| use strict; |
| use warnings; |
| use Test::More tests => 98; |
| use AI::MXNet qw(mx); |
| use AI::MXNet::TestUtils qw(mlp2 conv check_consistency zip assert enumerate); |
| use Storable qw(freeze thaw); |
| use PDL; |
| |
| sub test_symbol_compose |
| { |
| my $data = mx->symbol->Variable('data'); |
| my $net1 = mx->symbol->FullyConnected(data=>$data, name=>'fc1', num_hidden=>10); |
| $net1 = mx->symbol->FullyConnected(data=>$net1, name=>'fc2', num_hidden=>100); |
| is_deeply($net1->list_arguments(), ['data', |
| 'fc1_weight', 'fc1_bias', |
| 'fc2_weight', 'fc2_bias']); |
| |
| my $net2 = mx->symbol->FullyConnected(name=>'fc3', num_hidden=>10); |
| $net2 = mx->symbol->Activation(data=>$net2, act_type=>'relu'); |
| $net2 = mx->symbol->FullyConnected(data=>$net2, name=>'fc4', num_hidden=>20); |
| my $composed = &{$net2}(fc3_data=>$net1, name=>'composed'); |
| my $multi_out = mx->symbol->Group([$composed, $net1]); |
| ok(@{ $multi_out->list_outputs() } == 2); |
| } |
| |
| test_symbol_compose(); |
| |
| sub test_symbol_copy |
| { |
| my $data = mx->symbol->Variable('data'); |
| my $data_2 = $data->deepcopy; |
| is($data->tojson, $data_2->tojson); |
| } |
| |
| test_symbol_copy(); |
| |
| sub test_symbol_internal |
| { |
| my $data = mx->symbol->Variable('data'); |
| my $oldfc = mx->symbol->FullyConnected(data=>$data, name=>'fc1', num_hidden=>10); |
| my $net1 = mx->symbol->FullyConnected(data=>$oldfc, name=>'fc2', num_hidden=>100); |
| is_deeply($net1->list_arguments, ['data', 'fc1_weight', 'fc1_bias', 'fc2_weight', 'fc2_bias']); |
| |
| my $internal = $net1->get_internals(); |
| my $fc1 = $internal->slice('fc1_output'); |
| is_deeply($fc1->list_arguments, $oldfc->list_arguments); |
| } |
| |
| test_symbol_internal(); |
| |
| sub test_symbol_children |
| { |
| my $data = mx->symbol->Variable('data'); |
| my $oldfc = mx->symbol->FullyConnected(data=>$data, name=>'fc1', num_hidden=>10); |
| my $net1 = mx->symbol->FullyConnected(data=>$oldfc, name=>'fc2', num_hidden=>100); |
| |
| is_deeply($net1->get_children()->list_outputs(), ['fc1_output', 'fc2_weight', 'fc2_bias']); |
| is_deeply($net1->get_children()->get_children()->list_outputs() , ['data', 'fc1_weight', 'fc1_bias']); |
| is_deeply($net1->get_children()->slice('fc2_weight')->list_arguments(), ['fc2_weight']); |
| ok(not defined $net1->get_children()->slice('fc2_weight')->get_children()); |
| |
| $data = mx->sym->Variable('data'); |
| my $sliced = mx->sym->SliceChannel($data, num_outputs=>3, name=>'slice'); |
| my $concat = mx->sym->Concat(@{ $sliced }); |
| |
| is_deeply($concat->get_children()->list_outputs(), |
| ['slice_output0', 'slice_output1', 'slice_output2']); |
| is_deeply($sliced->get_children()->list_outputs(), ['data']); |
| } |
| |
| test_symbol_children(); |
| |
| sub test_symbol_storable |
| { |
| my $mlist = [mlp2(), conv()]; |
| my $data = freeze($mlist); |
| my $mlist2 = thaw($data); |
| zip(sub { |
| my ($x, $y) = @_; |
| is($x->tojson, $y->tojson); |
| }, $mlist, $mlist2); |
| } |
| |
| test_symbol_storable(); |
| |
| sub test_symbol_saveload |
| { |
| my $sym = mlp2(); |
| my $fname = 'tmp_sym.json'; |
| $sym->save($fname); |
| my $data2 = mx->symbol->load($fname); |
| # save because of order |
| is($sym->tojson, $data2->tojson); |
| unlink $fname; |
| } |
| |
| test_symbol_saveload(); |
| |
| sub test_symbol_infer_type |
| { |
| my $data = mx->symbol->Variable('data'); |
| my $f32data = mx->symbol->Cast(data=>$data, dtype=>'float32'); |
| my $fc1 = mx->symbol->FullyConnected(data => $f32data, name=>'fc1', num_hidden=>128); |
| my $mlp = mx->symbol->SoftmaxOutput(data => $fc1, name => 'softmax'); |
| |
| my ($arg, $out, $aux) = $mlp->infer_type(data=>'float16'); |
| is_deeply($arg, [qw/float16 float32 float32 float32/]); |
| is_deeply($out, ['float32']); |
| is_deeply($aux, []); |
| } |
| |
| test_symbol_infer_type(); |
| |
| sub test_symbol_infer_shape |
| { |
| my $num_hidden = 128; |
| my $num_dim = 64; |
| my $num_sample = 10; |
| |
| my $data = mx->symbol->Variable('data'); |
| my $prev = mx->symbol->Variable('prevstate'); |
| my $x2h = mx->symbol->FullyConnected(data=>$data, name=>'x2h', num_hidden=>$num_hidden); |
| my $h2h = mx->symbol->FullyConnected(data=>$prev, name=>'h2h', num_hidden=>$num_hidden); |
| |
| my $out = mx->symbol->Activation(data=>mx->sym->elemwise_add($x2h, $h2h), name=>'out', act_type=>'relu'); |
| |
| # shape inference will fail because information is not available for h2h |
| my @ret = $out->infer_shape(data=>[$num_sample, $num_dim]); |
| is_deeply(\@ret, [undef, undef, undef]); |
| |
| my ($arg_shapes, $out_shapes, $aux_shapes) = $out->infer_shape_partial(data=>[$num_sample, $num_dim]); |
| my %arg_shapes; |
| @arg_shapes{ @{ $out->list_arguments } } = @{ $arg_shapes }; |
| is_deeply($arg_shapes{data}, [$num_sample, $num_dim]); |
| is_deeply($arg_shapes{x2h_weight}, [$num_hidden, $num_dim]); |
| is_deeply($arg_shapes{h2h_weight}, []); |
| |
| # now we can do full shape inference |
| my $state_shape = $out_shapes->[0]; |
| ($arg_shapes, $out_shapes, $aux_shapes) = $out->infer_shape(data=>[$num_sample, $num_dim], prevstate=>$state_shape); |
| @arg_shapes{ @{ $out->list_arguments } } = @{ $arg_shapes }; |
| is_deeply($arg_shapes{data}, [$num_sample, $num_dim]); |
| is_deeply($arg_shapes{x2h_weight}, [$num_hidden, $num_dim]); |
| is_deeply($arg_shapes{h2h_weight}, [$num_hidden, $num_hidden]); |
| } |
| |
| test_symbol_infer_shape(); |
| |
| sub test_symbol_infer_shape_var |
| { |
| #Test specifying shape information when constructing a variable |
| my $shape = [2, 3]; |
| my $a = mx->symbol->Variable('a', shape=>$shape); |
| my $b = mx->symbol->Variable('b'); |
| my $c = mx->symbol->elemwise_add($a, $b); |
| my ($arg_shapes, $out_shapes, $aux_shapes) = $c->infer_shape(); |
| is_deeply($arg_shapes->[0], $shape); |
| is_deeply($arg_shapes->[1], $shape); |
| is_deeply($out_shapes->[0], $shape); |
| |
| $shape = [5, 6]; |
| ($arg_shapes, $out_shapes, $aux_shapes) = $c->infer_shape(a=>$shape); |
| is_deeply($arg_shapes->[0], $shape); |
| is_deeply($arg_shapes->[1], $shape); |
| is_deeply($out_shapes->[0], $shape); |
| } |
| |
| test_symbol_infer_shape_var(); |
| |
| sub check_symbol_consistency |
| { |
| my ($sym1, $sym2, $ctx) = @_; |
| is_deeply($sym1->list_arguments(), $sym2->list_arguments()); |
| is_deeply($sym1->list_auxiliary_states(), $sym2->list_auxiliary_states()); |
| is_deeply($sym1->list_outputs(), $sym2->list_outputs()); |
| check_consistency(sym => [$sym1, $sym2], ctx_list => [$ctx, $ctx]); |
| } |
| |
| sub test_load_000800 |
| { |
| my ($data, $weight, $fc1, $act1); |
| { |
| local($mx::AttrScope) = mx->AttrScope(ctx_group=>'stage1'); |
| $data = mx->symbol->Variable('data', lr_mult=>0.2); |
| $weight = mx->sym->Variable('fc1_weight', lr_mult=>1.2); |
| $fc1 = mx->symbol->FullyConnected(data => $data, weight=>$weight, name=>'fc1', num_hidden=>128, wd_mult=>0.3); |
| $act1 = mx->symbol->Activation(data => $fc1, name=>'relu1', act_type=>"relu"); |
| } |
| my ($fc2, $act2, $fc3, $sym1); |
| { |
| local($mx::AttrScope) = mx->AttrScope(ctx_group=>'stage2'); |
| $fc2 = mx->symbol->FullyConnected(data => $act1, name => 'fc2', num_hidden => 64, lr_mult=>0.01); |
| $act2 = mx->symbol->Activation(data => $fc2, name=>'relu2', act_type=>"relu"); |
| $fc3 = mx->symbol->FullyConnected(data => $act2, name=>'fc3', num_hidden=>10); |
| $fc3 = mx->symbol->BatchNorm($fc3, name=>'batchnorm0'); |
| $sym1 = mx->symbol->SoftmaxOutput(data => $fc3, name => 'softmax') |
| } |
| { local $/ = undef; my $json = <DATA>; open(F, ">save_000800.json"); print F $json; close(F); }; |
| my $sym2 = mx->sym->load('save_000800.json'); |
| unlink 'save_000800.json'; |
| |
| my %attr1 = %{ $sym1->attr_dict }; |
| my %attr2 = %{ $sym2->attr_dict }; |
| while(my ($k, $v1) = each %attr1) |
| { |
| ok(exists $attr2{ $k }); |
| my $v2 = $attr2{$k}; |
| while(my ($kk, $vv1) = each %{ $v1 }) |
| { |
| if($kk =~ /^__/ and $kk =~ /__$/) |
| { |
| ok(exists $v2->{$kk} and $v2->{$kk} eq $vv1); |
| } |
| } |
| } |
| |
| check_symbol_consistency($sym1, $sym2, |
| {ctx => mx->cpu(0), group2ctx =>{stage1 => mx->cpu(1), stage2 => mx->cpu(2) }, shapes => { data => [1,200] }} |
| ); |
| } |
| |
| test_load_000800(); |
| |
| __DATA__ |
| { |
| "nodes": [ |
| { |
| "op": "null", |
| "param": {}, |
| "name": "data", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage1", |
| "lr_mult": "0.2" |
| } |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "fc1_weight", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage1", |
| "wd_mult": "0.3", |
| "weight_lr_mult": "1.2" |
| } |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "fc1_bias", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage1", |
| "wd_mult": "0.3", |
| "weight_lr_mult": "1.2" |
| } |
| }, |
| { |
| "op": "FullyConnected", |
| "param": { |
| "no_bias": "False", |
| "num_hidden": "128" |
| }, |
| "name": "fc1", |
| "inputs": [[0, 0], [1, 0], [2, 0]], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage1", |
| "wd_mult": "0.3", |
| "weight_lr_mult": "1.2" |
| } |
| }, |
| { |
| "op": "Activation", |
| "param": {"act_type": "relu"}, |
| "name": "relu1", |
| "inputs": [[3, 0]], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage1"} |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "fc2_weight", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage2", |
| "lr_mult": "0.01" |
| } |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "fc2_bias", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage2", |
| "lr_mult": "0.01" |
| } |
| }, |
| { |
| "op": "FullyConnected", |
| "param": { |
| "no_bias": "False", |
| "num_hidden": "64" |
| }, |
| "name": "fc2", |
| "inputs": [[4, 0], [5, 0], [6, 0]], |
| "backward_source_id": -1, |
| "attr": { |
| "ctx_group": "stage2", |
| "lr_mult": "0.01" |
| } |
| }, |
| { |
| "op": "Activation", |
| "param": {"act_type": "relu"}, |
| "name": "relu2", |
| "inputs": [[7, 0]], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "fc3_weight", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "fc3_bias", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "FullyConnected", |
| "param": { |
| "no_bias": "False", |
| "num_hidden": "10" |
| }, |
| "name": "fc3", |
| "inputs": [[8, 0], [9, 0], [10, 0]], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "batchnorm0_gamma", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "batchnorm0_beta", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "BatchNorm", |
| "param": { |
| "eps": "0.001", |
| "fix_gamma": "True", |
| "momentum": "0.9", |
| "use_global_stats": "False" |
| }, |
| "name": "batchnorm0", |
| "inputs": [[11, 0], [12, 0], [13, 0]], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "null", |
| "param": {}, |
| "name": "softmax_label", |
| "inputs": [], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| }, |
| { |
| "op": "SoftmaxOutput", |
| "param": { |
| "grad_scale": "1", |
| "ignore_label": "-1", |
| "multi_output": "False", |
| "normalization": "null", |
| "out_grad": "False", |
| "preserve_shape": "False", |
| "use_ignore": "False" |
| }, |
| "name": "softmax", |
| "inputs": [[14, 0], [15, 0]], |
| "backward_source_id": -1, |
| "attr": {"ctx_group": "stage2"} |
| } |
| ], |
| "arg_nodes": [0, 1, 2, 5, 6, 9, 10, 12, 13, 15], |
| "heads": [[16, 0]] |
| } |
