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November 26, 2020 17:22
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PyTorch to RETURNN, Parallel WaveGAN example, full log output
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| /usr/local/bin/python3 "/Users/az/Library/Application Support/JetBrains/Toolbox/apps/PyCharm-C/ch-0/202.7660.27/PyCharm CE.app/Contents/plugins/python-ce/helpers/pydev/pydevd.py" --multiproc --qt-support=auto --client 127.0.0.1 --port 57798 --file /Users/az/Programmierung/import-parallel-wavegan/pytorch_to_returnn.py --pwg_config mb_melgan.v2.yaml --pwg_checkpoint mb_melgan_models/checkpoint-1000000steps.pkl --features data/features.npy | |
| pydev debugger: process 58079 is connecting | |
| Connected to pydev debugger (build 202.7660.27) | |
| 2020-11-26 18:21:44.088082: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA | |
| 2020-11-26 18:21:44.101822: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fabda13afa0 initialized for platform Host (this does not guarantee that XLA will be used). Devices: | |
| 2020-11-26 18:21:44.101846: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version | |
| CUDA_VISIBLE_DEVICES is not set. | |
| Collecting TensorFlow device list... | |
| Local devices available to TensorFlow: | |
| 1/2: name: "/device:CPU:0" | |
| device_type: "CPU" | |
| memory_limit: 268435456 | |
| locality { | |
| } | |
| incarnation: 3807773853604125641 | |
| 2/2: name: "/device:XLA_CPU:0" | |
| device_type: "XLA_CPU" | |
| memory_limit: 17179869184 | |
| locality { | |
| } | |
| incarnation: 8874893949683666076 | |
| physical_device_desc: "device: XLA_CPU device" | |
| Feature shape: (80, 80) | |
| >>> Running with standard reference imports... | |
| >>> Running with wrapped imports, wrapping original PyTorch... | |
| *** register sys.meta_path for ctx <WrapCtx 'pytorch_to_returnn.import_wrapper._torch_traced'> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.from_numpy' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.from_numpy'> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.from_numpy(...) | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan> | |
| *** <WrapCtx 'pytorch_to_returnn.import_wrapper._torch_traced'> extend by mod 'parallel_wavegan' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.models> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.models.melgan> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.causal_conv> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn' -> <WrappedIndirectModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.Module' -> <class 'pytorch_to_returnn.import_wrapper.torch_wrappers.module.WrappedModuleBase'> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.pqmf> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.residual_block> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.module.Module' -> <class 'pytorch_to_returnn.import_wrapper.torch_wrappers.module.WrappedModuleBase'> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.linear> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.Tensor' -> <class 'pytorch_to_returnn.import_wrapper.torch_wrappers.tensor.WrappedTorchTensor'> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.parameter.Parameter' -> <class 'pytorch_to_returnn.import_wrapper.torch_wrappers.parameter.WrappedTorchParameter'> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init' -> <WrappedIndirectModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.utils> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch._six.container_abcs' | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._size_1_t' | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._size_2_t' | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._size_3_t' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.xavier_uniform_' -> <function xavier_uniform_ at 0x10b91d440> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.constant_' -> <function constant_ at 0x10b91d4d0> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.xavier_normal_' -> <function xavier_normal_ at 0x10b91d560> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.loss> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.distance> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn._reduction' -> <WrappedIndirectModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn._reduction> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.container> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch._jit_internal._copy_to_script_wrapper' -> <function _copy_to_script_wrapper at 0x1552d0560> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch._jit_internal._copy_to_script_wrapper(...) | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.pooling> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._size_any_t' | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._ratio_3_t' | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._ratio_2_t' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.batchnorm> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules._functions> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.autograd.function.Function' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.instancenorm> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.normalization> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.Size' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.dropout> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._size_4_t' | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._size_6_t' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.sparse> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.rnn> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.utils.rnn.PackedSequence' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.utils.rnn.PackedSequence'> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch._VF' -> <WrappedIndirectModule pytorch_to_returnn.import_wrapper._torch_traced.torch._VF> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch._VF.rnn_tanh' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.rnn_tanh'> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch._VF.rnn_relu' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.rnn_relu'> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch._jit_internal._overload_method' -> <function _overload_method at 0x1553a8ef0> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch._jit_internal._overload_method(...) | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.pixelshuffle> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.upsampling> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.common_types._ratio_any_t' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.fold> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.adaptive> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.log_softmax' -> <function log_softmax at 0x15a90fd40> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.transformer> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.flatten> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.Conv1d' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.Conv1d'> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.residual_stack> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.upsample> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.Conv2d' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.Conv2d'> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.models.parallel_wavegan> | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.device' | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.models.melgan.MelGANGenerator(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.ReflectionPad1d' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding.ReflectionPad1d'> | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding.ReflectionPad1d(...) | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.Conv1d(...) | |
| **** torch tensor func __get__ | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.kaiming_uniform_' -> <function kaiming_uniform_ at 0x154d70e60> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.kaiming_uniform_(...) | |
| **** torch tensor func dim | |
| **** torch tensor func size | |
| **** torch tensor func __getitem__ | |
| **** torch tensor func uniform_ | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init._calculate_fan_in_and_fan_out' -> <function _calculate_fan_in_and_fan_out at 0x155360b90> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init._calculate_fan_in_and_fan_out(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.uniform_' -> <function uniform_ at 0x155360c20> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.init.uniform_(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.LeakyReLU' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation.LeakyReLU'> | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation.LeakyReLU(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.ConvTranspose1d' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.ConvTranspose1d'> | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.ConvTranspose1d(...) | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.residual_stack.ResidualStack(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.Sequential' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.container.Sequential'> | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.container.Sequential(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.Tanh' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation.Tanh'> | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation.Tanh(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.utils.weight_norm' -> <function weight_norm at 0x1553c1950> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.utils.weight_norm(...) | |
| **** torch tensor func norm_except_dim | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.load' -> <function load at 0x1553c18c0> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.load(...) | |
| **** torch tensor func copy_ | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.utils.remove_weight_norm' -> <function remove_weight_norm at 0x1553c1f80> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.utils.remove_weight_norm(...) | |
| **** torch tensor func is_floating_point | |
| **** torch tensor func to | |
| **** torch tensor func _has_compatible_shallow_copy_type | |
| **** torch tensor func __set__ | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.pqmf.PQMF(...) | |
| **** torch tensor func float | |
| **** torch tensor func unsqueeze | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.zeros' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.zeros'> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.zeros(...) | |
| **** torch tensor func __setitem__ | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.ConstantPad1d' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding.ConstantPad1d'> | |
| *** torch module create pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding.ConstantPad1d(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.no_grad' -> <class 'pytorch_to_returnn.import_wrapper._torch_traced.torch.autograd.grad_mode.no_grad'> | |
| *** WrappedClass pytorch_to_returnn.import_wrapper._torch_traced.torch.no_grad(...) | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.models.melgan.MelGANGenerator(...)(...) | |
| **** torch tensor func __hash__ | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.container.Sequential(...)(...) | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding.ReflectionPad1d(...)(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.pad' -> <function _pad at 0x1552f08c0> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.pad(...) | |
| **** torch tensor func _pad | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.Conv1d(...)(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.conv1d' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.conv1d'> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.conv1d(...) | |
| **** torch tensor func conv1d | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation.LeakyReLU(...)(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.leaky_relu' -> <function leaky_relu at 0x1552f0b90> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.leaky_relu(...) | |
| **** torch tensor func leaky_relu | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.conv.ConvTranspose1d(...)(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.conv_transpose1d' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.conv_transpose1d'> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.functional.conv_transpose1d(...) | |
| **** torch tensor func conv_transpose1d | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.parallel_wavegan.layers.residual_stack.ResidualStack(...)(...) | |
| **** torch tensor func add | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.activation.Tanh(...)(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.tanh' -> <class 'pytorch_to_returnn.import_wrapper.base_wrappers.function._VariableFunctionsClass.tanh'> | |
| *** func call pytorch_to_returnn.import_wrapper._torch_traced.torch.tanh(...) | |
| **** torch tensor func tanh | |
| **** torch tensor func mul | |
| *** torch module call pytorch_to_returnn.import_wrapper._torch_traced.torch.nn.modules.padding.ConstantPad1d(...)(...) | |
| *** indirect getattr 'pytorch_to_returnn.import_wrapper._torch_traced.torch.no_grad(...).prev' | |
| **** torch tensor func cpu | |
| **** torch tensor func numpy | |
| >>>> Module naming hierarchy: | |
| .tmp_root: (hidden) | |
| melgan: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry ReflectionPad1d((3, 3))> -> ... | |
| layer1: <ModuleEntry Conv1d(80, 384, kernel_size=(7,), stride=(1,))> -> ... | |
| layer2: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer3: <ModuleEntry ConvTranspose1d(384, 192, kernel_size=(10,), stride=(5,), padding=(3,), output_padding=(1,))> -> ... | |
| layer4: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((1, 1))> -> ... | |
| layer2: <ModuleEntry Conv1d(192, 192, kernel_size=(3,), stride=(1,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| layer5: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((3, 3))> -> ... | |
| layer2: <ModuleEntry Conv1d(192, 192, kernel_size=(3,), stride=(1,), dilation=(3,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| layer6: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((9, 9))> -> ... | |
| layer2: <ModuleEntry Conv1d(192, 192, kernel_size=(3,), stride=(1,), dilation=(9,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| layer7: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((27, 27))> -> ... | |
| layer2: <ModuleEntry Conv1d(192, 192, kernel_size=(3,), stride=(1,), dilation=(27,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(192, 192, kernel_size=(1,), stride=(1,))> -> ... | |
| layer8: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer9: <ModuleEntry ConvTranspose1d(192, 96, kernel_size=(10,), stride=(5,), padding=(3,), output_padding=(1,))> -> ... | |
| layer10: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((1, 1))> -> ... | |
| layer2: <ModuleEntry Conv1d(96, 96, kernel_size=(3,), stride=(1,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| layer11: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((3, 3))> -> ... | |
| layer2: <ModuleEntry Conv1d(96, 96, kernel_size=(3,), stride=(1,), dilation=(3,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| layer12: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((9, 9))> -> ... | |
| layer2: <ModuleEntry Conv1d(96, 96, kernel_size=(3,), stride=(1,), dilation=(9,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| layer13: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((27, 27))> -> ... | |
| layer2: <ModuleEntry Conv1d(96, 96, kernel_size=(3,), stride=(1,), dilation=(27,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(96, 96, kernel_size=(1,), stride=(1,))> -> ... | |
| layer14: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer15: <ModuleEntry ConvTranspose1d(96, 48, kernel_size=(4,), stride=(2,), padding=(1,))> -> ... | |
| layer16: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((1, 1))> -> ... | |
| layer2: <ModuleEntry Conv1d(48, 48, kernel_size=(3,), stride=(1,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| layer17: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((3, 3))> -> ... | |
| layer2: <ModuleEntry Conv1d(48, 48, kernel_size=(3,), stride=(1,), dilation=(3,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| layer18: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((9, 9))> -> ... | |
| layer2: <ModuleEntry Conv1d(48, 48, kernel_size=(3,), stride=(1,), dilation=(9,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| layer19: <ModuleEntry ResidualStack(...)> -> ... | |
| stack: <ModuleEntry Sequential(...)> -> ... | |
| layer0: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer1: <ModuleEntry ReflectionPad1d((27, 27))> -> ... | |
| layer2: <ModuleEntry Conv1d(48, 48, kernel_size=(3,), stride=(1,), dilation=(27,))> -> ... | |
| layer3: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer4: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| skip_layer: <ModuleEntry Conv1d(48, 48, kernel_size=(1,), stride=(1,))> -> ... | |
| layer20: <ModuleEntry LeakyReLU(negative_slope=0.2)> -> ... | |
| layer21: <ModuleEntry ReflectionPad1d((3, 3))> -> ... | |
| layer22: <ModuleEntry Conv1d(48, 4, kernel_size=(7,), stride=(1,))> -> ... | |
| layer23: <ModuleEntry Tanh()> -> ... | |
| pad_fn: <ModuleEntry ConstantPad1d(padding=(31, 31), value=0.0)> -> ... | |
| >>>> Root module calls: | |
| { | |
| 'melgan': <CallEntry #1 <ModuleEntry Sequential(...)>>, | |
| 'pad_fn': <CallEntry #0 <ModuleEntry ConstantPad1d(padding=(31, 31), value=0.0)>> | |
| } | |
| >>>> Modules with params: | |
| { | |
| 'melgan.layer1': Conv1d(80, 384, kernel_size=(7,), stride=(1,)), | |
| 'melgan.layer3': ConvTranspose1d(384, 192, kernel_size=(10,), stride=(5,), padding=(3,), output_padding=(1,)), | |
| 'melgan.layer4.stack.layer2': Conv1d(192, 192, kernel_size=(3,), stride=(1,)), | |
| 'melgan.layer4.stack.layer4': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer4.skip_layer': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer5.stack.layer2': Conv1d(192, 192, kernel_size=(3,), stride=(1,), dilation=(3,)), | |
| 'melgan.layer5.stack.layer4': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer5.skip_layer': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer6.stack.layer2': Conv1d(192, 192, kernel_size=(3,), stride=(1,), dilation=(9,)), | |
| 'melgan.layer6.stack.layer4': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer6.skip_layer': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer7.stack.layer2': Conv1d(192, 192, kernel_size=(3,), stride=(1,), dilation=(27,)), | |
| 'melgan.layer7.stack.layer4': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer7.skip_layer': Conv1d(192, 192, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer9': ConvTranspose1d(192, 96, kernel_size=(10,), stride=(5,), padding=(3,), output_padding=(1,)), | |
| 'melgan.layer10.stack.layer2': Conv1d(96, 96, kernel_size=(3,), stride=(1,)), | |
| 'melgan.layer10.stack.layer4': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer10.skip_layer': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer11.stack.layer2': Conv1d(96, 96, kernel_size=(3,), stride=(1,), dilation=(3,)), | |
| 'melgan.layer11.stack.layer4': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer11.skip_layer': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer12.stack.layer2': Conv1d(96, 96, kernel_size=(3,), stride=(1,), dilation=(9,)), | |
| 'melgan.layer12.stack.layer4': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer12.skip_layer': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer13.stack.layer2': Conv1d(96, 96, kernel_size=(3,), stride=(1,), dilation=(27,)), | |
| 'melgan.layer13.stack.layer4': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer13.skip_layer': Conv1d(96, 96, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer15': ConvTranspose1d(96, 48, kernel_size=(4,), stride=(2,), padding=(1,)), | |
| 'melgan.layer16.stack.layer2': Conv1d(48, 48, kernel_size=(3,), stride=(1,)), | |
| 'melgan.layer16.stack.layer4': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer16.skip_layer': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer17.stack.layer2': Conv1d(48, 48, kernel_size=(3,), stride=(1,), dilation=(3,)), | |
| 'melgan.layer17.stack.layer4': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer17.skip_layer': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer18.stack.layer2': Conv1d(48, 48, kernel_size=(3,), stride=(1,), dilation=(9,)), | |
| 'melgan.layer18.stack.layer4': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer18.skip_layer': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer19.stack.layer2': Conv1d(48, 48, kernel_size=(3,), stride=(1,), dilation=(27,)), | |
| 'melgan.layer19.stack.layer4': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer19.skip_layer': Conv1d(48, 48, kernel_size=(1,), stride=(1,)), | |
| 'melgan.layer22': Conv1d(48, 4, kernel_size=(7,), stride=(1,)) | |
| } | |
| >>>> Looks good! | |
| >>> Running with wrapped Torch import, wrapping replacement for PyTorch... | |
| *** register sys.meta_path for ctx <WrapCtx 'pytorch_to_returnn.import_wrapper._torch_returnn'> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan> | |
| *** <WrapCtx 'pytorch_to_returnn.import_wrapper._torch_returnn'> extend by mod 'parallel_wavegan' | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.models> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.models.melgan> | |
| WARNING:tensorflow:From /Users/az/Programmierung/import-parallel-wavegan/returnn/tf/network.py:352: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. | |
| Instructions for updating: | |
| Call initializer instance with the dtype argument instead of passing it to the constructor | |
| WARNING:tensorflow:From /Users/az/Library/Python/3.7/lib/python/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. | |
| Instructions for updating: | |
| If using Keras pass *_constraint arguments to layers. | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.layers> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.layers.causal_conv> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.layers.pqmf> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.layers.residual_block> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.layers.residual_stack> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.layers.upsample> | |
| *** exec mod <WrappedSourceModule pytorch_to_returnn.import_wrapper._torch_returnn.parallel_wavegan.models.parallel_wavegan> | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (384, 80, 7)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_weight' VariableLayer output: [384,80,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer1_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_Norm' MathNormLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (384, 80, 7)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_weight_v' VariableLayer output: [384,80,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer1_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_Norm_1' MathNormLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (384, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_weight_g' VariableLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer1_weight_g', 'layer1_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_truediv' CombineLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer1_weight_v', 'layer1_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_mul' CombineLayer output: [384,80,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (384, 192, 10)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_weight' VariableLayer output: [384,192,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer3_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_Norm' MathNormLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (384, 192, 10)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_weight_v' VariableLayer output: [384,192,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer3_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_Norm_1' MathNormLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (384, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_weight_g' VariableLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer3_weight_g', 'layer3_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_truediv' CombineLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer3_weight_v', 'layer3_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_mul' CombineLayer output: [384,192,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_weight' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_mul' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_weight' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_mul' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_weight' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_mul' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_weight' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_mul' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_weight' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 192, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_weight_v' VariableLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_mul' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 96, 10)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_weight' VariableLayer output: [192,96,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer9_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_Norm' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 96, 10)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_weight_v' VariableLayer output: [192,96,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer9_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_Norm_1' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (192, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_weight_g' VariableLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer9_weight_g', 'layer9_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_truediv' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer9_weight_v', 'layer9_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_mul' CombineLayer output: [192,96,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_weight' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_mul' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_weight' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_mul' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_weight' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_mul' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_weight' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_mul' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_weight' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 96, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_weight_v' VariableLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_mul' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 48, 4)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_weight' VariableLayer output: [96,48,F|4] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer15_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_Norm' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 48, 4)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_weight_v' VariableLayer output: [96,48,F|4] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer15_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_Norm_1' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (96, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_weight_g' VariableLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer15_weight_g', 'layer15_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_truediv' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer15_weight_v', 'layer15_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_mul' CombineLayer output: [96,48,F|4] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_weight' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_mul' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_weight' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_mul' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_weight' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_mul' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_weight' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 3)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_weight_v' VariableLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_mul' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_weight' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_Norm' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 48, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_weight_v' VariableLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_Norm_1' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (48, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_weight_g' VariableLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_truediv' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_mul' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_weight' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (4, 48, 7)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_weight' VariableLayer output: [4,48,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_Norm' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer22_weight'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_Norm' MathNormLayer output: [4,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_weight_v' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (4, 48, 7)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_weight_v' VariableLayer output: [4,48,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_Norm_1' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer22_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_Norm_1' MathNormLayer output: [4,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_weight_g' layer dict: {'class': 'variable', 'add_batch_axis': False, 'shape': (4, 1, 1)} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_weight_g' VariableLayer output: [4,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_truediv' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer22_weight_g', 'layer22_Norm_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_truediv' CombineLayer output: [4,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer22_weight_v', 'layer22_truediv']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_mul' CombineLayer output: [4,48,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer1_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_Norm_2' MathNormLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer1_weight_g', 'layer1_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_truediv_1' CombineLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer1_weight_v', 'layer1_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer1_mul_1' CombineLayer output: [384,80,F|7] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer3_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_Norm_2' MathNormLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer3_weight_g', 'layer3_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_truediv_1' CombineLayer output: [384,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer3_weight_v', 'layer3_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer3_mul_1' CombineLayer output: [384,192,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer4:subnet/'skip_layer_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer5:subnet/'skip_layer_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer6:subnet/'skip_layer_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [192,192,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer7:subnet/'skip_layer_mul_1' CombineLayer output: [192,192,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer9_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_Norm_2' MathNormLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer9_weight_g', 'layer9_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_truediv_1' CombineLayer output: [192,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer9_weight_v', 'layer9_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer9_mul_1' CombineLayer output: [192,96,F|10] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer10:subnet/'skip_layer_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer11:subnet/'skip_layer_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer12:subnet/'skip_layer_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [96,96,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer13:subnet/'skip_layer_mul_1' CombineLayer output: [96,96,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer15_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_Norm_2' MathNormLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer15_weight_g', 'layer15_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_truediv_1' CombineLayer output: [96,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer15_weight_v', 'layer15_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer15_mul_1' CombineLayer output: [96,48,F|4] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer16:subnet/'skip_layer_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer17:subnet/'skip_layer_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer18:subnet/'skip_layer_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer2_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer2_weight_g', 'layer2_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer2_weight_v', 'layer2_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer2_mul_1' CombineLayer output: [48,48,F|3] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer4_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer4_weight_g', 'layer4_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer4_weight_v', 'layer4_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/stack:subnet/'layer4_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'skip_layer_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_Norm_2' MathNormLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['skip_layer_weight_g', 'skip_layer_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_truediv_1' CombineLayer output: [48,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['skip_layer_weight_v', 'skip_layer_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/layer19:subnet/'skip_layer_mul_1' CombineLayer output: [48,48,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_Norm_2' layer dict: {'class': 'math_norm', 'p': 2, 'keep_dims': True, 'axes': ['static:1', 'F'], 'from': 'layer22_weight_v'} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_Norm_2' MathNormLayer output: [4,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_truediv_1' layer dict: {'class': 'combine', 'kind': 'truediv', 'from': ['layer22_weight_g', 'layer22_Norm_2']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_truediv_1' CombineLayer output: [4,1,F|1] | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_mul_1' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['layer22_weight_v', 'layer22_truediv_1']} | |
| *** root/.tmp_root:subnet/melgan:subnet/'layer22_mul_1' CombineLayer output: [4,48,F|7] | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast_PQMF_unnamed_const' layer dict: {'class': 'constant', 'value': array([[ 6.84837762e-06, -8.61604274e-20, -1.57372949e-05, | |
| 1.11185474e-05, 1.61987245e-04, 4.59894990e-04, | |
| 7.72398151e-04, 8.38639994e-04, 4.98372346e-04, | |
| -1.10215669e-18, 7.11952089e-05, 1.49111563e-03, | |
| 4.30004690e-03, 7.27269441e-03, 8.37646608e-03, | |
| 6.31989583e-03, 2.24644065e-03, 1.26586195e-19, | |
| 3.99743896e-03, 1.55457166e-02, 3.02382909e-02, | |
| 3.92091472e-02, 3.47761063e-02, 1.74983894e-02, | |
| -5.26998738e-04, 2.48157347e-18, 3.44539554e-02, | |
| 1.02453039e-01, 1.83332388e-01, 2.43451169e-01, | |
| 2.52647189e-01, 2.00818326e-01, 1.04649892e-01, | |
| 1.49070848e-17, -7.59387617e-02, -1.02453039e-01, | |
| -8.31792064e-02, -4.05271704e-02, -1.27228750e-03, | |
| 1.74983894e-02, 1.44047349e-02, 7.20260350e-18, | |
| -1.25251102e-02, -1.55457166e-02, -9.65067136e-03, | |
| -6.89103149e-04, 5.42338747e-03, 6.31989583e-03, | |
| 3.46964585e-03, 2.22662048e-18, -1.78113774e-03, | |
| -1.49111563e-03, -1.71880439e-04, 9.22816437e-04, | |
| 1.20317728e-03, 8.38639994e-04, 3.19937790e-04, | |
| -6.19814487e-19, -6.70973139e-05, -1.11185474e-05, | |
| 3.79931908e-05, 3.90753211e-05, 1.65334461e-05], | |
| [ 1.65334461e-05, -1.24443201e-19, -3.79931908e-05, | |
| 1.11185474e-05, -6.70973139e-05, -4.59894990e-04, | |
| -3.19937790e-04, 8.38639994e-04, 1.20317728e-03, | |
| 6.78645723e-19, 1.71880439e-04, 1.49111563e-03, | |
| -1.78113774e-03, -7.27269441e-03, -3.46964585e-03, | |
| 6.31989583e-03, 5.42338747e-03, -7.59943711e-19, | |
| 9.65067136e-03, 1.55457166e-02, -1.25251102e-02, | |
| -3.92091472e-02, -1.44047349e-02, 1.74983894e-02, | |
| -1.27228750e-03, 1.24078674e-17, 8.31792064e-02, | |
| 1.02453039e-01, -7.59387617e-02, -2.43451169e-01, | |
| -1.04649892e-01, 2.00818326e-01, 2.52647189e-01, | |
| 1.49070848e-17, -1.83332388e-01, -1.02453039e-01, | |
| 3.44539554e-02, 4.05271704e-02, 5.26998738e-04, | |
| 1.74983894e-02, 3.47761063e-02, 8.64555117e-17, | |
| -3.02382909e-02, -1.55457166e-02, 3.99743896e-03, | |
| 6.89103149e-04, -2.24644065e-03, 6.31989583e-03, | |
| 8.37646608e-03, 1.87081137e-17, -4.30004690e-03, | |
| -1.49111563e-03, 7.11952089e-05, -9.22816437e-04, | |
| -4.98372346e-04, 8.38639994e-04, 7.72398151e-04, | |
| 1.35198609e-18, -1.61987245e-04, -1.11185474e-05, | |
| -1.57372949e-05, -3.90753211e-05, -6.84837762e-06], | |
| [-1.65334461e-05, 1.91171999e-20, 3.79931908e-05, | |
| 1.11185474e-05, 6.70973139e-05, -4.59894990e-04, | |
| 3.19937790e-04, 8.38639994e-04, -1.20317728e-03, | |
| -3.39208620e-18, -1.71880439e-04, 1.49111563e-03, | |
| 1.78113774e-03, -7.27269441e-03, 3.46964585e-03, | |
| 6.31989583e-03, -5.42338747e-03, -1.94141486e-18, | |
| -9.65067136e-03, 1.55457166e-02, 1.25251102e-02, | |
| -3.92091472e-02, 1.44047349e-02, 1.74983894e-02, | |
| 1.27228750e-03, -8.93617306e-17, -8.31792064e-02, | |
| 1.02453039e-01, 7.59387617e-02, -2.43451169e-01, | |
| 1.04649892e-01, 2.00818326e-01, -2.52647189e-01, | |
| -4.47212543e-17, 1.83332388e-01, -1.02453039e-01, | |
| -3.44539554e-02, 4.05271704e-02, -5.26998738e-04, | |
| 1.74983894e-02, -3.47761063e-02, 3.84381550e-17, | |
| 3.02382909e-02, -1.55457166e-02, -3.99743896e-03, | |
| 6.89103149e-04, 2.24644065e-03, 6.31989583e-03, | |
| -8.37646608e-03, -2.31613547e-17, 4.30004690e-03, | |
| -1.49111563e-03, -7.11952089e-05, -9.22816437e-04, | |
| 4.98372346e-04, 8.38639994e-04, -7.72398151e-04, | |
| -3.38010756e-18, 1.61987245e-04, -1.11185474e-05, | |
| 1.57372949e-05, -3.90753211e-05, 6.84837762e-06], | |
| [-6.84837762e-06, 5.73999735e-20, 1.57372949e-05, | |
| 1.11185474e-05, -1.61987245e-04, 4.59894990e-04, | |
| -7.72398151e-04, 8.38639994e-04, -4.98372346e-04, | |
| 3.84299347e-18, -7.11952089e-05, 1.49111563e-03, | |
| -4.30004690e-03, 7.27269441e-03, -8.37646608e-03, | |
| 6.31989583e-03, -2.24644065e-03, -4.72716424e-18, | |
| -3.99743896e-03, 1.55457166e-02, -3.02382909e-02, | |
| 3.92091472e-02, -3.47761063e-02, 1.74983894e-02, | |
| 5.26998738e-04, 4.46934081e-17, -3.44539554e-02, | |
| 1.02453039e-01, -1.83332388e-01, 2.43451169e-01, | |
| -2.52647189e-01, 2.00818326e-01, -1.04649892e-01, | |
| -4.47212543e-17, 7.59387617e-02, -1.02453039e-01, | |
| 8.31792064e-02, -4.05271704e-02, 1.27228750e-03, | |
| 1.74983894e-02, -1.44047349e-02, -1.10464190e-16, | |
| 1.25251102e-02, -1.55457166e-02, 9.65067136e-03, | |
| -6.89103149e-04, -5.42338747e-03, 6.31989583e-03, | |
| -3.46964585e-03, 2.49516544e-17, 1.78113774e-03, | |
| -1.49111563e-03, 1.71880439e-04, 9.22816437e-04, | |
| -1.20317728e-03, 8.38639994e-04, -3.19937790e-04, | |
| -4.50282476e-19, 6.70973139e-05, -1.11185474e-05, | |
| -3.79931908e-05, 3.90753211e-05, -1.65334461e-05]])} | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast_PQMF_unnamed_const' ConstantLayer output: [4,F|63] | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast' layer dict: {'class': 'cast', 'from': 'Cast_PQMF_unnamed_const', 'dtype': 'float32'} | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast' CastLayer output: [4,F|63] | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast_PQMF_unnamed_const_1' layer dict: {'class': 'constant', 'value': array([[ 1.65334461e-05, 3.90753211e-05, 3.79931908e-05, | |
| -1.11185474e-05, -6.70973139e-05, -6.19814487e-19, | |
| 3.19937790e-04, 8.38639994e-04, 1.20317728e-03, | |
| 9.22816437e-04, -1.71880439e-04, -1.49111563e-03, | |
| -1.78113774e-03, 2.22662048e-18, 3.46964585e-03, | |
| 6.31989583e-03, 5.42338747e-03, -6.89103149e-04, | |
| -9.65067136e-03, -1.55457166e-02, -1.25251102e-02, | |
| 7.20260350e-18, 1.44047349e-02, 1.74983894e-02, | |
| -1.27228750e-03, -4.05271704e-02, -8.31792064e-02, | |
| -1.02453039e-01, -7.59387617e-02, 1.49070848e-17, | |
| 1.04649892e-01, 2.00818326e-01, 2.52647189e-01, | |
| 2.43451169e-01, 1.83332388e-01, 1.02453039e-01, | |
| 3.44539554e-02, 2.48157347e-18, -5.26998738e-04, | |
| 1.74983894e-02, 3.47761063e-02, 3.92091472e-02, | |
| 3.02382909e-02, 1.55457166e-02, 3.99743896e-03, | |
| 1.26586195e-19, 2.24644065e-03, 6.31989583e-03, | |
| 8.37646608e-03, 7.27269441e-03, 4.30004690e-03, | |
| 1.49111563e-03, 7.11952089e-05, -1.10215669e-18, | |
| 4.98372346e-04, 8.38639994e-04, 7.72398151e-04, | |
| 4.59894990e-04, 1.61987245e-04, 1.11185474e-05, | |
| -1.57372949e-05, -8.61604274e-20, 6.84837762e-06], | |
| [-6.84837762e-06, -3.90753211e-05, -1.57372949e-05, | |
| -1.11185474e-05, -1.61987245e-04, 1.35198609e-18, | |
| 7.72398151e-04, 8.38639994e-04, -4.98372346e-04, | |
| -9.22816437e-04, 7.11952089e-05, -1.49111563e-03, | |
| -4.30004690e-03, 1.87081137e-17, 8.37646608e-03, | |
| 6.31989583e-03, -2.24644065e-03, 6.89103149e-04, | |
| 3.99743896e-03, -1.55457166e-02, -3.02382909e-02, | |
| 8.64555117e-17, 3.47761063e-02, 1.74983894e-02, | |
| 5.26998738e-04, 4.05271704e-02, 3.44539554e-02, | |
| -1.02453039e-01, -1.83332388e-01, 1.49070848e-17, | |
| 2.52647189e-01, 2.00818326e-01, -1.04649892e-01, | |
| -2.43451169e-01, -7.59387617e-02, 1.02453039e-01, | |
| 8.31792064e-02, 1.24078674e-17, -1.27228750e-03, | |
| 1.74983894e-02, -1.44047349e-02, -3.92091472e-02, | |
| -1.25251102e-02, 1.55457166e-02, 9.65067136e-03, | |
| -7.59943711e-19, 5.42338747e-03, 6.31989583e-03, | |
| -3.46964585e-03, -7.27269441e-03, -1.78113774e-03, | |
| 1.49111563e-03, 1.71880439e-04, 6.78645723e-19, | |
| 1.20317728e-03, 8.38639994e-04, -3.19937790e-04, | |
| -4.59894990e-04, -6.70973139e-05, 1.11185474e-05, | |
| -3.79931908e-05, -1.24443201e-19, 1.65334461e-05], | |
| [ 6.84837762e-06, -3.90753211e-05, 1.57372949e-05, | |
| -1.11185474e-05, 1.61987245e-04, -3.38010756e-18, | |
| -7.72398151e-04, 8.38639994e-04, 4.98372346e-04, | |
| -9.22816437e-04, -7.11952089e-05, -1.49111563e-03, | |
| 4.30004690e-03, -2.31613547e-17, -8.37646608e-03, | |
| 6.31989583e-03, 2.24644065e-03, 6.89103149e-04, | |
| -3.99743896e-03, -1.55457166e-02, 3.02382909e-02, | |
| 3.84381550e-17, -3.47761063e-02, 1.74983894e-02, | |
| -5.26998738e-04, 4.05271704e-02, -3.44539554e-02, | |
| -1.02453039e-01, 1.83332388e-01, -4.47212543e-17, | |
| -2.52647189e-01, 2.00818326e-01, 1.04649892e-01, | |
| -2.43451169e-01, 7.59387617e-02, 1.02453039e-01, | |
| -8.31792064e-02, -8.93617306e-17, 1.27228750e-03, | |
| 1.74983894e-02, 1.44047349e-02, -3.92091472e-02, | |
| 1.25251102e-02, 1.55457166e-02, -9.65067136e-03, | |
| -1.94141486e-18, -5.42338747e-03, 6.31989583e-03, | |
| 3.46964585e-03, -7.27269441e-03, 1.78113774e-03, | |
| 1.49111563e-03, -1.71880439e-04, -3.39208620e-18, | |
| -1.20317728e-03, 8.38639994e-04, 3.19937790e-04, | |
| -4.59894990e-04, 6.70973139e-05, 1.11185474e-05, | |
| 3.79931908e-05, 1.91171999e-20, -1.65334461e-05], | |
| [-1.65334461e-05, 3.90753211e-05, -3.79931908e-05, | |
| -1.11185474e-05, 6.70973139e-05, -4.50282476e-19, | |
| -3.19937790e-04, 8.38639994e-04, -1.20317728e-03, | |
| 9.22816437e-04, 1.71880439e-04, -1.49111563e-03, | |
| 1.78113774e-03, 2.49516544e-17, -3.46964585e-03, | |
| 6.31989583e-03, -5.42338747e-03, -6.89103149e-04, | |
| 9.65067136e-03, -1.55457166e-02, 1.25251102e-02, | |
| -1.10464190e-16, -1.44047349e-02, 1.74983894e-02, | |
| 1.27228750e-03, -4.05271704e-02, 8.31792064e-02, | |
| -1.02453039e-01, 7.59387617e-02, -4.47212543e-17, | |
| -1.04649892e-01, 2.00818326e-01, -2.52647189e-01, | |
| 2.43451169e-01, -1.83332388e-01, 1.02453039e-01, | |
| -3.44539554e-02, 4.46934081e-17, 5.26998738e-04, | |
| 1.74983894e-02, -3.47761063e-02, 3.92091472e-02, | |
| -3.02382909e-02, 1.55457166e-02, -3.99743896e-03, | |
| -4.72716424e-18, -2.24644065e-03, 6.31989583e-03, | |
| -8.37646608e-03, 7.27269441e-03, -4.30004690e-03, | |
| 1.49111563e-03, -7.11952089e-05, 3.84299347e-18, | |
| -4.98372346e-04, 8.38639994e-04, -7.72398151e-04, | |
| 4.59894990e-04, -1.61987245e-04, 1.11185474e-05, | |
| 1.57372949e-05, 5.73999735e-20, -6.84837762e-06]])} | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast_PQMF_unnamed_const_1' ConstantLayer output: [4,F|63] | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast_1' layer dict: {'class': 'cast', 'from': 'Cast_PQMF_unnamed_const_1', 'dtype': 'float32'} | |
| *** root/.tmp_root:subnet/Cast_PQMF:subnet/'Cast_1' CastLayer output: [4,F|63] | |
| *** root/melgan:subnet/'layer0' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'data'} | |
| *** root/melgan:subnet/'layer0' PadLayer output: [B,F|80,T|'spatial:1:melgan/layer0'] | |
| *** root/melgan:subnet/'layer0' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| **** torch tensor func detach | |
| *** root/melgan:subnet/'layer1' layer dict: {'class': 'conv', 'from': 'layer0', 'activation': None, 'with_bias': True, 'n_out': 384, 'filter_size': (7,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/'layer1' ConvLayer output: [B,T|'time:var:extern_data:data',F|384] | |
| *** root/melgan:subnet/'layer1' ConvLayer importing params ['bias', 'weight'] ...) | |
| WARNING:tensorflow:From /Users/az/Programmierung/import-parallel-wavegan/pytorch_to_returnn/torch/nn/modules/conv.py:103: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. | |
| Instructions for updating: | |
| Prefer Variable.assign which has equivalent behavior in 2.X. | |
| *** root/melgan:subnet/'layer1' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer2' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer1'} | |
| *** root/melgan:subnet/'layer2' EvalLayer output: [B,T|'time:var:extern_data:data',F|384] | |
| *** root/melgan:subnet/'layer2' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer3' layer dict: {'class': 'transposed_conv', 'from': 'layer2', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (10,), 'strides': (5,), 'padding': 'valid', 'output_padding': (1,), 'remove_padding': (3,)} | |
| *** root/melgan:subnet/'layer3' TransposedConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer3' TransposedConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/'layer3' TransposedConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer4/stack/layer1',F|192] | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer4:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer4:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer4:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer4:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer4:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer4:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer4:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer4' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer4' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer5/stack/layer1',F|192] | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (3,)} | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer5:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer5:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer5:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer5:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer5:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer5:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer5:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer5' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer5' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer6/stack/layer1',F|192] | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (9,)} | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer6:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer6:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer6:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer6:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer6:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer6:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer6:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer6' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer6' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer7/stack/layer1',F|192] | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (27,)} | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer7:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 192, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer7:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer7:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer7:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer7:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/layer7:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer7:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer7' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer7' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer8' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer7'} | |
| *** root/melgan:subnet/'layer8' EvalLayer output: [B,T|'spatial:0:melgan/layer3',F|192] | |
| *** root/melgan:subnet/'layer8' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer9' layer dict: {'class': 'transposed_conv', 'from': 'layer8', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (10,), 'strides': (5,), 'padding': 'valid', 'output_padding': (1,), 'remove_padding': (3,)} | |
| *** root/melgan:subnet/'layer9' TransposedConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer9' TransposedConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/'layer9' TransposedConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer10/stack/layer1',F|96] | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer10:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer10:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer10:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer10:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer10:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer10:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer10:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer10' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer10' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer11/stack/layer1',F|96] | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (3,)} | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer11:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer11:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer11:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer11:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer11:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer11:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer11:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer11' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer11' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer12/stack/layer1',F|96] | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (9,)} | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer12:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer12:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer12:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer12:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer12:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer12:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer12:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer12' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer12' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer13/stack/layer1',F|96] | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (27,)} | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer13:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 96, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer13:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer13:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer13:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer13:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/layer13:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer13:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer13' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer13' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer14' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer13'} | |
| *** root/melgan:subnet/'layer14' EvalLayer output: [B,T|'spatial:0:melgan/layer9',F|96] | |
| *** root/melgan:subnet/'layer14' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer15' layer dict: {'class': 'transposed_conv', 'from': 'layer14', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (4,), 'strides': (2,), 'padding': 'valid', 'output_padding': (0,), 'remove_padding': (1,)} | |
| *** root/melgan:subnet/'layer15' TransposedConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer15' TransposedConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/'layer15' TransposedConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer16/stack/layer1',F|48] | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer16:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer16:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer16:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer16:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer16:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer16:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer16:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer16' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer16' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer17/stack/layer1',F|48] | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (3,)} | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer17:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer17:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer17:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer17:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer17:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer17:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer17:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer17' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer17' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer18/stack/layer1',F|48] | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (9,)} | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer18:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer18:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer18:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer18:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer18:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer18:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer18:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer18' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer18' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer0' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'} | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer0' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer0' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer1' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'} | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer1' PadLayer output: [B,T|'spatial:0:melgan/layer19/stack/layer1',F|48] | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer1' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer2' layer dict: {'class': 'conv', 'from': 'layer1', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (3,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (27,)} | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer2' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer2' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer2' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer3' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'} | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer3' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer3' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer4' layer dict: {'class': 'conv', 'from': 'layer3', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer4' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer4' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'layer4' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer4'} | |
| *** root/melgan:subnet/layer19:subnet/stack:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/'stack' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/'stack' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/'skip_layer' layer dict: {'class': 'conv', 'from': 'data', 'activation': None, 'with_bias': True, 'n_out': 48, 'filter_size': (1,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/layer19:subnet/'skip_layer' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/'skip_layer' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/layer19:subnet/'skip_layer' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/layer19:subnet/'add' layer dict: {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']} | |
| *** root/melgan:subnet/layer19:subnet/'add' CombineLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/layer19:subnet/'output' layer dict: {'class': 'copy', 'from': 'add'} | |
| *** root/melgan:subnet/layer19:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer19' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer19' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer20' layer dict: {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer19'} | |
| *** root/melgan:subnet/'layer20' EvalLayer output: [B,T|'spatial:0:melgan/layer15',F|48] | |
| *** root/melgan:subnet/'layer20' EvalLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer21' layer dict: {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer20'} | |
| *** root/melgan:subnet/'layer21' PadLayer output: [B,T|'spatial:0:melgan/layer21',F|48] | |
| *** root/melgan:subnet/'layer21' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer22' layer dict: {'class': 'conv', 'from': 'layer21', 'activation': None, 'with_bias': True, 'n_out': 4, 'filter_size': (7,), 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/melgan:subnet/'layer22' ConvLayer output: [B,T|'spatial:0:melgan/layer15',F|4] | |
| *** root/melgan:subnet/'layer22' ConvLayer importing params ['bias', 'weight'] ...) | |
| *** root/melgan:subnet/'layer22' ConvLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'layer23' layer dict: {'class': 'activation', 'activation': 'tanh', 'from': 'layer22'} | |
| *** root/melgan:subnet/'layer23' ActivationLayer output: [B,T|'spatial:0:melgan/layer15',F|4] | |
| *** root/melgan:subnet/'layer23' ActivationLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/melgan:subnet/'output' layer dict: {'class': 'copy', 'from': 'layer23'} | |
| *** root/melgan:subnet/'output' CopyLayer output: [B,T|'spatial:0:melgan/layer15',F|4] | |
| *** root/'melgan' SubnetworkLayer output: [B,T|'spatial:0:melgan/layer15',F|4] | |
| *** root/'melgan' SubnetworkLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/'PQMF_Cast_unnamed_const' layer dict: {'class': 'constant', 'value': array(4, dtype=int32)} | |
| *** root/'PQMF_Cast_unnamed_const' ConstantLayer output: [] | |
| *** root/'PQMF_Cast' layer dict: {'class': 'cast', 'from': 'PQMF_Cast_unnamed_const', 'dtype': 'float32'} | |
| *** root/'PQMF_Cast' CastLayer output: [] | |
| *** root/'PQMF_updown_filter' layer dict: {'class': 'constant', 'value': array([[[1., 0., 0., 0.], | |
| [0., 0., 0., 0.], | |
| [0., 0., 0., 0.], | |
| [0., 0., 0., 0.]], | |
| [[0., 0., 0., 0.], | |
| [1., 0., 0., 0.], | |
| [0., 0., 0., 0.], | |
| [0., 0., 0., 0.]], | |
| [[0., 0., 0., 0.], | |
| [0., 0., 0., 0.], | |
| [1., 0., 0., 0.], | |
| [0., 0., 0., 0.]], | |
| [[0., 0., 0., 0.], | |
| [0., 0., 0., 0.], | |
| [0., 0., 0., 0.], | |
| [1., 0., 0., 0.]]], dtype=float32)} | |
| *** root/'PQMF_updown_filter' ConstantLayer output: [4,4,F|4] | |
| *** root/'PQMF_mul' layer dict: {'class': 'combine', 'kind': 'mul', 'from': ['PQMF_updown_filter', 'PQMF_Cast']} | |
| *** root/'PQMF_mul' CombineLayer output: [4,4,F|4] | |
| *** root/'PQMF_FunctionalConvTransposed1d' layer dict: {'class': 'transposed_conv', 'from': 'melgan', 'n_out': 4, 'activation': None, 'with_bias': False, 'bias': None, 'filter_size': (4,), 'filter': 'PQMF_mul', 'filter_perm': {'static:0': 'F', 'static:1': 'static:1', 'F': 'static:0'}, 'padding': 'valid', 'output_padding': (0,), 'remove_padding': (0,), 'strides': (4,)} | |
| *** root/'PQMF_FunctionalConvTransposed1d' TransposedConvLayer output: [B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|4] | |
| *** root/'pad_fn' layer dict: {'class': 'pad', 'mode': 'constant', 'axes': 'spatial', 'padding': (31, 31), 'from': 'PQMF_FunctionalConvTransposed1d', 'value': 0.0} | |
| *** root/'pad_fn' PadLayer output: [B,T|'spatial:0:pad_fn',F|4] | |
| *** root/'pad_fn' PadLayer check RETURNN outputs given Torch inputs/outputs ... | |
| *** root/'PQMF_synthesis_filter' layer dict: {'class': 'constant', 'value': array([[[ 1.65334459e-05, 3.90753194e-05, 3.79931917e-05, | |
| -1.11185473e-05, -6.70973168e-05, -6.19814498e-19, | |
| 3.19937797e-04, 8.38639971e-04, 1.20317726e-03, | |
| 9.22816456e-04, -1.71880441e-04, -1.49111566e-03, | |
| -1.78113778e-03, 2.22662049e-18, 3.46964574e-03, | |
| 6.31989585e-03, 5.42338751e-03, -6.89103152e-04, | |
| -9.65067092e-03, -1.55457165e-02, -1.25251105e-02, | |
| 7.20260322e-18, 1.44047346e-02, 1.74983889e-02, | |
| -1.27228745e-03, -4.05271687e-02, -8.31792057e-02, | |
| -1.02453038e-01, -7.59387612e-02, 1.49070850e-17, | |
| 1.04649894e-01, 2.00818330e-01, 2.52647191e-01, | |
| 2.43451163e-01, 1.83332384e-01, 1.02453038e-01, | |
| 3.44539545e-02, 2.48157348e-18, -5.26998716e-04, | |
| 1.74983889e-02, 3.47761065e-02, 3.92091461e-02, | |
| 3.02382912e-02, 1.55457165e-02, 3.99743905e-03, | |
| 1.26586189e-19, 2.24644062e-03, 6.31989585e-03, | |
| 8.37646611e-03, 7.27269426e-03, 4.30004671e-03, | |
| 1.49111566e-03, 7.11952089e-05, -1.10215664e-18, | |
| 4.98372363e-04, 8.38639971e-04, 7.72398140e-04, | |
| 4.59895004e-04, 1.61987249e-04, 1.11185473e-05, | |
| -1.57372942e-05, -8.61604251e-20, 6.84837778e-06], | |
| [-6.84837778e-06, -3.90753194e-05, -1.57372942e-05, | |
| -1.11185473e-05, -1.61987249e-04, 1.35198608e-18, | |
| 7.72398140e-04, 8.38639971e-04, -4.98372363e-04, | |
| -9.22816456e-04, 7.11952089e-05, -1.49111566e-03, | |
| -4.30004671e-03, 1.87081140e-17, 8.37646611e-03, | |
| 6.31989585e-03, -2.24644062e-03, 6.89103152e-04, | |
| 3.99743905e-03, -1.55457165e-02, -3.02382912e-02, | |
| 8.64555147e-17, 3.47761065e-02, 1.74983889e-02, | |
| 5.26998716e-04, 4.05271687e-02, 3.44539545e-02, | |
| -1.02453038e-01, -1.83332384e-01, 1.49070850e-17, | |
| 2.52647191e-01, 2.00818330e-01, -1.04649894e-01, | |
| -2.43451163e-01, -7.59387612e-02, 1.02453038e-01, | |
| 8.31792057e-02, 1.24078672e-17, -1.27228745e-03, | |
| 1.74983889e-02, -1.44047346e-02, -3.92091461e-02, | |
| -1.25251105e-02, 1.55457165e-02, 9.65067092e-03, | |
| -7.59943702e-19, 5.42338751e-03, 6.31989585e-03, | |
| -3.46964574e-03, -7.27269426e-03, -1.78113778e-03, | |
| 1.49111566e-03, 1.71880441e-04, 6.78645703e-19, | |
| 1.20317726e-03, 8.38639971e-04, -3.19937797e-04, | |
| -4.59895004e-04, -6.70973168e-05, 1.11185473e-05, | |
| -3.79931917e-05, -1.24443197e-19, 1.65334459e-05], | |
| [ 6.84837778e-06, -3.90753194e-05, 1.57372942e-05, | |
| -1.11185473e-05, 1.61987249e-04, -3.38010752e-18, | |
| -7.72398140e-04, 8.38639971e-04, 4.98372363e-04, | |
| -9.22816456e-04, -7.11952089e-05, -1.49111566e-03, | |
| 4.30004671e-03, -2.31613549e-17, -8.37646611e-03, | |
| 6.31989585e-03, 2.24644062e-03, 6.89103152e-04, | |
| -3.99743905e-03, -1.55457165e-02, 3.02382912e-02, | |
| 3.84381566e-17, -3.47761065e-02, 1.74983889e-02, | |
| -5.26998716e-04, 4.05271687e-02, -3.44539545e-02, | |
| -1.02453038e-01, 1.83332384e-01, -4.47212551e-17, | |
| -2.52647191e-01, 2.00818330e-01, 1.04649894e-01, | |
| -2.43451163e-01, 7.59387612e-02, 1.02453038e-01, | |
| -8.31792057e-02, -8.93617311e-17, 1.27228745e-03, | |
| 1.74983889e-02, 1.44047346e-02, -3.92091461e-02, | |
| 1.25251105e-02, 1.55457165e-02, -9.65067092e-03, | |
| -1.94141482e-18, -5.42338751e-03, 6.31989585e-03, | |
| 3.46964574e-03, -7.27269426e-03, 1.78113778e-03, | |
| 1.49111566e-03, -1.71880441e-04, -3.39208613e-18, | |
| -1.20317726e-03, 8.38639971e-04, 3.19937797e-04, | |
| -4.59895004e-04, 6.70973168e-05, 1.11185473e-05, | |
| 3.79931917e-05, 1.91172006e-20, -1.65334459e-05], | |
| [-1.65334459e-05, 3.90753194e-05, -3.79931917e-05, | |
| -1.11185473e-05, 6.70973168e-05, -4.50282487e-19, | |
| -3.19937797e-04, 8.38639971e-04, -1.20317726e-03, | |
| 9.22816456e-04, 1.71880441e-04, -1.49111566e-03, | |
| 1.78113778e-03, 2.49516550e-17, -3.46964574e-03, | |
| 6.31989585e-03, -5.42338751e-03, -6.89103152e-04, | |
| 9.65067092e-03, -1.55457165e-02, 1.25251105e-02, | |
| -1.10464187e-16, -1.44047346e-02, 1.74983889e-02, | |
| 1.27228745e-03, -4.05271687e-02, 8.31792057e-02, | |
| -1.02453038e-01, 7.59387612e-02, -4.47212551e-17, | |
| -1.04649894e-01, 2.00818330e-01, -2.52647191e-01, | |
| 2.43451163e-01, -1.83332384e-01, 1.02453038e-01, | |
| -3.44539545e-02, 4.46934089e-17, 5.26998716e-04, | |
| 1.74983889e-02, -3.47761065e-02, 3.92091461e-02, | |
| -3.02382912e-02, 1.55457165e-02, -3.99743905e-03, | |
| -4.72716432e-18, -2.24644062e-03, 6.31989585e-03, | |
| -8.37646611e-03, 7.27269426e-03, -4.30004671e-03, | |
| 1.49111566e-03, -7.11952089e-05, 3.84299344e-18, | |
| -4.98372363e-04, 8.38639971e-04, -7.72398140e-04, | |
| 4.59895004e-04, -1.61987249e-04, 1.11185473e-05, | |
| 1.57372942e-05, 5.73999756e-20, -6.84837778e-06]]], | |
| dtype=float32)} | |
| *** root/'PQMF_synthesis_filter' ConstantLayer output: [1,4,F|63] | |
| *** root/'PQMF_FunctionalConv1d' layer dict: {'class': 'conv', 'from': 'pad_fn', 'n_out': 1, 'activation': None, 'with_bias': False, 'bias': None, 'filter_size': (63,), 'filter': 'PQMF_synthesis_filter', 'filter_perm': {'static:0': 'F', 'static:1': 'static:1', 'F': 'static:0'}, 'padding': 'valid', 'strides': (1,), 'dilation_rate': (1,)} | |
| *** root/'PQMF_FunctionalConv1d' ConvLayer output: [B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|1] | |
| *** root/'output' layer dict: {'class': 'copy', 'from': 'PQMF_FunctionalConv1d'} | |
| *** root/'output' CopyLayer output: [B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|1] | |
| RETURNN output: Data(name='output_output', shape=(None, 1), batch_shape_meta=[B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|1]) axis map RETURNN->Torch {0: 0, 2: 1, 1: 2} | |
| >>>> Module naming hierarchy: | |
| .tmp_root: (hidden) | |
| data: None -> None | |
| melgan: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,4,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|4] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,80,80) returnn_data:'layer0_output' [B,F|80,T|'spatial:1:melgan/layer0'] axes id> | |
| layer1: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,384,80) returnn_data:'layer1_output' [B,T|'time:var:extern_data:data',F|384] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,384,80) returnn_data:'layer2_output' [B,T|'time:var:extern_data:data',F|384] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <ConvTranspose1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer4/stack/layer1',F|192] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer5: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer5/stack/layer1',F|192] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer6: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer6/stack/layer1',F|192] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer7: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer7/stack/layer1',F|192] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer8: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,192,80) returnn_data:'layer8_output' [B,T|'spatial:0:melgan/layer3',F|192] axes {0:0,2:1,1:2}> | |
| layer9: <ModuleEntry <ConvTranspose1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer9_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer10: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer10/stack/layer1',F|96] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer11: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer11/stack/layer1',F|96] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer12: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer12/stack/layer1',F|96] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer13: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer13/stack/layer1',F|96] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer14: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,96,80) returnn_data:'layer14_output' [B,T|'spatial:0:melgan/layer9',F|96] axes {0:0,2:1,1:2}> | |
| layer15: <ModuleEntry <ConvTranspose1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer15_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer16: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer16/stack/layer1',F|48] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer17: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer17/stack/layer1',F|48] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer18: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer18/stack/layer1',F|48] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer19: <ModuleEntry <ResidualStack>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| stack: <ModuleEntry <Sequential>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| data: None -> None | |
| layer0: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer0_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer1: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer1_output' [B,T|'spatial:0:melgan/layer19/stack/layer1',F|48] axes {0:0,2:1,1:2}> | |
| layer2: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer2_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer3: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer3_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer4: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| skip_layer: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'skip_layer_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| add: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer20: <ModuleEntry <LeakyReLU>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer20_output' [B,T|'spatial:0:melgan/layer15',F|48] axes {0:0,2:1,1:2}> | |
| layer21: <ModuleEntry <ReflectionPad1d>> -> <TensorEntry name:? tensor:(1,48,80) returnn_data:'layer21_output' [B,T|'spatial:0:melgan/layer21',F|48] axes {0:0,2:1,1:2}> | |
| layer22: <ModuleEntry <Conv1d>> -> <TensorEntry name:? tensor:(1,4,80) returnn_data:'layer22_output' [B,T|'spatial:0:melgan/layer15',F|4] axes {0:0,2:1,1:2}> | |
| layer23: <ModuleEntry <Tanh>> -> <TensorEntry name:? tensor:(1,4,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|4] axes {0:0,2:1,1:2}> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,4,80) returnn_data:'output_output' [B,T|'spatial:0:melgan/layer15',F|4] axes {0:0,2:1,1:2}> | |
| PQMF_Cast: <ModuleEntry <Cast>> -> <TensorEntry name:? tensor:() returnn_data:'PQMF_Cast_output' [] axes id> | |
| PQMF_Cast_unnamed_const: <ModuleEntry <Constant>> -> <TensorEntry name:'value' tensor:() returnn_data:'PQMF_Cast_unnamed_const_const' [] axes id> | |
| PQMF_mul: <ModuleEntry <BinaryOperator>> -> <TensorEntry name:? tensor:(4,4,4) returnn_data:'PQMF_mul_output' [4,4,F|4] axes id> | |
| PQMF_updown_filter: <ModuleEntry <Constant>> -> <TensorEntry name:'updown_filter' tensor:(4,4,4) returnn_data:'PQMF_updown_filter_const' [4,4,F|4] axes id> | |
| PQMF_FunctionalConvTransposed1d: <ModuleEntry <FunctionalConvTransposed1d>> -> <TensorEntry name:? tensor:(1,4,80) returnn_data:'PQMF_FunctionalConvTransposed1d_output' [B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|4] axes {0:0,2:1,1:2}> | |
| pad_fn: <ModuleEntry <ConstantPad1d>> -> <TensorEntry name:? tensor:(1,4,80) returnn_data:'pad_fn_output' [B,T|'spatial:0:pad_fn',F|4] axes {0:0,2:1,1:2}> | |
| PQMF_FunctionalConv1d: <ModuleEntry <FunctionalConv1d>> -> <TensorEntry name:? tensor:(1,1,80) returnn_data:'output_output' [B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|1] axes {0:0,2:1,1:2}> | |
| PQMF_synthesis_filter: <ModuleEntry <Constant>> -> <TensorEntry name:'synthesis_filter' tensor:(1,4,63) returnn_data:'PQMF_synthesis_filter_const' [1,4,F|63] axes id> | |
| output: <ModuleEntry <Copy>> -> <TensorEntry name:? tensor:(1,1,80) returnn_data:'output_output' [B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|1] axes {0:0,2:1,1:2}> | |
| >>>> RETURNN net dict: | |
| { | |
| 'melgan': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'data'}, | |
| 'layer1': { | |
| 'class': 'conv', | |
| 'from': 'layer0', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 384, | |
| 'filter_size': (7,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'layer2': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer1'}, | |
| 'layer3': { | |
| 'class': 'transposed_conv', | |
| 'from': 'layer2', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (10,), | |
| 'strides': (5,), | |
| 'padding': 'valid', | |
| 'output_padding': (1,), | |
| 'remove_padding': (3,) | |
| }, | |
| 'layer4': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer3', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer5': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer4', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (3,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer6': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer5', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (9,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer7': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer6', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (27,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 192, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer8': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer7'}, | |
| 'layer9': { | |
| 'class': 'transposed_conv', | |
| 'from': 'layer8', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (10,), | |
| 'strides': (5,), | |
| 'padding': 'valid', | |
| 'output_padding': (1,), | |
| 'remove_padding': (3,) | |
| }, | |
| 'layer10': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer9', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer11': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer10', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (3,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer12': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer11', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (9,) | |
| }, | |
| 'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
| 'layer4': { | |
| 'class': 'conv', | |
| 'from': 'layer3', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'layer4'} | |
| } | |
| }, | |
| 'skip_layer': { | |
| 'class': 'conv', | |
| 'from': 'data', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (1,), | |
| 'padding': 'valid', | |
| 'strides': (1,), | |
| 'dilation_rate': (1,) | |
| }, | |
| 'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
| 'output': {'class': 'copy', 'from': 'add'} | |
| } | |
| }, | |
| 'layer13': { | |
| 'class': 'subnetwork', | |
| 'from': 'layer12', | |
| 'subnetwork': { | |
| 'stack': { | |
| 'class': 'subnetwork', | |
| 'from': 'data', | |
| 'subnetwork': { | |
| 'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
| 'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'}, | |
| 'layer2': { | |
| 'class': 'conv', | |
| 'from': 'layer1', | |
| 'activation': None, | |
| 'with_bias': True, | |
| 'n_out': 96, | |
| 'filter_size': (3,), | |
| 'padding': 'valid', | |
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| 0.012525110505521297, | |
| -1.1046418715961388e-16, | |
| -0.01440473459661007, | |
| 0.01749838888645172, | |
| 0.0012722874525934458, | |
| -0.04052716866135597, | |
| 0.08317920565605164, | |
| -0.10245303809642792, | |
| 0.07593876123428345, | |
| -4.4721255119883424e-17, | |
| -0.10464989393949509, | |
| 0.20081833004951477, | |
| -0.25264719128608704, | |
| 0.24345116317272186, | |
| -0.1833323836326599, | |
| 0.10245303809642792, | |
| -0.03445395454764366, | |
| 4.469340891174244e-17, | |
| 0.0005269987159408629, | |
| 0.01749838888645172, | |
| -0.03477610647678375, | |
| 0.03920914605259895, | |
| -0.03023829124867916, | |
| 0.015545716509222984, | |
| -0.003997439052909613, | |
| -4.7271643199925296e-18, | |
| -0.0022464406210929155, | |
| 0.006319895852357149, | |
| -0.008376466110348701, | |
| 0.007272694259881973, | |
| -0.0043000467121601105, | |
| 0.0014911156613379717, | |
| -7.119520887499675e-05, | |
| 3.842993444776436e-18, | |
| -0.0004983723629266024, | |
| 0.0008386399713344872, | |
| -0.0007723981398157775, | |
| 0.0004598950035870075, | |
| -0.0001619872491573915, | |
| 1.1118547263322398e-05, | |
| 1.5737294233986177e-05, | |
| 5.739997556022112e-20, | |
| -6.848377779533621e-06 | |
| ] | |
| ] | |
| ], dtype=numpy.float32) | |
| }, | |
| 'output': {'class': 'copy', 'from': 'PQMF_FunctionalConv1d'} | |
| } | |
| >>>> Root module calls: | |
| { | |
| 'melgan': <CallEntry #1 <ModuleEntry <Sequential>>>, | |
| 'PQMF_Cast': <CallEntry #0 <ModuleEntry <Cast>>>, | |
| 'PQMF_Cast_unnamed_const': <CallEntry #1 <ModuleEntry <Constant>>>, | |
| 'PQMF_mul': <CallEntry #0 <ModuleEntry <BinaryOperator>>>, | |
| 'PQMF_updown_filter': <CallEntry #1 <ModuleEntry <Constant>>>, | |
| 'PQMF_FunctionalConvTransposed1d': <CallEntry #0 <ModuleEntry <FunctionalConvTransposed1d>>>, | |
| 'pad_fn': <CallEntry #0 <ModuleEntry <ConstantPad1d>>>, | |
| 'PQMF_FunctionalConv1d': <CallEntry #0 <ModuleEntry <FunctionalConv1d>>>, | |
| 'PQMF_synthesis_filter': <CallEntry #1 <ModuleEntry <Constant>>>, | |
| 'output': <CallEntry #None <ModuleEntry <Copy>>> | |
| } | |
| >>>> Modules with params: | |
| { | |
| 'melgan.layer1': <Conv1d>, | |
| 'melgan.layer3': <ConvTranspose1d>, | |
| 'melgan.layer4.stack.layer2': <Conv1d>, | |
| 'melgan.layer4.stack.layer4': <Conv1d>, | |
| 'melgan.layer4.skip_layer': <Conv1d>, | |
| 'melgan.layer5.stack.layer2': <Conv1d>, | |
| 'melgan.layer5.stack.layer4': <Conv1d>, | |
| 'melgan.layer5.skip_layer': <Conv1d>, | |
| 'melgan.layer6.stack.layer2': <Conv1d>, | |
| 'melgan.layer6.stack.layer4': <Conv1d>, | |
| 'melgan.layer6.skip_layer': <Conv1d>, | |
| 'melgan.layer7.stack.layer2': <Conv1d>, | |
| 'melgan.layer7.stack.layer4': <Conv1d>, | |
| 'melgan.layer7.skip_layer': <Conv1d>, | |
| 'melgan.layer9': <ConvTranspose1d>, | |
| 'melgan.layer10.stack.layer2': <Conv1d>, | |
| 'melgan.layer10.stack.layer4': <Conv1d>, | |
| 'melgan.layer10.skip_layer': <Conv1d>, | |
| 'melgan.layer11.stack.layer2': <Conv1d>, | |
| 'melgan.layer11.stack.layer4': <Conv1d>, | |
| 'melgan.layer11.skip_layer': <Conv1d>, | |
| 'melgan.layer12.stack.layer2': <Conv1d>, | |
| 'melgan.layer12.stack.layer4': <Conv1d>, | |
| 'melgan.layer12.skip_layer': <Conv1d>, | |
| 'melgan.layer13.stack.layer2': <Conv1d>, | |
| 'melgan.layer13.stack.layer4': <Conv1d>, | |
| 'melgan.layer13.skip_layer': <Conv1d>, | |
| 'melgan.layer15': <ConvTranspose1d>, | |
| 'melgan.layer16.stack.layer2': <Conv1d>, | |
| 'melgan.layer16.stack.layer4': <Conv1d>, | |
| 'melgan.layer16.skip_layer': <Conv1d>, | |
| 'melgan.layer17.stack.layer2': <Conv1d>, | |
| 'melgan.layer17.stack.layer4': <Conv1d>, | |
| 'melgan.layer17.skip_layer': <Conv1d>, | |
| 'melgan.layer18.stack.layer2': <Conv1d>, | |
| 'melgan.layer18.stack.layer4': <Conv1d>, | |
| 'melgan.layer18.skip_layer': <Conv1d>, | |
| 'melgan.layer19.stack.layer2': <Conv1d>, | |
| 'melgan.layer19.stack.layer4': <Conv1d>, | |
| 'melgan.layer19.skip_layer': <Conv1d>, | |
| 'melgan.layer22': <Conv1d> | |
| } | |
| Output shape: (1, 16000, 1) | |
| Output seq lens: [16000] | |
| Output shape (converted to Torch): (1, 1, 16000) | |
| >>>> Looks good! | |
| RETURNN network layer topology: | |
| extern data: data: Data(shape=(80, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|80,T|'time:var:extern_data:data']) | |
| used data keys: ['data'] | |
| layers: | |
| layer subnetwork '.tmp_root' #: 80 | |
| layer cast 'PQMF_Cast' #: unknown | |
| layer constant 'PQMF_Cast_unnamed_const' #: unknown | |
| layer conv 'PQMF_FunctionalConv1d' #: 1 | |
| layer transposed_conv 'PQMF_FunctionalConvTransposed1d' #: 4 | |
| layer combine 'PQMF_mul' #: 4 | |
| layer constant 'PQMF_synthesis_filter' #: 63 | |
| layer constant 'PQMF_updown_filter' #: 4 | |
| layer source 'data' #: 80 | |
| layer subnetwork 'melgan' #: 4 | |
| layer copy 'output' #: 1 | |
| layer pad 'pad_fn' #: 4 | |
| net params #: 2128852 | |
| net trainable params: [<tf.Variable 'melgan/layer1/W:0' shape=(7, 80, 384) dtype=float32>, <tf.Variable 'melgan/layer1/bias:0' shape=(384,) dtype=float32>, <tf.Variable 'melgan/layer10/skip_layer/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer10/skip_layer/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer10/stack/layer2/W:0' shape=(3, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer10/stack/layer2/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer10/stack/layer4/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer10/stack/layer4/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer11/skip_layer/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer11/skip_layer/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer11/stack/layer2/W:0' shape=(3, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer11/stack/layer2/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer11/stack/layer4/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer11/stack/layer4/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer12/skip_layer/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer12/skip_layer/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer12/stack/layer2/W:0' shape=(3, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer12/stack/layer2/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer12/stack/layer4/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer12/stack/layer4/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer13/skip_layer/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer13/skip_layer/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer13/stack/layer2/W:0' shape=(3, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer13/stack/layer2/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer13/stack/layer4/W:0' shape=(1, 96, 96) dtype=float32>, <tf.Variable 'melgan/layer13/stack/layer4/bias:0' shape=(96,) dtype=float32>, <tf.Variable 'melgan/layer15/W_native_transposed_conv:0' shape=(4, 1, 48, 96) dtype=float32>, <tf.Variable 'melgan/layer15/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer16/skip_layer/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer16/skip_layer/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer16/stack/layer2/W:0' shape=(3, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer16/stack/layer2/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer16/stack/layer4/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer16/stack/layer4/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer17/skip_layer/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer17/skip_layer/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer17/stack/layer2/W:0' shape=(3, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer17/stack/layer2/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer17/stack/layer4/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer17/stack/layer4/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer18/skip_layer/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer18/skip_layer/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer18/stack/layer2/W:0' shape=(3, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer18/stack/layer2/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer18/stack/layer4/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer18/stack/layer4/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer19/skip_layer/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer19/skip_layer/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer19/stack/layer2/W:0' shape=(3, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer19/stack/layer2/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer19/stack/layer4/W:0' shape=(1, 48, 48) dtype=float32>, <tf.Variable 'melgan/layer19/stack/layer4/bias:0' shape=(48,) dtype=float32>, <tf.Variable 'melgan/layer22/W:0' shape=(7, 48, 4) dtype=float32>, <tf.Variable 'melgan/layer22/bias:0' shape=(4,) dtype=float32>, <tf.Variable 'melgan/layer3/W_native_transposed_conv:0' shape=(10, 1, 192, 384) dtype=float32>, <tf.Variable 'melgan/layer3/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer4/skip_layer/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer4/skip_layer/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer4/stack/layer2/W:0' shape=(3, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer4/stack/layer2/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer4/stack/layer4/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer4/stack/layer4/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer5/skip_layer/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer5/skip_layer/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer5/stack/layer2/W:0' shape=(3, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer5/stack/layer2/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer5/stack/layer4/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer5/stack/layer4/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer6/skip_layer/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer6/skip_layer/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer6/stack/layer2/W:0' shape=(3, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer6/stack/layer2/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer6/stack/layer4/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer6/stack/layer4/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer7/skip_layer/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer7/skip_layer/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer7/stack/layer2/W:0' shape=(3, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer7/stack/layer2/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer7/stack/layer4/W:0' shape=(1, 192, 192) dtype=float32>, <tf.Variable 'melgan/layer7/stack/layer4/bias:0' shape=(192,) dtype=float32>, <tf.Variable 'melgan/layer9/W_native_transposed_conv:0' shape=(10, 1, 96, 192) dtype=float32>, <tf.Variable 'melgan/layer9/bias:0' shape=(96,) dtype=float32>] | |
| Saving TF checkpoint to '/var/folders/fk/mt9zfm3n2853v1pcy0q3y25h0000gp/T/tmputue4givtmp-returnn-tf-checkpoint/model'... | |
| >>> Constructing RETURNN model, load TF checkpoint, run... | |
| layer <network via _run_returnn_standalone>/'data' output: Data(name='data', shape=(80, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|80,T|'time:var:extern_data:data']) | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5903935888), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5903936144), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5903933648), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5903936272)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5903936400)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904008016), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904008272), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904007696), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904008400)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904008528)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904051344), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904051600), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904048464), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904051728)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904051856)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904160208), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904160528), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904159376), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904161232)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904161104)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904250064), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904250320), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904249296), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904250448)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904250576)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904301584), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904301840), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904299344), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904301968)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904302032)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904406416), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904406672), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904405072), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904406800)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904406928)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904498832), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904499984), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5904499280), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904500240)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904500368)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904589264), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904589520), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5904588944), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904589648)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904589776)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904657168), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904657424), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5904654928), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904657552)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904657680)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904741520), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904741776), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5904740432), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904741904)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904742032)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904743248), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904831056), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5904831248), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904831184)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904831376)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904883280), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904883536), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5904881040), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904883664)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904941200)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5904251600), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904251792), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5904251984), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5904250064)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5904250512)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905060624), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905060880), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5905059984), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905061008)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905061136)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905150416), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905150672), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5905150224), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905150800)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905150928)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905230672), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905230928), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905228432), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905231056)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905231184)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905323216), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905323472), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905322448), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905323600)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905323728)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905274960), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905275088), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905274576), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905275216)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905274768)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905464208), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905464784), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905462288), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905464912)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905465040)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905564816), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905565072), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905564048), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905565200)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905565328)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905674320), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905674448), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905674384), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905674576)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905674704)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905712272), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905712656), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905712016), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905713552)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905712976)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905811472), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905811728), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905809552), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905811856)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905811984)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5906002064), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906002192), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5906002128), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5906002320)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906002448)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5906032720), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906034000), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5906032016), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5906034256)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906034384)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5906127312), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906127568), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5906125072), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5906127696)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906127824)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5906228048), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906228304), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5906227728), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5906228432)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5906228560)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905937872), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905937936), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5905937552), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905938064)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905892496)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5750462672), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5750461200), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5750463952), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5717892880)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5717893072)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5869215632), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5869215696), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5869168592), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5869167184)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5869167376)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]), | |
| Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5878956624), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5878956112), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=192, id=5878956816), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5878959184)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5878956368)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5874784272), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5874695184), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5874783632), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5874649488)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5874650960)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5878663248), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5878708176), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5878664528), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5866757520)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5866754832)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5868640208), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5868641680), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5868641424), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5868640976)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5868551504)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5866603920), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5855352720), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5855351824), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5855352272)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5855353680)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5854631120), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5854630480), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5854630672), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5854631888)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5854565584)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5866127888), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5854297680), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5854295184), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5854295632)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5854297808)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5851690320), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5851663440), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5851661840), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5851663248)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5851662352)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]), | |
| Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5828863056), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5828863568), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=96, id=5828864976), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5828863824)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5828864080)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5926735376), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5926733392), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5926735440), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5926732816)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5926732304)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5834235024), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5834234128), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5834235536), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5851924560)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5805873232)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5867604624), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5717876880), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5717877648), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5717878416)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5717879696)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5844809872), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5844754128), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5844753808), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5844753232)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5844754000)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5844435728), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5844437520), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5844438800), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5844435024)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5844438160)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5845135760), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5845136144), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5845137936), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5844703376)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5844703888)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5905863120), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905864144), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5905862992), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5905863952)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5905864656)] | |
| get_common_data( | |
| [Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]), | |
| Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])]), | |
| dim tags | |
| [DimensionTag(kind='batch', description='batch:output_output', id=5750452048), | |
| DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5750449744), | |
| DimensionTag(kind='feature', description='feature:output_output', dimension=48, id=5750450000), | |
| DimensionTag(kind='spatial', description='time:var-unk:skip_layer_output', id=5750450960)], | |
| largest source | |
| (Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48])) | |
| has incomplete dim tag info: | |
| [DimensionTag(kind='spatial', description='time:var-unk:output_output', id=5750449616)] | |
| layer <network via _run_returnn_standalone>/'melgan' output: Data(name='output_output', shape=(None, 4), batch_shape_meta=[B,T|?,F|4]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'data' output: Data(name='data', shape=(80, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|80,T|'time:var:extern_data:data']) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer0' output: Data(name='layer0_output', shape=(80, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|80,T|'time:var:extern_data:data']) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 384), batch_shape_meta=[B,T|?,F|384]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 384), batch_shape_meta=[B,T|'time:var:extern_data:data',F|384]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|?,F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer4' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/'data' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/'stack' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'data' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer4/stack/layer1',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/'add' output: Data(name='add_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer4:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer5' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/'data' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/'stack' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer5/stack/layer1',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/'add' output: Data(name='add_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer5:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer6' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/'data' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/'stack' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer6/stack/layer1',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/'add' output: Data(name='add_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer6:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer7' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/'data' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/'stack' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer7/stack/layer1',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/'add' output: Data(name='add_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer7:subnet/'output' output: Data(name='output_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer8' output: Data(name='layer8_output', shape=(None, 192), batch_shape_meta=[B,T|'spatial:0:melgan/layer3',F|192]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer9' output: Data(name='layer9_output', shape=(None, 96), batch_shape_meta=[B,T|?,F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer10' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/'data' output: Data(name='layer9_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/'stack' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'data' output: Data(name='layer9_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer10/stack/layer1',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/'add' output: Data(name='add_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer10:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer11' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/'data' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/'stack' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer11/stack/layer1',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/'add' output: Data(name='add_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer11:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer12' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/'data' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/'stack' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer12/stack/layer1',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/'add' output: Data(name='add_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer12:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer13' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/'data' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/'stack' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer13/stack/layer1',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/'add' output: Data(name='add_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer13:subnet/'output' output: Data(name='output_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer14' output: Data(name='layer14_output', shape=(None, 96), batch_shape_meta=[B,T|'spatial:0:melgan/layer9',F|96]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer15' output: Data(name='layer15_output', shape=(None, 48), batch_shape_meta=[B,T|?,F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer16' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/'data' output: Data(name='layer15_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/'stack' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'data' output: Data(name='layer15_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer16/stack/layer1',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/'add' output: Data(name='add_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer16:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer17' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/'data' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/'stack' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer17/stack/layer1',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/'add' output: Data(name='add_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer17:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer18' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/'data' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/'stack' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer18/stack/layer1',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/'add' output: Data(name='add_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer18:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer19' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/'data' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/'stack' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'data' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'layer0' output: Data(name='layer0_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'layer1' output: Data(name='layer1_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'layer2' output: Data(name='layer2_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer19/stack/layer1',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'layer3' output: Data(name='layer3_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'layer4' output: Data(name='layer4_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/stack:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/'skip_layer' output: Data(name='skip_layer_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/'add' output: Data(name='add_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/layer19:subnet/'output' output: Data(name='output_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer20' output: Data(name='layer20_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer21' output: Data(name='layer21_output', shape=(None, 48), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|48]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer22' output: Data(name='layer22_output', shape=(None, 4), batch_shape_meta=[B,T|'spatial:0:melgan/layer21',F|4]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'layer23' output: Data(name='layer23_output', shape=(None, 4), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|4]) | |
| layer <network via _run_returnn_standalone>/melgan:subnet/'output' output: Data(name='output_output', shape=(None, 4), batch_shape_meta=[B,T|'spatial:0:melgan/layer15',F|4]) | |
| layer <network via _run_returnn_standalone>/'PQMF_updown_filter' output: Data(name='PQMF_updown_filter_const', shape=(4, 4, 4), batch_dim_axis=None, time_dim_axis=None, batch_shape_meta=[4,4,F|4]) | |
| layer <network via _run_returnn_standalone>/'PQMF_Cast_unnamed_const' output: Data(name='PQMF_Cast_unnamed_const_const', shape=(), dtype='int32', batch_dim_axis=None, time_dim_axis=None, batch_shape_meta=[]) | |
| layer <network via _run_returnn_standalone>/'PQMF_Cast' output: Data(name='PQMF_Cast_output', shape=(), batch_dim_axis=None, time_dim_axis=None, batch_shape_meta=[]) | |
| layer <network via _run_returnn_standalone>/'PQMF_mul' output: Data(name='PQMF_mul_output', shape=(4, 4, 4), batch_dim_axis=None, time_dim_axis=None, batch_shape_meta=[4,4,F|4]) | |
| layer <network via _run_returnn_standalone>/'PQMF_FunctionalConvTransposed1d' output: Data(name='PQMF_FunctionalConvTransposed1d_output', shape=(None, 4), batch_shape_meta=[B,T|?,F|4]) | |
| layer <network via _run_returnn_standalone>/'pad_fn' output: Data(name='pad_fn_output', shape=(None, 4), batch_shape_meta=[B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|4]) | |
| layer <network via _run_returnn_standalone>/'PQMF_synthesis_filter' output: Data(name='PQMF_synthesis_filter_const', shape=(1, 4, 63), batch_dim_axis=None, time_dim_axis=None, batch_shape_meta=[1,4,F|63]) | |
| layer <network via _run_returnn_standalone>/'PQMF_FunctionalConv1d' output: Data(name='PQMF_FunctionalConv1d_output', shape=(None, 1), batch_shape_meta=[B,T|'spatial:0:pad_fn',F|1]) | |
| layer <network via _run_returnn_standalone>/'output' output: Data(name='output_output', shape=(None, 1), batch_shape_meta=[B,T|'spatial:0:PQMF_FunctionalConvTransposed1d',F|1]) | |
| Output shape: (1, 16000, 1) | |
| >>>> Looks good! | |
| Wrote out.wav. | |
| Process finished with exit code 0 |
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