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未验证 提交 f16ead9e 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #95 from Channingss/develop 

support new model & fix bug
上级 53159db3 a050d428
隐藏空白更改
内联 并排
Showing with 1746 addition and 122 deletion
x2paddle/convert.py
浏览文件 @ f16ead9e
......@@ -137,14 +137,17 @@ def onnx2paddle(model_path, save_dir):
except:
print("onnx is not installed, use \"pip install onnx==1.5.0\".")
return
print("Now translating model from onnx to paddle.")
from x2paddle.decoder.onnx_decoder import ONNXDecoder
from x2paddle.op_mapper.onnx_op_mapper import ONNXOpMapper
from x2paddle.optimizer.onnx_optimizer import ONNXOptimizer
print("Now translating model from onnx to paddle.")
model = ONNXDecoder(model_path)
from x2paddle.op_mapper.onnx_op_mapper import ONNXOpMapper
mapper = ONNXOpMapper(model)
from x2paddle.optimizer.onnx_optimizer import ONNXOptimizer
optimizer = ONNXOptimizer(mapper)
optimizer.delete_redundance_code()
mapper.save_inference_model(save_dir)
......
x2paddle/decoder/onnx_backend.py 0 → 100644
浏览文件 @ f16ead9e
# 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.
# Part of the following code in this file refs to https://github.com/pytorch/pytorch/blob/master/caffe2/python/onnx/backend.py
# PyTorch is BSD-style licensed, as found in the LICENSE file: https://github.com/pytorch/pytorch/blob/master/LICENSE
"""Backend for running ONNX on Caffe2
To run this, you will need to have Caffe2 installed as well.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import collections
from subprocess import Popen, PIPE
import zipfile
import itertools
# When onnx is built against a version of protobuf that is older than
# that which is vendored with caffe2, onnx will crash if caffe2's
# vendored protobuf is loaded first. We can work around this by
# importing onnx first, which will cause it to go out and pick up the
# system protobuf.
import onnx.backend
import caffe2
from caffe2.python import core, workspace, rnn_cell, gru_cell
from caffe2.python.compatibility import container_abcs
from caffe2.python.model_helper import ModelHelper
from caffe2.proto import caffe2_pb2
import caffe2.python.utils
import numpy as np
import onnx
from onnx import checker, GraphProto, TensorProto, AttributeProto, ModelProto
import onnx.numpy_helper
import onnx.defs
import onnx.optimizer
import onnx.shape_inference
import onnx.utils
from onnx.backend.base import Backend, Device, DeviceType, namedtupledict
from caffe2.python.onnx.workspace import Workspace
from caffe2.python.onnx.backend_rep import Caffe2Rep
from caffe2.python.onnx.backend_cpp_rep import Caffe2CppRep
import caffe2.python._import_c_extension as C
import warnings
def force_unicode(s):
try:
return s.decode('utf-8')
except AttributeError:
return s
def get_device_option(device):
m = {
DeviceType.CPU: caffe2_pb2.CPU,
DeviceType.CUDA: workspace.GpuDeviceType
}
return core.DeviceOption(m[device.type], device.device_id)
class OnnxAttributes(dict):
"""
This is a more convenient way to work with ONNX/Caffe2 attributes
that is not the protobuf representation.
"""
@staticmethod
def from_onnx(args):
d = OnnxAttributes()
for arg in args:
d[arg.name] = convertAttributeProto(arg)
return d
def caffe2(self, kmap=lambda k: k):
for k, v in self.items():
if kmap(k) != '':
yield caffe2.python.utils.MakeArgument(kmap(k), v)
# TODO: Move this into ONNX main library
def convertAttributeProto(onnx_arg):
"""
Convert an ONNX AttributeProto into an appropriate Python object
for the type.
NB: Tensor attribute gets returned as the straight proto.
"""
if onnx_arg.HasField('f'):
return onnx_arg.f
elif onnx_arg.HasField('i'):
return onnx_arg.i
elif onnx_arg.HasField('s'):
return onnx_arg.s
elif onnx_arg.HasField('t'):
return onnx_arg.t # this is a proto!
elif onnx_arg.HasField('g'):
return Caffe2Backend._graph_to_net(onnx_arg.g,
Caffe2Backend._known_opset_version)
elif len(onnx_arg.floats):
return list(onnx_arg.floats)
elif len(onnx_arg.ints):
return list(onnx_arg.ints)
elif len(onnx_arg.strings):
return list(onnx_arg.strings)
elif len(onnx_arg.graphs):
retval = []
# TODO: this doesn't work with RNN ops
for g in onnx_arg.graphs:
retval.append(
Caffe2Backend._graph_to_net(g,
Caffe2Backend._known_opset_version))
return retval
else:
raise ValueError("Unsupported ONNX attribute: {}".format(onnx_arg))
# TODO: Move this into ONNX main library
class OnnxNode(object):
"""
Reimplementation of NodeProto from ONNX, but in a form
more convenient to work with from Python.
We may temporarily edit these nodes to get them into Caffe2 form,
before actually translating into the Caffe2 protobuf, since this
is easier than decomposing everything, and putting it back together
when we're ready.
"""
def __init__(self, node):
self.name = str(node.name)
self.op_type = str(node.op_type)
self.attrs = OnnxAttributes.from_onnx(node.attribute)
self.inputs = list(node.input)
self.outputs = list(node.output)
Caffe2Ops = collections.namedtuple('Caffe2Ops',
['ops', 'init_ops', 'interface_blobs'])
class Caffe2Backend(Backend):
# The greatest version of the ONNX operator set which we are aware of.
# Models whose version is larger than this will cause us to emit a warning
# that we are attempting to translate on a "best effort" basis.
#
# If you increase this, make SURE you cross-reference all BC-breaking
# changes from one version to the next, and any that you did not
# implement, mark as broken in _broken_operators
_known_opset_version = 9
# This dictionary will record operators which are KNOWN to be
# broken, so we give a good error message rather than do something
# bogus and then fail.
_broken_operators = {
# 'BrokenOp': version_it_was_broken_in
}
# Operators that are different between Caffe2 and
# ONNX but only in their name.
# In most cases, this should be empty - as the effort of ONNX is
# to unify the operator definitions.
_renamed_operators = {
'GlobalMaxPool': 'MaxPool',
'GlobalAveragePool': 'AveragePool',
'Pad': 'PadImage',
'Neg': 'Negative',
'BatchNormalization': 'SpatialBN',
'InstanceNormalization': 'InstanceNorm',
'MatMul': 'BatchMatMul',
'Upsample': 'ResizeNearest',
'Identity': 'Copy',
'InstanceNormalization': 'InstanceNorm',
'Equal': 'EQ',
'Less': 'LT',
'Greater': 'GT',
'Unsqueeze': 'ExpandDims',
'Loop': 'ONNXWhile',
'Tile': 'NumpyTile',
'RandomNormal': 'GaussianFill',
'RandomUniform': 'UniformFill',
}
_global_renamed_attrs = {'kernel_shape': 'kernels'}
_per_op_renamed_attrs = {
'Squeeze': {
'axes': 'dims'
},
'Unsqueeze': {
'axes': 'dims'
},
'Transpose': {
'perm': 'axes'
},
'Upsample': {
'mode': '',
'scales': ''
},
'ConvTranspose': {
'output_padding': 'adjs'
},
'Selu': {
'gamma': 'scale'
},
'If': {
'then_branch': 'then_net',
'else_branch': 'else_net'
},
'RandomUniform': {
'low': 'min',
'high': 'max'
}
}
# operators whose behavior is different beyond renaming
# the value is an attribute of this class that is a
# function from ToffeIR node_def to caffe2 op_def
_special_operators = {
'LSTM': '_create_rnn_variant',
'GRU': '_create_rnn_variant',
'RNN': '_create_rnn_variant',
'Loop': '_create_loop',
'If': '_create_if',
'Upsample': '_create_upsample',
'RandomNormal': '_create_gaussian_fill'
}
# Dummy name generator
_dummy_name = C.DummyName()
@classmethod
def dummy_name(cls):
return cls._dummy_name.new_dummy_name()
# NB: By default, you will use the LATEST definition of the operator,
# so this interface MAY make BC-breaking changes. Specify an
# opset_version if you don't want this to version.
@classmethod
def run_node(cls,
node,
inputs,
device='CPU',
opset_version=_known_opset_version,
outputs_info=None):
super(Caffe2Backend, cls).run_node(node,
inputs,
device=device,
outputs_info=outputs_info,
opset_version=opset_version)
value_infos = []
device_option = get_device_option(Device(device))
ws = Workspace()
with core.DeviceScope(device_option): # temporary!
if isinstance(inputs, dict):
for key, value in inputs.items():
ws.FeedBlob(key, value)
value_infos.append(
onnx.helper.make_tensor_value_info(
name=key,
elem_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[
value.dtype],
shape=value.shape).SerializeToString())
else:
assert len(node.input) == len(
inputs), "{}: expected {} but got {}".format(
node.op_type, len(node.input), len(inputs))
for key, value in zip(node.input, inputs):
ws.FeedBlob(key, value)
value_infos.append(
onnx.helper.make_tensor_value_info(
name=key,
elem_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[
value.dtype],
shape=value.shape).SerializeToString())
ops = []
cbackend = C.Caffe2Backend(cls._dummy_name)
ops_str = cbackend.convert_node(node.SerializeToString(),
value_infos, opset_version)
for s in ops_str[0] + ops_str[1]:
op = caffe2_pb2.OperatorDef()
op.ParseFromString(s)
op.device_option.CopyFrom(device_option)
ops.append(op)
ws.RunOperatorsOnce(ops)
output_values = [ws.FetchBlob(name) for name in node.output]
return namedtupledict('Outputs', node.output)(*output_values)
@classmethod
def _create_tensor_filling_op(cls, onnx_tensor, name=None):
"""
Given an Onnx TensorProto, translate it into a Caffe2 operator
which produces the given tensor filling op.
"""
assert name or onnx_tensor.name
name = name or onnx_tensor.name
c2_op = caffe2_pb2.OperatorDef()
c2_values = c2_op.arg.add()
c2_values.name = "values"
def tensor2list(onnx_tensor):
# Use the onnx.numpy_helper because the data may be raw
return onnx.numpy_helper.to_array(onnx_tensor).flatten().tolist()
if onnx_tensor.data_type in [TensorProto.FLOAT]:
c2_op.type = 'GivenTensorFill'
c2_values.floats.extend(tensor2list(onnx_tensor))
elif onnx_tensor.data_type in [TensorProto.DOUBLE]:
c2_op.type = 'GivenTensorDoubleFill'
c2_values.floats.extend(tensor2list(onnx_tensor))
elif onnx_tensor.data_type in [TensorProto.INT64, TensorProto.UINT32]:
c2_op.type = 'GivenTensorInt64Fill'
c2_values.ints.extend(tensor2list(onnx_tensor))
elif onnx_tensor.data_type in [
TensorProto.UINT8, TensorProto.INT8, TensorProto.UINT16,
TensorProto.INT16, TensorProto.INT32
]:
c2_op.type = 'GivenTensorIntFill'
c2_values.ints.extend(tensor2list(onnx_tensor))
elif onnx_tensor.data_type == TensorProto.BOOL:
c2_op.type = 'GivenTensorBoolFill'
c2_values.ints.extend(tensor2list(onnx_tensor))
elif onnx_tensor.data_type == TensorProto.STRING:
c2_op.type = 'GivenTensorStringFill'
c2_values.strings.extend(onnx_tensor.string_data)
else:
raise RuntimeError("unrecognized tensor type {}".format(
onnx_tensor.data_type))
c2_shape = c2_op.arg.add()
c2_shape.name = "shape"
c2_shape.ints.extend(onnx_tensor.dims)
c2_op.output.append(name)
return c2_op
@classmethod
def _rnn_reform_weights(cls, reforms, name, hidden_size, init_net, gates,
reorder_indices):
for name_from, name_to, do_concat, extra_dims in reforms:
gate_blobs = [
'%s/%s_%s' % (name, prefix, name_to) for prefix in gates
]
for i, x in enumerate(gate_blobs):
dim0 = i * hidden_size, (i + 1) * hidden_size
starts, ends = zip(dim0, *extra_dims)
init_net.Slice(name_from, x, starts=starts, ends=ends)
if do_concat:
reordered_gate_blobs = [gate_blobs[i] for i in reorder_indices]
init_net.Concat(reordered_gate_blobs,
['%s/%s' % (name, name_to),
cls.dummy_name()],
axis=0)
@classmethod
def _make_rnn_direction(cls, input_blob, B, W, R, initial_states_and_names,
sequence_lens, pred_mh, init_net, input_size,
hidden_size, num_gates, direction_offset, Bi, Br,
W_, R_, reform, make_cell, keep_outputs):
name = cls.dummy_name()
# input and recurrence biases are squashed together in onnx
# but not in caffe2
gates_hidden_size = num_gates * hidden_size
bias_offset = 2 * direction_offset * gates_hidden_size
weight_offset = direction_offset * gates_hidden_size
Bi = init_net.Slice(B,
name + Bi,
starts=[bias_offset + 0 * gates_hidden_size],
ends=[bias_offset + 1 * gates_hidden_size])
Br = init_net.Slice(B,
name + Br,
starts=[bias_offset + 1 * gates_hidden_size],
ends=[bias_offset + 2 * gates_hidden_size])
W_ = init_net.Slice(W,
name + W_,
starts=[weight_offset + 0 * gates_hidden_size, 0],
ends=[weight_offset + 1 * gates_hidden_size, -1])
R_ = init_net.Slice(R,
name + R_,
starts=[weight_offset + 0 * gates_hidden_size, 0],
ends=[weight_offset + 1 * gates_hidden_size, -1])
initial_states_sliced = []
for initial_state, name_suffix in initial_states_and_names:
initial_states_sliced.append(
pred_mh.net.Slice(initial_state,
name + name_suffix,
starts=[direction_offset + 0, 0, 0],
ends=[direction_offset + 1, -1, -1]))
if direction_offset == 1:
if sequence_lens is not None:
seq_lens_for_reverse = sequence_lens
else:
input_shape = pred_mh.net.Shape(input_blob,
name + '/input_shape')
batch_size = pred_mh.net.Slice(input_shape,
name + '/batch_size_slice',
starts=[1],
ends=[2])
seq_len = pred_mh.net.Slice(input_shape,
name + '/seq_len_slice',
starts=[0],
ends=[1])
dummy_sequence_lens = pred_mh.net.Tile([seq_len, batch_size],
name +
'/dummy_sequence_lens',
axis=0)
pred_mh.net.Reshape(
dummy_sequence_lens,
[dummy_sequence_lens, cls.dummy_name()],
shape=[-1])
seq_lens_for_reverse = pred_mh.net.Cast(dummy_sequence_lens,
name +
'/seq_lens_for_reverse',
to=core.DataType.INT32)
reform(Bi, Br, W_, R_, name, hidden_size, init_net)
if direction_offset == 1:
input = pred_mh.net.ReversePackedSegs(
[input_blob, seq_lens_for_reverse], name + "/input-reversed")
else:
input = input_blob
outputs = keep_outputs(
list(
make_cell(
pred_mh,
input,
sequence_lens,
initial_states_sliced,
input_size,
hidden_size,
name,
drop_states=False,
forward_only=True,
)))
if direction_offset == 1:
outputs[0] = pred_mh.net.ReversePackedSegs(
[outputs[0], seq_lens_for_reverse], name + "/output-reversed")
return outputs
@classmethod
def _create_rnn_variant(cls, init_model, pred_model, n, opset_version):
assert init_model is not None, "cannot convert RNNs without access to the full model"
assert pred_model is not None, "cannot convert RNNs without access to the full model"
attrs = dict(n.attrs) # make a copy, which is safe to mutate
hidden_size = attrs.pop('hidden_size')
direction = force_unicode(attrs.pop('direction', 'forward'))
if n.op_type == 'RNN':
activation = force_unicode(
attrs.pop('activations', ('tanh', ))[0].lower())
elif n.op_type == 'GRU':
linear_before_reset = attrs.pop('linear_before_reset', 0)
assert not attrs, "unsupported RNN attributes: " + str(attrs.keys())
assert direction in ['forward', 'bidirectional'
], "unsupported backwards RNN/GRU/LSTM"
if n.op_type in ['RNN', 'GRU']:
input_blob, W, R, B, sequence_lens, initial_h = n.inputs
elif n.op_type == 'LSTM':
input_blob, W, R, B, sequence_lens, initial_h, initial_c = n.inputs
if sequence_lens == "":
sequence_lens = None
for x in itertools.chain(init_model.graph.input,
init_model.graph.value_info,
pred_model.graph.input,
pred_model.graph.value_info):
if x.name == W:
input_size = x.type.tensor_type.shape.dim[2].dim_value
break
else:
raise RuntimeError(
"best-effort shape inference for RNN/GRU/LSTM failed")
pred_mh = ModelHelper()
init_net = core.Net("init-net")
init_net.Reshape(W, [W, cls.dummy_name()], shape=[1, -1, 0])
init_net.Squeeze(W, W, dims=[0])
init_net.Reshape(R, [R, cls.dummy_name()], shape=[1, -1, 0])
init_net.Squeeze(R, R, dims=[0])
init_net.Reshape(B, [B, cls.dummy_name()], shape=[1, -1])
init_net.Squeeze(B, B, dims=[0])
if n.op_type == 'RNN':
def reform(*args):
pass
def make_cell(*args, **kwargs):
return rnn_cell.BasicRNN(*args, activation=activation, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h')],
sequence_lens, pred_mh, init_net, input_size, hidden_size,
1, direction_offset, "/i2h_b", "/gates_t_b", "/i2h_w",
"/gates_t_w", reform, make_cell, lambda x: x)
elif n.op_type == 'GRU':
def reform(Bi, Br, W_, R_, name, hidden_size, init_net):
# caffe2 has a different order from onnx. We need to rearrange
# z r h -> r z h
reforms = ((W_, 'i2h_w', True, [(0, -1)]), (R_, 'gate_t_w',
False, [(0, -1)]),
(Bi, 'i2h_b', True, []), (Br, 'gate_t_b', False, []))
cls._rnn_reform_weights(reforms, name, hidden_size, init_net,
['update', 'reset', 'output'],
[1, 0, 2])
def make_cell(*args, **kwargs):
return gru_cell.GRU(*args,
linear_before_reset=linear_before_reset,
**kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h')],
sequence_lens, pred_mh, init_net, input_size, hidden_size,
3, direction_offset, "_bias_i2h", "_bias_gates",
"/i2h_w_pre", "/gates_t_w_pre", reform, make_cell,
lambda x: x)
elif n.op_type == 'LSTM':
def reform(Bi, Br, W_, R_, name, hidden_size, init_net):
# caffe2 has a different order from onnx. We need to rearrange
# i o f c -> i f o c
reforms = ((W_, 'i2h_w', True, [(0, -1)]), (R_, 'gates_t_w',
True, [(0, -1)]),
(Bi, 'i2h_b', True, []), (Br, 'gates_t_b', True, []))
cls._rnn_reform_weights(reforms, name, hidden_size, init_net,
['input', 'output', 'forget', 'cell'],
[0, 2, 1, 3])
def make_cell(*args, **kwargs):
return rnn_cell.LSTM(*args, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h'),
(initial_c, '/initial_c')],
sequence_lens, pred_mh, init_net, input_size, hidden_size,
4, direction_offset, "/i2h_b", "/gates_t_b", "/i2h_w",
"/gates_t_w", reform, make_cell,
lambda x: [x[0], x[1], x[3]])
if direction == 'forward':
outputs = make_rnn(0)
# in the forward case, storage is shared between the
# last outputs. We need to decouple them so that the
# VariableLengthSequencePadding only mutates
# n.outputs[0]
for i in range(1, len(outputs)):
pred_mh.net.Copy(outputs[i], n.outputs[i])
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[outputs[0], sequence_lens], [outputs[0]])
pred_mh.net.ExpandDims([outputs[0]], [n.outputs[0]], dims=[1])
elif direction == 'bidirectional':
outputs_f = make_rnn(0)
outputs_b = make_rnn(1)
concatted_output, _ = pred_mh.net.Concat(
[outputs_f[0], outputs_b[0]],
[cls.dummy_name(), cls.dummy_name()],
axis=2)
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[concatted_output, sequence_lens], [concatted_output])
reshaped_output, _ = pred_mh.net.Reshape(
concatted_output,
[cls.dummy_name(), cls.dummy_name()],
shape=[0, 0, -1, 2])
pred_mh.net.Transpose(reshaped_output,
n.outputs[0],
axes=[0, 2, 1, 3])
for i in range(1, len(n.outputs)):
pred_mh.net.Concat(
[outputs_f[i], outputs_b[i]],
[n.outputs[i], cls.dummy_name()],
axis=0)
# We want to decide whether to put all of our weight-reshaping
# operators in the init net or the predict net. We can put
# them in the init net iff the inputs to those operators are
# already available, either as graph initializers, or as the
# output of other operators in the init net. The latter case
# occurs, for example, when exporting from pytorch to onnx.
# In most production use, we expect has_initializers to be
# true.
initializers = {i.name for i in init_model.graph.initializer}
outputs = {
output
for node in init_model.graph.node for output in node.output
}
has_initializers = all(x in initializers or x in outputs
for x in (W, R, B))
pred_ops = []
init_ops = []
(init_ops if has_initializers else pred_ops).extend(init_net.Proto().op)
pred_ops.extend(pred_mh.Proto().op)
return Caffe2Ops(pred_ops, init_ops,
list(pred_mh.Proto().external_input))
@classmethod
def _create_control_op(cls, init_model, pred_model, n, opset_version):
control_inputs = []
if '__control_inputs' in n.attrs:
control_inputs.extend(n.attrs['__control_inputs'])
node = cls._common_onnx_node_to_caffe2_op(init_model, pred_model, n,
opset_version)
node.control_input.extend(control_inputs)
return Caffe2Ops([node], [], [])
@classmethod
def _remove_ssa(cls, net, remap_dict):
for op in net.op:
for i, name in enumerate(op.output):
if name in remap_dict:
op.output[i] = remap_dict[name]
for i, out in enumerate(net.external_output):
if out in remap_dict:
net.external_output[i] = remap_dict[out]
@classmethod
def _create_if(cls, init_model, pred_model, n, opset_version):
ops = cls._create_control_op(init_model, pred_model, n, opset_version)
assert ops[0][0].type == 'If'
if_op = ops[0][0]
then_net = else_net = None
control_inputs = []
for arg in if_op.arg:
if arg.name == 'then_net':
then_net = arg.n
if arg.name == 'else_net':
else_net = arg.n
if arg.name == '__control_inputs':
control_inputs = arg.strings
assert then_net and else_net
then_net_outs = then_net.external_output
else_net_outs = else_net.external_output
op_outputs = if_op.output
assert len(then_net_outs) == len(else_net_outs)
assert len(else_net_outs) == len(op_outputs)
for arg in if_op.arg:
if arg.name == 'then_net':
arg.n.external_input.extend(control_inputs)
if arg.name == 'else_net':
arg.n.external_input.extend(control_inputs)
return ops
@classmethod
def _create_loop(cls, init_model, pred_model, n, opset_version):
ops = cls._create_control_op(init_model, pred_model, n, opset_version)
assert ops[0][0].type == 'ONNXWhile'
while_op = ops[0][0]
while_op.arg.extend(
[caffe2.python.utils.MakeArgument('has_trip_count', True)])
while_op.arg.extend(
[caffe2.python.utils.MakeArgument('has_cond', True)])
while_op.arg.extend(
[caffe2.python.utils.MakeArgument('disable_scopes', True)])
control_inputs = []
for arg in while_op.arg:
if arg.name == '__control_inputs':
control_inputs = arg.strings
num_loop_carried_deps = 0
for arg in while_op.arg:
if arg.name == 'body':
num_loop_carried_deps = len(arg.n.external_input) - 2
arg.n.external_input.extend(control_inputs)
while_op.arg.extend([
caffe2.python.utils.MakeArgument('num_loop_carried_deps',
num_loop_carried_deps)
])
return ops
@classmethod
def _substitute_raw_value(cls, tp, raw_values_dict):
if tp.HasField('raw_data') and tp.raw_data == bytes(b'__EXTERNAL'):
if tp.name not in raw_values_dict:
raise RuntimeError(
'TensorProto for value {} referenced raw data but it was not found!'
.format(tp.name))
else:
tp.raw_data = raw_values_dict[tp.name]
@classmethod
def _visit_and_substitute_raw_values(cls, nodes, raw_values_dict):
for node in nodes:
for attr in node.attribute:
if attr.HasField('t'):
cls._substitute_raw_value(attr.t, raw_values_dict)
for t in attr.tensors:
cls._substitute_raw_value(t, raw_values_dict)
if attr.HasField('g'):
cls._visit_and_substitute_raw_values(
attr.g.node, raw_values_dict)
for g in attr.graphs:
cls._visit_and_substitute_raw_values(
g.node, raw_values_dict)
@classmethod
def _external_value_resolution_pass(cls, model, raw_values_dict):
for init in model.graph.initializer:
cls._substitute_raw_value(init, raw_values_dict)
cls._visit_and_substitute_raw_values(model.graph.node, raw_values_dict)
@classmethod
def _direct_initialize_parameters(cls, initializer, ws, device_option):
for tp in initializer:
ws.FeedBlob(tp.name, onnx.numpy_helper.to_array(tp), device_option)
@classmethod
def _direct_initialize_inputs(cls, inputs, initialized, ws, device_option):
for value_info in inputs:
if value_info.name in initialized:
continue
shape = list(d.dim_value
for d in value_info.type.tensor_type.shape.dim)
ws.FeedBlob(
value_info.name,
np.ones(shape,
dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[
value_info.type.tensor_type.elem_type]),
device_option)
@staticmethod
def optimize_onnx(input, init=False, predict=False):
passes = [
'fuse_consecutive_transposes', 'eliminate_nop_transpose',
'fuse_transpose_into_gemm', 'lift_lexical_references'
]
if init:
passes.append('split_init')
if predict:
passes.append('split_predict')
out = onnx.optimizer.optimize(input, passes)
return out
@classmethod
def prepare_zip_archive(cls, file, device='CPU', **kwargs):
with zipfile.ZipFile(file, mode='r') as z:
with z.open('__MODEL_PROTO', 'r') as f:
model = onnx.load(f)
blob_names = set(z.namelist()) - set('__MODEL_PROTO')
# TODO: make this more efficient
raw_values_dict = {}
for name in blob_names:
with z.open(name, 'r') as blob_file:
raw_values_dict[name] = blob_file.read()
return cls.prepare(model,
device,
raw_values_dict=raw_values_dict,
**kwargs)
@classmethod
def prepare(cls, model, device='CPU', raw_values_dict=None, **kwargs):
'''
For Onnx Caffe2Backend, we require that init_graph don't initialize the actual input of the predict_graph,
for example, if "img" is the input blob for the predict_net, we require that in init_graph and in
initializer of the predict_graph, "img" is not initalized. We don't have a check for this, since
there is no way we can know which blob is the input of the predict_graph.
'''
if not kwargs.pop('no_check_UNSAFE', False):
super(Caffe2Backend, cls).prepare(model, device, **kwargs)
opset_version = None
for imp in model.opset_import:
if not imp.HasField("domain") or imp.domain == "":
opset_version = imp.version
if imp.version > cls._known_opset_version:
warnings.warn(
"This version of onnx-caffe2 targets ONNX operator set version {}, but the model we are trying to import uses version {}. We will try to import it anyway, but if the model uses operators which had BC-breaking changes in the intervening versions, import will fail."
.format(cls._known_opset_version, imp.version))
else:
warnings.warn("Unrecognized operator set {}".format(imp.domain))
if opset_version is None:
if model.ir_version >= 0x00000003:
raise RuntimeError(
"Model with IR version >= 3 did not specify ONNX operator set version (onnx-caffe2 requires it)"
)
else:
opset_version = 1
ws = Workspace()
device_option = get_device_option(Device(device))
init_net, predict_net = cls._onnx_model_to_caffe2_net(
model, device, opset_version, False)
if raw_values_dict:
cls._external_value_resolution_pass(model, raw_values_dict)
# Directly load initializer data into blobs in workspace
cls._direct_initialize_parameters(
model.graph.initializer,
ws,
device_option,
)
initialized = {init.name for init in model.graph.initializer}
cls._direct_initialize_inputs(
model.graph.input,
initialized,
ws,
device_option,
)
uninitialized = [
value_info.name for value_info in model.graph.input
if value_info.name not in initialized
]
retval = Caffe2Rep(init_net, predict_net, ws, uninitialized)
return retval
@classmethod
# TODO: This method needs a refactor for clarity
def _onnx_node_to_caffe2_op(cls, init_model, pred_model, node_def,
opset_version):
cbackend = C.Caffe2Backend(cls._dummy_name)
if cbackend.support_onnx_import(node_def.op_type):
# extract value infos from pred model (value infos of
# node's inputs that are in init model should be all
# available in pred model)
value_infos = []
for name in node_def.input:
if pred_model is not None:
for vi in itertools.chain(pred_model.graph.input,
pred_model.graph.output,
pred_model.graph.value_info):
if vi.name == name:
value_infos.append(vi.SerializeToString())
op_strs = cbackend.convert_node(node_def.SerializeToString(),
value_infos, opset_version)
init_ops = []
for s in op_strs[0]:
op = caffe2_pb2.OperatorDef()
op.ParseFromString(s)
init_ops.append(op)
ops = []
for s in op_strs[1]:
op = caffe2_pb2.OperatorDef()
op.ParseFromString(s)
ops.append(op)
return Caffe2Ops(ops, init_ops, [])
if node_def.op_type in cls._special_operators:
translator = getattr(cls, cls._special_operators[node_def.op_type])
else:
translator = cls._common_onnx_node_to_caffe2_op
ops = translator(init_model, pred_model, OnnxNode(node_def),
opset_version)
if isinstance(ops, Caffe2Ops):
return ops
if not isinstance(ops, container_abcs.Iterable):
ops = [ops]
return Caffe2Ops(ops, [], [])
_broadcast_operators = {
'Add',
'Sub',
}
@classmethod
def _common_onnx_node_to_caffe2_op(cls, init_model, pred_model, onnx_node,
opset_version):
"""
This translator performs the basic translation of ONNX nodes into
Caffe2 operators. Besides doing a straightforward marshalling from
one format to another, it also does these extra things:
- Renames operators based on '_renamed_operators'
- Renames attributes based on '_global_renamed_attrs' and
'_per_op_renamed_attrs'
If you're writing a custom translator, consider calling this first,
and then fixing things up further.
"""
c2_op = caffe2_pb2.OperatorDef()
c2_op.input.extend(onnx_node.inputs)
c2_op.output.extend(onnx_node.outputs)
c2_op.name = onnx_node.name
onnx_op_type = onnx_node.op_type
broken_version = cls._broken_operators.get(onnx_op_type, float('Inf'))
if broken_version <= opset_version:
raise ValueError(
"Don't know how to translate op {} in ONNX operator set v{} (I only support prior to v{})"
.format(onnx_op_type, opset_version, broken_version))
c2_op.type = cls._renamed_operators.get(onnx_op_type, onnx_op_type)
if not core.IsOperator(c2_op.type):
raise ValueError(
"Don't know how to translate op {}".format(onnx_op_type))
def kmap(k):
if (onnx_op_type in cls._per_op_renamed_attrs
and k in cls._per_op_renamed_attrs[onnx_op_type]):
return cls._per_op_renamed_attrs[onnx_op_type][k]
if k in cls._global_renamed_attrs:
return cls._global_renamed_attrs[k]
return k
c2_op.arg.extend(onnx_node.attrs.caffe2(kmap=kmap))
if opset_version < 7:
# onnx opset 7 and newest caffe2 have adopted full onnx broadcast semantics
# so we don't need this hack anymore
if c2_op.type in cls._broadcast_operators:
already_broadcast = False
for arg in c2_op.arg:
if arg.name == 'broadcast':
already_broadcast = True
if not already_broadcast:
c2_op.arg.extend(
[caffe2.python.utils.MakeArgument('broadcast', 1)])
return c2_op
@staticmethod
def _all_names_in_graph(graph):
if graph is None:
return set()
names = set()
names.update(value_info.name for value_info in graph.input)
names.update(value_info.name for value_info in graph.output)
for node in graph.node:
names.update(node.input)
names.update(node.output)
return names
@classmethod
def _graph_to_net(cls, onnx_graph, opset_version):
net = caffe2_pb2.NetDef()
for node in onnx_graph.node:
try:
c2ops = cls._onnx_node_to_caffe2_op(None, None, node,
opset_version)
except Exception as e:
print('ONNX FATAL:', e)
continue
net.op.extend(c2ops.init_ops)
net.op.extend(c2ops.ops)
net.external_input.extend(c2ops.interface_blobs)
net.external_output.extend(value_info.name
for value_info in onnx_graph.output)
net.external_input.extend(value_info.name
for value_info in onnx_graph.input)
return net
@classmethod
def _onnx_model_to_caffe2_net(cls, onnx_model, device, opset_version,
include_initializers):
device_option = get_device_option(Device(device))
# init_model = cls.optimize_onnx(onnx_model, init=True)
# pred_model = cls.optimize_onnx(onnx_model, predict=True)
init_model = onnx_model
pred_model = onnx_model
init_net = caffe2_pb2.NetDef()
pred_net = caffe2_pb2.NetDef()
init_net.name = onnx_model.graph.name + '_init'
pred_net.name = onnx_model.graph.name + '_predict'
if include_initializers:
init_net.op.extend(
cls._create_tensor_filling_op(tp)
for tp in onnx_model.graph.initializer)
cls._dummy_name.reset(
cls._all_names_in_graph(init_model.graph)
| cls._all_names_in_graph(pred_model.graph))
success = True
for net, model in ((init_net, init_model), (pred_net, pred_model)):
net.device_option.CopyFrom(device_option)
for node in model.graph.node:
try:
c2ops = cls._onnx_node_to_caffe2_op(init_model, pred_model,
node, opset_version)
except Exception as e:
success = False
print('ONNX FATAL:', e)
continue
init_net.op.extend(c2ops.init_ops)
net.op.extend(c2ops.ops)
net.external_input.extend(c2ops.interface_blobs)
net.external_output.extend(value_info.name
for value_info in model.graph.output)
net.external_input.extend(value_info.name
for value_info in model.graph.input)
if not success:
raise RuntimeError('ONNX conversion failed')
return init_net, pred_net
# wrapper for backwards compatability
@classmethod
def onnx_graph_to_caffe2_net(cls,
model,
device="CPU",
opset_version=_known_opset_version):
return cls._onnx_model_to_caffe2_net(model,
device=device,
opset_version=opset_version,
include_initializers=True)
@classmethod
def supports_device(cls, device_str):
device = Device(device_str)
if device.type == DeviceType.CPU:
return True
elif core.IsGPUDeviceType(device.type):
return workspace.has_gpu_support
return False
@classmethod
def is_compatible(cls, model, device='CPU', **kwargs):
if hasattr(super(Caffe2Backend, cls), 'is_compatible') \
and callable(super(Caffe2Backend, cls).is_compatible):
if not super(Caffe2Backend, cls).is_compatible(
model, device, **kwargs):
return False
# TODO: should have an unspported list of operators, be optimistic for now
return True
prepare = Caffe2Backend.prepare
prepare_zip_archive = Caffe2Backend.prepare_zip_archive
run_node = Caffe2Backend.run_node
run_model = Caffe2Backend.run_model
supports_device = Caffe2Backend.supports_device # noqa
is_compatible = Caffe2Backend.is_compatible
x2paddle/decoder/onnx_decoder.py
浏览文件 @ f16ead9e
......@@ -23,6 +23,7 @@ from onnx.helper import get_attribute_value, make_attribute
from onnx.shape_inference import infer_shapes
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
from onnx.numpy_helper import to_array
from onnx import AttributeProto, TensorProto, GraphProto
from collections import OrderedDict as Dict
import onnx
import numpy as np
......@@ -44,7 +45,7 @@ class ONNXGraphNode(GraphNode):
self.attr_map = self.get_attr_map()
self.dtype_map = {1: "float32", 3: "int32", 9: "int64"}
self.weight_inputs = list()
self.out_shapes = None
self.out_shapes = list()
self.dtype = None
def get_attr_map(self):
......@@ -58,11 +59,10 @@ class ONNXGraphNode(GraphNode):
@property
def value(self):
assert 'Constant' in self.layer_type, "Only Constant node has value."
attr = self.layer.attr['value']
if 'value' in self.attr_map:
return default
assert 'Constant' in self.layer_type, "Only Constant | ConstantOfShape node has value."
attr = self.layer.attribute['value']
if 'value' not in self.attr_map:
return None
return self.attr_map[name]
def get_attribute_value2(self, attr):
......@@ -110,23 +110,26 @@ class ONNXGraphDataNode(GraphNode):
def out_shapes(self):
values = self.layer.type.tensor_type.shape.dim
out_shapes = list()
out_shapes = [dim.dim_value for dim in values]
out_shapes.append([dim.dim_value for dim in values])
return out_shapes
@property
def dtype(self):
dtype = self.layer.type.tensor_type.elem_type
return TENSOR_TYPE_TO_NP_TYPE[dtype]
class ONNXGraph(Graph):
def __init__(self, model):
super(ONNXGraph, self).__init__(model)
def __init__(self, graph, onnx_model):
super(ONNXGraph, self).__init__(graph)
self.onnx_model = onnx_model
self.initializer = {}
self.place_holder_nodes = list()
self.get_place_holder_nodes()
self.value_infos = self.inferred_model_value_info(graph)
self.results_of_inference = dict()
def get_inner_nodes(self):
"""
generate inner node of ONNX model
......@@ -162,17 +165,22 @@ class ONNXGraph(Graph):
"""
build topo_sort of ONNX model
"""
data_node = self.place_holder_nodes[0]
value_info = self.value_infos[data_node]
input_shape = value_info['shape']
self.get_results_of_inference(self.onnx_model, input_shape)
for layer in self.model.node:
self.node_map[layer.name] = ONNXGraphNode(layer)
#set op node's dtype and out_shapes
for item in self.model.value_info:
if item.name in self.node_map:
self.node_map[item.name].dtype = TENSOR_TYPE_TO_NP_TYPE[
item.type.tensor_type.elem_type]
self.node_map[item.name].out_shapes = [
dim.dim_value for dim in item.type.tensor_type.shape.dim
]
node = ONNXGraphNode(layer)
self.node_map[layer.name] = node
for opt in layer.output:
if opt in self.value_infos:
value_info = self.value_infos[opt]
node.dtype = value_info['dtype']
node.out_shapes.append(value_info['shape'])
else:
_, dtype, shape = self.get_dynamic_shape(opt)
node.dtype = dtype
node.out_shapes.append(shape)
for layer in self.model.input:
if layer.name not in self.node_map:
......@@ -199,7 +207,6 @@ class ONNXGraph(Graph):
format(in_node, layer_name))
else:
self.connect(in_node, layer_name)
#generate topo
super(ONNXGraph, self).build()
......@@ -227,31 +234,108 @@ class ONNXGraph(Graph):
weight = to_array(initializer)
yield name, weight
def inferred_model_value_info(self, graph):
"""
collect value/type info for an ONNX model
"""
assert isinstance(graph,
onnx.GraphProto), 'model is not a ModelProto instance'
value_info = Dict()
for item in graph.value_info:
value_info[item.name] = {
'dtype':
TENSOR_TYPE_TO_NP_TYPE[item.type.tensor_type.elem_type],
'shape':
[dim.dim_value for dim in item.type.tensor_type.shape.dim],
'external': False
}
for item in graph.input:
assert item.name not in value_info
value_info[item.name] = {
'dtype':
TENSOR_TYPE_TO_NP_TYPE[item.type.tensor_type.elem_type],
'shape':
[dim.dim_value for dim in item.type.tensor_type.shape.dim],
'external': True
}
for item in graph.output:
assert item.name not in value_info
value_info[item.name] = {
'dtype':
TENSOR_TYPE_TO_NP_TYPE[item.type.tensor_type.elem_type],
'shape':
[dim.dim_value for dim in item.type.tensor_type.shape.dim],
'external': True
}
return value_info
def get_results_of_inference(self, model, shape):
try:
import torch
version = torch.__version__
if '1.1.0' not in version:
print("your model have dynamic graph, torch==1.1.0 is required")
return
except:
print(
"your model have dynamic graph, we use caff2 to inference graph, please use \"pip install torch==1.1.0\"."
)
return
from x2paddle.decoder.onnx_backend import prepare
np_images = np.random.rand(shape[0], shape[1], shape[2],
shape[3]).astype('float32')
outputs = []
for node in model.graph.node:
value_info = helper.make_tensor_value_info(node.name,
TensorProto.UNDEFINED,
[])
outputs.append(value_info)
while len(outputs) > 0:
tmp_outputs = outputs[:254]
model.graph.ClearField('output')
model.graph.output.MergeFrom(tmp_outputs)
prepared_backend = prepare(model,
device='CPU',
no_check_UNSAFE=True)
res = prepared_backend.run(inputs=np_images)
for idx, info in enumerate(tmp_outputs):
self.results_of_inference[info.name] = res[idx]
outputs = outputs[254:]
return
def get_dynamic_shape(self, layer):
"""
get dynamic shape from caffe2.backend
"""
output = self.results_of_inference[layer]
return output.tolist(), output.dtype, output.shape
class ONNXDecoder(object):
def __init__(self, onnx_model):
model = onnx.load(onnx_model)
print('model ir_version: {}, op version: {}'.format(
model.ir_version, model.opset_import[0].version))
if model.opset_import[0].version < 9:
_logger.warning(
'Now, onnx2paddle main support convert onnx model opset_verison == 9,'
'opset_verison of your onnx model is %d < 9,'
'some operator may cannot convert.',
model.opset_import[0].version)
check_model(model)
model = polish_model(model)
check_model(model)
model = onnx.shape_inference.infer_shapes(model)
model = self.optimize_model_skip_op_for_inference(model)
model = self.optimize_model_strip_initializer(model)
self.standardize_variable_name(model.graph)
self.model = model
graph_def = model.graph
self.onnx_graph = ONNXGraph(graph_def)
self.onnx_graph = ONNXGraph(graph_def, model)
self.onnx_graph.build()
def build_value_refs(self, nodes):
......@@ -334,9 +418,13 @@ class ONNXDecoder(object):
output_name, output_refs)
else:
processed = -1
if processed > 0:
nodes_to_remove.append(node_idx)
for value_info in ret.graph.value_info:
for output in node.output:
if value_info.name == output:
ret.graph.value_info.remove(value_info)
print('skip op {}: {} -> {} -> {}'.format(
node_idx, input_name, node.op_type, output_name))
elif processed == 0:
......@@ -396,7 +484,6 @@ class ONNXDecoder(object):
"""
standardize variable name for paddle's code
"""
for initializer in graph.initializer:
initializer.name = self.make_variable_name(initializer.name)
for ipt in graph.input:
......@@ -455,43 +542,3 @@ class ONNXDecoder(object):
raise RuntimeError("Input mismatch {} != {}".format(
len(onnx_model.input), len(model.input)))
return onnx_model
def get_dynamic_shape_from_caffe2(self, layer, input_shapes):
"""
get dynamic shape from caffe2.backend
"""
try:
import torch
version = torch.__version__
if '1.1.0' not in version:
print("your model have dynamic graph, torch==1.1.0 is required")
return
except:
print(
"your model have dynamic graph, we use caff2 to inference graph, please use \"pip install torch==1.1.0\"."
)
return
from caffe2.python.onnx.backend import prepare
shape = input_shapes[0]
np_images = np.random.rand(shape[0], shape[1], shape[2],
shape[3]).astype('float32')
num_onnx = self.split_model(self.model, layer)
prepared_backend = prepare(num_onnx, device='CPU')
output = prepared_backend.run(inputs=np_images)
return output[0].tolist()
def get_dynamic_shape_from_onnx(self, layer, input_shapes):
"""
get dynamic shape from onnxruntime
"""
import onnxruntime as rt
from onnxruntime.backend import prepare
import numpy as np
num_onnx = self.split_model(self.model, layer)
sess = prepare(num_onnx)
shape = input_shapes[0]
print(shape)
np_images = np.random.rand(shape[0], shape[1], shape[2],
shape[3]).astype('float32')
output = sess.run(model=sess, inputs=np_images)
return output[0].tolist()
x2paddle/op_mapper/onnx_custom_layer/InstanceNormalization.py 0 → 100644
浏览文件 @ f16ead9e
# 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 .register import register
def InstanceNormalization_shape(input_shape):
return input_shape
def InstanceNormalization_layer(inputs, name=None):
# TODO(lvmengsi@baidu.com): Check the accuracy when using fluid.layers.layer_norm.
epsilon = 1e-5
mean = fluid.layers.reduce_mean(inputs, dim=[2, 3], keep_dim=True)
var = fluid.layers.reduce_mean(fluid.layers.square(inputs - mean),
dim=[2, 3],
keep_dim=True)
if name is not None:
scale_name = name + "_scale"
offset_name = name + "_offset"
scale_param = fluid.ParamAttr(name=scale_name,
initializer=fluid.initializer.Constant(1.0),
trainable=True)
offset_param = fluid.ParamAttr(name=offset_name,
initializer=fluid.initializer.Constant(0.0),
trainable=True)
scale = fluid.layers.create_parameter(attr=scale_param,
shape=inputs.shape[1:2],
dtype="float32")
offset = fluid.layers.create_parameter(attr=offset_param,
shape=inputs.shape[1:2],
dtype="float32")
tmp = fluid.layers.elementwise_mul(x=(inputs - mean), y=scale, axis=1)
tmp = tmp / fluid.layers.sqrt(var + epsilon)
tmp = fluid.layers.elementwise_add(tmp, offset, axis=1)
return tmp
def InstanceNormalization_weights(name, data=None):
weights_name = [name + '_scale']
return weights_name
register(kind='InstanceNormalization',
shape=InstanceNormalization_shape,
layer=InstanceNormalization_layer,
weights=InstanceNormalization_weights)
x2paddle/op_mapper/onnx_custom_layer/__init__.py 0 → 100644
浏览文件 @ f16ead9e
# 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 .register import get_registered_layers
#custom layer import begins
from . import InstanceNormalization
#custom layer import ends
custom_layers = get_registered_layers()
def set_args(f, params):
""" set args for function 'f' using the parameters in node.layer.param
Args:
f (function): a python function object
params (object): a object contains attributes needed by f's arguments
Returns:
arg_names (list): a list of argument names
kwargs (dict): a dict contains needed arguments
"""
argc = f.__code__.co_argcount
arg_list = f.__code__.co_varnames[0:argc]
kwargs = {}
for arg_name in arg_list:
if hasattr(params, arg_name) and params is not None:
kwargs[arg_name] = getattr(params, arg_name)
return arg_list, kwargs
def has_layer(layer_type):
""" test whether this layer exists in custom layer
"""
return layer_type in custom_layers
def get_params(layer, layer_type):
import re
if layer_type.lower() == "deconvolution" or layer_type.lower(
) == "convolutiondepthwise":
param_name = '_'.join(('convolution', 'param'))
elif layer_type.lower() == "normalize":
param_name = '_'.join(('norm', 'param'))
elif len(layer_type) - len(re.sub("[A-Z]", "", layer_type)) >= 2:
s = ''
tmp_name = ''
for i, ch in enumerate(layer_type):
if i == 0:
s += ch.lower()
continue
elif ch.isupper() and layer_type[i - 1].islower():
tmp_name += (s + '_')
s = ''
s += ch.lower()
tmp_name += s
param_name = '_'.join((tmp_name, 'param'))
else:
param_name = '_'.join((layer_type.lower(), 'param'))
return getattr(layer, param_name, None)
def compute_output_shape(node):
""" compute the output shape of custom layer
"""
layer_type = node.layer_type
assert layer_type in custom_layers, "layer[%s] not exist in custom layers" % (
layer_type)
shape_func = custom_layers[layer_type]['shape']
layer = node.layer
params = get_params(layer, layer_type)
arg_names, kwargs = set_args(shape_func, params)
input_shape = node.input_shape
return shape_func(input_shape, **kwargs)
def make_custom_layer(node):
""" get the code which implement the custom layer function
"""
layer_type = node.layer_type
assert layer_type in custom_layers, "layer[%s] not exist in custom layers" % (
layer_type)
layer_func = custom_layers[layer_type]['layer']
import inspect
return inspect.getsource(layer_func), layer_func
def deal_weights(node, data=None):
""" deal the weights of the custom layer
"""
layer_type = node.layer_type
weights_func = custom_layers[layer_type]['weights']
name = node.layer_name
return weights_func(name, data)
x2paddle/op_mapper/onnx_custom_layer/register.py 0 → 100644
浏览文件 @ f16ead9e
# 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.
""" this module provides 'register' for registering customized layers
"""
g_custom_layers = {}
def register(kind, shape, layer, weights):
""" register a custom layer or a list of custom layers
Args:
@kind (str or list): type name of the layer
@shape (function): a function to generate the shape of layer's output
@layer (function): a function to generate the paddle code of layer
@weights (function): a function to deal with weights data
Returns:
None
"""
assert type(shape).__name__ == 'function', 'shape should be a function'
assert type(layer).__name__ == 'function', 'layer should be a function'
if type(kind) is str:
kind = [kind]
else:
assert type(
kind
) is list, 'invalid param "kind" for register, not a list or str'
for k in kind:
assert type(
k) is str, 'invalid param "kind" for register, not a list of str'
assert k not in g_custom_layers, 'this type[%s] has already been registered' % (
k)
print('register layer[%s]' % (k))
g_custom_layers[k] = {
'shape': shape,
'layer': layer,
'weights': weights
}
def get_registered_layers():
return g_custom_layers
x2paddle/op_mapper/onnx_directly_map.py
浏览文件 @ f16ead9e
......@@ -24,6 +24,7 @@ default_op_mapping_field_values['DEFAULTS'] = dict()
default_op_mapping_field_values['INPUT_PERM'] = None
default_op_mapping_field_values['OUTPUT_PERM'] = None
default_op_mapping_field_values['FILL_NAME_FIELD'] = True
default_op_mapping = {
'Gather': ['gather', ['X'], ['Out'],
dict(axis='')],
......@@ -46,8 +47,44 @@ default_op_mapping = {
dict(axes='dim', keepdims='keep_dim'),
dict(keep_dim=1)
],
'ReduceSum': [
'reduce_sum', ['X'], ['Out'],
dict(axes='dim', keepdims='keep_dim'),
dict(keep_dim=1)
],
#active function
'Relu': ['relu', ['X'], ['Out']],
'LeakyRelu': ['leaky_relu', ['X'], ['Out'],
dict(), dict(alpha=.01)],
'Elu': ['elu', ['X'], ['Out'],
dict(), dict(alpha=1.)],
'ThresholdedRelu': [
'thresholded_relu', ['X'], ['Out'],
dict(alpha='threshold'),
dict(alpha=1.)
],
'Tanh': ['tanh', ['X'], ['Out']],
'Sigmoid': ['sigmoid', ['X'], ['Out']],
'Pow': ['elementwise_pow', ['X', 'Y'], ['Out'],
dict(),
dict(axis=-1)], # TODO: pow for scalar exponent
'HardSigmoid': [
'hard_sigmoid', ['X'], ['Out'],
dict(alpha='slope', beta='offset'),
dict(slope=.2, offset=.5)
],
'Softsign': ['softsign', ['X'], ['Out']],
'Softplus': ['softplus', ['X'], ['Out']],
'Exp': ['exp', ['X'], ['Out']],
'Softmax': ['softmax', ['X'], ['Out'],
dict(axis=''),
dict(axis=1)],
}
activefunc_op_mapping = {
'LeakyRelu': ['leaky_relu', ['X'], ['Out'],
dict(), dict(alpha=.01)]
dict(), dict(alpha=.01)],
}
default_ioa_constraint = {
......
x2paddle/op_mapper/onnx_op_mapper.py
浏览文件 @ f16ead9e
......@@ -14,14 +14,16 @@
from x2paddle.core.graph import GraphNode
from x2paddle.core.op_mapper import OpMapper
from x2paddle.core.util import *
from x2paddle.core.fluid_code import Layer
from x2paddle.core.fluid_code import FluidCode
from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
from x2paddle.op_mapper.onnx_directly_map import default_op_mapping_field_values
from x2paddle.op_mapper.onnx_directly_map import default_op_mapping
from x2paddle.op_mapper.onnx_directly_map import default_ioa_constraint
from x2paddle.op_mapper.onnx_custom_layer import *
from x2paddle.core.util import string
import numpy as np
import onnx.numpy_helper as numpy_helper
import logging as _logging
from collections import OrderedDict as _dict
......@@ -52,12 +54,12 @@ class ONNXOpMapper(OpMapper):
self.input_shapes = []
self.weights = dict()
self.omit_nodes = list()
self.used_custom_layers = dict()
if not self.op_checker():
raise Exception("Model are not supported yet.")
#mapping op
print("Total nodes: {}".format(
sum([
isinstance(node, ONNXGraphNode)
......@@ -71,13 +73,17 @@ class ONNXOpMapper(OpMapper):
func(node)
elif op in default_op_mapping:
self.directly_map(node)
elif op in custom_layers:
self.deal_custom_layer(node)
def op_checker(self):
unsupported_ops = set()
for node_name in self.graph.topo_sort:
node = self.graph.get_node(node_name)
op = node.layer_type
if not hasattr(self, op) and op not in default_op_mapping:
if not hasattr(
self, op
) and op not in default_op_mapping and op not in custom_layers:
unsupported_ops.add(op)
if len(unsupported_ops) == 0:
return True
......@@ -133,11 +139,28 @@ class ONNXOpMapper(OpMapper):
output=val_outs[0],
param_attr=attr)
def deal_custom_layer(self, node):
op = node.layer_type
val_x = self.graph.get_node(node.layer.input[0], copy=True)
custom_code, func = make_custom_layer(node)
params = get_params(node.layer, node.layer_type)
arg_names, kwargs = set_args(func, params)
kwargs['name'] = string(node.layer_name)
inputs_node = []
inputs_node.append(node.inputs[0])
node.fluid_code.add_layer(func.__code__.co_name,
inputs=inputs_node[0],
output=node,
param_attr=kwargs,
is_custom_layer=True)
if op not in self.used_custom_layers:
self.used_custom_layers[op] = custom_code
def place_holder(self, node):
self.input_shapes.append(node.out_shapes)
self.input_shapes.append(node.out_shapes[0])
attr = {
"dtype": string(node.dtype),
"shape": node.out_shapes,
"shape": node.out_shapes[0],
"name": string(node.layer_name),
"append_batch_size": 'False'
}
......@@ -151,7 +174,7 @@ class ONNXOpMapper(OpMapper):
if parameter is not None:
node = parameter
dtype = node.dtype
shape = node.out_shapes
shape = node.out_shapes[0]
self.weights[node.layer_name] = node.weight
attr = {
......@@ -179,13 +202,55 @@ class ONNXOpMapper(OpMapper):
val_padded = self.Pad(node, op_independent=False)
return [0] * ndims, val_padded
def _interpolate(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_scales = self.graph.get_node(node.layer.input[1], copy=True)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
out_shape_ = val_y.out_shapes[0]
if out_shape_ is not None:
assert len(out_shape_) == 4, 'only 4-D Tensor as X and Y supported'
out_shape_ = out_shape_[2:]
scales = _const_weight_or_none(val_scales)
if scales is not None:
assert len(scales) == 4, 'only 4-D Tensor as X and Y supported'
assert scales[0] == 1 and scales[
1] == 1, 'only scale on (NC)HW supported'
assert scales[2] == scales[
3], 'only aspect-ratio-invariant scale supported'
scale = scales[2] if scales else None
if scale is None:
assert out_shape_, 'neither scales nor output shape is available'
out_shape = out_shape_
else:
out_shape = None
if out_shape_ is None:
in_shape = val_x.out_shapes[0]
assert in_shape is not None, 'out_shape required but not inferrable'
assert len(
in_shape) == 4, 'only 4-D Tensor as X and Y supported'
out_shape_ = [in_shape[2] * scale, in_shape[3] * scale]
mode = node.get_attr('mode', 'nearest')
fluid_op = 'resize_{}'.format(mode)
attr = {
'scale': scale,
'out_shape': out_shape,
'name': string(node.layer_name)
}
node.fluid_code.add_layer(fluid_op,
inputs=val_x,
output=node,
param_attr=attr)
def Pad(self, node, op_independent=True):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
pads = node.get_attr('pads')
mode = node.get_attr('mode', 'constant')
value = node.get_attr('value', 0.)
data_shape = val_x.out_shapes
output_shape = node.out_shapes
data_shape = val_x.out_shapes[0]
output_shape = node.out_shapes[0]
assume_pad2d = False
attr = {}
if len(pads) == 4:
......@@ -200,8 +265,6 @@ class ONNXOpMapper(OpMapper):
attr['mode'] = string(mode)
else:
attr = {'pad_value': value}
assert mode == 'constant', 'mode {} is supported only in pad2d'.format(
mode)
fluid_op = 'pad'
if len(pads) == 4:
paddings = np.array(pads).reshape(
......@@ -209,6 +272,10 @@ class ONNXOpMapper(OpMapper):
elif len(pads) == 8:
paddings = np.array(pads).reshape(
(-1, 4)).transpose().flatten().tolist() # SSEE -> SESE
if sum(paddings[:4]) == 0:
fluid_op = 'pad2d'
paddings = paddings[4:]
attr['mode'] = string(mode)
attr['paddings'] = paddings
if op_independent:
attr['name'] = string(node.layer_name)
......@@ -233,6 +300,17 @@ class ONNXOpMapper(OpMapper):
output=node,
param_attr=attr)
def Shrink(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
bias = node.get_attr('bias')
lambd = node.get_attr('lambd')
assert bias == 0.0, 'not support bias!=0'
attr = {'threshold': lambd, 'name': node.layer_name}
node.fluid_code.add_layer('hard_shrink',
inputs=val_x,
output=node,
param_attr=attr)
def Constant(self, node):
val_output = self.graph.get_node(node.layer.output[0], copy=True)
......@@ -244,7 +322,7 @@ class ONNXOpMapper(OpMapper):
shape = node.get_attr('shape', None)
if shape is None:
shape = val_output.out_shapes
shape = val_output.out_shapes[0]
if shape is None:
shape = list(value.shape)
_logger.warning(
......@@ -253,8 +331,8 @@ class ONNXOpMapper(OpMapper):
'using value as 1-D tensor may lead to fails',
val_output.layer_name, val_output.layer_name)
value = value.tolist()
if len(value) == 1: # scalar
value = value.tolist()
shape = [1]
value = value[0]
if dtype.name == 'int64':
......@@ -264,14 +342,27 @@ class ONNXOpMapper(OpMapper):
inputs=None,
output=node,
param_attr=attr)
else:
value = np.reshape(value, shape)
self.weights[node.layer_name] = value
attr = {
'dtype': string(dtype),
'shape': shape,
'name': string(node.layer_name),
'attr': string(node.layer_name),
'default_initializer': 'Constant(0.0)'
}
node.fluid_code.add_layer("create_parameter",
inputs=None,
output=node,
param_attr=attr)
def Resize(self, node):
# I/O
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_scales = self.graph.get_node(node.layer.input[1], copy=True)
val_y, = self.graph.get_node(node.layer.output[0], copy=True)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
out_shape_ = val_y.out_shapes
out_shape_ = val_y.out_shapes[0]
if out_shape_ is not None:
assert len(out_shape_) == 4, 'only 4-D Tensor as X and Y supported'
out_shape_ = out_shape_[2:]
......@@ -289,7 +380,7 @@ class ONNXOpMapper(OpMapper):
else:
out_shape = None
if out_shape_ is None:
in_shape = val_x.out_shapes
in_shape = val_x.out_shapes[0]
assert in_shape is not None, 'out_shape required but not inferrable'
assert len(
in_shape) == 4, 'only 4-D Tensor as X and Y supported'
......@@ -297,8 +388,6 @@ class ONNXOpMapper(OpMapper):
mode = node.get_attr('mode', 'nearest')
fluid_op = 'resize_{}'.format(mode)
name_attr = ', name={}'.format(repr(name)) if name else ''
attr = {
'scale': scale,
'out_shape': out_shape,
......@@ -309,13 +398,40 @@ class ONNXOpMapper(OpMapper):
output=node,
param_attr=attr)
def Upsample(self, node):
self._interpolate(node)
def Slice(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
axes = node.get_attr('axes')
starts = node.get_attr('starts')
ends = node.get_attr('ends')
shape = val_x.out_shapes[0]
if shape is not None:
for idx, value in enumerate(starts):
if value > 2**63 - 1 // 2:
value = value - ONNX_INT_MAX
starts[idx] = shape[axes[idx]] + value
for idx, value in enumerate(ends):
if value > 2**63 - 1 // 2:
value = value - ONNX_INT_MAX
ends[idx] = shape[axes[idx]] + value
attr = {"axes": axes, "starts": starts, "ends": ends}
node.fluid_code.add_layer('slice',
inputs=val_x,
output=node,
param_attr=attr)
def ConstantOfShape(self, node):
val_shape = self.graph.get_node(node.layer.input[0], copy=True)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
shape = _const_weight_or_none(val_shape)
if shape is None:
shape = node.out_shapes
shape = node.out_shapes[0]
assert shape is not None, (
'given shape is neither const value nor deductible from output, '
......@@ -359,10 +475,10 @@ class ONNXOpMapper(OpMapper):
# catch dynamic graph shape
if isinstance(val_shape, ONNXGraphNode):
shape = self.decoder.get_dynamic_shape_from_caffe2(
val_shape.layer_name, self.input_shapes)
shape, _, _ = self.decoder.onnx_graph.get_dynamic_shape(
val_shape.layer_name)
if shape is None:
shape = val_reshaped.out_shapes
shape = val_reshaped.out_shapes[0]
shape_dtype = val_shape.dtype
......@@ -415,9 +531,10 @@ class ONNXOpMapper(OpMapper):
pads = node.get_attr('pads', [0] * (poolnd * 2))
fluid_op = 'pool{}d'.format(poolnd)
assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
input_shape = val_x.out_shapes[0]
paddings, val_x = self._pad_if_asymmetric(node, pads, val_x)
input_shape = val_x.out_shapes
if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
pad_h = get_same_padding(input_shape[2], kernel_shape[0],
strides[0])
......@@ -572,14 +689,6 @@ class ONNXOpMapper(OpMapper):
output=node,
param_attr=attr)
def Softmax(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
attr = {"name": string(node.layer_name)}
node.fluid_code.add_layer("softmax",
inputs=val_x,
output=node,
param_attr=attr)
def Transpose(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
perm = node.get_attr('perm')
......@@ -589,15 +698,79 @@ class ONNXOpMapper(OpMapper):
output=node,
param_attr=attr)
def Mul(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_y = self.graph.get_node(node.layer.input[1], copy=True)
val_x_shape = val_x.out_shapes[0]
val_y_shape = val_y.out_shapes[0]
slice_idx = 0
for dim in val_y_shape:
if dim == 1:
slice_idx += 1
else:
break
attr = {"name": string(node.layer_name)}
if slice_idx < len(val_y_shape) and slice_idx > 0:
val_y_reshaped = val_y_shape[slice_idx:]
var_y_reshaped = val_y.layer_name + '_reshaped'
attr_reshaped = {
'shape': val_y_reshaped,
'name': string(var_y_reshaped)
}
node.fluid_code.add_layer('reshape',
inputs=val_y,
output=var_y_reshaped,
param_attr=attr_reshaped)
inputs = {'x': val_x, 'y': var_y_reshaped}
node.fluid_code.add_layer("elementwise_mul",
inputs=inputs,
output=node,
param_attr=attr)
else:
inputs = {'x': val_x, 'y': val_y}
node.fluid_code.add_layer("elementwise_mul",
inputs=inputs,
output=node,
param_attr=attr)
def Div(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_y = self.graph.get_node(node.layer.input[1], copy=True)
inputs = {'x': val_x, 'y': val_y}
val_x_shape = val_x.out_shapes[0]
val_y_shape = val_y.out_shapes[0]
slice_idx = 0
for dim in val_y_shape:
if dim == 1:
slice_idx += 1
else:
break
attr = {"name": string(node.layer_name)}
node.fluid_code.add_layer("elementwise_div",
inputs=inputs,
output=node,
param_attr=attr)
if slice_idx < len(val_y_shape) and slice_idx > 0:
val_y_reshaped = val_y_shape[slice_idx:]
var_y_reshaped = val_y.layer_name + '_reshaped'
attr_reshaped = {
'shape': val_y_reshaped,
'name': string(var_y_reshaped)
}
node.fluid_code.add_layer('reshape',
inputs=val_y,
output=var_y_reshaped,
param_attr=attr_reshaped)
inputs = {'x': val_x, 'y': var_y_reshaped}
node.fluid_code.add_layer("elementwise_div",
inputs=inputs,
output=node,
param_attr=attr)
else:
inputs = {'x': val_x, 'y': val_y}
node.fluid_code.add_layer("elementwise_div",
inputs=inputs,
output=node,
param_attr=attr)
def Relu(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
......@@ -610,12 +783,17 @@ class ONNXOpMapper(OpMapper):
def PRelu(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_slope = self.graph.get_node(node.layer.input[1], copy=True)
attr = {"name": string(node.layer_name), "mode": string('channel')}
if isinstance(val_slope, str):
attr["param_attr"] = string(val_slope.layer_name)
else:
attr["param_attr"] = string(val_slope.layer_name)
mode = 'channel'
shape_slope = val_slope.out_shapes[0]
if len(shape_slope) == 1:
mode = 'all'
elif len(shape_slope) > 2:
mode = 'element'
attr = {
"param_attr": string(val_slope.layer_name),
'mode': string(mode)
}
node.fluid_code.add_layer("prelu",
inputs=val_x,
output=node,
......@@ -649,9 +827,10 @@ class ONNXOpMapper(OpMapper):
pads = node.get_attr('pads', [0] * (poolnd * 2)) # optional
fluid_op = 'pool{}d'.format(poolnd)
assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
input_shape = val_x.out_shapes[0]
paddings, val_x = self._pad_if_asymmetric(node, pads, val_x)
input_shape = val_x.out_shapes
if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
pad_h = get_same_padding(input_shape[2], kernel_shape[0],
strides[0])
......@@ -676,8 +855,8 @@ class ONNXOpMapper(OpMapper):
def GlobalAveragePool(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
input_shape = val_x.out_shapes
output_shape = val_y.out_shapes
input_shape = val_x.out_shapes[0]
output_shape = val_y.out_shapes[0]
assert input_shape is not None or output_shape is not None, 'poolnd not inferred' # N
if input_shape:
poolnd = len(input_shape) - 2 # NC...
......@@ -701,7 +880,6 @@ class ONNXOpMapper(OpMapper):
val_y = self.graph.get_node(node.layer.output[0], copy=True)
self.omit_nodes.append(val_w.layer_name)
input_shape = val_x.out_shapes
has_bias = len(node.layer.input) == 3
if has_bias:
......@@ -709,12 +887,12 @@ class ONNXOpMapper(OpMapper):
self.omit_nodes.append(val_b.layer_name)
auto_pad = node.get_attr('auto_pad', 'NOTSET')
kernel_shape = val_w.out_shapes[2:] # OI...
kernel_shape = val_w.out_shapes[0][2:] # OI...
assert kernel_shape == node.get_attr(
'kernel_shape'), 'kernel_shape in attr unmatches value_info' # HW
convnd = len(kernel_shape)
assert 2 <= convnd <= 3, 'only conv2d and conv3d is supported'
num_out_channels = val_w.out_shapes[0] # OI...
num_out_channels = val_w.out_shapes[0][0] # OI...
fluid_op = 'conv{}d'.format(convnd)
num_groups = node.get_attr('group', 1)
......@@ -722,6 +900,7 @@ class ONNXOpMapper(OpMapper):
dilations = node.get_attr('dilations', [1] * convnd) # optional
pads = node.get_attr('pads', [0] * (convnd * 2)) # optional
input_shape = val_x.out_shapes[0]
paddings, val_x = self._pad_if_asymmetric(node, pads, val_x)
if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
......@@ -749,3 +928,55 @@ class ONNXOpMapper(OpMapper):
inputs=val_x,
output=node,
param_attr=attr)
def ConvTranspose(self, node):
val_x = self.graph.get_node(node.layer.input[0], copy=True)
val_w = self.graph.get_node(node.layer.input[1], copy=True)
val_b = self.graph.get_node(node.layer.input[2], copy=True)
self.omit_nodes.append(val_w.layer_name)
self.omit_nodes.append(val_b.layer_name)
val_y = self.graph.get_node(node.layer.output[0], copy=True)
auto_pad = node.get_attr('auto_pad', 'NOTSET')
out_padding = node.get_attr('output_padding', [0, 0])
kernel_shape = node.get_attr('kernel_shape', val_w.out_shapes[0][2:])
assert kernel_shape, 'kernel_shape not inferred'
convnd = len(kernel_shape)
assert 2 <= convnd <= 3, 'only conv2d_transpose and conv3d_transpose supported'
num_out_channels = val_w.out_shapes[0][1]
fluid_op = 'conv{}d_transpose'.format(convnd)
num_groups = node.get_attr('group', 1)
strides = node.get_attr('strides', [1] * convnd)
dilations = node.get_attr('dilations', [1] * convnd)
output_size = node.get_attr('output_shape', [])
pads = node.get_attr('pads', [0] * (convnd * 2))
paddings, var_x = self._pad_if_asymmetric(node, pads, val_x)
output_size = [0, 0]
output_size[0] = (val_x.out_shapes[0][2] -
1) * strides[0] - 2 * paddings[0] + dilations[0] * (
kernel_shape[0] - 1) + 1 + out_padding[0]
output_size[1] = (val_x.out_shapes[0][3] -
1) * strides[1] - 2 * paddings[1] + dilations[1] * (
kernel_shape[1] - 1) + 1 + out_padding[1]
attr = {
'num_filters': num_out_channels,
'output_size': output_size or None,
'filter_size': kernel_shape,
'padding': paddings,
'stride': strides,
'dilation': dilations,
'groups': num_groups,
'param_attr': string(val_w.layer_name),
'bias_attr': string(val_b.layer_name),
'name': string(node.layer_name),
}
node.fluid_code.add_layer(fluid_op,
inputs=val_x,
output=node,
param_attr=attr)
x2paddle/optimizer/onnx_optimizer.py
浏览文件 @ f16ead9e
......@@ -14,7 +14,6 @@
# TODO useless node remove
from x2paddle.op_mapper.onnx_op_mapper import ONNXOpMapper
from x2paddle.core.util import *
class ONNXOptimizer(object):
......
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