optimized:
break
if not can_be_optimized:
continue
concat_layer_ids = list(set(concat_layer_ids))
for l in concat_layer_ids:
axis = _graph.layers[l].attrs.get('axis', 0)
_graph.layers[l].attrs['axis'] = [0, 2, 3, 1][axis]
modified_attrs[l] = _graph.layers[l].attrs
parameter_layers = list(set(parameter_layers))
for l in parameter_layers:
for o in _graph.edges_out[l]:
if _graph.layers[o].kernel in self.elementwise_layers:
axis = _graph.layers[o].attrs.get('axis', -1)
_graph.layers[o].attrs['axis'] = [0, 3, 1, 2][axis]
modified_attrs[o] = _graph.layers[o].attrs
else:
can_be_optimized = False
break
if not can_be_optimized:
break
s = _graph.layers[l].attrs['shape']
p = _graph.parameters[_graph.layers[l].outputs[0]]
if len(s) == 4:
_graph.layers[l].attrs[
'shape'] = [s[0], s[3], s[1], s[2]]
modified_attrs[l] = _graph.layers[l].attrs
parameters[_graph.layers[l].outputs[0]] = np.transpose(
p, (0, 3, 1, 2))
elif len(s) == 3:
_graph.layers[l].attrs['shape'] = [s[2], s[0], s[1]]
modified_attrs[l] = _graph.layers[l].attrs
parameters[_graph.layers[l].outputs[0]] = np.transpose(
p, (2, 0, 1))
if not can_be_optimized:
continue
transpose_layer_ids.append(layer_id)
transpose_layer_ids = list(set(transpose_layer_ids))
for transpose_layer_id in transpose_layer_ids:
_graph.del_layer(transpose_layer_id)
optimized_transpose_layers.extend(transpose_layer_ids)
modified_layer_attrs.update(modified_attrs)
modified_parameters.update(parameters)
return True
return False
before_transpose_num = self.get_transpose_num(graph)
opt_graph = self.strip_direct_layers(graph)
total_layer_num = len(opt_graph.layers)
while strip_transpose(opt_graph):
pass
for layer_id in optimized_transpose_layers:
graph.del_layer(layer_id)
for layer_id, attrs in modified_layer_attrs.items():
graph.layers[layer_id].attrs = attrs
for name, parameter in modified_parameters.items():
graph.parameters[name] = parameter
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))
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