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“86673b1567a4667ee22869e6567cc55468c25918”上不存在“en/device-dev/guide/overview-0.md”
提交 fb61a10b 编写于 作者: C Channingss
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add lstm & mapping weight by rename to paddle's naming rule

上级 03f2420d 62c6e875
隐藏空白更改
内联 并排
Showing with 1036 addition and 309 deletion
x2paddle/core/program.py
浏览文件 @ fb61a10b
......@@ -166,7 +166,6 @@ class PaddleGraph(object):
self.clear_edges()
outputs_from_nodes = dict()
for layer_id, layer in self.layers.items():
print(layer.kernel, layer.outputs ,layer.inputs)
for input_key, input_var in layer.inputs.items():
vs = input_var
if not isinstance(vs, (list, tuple)):
......@@ -211,9 +210,12 @@ class PaddleGraph(object):
if self.edges_in.get(layer_id, 0) == 0 and self.edges_out.get(
layer_id, 0) == 0 and layer.kernel != "prim.assert" \
and layer.kernel != "prim.exception" \
and layer.kernel != "prim.warnings":
if layer.kernel == "paddle.to_tensor":
and layer.kernel != "prim.warnings" \
and layer.outputs[0] not in self.outputs:
if layer.kernel == "paddle.to_tensor" and layer.outputs[0] in self.inputs_info:
self.inputs_info.pop(layer.outputs[0])
if layer.outputs[0] in self.inputs:
self.inputs.pop(self.inputs.index(layer.outputs[0]))
invalid_list.append(layer_id)
for layer_id in invalid_list:
self.layers.pop(layer_id)
......@@ -323,6 +325,9 @@ class PaddleGraph(object):
if self.source_type == "caffe":
custom_import = "from x2paddle.op_mapper.static.caffe2paddle " + \
"import caffe_custom_layer as x2paddle_nn"
elif self.source_type == "onnx":
custom_import = "from x2paddle.op_mapper.static.onnx2paddle " + \
"import onnx_custom_layer as x2paddle_nn"
else:
custom_import = ""
......@@ -352,7 +357,9 @@ class PaddleGraph(object):
remove_default_attrs(layer.kernel, layer.attrs)
edges_in = self.edges_in.get(layer_id, [])
edges_out = self.edges_out.get(layer_id, [])
if len(edges_in) == 0 and len(edges_out) == 0:
if len(edges_in) == 0 and len(edges_out) == 0 and layer.outputs[0] not in self.outputs:
if layer.outputs[0] in self.inputs:
self.inputs.pop(self.inputs.index(layer.outputs[0]))
continue
line = ""
......@@ -472,6 +479,9 @@ class PaddleGraph(object):
elif self.source_type == "pytorch":
custom_import = "from x2paddle.op_mapper.dygraph.pytorch2paddle " + \
"import pytorch_custom_layer as x2paddle_nn"
elif self.source_type == "onnx":
custom_import = "from x2paddle.op_mapper.dygraph.onnx2paddle " + \
"import onnx_custom_layer as x2paddle_nn"
else:
custom_import = ""
self.head = gen_codes(
......@@ -580,7 +590,7 @@ class PaddleGraph(object):
elif len(layer.outputs) == 2:
line = layer.outputs[1]
else:
if layer.kernel == "paddle.nn.LSTM":
if layer.kernel in ["paddle.nn.LSTM", 'custom_layer:LSTM']:
line = "{}, ({})".format(layer.outputs[1], ', '.join(layer.outputs[-2:]))
else:
line = ','.join(layer.outputs[1:])
......@@ -589,8 +599,13 @@ class PaddleGraph(object):
line += " = self.{}".format(layer.outputs[0])
else:
line += " = self.{}(".format(layer.outputs[0])
for k, v in layer.inputs.items():
line += "{}, ".format(v)
for v in layer.inputs.values():
if isinstance(v, list):
line += "[{}], ".format(", ".join(v))
elif isinstance(v, tuple):
line += "({}), ".format(", ".join(v))
else:
line += "{}, ".format(v)
line = line.strip(", ")
line += ")"
self.forward_func.extend(gen_codes([line], indent=indent))
......
x2paddle/decoder/onnx_decoder.py
浏览文件 @ fb61a10b
......@@ -31,6 +31,7 @@ import numpy as np
from copy import deepcopy
import logging as _logging
import os
import copy
default_op_domain = 'ai.onnx'
_logger = _logging.getLogger(__name__)
......@@ -98,9 +99,7 @@ class ONNXGraphNode(GraphNode):
def output(self, index=0):
if index >0 and len(self.layer.output) <= index:
raise IndexError('Output numbers of Node:{} is {} <= index:{}'.format(self.layer_name, len(self.layer.output), index))
if index > 0:
return "{}_p{}".format(self.layer_name, index)
return self.layer_name
return self.layer.output[index]
class ONNXGraphDataNode(GraphNode):
......@@ -132,6 +131,17 @@ class ONNXGraphDataNode(GraphNode):
shape.append(dim.dim_value)
out_shapes.append(shape)
return out_shapes
elif isinstance(self.layer, TensorProto):
values = self.layer.dims
out_shapes = list()
shape = list()
for dim in values:
if dim == 0:
shape.append(-1)
else:
shape.append(dim)
out_shapes.append(shape)
return out_shapes
else:
values = self.layer.dims
out_shapes = list()
......@@ -241,11 +251,12 @@ class ONNXGraph(Graph):
"""
generate output_nodes node of ONNX model
"""
inner_nodes = self.get_inner_nodes()
output_nodes = [value.name for value in self.graph.output]
for opt_data in output_nodes:
if opt_data not in inner_nodes:
self.output_nodes.append(opt_data)
#n = super(ONNXGraph, self).get_node(opt_data)
#if n is None:
# self.topo_sort.append(self.node_map[opt_data])
self.output_nodes.append(opt_data)
def is_place_holder_nodes(self, layer):
"""
......@@ -293,7 +304,7 @@ class ONNXGraph(Graph):
#generate topo
super(ONNXGraph, self).build()
self.input_nodes = self.place_holder_nodes
self.input_nodes = copy.deepcopy(self.place_holder_nodes)
def build_connection(self, layer_name, node):
"""
......@@ -410,10 +421,8 @@ class ONNXDecoder(object):
check_model(onnx_model)
onnx_model = self.optimize_model_skip_op(onnx_model)
onnx_model = self.optimize_model_strip_initializer(onnx_model)
onnx_model = self.optimize_node_name(onnx_model)
self.graph = ONNXGraph(onnx_model)
#self.onnx_model = onnx_model
def build_value_refs(self, nodes):
"""
......
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/__init__.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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 .one_hot import OneHot
from .rnn import LSTM
from .pad_two_input import PadWithTwoInput
from .pad_all_dim2 import PadAllDim2
from .pad_all_dim4 import PadAllDim4
from .pad_all_dim4_one_input import PadAllDim4WithOneInput
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/one_hot.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
class OneHot(object):
def __init__(self, axis):
self.axis = axis
def __call__(self, indices, depth, values):
indices_shape = indices.shape
rank = len(indices.shape)
real_axis = self.axis
if self.axis < 0:
real_axis = self.axis + rank + 1
depth_range = paddle.arange(end=depth)
ls = tuple(indices_shape[0: real_axis])
rs = tuple(indices_shape[real_axis: rank])
targets = paddle.reshape(depth_range, (1,) * (real_axis-0) + tuple(depth_range.shape) + (1,) * (rank-real_axis))
mod = paddle.mod(indices, depth)
v = paddle.reshape(mod, ls + (1,) + rs)
out = targets == v
out = paddle.cast(out, "float32")
on_value = paddle.slice(values, axes=[0], starts=[1], ends=[2])
off_value = paddle.slice(values, axes=[0], starts=[0], ends=[1])
out = out * (on_value - off_value) + off_value
return out
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/pad_all_dim2.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
from x2paddle.core.util import *
class PadAllDim2(object):
def __init__(self, value, mode):
self.layer_attrs = {}
self.layer_attrs['mode'] = mode
self.layer_attrs['data_format'] = 'NCHW'
self.layer_attrs['value'] = value
def __call__(self, x, pad):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
x = paddle.unsqueeze(x, axis=[0, 1])
out = paddle.nn.functional.pad(x=x, pad=pad, **self.layer_attrs)
out = paddle.squeeze(out, axis=[0, 1])
return out
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/pad_all_dim4.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
from x2paddle.core.util import *
class PadAllDim4(object):
def __init__(self, value, mode):
self.layer_attrs = {}
self.layer_attrs['mode'] = mode
self.layer_attrs['data_format'] = 'NCHW'
self.layer_attrs['value'] = value
def __call__(self, x, pad):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
pad1, pad2 = paddle.split(pad, num_or_sections=2, axis=0)
x = paddle.nn.functional.pad(x=x, pad=pad1, **self.layer_attrs)
x = paddle.transpose(x, perm=[2, 3, 0, 1])
x = paddle.nn.functional.pad(x=x, pad=pad2, **self.layer_attrs)
out = paddle.transpose(x, perm=[2, 3, 0, 1])
return out
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
from x2paddle.core.util import *
class PadAllDim4WithOneInput(object):
def __init__(self, pad, value, mode):
self.layer_attrs = {}
self.layer_attrs['mode'] = mode
self.layer_attrs['data_format'] = 'NCHW'
self.layer_attrs['value'] = value
self.pad1 = pad[0: 4]
self.pad2 = pad[4: 9]
def __call__(self, x):
x = paddle.nn.functional.pad(x=x, pad=self.pad1, **self.layer_attrs)
x = paddle.transpose(x, perm=[2, 3, 0, 1])
x = paddle.nn.functional.pad(x=x, pad=self.pad2, **self.layer_attrs)
out = paddle.transpose(x, perm=[2, 3, 0, 1])
return out
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/pad_two_input.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
from x2paddle.core.util import *
class PadWithTwoInput(object):
def __init__(self, value, mode, data_format):
self.layer_attrs = {}
self.layer_attrs['mode'] = mode
self.layer_attrs['data_format'] = data_format
self.layer_attrs['value'] = value
def __call__(self, x, pad):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
out = paddle.nn.functional.pad(x=x, pad=pad, **self.layer_attrs)
return out
\ No newline at end of file
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
浏览文件 @ fb61a10b
......@@ -104,6 +104,9 @@ class OpSet9():
'ReduceMax': ['paddle.max',
dict(axes='axis', keepdims='keepdim'),
dict(keepdim=1)],
'ReduceProd': ['paddle.prod',
dict(axes='axis', keepdims='keepdim'),
dict(keepdim=1)],
# active function
'Relu': ['paddle.nn.ReLU'],
'LeakyRelu': ['paddle.nn.LeakyReLU',
......@@ -142,6 +145,7 @@ class OpSet9():
self.inputs_info = dict()
self.weights = dict()
self.nn_name2id = dict()
self.done_weight_list = list()
@print_mapping_info
def directly_map(self, node, *args, **kwargs):
......@@ -163,11 +167,12 @@ class OpSet9():
layer_attrs[pd_attr_name] = onnx_attrs[onnx_attr_name]
else:
layer_attrs[pd_attr_name] = op_info[2][onnx_attr_name]
if paddle_op.startswith("paddle.nn"):
if paddle_op.startswith("paddle.nn") and 'functional' not in paddle_op:
op_name = paddle_op[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=paddle_op,
inputs={"x": input.name},
......@@ -215,7 +220,7 @@ class OpSet9():
node = parameter
dtype = node.dtype
shape = node.out_shapes[0]
if len(node.weight.shape) == 0:
if hasattr(node.weight, "shape") and len(node.weight.shape) == 0:
self.paddle_graph.add_layer(
"paddle.full",
inputs={},
......@@ -232,8 +237,7 @@ class OpSet9():
shape=shape,
attr=string(node.name),
dtype=string(dtype),
default_initializer="paddle.nn.initializer.Constant(value=0.0)")
default_initializer="paddle.nn.initializer.Constant(value=0.0)")
def _pad_if_asymmetric(self, node, pads, val_name): # pads: SSEE
assert len(pads) & 1 == 0
......@@ -299,6 +303,10 @@ class OpSet9():
attrs.update({"align_corners": False,
"mode": string(mode),
"align_mode": 1})
val_x_shape = val_x.out_shapes[0]
if mode == "linear" and len(val_x_shape) == 4:
attrs["mode"] = string("bilinear")
attrs["align_corners"] = True
self.paddle_graph.add_layer(
kernel="paddle.nn.functional.interpolate",
inputs=inputs,
......@@ -382,63 +390,131 @@ class OpSet9():
def Pad(self, node, op_independent=True):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
pads = node.get_attr('pads')
is_pads_attr = True
if pads is None:
val_pad = self.graph.get_input_node(node, idx=1, copy=True)
pad_shape = val_pad.out_shapes[0]
is_pads_attr = False
pads = _const_weight_or_none(val_pad)
if pads is not None:
is_pads_attr = True
mode = node.get_attr('mode', 'constant')
value = node.get_attr('value', 0.)
data_shape = val_x.out_shapes[0]
output_shape = node.out_shapes[0]
assume_pad2d = False
assume_pad = False
layer_attrs = {}
layer_attrs['mode'] = string(mode)
paddings = []
if len(pads) == 4:
assume_pad2d |= mode != 'constant'
if data_shape:
assume_pad2d |= data_shape and len(data_shape) == 4 # NCHW
if output_shape:
assume_pad2d |= output_shape and len(output_shape) == 4 # NCHW
if assume_pad2d:
paddle_op = 'paddle.nn.Pad2D'
layer_attrs['data_format'] = string('NCHW')
layer_attrs['value'] = value
else:
paddle_op = 'paddle.fluid.layers.pad'
layer_attrs["pad_value"] = value
if len(pads) == 4:
paddings = np.array(pads).reshape(
(-1, 2)).transpose().flatten().tolist() # SSEE -> SESE
elif len(pads) == 8:
paddings = np.array(pads).reshape(
(-1, 4)).transpose().flatten().tolist() # SSEE -> SESE
if sum(paddings[:4]) == 0:
paddle_op = 'paddle.nn.Pad2D'
paddings = paddings[4:]
layer_attrs['value'] = value
if 'pad_value' in layer_attrs:
layer_attrs.pop('pad_value')
tmp_paddings = copy.deepcopy(paddings)
paddings[0] = tmp_paddings[2]
paddings[1] = tmp_paddings[3]
paddings[2] = tmp_paddings[0]
paddings[3] = tmp_paddings[1]
if paddle_op == 'paddle.nn.Pad2D':
layer_attrs['padding'] = paddings
nn_op_name = name_generator("pad2d", self.nn_name2id)
layer_attrs['value'] = value
if not op_independent:
output_name = node.name + '_paded'
else:
layer_attrs['paddings'] = paddings
if op_independent:
output_name = node.name
nn_op_name = name_generator("pad", self.nn_name2id)
layer_outputs = [nn_op_name, output_name]
if is_pads_attr:
paddings = []
if len(pads) in [2, 4, 6]:
if data_shape:
assume_pad |= data_shape and 2 * (len(data_shape) - 2) == len(pads) # NCHW
if output_shape:
assume_pad |= output_shape and 2 * (len(output_shape) - 2) == len(pads) # NCHW
if assume_pad:
paddle_op = 'paddle.nn.Pad{}D'.format(len(output_shape) - 2)
paddings = np.array(pads).reshape(
(2, -1)).transpose().astype("int32")
paddings = np.flip(paddings, axis=0).flatten().tolist()
layer_attrs['padding'] = paddings
else:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == len(pads) # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == len(pads) # NCHW
if assume_pad:
paddle_op = 'paddle.nn.functional.pad'
paddings = np.array(pads).reshape(
(2, -1)).transpose().astype("int32").flatten().tolist()
layer_attrs['pad'] = paddings
else:
raise Exception("The padding value {} is wrong!".format(pads))
elif len(pads) == 8:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == len(pads) # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == len(pads) # NCHW
if assume_pad:
paddle_op = 'paddle.nn.Pad2D'
paddings = np.array(pads).reshape(
(2, -1)).transpose().astype("int32")
paddings = np.flip(paddings, axis=0).flatten().tolist()
if sum(paddings[:4]) == 0:
paddings = paddings[4:]
layer_attrs['padding'] = paddings
else:
layer_attrs["pad"] = paddings
paddle_op = "custom_layer:PadAllDim4WithOneInput"
else:
raise Exception("The padding value {} is wrong!".format(pads))
self.paddle_graph.add_layer(
paddle_op,
inputs={'x': val_x.name},
outputs=[nn_op_name, node.name] if paddle_op == 'paddle.nn.Pad2D' else [node.name],
outputs=layer_outputs[1:] if paddle_op == 'paddle.nn.functional.pad' else layer_outputs,
**layer_attrs)
if not op_independent:
return node.name + '_paded'
else:
self.paddle_graph.add_layer(
paddle_op,
inputs={'x': val_x.name},
outputs=[nn_op_name, node.name + '_paded'] if paddle_op == 'paddle.nn.Pad2D' \
else [node.name + '_paded'],
**layer_attrs)
return node.name + '_paded'
pads_len = val_pad.out_shapes[0][0]
if pads_len in [2, 4, 6]:
if data_shape:
assume_pad |= data_shape and 2 * (len(data_shape) - 2) == pads_len # NCHW
if output_shape:
assume_pad |= output_shape and 2 * (len(output_shape) - 2) == pads_len # NCHW
if assume_pad:
if pads_len == 2:
data_format = "NCL"
elif pads_len == 4:
data_format = "NCHW"
else:
data_format = "NCDHW"
self.paddle_graph.add_layer(
"custom_layer:PadWithTwoInput",
inputs={'x': val_x.name, 'pad': val_pad.name},
outputs=layer_outputs,
value=value,
mode=string(mode),
data_format=string(data_format))
else:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == pads_len # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == pads_len # NCHW
if assume_pad:
if pads_len == 4:
self.paddle_graph.add_layer(
"custom_layer:PadAllDim2",
inputs={'x': val_x.name, 'pad': val_pad.name},
outputs=layer_outputs,
value=value,
mode=string(mode))
else:
raise Exception("The padding value is wrong!")
elif pads_len == 8:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == pads_len # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == pads_len # NCHW
if assume_pad:
self.paddle_graph.add_layer(
"custom_layer:PadAllDim4",
inputs={'x': val_x.name, 'pad': val_pad.name},
outputs=layer_outputs,
value=value,
mode=string(mode))
else:
print(pads_len)
raise Exception("The padding value is wrong!")
if not op_independent:
return node.name + '_paded'
@print_mapping_info
def Unsqueeze(self, node):
......@@ -530,11 +606,13 @@ class OpSet9():
val_scale = self.graph.get_input_node(node, idx=1, copy=True)
val_b = self.graph.get_input_node(node, idx=2, copy=True)
epsilon = node.get_attr('epsilon', 1e-5)
self.weights[op_name+'.scale'] = self.weights[val_scale.name]
self.weights[op_name+'.bias'] = self.weights[val_b.name]
layer_attrs = {
'num_features': node.out_shapes[0][1],
'epsilon': epsilon,
'weight_attr': string(val_scale.name),
'bias_attr': string(val_b.name)
#'weight_attr': string(val_scale.name),
#'bias_attr': string(val_b.name)
}
dim = len(val_x.out_shapes[0])
if dim == 3:
......@@ -640,7 +718,7 @@ class OpSet9():
self.paddle_graph.add_layer(
'paddle.cast',
inputs={"x": indices.name},
outputs=indices_cast,
outputs=[indices_cast],
dtype=string('int64'))
op_name = name_generator("embedding", self.nn_name2id)
output_name = node.name
......@@ -649,8 +727,9 @@ class OpSet9():
'paddle.nn.Embedding',
inputs={"x": indices_cast},
outputs=layer_outputs,
param_attr=string(val_x.name),
size=val_x.out_shapes[0])
weight_attr=string(val_x.name),
num_embeddings=val_x.out_shapes[0][0],
embedding_dim=val_x.out_shapes[0][1])
else:
from functools import reduce
reshape_shape = reduce(lambda x, y: x * y, indices_shape)
......@@ -822,20 +901,27 @@ class OpSet9():
starts = self.graph.get_input_node(node, idx=1, copy=True)
ends = self.graph.get_input_node(node, idx=2, copy=True)
starts_value = _const_weight_or_none(starts)
if starts_value is not None:
starts_value = starts_value.tolist()
ends_value = _const_weight_or_none(ends)
if ends_value is not None:
ends_value = ends_value.tolist()
if len(node.inputs) > 2:
s_len = len(val_x.out_shapes[0])
axes = list(range(s_len))
if len(node.inputs) > 3:
axes = self.graph.get_input_node(node, idx=3, copy=True)
axes = _const_weight_or_none(axes, necessary=True)
axes_node = self.graph.get_input_node(node, idx=3, copy=True)
axes = _const_weight_or_none(axes_node, necessary=True).tolist()
if len(node.inputs) > 4:
steps = self.graph.get_input_node(node, idx=4, copy=True)
steps = _const_weight_or_none(steps)
steps = _const_weight_or_none(steps).tolist()
layer_attrs = {
"axes": axes,
"starts": starts.name,
"ends": ends.name
}
if starts_value is not None and ends_value is not None:
if starts_value is not None and ends_value is not None and axes is not None:
starts_value = starts_value.copy()
ends_value = ends_value.copy()
#for idx in range(len(ends_value)):
......@@ -865,6 +951,8 @@ class OpSet9():
layer_attrs['starts'] = starts_cast
if ends.dtype != 'int32':
ends_cast = ends.name + '_cast'
else:
ends_cast = ends.name
self.paddle_graph.add_layer(
'paddle.cast',
inputs={"x": ends.name},
......@@ -880,6 +968,7 @@ class OpSet9():
ends[idx] = 2**31 - 1
layer_attrs = {"axes": axes, "starts": starts, "ends": ends}
if steps is not None:
layer_attrs['strides'] = steps
self.paddle_graph.add_layer(
......@@ -1005,11 +1094,17 @@ class OpSet9():
inputs={'x': val_shape.name},
outputs=[val_shape.name],
shape=val_shape.out_shapes[0])
if val_shape.dtype != "int32":
self.paddle_graph.add_layer(
'paddle.cast',
inputs={'x': val_shape.name},
outputs=[val_shape.name],
dtype=string("int32"))
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={'x': val_x.name,
'shape': val_shape.name},
outputs=node)
outputs=[node.name])
@print_mapping_info
def Cast(self, node):
......@@ -1227,6 +1322,10 @@ class OpSet9():
epsilon = node.get_attr('epsilon', 1e-5)
c = val_x.out_shapes[0][1]
self.weights[op_name + '.weight'] = self.weights[val_scale.name]
self.weights[op_name + '.bias'] = self.weights[val_b.name]
self.weights[op_name + '._variance'] = self.weights[val_var.name]
self.weights[op_name + '._mean'] = self.weights[val_mean.name]
# Attribute: spatial is used in BatchNormalization-1,6,7
spatial = bool(node.get_attr('spatial'))
layer_attrs = {
......@@ -1234,10 +1333,10 @@ class OpSet9():
"momentum": momentum,
"epsilon": epsilon,
"is_test": True,
"param_attr": string(val_scale.name),
"bias_attr": string(val_b.name),
"moving_mean_name": string(val_mean.name),
"moving_variance_name": string(val_var.name),
#"param_attr": string(val_scale.name),
#"bias_attr": string(val_b.name),
#"moving_mean_name": string(val_mean.name),
#"moving_variance_name": string(val_var.name),
"use_global_stats": False,
}
self.paddle_graph.add_layer(
......@@ -1249,7 +1348,10 @@ class OpSet9():
@print_mapping_info
def Transpose(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
perm = node.get_attr('perm')
s_len = len(val_x.out_shapes[0])
perm_default = list(range(s_len))
perm_default.reverse()
perm = node.get_attr('perm', perm_default)
self.paddle_graph.add_layer(
"paddle.transpose",
inputs={"x": val_x.name},
......@@ -1266,26 +1368,51 @@ class OpSet9():
mode = 'channel'
shape_slope = val_slope.out_shapes[0]
if shape_slope == [1]:
if shape_slope == [1] * len(shape_slope):
mode = 'all'
elif len(shape_slope) > 2:
raise Exception("The 'element' mode is not supported yet!")
if mode == 'channel' and len(shape_slope) == 1:
# paddle params shape need be [1, channel]
slope_data = _const_weight_or_none(val_slope)
slope_data = np.reshape(slope_data, [1] + shape_slope)
self.weights[val_slope.name] = slope_data
num_parameters = val_x.out_shapes[0][1]
else:
num_parameters = 1
self.paddle_graph.add_layer(
"paddle.nn.PReLU",
inputs={"x": val_x.name},
outputs=layer_outputs,
num_parameters=num_parameters,
weight_attr=string(val_slope.name))
if mode == "element":
self.paddle_graph.add_layer(
"paddle.zeros",
inputs={},
outputs=[output_name + "__zeros"],
shape=shape_slope,
dtype=string(node.dtype))
self.paddle_graph.add_layer(
"paddle.maximum",
inputs={"x": val_x.name,
"y": output_name + "__zeros"},
outputs=[output_name + "__max"])
self.paddle_graph.add_layer(
"paddle.minimum",
inputs={"x": val_x.name,
"y": output_name + "__zeros"},
outputs=[output_name + "__max"])
self.paddle_graph.add_layer(
"paddle.multiply",
inputs={"x": val_slope.name,
"y": output_name + "__min"},
outputs=[output_name + "__mul"])
self.paddle_graph.add_layer(
"paddle.add",
inputs={"x": output_name + "__max",
"y": output_name + "__mul"},
outputs=[output_name])
else:
if mode == 'channel':
slope_data = _const_weight_or_none(val_slope)
if len(shape_slope) > 1:
#self.weights[val_slope.name] = np.reshape(slope_data, shape_slope[0])
self.weights[op_name+'._weight'] = np.reshape(slope_data, shape_slope[0])
num_parameters = val_x.out_shapes[0][1]
else:
num_parameters = 1
self.weights[op_name+'._weight'] = np.reshape(self.weights[val_slope.name], [1])
self.paddle_graph.add_layer(
"paddle.nn.PReLU",
inputs={"x": val_x.name},
outputs=layer_outputs,
num_parameters=num_parameters)
@print_mapping_info
def Squeeze(self, node):
......@@ -1553,6 +1680,7 @@ class OpSet9():
strides[1])
paddings = pad_h + pad_w
layer_inputs = {'x': val_x if isinstance(val_x, str) else val_x.name}
layer_attrs = {
"in_channels": num_in_channels * num_groups,
"out_channels": num_out_channels,
......@@ -1561,20 +1689,41 @@ class OpSet9():
"padding": paddings,
"dilation": dilations,
"groups": num_groups,
'weight_attr': string(val_w.name),
}
val_w_name = val_w.name
while val_w_name in self.done_weight_list:
val_w_name += "__repeat"
self.done_weight_list.append(val_w_name)
self.weights[op_name + '.weight'] = self.weights[val_w.name]
if has_bias:
layer_attrs["bias_attr"] = string(val_b.name)
val_b_name = val_b.name
while val_b_name in self.done_weight_list:
val_b_name += "__repeat"
self.done_weight_list.append(val_b_name)
self.weights[op_name + '.bias'] = self.weights[val_b.name]
else:
layer_attrs["bias_attr"] = False
input_shape = val_x.out_shapes[0]
if reduce(lambda x,y:x*y, input_shape) in [1, -1] and 1 not in input_shape:
input_shape[1] = num_in_channels * num_groups
input_shape[0] = 0
input_shape[2] = 0
self.paddle_graph.add_layer(
"paddle.reshape",
inputs=layer_inputs,
outputs=[layer_inputs["x"]],
shape=input_shape)
self.paddle_graph.add_layer(
paddle_op,
inputs={'x': val_x if isinstance(val_x, str) else val_x.name},
inputs=layer_inputs,
outputs=layer_outputs,
**layer_attrs)
@print_mapping_info
def ConvTranspose(self, node):
op_name = name_generator("conv_trans", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
val_x = self.graph.get_input_node(node, idx=0, copy=True)
val_w = self.graph.get_input_node(node, idx=1, copy=True)
val_b = None
......@@ -1588,7 +1737,7 @@ class OpSet9():
assert 2 <= convnd <= 3, 'only Conv2DTranspose and Conv3DTranspose supported'
num_in_channels = val_w.out_shapes[0][0]
num_out_channels = val_w.out_shapes[0][1]
paddle_op = 'paddle.nn.functional.conv{}d_transpose'.format(convnd)
paddle_op = 'paddle.nn.Conv{}DTranspose'.format(convnd)
num_groups = node.get_attr('group', 1)
strides = node.get_attr('strides', [1] * convnd)
......@@ -1607,22 +1756,22 @@ class OpSet9():
) * strides[1] - 2 * paddings[1] + dilations[1] * (
kernel_shape[1] - 1) + 1 + out_padding[1]
# Conv2DTranspose缺少output_size，只能在forward里头传进output_size
inputs_dict = {'x': val_x if isinstance(val_x, str) else val_x.name,
"weight": val_w.name}
inputs_dict = {'x': val_x if isinstance(val_x, str) else val_x.name}
layer_attrs = {
"in_channels": num_in_channels,
"out_channels": num_out_channels,
'kernel_size': kernel_shape,
"stride": strides,
"dilation": dilations,
"padding": paddings,
"groups": num_groups,
"output_size": node.out_shapes[0][2:]}
"groups": num_groups}
self.weights[op_name + '.weight'] = self.weights[val_w.name]
if val_b is not None:
inputs_dict["bias"] = val_b.name
else:
layer_attrs["bias"] = None
self.weights[op_name + '.bias'] = self.weights[val_b.name]
self.paddle_graph.add_layer(
kernel="paddle.nn.functional.conv2d_transpose",
kernel=paddle_op,
inputs=inputs_dict,
outputs=[node.name],
outputs=layer_outputs,
**layer_attrs)
@print_mapping_info
......@@ -1638,64 +1787,158 @@ class OpSet9():
outputs=[node.name],
**layer_attrs)
@print_mapping_info
def LSTM(self, node):
# parameters order in paddle:lstm:
# 1. gate order in paddle is: input, forget, cell, output.
# 2. gate orfer in onnx is: input, output, forget, cell.
def reform_weights(w, n, intervals):
slices = [w[:,x * n: y * n] for x, y in intervals]
return np.concatenate(slices, axis=1)
def transform_weight_with_bias(weights, n, intervals):
return [reform_weights(w, n, intervals) for w in weights]
def Size(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
self.paddle_graph.add_layer(
"paddle.shape",
inputs={"input": val_x.name},
outputs=[node.name])
self.paddle_graph.add_layer(
'paddle.cast',
inputs={"x": node.name},
outputs=[node.name],
dtype=string('int64'))
self.paddle_graph.add_layer(
"paddle.prod",
inputs={"x": node.name},
outputs=[node.name])
@print_mapping_info
def Sign(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
if node.dtype not in ["float16", "float32", "float64"]:
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": val_x.name},
outputs=[val_x.name],
dtype=string("float32"))
self.paddle_graph.add_layer(
"paddle.sign",
inputs={"x": val_x.name},
outputs=[node.name])
if node.dtype not in ["float16", "float32", "float64"]:
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": node.name},
outputs=[node.name],
dtype=string(node.dtype))
@print_mapping_info
def OneHot(self, node):
nn_op_name = name_generator("onehot", self.nn_name2id)
output_name = node.name
layer_outputs = [nn_op_name, output_name]
indices = self.graph.get_input_node(node, idx=0, copy=True)
depth = self.graph.get_input_node(node, idx=1, copy=True)
values = self.graph.get_input_node(node, idx=2, copy=True)
axis = node.get_attr('axis', -1)
self.paddle_graph.add_layer(
"custom_layer:OneHot",
inputs={"indices": indices.name,
"depth": depth.name,
"values": values.name},
outputs=layer_outputs,
axis=axis)
@print_mapping_info
def Reciprocal(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
self.paddle_graph.add_layer(
"paddle.reciprocal",
inputs={"x": val_x.name},
outputs=[node.name])
print(node.layer.input)
@print_mapping_info
def LSTM(self, node):
x = self.graph.get_input_node(node, idx=0, copy=True)
input_weight = self.graph.get_input_node(node, idx=1, copy=True)
hidden_weight = self.graph.get_input_node(node, idx=2, copy=True)
input_nums = len(node.layer.input)
exist_input_nums = 3
have_bias = False
if input_nums > 3 and node.layer.input[3] != '':
bias = self.graph.get_input_node(node, idx=exist_input_nums, copy=True)
have_bias = True
exist_input_nums += 1
if input_nums > 4 and node.layer.input[4] != '':
sequence_lens = self.graph.get_input_node(node, idx=exist_input_nums, copy=True)
exist_input_nums += 1
if input_nums > 5 and node.layer.input[5] != '':
init_h = self.graph.get_input_node(node, idx=exist_input_nums, copy=True)
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={"x": init_h.name},
outputs=[init_h.name],
shape=init_h.out_shapes[0]
)
exist_input_nums += 1
if input_nums > 6 and node.layer.input[6] != '':
init_c = self.graph.get_input_node(node, idx=exist_input_nums, copy=True)
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={"x": init_c.name},
outputs=[init_c.name],
shape=init_c.out_shapes[0]
)
input_weight_np = _const_weight_or_none(input_weight)
hidden_size = node.get_attr('hidden_size', input_weight_np.shape[1]/3)
hidden_size = node.get_attr('hidden_size', input_weight_np.shape[1]/4)
input_size = input_weight_np.shape[2]
hidden_weight_np = _const_weight_or_none(hidden_weight)
bias_np = _const_weight_or_none(bias)
input_bias_np = bias_np[:, :3*hidden_size]
hidden_bias_np = bias_np[:, 3*hidden_size:]
input_bias_np = bias_np[:, :4*hidden_size]
hidden_bias_np = bias_np[:, 4*hidden_size:]
# parameters order in paddle:lstm:
# 1. gate order in paddle is: input, forget, cell, output.
# 2. gate orfer in onnx is: input, output, forget, cell.
def reform_weights(w, n, intervals):
slices = [w[:,x * n: y * n] for x, y in intervals]
return np.concatenate(slices, axis=1)
reform_permutation = [(0, 1), (3, 4), (1, 3)]
def transform_weight_with_bias(weights, n, intervals):
return [reform_weights(w, n, intervals) for w in weights]
reform_permutation = [(0, 1), (2, 4), (1, 2)]
input_weight_np, hidden_weight_np, input_bias_np, hidden_bias_np = transform_weight_with_bias(
[input_weight_np, hidden_weight_np, input_bias_np, hidden_bias_np],
hidden_size, reform_permutation)
self.weights[input_weight.name] = input_weight_np
self.weights[hidden_weight.name] = hidden_weight_np
input_bias_name = bias.name + '_input'
hidden_bias_name = bias.name + '_hidden'
self.weights[input_bias_name] = input_bias_np
self.weights[hidden_bias_name] = hidden_bias_np
op_name = name_generator("lstm", self.nn_name2id)
y_out = node.output(0)
yh_out = node.output(1)
yc_out = node.output(2)
direction = node.get_attr('direction', 'forward')
if direction == 'backward':
raise Exception("LSTM support 'forward' or 'bidirectional', except '{}'.".format(direction))
elif direction == 'forward':
self.weights[input_weight.name] = input_weight_np.squeeze(0)
self.weights[hidden_weight.name] = hidden_weight_np.squeeze(0)
self.weights[input_bias_name] = input_bias_np.squeeze(0)
self.weights[hidden_bias_name] = hidden_bias_np.squeeze(0)
else:
param_names = []
for direct in range(2):
suffix = '_reverse' if direct == 1 else ''
param_names.extend(['{}.weight_ih_l0{}', '{}.weight_hh_l0{}'])
if have_bias != False: param_names.append('{}.bias_ih_l0{}')
if have_bias != False: param_names.append('{}.bias_hh_l0{}')
param_names = [x.format(op_name, suffix) for x in param_names]
self.weights[param_names[0]] = input_weight_np[0]
self.weights[param_names[4]] = input_weight_np[1]
self.weights[param_names[1]] = hidden_weight_np[0]
self.weights[param_names[5]] = hidden_weight_np[1]
self.weights[param_names[2]] = input_bias_np[0]
self.weights[param_names[6]] = input_bias_np[1]
self.weights[param_names[3]] = hidden_bias_np[0]
self.weights[param_names[7]] = hidden_bias_np[1]
self.paddle_graph.add_layer(
'paddle.nn.LSTM',
inputs={'input': x.name, 'initial_states': (init_h.name, init_c.name)},
......@@ -1703,18 +1946,14 @@ class OpSet9():
input_size=input_size,
hidden_size=hidden_size,
num_layers=1,
weight_ih_attr=string(input_weight.name),
weight_hh_attr=string(hidden_weight.name),
bias_ih_attr=string(input_bias_name),
bias_hh_attr=string(hidden_bias_name),
direction=string(node.get_attr('direction')),
direction=string(direction),
time_major=True)
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={"x": y_out},
outputs=[y_out],
shape=[-1, -1, -1, hidden_size]
shape=[0, 0, -1, hidden_size]
)
self.paddle_graph.add_layer(
'paddle.transpose',
......
x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/gather.py
浏览文件 @ fb61a10b
......@@ -13,8 +13,6 @@
# limitations under the License.
import paddle
from itertools import product
import numpy as np
class Gather(object):
def __init__(self, dim):
......
x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py
浏览文件 @ fb61a10b
......@@ -642,27 +642,6 @@ class TFOpMapper(OpMapper):
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={"x": 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={"x": input.name},
......@@ -670,31 +649,11 @@ class TFOpMapper(OpMapper):
pad=paddings)
def MirrorPad(self, node):
op_name = name_generator("pad", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_input_node(node, 0)
paddings = self.graph.get_input_node(node, 1)
assert paddings.layer_type == "Const", "Padding should be Const"
new_paddings = numpy.flip(paddings.value, 0).flatten().tolist()
dim = int(len(new_paddings) / 2)
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.Pad{}D".format(dim),
inputs={"x": transpose_name},
outputs=layer_outputs,
pad=new_paddings)
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
self.Pad(node)
def PadV2(self, node):
self.Pad(node)
def Squeeze(self, node):
input = self.graph.get_input_node(node, 0)
......
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/__init__.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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 .one_hot import one_hot
from .pad_two_input import pad_with_two_input
from .pad_all_dim2 import pad_all_dim2
from .pad_all_dim4 import pad_all_dim4
from .pad_all_dim4_one_input import pad_all_dim4_one_input
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/one_hot.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
def one_hot(indices, depth, values, axis):
indices_shape = indices.shape
rank = len(indices.shape)
real_axis = axis
if axis < 0:
real_axis = axis + rank + 1
depth_range = paddle.arange(end=depth)
ls = tuple(indices_shape[0: real_axis])
rs = tuple(indices_shape[real_axis: rank])
targets = paddle.reshape(depth_range, (1,) * (real_axis-0) + tuple(depth_range.shape) + (1,) * (rank-real_axis))
mod = paddle.mod(indices, depth)
v = paddle.reshape(mod, ls + (1,) + rs)
out = targets == v
out = paddle.cast(out, "float32")
on_value = paddle.slice(values, axes=[0], starts=[1], ends=[2])
off_value = paddle.slice(values, axes=[0], starts=[0], ends=[1])
out = out * (on_value - off_value) + off_value
return out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim2.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
def pad_all_dim2(x, pad, value, mode):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
x = paddle.unsqueeze(x, axis=[0, 1])
out = paddle.nn.functional.pad(x=x,
pad=pad,
mode=mode,
data_format='NCHW',
value=value)
out = paddle.squeeze(out, axis=[0, 1])
return out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
def pad_all_dim4(x, pad, value, mode):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
pad1, pad2 = paddle.split(pad, num_or_sections=2, axis=0)
x = paddle.nn.functional.pad(x=x,
pad=pad1,
mode=mode,
data_format='NCHW',
value=value)
x = paddle.transpose(x, perm=[2, 3, 0, 1])
x = paddle.nn.functional.pad(x=x,
pad=pad2,
mode=mode,
data_format='NCHW',
value=value)
out = paddle.transpose(x, perm=[2, 3, 0, 1])
return out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_all_dim4_one_input.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
def pad_all_dim4_one_input(x, pad, value, mode):
x = paddle.nn.functional.pad(x=x,
pad=pad[0: 4],
mode=mode,
data_format='NCHW',
value=value)
x = paddle.transpose(x, perm=[2, 3, 0, 1])
x = paddle.nn.functional.pad(x=x,
pad=pad[4: 9],
mode=mode,
data_format='NCHW',
value=value)
out = paddle.transpose(x, perm=[2, 3, 0, 1])
return out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/pad_two_input.py 0 → 100644
浏览文件 @ fb61a10b
# Copyright (c) 2020 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.
import paddle
def pad_with_two_input(x, pad, value, mode, data_format):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
out = paddle.nn.functional.pad(x=x,
pad=pad,
value=value,
mode=mode,
data_format=data_format)
return out
\ No newline at end of file
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
浏览文件 @ fb61a10b
......@@ -106,6 +106,9 @@ class OpSet9():
'ReduceMax': ['paddle.max',
dict(axes='axis', keepdims='keepdim'),
dict(keepdim=1)],
'ReduceProd': ['paddle.prod',
dict(axes='axis', keepdims='keepdim'),
dict(keepdim=1)],
# active function
'Relu': ['paddle.nn.functional.relu'],
'LeakyRelu': ['paddle.nn.functional.leaky_relu',
......@@ -203,7 +206,7 @@ class OpSet9():
node = parameter
dtype = node.dtype
shape = node.out_shapes[0]
if len(node.weight.shape) == 0:
if hasattr(node.weight, "shape") and len(node.weight.shape) == 0:
self.paddle_graph.add_layer(
"paddle.full",
inputs={},
......@@ -286,6 +289,10 @@ class OpSet9():
attrs.update({"align_corners": False,
"mode": string(mode),
"align_mode": 1})
val_x_shape = val_x.out_shapes[0]
if mode == "linear" and len(val_x_shape) == 4:
attrs["mode"] = string("bilinear")
attrs["align_corners"] = True
self.paddle_graph.add_layer(
kernel="paddle.nn.functional.interpolate",
inputs=inputs,
......@@ -368,61 +375,136 @@ class OpSet9():
def Pad(self, node, op_independent=True):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
pads = node.get_attr('pads')
is_pads_attr = True
if pads is None:
val_pad = self.graph.get_input_node(node, idx=1, copy=True)
pad_shape = val_pad.out_shapes[0]
is_pads_attr = False
pads = _const_weight_or_none(val_pad)
if pads is not None:
is_pads_attr = True
mode = node.get_attr('mode', 'constant')
value = node.get_attr('value', 0.)
data_shape = val_x.out_shapes[0]
output_shape = node.out_shapes[0]
assume_pad2d = False
assume_pad = False
layer_attrs = {}
layer_attrs['mode'] = string(mode)
paddings = []
if len(pads) == 4:
assume_pad2d |= mode != 'constant'
if data_shape:
assume_pad2d |= data_shape and len(data_shape) == 4 # NCHW
if output_shape:
assume_pad2d |= output_shape and len(output_shape) == 4 # NCHW
if assume_pad2d:
paddle_op = 'paddle.nn.functional.pad'
layer_attrs['data_format'] = string('NCHW')
layer_attrs['value'] = value
layer_attrs['value'] = value
if not op_independent:
output_name = node.name + '_paded'
else:
paddle_op = 'paddle.fluid.layers.pad'
layer_attrs["pad_value"] = value
if len(pads) == 4:
paddings = np.array(pads).reshape(
(-1, 2)).transpose().flatten().tolist() # SSEE -> SESE
elif len(pads) == 8:
paddings = np.array(pads).reshape(
(-1, 4)).transpose().flatten().tolist() # SSEE -> SESE
if sum(paddings[:4]) == 0:
paddle_op = 'paddle.nn.functional.pad'
paddings = paddings[4:]
layer_attrs['value'] = value
if 'pad_value' in layer_attrs:
layer_attrs.pop('pad_value')
tmp_paddings = copy.deepcopy(paddings)
paddings[0] = tmp_paddings[2]
paddings[1] = tmp_paddings[3]
paddings[2] = tmp_paddings[0]
paddings[3] = tmp_paddings[1]
if paddle_op == 'paddle.nn.functional.pad':
layer_attrs['pad'] = paddings
else:
layer_attrs['paddings'] = paddings
if op_independent:
output_name = node.name
layer_outputs = [output_name]
if is_pads_attr:
paddings = []
paddle_op = 'paddle.nn.functional.pad'
if len(pads) in [2, 4, 6]:
if data_shape:
assume_pad |= data_shape and 2 * (len(data_shape) - 2) == len(pads) # NCHW
if output_shape:
assume_pad |= output_shape and 2 * (len(output_shape) - 2) == len(pads) # NCHW
if assume_pad:
if len(pads) == 2:
data_format = "NCL"
elif len(pads) == 4:
data_format = "NCHW"
else:
data_format = "NCDHW"
paddings = np.array(pads).reshape(
(2, -1)).transpose().astype("int32")
paddings = np.flip(paddings, axis=0).flatten().tolist()
layer_attrs['pad'] = paddings
layer_attrs['data_format'] = data_format
else:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == len(pads) # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == len(pads) # NCHW
if assume_pad:
paddings = np.array(pads).reshape(
(2, -1)).transpose().astype("int32").flatten().tolist()
layer_attrs['pad'] = paddings
else:
raise Exception("The padding value {} is wrong!".format(pads))
elif len(pads) == 8:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == len(pads) # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == len(pads) # NCHW
if assume_pad:
paddings = np.array(pads).reshape(
(2, -1)).transpose().astype("int32")
paddings = np.flip(paddings, axis=0).flatten().tolist()
if sum(paddings[:4]) == 0:
paddings = paddings[4:]
layer_attrs['pad'] = paddings
else:
layer_attrs['pad'] = paddings
paddle_op = "custom_layer:pad_all_dim4_one_input"
else:
raise Exception("The padding value {} is wrong!".format(pads))
self.paddle_graph.add_layer(
paddle_op,
inputs={'x': val_x.name},
outputs=[node.name],
outputs=layer_outputs,
**layer_attrs)
if not op_independent:
return node.name + '_paded'
else:
self.paddle_graph.add_layer(
paddle_op,
inputs={'x': val_x.name},
outputs=[node.name + '_paded'],
**layer_attrs)
return node.name + '_paded'
pads_len = val_pad.out_shapes[0][0]
if pads_len in [2, 4, 6]:
if data_shape:
assume_pad |= data_shape and 2 * (len(data_shape) - 2) == pads_len # NCHW
if output_shape:
assume_pad |= output_shape and 2 * (len(output_shape) - 2) == pads_len # NCHW
if assume_pad:
if pads_len == 2:
data_format = "NCL"
elif pads_len == 4:
data_format = "NCHW"
else:
data_format = "NCDHW"
self.paddle_graph.add_layer(
"custom_layer:pad_with_two_input",
inputs={'x': val_x.name, 'pad': val_pad.name},
outputs=layer_outputs,
value=value,
mode=string(mode),
data_format=string(data_format))
else:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == pads_len # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == pads_len # NCHW
if assume_pad:
if pads_len == 4:
self.paddle_graph.add_layer(
"custom_layer:pad_all_dim2",
inputs={'x': val_x.name, 'pad': val_pad.name},
outputs=layer_outputs,
value=value,
mode=string(mode))
else:
raise Exception("The padding value is wrong!")
elif pads_len == 8:
if data_shape:
assume_pad |= data_shape and 2 * len(data_shape) == pads_len # NCHW
if output_shape:
assume_pad |= output_shape and 2 * len(output_shape) == pads_len # NCHW
if assume_pad:
self.paddle_graph.add_layer(
"custom_layer:pad_all_dim4",
inputs={'x': val_x.name, 'pad': val_pad.name},
outputs=layer_outputs,
value=value,
mode=string(mode))
else:
print(pads_len)
raise Exception("The padding value is wrong!")
if not op_independent:
return node.name + '_paded'
@print_mapping_info
def Unsqueeze(self, node):
......@@ -622,17 +704,13 @@ class OpSet9():
self.paddle_graph.add_layer(
'paddle.cast',
inputs={"x": indices.name},
outputs=indices_cast,
outputs=[indices_cast],
dtype=string('int64'))
op_name = name_generator("embedding", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
self.paddle_graph.add_layer(
'paddle.nn.Embedding',
inputs={"x": indices_cast},
outputs=layer_outputs,
param_attr=string(val_x.name),
size=val_x.out_shapes[0])
'paddle.nn.functional.embedding',
inputs={"x": indices_cast,
"weight": val_x.name},
outputs=[node.name])
else:
from functools import reduce
reshape_shape = reduce(lambda x, y: x * y, indices_shape)
......@@ -804,20 +882,27 @@ class OpSet9():
starts = self.graph.get_input_node(node, idx=1, copy=True)
ends = self.graph.get_input_node(node, idx=2, copy=True)
starts_value = _const_weight_or_none(starts)
if starts_value is not None:
starts_value = starts_value.tolist()
ends_value = _const_weight_or_none(ends)
if ends_value is not None:
ends_value = ends_value.tolist()
if len(node.inputs) > 2:
s_len = len(val_x.out_shapes[0])
axes = list(range(s_len))
if len(node.inputs) > 3:
axes = self.graph.get_input_node(node, idx=3, copy=True)
axes = _const_weight_or_none(axes, necessary=True)
axes_node = self.graph.get_input_node(node, idx=3, copy=True)
axes = _const_weight_or_none(axes_node, necessary=True).tolist()
if len(node.inputs) > 4:
steps = self.graph.get_input_node(node, idx=4, copy=True)
steps = _const_weight_or_none(steps)
steps = _const_weight_or_none(steps).tolist()
layer_attrs = {
"axes": axes,
"starts": starts.name,
"ends": ends.name
}
if starts_value is not None and ends_value is not None:
if starts_value is not None and ends_value is not None and axes is not None:
starts_value = starts_value.copy()
ends_value = ends_value.copy()
#for idx in range(len(ends_value)):
......@@ -847,6 +932,8 @@ class OpSet9():
layer_attrs['starts'] = starts_cast
if ends.dtype != 'int32':
ends_cast = ends.name + '_cast'
else:
ends_cast = ends.name
self.paddle_graph.add_layer(
'paddle.cast',
inputs={"x": ends.name},
......@@ -862,6 +949,7 @@ class OpSet9():
ends[idx] = 2**31 - 1
layer_attrs = {"axes": axes, "starts": starts, "ends": ends}
if steps is not None:
layer_attrs['strides'] = steps
self.paddle_graph.add_layer(
......@@ -986,11 +1074,17 @@ class OpSet9():
inputs={'x': val_shape.name},
outputs=[val_shape.name],
shape=val_shape.out_shapes[0])
if val_shape.dtype != "int32":
self.paddle_graph.add_layer(
'paddle.cast',
inputs={'x': val_shape.name},
outputs=[val_shape.name],
dtype=string("int32"))
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={'x': val_x.name,
'shape': val_shape.name},
outputs=node)
outputs=[node.name])
@print_mapping_info
def Cast(self, node):
......@@ -1221,7 +1315,10 @@ class OpSet9():
@print_mapping_info
def Transpose(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
perm = node.get_attr('perm')
s_len = len(val_x.out_shapes[0])
perm_default = list(range(s_len))
perm_default.reverse()
perm = node.get_attr('perm', perm_default)
self.paddle_graph.add_layer(
"paddle.transpose",
inputs={"x": val_x.name},
......@@ -1230,9 +1327,6 @@ class OpSet9():
@print_mapping_info
def PRelu(self, node):
op_name = name_generator("prelu", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
val_x = self.graph.get_input_node(node, idx=0, copy=True)
val_slope = self.graph.get_input_node(node, idx=1, copy=True)
......@@ -1240,20 +1334,27 @@ class OpSet9():
shape_slope = val_slope.out_shapes[0]
if shape_slope == [1]:
mode = 'all'
elif len(shape_slope) > 2:
raise Exception("The 'element' mode is not supported yet!")
if mode == 'channel' and len(shape_slope) == 1:
# paddle params shape need be [1, channel]
slope_data = _const_weight_or_none(val_slope)
slope_data = np.reshape(slope_data, [1] + shape_slope)
self.params[val_slope.name] = slope_data
self.paddle_graph.add_layer(
"paddle.nn.functional.prelu",
inputs={"x": val_x.name,
"weight": val_slope.name},
outputs=[node.name])
if mode == "element":
self.paddle_graph.add_layer(
"paddle.static.nn.prelu",
inputs={"x": val_x.name,
"param_attr": val_slope.name},
outputs=[node.name],
mode="element")
else:
if mode == 'channel':
if len(shape_slope) > 1:
self.paddle_graph.add_layer(
"paddle.reshape",
inputs={"x": val_slope.name},
outputs=[val_slope.name],
shape=[shape_slope[0]])
self.paddle_graph.add_layer(
"paddle.nn.functional.prelu",
inputs={"x": val_x.name,
"weight": val_slope.name},
outputs=[node.name])
@print_mapping_info
def Squeeze(self, node):
......@@ -1521,6 +1622,16 @@ class OpSet9():
}
if has_bias:
layer_inputs["bias"] = val_b.name
input_shape = val_x.out_shapes[0]
if reduce(lambda x,y:x*y, input_shape) in [1, -1] and 1 not in input_shape:
input_shape[1] = num_in_channels * num_groups
input_shape[0] = 0
input_shape[2] = 0
self.paddle_graph.add_layer(
"paddle.reshape",
inputs={"x": layer_inputs["x"]},
outputs=[layer_inputs["x"]],
shape=input_shape)
self.paddle_graph.add_layer(
paddle_op,
inputs=layer_inputs,
......@@ -1588,4 +1699,62 @@ class OpSet9():
inputs={"x": val_x.name},
outputs=[node.name],
**layer_attrs)
@print_mapping_info
def Size(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
self.paddle_graph.add_layer(
"paddle.shape",
inputs={"input": val_x.name},
outputs=[node.name])
self.paddle_graph.add_layer(
'paddle.cast',
inputs={"x": node.name},
outputs=[node.name],
dtype=string('int64'))
self.paddle_graph.add_layer(
"paddle.prod",
inputs={"x": node.name},
outputs=[node.name])
@print_mapping_info
def Sign(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
if node.dtype not in ["float16", "float32", "float64"]:
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": val_x.name},
outputs=[val_x.name],
dtype=string("float32"))
self.paddle_graph.add_layer(
"paddle.sign",
inputs={"x": val_x.name},
outputs=[node.name])
if node.dtype not in ["float16", "float32", "float64"]:
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": node.name},
outputs=[node.name],
dtype=string(node.dtype))
@print_mapping_info
def OneHot(self, node):
indices = self.graph.get_input_node(node, idx=0, copy=True)
depth = self.graph.get_input_node(node, idx=1, copy=True)
values = self.graph.get_input_node(node, idx=2, copy=True)
axis = node.get_attr('axis', -1)
self.paddle_graph.add_layer(
"custom_layer:one_hot",
inputs={"indices": indices.name,
"depth": depth.name,
"values": values.name},
outputs=[node.name],
axis=axis)
@print_mapping_info
def Reciprocal(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
self.paddle_graph.add_layer(
"paddle.reciprocal",
inputs={"x": val_x.name},
outputs=[node.name])
x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py
浏览文件 @ fb61a10b
......@@ -625,32 +625,11 @@ class TFOpMapper(OpMapper):
shape=out_shape.tolist())
def Pad(self, node):
input = self.graph.get_node(node.layer.input[0])
paddings = self.graph.get_node(node.layer.input[1])
input = self.graph.get_input_node(node, 0)
paddings = self.graph.get_input_node(node, 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={"x": 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={"x": input.name},
......@@ -658,26 +637,11 @@ class TFOpMapper(OpMapper):
pad=paddings)
def MirrorPad(self, node):
input = self.graph.get_input_node(node, 0)
paddings = self.graph.get_input_node(node, 1)
assert paddings.layer_type == "Const", "Padding should be Const"
new_paddings = numpy.flip(paddings.value, 0).flatten().tolist()
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".format(dim),
inputs={"x": transpose_name},
outputs=[node.name],
pad=new_paddings)
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
self.Pad(node)
def PadV2(self, node):
self.Pad(node)
def Squeeze(self, node):
input = self.graph.get_input_node(node, 0)
......
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