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albertz/net_dict.py

Last active November 26, 2020 11:25
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Parallel WaveGAN, converted PyTorch to RETURNN net dict, via https://github.com/albertz/pytorch-to-returnn
Raw
net_dict.py
{
'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',
'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': 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'}
}
},
'layer14': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer13'},
'layer15': {
'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,)
},
'layer16': {
'class': 'subnetwork',
'from': 'layer15',
'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': 48,
'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': 48,
'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': 48,
'filter_size': (1,),
'padding': 'valid',
'strides': (1,),
'dilation_rate': (1,)
},
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']},
'output': {'class': 'copy', 'from': 'add'}
}
},
'layer17': {
'class': 'subnetwork',
'from': 'layer16',
'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': 48,
'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': 48,
'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': 48,
'filter_size': (1,),
'padding': 'valid',
'strides': (1,),
'dilation_rate': (1,)
},
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']},
'output': {'class': 'copy', 'from': 'add'}
}
},
'layer18': {
'class': 'subnetwork',
'from': 'layer17',
'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': 48,
'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': 48,
'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': 48,
'filter_size': (1,),
'padding': 'valid',
'strides': (1,),
'dilation_rate': (1,)
},
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']},
'output': {'class': 'copy', 'from': 'add'}
}
},
'layer19': {
'class': 'subnetwork',
'from': 'layer18',
'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': 48,
'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': 48,
'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': 48,
'filter_size': (1,),
'padding': 'valid',
'strides': (1,),
'dilation_rate': (1,)
},
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']},
'output': {'class': 'copy', 'from': 'add'}
}
},
'layer20': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer19'},
'layer21': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer20'},
'layer22': {
'class': 'conv',
'from': 'layer21',
'activation': None,
'with_bias': True,
'n_out': 4,
'filter_size': (7,),
'padding': 'valid',
'strides': (1,),
'dilation_rate': (1,)
},
'layer23': {'class': 'activation', 'activation': 'tanh', 'from': 'layer22'},
'output': {'class': 'copy', 'from': 'layer23'}
}
},
'PQMF_Cast': {'class': 'cast', 'from': 'PQMF_Cast_unnamed_const', 'dtype': 'float32'},
'PQMF_Cast_unnamed_const': {'class': 'constant', 'value': numpy.array(4, dtype=numpy.int32)},
'PQMF_mul': {'class': 'combine', 'kind': 'mul', 'from': ['PQMF_updown_filter', 'PQMF_Cast']},
'PQMF_updown_filter': {
'class': 'constant',
'value': numpy.array([
[[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 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, 0.0, 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, 0.0, 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]]
], dtype=numpy.float32)
},
'PQMF_FunctionalConvTransposed1d': {
'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,)
},
'pad_fn': {
'class': 'pad',
'mode': 'constant',
'axes': 'spatial',
'padding': (31, 31),
'from': 'PQMF_FunctionalConvTransposed1d',
'value': 0.0
},
'PQMF_FunctionalConv1d': {
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},
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], dtype=numpy.float32)
},
'output': {'class': 'copy', 'from': 'PQMF_FunctionalConv1d'}
}
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