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wenyuanbo
tic
Commits
af974c34
Unverified
Commit
af974c34
authored
Oct 27, 2018
by
Tianqi Chen
Committed by
GitHub
Oct 27, 2018
Browse files
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Browse Files
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Plain Diff
[RELAY][OP] Fix conv2d NHWC type inference. (#2019)
parent
42dc24a3
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Inline
Side-by-side
Showing
5 changed files
with
40 additions
and
16 deletions
+40
-16
include/tvm/packed_func_ext.h
+5
-3
src/relay/ir/op.cc
+5
-0
src/relay/op/nn/convolution.cc
+11
-7
src/relay/op/nn/layout.h
+1
-3
tests/python/relay/test_op_level2.py
+18
-3
No files found.
include/tvm/packed_func_ext.h
View file @
af974c34
...
...
@@ -35,6 +35,8 @@ struct NodeTypeChecker {
// It can be turned off, but will make non strict checking.
// TODO(tqchen) possibly find alternative to turn of RTTI
using
ContainerType
=
typename
T
::
ContainerType
;
// always allow nullptr.
if
(
sptr
==
nullptr
)
return
true
;
return
sptr
->
derived_from
<
ContainerType
>
();
}
static
inline
void
PrintName
(
std
::
ostringstream
&
os
)
{
// NOLINT(*)
...
...
@@ -46,7 +48,7 @@ struct NodeTypeChecker {
template
<
typename
T
>
struct
NodeTypeChecker
<
Array
<
T
>
>
{
static
inline
bool
Check
(
Node
*
sptr
)
{
if
(
sptr
==
nullptr
)
return
fals
e
;
if
(
sptr
==
nullptr
)
return
tru
e
;
if
(
!
sptr
->
is_type
<
ArrayNode
>
())
return
false
;
ArrayNode
*
n
=
static_cast
<
ArrayNode
*>
(
sptr
);
for
(
const
auto
&
p
:
n
->
data
)
{
...
...
@@ -64,7 +66,7 @@ struct NodeTypeChecker<Array<T> > {
template
<
typename
V
>
struct
NodeTypeChecker
<
Map
<
std
::
string
,
V
>
>
{
static
inline
bool
Check
(
Node
*
sptr
)
{
if
(
sptr
==
nullptr
)
return
fals
e
;
if
(
sptr
==
nullptr
)
return
tru
e
;
if
(
!
sptr
->
is_type
<
StrMapNode
>
())
return
false
;
StrMapNode
*
n
=
static_cast
<
StrMapNode
*>
(
sptr
);
for
(
const
auto
&
kv
:
n
->
data
)
{
...
...
@@ -83,7 +85,7 @@ struct NodeTypeChecker<Map<std::string, V> > {
template
<
typename
K
,
typename
V
>
struct
NodeTypeChecker
<
Map
<
K
,
V
>
>
{
static
inline
bool
Check
(
Node
*
sptr
)
{
if
(
sptr
==
nullptr
)
return
fals
e
;
if
(
sptr
==
nullptr
)
return
tru
e
;
if
(
!
sptr
->
is_type
<
MapNode
>
())
return
false
;
MapNode
*
n
=
static_cast
<
MapNode
*>
(
sptr
);
for
(
const
auto
&
kv
:
n
->
data
)
{
...
...
src/relay/ir/op.cc
View file @
af974c34
...
...
@@ -150,5 +150,10 @@ TVM_REGISTER_NODE_TYPE(OpNode)
return
static_cast
<
const
OpNode
*>
(
n
)
->
name
;
});
TVM_STATIC_IR_FUNCTOR_REGISTER
(
IRPrinter
,
vtable
)
.
set_dispatch
<
OpNode
>
([](
const
OpNode
*
node
,
tvm
::
IRPrinter
*
p
)
{
p
->
stream
<<
"Op("
<<
node
->
name
<<
")"
;
});
}
// namespace relay
}
// namespace tvm
src/relay/op/nn/convolution.cc
View file @
af974c34
...
...
@@ -21,7 +21,6 @@ bool Conv2DRel(const Array<Type>& types,
const
auto
*
data
=
types
[
0
].
as
<
TensorTypeNode
>
();
const
auto
*
weight
=
types
[
1
].
as
<
TensorTypeNode
>
();
if
(
data
==
nullptr
)
return
false
;
static
const
Layout
kNCHW
(
"NCHW"
);
static
const
Layout
kOIHW
(
"OIHW"
);
...
...
@@ -42,6 +41,9 @@ bool Conv2DRel(const Array<Type>& types,
<<
"Conv only support output layouts that are convertible from NCHW."
<<
" But got "
<<
out_layout
;
std
::
vector
<
IndexExpr
>
dshape_nchw
=
ConvertLayout
(
data
->
shape
,
in_layout
,
kNCHW
);
IndexExpr
channels
,
dilated_ksize_y
,
dilated_ksize_x
;
// infer weight if the kernel_size and channels are defined
if
(
param
->
kernel_size
.
defined
()
&&
param
->
channels
.
defined
())
{
...
...
@@ -49,7 +51,7 @@ bool Conv2DRel(const Array<Type>& types,
CHECK_EQ
(
param
->
dilation
.
size
(),
2
);
std
::
vector
<
IndexExpr
>
wshape
(
{
param
->
channels
/
param
->
groups
,
d
ata
->
shape
[
1
]
/
param
->
groups
,
d
shape_nchw
[
1
]
/
param
->
groups
,
param
->
kernel_size
[
0
],
param
->
kernel_size
[
1
]});
wshape
=
ConvertLayout
(
wshape
,
kOIHW
,
kernel_layout
);
...
...
@@ -78,16 +80,16 @@ bool Conv2DRel(const Array<Type>& types,
<<
" channels="
<<
param
->
channels
<<
" wshape="
<<
Array
<
IndexExpr
>
(
wshape
);
}
CHECK
(
reporter
->
AssertEQ
(
d
ata
->
shape
[
1
]
/
param
->
groups
,
wshape
[
1
]));
CHECK
(
reporter
->
AssertEQ
(
d
shape_nchw
[
1
]
/
param
->
groups
,
wshape
[
1
]));
channels
=
wshape
[
0
];
dilated_ksize_y
=
1
+
(
wshape
[
2
]
-
1
)
*
param
->
dilation
[
0
];
dilated_ksize_x
=
1
+
(
wshape
[
3
]
-
1
)
*
param
->
dilation
[
1
];
}
// dilation
std
::
vector
<
IndexExpr
>
oshape
({
d
ata
->
shape
[
0
],
channels
,
0
,
0
});
std
::
vector
<
IndexExpr
>
oshape
({
d
shape_nchw
[
0
],
channels
,
0
,
0
});
oshape
[
2
]
=
(
d
ata
->
shape
[
2
]
+
param
->
padding
[
0
]
*
2
-
dilated_ksize_y
)
/
param
->
strides
[
0
]
+
1
;
oshape
[
3
]
=
(
d
ata
->
shape
[
3
]
+
param
->
padding
[
1
]
*
2
-
dilated_ksize_x
)
/
param
->
strides
[
1
]
+
1
;
oshape
[
2
]
=
(
d
shape_nchw
[
2
]
+
param
->
padding
[
0
]
*
2
-
dilated_ksize_y
)
/
param
->
strides
[
0
]
+
1
;
oshape
[
3
]
=
(
d
shape_nchw
[
3
]
+
param
->
padding
[
1
]
*
2
-
dilated_ksize_x
)
/
param
->
strides
[
1
]
+
1
;
DataType
out_dtype
=
param
->
out_dtype
;
if
(
out_dtype
.
bits
()
==
0
)
{
out_dtype
=
data
->
dtype
;
...
...
@@ -183,7 +185,9 @@ bool Conv2DTransposeRel(const Array<Type>& types,
<<
" But got "
<<
kernel_layout
;
IndexExpr
channels
,
dilated_ksize_y
,
dilated_ksize_x
;
const
auto
dshape_nchw
=
ConvertLayout
(
data
->
shape
,
in_layout
,
kNCHW
);
auto
dshape_nchw
=
ConvertLayout
(
data
->
shape
,
in_layout
,
kNCHW
);
// infer weight if the kernel_size and channels are defined
if
(
param
->
kernel_size
.
defined
()
&&
param
->
channels
.
defined
())
{
CHECK_EQ
(
param
->
kernel_size
.
size
(),
2
);
...
...
src/relay/op/nn/layout.h
View file @
af974c34
...
...
@@ -495,9 +495,7 @@ inline std::vector<IndexExpr> ConvertLayout(
IndexExpr
src_dim_size
=
src
[
i
];
if
(
src_minor_pos
>=
0
)
{
const
int64_t
*
minor_size
=
as_const_int
(
src
[
src_minor_pos
]);
CHECK
(
minor_size
==
nullptr
&&
src_factor
==
minor_size
[
0
])
CHECK
(
is_const_int
(
src
[
src_minor_pos
],
src_factor
))
<<
"src shape "
<<
Array
<
IndexExpr
>
(
src
)
<<
" does not agree with layout "
<<
src_layout
;
...
...
tests/python/relay/test_op_level2.py
View file @
af974c34
...
...
@@ -32,9 +32,9 @@ def test_conv2d_infer_type():
# Infer with a different layout
n
,
c
,
h
,
w
=
4
,
32
,
224
,
224
x
=
relay
.
var
(
"x"
,
relay
.
TensorType
((
n
,
c
,
h
,
w
),
"int8"
))
w
=
relay
.
var
(
"w"
)
y
=
relay
.
nn
.
conv2d
(
x
,
w
,
x
=
relay
.
var
(
"x"
,
relay
.
TensorType
((
n
//
4
,
c
//
4
,
h
,
w
,
4
,
4
),
"int8"
))
w
t
=
relay
.
var
(
"w"
)
y
=
relay
.
nn
.
conv2d
(
x
,
w
t
,
kernel_size
=
(
3
,
3
),
padding
=
(
1
,
1
),
channels
=
16
,
...
...
@@ -47,6 +47,21 @@ def test_conv2d_infer_type():
assert
yy
.
args
[
1
]
.
checked_type
==
relay
.
TensorType
(
(
4
,
8
,
3
,
3
,
4
,
4
),
"int8"
)
# Infer with NHWC
n
,
c
,
h
,
w
=
4
,
32
,
224
,
224
x
=
relay
.
var
(
"x"
,
relay
.
TensorType
((
n
,
h
,
w
,
c
),
"int8"
))
wt
=
relay
.
var
(
"w"
)
y
=
relay
.
nn
.
conv2d
(
x
,
wt
,
kernel_size
=
(
3
,
3
),
padding
=
(
1
,
1
),
channels
=
16
,
data_layout
=
"NHWC"
,
out_dtype
=
"int32"
)
yy
=
relay
.
ir_pass
.
infer_type
(
y
)
assert
yy
.
checked_type
==
relay
.
TensorType
(
(
n
,
h
,
w
,
16
),
"int32"
)
def
test_conv2d_transpose_infer_type
():
# symbolic in batch dimension
n
,
c
,
h
,
w
=
tvm
.
var
(
"n"
),
10
,
10
,
12
...
...
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