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“29b500e79aad6c9ddc859aa14156ddd4e156b90c”上不存在“develop/doc_cn/howto/cmd_parameter/use_case_cn.html”
未验证 提交 e57badeb 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #311 from Channingss/paddle_onnx 

add copyright & delete extra file
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x2paddle/op_mapper/onnx2paddle/opsets/_shape_inference.py 已删除 100644 → 0
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
import numpy as np
import sympy
def handle_negative_axis(axis, rank):
return axis if axis >= 0 else axis + rank
class ShapeInference():
def __init__(self, decoder, auto_merge=False):
self.decoder = decoder
self.fluid_data = {}
self.suggested_merge_ = {}
self.symbolic_dims_ = {}
self.auto_merge_ = auto_merge
self.dispatcher = {
# activation ops
'Relu': self.activation_ops,
'LeakyRelu': self.activation_ops,
'Elu': self.activation_ops,
'ThresholdRelu': self.activation_ops,
'Prelu': self.activation_ops,
'Tanh': self.activation_ops,
'Sigmoid': self.activation_ops,
'Softplus': self.activation_ops,
'Softsign': self.activation_ops,
'HardSigmoid': self.activation_ops,
'Shrink': self.activation_ops,
'Exp': self.activation_ops,
'Clip': self.activation_ops,
# elementwise ops
'Add': self.elementwise_ops,
'Div': self.elementwise_ops,
'Sub': self.elementwise_ops,
'Mul': self.elementwise_ops,
'Pow': self.elementwise_ops,
'Sqrt': self.elementwise_ops,
'Softmax': self.elementwise_ops,
'Constant': self.constant,
'AveragePool': self.pool,
'MaxPool': self.pool,
'Cast': self.cast,
'Conv': self.conv,
'BatchNormalization': self.batch_norm,
'Pad': self.pad,
'Gather': self.gather,
'Split': self.split,
'Transpose': self.transpose,
'Reshape': self.reshape,
'MatMul': self.matmul,
'Squeeze': self.squeeze,
'Unsqueeze': self.unsqueeze,
'Concat': self.concat,
}
self.run_ = True
self.suggested_merge_ = {}
self.symbolic_dims_ = {}
self.input_symbols_ = {}
def __call__(self):
"""
run shape inference
"""
nodes = self.decoder.model.graph.node
node_map = self.decoder.onnx_graph.node_map
value_infos = self.decoder.onnx_graph.value_infos
onnx_model = self.decoder.model
#self._apply_suggested_merge(graph_input_only=True)
for layer in nodes:
node = node_map[layer.name]
for opt in layer.output:
if opt in value_infos:
value_info = value_infos[opt]
#if len(value_info['shape']) == 0 or value_info[
# 'dtype'] is None or 0 in value_info['shape']:
# #TODO add node shape inference
# if self.is_support_inference(node):
# op_infer = self.dispatcher[node.layer_type]
# #shapes = op_infer(node)
# print(node.layer_name + ': ')
# print(node.layer_type + ': ')
#else:
# print(node.layer_name)
node.dtype = value_info['dtype']
node.out_shapes.append(value_info['shape'])
else:
#TODO add node shape inference
if self.is_support_inference(node):
op_infer = self.dispatcher[node.layer_type]
#shapes = op_infer(node)
#print(node.layer_name + ': ')
#print(node.layer_type + ': ')
def get_input_node(self, node, idx, copy=False):
return self.decoder.onnx_graph.get_input_node(node, idx=idx, copy=copy)
def get_fluid_data(self, node, return_ndarray=False):
data = None
if node.layer_name in self.fluid_data:
data = self.fluid_data[node.layer_name]
elif isinstance(node, ONNXGraphDataNode):
data = node.weight
elif isinstance(node, ONNXGraphNode):
data = node.value
if return_ndarray:
return data
else:
return data.tolist()
def is_support_inference(self, node):
if node.layer_type not in self.dispatcher:
print(
"[WARNNING] Shape inference not support Node[{}](op type: {}) ".
format(node.layer_name, node.layer_type))
return False
return True
def _try_get_value(self, node, idx):
if idx >= len(node.inputs):
return None
return self.get_input_node(node, idx=idx, return_ndarray=True)
def _get_int_values(self, node, broadcast=False):
values = [self._try_get_value(node, i) for i in range(len(node.input))]
if all([v is not None for v in values]):
# some shape compute is in floating point, cast to int for sympy
for i, v in enumerate(values):
if type(v) != np.ndarray:
continue
if len(v.shape) > 1:
new_v = None # ignore value for rank > 1
elif len(v.shape) == 0:
new_v = int(np.asscalar(v))
else:
assert len(v.shape) == 1
new_v = [int(vv) for vv in v]
values[i] = new_v
values_len = [len(v) if type(v) == list else 0 for v in values]
max_len = max(values_len)
if max_len >= 1 and broadcast:
# broadcast
for i, v in enumerate(values):
if v is None:
continue # don't broadcast if value is unknown
if type(v) == list:
if len(v) < max_len:
values[i] = v * max_len
else:
assert len(v) == max_len
else:
values[i] = [v] * max_len
return values
def _compute_on_sympy_data(self, node, op_func):
assert len(node.outputs) == 1
values = self._get_int_values(node, broadcast=True)
if all([v is not None for v in values]):
is_list = [type(v) == list for v in values]
as_list = any(is_list)
if as_list:
data = [op_func(vs) for vs in zip(*values)]
self.fluid_data[node.layer_name] = data
node.out_shapes.append(data.shape)
print('*' * 10, data)
else:
data = op_func(values)
self.fluid_data[node.layer_name] = data
print('*' * 10, data)
node.out_shapes.append(data.shape)
def _pass_on_sympy_data(self, node):
assert len(node.inputs) == 1 or node.layer_type == 'Reshape'
self._compute_on_sympy_data(node, lambda x: x[0])
def _get_sympy_shape(self, node, idx):
sympy_shape = []
for d in self._get_shape(node, idx):
if type(d) == str:
sympy_shape.append(self.symbolic_dims_[d] if d in
self.symbolic_dims_ else sympy.Symbol(
d, integer=True))
else:
assert None != d
sympy_shape.append(d)
return sympy_shape
def _check_merged_dims(self, dims, allow_broadcast=True):
if allow_broadcast:
dims = [d for d in dims if not (is_literal(d) and int(d) <= 1)]
if not all([d == dims[0] for d in dims]):
self._add_suggested_merge(dims, apply=True)
def check_specific_shape(self, input_node, output_node, shape):
if -1 in input_node.out_shapes[0]:
assert "Shape inference failed, when calculate output_node[{}]'s \
shape need specific shape, but got input_node[{}]'s shape: {}".format(
output_node.layer_name, input_node.layer_name,
input_node.out_shapes[0])
def _add_suggested_merge(self, symbols, apply=False):
assert all([(type(s) == str and s in self.symbolic_dims_) or
is_literal(s) for s in symbols])
symbols = set(symbols)
for k, v in self.suggested_merge_.items():
if k in symbols:
symbols.remove(k)
symbols.add(v)
map_to = None
# if there is literal, map to it first
for s in symbols:
if is_literal(s):
map_to = s
break
# when no literals, map to input symbolic dims, then existing symbolic dims
if map_to is None:
for s in symbols:
if s in self.input_symbols_:
map_to = s
break
if map_to is None:
for s in symbols:
if type(self.symbolic_dims_[s]) == sympy.Symbol:
map_to = s
break
# when nothing to map to, use the shorter one
if map_to is None:
if self.verbose_ > 0:
print(
'Potential unsafe merge between symbolic expressions: ({})'.
format(','.join(symbols)))
symbols_list = list(symbols)
lens = [len(s) for s in symbols_list]
map_to = symbols_list[lens.index(min(lens))]
symbols.remove(map_to)
def _merge_symbols(self, dims):
if not all([type(d) == str for d in dims]):
if self.auto_merge_:
assert len(
dims
) == 2 # only allow symbol->int merge in binary ops for now
is_int = [is_literal(d) for d in dims]
if sum(is_int) == 1:
int_dim = is_int.index(1)
if self.verbose_ > 0:
print('dim {} has been merged with value {}'.format(
dims[1 - int_dim], dims[int_dim]))
self._check_merged_dims(dims, allow_broadcast=False)
return dims[int_dim]
else:
if self.verbose_ > 0:
print('dim {} has been mergd with dim {}'.format(dims[
0], dims[1]))
return dims[0]
else:
return None
if all([d == dims[0] for d in dims]):
return dims[0]
merged = [
self.suggested_merge_[d] if d in self.suggested_merge_ else d
for d in dims
]
if all([d == merged[0] for d in merged]):
assert merged[0] in self.symbolic_dims_
return merged[0]
else:
return None
# broadcast from right to left, and merge symbolic dims if needed
def _broadcast_shapes(self, shape1, shape2):
new_shape = []
rank1 = len(shape1)
rank2 = len(shape2)
new_rank = max(rank1, rank2)
for i in range(new_rank):
dim1 = shape1[rank1 - 1 - i] if i < rank1 else 1
dim2 = shape2[rank2 - 1 - i] if i < rank2 else 1
if dim1 == 1 or dim1 == dim2:
new_dim = dim2
elif dim2 == 1:
new_dim = dim1
else:
new_dim = self._merge_symbols([dim1, dim2])
if not new_dim:
# warning about unsupported broadcast when not auto merge
# note that auto merge has the risk of incorrectly merge symbols while one of them being 1
# for example, 'a' = 1, 'b' = 5 at runtime is valid broadcasting, but with auto merge 'a' == 'b'
if self.auto_merge_:
self._add_suggested_merge([dim1, dim2], apply=True)
else:
print('unsupported broadcast between ' + str(dim1) + ' '
+ str(dim2))
new_shape = [new_dim] + new_shape
return new_shape
def _apply_suggested_merge(self, graph_input_only=False):
if not self.suggested_merge_:
return
for i in list(self.decoder.model.graph.input) + (
[] if graph_input_only else
list(self.decoder.model.graph.value_info)):
for d in i.type.tensor_type.shape.dim:
if d.dim_param in self.suggested_merge_:
v = self.suggested_merge_[d.dim_param]
if is_literal(v):
d.dim_value = int(v)
else:
d.dim_param = v
def _add_suggested_merge(self, symbols, apply=False):
assert all([(type(s) == str and s in self.symbolic_dims_) or
is_literal(s) for s in symbols])
symbols = set(symbols)
for k, v in self.suggested_merge_.items():
if k in symbols:
symbols.remove(k)
symbols.add(v)
map_to = None
# if there is literal, map to it first
for s in symbols:
if is_literal(s):
map_to = s
break
# when no literals, map to input symbolic dims, then existing symbolic dims
if map_to is None:
for s in symbols:
if s in self.input_symbols_:
map_to = s
break
if map_to is None:
for s in symbols:
if type(self.symbolic_dims_[s]) == sympy.Symbol:
map_to = s
break
# when nothing to map to, use the shorter one
if map_to is None:
if self.verbose_ > 0:
print(
'Potential unsafe merge between symbolic expressions: ({})'.
format(','.join(symbols)))
symbols_list = list(symbols)
lens = [len(s) for s in symbols_list]
map_to = symbols_list[lens.index(min(lens))]
symbols.remove(map_to)
for s in symbols:
if s == map_to:
continue
if is_literal(map_to) and is_literal(s):
assert int(map_to) == int(s)
self.suggested_merge_[s] = int(map_to) if is_literal(
map_to) else map_to
for k, v in self.suggested_merge_.items():
if v == s:
self.suggested_merge_[k] = map_to
if apply and self.auto_merge_:
self._apply_suggested_merge()
def pool_conv_ops(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
if len(node.inputs) > 1:
W_shape = self.get_input_node(node, idx=1).out_shapes[0]
rank = len(W_shape) - 2 # number of spatial axes
kernel_shape = W_shape[-rank:]
sympy_shape[1] = W_shape[0]
else:
W_shape = None
kernel_shape = node.get_attr('kernel_shape')
rank = len(kernel_shape)
dilations = node.get_attr('dilations', [1] * rank)
strides = node.get_attr('strides', [1] * rank)
pads = node.get_attr('pads')
effective_kernel_shape = [(k - 1) * d + 1
for k, d in zip(kernel_shape, dilations)]
if pads is None:
pads = [0] * (2 * rank)
auto_pad = node.get_attr('auto_pad', b'NOTSET').decode('utf-8')
if auto_pad != 'VALID' and auto_pad != 'NOTSET':
try:
residual = [
sympy.Mod(d, s)
for d, s in zip(fluid_shape[-rank:], strides)
]
total_pads = [
max(0, (k - s) if r == 0 else (k - r))
for k, s, r in zip(effective_kernel_shape, strides,
residual)
]
except TypeError: # sympy may throw TypeError: cannot determine truth value of Relational
total_pads = [
max(0, (k - s))
for k, s in zip(effective_kernel_shape, strides)
] # assuming no residual if sympy throws error
elif auto_pad == 'VALID':
total_pads = []
else:
total_pads = [0] * rank
else:
assert len(pads) == 2 * rank
total_pads = [p1 + p2 for p1, p2 in zip(pads[:rank], pads[rank:])]
ceil_mode = node.get_attr('ceil_mode', 0)
for i in range(rank):
effective_input_size = fluid_shape[-rank + i]
if len(total_pads) > 0:
effective_input_size = effective_input_size + total_pads[i]
if ceil_mode:
strided_kernel_positions = sympy.ceiling(
(effective_input_size - effective_kernel_shape[i]) /
strides[i])
else:
strided_kernel_positions = (
effective_input_size - effective_kernel_shape[i]
) // strides[i]
fluid_shape[-rank + i] = strided_kernel_positions + 1
node.out_shapes.append(fluid_shape)
return fluid_shape
def cast(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shape[0]
node.out_shapes.append(fluid_shape)
return fluid_shape
def pool(self, node):
return self.conv_pool_ops(node)
def conv(self, node):
return self.conv_pool_ops(node)
def batch_norm(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
node.out_shapes.append(fluid_shape)
return fluid_shape
def activation_ops(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
node.out_shapes.append(fluid_shape)
return fluid_shape
def elementwise_ops(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
node.out_shapes.append(fluid_shape)
return fluid_shape
def pad(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
# op_set <= 10
pads = node.get_attr('pads')
rank = len(fluid_shape)
fluid_shape = [
d + pad_up + pad_down
for d, pad_up, pad_down in zip(fluid_shape, pads[:rank], pads[
rank:])
]
node.out_shapes.append(fluid_shape)
return fluid_shape
def gather(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
axis = handle_negative_axis(node.get_attr('axis', 0), len(fluid_shape))
indices_shape = self.get_input_node(node, idx=1).out_shapes[0]
fluid_shape = fluid_shape[:axis] + list(indices_shape) + fluid_shape[
axis + 1:]
input = self.get_input_node(node, 0)
if input.layer_name in self.fluid_data:
assert 0 == axis # only handle 1D sympy compute
idx = self.get_fluid_date(indices_shape)
data = self.fluid_data[input.layer_name]
if type(data) == list:
if type(idx) == np.ndarray and len(idx.shape) == 1:
self.fluid_data[
node.layer_name] = [data[int(i)] for i in idx]
else:
self.fluid_data[node.layer_name] = data[int(idx)]
else:
assert idx == 0
self.fluid_data[node.layer_name] = data
node.out_shapes.append(fluid_shape)
return fluid_shape
def constant(self, node):
if isinstance(node, ONNXGraphNode):
fluid_shape = node.value.shape
else:
fluid_shape = node.weight.shape
node.out_shapes.append(fluid_shape)
return fluid_shape
def split(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
axis = handle_negative_axis(node.get_attr('axis', 0), len(fluid_shape))
split = node.get_attr('split')
if not split:
num_outputs = len(node.outputs)
split = [fluid_shape[axis] /
sympy.Integer(num_outputs)] * num_outputs
else:
split = [sympy.Integer(s) for s in split]
shapes = []
for i_o in range(len(split)):
shape = fluid_shape[:axis] + [split[i_o]] + fluid_shape[axis + 1:]
shapes.append(shape)
node.out_shapes += shapes
return shapes
def shape(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
fluid_shape = [len(fluid_shape), ]
node.out_shapes.append(fluid_shape)
self.fluid_data[node.layer_name] = np.array(fluid_shape)
return fluid_shape
def transpose(self, node):
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
perm = node.get_attr('perm')
fulid_shape = np.array(fluid_shape)[perm].tolist()
node.out_shapes.append(fluid_shape)
return fluid_shape
def reshape(self, node):
shape = self.get_input_node(node, idx=1)
shape_data = self.get_fluid_data(shape)
if shape_data is not None:
if -1 in shape_data:
fluid_shape = self.get_input_node(node, idx=0).out_shapes[0]
print(fluid_shape)
index = shape_data.index(-1)
total_elements = 1
for dim in fluid_shape:
total_elements *= dim
part_elements = 1
for dim in shape_data:
if dim != -1:
part_elements *= dim
shape_data[index] = total_elements // part_elements
node.out_shapes.append(shape_data)
else:
pass
return shape_data
def matmul(self, node):
x_shape = self.get_input_node(node, idx=0).out_shapes[0]
y_shape = self.get_input_node(node, idx=1).out_shapes[0]
x_rank = len(x_shape)
y_rank = len(y_shape)
if x_rank == 1 and y_rank == 1:
new_shape = []
elif x_rank == 1:
y_reduce_dim = -2
new_shape = x_shape[:y_reduce_dim] + [x_shape[-1]]
elif y_rank == 1:
x_reduce_dim = -1
new_shape = x_shape[:x_reduce_dim]
else:
x_reduce_dim = -1
y_reduce_dim = -2
new_shape = self._broadcast_shapes(
x_shape[:-2], y_shape[:-2]) + [x_shape[-2]] + [y_shape[-1]]
node.out_shapes.append(new_shape)
return new_shape
def squeeze(self, node):
self._pass_on_sympy_data(node)
def unsqueeze(self, node):
self._pass_on_sympy_data(node)
def concat(self, node):
if any([i in self.fluid_data for i in node.inputs]):
values = self._get_int_values(node)
if all([v is not None for v in values]):
assert 0 == get_attribute(node, 'axis')
self.fluid_data[node.layer_name] = []
for i in range(len(node.input)):
value = values[i]
if type(value) == list:
self.fluid_data[node.layer_name].extend(value)
else:
self.fluid_data[node.layer_name].append(value)
x2paddle/op_mapper/paddle2onnx/opset10/paddle_custom_layer/im2sequence.py
浏览文件 @ e57badeb
# 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 onnx
import numpy as np
from onnx import onnx_pb, helper
......
x2paddle/op_mapper/paddle2onnx/opset10/paddle_custom_layer/yolo_box.py
浏览文件 @ e57badeb
# 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 onnx
import numpy as np
from onnx import onnx_pb, helper
......
x2paddle/op_mapper/paddle2onnx/opset11/opset.py
浏览文件 @ e57badeb
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......
x2paddle/op_mapper/paddle2onnx/opset11/paddle_custom_layer/im2sequence.py
浏览文件 @ e57badeb
# 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 onnx
import numpy as np
from onnx import onnx_pb, helper
......
x2paddle/op_mapper/paddle2onnx/opset11/paddle_custom_layer/yolo_box.py
浏览文件 @ e57badeb
# 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 onnx
import numpy as np
from onnx import onnx_pb, helper
......
x2paddle/op_mapper/paddle2onnx/opset9/opset.py
浏览文件 @ e57badeb
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/im2sequence.py
浏览文件 @ e57badeb
# 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 onnx
import numpy as np
from onnx import onnx_pb, helper
......
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/multiclass_nms.py
浏览文件 @ e57badeb
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......
x2paddle/op_mapper/paddle2onnx/opset9/paddle_custom_layer/yolo_box.py
浏览文件 @ e57badeb
# 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 onnx
import numpy as np
from onnx import onnx_pb, helper
......
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