81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
from abc import ABC
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from paddle import nn
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import re
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class Identity(nn.Layer):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, inputs):
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return inputs
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class TheseusLayer(nn.Layer):
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def __init__(self, *args, return_patterns=None, **kwargs):
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super(TheseusLayer, self).__init__()
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self.res_dict = None
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if return_patterns is not None:
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self._update_res(return_patterns)
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def forward(self, *input, res_dict=None, **kwargs):
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if res_dict is not None:
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self.res_dict = res_dict
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# stop doesn't work when stop layer has a parallel branch.
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def stop_after(self, stop_layer_name: str):
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after_stop = False
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for layer_i in self._sub_layers:
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if after_stop:
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self._sub_layers[layer_i] = Identity()
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continue
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layer_name = self._sub_layers[layer_i].full_name()
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if layer_name == stop_layer_name:
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after_stop = True
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continue
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if isinstance(self._sub_layers[layer_i], TheseusLayer):
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after_stop = self._sub_layers[layer_i].stop_after(
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stop_layer_name)
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return after_stop
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def _update_res(self, return_layers):
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for layer_i in self._sub_layers:
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layer_name = self._sub_layers[layer_i].full_name()
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for return_pattern in return_layers:
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if return_layers is not None and re.match(return_pattern,
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layer_name):
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self._sub_layers[layer_i].register_forward_post_hook(
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self._save_sub_res_hook)
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def replace_sub(self, layer_name_pattern, replace_function,
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recursive=True):
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for k in self._sub_layers.keys():
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layer_name = self._sub_layers[k].full_name()
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if re.match(layer_name_pattern, layer_name):
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self._sub_layers[k] = replace_function(self._sub_layers[k])
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if recursive:
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if isinstance(self._sub_layers[k], TheseusLayer):
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self._sub_layers[k].replace_sub(
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layer_name_pattern, replace_function, recursive)
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elif isinstance(self._sub_layers[k],
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nn.Sequential) or isinstance(
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self._sub_layers[k], nn.LayerList):
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for kk in self._sub_layers[k]._sub_layers.keys():
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self._sub_layers[k]._sub_layers[kk].replace_sub(
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layer_name_pattern, replace_function, recursive)
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else:
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pass
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'''
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example of replace function:
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def replace_conv(origin_conv: nn.Conv2D):
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new_conv = nn.Conv2D(
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in_channels=origin_conv._in_channels,
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out_channels=origin_conv._out_channels,
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kernel_size=origin_conv._kernel_size,
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stride=2
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)
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return new_conv
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'''
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