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99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
"""MaskNet: Wang et al. (https://arxiv.org/abs/2102.07619)."""
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from tml.projects.home.recap.model import config, mlp
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import torch
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def _init_weights(module):
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if isinstance(module, torch.nn.Linear):
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torch.nn.init.xavier_uniform_(module.weight)
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torch.nn.init.constant_(module.bias, 0)
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class MaskBlock(torch.nn.Module):
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def __init__(
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self, mask_block_config: config.MaskBlockConfig, input_dim: int, mask_input_dim: int
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) -> None:
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super(MaskBlock, self).__init__()
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self.mask_block_config = mask_block_config
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output_size = mask_block_config.output_size
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if mask_block_config.input_layer_norm:
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self._input_layer_norm = torch.nn.LayerNorm(input_dim)
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else:
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self._input_layer_norm = None
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if mask_block_config.reduction_factor:
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aggregation_size = int(mask_input_dim * mask_block_config.reduction_factor)
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elif mask_block_config.aggregation_size is not None:
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aggregation_size = mask_block_config.aggregation_size
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else:
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raise ValueError("Need one of reduction factor or aggregation size.")
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self._mask_layer = torch.nn.Sequential(
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torch.nn.Linear(mask_input_dim, aggregation_size),
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torch.nn.ReLU(),
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torch.nn.Linear(aggregation_size, input_dim),
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)
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self._mask_layer.apply(_init_weights)
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self._hidden_layer = torch.nn.Linear(input_dim, output_size)
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self._hidden_layer.apply(_init_weights)
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self._layer_norm = torch.nn.LayerNorm(output_size)
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def forward(self, net: torch.Tensor, mask_input: torch.Tensor):
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if self._input_layer_norm:
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net = self._input_layer_norm(net)
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hidden_layer_output = self._hidden_layer(net * self._mask_layer(mask_input))
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return self._layer_norm(hidden_layer_output)
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class MaskNet(torch.nn.Module):
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def __init__(self, mask_net_config: config.MaskNetConfig, in_features: int):
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super().__init__()
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self.mask_net_config = mask_net_config
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mask_blocks = []
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if mask_net_config.use_parallel:
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total_output_mask_blocks = 0
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for mask_block_config in mask_net_config.mask_blocks:
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mask_blocks.append(MaskBlock(mask_block_config, in_features, in_features))
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total_output_mask_blocks += mask_block_config.output_size
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self._mask_blocks = torch.nn.ModuleList(mask_blocks)
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else:
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input_size = in_features
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for mask_block_config in mask_net_config.mask_blocks:
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mask_blocks.append(MaskBlock(mask_block_config, input_size, in_features))
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input_size = mask_block_config.output_size
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self._mask_blocks = torch.nn.ModuleList(mask_blocks)
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total_output_mask_blocks = mask_block_config.output_size
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if mask_net_config.mlp:
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self._dense_layers = mlp.Mlp(total_output_mask_blocks, mask_net_config.mlp)
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self.out_features = mask_net_config.mlp.layer_sizes[-1]
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else:
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self.out_features = total_output_mask_blocks
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self.shared_size = total_output_mask_blocks
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def forward(self, inputs: torch.Tensor):
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if self.mask_net_config.use_parallel:
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mask_outputs = []
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for mask_layer in self._mask_blocks:
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mask_outputs.append(mask_layer(mask_input=inputs, net=inputs))
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# Share the outputs of the MaskBlocks.
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all_mask_outputs = torch.cat(mask_outputs, dim=1)
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output = (
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all_mask_outputs
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if self.mask_net_config.mlp is None
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else self._dense_layers(all_mask_outputs)["output"]
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)
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return {"output": output, "shared_layer": all_mask_outputs}
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else:
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net = inputs
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for mask_layer in self._mask_blocks:
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net = mask_layer(net=net, mask_input=inputs)
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# Share the output of the stacked MaskBlocks.
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output = net if self.mask_net_config.mlp is None else self._dense_layers[net]["output"]
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return {"output": output, "shared_layer": net}
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