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提交 8aa1008b 编写于 作者: S SunAhong1993
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x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py 0 → 100644
浏览文件 @ 8aa1008b
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from x2paddle.decoder.tf_decoder import TFGraph
from x2paddle.core.program import PaddleGraph
from x2paddle.core.op_mapper import OpMapper
from x2paddle.core.util import *
import traceback
import math
import inspect
import numpy
import sys
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
# compute padding size for SAME mode
def get_same_padding(in_size, kernel_size, stride):
new_size = int(math.ceil(in_size * 1.0 / stride))
pad_size = (new_size - 1) * stride + kernel_size - in_size
if pad_size < 0:
pad_size = 0
pad0 = int(pad_size / 2)
pad1 = pad_size - pad0
return [pad0, pad1]
class TFOpMapper(OpMapper):
directly_map_ops = {
'Relu': ['paddle.nn.ReLU'],
'Relu6': ['paddle.nn.ReLU6'],
'Abs': ['paddle.abs'],
'Sigmoid': ['paddle.nn.Sigmoid'],
'Exp': ['paddle.exp'],
'Rsqrt': ['paddle.rsqrt'],
'Sqrt': ['paddle.sqrt'],
'swish_f32': ['paddle.nn.Swish'],
'Tanh': ['paddle.nn.Tanh'],
'Softplus': ['paddle.nn.Softplus'],
'LeakyRelu': ['paddle.nn.LeakyReLU', {
'alpha': 'negative_slope'
}],
'Softmax': ['paddle.nn.Softmax', {'axis': 'axis'}],
'Floor': ['paddle.floor'],
'Erf': ['paddle.erf'],
'Square': ['paddle.square']
}
elementwise_ops = {
'Add': 'paddle.add',
'AddV2': 'paddle.add',
'RealDiv': 'paddle.divide',
'Sub': 'fluid.layers.elementwise_sub',
'Maximum': 'paddle.maximum',
'Minimum': 'paddle.minimum',
'LessEqual': 'paddle.less_equal',
'GreaterEqual': 'paddle.greater_equal',
'Mul': 'paddle.multiply',
'FloorDiv': 'fluid.layers.elementwise_floordiv'
}
def __init__(self, decoder):
super(TFOpMapper, self).__init__()
self.decoder = decoder
self.graph = decoder.tf_graph
self.params = dict()
self.nn_name2id = dict()
self.input_index = 0
self.paddle_graph = PaddleGraph(parent_layer=None, graph_type="dygraph", source_type="tf")
self.used_custom_layers = dict()
self.inputs_info = dict()
not_placeholder = list()
for name in self.graph.input_nodes:
if self.graph.get_node(
name).layer_type != "Placeholder" and self.graph.get_node(
name
).layer_type != "OneShotIterator" and self.graph.get_node(
name).layer_type != "IteratorV2":
not_placeholder.append(name)
for name in not_placeholder:
idx = self.graph.input_nodes.index(name)
del self.graph.input_nodes[idx]
self.paddle_graph.outputs = self.graph.output_nodes
unsupported_ops = set()
sys.stderr.write("Total nodes: {}\n".format(len(self.graph.topo_sort)))
for i, node_name in enumerate(self.graph.topo_sort):
sys.stderr.write("\rConverting node {} ... ".format(i + 1))
node = self.graph.get_node(node_name)
op = node.layer_type
if op in self.directly_map_ops:
if len(unsupported_ops) > 0:
continue
self.directly_map(node)
elif op in self.elementwise_ops:
if len(unsupported_ops) > 0:
continue
self.elementwise_map(node)
elif hasattr(self, op):
if len(unsupported_ops) > 0:
continue
func = getattr(self, op)
try:
func(node)
except Exception as e:
unsupported_ops.add(op)
print("\n{}\n".format(traceback.format_exc()))
else:
unsupported_ops.add(op)
if len(unsupported_ops) > 0:
print("\n========= {} OPs are not supported yet ===========".format(
len(unsupported_ops)))
for op in unsupported_ops:
print("========== {} ============".format(op))
sys.exit(-1)
sys.stderr.write("\nDone!\n")
self.paddle_graph.set_name(self.graph.graph_name)
self.paddle_graph.set_parameters(self.params)
self.paddle_graph.set_inputs_info(self.inputs_info)
def directly_map(self, node):
assert node.layer_type in self.directly_map_ops
op_info = self.directly_map_ops[node.layer_type]
input = self.graph.get_node(node.layer.input[0])
layer_attrs = dict()
for param in op_info[1:]:
tf_param_name = list(param.keys())[0]
pd_param_name = list(param.values())[0]
tf_param = node.get_attr(tf_param_name)
layer_attrs[pd_param_name] = tf_param
if op_info[0].startswith("paddle.nn"):
op_name = op_info[0][10:].lower()
op_name = name_generator(op_name, self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
self.paddle_graph.add_layer(
kernel=op_info[0],
inputs={"x": input.name},
outputs=layer_outputs,
**layer_attrs)
else:
self.paddle_graph.add_layer(
kernel=op_info[0],
inputs={"x": input.name},
outputs=[node.name],
**layer_attrs)
def elementwise_map(self, node):
assert node.layer_type in self.elementwise_ops
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])
x_shape = x.out_shapes[0]
y_shape = y.out_shapes[0]
self.paddle_graph.add_layer(
kernel=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])
self.paddle_graph.add_layer(
kernel="paddle.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(
node.layer_name)
dtype = node.dtype
self.paddle_graph.add_layer(
kernel="paddle.to_tensor",
inputs={},
outputs=[node.name],
data="x{}".format(self.input_index))
self.inputs_info["x{}".format(self.input_index)] = [shape, node.dtype]
self.input_index += 1
def Const(self, node):
shape = node.out_shapes[0]
dtype = node.dtype
value = node.value
if len(shape) == 0:
assert value.size == 1, "Unexpected situation happend"
if value == float('inf'):
value = "float('inf')"
self.paddle_graph.add_layer(
"paddle.full",
inputs={},
outputs=[node.name],
dtype=string(dtype),
shape=[1],
fill_value=value)
return
self.params[node.name] = node.value
self.paddle_graph.add_layer(
"self.create_parameter",
inputs={},
outputs=[node.name],
shape=shape,
attr=string(node.name))
def Transpose(self, node):
input = self.graph.get_node(node.layer.input[0])
perm = self.graph.get_node(node.layer.input[1])
assert perm.layer_type == "Const", "Perm of transpose OP should be Const"
perm = perm.value.tolist()
self.paddle_graph.add_layer(
"paddle.transpose",
inputs={"x": input.name},
outputs=[node.name],
perm=perm)
def Fill(self, node):
dims = self.graph.get_node(node.layer.input[0])
input_value = self.graph.get_node(node.layer.input[1])
inputs = dict()
layer_attrs = dict()
assert input_value.layer_type == "Const", "Value of fill OP should be Const"
if dims.layer_type == "Const":
layer_attrs["shape"] = dims.value.tolist()
else:
inputs["shape"] = dims.name
layer_attrs["dtype"] = string(input_value.dtype)
layer_attrs["value"] = input_value.value
self.paddle_graph.add_layer(
"paddle.full",
inputs=inputs,
outputs=[node.name],
**layer_attrs)
def DepthToSpace(self, node):
input = self.graph.get_node(node.layer.input[0])
block_size = node.get_attr("block_size")
data_format = node.get_attr("data_format").decode()
if data_format == "NHWC":
n, h, w, c = input.out_shapes[0]
else:
n, c, h, w = input.out_shapes[0]
input_name = input.name
if data_format == "NHWC":
transpose_name = gen_name("depth_to_space", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
input_name = transpose_name
shape = [0, block_size * block_size, -1, h, w]
reshape_name = gen_name("depth_to_space", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": input_name},
outputs=[reshape_name],
shape=shape)
transpose_name = gen_name("depth_to_space", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": reshape_name},
outputs=[transpose_name],
perm=[0, 2, 1, 3, 4])
reshape_name = gen_name("depth_to_space", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": transpose_name},
outputs=[reshape_name],
shape=[0, c, h, w])
self.paddle_graph.add_layer(
kernel="fluid.layers.pixel_shuffle",
inputs={"x": reshape_name},
outputs=[node.name],
upscale_factor=block_size)
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def MaxPool(self, node):
input = self.graph.get_node(node.layer.input[0])
k_size = node.get_attr("ksize")
strides = node.get_attr("strides")
data_format = node.get_attr("data_format").decode()
pad_mode = node.get_attr("padding").decode()
input_name = input.name
if data_format == "NHWC":
transpose_name = gen_name("max_pool", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
strides = [strides[i] for i in [0, 3, 1, 2]]
k_size = [k_size[i] for i in [0, 3, 1, 2]]
input_name = transpose_name
op_name = name_generator("pool", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
self.paddle_graph.add_layer(
kernel="paddle.nn.Pool2D",
inputs={"input": input_name},
outputs=layer_outputs,
pool_size=k_size[2:4],
pool_type=string("max"),
pool_stride=strides[2:4],
pool_padding=string(pad_mode))
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Conv2D(self, node):
op_name = name_generator("conv", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_node(node.layer.input[0])
kernel = self.graph.get_node(node.layer.input[1])
k_size = kernel.out_shapes[0]
strides = node.get_attr("strides")
dilations = node.get_attr("dilations")
data_format = node.get_attr("data_format").decode()
pad_mode = node.get_attr("padding").decode()
if data_format == "NHWC":
n, h, w, c = input.out_shapes[0]
else:
n, c, h, w = input.out_shapes[0]
if kernel.layer_type == 'Const':
kernel_value = kernel.value
else:
kernel_value = self.decoder.infer_tensor(kernel)
kernel_weight_name = op_name + ".weight"
self.params[kernel_weight_name] = numpy.transpose(kernel_value,
(3, 2, 0, 1))
input_name = input.name
if data_format == "NHWC":
strides = [strides[i] for i in [0, 3, 1, 2]]
dilations = [dilations[i] for i in [0, 3, 1, 2]]
transpose_name = gen_name("conv2d", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
input_name = transpose_name
if c == -1:
attr = {"shape": [0, k_size[2], 0, 0]}
node.fluid_code.add_layer(
"reshape", inputs=input, output=input, param_attr=attr)
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": input_name},
outputs=[input_name],
shape=[0, k_size[2], 0, 0])
self.paddle_graph.add_layer(
kernel="paddle.nn.Conv2D",
inputs={"input": input_name},
outputs=layer_outputs,
weight_attr=string(kernel_weight_name),
bias_attr=False,
in_channels=k_size[2],
out_channels=k_size[3],
kernel_size=k_size[0:2],
stride=strides[2:4],
dilation=dilations[2:4],
padding=string(pad_mode))
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def BiasAdd(self, node):
input = self.graph.get_node(node.layer.input[0])
bias = self.graph.get_node(node.layer.input[1])
self.paddle_graph.add_layer(
kernel="paddle.add",
inputs={"x": input.name,
"y": bias.name},
outputs=[node.name])
def FusedBatchNorm(self, node):
op_name = name_generator("bn", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_node(node.layer.input[0])
if data_format == "NHWC":
n, h, w, c = input.out_shapes[0]
else:
n, c, h, w = input.out_shapes[0]
gamma = self.graph.get_node(node.layer.input[1])
beta = self.graph.get_node(node.layer.input[2])
moving_mean = self.graph.get_node(node.layer.input[3])
moving_var = self.graph.get_node(node.layer.input[4])
data_format = node.get_attr("data_format").decode()
assert gamma.layer_type == "Const"
assert beta.layer_type == "Const"
assert moving_mean.layer_type == "Const"
assert moving_var.layer_type == "Const"
input_name = input.name
if data_format == "NHWC":
transpose_name = gen_name("batch_norm", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
input_name = transpose_name
self.paddle_graph.add_layer(
kernel="paddle.nn.BatchNorm",
inputs={"input": input_name},
outputs=layer_outputs,
num_features=c,
epsilon=node.get_attr("epsilon"),
weight_attr=string(gamma.name),
bias_attr=string(beta.name),
moving_mean_name=string(moving_mean.name),
moving_variance_name=string(moving_var.name),
is_test=True)
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Mean(self, node):
input = self.graph.get_node(node.layer.input[0])
reduce_idx = self.graph.get_node(node.layer.input[1])
assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
dims = reduce_idx.value.tolist()
keep_dims = node.get_attr("keep_dims")
self.paddle_graph.add_layer(
kernel="paddle.mean",
inputs={"x": input.name},
outputs=[node.name],
axis=dims,
keepdim=keep_dims)
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')
self.paddle_graph.add_layer(
kernel="paddle.cast",
inputs={"x": input_name},
outputs=[cast_name],
dtype="'int32'")
input_name = cast_name
if param.layer_type == "Const":
shape = param.value.tolist()
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": input_name},
outputs=[node.name],
shape=shape)
else:
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": input_name,
"shape": param.name},
outputs=[node.name])
if param.layer_type != "Const":
out_shape = numpy.array(node.out_shapes[0])
if (out_shape > 0).any():
out_shape[out_shape < 0] = 0
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=out_shape.tolist())
if input.dtype == 'bool':
self.paddle_graph.add_layer(
kernel="paddle.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])
assert paddings.layer_type == "Const", "Padding should be Const"
paddings = paddings.value.flatten().tolist()
if len(input.out_shapes[0]) == 4:
if paddings[0] + paddings[1] + paddings[6] + paddings[7] == 0:
new_padding = paddings[2:6]
transpose_name = gen_name("pad", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
self.paddle_graph.add_layer(
kernel="paddle.nn.functional.pad",
inputs={"input": transpose_name},
outputs=[node.name],
pad=new_padding)
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
return
self.paddle_graph.add_layer(
kernel="paddle.nn.functional.pad",
inputs={"input": input.name},
outputs=[node.name],
pad=paddings)
def Squeeze(self, node):
input = self.graph.get_node(node.layer.input[0])
squeeze_dims = node.get_attr('squeeze_dims')
self.paddle_graph.add_layer(
kernel="paddle.squeeze",
inputs={"x": input.name},
outputs=[node.name],
axis=squeeze_dims)
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')
self.paddle_graph.add_layer(
kernel="paddle.cast",
inputs={"x": input.name},
outputs=[cast_name],
dtype=string("int32"))
input_name = cast_name
self.paddle_graph.add_layer(
kernel="paddle.shape",
inputs={"input": input_name},
outputs=[node.name])
def ArgMax(self, node):
input = self.graph.get_node(node.layer.input[0])
axis = self.graph.get_node(node.layer.input[1])
assert axis.layer_type == "Const", "ArgMax only support Const parameter"
axis = axis.value
self.paddle_graph.add_layer(
kernel="paddle.argmax",
inputs={"x": input.name},
outputs=[node.name],
axis=axis)
def MatMul(self, node):
x = self.graph.get_node(node.layer.input[0])
y = self.graph.get_node(node.layer.input[1])
transpose_a = node.get_attr('transpose_a')
transpose_b = node.get_attr('transpose_b')
if transpose_a is None:
transpose_a = node.get_attr('adj_x')
if transpose_b is None:
transpose_b = node.get_attr('adj_y')
self.paddle_graph.add_layer(
kernel="paddle.matmul",
inputs={"x": x.name,
"y": y.name},
outputs=[node.name],
transpose_x=transpose_a,
transpose_y=transpose_b)
def BatchMatMul(self, node):
return self.MatMul(node)
def BatchMatMulV2(self, node):
return self.MatMul(node)
def DepthwiseConv2dNative(self, node):
op_name = name_generator("conv", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_node(node.layer.input[0])
kernel = self.graph.get_node(node.layer.input[1])
assert kernel.layer_type == "Const", "Kernel of DepthwiseConv2DNative should be Const"
in_shape = input.out_shapes[0]
k_size = kernel.out_shapes[0]
strides = node.get_attr("strides")
dilations = node.get_attr("dilations")
data_format = node.get_attr("data_format").decode()
pad_mode = node.get_attr("padding").decode()
kernel_weight_name = op_name + ".weight"
self.params[kernel_weight_name] = numpy.transpose(kernel.value,
(2, 3, 0, 1))
input_name = input.name
if data_format == "NHWC":
in_shape = [in_shape[i] for i in [0, 3, 1, 2]]
strides = [strides[i] for i in [0, 3, 1, 2]]
dilations = [dilations[i] for i in [0, 3, 1, 2]]
transpose_name = gen_name('depthwise_conv2d', 'transpose')
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
input_name = transpose_name
self.paddle_graph.add_layer(
kernel="paddle.nn.Conv2D",
inputs={"input": input_name},
outputs=layer_outputs,
weight_attr=string(kernel_weight_name),
bias_attr=False,
in_channels=in_shape[1],
out_channels=k_size[2],
kernel_size=k_size[0:2],
stride=strides[2:4],
dilation=dilations[2:4],
groups=k_size[3] * in_shape[1],
padding=string(pad_mode))
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def AvgPool(self, node):
input = self.graph.get_node(node.layer.input[0])
k_size = node.get_attr("ksize")
strides = node.get_attr("strides")
data_format = node.get_attr("data_format").decode()
pad_mode = node.get_attr("padding").decode()
input_name = input.name
if data_format == "NHWC":
transpose_name = gen_name("avg_pool", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
strides = [strides[i] for i in [0, 3, 1, 2]]
k_size = [k_size[i] for i in [0, 3, 1, 2]]
input_name = transpose_name
op_name = name_generator("pool", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
self.paddle_graph.add_layer(
kernel="paddle.nn.Pool2D",
inputs={"input": input_name},
outputs=layer_outputs,
pool_size=k_size[2:4],
pool_type=string("avg"),
pool_stride=strides[2:4],
pool_padding=string(pad_mode))
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
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")
self.paddle_graph.add_layer(
kernel="paddle.stack",
inputs={"x": input_names},
outputs=[node.name],
axis=axis)
if len(node.out_shapes[0]) == 1:
self.paddle_graph.add_layer(
kernel="paddle.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]:
self.paddle_graph.add_layer(
kernel="paddle.unsqueeze",
inputs={"x": input.name},
outputs=[node.name],
axis=[0])
input_name = node.name
axis = 1
else:
raise Exception("Unexpected situation happend in Unpack OP")
self.paddle_graph.add_layer(
kernel="paddle.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]]
axis = self.graph.get_node(node.layer.input[-1])
assert axis.layer_type == "Const", "axis for ConcatV2 must be type Const"
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 ipt.dtype == 'bool':
cast_name = gen_name('concat', 'cast')
self.paddle_graph.add_layer(
kernel="paddle.cast",
inputs={"x": ipt.name},
outputs=[cast_name],
dtype="'int32'")
input_names[i] = cast_name
self.paddle_graph.add_layer(
kernel="paddle.concat",
inputs={"input": input_names},
outputs=[node.name],
axis=axis)
if node.dtype == 'bool':
self.paddle_graph.add_layer(
kernel="paddle.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])
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
begin_mask = node.get_attr('begin_mask')
end_mask = node.get_attr('end_mask')
ellipsis_mask = node.get_attr('ellipsis_mask')
new_axis_mask = node.get_attr('new_axis_mask')
shrink_axis_mask = node.get_attr('shrink_axis_mask')
assert ellipsis_mask == 0, "(OP:{} Name:{})Only support ellipsis_mask be 0[now: {}] n StridedSlice OP".format(
node.layer_type, node.layer.name, ellipsis_mask)
# TODO codes without validation
# Use it carefully
new_begin = list()
new_end = list()
new_axes = list()
shrink_axes = list()
for i, item in enumerate(begin):
mask = (new_axis_mask >> i) & 1
if mask != 0:
new_axes.append(i)
continue
mask = (shrink_axis_mask >> i) & 1
if mask != 0:
shrink_axes.append(i)
mask = (begin_mask >> i) & 1
if mask != 0:
new_begin.append(0)
else:
new_begin.append(item)
mask = (end_mask >> i) & 1
if mask != 0:
new_end.append(999999)
else:
new_end.append(end[i])
self.paddle_graph.add_layer(
kernel="paddle.slice",
inputs={"input": input.name},
outputs=[node.name],
axes=[i for i in range(len(new_begin))],
starts=new_begin,
ends=new_end)
if len(new_axes) > 0:
self.paddle_graph.add_layer(
kernel="paddle.unsqueeze",
inputs={"x": node.name},
outputs=[node.name],
axis=new_axes)
if len(shrink_axes) > 0:
if len(input.out_shapes[0]) + len(new_axes) <= 1:
pass
else:
self.paddle_graph.add_layer(
kernel="paddle.squeeze",
inputs={"x": node.name},
outputs=[node.name],
axis=shrink_axes)
def Split(self, node):
dim = self.graph.get_node(node.layer.input[0])
input = self.graph.get_node(node.layer.input[1])
assert dim.layer_type == "Const"
num_split = node.get_attr('num_split')
dim = dim.value
self.paddle_graph.add_layer(
kernel="paddle.split",
inputs={"input": input.name},
outputs=[
"{}_p{}".format(node.layer_name, i) for i in range(num_split)
],
num_or_sections=num_split,
dim=dim)
def Slice(self, node):
input = self.graph.get_node(node.layer.input[0])
begin = self.graph.get_node(node.layer.input[1])
size = self.graph.get_node(node.layer.input[2])
inputs = {"x": input.name}
attrs = {}
if begin.layer_type == "Const":
begin = begin.value.tolist()
attrs['offsets'] = begin
else:
# shape = begin.out_shapes[0]
# reshape_name = gen_name("slice", "reshape")
# self.paddle_graph.add_layer(
# kernel="fluid.layers.reshape",
# inputs={"x": begin.name},
# outputs=[reshape_name],
# shape=shape)
# inputs['offsets'] = reshape_name
begin = self.decoder.infer_tensor(begin).tolist()
attrs['offsets'] = begin
if size.layer_type == "Const":
size = size.value.tolist()
attrs['shape'] = size
else:
shape = size.out_shapes[0]
reshape_name = gen_name("slice", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": size.name},
outputs=[reshape_name],
shape=shape)
inputs['shape'] = reshape_name
self.paddle_graph.add_layer(
kernel="paddle.crop",
inputs=inputs,
outputs=[node.name],
**attrs)
def ResizeNearestNeighbor(self, node):
input = self.graph.get_node(node.layer.input[0])
resize_shape = self.graph.get_node(node.layer.input[1])
data_format = "NHWC"
inputs = {"input": input.name}
attrs = {"align_corner": node.get_attr("align_corners"),
"mode": "nearest"}
if resize_shape.layer_type == "Const":
resize_shape = resize_shape.value.tolist()
attrs["size"] = resize_shape
else:
shape = resize_shape.out_shapes[0]
reshape_name = gen_name("resize_nearest", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": resize_shape.name},
outputs=[reshape_name],
shape=shape)
inputs["out_shape"] = reshape_name
if data_format == "NHWC":
transpose_name = gen_name("resize_nearest", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
inputs["input"] = transpose_name
self.paddle_graph.add_layer(
kernel="paddle.nn.functioanl.interpolate",
inputs=inputs,
outputs=[node.name],
**attrs)
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def ResizeBilinear(self, node):
input = self.graph.get_node(node.layer.input[0])
resize_shape = self.graph.get_node(node.layer.input[1])
data_format = "NHWC"
inputs = {"input": input.name}
attrs = {"align_corners": node.get_attr("align_corners"),
"mode": "bilinear"}
if resize_shape.layer_type == "Const":
resize_shape = resize_shape.value.tolist()
attrs["size"] = resize_shape
else:
shape = resize_shape.out_shapes[0]
reshape_name = gen_name("resize_bilinear", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": resize_shape.name},
outputs=[reshape_name],
shape=shape)
inputs["out_shape"] = reshape_name
if data_format == "NHWC":
transpose_name = gen_name("resize_bilinear", "reshape")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
inputs["input"] = transpose_name
self.paddle_graph.add_layer(
kernel="paddle.nn.functioanl.interpolate",
inputs=inputs,
outputs=[node.name],
**attrs)
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="fluid.layers.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Cast(self, node):
input = self.graph.get_node(node.layer.input[0])
dtype = node.dtype
self.paddle_graph.add_layer(
kernel="paddle.cast",
inputs={"x": input.name},
outputs=[node.name],
dtype=string(dtype))
def Sum(self, node):
input = self.graph.get_node(node.layer.input[0])
reduce_idx = self.graph.get_node(node.layer.input[1])
assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
keep_dims = node.get_attr("keep_dims")
dim = reduce_idx.value.tolist()
self.paddle_graph.add_layer(
kernel="paddle.sum",
inputs={"input": input.name},
outputs=[node.name],
axis=dim,
keepdim=keep_dims)
def Max(self, node):
input = self.graph.get_node(node.layer.input[0])
reduce_idx = self.graph.get_node(node.layer.input[1])
assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
keep_dims = node.get_attr("keep_dims")
dim = reduce_idx.value.tolist()
self.paddle_graph.add_layer(
kernel="paddle.max",
inputs={"input": input.name},
outputs=[node.name],
axis=dim,
keepdim=keep_dims)
def RandomUniform(self, node):
shape = self.graph.get_node(node.layer.input[0])
if shape.layer_type == "Const":
shape = shape.value.tolist()
self.paddle_graph.add_layer(
kernel="paddle.uniform",
inputs={},
outputs=[node.name],
shape=shape,
min=0.0,
max=0.9999)
else:
self.paddle_graph.add_layer(
kernel="paddle.uniform",
inputs={'shape': shape.name},
outputs=[node.name],
min=0.0,
max=0.9999)
def Conv2DBackpropInput(self, node):
op_name = name_generator("conv", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
out_shape = self.graph.get_node(node.layer.input[0])
kernel = self.graph.get_node(node.layer.input[1])
input = self.graph.get_node(node.layer.input[2])
assert kernel.layer_type == "Const", "Kernel of Conv2DBackpropInput should be Const"
if out_shape.layer_type == "Const":
out_shape = out_shape.value.tolist()
else:
out_shape = self.decoder.infer_shape_tensor(out_shape,
node.out_shapes[0])
in_shape = input.out_shapes[0]
if in_shape.count(-1) > 2:
in_shape = self.decoder.infer_tensor(input).shape
k_size = kernel.out_shapes[0]
if k_size.count(-1) > 2:
k_size = self.decoder.infer_tensor(kernel).shape
pad_mode = node.get_attr("padding").decode()
strides = node.get_attr("strides")
dilations = node.get_attr("dilations")
data_format = node.get_attr("data_format").decode()
kernel_name = op_name + ".weight"
self.params[kernel_name] = numpy.transpose(kernel.value, (3, 2, 0, 1))
input_name = input.name
if data_format == "NHWC":
in_shape = [in_shape[i] for i in [0, 3, 1, 2]]
strides = [strides[i] for i in [0, 3, 1, 2]]
dilations = [dilations[i] for i in [0, 3, 1, 2]]
transpose_name = gen_name("conv2dbackpropinput", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
input_name = transpose_name
self.paddle_graph.add_layer(
kernel="paddle.nn.Conv2DTranspose",
inputs={"input": input_name},
outputs=layer_outputs,
bias_attr=False,
in_channels=k_size[3],
out_channels=k_size[2],
kernel_size=k_size[0:2],
stride=strides[2:4],
dilation=dilations[2:4],
padding=string(pad_mode))
if data_format == "NHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Tile(self, node):
input = self.graph.get_node(node.layer.input[0])
expand_times = self.graph.get_node(node.layer.input[1])
inputs = {"x": input.name}
attr = dict()
in_shape = input.out_shapes[0]
if expand_times.layer_type == "Const":
expand_times = expand_times.value.tolist()
attr["repeat_times"] = expand_times
else:
inputs["repeat_times"] = expand_times.name
self.paddle_graph.add_layer(
kernel="paddle.tile",
inputs=inputs,
outputs=[node.name],
**attr)
def Range(self, node):
start = self.graph.get_node(node.layer.input[0])
limit = self.graph.get_node(node.layer.input[1])
delta = self.graph.get_node(node.layer.input[2])
inputs = dict()
attr = dict()
dtype = 'int32'
if start.dtype.startswith('float'):
dtype = start.dtype
if start.layer_type == "Const":
attr["start"] = start.value
else:
inputs["start"] = start.name
if limit.dtype.startswith('float'):
dtype = limit.dtype
if limit.layer_type == "Const":
attr["end"] = limit.value
else:
inputs["end"] = limit.name
if delta.dtype.startswith('float'):
dtype = delta.dtype
if delta.layer_type == "Const":
attr["step"] = delta.value
else:
inputs["step"] = delta.name
node.set_dtype(dtype)
attr["dtype"] = string(node.dtype)
self.paddle_graph.add_layer(
kernel="paddle.arange",
inputs=inputs,
outputs=[node.name],
**attr)
def SquaredDifference(self, node):
x = self.graph.get_node(node.layer.input[0])
y = self.graph.get_node(node.layer.input[1])
inputs = {"x": x.name, "y": y.name}
x_shape = x.out_shapes[0]
y_shape = y.out_shapes[0]
layer_id = self.paddle_graph.add_layer(
"paddle.fluid.layers.elementwise_sub", inputs=inputs, outputs=[node.name])
# program.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape}
inputs = {"x": node.name, "y": node.name}
x_shape = node.out_shapes[0]
y_shape = node.out_shapes[0]
layer_id = self.paddle_graph.add_layer(
"paddle.multiply", inputs=inputs, outputs=[node.name])
# program.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape}
def OneHot(self, node):
input = self.graph.get_node(node.layer.input[0])
depth = self.graph.get_node(node.layer.input[1])
on_value = self.graph.get_node(node.layer.input[2])
off_value = self.graph.get_node(node.layer.input[3])
assert depth.layer_type == 'Const', 'Parameter depth should be Const in OneHot'
assert on_value.layer_type == 'Const', 'Parameter on_value should be Const in OneHot'
assert off_value.layer_type == 'Const', 'Parameter off_value should be Const in OneHot'
attr = {'depth': depth.value}
on_value = on_value.value
off_value = off_value.value
assert math.fabs(on_value -
1.0) < 1e-06, "on_value should be 1 in OneHot"
assert math.fabs(off_value -
0.0) < 1e-06, "off_value should be 0 in OneHot"
self.paddle_graph.add_layer(
"paddle.nn.functional.one_hot",
inputs={"x": input.name},
outputs=[node.name],
num_classes=depth.value)
def Pow(self, node):
x = self.graph.get_node(node.layer.input[0])
factor = self.graph.get_node(node.layer.input[1])
inputs = {"x": x.name}
attr = dict()
if factor.layer_type == 'Const':
attr["y"] = factor.value.tolist()
else:
inputs["y"] = factor.name
self.paddle_graph.add_layer(
"paddle.pow", inputs=inputs, outputs=[node.name], **attr)
def All(self, node):
input = self.graph.get_node(node.layer.input[0])
reduce_idx = self.graph.get_node(node.layer.input[1])
assert reduce_idx.layer_type == "Const", "Only support Const parameter[reduce_idx]"
attr = dict()
attr["axis"] = reduce_idx.value.tolist()
attr["keepdim"] = node.get_attr("keep_dims")
input_name = input.name
if input.dtype != "bool":
input_name = gen_name("all", "cast")
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": input.name},
outputs=[input_name],
dtype=string("bool"))
self.paddle_graph.add_layer(
"paddle.all",
inputs={"x": input_name},
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])
axis = self.graph.get_node(node.layer.input[2])
assert axis.layer_type == 'Const', "Only support Const parameter[axis]"
axis = axis.value.tolist()
assert axis == 0, "Only support axis=0 in GatherV2 OP"
index_name = index.name
if len(index.out_shapes[0]) != 1:
reshape_name = gen_name("gather", "reshape")
index_name = reshape_name
self.paddle_graph.add_layer(
"paddle.reshape",
inputs={"x": index.name},
outputs=[reshape_name],
shape=[-1])
inputs = {'x': embeddings.name, 'index': index_name}
self.paddle_graph.add_layer(
"paddle.gather",
inputs=inputs,
outputs=[node.name])
if len(index.out_shapes[0]) != 1:
out_shape = node.out_shapes[0]
self.paddle_graph.add_layer(
kernel="paddle.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)
y = self.graph.get_node(node.layer.input[1], copy=True)
inputs = {"x": x.name}
attr = dict()
if y.layer_type == 'Const':
dim = y.value.tolist()
if not isinstance(dim, list):
dim = [dim]
attr['axis'] = dim
else:
inputs['axis'] = y.name
self.paddle_graph.add_layer(
"paddle.unsqueeze",
inputs=inputs,
outputs=[node.name],
**attr)
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