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提交 20eef555 编写于 作者: W wjj19950828
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rm SymbolicShapeInference

上级 d740acce
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Showing with 4 addition and 1620 deletion
x2paddle/decoder/onnx_decoder.py
浏览文件 @ 20eef555
......@@ -13,7 +13,6 @@
# limitations under the License.
from x2paddle.core.graph import GraphNode, Graph
from x2paddle.decoder.onnx_shape_inference import SymbolicShapeInference
from onnx.checker import ValidationError
from onnx.checker import check_model
from onnx import helper, shape_inference
......@@ -184,14 +183,10 @@ class ONNXGraph(Graph):
self.value_infos = {}
self.graph = onnx_model.graph
self.get_place_holder_nodes()
print("shape inferencing ...")
self.graph = SymbolicShapeInference.infer_shapes(
onnx_model, fixed_input_shape=self.fixed_input_shape)
if self.graph is None:
print('[WARNING] Shape inference by ONNX offical interface.')
onnx_model = shape_inference.infer_shapes(onnx_model)
self.graph = onnx_model.graph
print("shape inferenced.")
print("Shape inferencing ...")
onnx_model = shape_inference.infer_shapes(onnx_model)
self.graph = onnx_model.graph
print("Shape inferenced.")
self.build()
self.collect_value_infos()
self.allocate_shapes()
......
x2paddle/decoder/onnx_shape_inference.py 已删除 100644 → 0
浏览文件 @ d740acce
# 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.
# Reference Code from https://github.com/microsoft/onnxruntime, Licensed under the MIT License.
import argparse
import numpy as np
import onnx
import sys
from onnx import helper, numpy_helper, shape_inference
import sympy
from packaging import version
def get_attribute(node, attr_name, default_value=None):
found = [attr for attr in node.attribute if attr.name == attr_name]
if found:
return helper.get_attribute_value(found[0])
return default_value
def get_dim_from_type_proto(dim):
return getattr(dim, dim.WhichOneof('value')) if type(
dim.WhichOneof('value')) == str else None
def get_shape_from_type_proto(type_proto):
return [
get_dim_from_type_proto(d) for d in type_proto.tensor_type.shape.dim
]
def get_shape_from_sympy_shape(sympy_shape):
return [
None if i is None else (int(i) if is_literal(i) else str(i))
for i in sympy_shape
]
def is_literal(dim):
return type(dim) in [int, np.int64, np.int32, sympy.Integer] or (hasattr(
dim, 'is_number') and dim.is_number)
def handle_negative_axis(axis, rank):
assert axis < rank and axis >= -rank
return axis if axis >= 0 else rank + axis
def get_opset(mp, domain=['', 'onnx', 'ai.onnx']):
if type(domain) != list:
domain = [domain]
for opset in mp.opset_import:
if opset.domain in domain:
return opset.version
return None
def as_scalar(x):
if type(x) == list:
assert len(x) == 1
return x[0]
elif type(x) == np.ndarray:
return np.asscalar(x)
else:
return x
def as_list(x, keep_none):
if type(x) == list:
return x
elif type(x) == np.ndarray:
return list(x)
elif keep_none and x is None:
return None
else:
return [x]
def sympy_reduce_product(x):
if type(x) == list:
value = sympy.Integer(1)
for v in x:
value = value * v
else:
value = x
return value
class SymbolicShapeInference:
def __init__(self, int_max, auto_merge, guess_output_rank, verbose):
self.dispatcher_ = {
'Add': self._infer_symbolic_compute_ops,
'ArrayFeatureExtractor': self._infer_ArrayFeatureExtractor,
'AveragePool': self._infer_Pool,
'Cast': self._infer_Cast,
'CategoryMapper': self._infer_CategoryMapper,
'Compress': self._infer_Compress,
'Concat': self._infer_Concat,
'ConstantOfShape': self._infer_ConstantOfShape,
'Conv': self._infer_Conv,
'CumSum': self._pass_on_shape_and_type,
'Div': self._infer_symbolic_compute_ops,
'Expand': self._infer_Expand,
'Equal': self._infer_symbolic_compute_ops,
'Floor': self._infer_symbolic_compute_ops,
'Gather': self._infer_Gather,
'GatherElements': self._infer_GatherElements,
'GatherND': self._infer_GatherND,
'If': self._infer_If,
'Loop': self._infer_Loop,
'MatMul': self._infer_MatMul,
'MatMulInteger16': self._infer_MatMulInteger,
'MaxPool': self._infer_Pool,
'Max': self._infer_symbolic_compute_ops,
'Min': self._infer_symbolic_compute_ops,
'Mul': self._infer_symbolic_compute_ops,
'NonMaxSuppression': self._infer_NonMaxSuppression,
'NonZero': self._infer_NonZero,
'OneHot': self._infer_OneHot,
'Pad': self._infer_Pad,
'Range': self._infer_Range,
'ReduceProd': self._infer_ReduceProd,
'Reshape': self._infer_Reshape,
'Resize': self._infer_Resize,
'Round': self._pass_on_shape_and_type,
'Scan': self._infer_Scan,
'ScatterElements': self._infer_ScatterElements,
'Shape': self._infer_Shape,
'Size': self._infer_Size,
'Slice': self._infer_Slice,
'Split': self._infer_Split,
'SplitToSequence': self._infer_SplitToSequence,
'Squeeze': self._infer_Squeeze,
'Sub': self._infer_symbolic_compute_ops,
'Tile': self._infer_Tile,
'TopK': self._infer_TopK,
'Unsqueeze': self._infer_Unsqueeze,
'Where': self._infer_symbolic_compute_ops,
'ZipMap': self._infer_ZipMap
}
self.run_ = True
self.suggested_merge_ = {}
self.symbolic_dims_ = {}
self.input_symbols_ = {}
self.auto_merge_ = auto_merge
self.guess_output_rank_ = guess_output_rank
self.verbose_ = verbose
self.int_max_ = int_max
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 _apply_suggested_merge(self, graph_input_only=False):
if not self.suggested_merge_:
return
for i in list(self.out_mp_.graph.input) + (
[] if graph_input_only else list(self.out_mp_.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 _preprocess(self, in_mp, input_shapes=None):
out_mp = onnx.ModelProto()
out_mp.CopyFrom(in_mp)
out_mp.graph.ClearField('node')
self.out_mp_ = out_mp
defined = set([
i.name
for i in list(in_mp.graph.input) + list(in_mp.graph.initializer)
])
pending_nodes = []
# returns True if no more ready nodes
def _insert_ready_nodes():
ready_nodes = [
pn for pn in pending_nodes
if all([i in defined for i in pn.input if i])
]
for rn in ready_nodes:
self.out_mp_.graph.node.add().CopyFrom(rn)
for o in rn.output:
defined.add(o)
pending_nodes.remove(rn)
return not ready_nodes
# constant op -> initializer, topological sort
for in_n in in_mp.graph.node:
if in_n.op_type == 'Constant':
t = get_attribute(in_n, 'value')
t.name = in_n.output[0]
self.out_mp_.graph.initializer.add().CopyFrom(t)
defined.add(t.name)
else:
pending_nodes.append(in_n)
_insert_ready_nodes()
while pending_nodes:
if _insert_ready_nodes():
break
if pending_nodes and self.verbose_ > 0:
print('SymbolicShapeInference: orphaned nodes discarded: ')
print(
*[n.op_type + ': ' + n.output[0] for n in pending_nodes],
sep='\n')
if input_shapes is not None:
for input_name, shape in input_shapes.items():
for idx in range(len(self.out_mp_.graph.input)):
if self.out_mp_.graph.input[idx].name == input_name:
value_info = self.out_mp_.graph.input[idx]
del self.out_mp_.graph.input[idx]
self.out_mp_.graph.input.append(
helper.make_tensor_value_info(
value_info.name,
value_info.type.tensor_type.elem_type, shape))
self.initializers_ = dict(
[(i.name, i) for i in self.out_mp_.graph.initializer])
self.known_vi_ = dict(
[(i.name, i) for i in list(self.out_mp_.graph.input)])
self.known_vi_.update(
dict([(i.name, helper.make_tensor_value_info(i.name, i.data_type,
list(i.dims)))
for i in self.out_mp_.graph.initializer]))
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 _get_shape(self, node, idx):
name = node.input[idx]
if name in self.known_vi_:
return get_shape_from_type_proto(self.known_vi_[name].type)
else:
assert name in self.initializers_
return list(self.initializers_[name].dims)
def _get_shape_rank(self, node, idx):
return len(self._get_shape(node, idx))
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 _get_value(self, node, idx):
name = node.input[idx]
assert name in self.sympy_data_ or name in self.initializers_
return self.sympy_data_[
name] if name in self.sympy_data_ else numpy_helper.to_array(
self.initializers_[name])
def _try_get_value(self, node, idx):
if idx >= len(node.input):
return None
name = node.input[idx]
if name in self.sympy_data_ or name in self.initializers_:
return self._get_value(node, idx)
return None
def _update_computed_dims(self, new_sympy_shape):
for i, new_dim in enumerate(new_sympy_shape):
if not is_literal(new_dim) and not type(new_dim) == str:
str_dim = str(new_dim)
if str_dim in self.suggested_merge_:
new_sympy_shape[i] = self.symbolic_dims_[
self.suggested_merge_[str_dim]]
else:
# add new_dim if it's a computational expression
if not str(new_dim) in self.symbolic_dims_:
self.symbolic_dims_[str(new_dim)] = new_dim
def _onnx_infer_single_node(self, node):
# skip onnx shape inference for Scan/Loop
skip_infer = node.op_type in ['Scan', 'Loop']
if not skip_infer:
# run single node inference with self.known_vi_ shapes
# note that inference rely on initializer values is not handled
# as we don't copy initializer weights to tmp_graph for inference speed purpose
if node.op_type == 'SplitToSequence':
make_value_info_func = helper.make_sequence_value_info
else:
make_value_info_func = helper.make_tensor_value_info
tmp_graph = helper.make_graph(
[node], 'tmp', [self.known_vi_[i] for i in node.input if i], [
make_value_info_func(i, onnx.TensorProto.UNDEFINED, None)
for i in node.output
])
self.tmp_mp_.graph.CopyFrom(tmp_graph)
self.tmp_mp_ = shape_inference.infer_shapes(self.tmp_mp_)
for i_o in range(len(node.output)):
o = node.output[i_o]
vi = self.out_mp_.graph.value_info.add()
if not skip_infer:
vi.CopyFrom(self.tmp_mp_.graph.output[i_o])
self.known_vi_[o] = vi
def _onnx_infer_subgraph(self, node, subgraph, use_node_input=True):
if self.verbose_ > 2:
print('Inferencing subgraph of node {} with output({}...): {}'.
format(node.name, node.output[0], node.op_type))
# node inputs are not passed directly to the subgraph
# it's up to the node dispatcher to prepare subgraph input
# for example, with Scan/Loop, subgraph input shape would be trimmed from node input shape
# besides, inputs in subgraph could shadow implicit inputs
subgraph_inputs = set([
i.name for i in list(subgraph.initializer) + list(subgraph.input)
])
subgraph_implicit_input = set([
name for name in self.known_vi_.keys()
if not name in subgraph_inputs
])
tmp_graph = helper.make_graph(
list(subgraph.node), 'tmp',
list(subgraph.input) +
[self.known_vi_[i] for i in subgraph_implicit_input], [
helper.make_tensor_value_info(i.name,
onnx.TensorProto.UNDEFINED, None)
for i in subgraph.output
])
tmp_graph.initializer.extend([
i for i in self.out_mp_.graph.initializer
if i.name in subgraph_implicit_input
])
tmp_graph.initializer.extend(subgraph.initializer)
self.tmp_mp_.graph.CopyFrom(tmp_graph)
symbolic_shape_inference = SymbolicShapeInference(
self.int_max_, self.auto_merge_, self.guess_output_rank_,
self.verbose_)
all_shapes_inferred = False
symbolic_shape_inference._preprocess(self.tmp_mp_)
# note that after _preprocess, Constant node will be converted to initializer and should be appended to subgraph.initializer
subgraph.initializer.extend([
i for i in symbolic_shape_inference.out_mp_.graph.initializer
if i.name not in subgraph_implicit_input and i.name not in
subgraph_inputs
])
symbolic_shape_inference.suggested_merge_ = self.suggested_merge_.copy()
while symbolic_shape_inference.run_:
all_shapes_inferred = symbolic_shape_inference._infer_impl(
self.tmp_mp_, self.sympy_data_.copy())
symbolic_shape_inference._update_output_from_vi()
if use_node_input:
# if subgraph uses node input, it needs to update to merged dims
subgraph.ClearField('input')
subgraph.input.extend(
symbolic_shape_inference.out_mp_.graph.input[:len(node.input)])
subgraph.ClearField('output')
subgraph.output.extend(symbolic_shape_inference.out_mp_.graph.output)
subgraph.ClearField('value_info')
subgraph.value_info.extend(
symbolic_shape_inference.out_mp_.graph.value_info)
subgraph.ClearField('node')
subgraph.node.extend(symbolic_shape_inference.out_mp_.graph.node)
# for new symbolic dims from subgraph output, add to main graph symbolic dims
subgraph_shapes = [
get_shape_from_type_proto(o.type)
for o in symbolic_shape_inference.out_mp_.graph.output
]
subgraph_new_symbolic_dims = set([
d for s in subgraph_shapes
if s for d in s if type(d) == str and not d in self.symbolic_dims_
])
new_dims = {}
for d in subgraph_new_symbolic_dims:
assert d in symbolic_shape_inference.symbolic_dims_
new_dims[d] = symbolic_shape_inference.symbolic_dims_[d]
self.symbolic_dims_.update(new_dims)
return symbolic_shape_inference
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.output) == 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:
self.sympy_data_[node.output[
0]] = [op_func(vs) for vs in zip(*values)]
else:
self.sympy_data_[node.output[0]] = op_func(values)
def _pass_on_sympy_data(self, node):
assert len(node.input) == 1 or node.op_type == 'Reshape'
self._compute_on_sympy_data(node, lambda x: x[0])
def _pass_on_shape_and_type(self, node):
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type,
self._get_shape(node, 0)))
def _new_symbolic_dim(self, prefix, dim):
new_dim = '{}_d{}'.format(prefix, dim)
if new_dim in self.suggested_merge_:
v = self.suggested_merge_[new_dim]
new_dim = sympy.Integer(int(v)) if is_literal(v) else v
else:
self.symbolic_dims_[new_dim] = sympy.Symbol(new_dim, integer=True)
return new_dim
def _new_symbolic_dim_from_output(self, node, out_idx=0, dim=0):
return self._new_symbolic_dim('{}{}_o{}_'.format(
node.op_type, list(self.out_mp_.graph.node).index(node), out_idx),
dim)
def _new_symbolic_shape(self, rank, node, out_idx=0):
return [
self._new_symbolic_dim_from_output(node, out_idx, i)
for i in range(rank)
]
def _compute_conv_pool_shape(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
if len(node.input) > 1:
W_shape = self._get_sympy_shape(node, 1)
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 = get_attribute(node, 'kernel_shape')
rank = len(kernel_shape)
assert len(sympy_shape) == rank + 2
# only need to symbolic shape inference if input has symbolic dims in spatial axes
is_symbolic_dims = [not is_literal(i) for i in sympy_shape[-rank:]]
if not any(is_symbolic_dims):
shape = get_shape_from_type_proto(self.known_vi_[node.output[0]]
.type)
if len(shape) > 0:
assert len(sympy_shape) == len(shape)
sympy_shape[-rank:] = [sympy.Integer(d) for d in shape[-rank:]]
return sympy_shape
dilations = get_attribute(node, 'dilations', [1] * rank)
strides = get_attribute(node, 'strides', [1] * rank)
effective_kernel_shape = [(k - 1) * d + 1
for k, d in zip(kernel_shape, dilations)]
pads = get_attribute(node, 'pads')
if pads is None:
pads = [0] * (2 * rank)
auto_pad = get_attribute(node, '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(sympy_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 = get_attribute(node, 'ceil_mode', 0)
for i in range(rank):
effective_input_size = sympy_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]
sympy_shape[-rank + i] = strided_kernel_positions + 1
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 _compute_matmul_shape(self, node, output_dtype=None):
lhs_shape = self._get_shape(node, 0)
rhs_shape = self._get_shape(node, 1)
lhs_rank = len(lhs_shape)
rhs_rank = len(rhs_shape)
lhs_reduce_dim = 0
rhs_reduce_dim = 0
assert lhs_rank > 0 and rhs_rank > 0
if lhs_rank == 1 and rhs_rank == 1:
new_shape = []
elif lhs_rank == 1:
rhs_reduce_dim = -2
new_shape = rhs_shape[:rhs_reduce_dim] + [rhs_shape[-1]]
elif rhs_rank == 1:
lhs_reduce_dim = -1
new_shape = lhs_shape[:lhs_reduce_dim]
else:
lhs_reduce_dim = -1
rhs_reduce_dim = -2
new_shape = self._broadcast_shapes(
lhs_shape[:-2],
rhs_shape[:-2]) + [lhs_shape[-2]] + [rhs_shape[-1]]
# merge reduce dim
self._check_merged_dims(
[lhs_shape[lhs_reduce_dim], rhs_shape[rhs_reduce_dim]],
allow_broadcast=False)
if output_dtype is None:
# infer output_dtype from input type when not specified
output_dtype = self.known_vi_[node.input[
0]].type.tensor_type.elem_type
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], output_dtype,
new_shape))
def _infer_ArrayFeatureExtractor(self, node):
data_shape = self._get_shape(node, 0)
indices_shape = self._get_shape(node, 1)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type, data_shape[:-1] +
indices_shape))
def _infer_symbolic_compute_ops(self, node):
funcs = {
'Add': lambda l: l[0] + l[1],
'Div': lambda l: l[0] // l[1], # integer div in sympy
'Equal': lambda l: l[0] == l[1],
'Floor': lambda l: sympy.floor(l[0]),
'Max':
lambda l: l[1] if is_literal(l[0]) and int(l[0]) < -self.int_max_ else (l[0] if is_literal(l[1]) and int(l[1]) < -self.int_max_ else sympy.Max(l[0], l[1])),
'Min':
lambda l: l[1] if is_literal(l[0]) and int(l[0]) > self.int_max_ else (l[0] if is_literal(l[1]) and int(l[1]) > self.int_max_ else sympy.Min(l[0], l[1])),
'Mul': lambda l: l[0] * l[1],
'Sub': lambda l: l[0] - l[1],
'Where': lambda l: l[1] if l[0] else l[2]
}
assert node.op_type in funcs
self._compute_on_sympy_data(node, funcs[node.op_type])
def _infer_Cast(self, node):
self._pass_on_sympy_data(node)
def _infer_CategoryMapper(self, node):
input_type = self.known_vi_[node.input[0]].type.tensor_type.elem_type
if input_type == onnx.TensorProto.STRING:
output_type = onnx.TensorProto.INT64
else:
output_type = onnx.TensorProto.STRING
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], output_type,
self._get_shape(node, 0)))
def _infer_Compress(self, node):
input_shape = self._get_shape(node, 0)
# create a new symbolic dimension for Compress output
compress_len = self._new_symbolic_dim_from_output(node)
axis = get_attribute(node, 'axis')
if axis == None:
# when axis is not specified, input is flattened before compress so output is 1D
output_shape = [compress_len]
else:
output_shape = input_shape
output_shape[handle_negative_axis(axis, len(
input_shape))] = compress_len
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type, output_shape))
def _infer_Concat(self, node):
if any([i in self.sympy_data_ for i in node.input]):
values = self._get_int_values(node)
if all([v is not None for v in values]):
assert 0 == get_attribute(node, 'axis')
self.sympy_data_[node.output[0]] = []
for i in range(len(node.input)):
value = values[i]
if type(value) == list:
self.sympy_data_[node.output[0]].extend(value)
else:
self.sympy_data_[node.output[0]].append(value)
sympy_shape = self._get_sympy_shape(node, 0)
axis = handle_negative_axis(
get_attribute(node, 'axis'), len(sympy_shape))
for i_idx in range(1, len(node.input)):
input_shape = self._get_sympy_shape(node, i_idx)
if input_shape:
sympy_shape[axis] = sympy_shape[axis] + input_shape[axis]
self._update_computed_dims(sympy_shape)
# merge symbolic dims for non-concat axes
for d in range(len(sympy_shape)):
if d == axis:
continue
dims = [
self._get_shape(node, i_idx)[d]
for i_idx in range(len(node.input))
if self._get_shape(node, i_idx)
]
if all([d == dims[0] for d in dims]):
continue
merged = self._merge_symbols(dims)
if type(merged) == str:
sympy_shape[d] = self.symbolic_dims_[merged] if merged else None
else:
sympy_shape[d] = merged
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], self.known_vi_[node.input[0]].type.tensor_type.
elem_type, get_shape_from_sympy_shape(sympy_shape)))
def _infer_Conv(self, node):
sympy_shape = self._compute_conv_pool_shape(node)
self._update_computed_dims(sympy_shape)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(sympy_shape)))
def _infer_ConstantOfShape(self, node):
sympy_shape = self._get_int_values(node)[0]
vi = self.known_vi_[node.output[0]]
if sympy_shape is not None:
if type(sympy_shape) != list:
sympy_shape = [sympy_shape]
self._update_computed_dims(sympy_shape)
# update sympy data if output type is int, and shape is known
if vi.type.tensor_type.elem_type == onnx.TensorProto.INT64 and all(
[is_literal(x) for x in sympy_shape]):
self.sympy_data_[node.output[0]] = np.ones(
[int(x) for x in sympy_shape],
dtype=np.int64) * numpy_helper.to_array(
get_attribute(node, 'value', 0))
else:
# create new dynamic shape
sympy_shape = self._new_symbolic_shape(
self._get_shape_rank(node, 0), node)
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(sympy_shape)))
def _infer_Expand(self, node):
expand_to_shape = self._try_get_value(node, 1)
if expand_to_shape is not None:
# new_shape's dim can come from shape value
self._update_computed_dims(expand_to_shape)
shape = self._get_shape(node, 0)
new_shape = self._broadcast_shapes(
shape, get_shape_from_sympy_shape(expand_to_shape))
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type, new_shape))
def _infer_Gather(self, node):
data_shape = self._get_shape(node, 0)
axis = handle_negative_axis(
get_attribute(node, 'axis', 0), len(data_shape))
indices_shape = self._get_shape(node, 1)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type, data_shape[:axis]
+ indices_shape + data_shape[axis + 1:]))
if node.input[0] in self.sympy_data_:
assert 0 == get_attribute(node, 'axis',
0) # only handle 1D sympy compute
idx = self._get_value(node, 1)
data = self.sympy_data_[node.input[0]]
if type(data) == list:
if type(idx) == np.ndarray and len(idx.shape) == 1:
self.sympy_data_[node.output[
0]] = [data[int(i)] for i in idx]
else:
self.sympy_data_[node.output[0]] = data[int(idx)]
else:
assert idx == 0
self.sympy_data_[node.output[0]] = data
def _infer_GatherElements(self, node):
indices_shape = self._get_shape(node, 1)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type, indices_shape))
def _infer_GatherND(self, node):
data_shape = self._get_shape(node, 0)
data_rank = len(data_shape)
indices_shape = self._get_shape(node, 1)
indices_rank = len(indices_shape)
last_index_dimension = indices_shape[-1]
assert is_literal(
last_index_dimension) and last_index_dimension <= data_rank
new_shape = indices_shape[:-1] + data_shape[last_index_dimension:]
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type, new_shape))
def _infer_If(self, node):
# special case for constant condition, in case there are mismatching shape from the non-executed branch
subgraphs = [
get_attribute(node, 'then_branch'), get_attribute(node,
'else_branch')
]
cond = self._try_get_value(node, 0)
if cond is not None:
if cond > 0:
subgraphs[1].CopyFrom(subgraphs[0])
else:
subgraphs[0].CopyFrom(subgraphs[1])
for i_sub, subgraph in enumerate(subgraphs):
subgraph_infer = self._onnx_infer_subgraph(
node, subgraph, use_node_input=False)
for i_out in range(len(node.output)):
vi = self.known_vi_[node.output[i_out]]
if i_sub == 0:
vi.CopyFrom(subgraph.output[i_out])
vi.name = node.output[i_out]
else:
assert all([
d1 == d2
for d1, d2 in zip(vi.type.tensor_type.shape.dim,
subgraph.output[
i_out].type.tensor_type.shape.dim)
])
# pass on sympy data from subgraph, if cond is constant
if cond is not None and i_sub == (0 if cond > 0 else 1):
if subgraph.output[
i_out].name in subgraph_infer.sympy_data_:
self.sympy_data_[vi.name] = subgraph_infer.sympy_data_[
subgraph.output[i_out].name]
def _infer_Loop(self, node):
subgraph = get_attribute(node, 'body')
assert len(subgraph.input) == len(node.input)
for i, si in enumerate(subgraph.input):
subgraph_name = si.name
si.CopyFrom(self.known_vi_[node.input[i]])
si.name = subgraph_name
self._onnx_infer_subgraph(node, subgraph)
# create a new symbolic dimension for iteration dependent dimension
loop_iter_dim = self._new_symbolic_dim_from_output(node)
num_loop_carried = len(node.input) - 2
for i in range(len(node.output)):
vi = self.known_vi_[node.output[i]]
vi.CopyFrom(
subgraph.output[i + 1]
) # first subgraph output is condition, not in node output
if i >= num_loop_carried:
subgraph_vi_dim = subgraph.output[i +
1].type.tensor_type.shape.dim
vi.type.tensor_type.shape.ClearField('dim')
vi_dim = vi.type.tensor_type.shape.dim
vi_dim.add().dim_param = loop_iter_dim
vi_dim.extend(list(subgraph_vi_dim))
vi.name = node.output[i]
def _infer_MatMul(self, node):
self._compute_matmul_shape(node)
def _infer_MatMulInteger(self, node):
self._compute_matmul_shape(node, onnx.TensorProto.INT32)
def _infer_NonMaxSuppression(self, node):
selected = self._new_symbolic_dim_from_output(node)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[
0], onnx.TensorProto.INT64, [selected, 3]))
def _infer_NonZero(self, node):
input_rank = self._get_shape_rank(node, 0)
# create a new symbolic dimension for NonZero output
nz_len = self._new_symbolic_dim_from_output(node, 0, 1)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[
0], vi.type.tensor_type.elem_type, [input_rank, nz_len]))
def _infer_OneHot(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
depth = self._try_get_value(node, 1)
axis = get_attribute(node, 'axis', -1)
axis = handle_negative_axis(axis, len(sympy_shape) + 1)
new_shape = get_shape_from_sympy_shape(sympy_shape[:axis] + [
self._new_symbolic_dim_from_output(node)
if not is_literal(depth) else depth
] + sympy_shape[axis:])
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[2]].type.tensor_type.elem_type, new_shape))
def _infer_Pad(self, node):
if get_opset(self.out_mp_) <= 10:
pads = get_attribute(node, 'pads')
else:
pads = self._try_get_value(node, 1)
vi = self.known_vi_[node.output[0]]
output_shape = get_shape_from_type_proto(vi.type)
if len(output_shape) == 0 or None in output_shape:
sympy_shape = self._get_sympy_shape(node, 0)
rank = len(sympy_shape)
if pads is not None:
assert len(pads) == 2 * rank
new_sympy_shape = [
d + pad_up + pad_down
for d, pad_up, pad_down in zip(sympy_shape, pads[:rank],
pads[rank:])
]
self._update_computed_dims(new_sympy_shape)
else:
# dynamic pads, create new symbolic dimensions
new_sympy_shape = self._new_symbolic_shape(rank, node)
output_tp = self.known_vi_[node.input[0]].type.tensor_type.elem_type
vi.CopyFrom(
helper.make_tensor_value_info(node.output[
0], output_tp, get_shape_from_sympy_shape(new_sympy_shape)))
def _infer_Pool(self, node):
sympy_shape = self._compute_conv_pool_shape(node)
self._update_computed_dims(sympy_shape)
for o in node.output:
if not o:
continue
vi = self.known_vi_[o]
vi.CopyFrom(
helper.make_tensor_value_info(o, vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(
sympy_shape)))
def _infer_Range(self, node):
vi = self.known_vi_[node.output[0]]
input_data = self._get_int_values(node)
if all([i is not None for i in input_data]):
start = as_scalar(input_data[0])
limit = as_scalar(input_data[1])
delta = as_scalar(input_data[2])
new_sympy_shape = [
sympy.Max(sympy.ceiling((limit - start) / delta), 0)
]
else:
new_dim = self._new_symbolic_dim_from_output(node)
new_sympy_shape = [self.symbolic_dims_[new_dim]]
self._update_computed_dims(new_sympy_shape)
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], self.known_vi_[node.input[0]].type.tensor_type.
elem_type, get_shape_from_sympy_shape(new_sympy_shape)))
def _infer_ReduceProd(self, node):
axes = get_attribute(node, 'axes')
keep_dims = get_attribute(node, 'keepdims')
if keep_dims == 0 and axes == [0]:
data = self._get_int_values(node)[0]
if data is not None:
self.sympy_data_[node.output[0]] = sympy_reduce_product(data)
def _infer_Reshape(self, node):
shape_value = self._try_get_value(node, 1)
vi = self.known_vi_[node.output[0]]
if shape_value is None:
shape_shape = self._get_shape(node, 1)
assert len(shape_shape) == 1
shape_rank = shape_shape[0]
assert is_literal(shape_rank)
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(
self._new_symbolic_shape(shape_rank, node))))
else:
input_shape = self._get_shape(node, 0)
input_sympy_shape = self._get_sympy_shape(node, 0)
total = int(1)
for d in input_sympy_shape:
total = total * d
new_sympy_shape = []
deferred_dim_idx = -1
non_deferred_size = int(1)
for i, d in enumerate(shape_value):
if type(d) == sympy.Symbol:
new_sympy_shape.append(d)
elif d == 0:
new_sympy_shape.append(input_sympy_shape[i])
non_deferred_size = non_deferred_size * input_sympy_shape[i]
else:
new_sympy_shape.append(d)
if d == -1:
deferred_dim_idx = i
elif d != 0:
non_deferred_size = non_deferred_size * d
assert new_sympy_shape.count(-1) < 2
if -1 in new_sympy_shape:
new_dim = total // non_deferred_size
new_sympy_shape[deferred_dim_idx] = new_dim
self._update_computed_dims(new_sympy_shape)
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(new_sympy_shape)))
self._pass_on_sympy_data(node)
def _infer_Resize(self, node):
vi = self.known_vi_[node.output[0]]
input_sympy_shape = self._get_sympy_shape(node, 0)
if get_opset(self.out_mp_) <= 10:
scales = self._try_get_value(node, 1)
if scales is not None:
new_sympy_shape = [
sympy.simplify(sympy.floor(d * s))
for d, s in zip(input_sympy_shape, scales)
]
self._update_computed_dims(new_sympy_shape)
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], self.known_vi_[node.input[
0]].type.tensor_type.elem_type,
get_shape_from_sympy_shape(new_sympy_shape)))
else:
roi = self._try_get_value(node, 1)
scales = self._try_get_value(node, 2)
sizes = self._try_get_value(node, 3)
if sizes is not None:
new_sympy_shape = [
sympy.simplify(sympy.floor(s)) for s in sizes
]
self._update_computed_dims(new_sympy_shape)
elif scales is not None:
rank = len(scales)
if get_attribute(node, 'coordinate_transformation_mode'
) == 'tf_crop_and_resize':
assert len(roi) == 2 * rank
roi_start = list(roi)[:rank]
roi_end = list(roi)[rank:]
else:
roi_start = [0] * rank
roi_end = [1] * rank
scales = list(scales)
new_sympy_shape = [
sympy.simplify(sympy.floor(d * (end - start) * scale))
for d, start, end, scale in zip(input_sympy_shape,
roi_start, roi_end, scales)
]
self._update_computed_dims(new_sympy_shape)
else:
new_sympy_shape = self._new_symbolic_shape(
self._get_shape_rank(node, 0), node)
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type,
get_shape_from_sympy_shape(
new_sympy_shape)))
def _infer_Scan(self, node):
subgraph = get_attribute(node, 'body')
num_scan_inputs = get_attribute(node, 'num_scan_inputs')
scan_input_axes = get_attribute(node, 'scan_input_axes',
[0] * num_scan_inputs)
num_scan_states = len(node.input) - num_scan_inputs
scan_input_axes = [
handle_negative_axis(
ax, self._get_shape_rank(node, i + num_scan_states))
for i, ax in enumerate(scan_input_axes)
]
# We may have cases where the subgraph has optionial inputs that appear in both subgraph's input and initializer,
# but not in the node's input. In such cases, the input model might be invalid, but let's skip those optional inputs.
assert len(subgraph.input) >= len(node.input)
subgraph_inputs = subgraph.input[:len(node.input)]
for i, si in enumerate(subgraph_inputs):
subgraph_name = si.name
si.CopyFrom(self.known_vi_[node.input[i]])
if i >= num_scan_states:
scan_input_dim = si.type.tensor_type.shape.dim
scan_input_dim.remove(scan_input_dim[scan_input_axes[
i - num_scan_states]])
si.name = subgraph_name
self._onnx_infer_subgraph(node, subgraph)
num_scan_outputs = len(node.output) - num_scan_states
scan_output_axes = get_attribute(node, 'scan_output_axes',
[0] * num_scan_outputs)
scan_input_dim = get_shape_from_type_proto(self.known_vi_[node.input[
-1]].type)[scan_input_axes[-1]]
for i, o in enumerate(node.output):
vi = self.known_vi_[o]
if i >= num_scan_states:
shape = get_shape_from_type_proto(subgraph.output[i].type)
new_dim = handle_negative_axis(
scan_output_axes[i - num_scan_states], len(shape) + 1)
shape = shape[:new_dim] + [scan_input_dim] + shape[new_dim:]
vi.CopyFrom(
helper.make_tensor_value_info(o, subgraph.output[
i].type.tensor_type.elem_type, shape))
else:
vi.CopyFrom(subgraph.output[i])
vi.name = o
def _infer_ScatterElements(self, node):
data_shape = self._get_shape(node, 0)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[0], self.known_vi_[
node.input[0]].type.tensor_type.elem_type, data_shape))
def _infer_Shape(self, node):
self.sympy_data_[node.output[0]] = self._get_sympy_shape(node, 0)
def _infer_Size(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
self.sympy_data_[node.output[0]] = sympy_reduce_product(sympy_shape)
self.known_vi_[node.output[0]].CopyFrom(
helper.make_tensor_value_info(node.output[0],
onnx.TensorProto.INT64, []))
def _infer_Slice(self, node):
if get_opset(self.out_mp_) <= 9:
axes = get_attribute(node, 'axes')
starts = get_attribute(node, 'starts')
ends = get_attribute(node, 'ends')
steps = [1] * len(axes)
else:
starts = as_list(self._try_get_value(node, 1), keep_none=True)
ends = as_list(self._try_get_value(node, 2), keep_none=True)
axes = self._try_get_value(node, 3)
steps = self._try_get_value(node, 4)
if axes is None and not (starts is None and ends is None):
axes = list(
range(0, len(starts if starts is not None else ends)))
if steps is None and not (starts is None and ends is None):
steps = [1] * len(starts if starts is not None else ends)
axes = as_list(axes, keep_none=True)
steps = as_list(steps, keep_none=True)
new_sympy_shape = self._get_sympy_shape(node, 0)
if starts is None or ends is None:
if axes is None:
for i in range(len(new_sympy_shape)):
new_sympy_shape[i] = self._new_symbolic_dim_from_output(
node, 0, i)
else:
new_sympy_shape = get_shape_from_sympy_shape(new_sympy_shape)
for i in axes:
new_sympy_shape[i] = self._new_symbolic_dim_from_output(
node, 0, i)
else:
for i, s, e, t in zip(axes, starts, ends, steps):
idx = handle_negative_axis(i, len(new_sympy_shape))
if is_literal(e):
if e >= self.int_max_:
e = new_sympy_shape[i]
elif e <= -self.int_max_:
e = 0 if s > 0 else -1
elif is_literal(new_sympy_shape[i]):
if e < 0:
e = e + new_sympy_shape[i]
e = min(e, new_sympy_shape[i])
else:
if e > 0:
e = sympy.Min(
e, new_sympy_shape[i]
) if e > 1 else e #special case for slicing first to make computation easier
else:
e = new_sympy_shape[i] + e
else:
if is_literal(new_sympy_shape[i]):
e = sympy.Min(e, new_sympy_shape[i])
else:
try:
if e >= new_sympy_shape[i]:
e = new_sympy_shape[i]
except Exception:
print(
'Unable to determine if {} <= {}, treat as equal'.
format(e, new_sympy_shape[i]))
e = new_sympy_shape[i]
if is_literal(s) and int(s) < 0:
s = new_sympy_shape[i] + s
new_sympy_shape[idx] = (e - s + t + (-1 if t > 0 else 1)) // t
self._update_computed_dims(new_sympy_shape)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(new_sympy_shape)))
# handle sympy_data if needed, for slice in shape computation
if node.input[0] in self.sympy_data_:
assert [0] == axes
assert len(starts) == 1
assert len(ends) == 1
self.sympy_data_[node.output[0]] = self.sympy_data_[node.input[0]][
starts[0]:ends[0]]
def _infer_Split_Common(self, node, make_value_info_func):
input_sympy_shape = self._get_sympy_shape(node, 0)
axis = handle_negative_axis(
get_attribute(node, 'axis', 0), len(input_sympy_shape))
split = get_attribute(node, 'split')
if not split:
num_outputs = len(node.output)
split = [input_sympy_shape[axis] /
sympy.Integer(num_outputs)] * num_outputs
self._update_computed_dims(split)
else:
split = [sympy.Integer(s) for s in split]
for i_o in range(len(split)):
vi = self.known_vi_[node.output[i_o]]
vi.CopyFrom(
make_value_info_func(node.output[i_o], self.known_vi_[
node.input[0]].type.tensor_type.elem_type,
get_shape_from_sympy_shape(
input_sympy_shape[:axis] + [
split[i_o]
] + input_sympy_shape[axis + 1:])))
self.known_vi_[vi.name] = vi
def _infer_Split(self, node):
self._infer_Split_Common(node, helper.make_tensor_value_info)
def _infer_SplitToSequence(self, node):
self._infer_Split_Common(node, helper.make_sequence_value_info)
def _infer_Squeeze(self, node):
self._pass_on_sympy_data(node)
def _infer_Tile(self, node):
repeats_value = self._get_value(node, 1)
input_sympy_shape = self._get_sympy_shape(node, 0)
new_sympy_shape = []
for i, d in enumerate(input_sympy_shape):
new_dim = d * repeats_value[i]
new_sympy_shape.append(new_dim)
self._update_computed_dims(new_sympy_shape)
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], vi.type.tensor_type.elem_type,
get_shape_from_sympy_shape(new_sympy_shape)))
def _infer_TopK(self, node):
rank = self._get_shape_rank(node, 0)
axis = handle_negative_axis(get_attribute(node, 'axis', -1), rank)
new_shape = self._get_shape(node, 0)
if get_opset(self.out_mp_) <= 9:
k = get_attribute(node, 'k')
else:
k = self._get_int_values(node)[1]
if k == None:
k = self._new_symbolic_dim_from_output(node)
else:
k = as_scalar(k)
if type(k) in [int, str]:
new_shape[axis] = k
else:
new_sympy_shape = self._get_sympy_shape(node, 0)
new_sympy_shape[axis] = k
self._update_computed_dims(
new_sympy_shape
) # note that TopK dim could be computed in sympy_data, so need to update computed_dims when it enters shape
new_shape = get_shape_from_sympy_shape(new_sympy_shape)
for i_o in range(len(node.output)):
vi = self.known_vi_[node.output[i_o]]
vi.CopyFrom(
helper.make_tensor_value_info(node.output[
i_o], vi.type.tensor_type.elem_type, new_shape))
def _infer_Unsqueeze(self, node):
self._pass_on_sympy_data(node)
def _infer_ZipMap(self, node):
map_key_type = None
if get_attribute(node, 'classlabels_int64s') is not None:
map_key_type = onnx.TensorProto.INT64
elif get_attribute(node, 'classlabels_strings') is not None:
map_key_type = onnx.TensorProto.STRING
assert map_key_type is not None
new_vi = onnx.ValueInfoProto()
new_vi.name = node.output[0]
new_vi.type.sequence_type.elem_type.map_type.value_type.tensor_type.elem_type = onnx.TensorProto.FLOAT
new_vi.type.sequence_type.elem_type.map_type.key_type = map_key_type
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(new_vi)
def _infer_impl(self, in_mp, start_sympy_data={}):
self.sympy_data_ = start_sympy_data
self.out_mp_.graph.ClearField('value_info')
self._apply_suggested_merge(graph_input_only=True)
self.input_symbols_ = set()
for i in self.out_mp_.graph.input:
input_dims = i.type.tensor_type.shape.dim
for i_dim in range(len(input_dims)):
if get_dim_from_type_proto(input_dims[i_dim]) is None:
# some models use None for symbolic dim in input, replace it with a string
input_dims[i_dim].dim_param = self._new_symbolic_dim(i.name,
i_dim)
self.input_symbols_.update([
d for d in get_shape_from_type_proto(i.type) if type(d) == str
])
for s in self.input_symbols_:
if s in self.suggested_merge_:
s_merge = self.suggested_merge_[s]
assert s_merge in self.symbolic_dims_
self.symbolic_dims_[s] = self.symbolic_dims_[s_merge]
else:
self.symbolic_dims_[s] = sympy.Symbol(s, integer=True)
# create a temporary ModelProto for single node inference
# note that we remove initializer to have faster inference
# for tensor ops like Reshape/Tile/Expand that read initializer, we need to do sympy computation based inference anyways
self.tmp_mp_ = onnx.ModelProto()
self.tmp_mp_.CopyFrom(self.out_mp_)
self.tmp_mp_.graph.ClearField('initializer')
for node in self.out_mp_.graph.node:
assert all([i in self.known_vi_ for i in node.input if i])
self._onnx_infer_single_node(node)
if node.op_type in self.dispatcher_:
self.dispatcher_[node.op_type](node)
elif node.op_type in ['ConvTranspose']:
# onnx shape inference ops like ConvTranspose may have empty shape for symbolic input
# before adding symbolic compute for them
# mark the output type as UNDEFINED to allow guessing of rank
vi = self.known_vi_[node.output[0]]
if len(vi.type.tensor_type.shape.dim) == 0:
vi.type.tensor_type.elem_type = onnx.TensorProto.UNDEFINED
if self.verbose_ > 2:
print(node.op_type + ': ' + node.name)
for i, name in enumerate(node.input):
print(' Input {}: {} {}'.format(
i, name, 'initializer'
if name in self.initializers_ else ''))
# onnx automatically merge dims with value, i.e. Mul(['aaa', 'bbb'], [1000, 1]) -> [1000, 'bbb']
# symbolic shape inference needs to apply merge of 'aaa' -> 1000 in this case
if node.op_type in [
'Add', 'Sub', 'Mul', 'Div', 'MatMul', 'MatMulInteger',
'MatMulInteger16', 'Where', 'Sum'
]:
vi = self.known_vi_[node.output[0]]
out_rank = len(get_shape_from_type_proto(vi.type))
in_shapes = [
self._get_shape(node, i) for i in range(len(node.input))
]
for d in range(out_rank - (2 if node.op_type in [
'MatMul', 'MatMulInteger', 'MatMulInteger16'
] else 0)):
in_dims = [
s[len(s) - out_rank + d] for s in in_shapes
if len(s) + d >= out_rank
]
if len(in_dims) > 1:
self._check_merged_dims(in_dims, allow_broadcast=True)
for i_o in range(len(node.output)):
vi = self.known_vi_[node.output[i_o]]
out_type = vi.type
out_type_kind = out_type.WhichOneof('value')
# only TensorProto and SparseTensorProto have shape
if out_type_kind != 'tensor_type' and out_type_kind != 'sparse_tensor_type':
continue
out_shape = get_shape_from_type_proto(vi.type)
out_type_undefined = out_type.tensor_type.elem_type == onnx.TensorProto.UNDEFINED
if self.verbose_ > 2:
print(' {}: {} {}'.format(node.output[
i_o], str(out_shape), vi.type.tensor_type.elem_type))
if node.output[i_o] in self.sympy_data_:
print(' Sympy Data: ' + str(self.sympy_data_[
node.output[i_o]]))
if None in out_shape or out_type_undefined:
if self.auto_merge_:
if node.op_type in [
'Add', 'Sub', 'Mul', 'Div', 'MatMul',
'MatMulInteger', 'MatMulInteger16', 'Concat',
'Where', 'Sum'
]:
shapes = [
self._get_shape(node, i)
for i in range(len(node.input))
]
if node.op_type in [
'MatMul', 'MatMulInteger', 'MatMulInteger16'
]:
if None in out_shape:
idx = out_shape.index(None)
dim_idx = [
len(s) - len(out_shape) + idx
for s in shapes
]
# only support auto merge for MatMul for dim < rank-2 when rank > 2
assert len(shapes[0]) > 2 and dim_idx[
0] < len(shapes[0]) - 2
assert len(shapes[1]) > 2 and dim_idx[
1] < len(shapes[1]) - 2
elif node.op_type == 'Expand':
# auto merge for cases like Expand([min(batch, 1), min(seq, 512)], [batch, seq])
shapes = [
self._get_shape(node, 0), self._get_value(node,
1)
]
else:
shapes = []
if shapes:
for idx in range(len(out_shape)):
if out_shape[idx] is not None:
continue
dim_idx = [
len(s) - len(out_shape) + idx
for s in shapes
]
assert all([d >= 0 for d in dim_idx])
self._add_suggested_merge([
s[i] if is_literal(s[i]) else str(s[i])
for s, i in zip(shapes, dim_idx)
])
self.run_ = True
else:
self.run_ = False
else:
self.run_ = False
# create new dynamic dims for ops not handled by symbolic shape inference
if self.run_ == False and not node.op_type in self.dispatcher_:
is_unknown_op = (out_type_undefined and
len(out_shape) == 0)
if is_unknown_op:
# unknown op to ONNX, maybe from higher opset or other domain
# only guess the output rank from input 0 when using guess_output_rank option
out_rank = self._get_shape_rank(
node, 0) if self.guess_output_rank_ else -1
else:
# valid ONNX op, but not handled by symbolic shape inference, just assign dynamic shape
out_rank = len(out_shape)
if out_rank >= 0:
new_shape = self._new_symbolic_shape(out_rank, node,
i_o)
vi.CopyFrom(
helper.make_tensor_value_info(
vi.name, self.known_vi_[node.input[
0]].type.tensor_type.elem_type,
get_shape_from_sympy_shape(new_shape)))
if self.verbose_ > 0:
if is_unknown_op:
print(
"Possible unknown op: {} node: {}, guessing {} shape".
format(node.op_type, node.name,
vi.name))
if self.verbose_ > 2:
print(' {}: {} {}'.format(
node.output[i_o],
str(new_shape),
vi.type.tensor_type.elem_type))
self.run_ = True
continue # continue the inference after guess, no need to stop as no merge is needed
if self.verbose_ > 0 or not self.auto_merge_ or out_type_undefined:
print('Stopping at incomplete shape inference at ' +
node.op_type + ': ' + node.name)
print('node inputs:')
for i in node.input:
print(self.known_vi_[i])
print('node outputs:')
for o in node.output:
print(self.known_vi_[o])
if self.auto_merge_ and not out_type_undefined:
print('Merging: ' + str(self.suggested_merge_))
return False
self.run_ = False
return True
def _update_output_from_vi(self):
for output in self.out_mp_.graph.output:
if output.name in self.known_vi_:
output.CopyFrom(self.known_vi_[output.name])
@staticmethod
def infer_shapes(in_mp,
fixed_input_shape=None,
int_max=2**31 - 1,
auto_merge=False,
guess_output_rank=False,
verbose=0):
assert version.parse(onnx.__version__) >= version.parse("1.5.0")
onnx_opset = get_opset(in_mp)
if not onnx_opset or onnx_opset < 7:
print('[WARNING] Symbolic shape inference only support models of onnx opset 7 and above.')
return
symbolic_shape_inference = SymbolicShapeInference(
int_max, auto_merge, guess_output_rank, verbose)
all_shapes_inferred = False
symbolic_shape_inference._preprocess(
in_mp, input_shapes=fixed_input_shape)
try:
while symbolic_shape_inference.run_:
all_shapes_inferred = symbolic_shape_inference._infer_impl(
in_mp)
symbolic_shape_inference._update_output_from_vi()
if not all_shapes_inferred:
print('!' * 10)
symbolic_shape_inference.out_mp_ = shape_inference.infer_shapes(
symbolic_shape_inference.out_mp_)
except:
print('[WARNING] Incomplete symbolic shape inference')
symbolic_shape_inference.out_mp_ = shape_inference.infer_shapes(
symbolic_shape_inference.out_mp_)
return symbolic_shape_inference.out_mp_.graph
\ No newline at end of file
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