AI-MXNet
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lib/AI/MXNet/Base.pm view on Meta::CPAN
$c->slice(('X')x$rem, $i) .= $pdl->slice(('X')x$rem, $shuffle[$i])
}
$c;
}
=head2 assert
Parameters
-----------
Bool $input
Str $error_str
Calls Carp::confess with $error_str//"AssertionError" if the $input is false
=cut
sub assert
{
my ($input, $error_str) = @_;
local($Carp::CarpLevel) = 1;
Carp::confess($error_str//'AssertionError')
unless $input;
}
=head2 check_call
Checks the return value of C API call
This function will raise an exception when error occurs.
Every API call is wrapped with this function.
Returns the C API call return values stripped of first return value,
checks for return context and returns first element in
the values list when called in scalar context.
=cut
sub check_call
{
Carp::confess(AI::MXNetCAPI::GetLastError()) if shift;
lib/AI/MXNet/Executor.pm view on Meta::CPAN
Parameters
----------
arg_params : HashRef[AI::MXNet::NDArray]
Parameters, hash ref of name to NDArray of arguments
aux_params : Maybe[HashRef[AI::MXNet::NDArray]], optional
Parameters, hash ref of name to NDArray of auxiliary states.
allow_extra_params : boolean, optional
Whether to allow extra parameters that are not needed by symbol
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.
=cut
method copy_params_from(
HashRef[AI::MXNet::NDArray] $arg_params,
Maybe[HashRef[AI::MXNet::NDArray]] $aux_params=,
Maybe[Bool] $allow_extra_params=
)
{
my %arg_dict = %{ $self->arg_dict };
lib/AI/MXNet/Executor.pm view on Meta::CPAN
{
$new_grad_dict{ $name } = $darr->reshape($new_shape);
}
}
}
else
{
confess(
"Shape of unspecified array arg:$name changed. "
."This can cause the new executor to not share parameters "
."with the old one. Please check for error in network."
."If this is intended, set partial_shaping=True to suppress this warning."
);
}
$i++;
}
my %new_aux_dict;
$i = 0;
for my $name (@{ $self->_symbol->list_auxiliary_states() })
{
my $new_shape = $aux_shapes->[$i];
lib/AI/MXNet/Executor.pm view on Meta::CPAN
else
{
$new_aux_dict{ $name } = $arr->reshape($new_shape);
}
}
else
{
confess(
"Shape of unspecified array aux:$name changed. "
."This can cause the new executor to not share parameters "
."with the old one. Please check for error in network."
."If this is intended, set partial_shaping=True to suppress this warning."
);
}
$i++;
}
return $self->_symbol->bind(
ctx => $self->_ctx,
args => \%new_arg_dict,
args_grad => \%new_grad_dict,
grad_req => $self->_grad_req,
lib/AI/MXNet/Initializer.pm view on Meta::CPAN
{
my $x = $i % $shape->[3];
my $y = ($i / $shape->[3]) % $shape->[2];
$weight->index($i) .= (1 - abs($x / $f - $c)) * (1 - abs($y / $f - $c));
}
$arr .= $weight->reshape(reverse @{ $shape });
}
method _init_loc_bias($name, $arr)
{
confess("assert error shape[0] == 6")
unless $arr->shape->[0] == 6;
$arr .= [1.0, 0, 0, 0, 1.0, 0];
}
method _init_zero($name, $arr)
{
$arr .= 0;
}
method _init_one($name, $arr)
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
If not undef, should be a hash ref of existing arg_params.
:$aux_params : Maybe[HashRef[AI::MXNet::NDArray]]
If not undef, should be a hash ref of existing aux_params.
:$allow_missing=0 : Bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
:$force_init=0 : Bool
If true, will force re-initialize even if already initialized.
:$allow_extra=0 : Boolean, optional
Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.
=cut
method init_params(
Maybe[AI::MXNet::Initializer] :$initializer=AI::MXNet::Initializer->Uniform(0.01),
Maybe[HashRef[AI::MXNet::NDArray]] :$arg_params=,
Maybe[HashRef[AI::MXNet::NDArray]] :$aux_params=,
Bool :$allow_missing=0,
Bool :$force_init=0,
Bool :$allow_extra=0
lib/AI/MXNet/Module/Base.pm view on Meta::CPAN
Hash ref of name to value (NDArray) mapping.
$aux_params= : Maybe[HashRef[AI::MXNet::NDArray]]
Hash Ref of name to value (`NDArray`) mapping.
:$allow_missing=0 : Bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
:$force_init=0 : Bool
If true, will force re-initialize even if already initialized.
:$allow_extra=0 : Bool
Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.
=cut
method set_params(
Maybe[HashRef[AI::MXNet::NDArray]] $arg_params=,
Maybe[HashRef[AI::MXNet::NDArray]] $aux_params=,
Bool :$allow_missing=0,
Bool :$force_init=0,
Bool :$allow_extra=0
)
lib/AI/MXNet/Symbol.pm view on Meta::CPAN
{
return scalar(check_call(AI::NNVMCAPI::SymbolListInputNames($self->handle, 0)));
}
=head2 infer_type
Infer the type of outputs and arguments of given known types of arguments.
User can either pass in the known types in positional way or keyword argument way.
Tuple of Nones is returned if there is not enough information passed in.
An error will be raised if there is inconsistency found in the known types passed in.
Parameters
----------
args : Array
Provide type of arguments in a positional way.
Unknown type can be marked as None
kwargs : Hash ref, must ne ssupplied as as sole argument to the method.
Provide keyword arguments of known types.
lib/AI/MXNet/Symbol.pm view on Meta::CPAN
return (undef, undef, undef);
}
}
=head2 infer_shape
Infer the shape of outputs and arguments of given known shapes of arguments.
User can either pass in the known shapes in positional way or keyword argument way.
Tuple of Nones is returned if there is not enough information passed in.
An error will be raised if there is inconsistency found in the known shapes passed in.
Parameters
----------
*args :
Provide shape of arguments in a positional way.
Unknown shape can be marked as undef
**kwargs :
Provide keyword arguments of known shapes.
lib/AI/MXNet/TestUtils.pm view on Meta::CPAN
sub enumerate
{
my ($sub, @arrays) = @_;
my $len = @{ $arrays[0] };
zip($sub, [0..$len-1], @arrays);
}
sub assert
{
my ($input, $error_str) = @_;
local($Carp::CarpLevel) = 1;
Carp::confess($error_str//'AssertionError')
unless $input;
}
=head2 same_array
Check whether two NDArrays sharing the same memory block
Parameters
----------
t/test_infer_shape.t view on Meta::CPAN
is_deeply($out_shapes->[0], [100, 10]);
my %true_shapes = (
fc2_bias => [10],
fc2_weight => [10, 1000],
fc1_bias => [1000],
fc1_weight => [1000,100]
);
_test_shapes($out, $arg_shapes, %true_shapes);
}
sub test_mlp2_infer_error
{
# Test shape inconsistent case
my $out = mlp2();
my $weight_shape = [1, 100];
my $data_shape = [100, 100];
eval { $out->infer_shape(data=>$data_shape, fc1_weight=>$weight_shape) };
like($@, qr/Shape inconsistent/);
}
sub test_backward_infer
t/test_infer_shape.t view on Meta::CPAN
my $a = mx->sym->Variable('a', shape=>[0, 10]);
my $b = mx->sym->Variable('b', shape=>[0, 5]);
my $c = mx->sym->Concat($a, $b, num_args=>2, dim=>1);
my $d = mx->sym->Variable('d', shape=>[2, 0]);
$d = $d + $c;
my ($arg_shapes) = $d->infer_shape();
_test_shapes($d, $arg_shapes, a=>[2,10], b=>[2,5], d=>[2,15]);
}
test_mlp2_infer_shape();
test_mlp2_infer_error();
test_backward_infer();
test_incomplete_infer_elewise();
test_incomplete_infer_mlp();
test_incomplete_infer_slicechannel();
test_incomplete_infer_convolution();
test_incomplete_infer_concat();
t/test_module.t view on Meta::CPAN
$mod->set_params($arg_params_correct, {}, force_init=>1);
# test allow missing
$mod->set_params($arg_params_missing, {}, allow_missing=>1, force_init=>1);
ok(dies_like(sub { $mod->set_params($arg_params_missing, {}, force_init=>1, allow_missing=>0); }, qr/fc_/));
# test allow extra
$mod->set_params($arg_params_extra, {}, force_init=>1, allow_missing=>1, allow_extra=>1);
ok(dies_like(sub { $mod->set_params($arg_params_extra, {}, force_init=>1, allow_missing=>1, allow_extra=>0); }, qr/fc_/));
# test allow missing + extra, this will throw a runtime error
ok(dies_like(sub { $mod->set_params($arg_params_missing_extra, {}, force_init=>1, allow_missing=>1, allow_extra=>0); }, qr/fc_/));
}
sub test_forward_reshape
{
my $num_class = 10;
my $data1 = mx->sym->Variable('data1');
my $data2 = mx->sym->Variable('data2');
my $conv1 = mx->sym->Convolution(data=>$data1, kernel=>[2, 2], num_filter=>2, stride=>[2, 2]);
my $conv2 = mx->sym->Convolution(data=>$data2, kernel=>[3, 3], num_filter=>3, stride=>[1, 1]);
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